But this week, the market is facing another round of pressure. Rising tensions around Iran and the Strait of Hormuz have pushed oil prices higher, raising new questions about inflation, interest rates and whether the market can maintain the momentum.

Meanwhile, this quarter’s results are coming in much hotter than expected. According to FactSet, S&P 500 earnings growth has surged to its highest level since 2021, driven mainly by three Magnificent Seven stocks that reported last week: Alphabet Inc. (GOOG), Amazon.com, Inc. (AMZN) and Meta Platforms, Inc. (META).

As I mentioned last week, several of those same names are delivering strong results as AI spending ramps even higher.

But that raises a bigger question: Can investors actually trust those big earnings numbers?

So, in this week’s Navellier Market Buzz, I invited accounting expert Rob Spivey from Altimetry, our corporate affiliate, to answer that question for us. He explains which accounting metrics investors should be looking for, his top 3 AI power infrastructure picks best positioned to benefit from this environment – and more.

Click the image below to watch now.

To see more of my videos, click here to subscribe to my YouTube channel. And to learn more about Rob, click here.

Plus, the grades in Stock Grader (subscription required) have been updated this week! Click here to plug in your own stocks and see how they’re rated.

What’s Really Driving This Earnings Surge

With earnings coming in stronger than expected, it’s easy to follow the temptation to sit back and watch the profits roll in.

I think that’s a mistake. Because you should always be on the lookout for what’s next.

Right now, companies are spending billions to build out AI – data centers, power infrastructure, computing systems and more.

But according to my research, the next phase in the AI boom is happening in a little-known lab in Tennessee.

Hardly anyone is talking about it. But President Trump even compared the size and scope of this project to the Manhattan Project, which led to the creation of the atomic bomb.

The goal of these systems isn’t to make small improvements. It’s to speed things up.

The result? We’re talking about major technological and scientific breakthroughs that happen in days, not years.

The impact will be huge. In fact, I believe it could spark a $100 trillion reset of the AI market, beginning this year.

When that happens, some companies will benefit and become clear leaders. While others, even if they look strong today, could fall behind.

I’ve identified seven stocks that I believe are positioned to benefit most from this reset.

Click here to learn more about these picks and how you can prepare yourself for this massive reset.

Sincerely,

Louis Navellier

Editor, Market 360

The post Earnings Season Is on Fire – Are the Numbers Legit? Plus, 3 Hidden AI Stock Picks appeared first on InvestorPlace.

Editor’s Note:🔊 Prefer to listen? Tap the play button above to hear today’s Digest.

One of Eric Fry’s favorite ways to play the AI infrastructure boom is in the middle of a double-digit pullback.

Is it time to buy the dip – or reevaluate the investment thesis?

Eric – our global macroeconomic investing expert and editor of The Speculator – has been making the case for copper as one of the most structurally compelling commodities. The reason is straightforward: every major theme reshaping the global economy – AI data centers, electrification, EVs, power grid expansion – runs through copper.

As just one illustration of the related demand, take today’s hyperscale data centers…

They’re essentially a copper-and-aluminum exoskeleton wrapped around racks of silicon. So, as the need for those facilities accelerates, so too does demand for copper. S&P Global sees global copper demand rising from roughly 28 million metric tons today to 42 million by 2040.

But supply is where things get complicated – and where today’s price action gets harder to read

Two disruptions are hitting simultaneously.

First, the war in the Middle East is rerouting cargo ships away from the Strait of Hormuz, creating bottlenecks and delaying copper shipments. And when supply temporarily shrinks, traders price in scarcity fast – spiking spot premiums, especially in import-heavy regions.

Second, Eric writes that beginning last Friday, China started restricting exports of sulfuric acid, a chemical essential to copper mining. That’s likely to constrain production at the source before actual shortages even materialize. Traders have been pricing in that risk.

The result this year has been a rally driven by two distinct factors – genuine demand growth and supply-risk stress. Eric suggests investors play both angles:

Because copper’s rally is being driven by two distinctive forces – genuine demand growth and supply risk stress – the best approach is to invest in stocks that win in either scenario.

On the demand side, the winners are energy and raw materials companies that benefit as copper gets consumed at record rates by AI infrastructure, power grids, and industrial expansion.

On the supply side, the winners are copper mines, whose profit margins expand when prices spike faster than their production costs.

The stock that sits at the intersection of both is Freeport-McMoRan Inc. (FCX) – the world’s largest publicly traded copper miner.

Eric has been trading it for years. Back in 2020, he put Speculator subscribers into call options on FCX, predicting a new “commodity supercycle” would send it soaring. 

He was right.  By July 2021, those subscribers closed their trade for a 1,000%+ return.

Which brings us back to the double-digit drawdown we mentioned at the top of today’s Digest – now hitting FCX.

Is this a buying opportunity?

Two weeks ago, the copper mining giant hit a new all-time high.

But just days later, when Freeport reported earnings, it cut its 2026 copper sales guidance from 3.4 billion to 3.1 billion pounds due to operational setbacks at its Grasberg mine in Indonesia.

The market didn’t like it. Since its high, FCX has pulled back nearly 20%.

Is this a reason to turn cautious on FCX? Or a good entry point for longer-term investors?

Well, let’s start with the most important related question…

Has the structural story that made FCX compelling changed?

No. We still have a massive demand from the AI infrastructure buildout, and a supply side that can’t keep pace – either way, FCX wins.

Here’s Eric:

When demand spikes, FCX sells more copper into a strong market.

When supply shock hits, copper prices jump faster than FCX’s costs do, expanding margins either way.

Eric still holds FCX in his Fry’s Investment Report portfolio with subscribers sitting on 247% returns.

Here’s the twist many investors are missing today

The type of booms that create tailwinds for commodities like copper don’t always reward the companies that have spent billions on the infrastructure as the megatrend matures.

In fact, this pattern of “the builders don’t always win long term” has repeated across nearly every major technological shift in modern history. As Eric puts it:

The builders struggled. The appliers got rich.

The reason comes down to economics.

Builders face enormous upfront costs, constant reinvestment, and rising competition. As more capital floods in, returns get competed away. Margins compress. And even if demand ultimately arrives, it often shows up too late for early investors.

In Friday’s Digest takeover, Eric walked through the historical evidence (railroads in the 1800s, the dot-com fiber buildout) and explained why he believes that same shift is beginning to play out in AI today. It’s worth reading if you missed it, but here’s the short version…

Companies that didn’t build new technology infrastructure but used it were able to scale faster, operate more efficiently, and compound returns than the infrastructure build-out companies that carried the enormous capital burden of the buildout.

So, what’s an example of an applier poised to benefit today?

PayPal (PYPL).

Eric says it’s deploying AI within an existing payments ecosystem rather than building infrastructure from scratch, and that revenue per employee has surged more than 50% since 2022 as a result.

For a deeper dive into the winners and losers of this transition, Eric just released a special new broadcast that explains this shift

It highlights popular stocks could be at risk as the cycle evolves, and details which lesser-known “appliers” are positioned to benefit as the focus moves from building the technology… to putting it to work.

You can check it out right here. Eric even gives away his No. 1 applier stock to buy today.

Now, to avoid confusion,FCX isn’t a builder in Eric’s framework – it’s not pouring tens of billions into data centers and chips. Rather, it’s supplying the raw materials that make the buildout possible.

Eric writes that the real builders are the hyperscalers: Alphabet (GOOGL), Amazon (AMZN), Meta Platforms Inc. (META), Microsoft Corp. (MSFT).

These companies look dominant today – and in many respects, they are. But history suggests that as the buildout matures, more capital floods in, competition intensifies, and the returns on that infrastructure investment get competed away.

So, keep timing in mind…

While the builders often outperform in the early innings, the question Eric is asking is who wins in the middle and later ones.

But not every “applier” is the kind Eric is recommending

This brings us to an important distinction – one our trading expert Jonathan Rose, editor of Masters in Trading: Live, has been focusing on.

Eric’s appliers are companies using AI to compound an existing competitive moat: a massive installed user base, proprietary data, or a dominant market position. AI makes a strong business stronger.

But Jonathan just flagged a handful of companies he’s bearish on that appear to be in the same applier category – but they carry far greater risk.

We’re talking about software businesses that are deploying AI, but they’re using it to try to defend a deteriorating model rather than extend a durable one.

The moat was already eroding before AI arrived. Today, AI is accelerating that erosion, and in some cases actively cannibalizing their revenue.

Jonathan laid out his full case in last Thursday’s free Masters in Trading: Live episode.

He flagged 12 names – with a combined market cap of $1.4 trillion – that he believes face substantial downside risk over the next 24 months.

Jonathan’s framework centers on four warning signals

He calls them the “Four Tells”:

• Coordinated insider sales
• Senior talent defecting to AI-native competitors
• A pivot away from per-seat pricing toward consumption models
• And CEO language that matches to every prior disruption cycle – the “AI augments, don’t replace” playbook.

And though the software sector has already suffered a painful drawdown in recent months, Jonathan believes there’s more to come:

The disruption isn’t done; it’s rotating.

We’ve watched this script before – Chegg, Teleperformance, Fiverr – and the pattern repeats. 

I’ll give you three companies on Jonathan’s red-flag radar. The first two are Salesforce (CRM) and ServiceNow (NOW).

Salesforce is the dominant customer relationship management platform – the software that helps companies manage sales pipelines, customer data and marketing campaigns. For years, it was the gold standard of enterprise software.

Meanwhile, ServiceNow sells workflow automation software – the systems that route IT help tickets, HR requests and approval processes through large enterprises. It’s been one of the most beloved names in enterprise software for the better part of a decade

Jonathan sees both as risks to your portfolio as AI continues to proliferate.

The third company is likely to raise a few eyebrows…

It’s Palantir (PLTR) – one of the highest-returning stocks in the entire market over the last few years.

It has minted enormous returns for investors who rode it up and become something of a cult name in the retail investing community.

While its defenders will argue it’s one of the rare software names actually built for the AI era rather than threatened by it, Jonathan sees something else:

CEO Alex Karp and four senior officers filed sales on the same day at the same reference price — $205 million in coordinated insider intent.

Smart money inside is ringing the register.

When the people closest to the business are positioning for the exit in a coordinated way, Jonathan takes it seriously – regardless of the narrative.

So, how far could PLTR fall?

Jonathan’s model suggests it could be as much as 45%.

PLTR reports earnings today after the bell – they could already be out by the time you read this. If Jonathan reviews the numbers, we’ll update you here in the Digest.

But for now, you can hear more of his case against PLTR – and access the remaining nine names on his watch list – in last Thursday’s free Masters in Trading: Live episode right here.

And to sign up for Masters in Trading: Live so that you don’t miss any of Jonathan’s free market analysis and updates, click here.

Wrapping up

We have a simple through-line today…

AI is reshaping where value accumulates in the economy, and the winners aren’t always what the consensus expects.

Copper sits upstream of the buildout. True appliers sit downstream. And some of the most celebrated software names of the last decade may be caught in the middle – with AI eroding the very moats that made them great.

We’ll keep tracking all three dynamics here in the Digest.

Have a good evening,

Jeff Remsburg

The post Copper, the AI Gold Rush, and 12 Stocks to Avoid appeared first on InvestorPlace.

92,000.

That’s the staggering number of tech workers that have been laid off so far this year, according to Layoffs.fyi.

AI is a large contributor to this number, as Big Tech companies shift money toward AI investments – and make steep cuts in the process.

For example…

• Oracle Corp. (ORCL) laid off 30,000 workers in the first quarter as it pivots toward AI. CTO Larry Ellison admitted as much at Oracle’s development conference last month: “The code that Oracle is writing, Oracle isn’t writing. Our AI models are writing,” he said.
• According to TD Cowen analysts, cutting 20,000 to 30,000 employees could lead to $8 billion to $10 billion in incremental free cash flow for Oracle.
• Amazon.com Inc. (AMZN) confirmed plans to cut 16,000 corporate jobs back in January, following a 14,000 layoff in October.
• Meta Platform’s Inc. (META) recently said it will be cutting 10% of its workforce, about 8,000 job, beginning on May 20. And Microsoft Corp. (MSFT) just announced that it will offer voluntary buyouts for the first time in its 51-year history. With about 125,000 U.S. employees, that could add up to 8,750 cuts.

This trend will only worsen. That’s because for the past three years, AI has essentially been a very smart assistant. You ask it a question, and it gives you an answer. Or you give it a task, and it helps you do it more quickly.

But what’s rolling out now is fundamentally different.

There is a new type of AI making its debut that doesn’t answer questions or wait for instructions. It goes out into the world, makes decisions, takes actions, and completes entire jobs – from start to finish – without a human being involved.

Step by step. On its own.

I call this new technology “A-AI.”

And A-AI will be “employed” in the U.S. workforce, working alongside people, managing other AI systems, and running entire business operations from end to end.

Erik Brynjolfsson, a major economist from Stanford predicted that soon private individuals in the workforce will command fleets of A-AI “employees” bigger than the biggest multinational corporations do today.

But here’s the overlooked threat: A-AI’s earliest impact on the job market isn’t who gets fired; it’s who never gets hired.

The Federal Reserve’s Beige Book has noted repeatedly that firms are using AI tools to reduce the need for entry-level hiring, customer support staff, junior analysts, and back-office roles. Existing employees, augmented by software, are doing more work. Positions that would normally be backfilled are quietly left open.

That alone is enough to stall job growth.

Recent productivity data reinforce the point: Output per hour has risen faster than total hours worked. In plain English, the economy is producing more output without needing more labor.

If that dynamic persists – or accelerates – employment growth can turn negative, even if GDP remains positive.

Perhaps the most underappreciated signal is coming from a group that rarely worries about employment: recent college graduates.

According to data tracked by the Federal Reserve Bank of New York, unemployment among recent college graduates has risen, and underemployment has climbed to levels last seen during the pandemic disruption. More than 40% of recent grads are now working in jobs that do not require a degree.

That is not a coincidence. Entry-level hiring is exactly where AI-driven efficiency shows up first. When firms can automate basic research, coding assistance, customer support, and routine analysis, junior roles become optional… or obsolete.

But April layoffs bring May trade-offs.

Main jobs will become vulnerable to the threat of A-AI, as will the companies that provide them. You’ll want to steer clear of these firms.

But certain companies will get stronger because of this technology. You’ll want to incorporate these into your portfolio.

I’ll share this trade-off below. But first, here are other ways you can protect against – and profitably play – the A-AI revolution…

Smart Money Roundup

May 3, 2026

Missed the Mag 7 Gains? Here’s Your Second Chance

The first wave of AI gains went to the Mag 7, but the next opportunity may be forming in a very different part of the market. What lies ahead is the single greatest second chance I’ve seen in my decades-long career.

May 2, 2026

Don’t Let These 3 AI Investing Mistakes Destroy Your Gains

Market shifts tend to trigger three common mistakes every time a breakthrough technology emerges. Millions of portfolios will suffer losses because of them. I’ll break down each costly mistake and show you how to stay on the right side of this technology transition. Then, I’ll share the action you need to take now.

April 30, 2026

Don’t Buy the Railroads. Buy the Campbell’s

Every technological boom begins the same way. Investors applaud these visionaries, but then optimistic vision collides with reality. Then, a follow-on group emerges and capitalizes on those failures. They don’t build the new technology. They use it. This shift is already beginning to play out in the AI sector.

April 29, 2026

The Software Crash That’s Creating the Next 1,000% Winners

Anthropic’s Mythos isn’t just a better chatbot; it’s a signal that AI has crossed an important threshold, moving from a tool that responds to instructions… to one that can act, adapt, and solve problems on its own. And that changes everything for your portfolio…

How to Ride Out the A-AI Revolution

One stock I suggest selling is a global staffing and recruitment firm that could suffer multiple blows from A-AI.

For starters, the technology can now perform some of the tasks that temporary workers and contractors currently perform. Job categories from customer service agents to computer programmers could be at risk.

Second, A-AI will boost the efficiency of existing workers to such a meaningful extent that employers would need fewer part-time employees.

As demand for both temporary staff and skilled full-time workers falls, demand for the company’s recruitment and placement services will also fall.

As I see it, this company sits directly in the crosshairs of the A-AI threat. There are better places to park your money.

For instance, one of my newest “Buy” recommendations is a company that will thrive in the new era of A-AI. It designs and sells most of the essential components of modern automated factories – including the factories that make semiconductors.

I reveal the name of this company – for free – here.

Regards,

Eric Fry

The post The AI Job Shake-Up Could Create Big Winners – and Losers appeared first on InvestorPlace.

This Week’s Ratings Changes:

Upgraded: Strong to Very Strong

Downgraded: Very Strong to Strong

Upgraded: Neutral to Strong

Downgraded: Strong to Neutral

Upgraded: Weak to Neutral

Downgraded: Neutral to Weak

Upgraded: Very Weak to Weak

Downgraded: Weak to Very Weak

To stay on top of my latest stock ratings, plug your holdings into Stock Grader, my proprietary stock screening tool. But, you must be a subscriber to one of my premium services.

To learn more about my premium service, Growth Investor, and get my latest picks, go here. Or, if you are a member of one of my premium services, you can go here to get started.

Sincerely,

Louis Navellier

Editor, Market 360

The post Reddit Upgraded, Spotify Downgraded: Updated Rankings on Top Blue-Chip Stocks appeared first on InvestorPlace.

That was the Dot Com Boom. This is the AI Boom. And right now, it looks identical.

Nvidia (NVDA) is minting money. Jensen Huang believes Blackwell chips are a “one-trillion-dollar” opportunity. Broadcom (AVGO) and Marvell (MRVL) are printing records. Oracle (ORCL) just reported $553 billion in remaining performance obligations. The hyperscalers — Amazon (AMZN), Microsoft (MSFT), Alphabet (GOOGL), Meta (META) — are spending more than half a trillion dollars this year alone on AI infrastructure. Both OpenAI and Anthropic are eyeing $1 trillion IPOs. SpaceX is approaching a $2 trillion IPO.

This boom is unstoppable. Generational.

That’s what they said in 1999, too.

The smart money is already asking: what ends the AI Boom, and how much time do we have? 

We think we know the answer. And the clock is ticking.

The Real Risk to the AI Boom 

The force that will derail the AI Boom is not a technological failure, demand collapse, or even a recession.

It is politics — specifically, a populist backlash against AI that is already building momentum, fueled by the growing economic pain hitting American households right now. And it’s on a trajectory to reach full force right around the 2028 presidential election cycle.

The evidence is already stacking up in ways that are hard to ignore.

Rising Energy Costs Are Fueling the AI Backlash 

Every time a hyperscaler announces another gigawatt of data center capacity, somewhere in America, a family’s electricity bill goes up.

A recent CBS News investigation found that Georgia Power — the state’s largest utility — imposed six rate hikes in just three years. The average monthly bill jumped 50%. And this is not just a Georgia problem. According to a Bloomberg analysis, Americans living near data centers are paying as much as 267% more per month for electricity than they were five years ago. This is now affecting at least 13 states — and spreading.

The AI infrastructure buildout requires staggering amounts of power. A single large data center can consume as much electricity as a small city. The companies building them — Amazon, Microsoft, Google, Meta — negotiate discounted power rates with utilities. Residential customers make up the difference. The woman CBS News interviewed in Atlanta is now living in a ski suit inside her own home because she can’t afford heating — all because she’s forced to subsidize data center customers.

That story has a face now. And faces win elections.

Public Opinion Is Turning Against AI 

Pew Research Center’s latest data also tells a story the industry doesn’t want to hear. 

Among American adults, those concerned about AI outnumber those excited by 5 to 1.  And concern has been rising steadily since 2021.

The numbers get even worse when you focus on specific applications. Only 23% of Americans believe AI will have a positive impact on how people do their jobs. Only 24% think it will be good for education. More than half say AI will worsen people’s ability to think creatively and form meaningful relationships. And — critically — more than half of Americans say they want more control over AI in their lives.

That last data point is like a pre-written mandate for regulation. Any politician who runs on “you should be in charge of this technology, not them” will already have majority support before they’ve said another word. And a cross-the-aisle convergence makes this doubly dangerous for the AI industry: Republicans and Democrats are now equally concerned about AI in daily life. 

This is a bipartisan pressure cooker.

AI-Driven Layoffs Are Accelerating

If rising energy costs are a slow burn, the layoffs are an accelerant.

The AI-driven job cut announcements of the past 12 months have been extraordinary — not just in scale, but in the brazenness with which executives are attributing them directly to artificial intelligence:

• Block (XYZ) CEO Jack Dorsey cut 40% of the company’s workforce (4,000 people) in February 2026. His justification? “Intelligence tools have changed what it means to build and run a company.” 
• Amazon eliminated 14,000 corporate jobs — the largest layoff in the company’s history — with AI explicitly cited as enabling “leaner structures and faster innovation.”
• Meta cut 8,000 jobs in April 2026, on top of the 25,000 already eliminated since 2022. 
• Oracle has erased up to 30,000 positions. 
• Salesforce (CRM) dismissed 4,000 customer support roles, with CEO Marc Benioff declaring “I need less heads.” 

In total, AI was explicitly cited for nearly 55,000 U.S. layoffs in 2025 alone — out of a total 1.17 million job cuts, the highest since the pandemic. In 2026, the pace has accelerated to over 860 tech layoffs per day.

Meta Shows How the Narrative Is Forming 

No single company compressed the anti-AI narrative into a tighter window than Meta did over three weeks this spring. 

In early March, Mark Zuckerberg purchased a $170 million mansion on Miami’s “Billionaire Bunker” — Indian Creek Island, a private, guarded enclave with its own 24-hour armed marine patrol, a few doors down from Jeff Bezos. It set the all-time record for the most expensive home sale in Miami-Dade County history.

In late April, Meta announced it would lay off 10% of its workforce — 8,000 people — starting May 20.

Then in the same week, Meta disclosed a new internal program called the Model Capability Initiative (MCI), which installs tracking software on all U.S. employees’ work computers, recording their mouse movements, keystrokes, clicks, and periodic screenshots. The purpose: to harvest their behavioral data to train AI agents designed to perform white-collar tasks autonomously. Workers cannot opt out. (Note: Meta’s European employees are exempt, because GDPR would require their explicit consent. American workers get no such protection.)

One bucket of Meta employees is being fired. A second is being surveilled — their daily work habits harvested as training data for the bots that will eventually replace them. 

All this while the CEO and his peers are buying $170 million compounds on private islands with private police forces.

This is the story that will be told — in campaign ads, union halls, and congressional hearings — because it is true and devastating. 

The 2028 Election Could Trigger AI Regulation 

Add it all up, and the narrative nearly writes itself. The only question left is when it reaches Washington — and what happens to your portfolio when it does. 

Over the next 12 to 18 months, the three pressure points — rising energy costs, accelerating layoffs, and widening wealth inequality — will continue to compound. Public concern about AI will keep rising. More states will see legislative battles over data center construction. And more CEOs will cite AI for why they’re cutting headcount.

We expect that by 2027, this is a dominant political narrative. Candidates in both parties — facing a presidential election year in 2028 — will begin incorporating anti-AI messaging into their platforms. The China counter-argument (“we can’t fall behind Beijing”) will likely provide some suppression, but it won’t be sufficient to contain what has become a kitchen-table economic issue for tens of millions of Americans.

In November 2028, some of those candidates win. In 2029, proposed legislation arrives: an AI tax, restrictions on data center construction, energy cost regulation, labor displacement provisions. The market prices this risk before the bills are even introduced. AI infrastructure stocks — which by then have had a spectacular multi-year run — begin to roll over.

That is the scenario that ends the AI Boom. And it is not a remote tail risk. It is the base case if current trajectories hold.

The Final Word

Make your money now.

The window for transformational wealth creation in this AI cycle is the next two to three years. The fundamental thesis — AI infrastructure buildout, semiconductor supercycle, power and cooling demand — remains intact and powerful. 

But be clear-eyed about what’s to come. 

Every boom creates its own backlash.

We know what could end this one. The question is what comes next.

The last phase of a boom is always about extraction.

Not just who builds the technology… but who controls the flow of money around it.

That’s why we’re tracking a separate story most investors haven’t connected yet — what Elon Musk is building inside X, and how it could determine how money actually moves in the next cycle.

If you want to get ahead of that shift, you can dive into it right here.

The post This Is How the AI Boom Ends appeared first on InvestorPlace.

Hollywood loves a “second chance” story.

It’s no surprise that It’s a Wonderful Life ranks No. 1 on the American Film Institute’s 100 YEARS…100 CHEERS list of the most inspiring films of all time.

Reluctant hero George Bailey recognizes the immense value of his existence with the help of his guardian angel. (Remember: Every time a bell rings, an angel gets its wings.)

It’s a timeless example of the power of a second chance.

We investors rival Hollywood with our appreciation for a good do-over.

Luckily, missed opportunities often come back again in new forms, offering another chance to get it right.

The AI Boom brought immense wealth to early investors. Those who bought Nvidia Corp. (NVDA) after the launch of OpenAI’s ChatGPT in late 2022 would have achieved over 1,000% gains today.

For several years, Nvidia and the so-called Magnificent Seven technology stocks have driven the entire U.S. stock market, their soaring valuations lifting index funds, pension portfolios, and retirement accounts across the country.

But the financial slack they once enjoyed is disappearing. It’s only a matter of time before they lose ground.

So, to those who watched others make huge gains in AI stocks and thought, “I missed it”…

You didn’t miss it. The real money hasn’t yet been made.

Of course, I’m no mystical guardian angel. But I’d like to update the cinematic turn-of-phrase: “When the market bell rings, a second chance it often brings.”

In today’s Smart Money, I’ll share why the heyday of the Mag 7 companies is decidedly over – and why the next opportunity may be forming in a very different part of the market.

What lies ahead is the single greatest second chance I’ve seen in my decades-long career.

No Hollywood magic necessary.

Mag 7 AI Spending Hits New Highs

While the Mag 7 companies – Nvidia, Meta Platforms Inc. (META), Microsoft Inc. (MSFT), Alphabet Inc. (GOOGL), Apple Inc. (APPL), Amazon.com Inc. (AMZN), and Tesla Inc. (TSLA) – brought the first wave of massive returns, they won’t bring the second. Or the biggest.

They have staked their futures on a massive AI bet, evident from their latest earnings reports. But those bets rely on rosy assumptions about the future that may not come to pass.

All but Nvidia reported earnings over the last two weeks, and all declared an increase in AI spending and infrastructure.

Meta raised its 2026 capital expenditure (CapEx) – the funds spent on big, long-term investments – forecast to roughly $125 billion–$145 billion, while Alphabet increased CapEx to $180 billion–$190 billion. Tesla plans to spend more than $25 billion in 2026, a massive increase from under $9 billion in 2025.

Microsoft said it is continuing heavy capital spending on data centers and AI infrastructure to keep up with demand; and Apple reported that its research and development spending reached a record high as it continues investing in AI features and Apple Intelligence. (The company is still spending far less on AI infrastructure than Meta, Microsoft, Alphabet, or Amazon.)

Last year alone, Amazon, Microsoft, Meta, and Alphabet collectively poured nearly $300 billion into capital expenditure. That figure will more than double this year to an eye-watering $635 billion.

As the hyperscalers deplete their cash reserves to build AI infrastructure, they are tapping the credit markets for additional financing. Annual issuance of debt tied to AI and data centers surged from $166 billion in 2023 to $625 billion last year.

Of course, the chief executives of Amazon, Microsoft, Meta, and Alphabet are not novices. They understand that they may be over-investing.

As Alphabet’s CEO Sundar Pichai has argued, “The risk of under-investing is dramatically greater than the risk of over-investing.” Meta’s CEO, Mark Zuckerberg, has made essentially the same case.

This is the logic of what economists call a “prisoner’s dilemma.” Each individual player acts rationally given their own circumstances, but the collective result is that everyone over-invests simultaneously, competition destroys returns, and the industry as a whole burns the very value it set out to create.

OpenAI, the prominent poster child of the AI boom, also offers a fascinating case study in the arithmetic of ambition. The company lost approximately $8 billion last year on revenues of just $12 billion. This year will be worse, and 2027 worse still. OpenAI expects its losses to double to $17 billion in 2026 and double again to $35 billion in 2027.

To be clear, the leading technology companies are not “zeros.” They generate robust revenues, profits, and cash flows from their existing businesses. Furthermore, AI may well prove as transformative as its most enthusiastic cheerleaders claim.

But AI is becoming a “cost center” rather than a powerful growth driver. That means that even if revenues keep growing, margins may compress, expectations reset, and valuation multiples shrink.

The question is not whether AI will change the world – it certainly will – but how successfully the leading AI companies will capitalize on that change.

The Second Chance Ahead

Too often, investors assume that the builders of a new technology will automatically capture huge returns from that technology. But that’s rarely the case.

The Magnificent Seven may remain magnificent for some time yet. But the foundations beneath them are far less solid than the mythology suggests – and the distance from the current altitude to the ground below is very, very far.

That’s why I’ve been recommending that investors steer clear of the priciest, diciest AI names and pivot toward the vast universe of stocks that offer a more compelling risk-reward profile.

This is where the second chance lies.

The initial phase of the AI Boom brought gains to those that pioneered the technological revolution – the very companies now burning through cash. Be careful; don’t get too close to the flames.

The next chance won’t come from these aged “money-makers.” The likes of the Mag 7 are headed for retirement.

Instead, it will come from the companies using the technology that they have built.

These are firms hiding in plain sight, not typically thought of as AI companies. And yet, they are becoming AI companies quickly, effectively, and without nosebleed valuations. This provides the ability to pay ordinary, or even low, valuations for a company that can grow rapidly in the future.

It’s like getting in on Nvidia all of those years ago.

I detail these “second chance companies” in my latest presentation. This is when the real money will be made.

And it’s an opportunity I don’t want you to miss out on.

Click here to watch my free, special broadcast.

Regards,

Eric Fry

The post Missed the Mag 7 Gains? Here’s Your Second Chance appeared first on InvestorPlace.

• October 1, 1908: Ford Motor Co. (F) introduced the Model T.
• October 26, 1958: Pan Am’s Boeing 707 completed the first scheduled transatlantic jet service from New York to Paris.
• January 9, 2007: Steve Jobs walked onstage and introduced the iPhone.
• November 30, 2022: OpenAI released ChatGPT.

These were not quiet, obscure breakthroughs buried in a lab.

They arrived with fanfare.

Pan Am’s flight featured a full marching band and global press coverage. The iPhone announcement drew roughly 7,500 people in person, and liveblogs attracted at least another 400,000. ChatGPT was possibly the flashiest: It reached 1 million users in five days, then an estimated 100 million monthly active users two months later.

And yet, Wall Street has a remarkable habit of acting surprised when a new technological age knocks on the door. Railroad stocks kept rising for at least eight years after the world entered the jet age. (They would eventually collapse in the late 1960s.) Nokia and BlackBerry Ltd.’s (BB) shares went up for months after the iPhone was announced. And software stocks kept inflating until the middle of last year, despite clear signs that AI was getting proficient at coding.

Investors could see the future onstage, but many kept valuing the past as if nothing had changed.

That matters because May 19 could mark another one of those before-and-after moments.

On that day, one of the world’s most prominent artificial intelligence companies will launch what InvestorPlace senior analyst Eric Fry and I believe will be a game-changing suite of AI products. They will be powered by a new form of the technology Eric is calling “A-AI.”

If history is any guide, the market’s first reaction will be dangerously incomplete.

Fortunately, many industry insiders are already giving clues about which firms will be in the “splash zone” of this disruption. They’re selling shares in the companies they run, and they’re doing it faster than you might expect.

In this update, I’d like to highlight three firms with unusual insider sales as a clear sign to get out before May 19.

Stock to Sell No. 1: The Vanishing Freelancer Model

The first candidate to sell is a global online marketplace specializing in freelance work:

Fiverr International Ltd. (FVRR).

Shares rose almost 20% last week after management announced surprisingly strong results. Revenue of $105 million and earnings per share of $0.62 beat forecasts by 1% each, reassuring markets that the freelancer marketplace was still alive.

However, a significant share sale by its CEO should give investors a reason to pause.

On April 30, an SEC filing revealed that Fiverr CEO Micha Kaufman had significantly accelerated the pace of his preauthorized stock sales from around 20,000 per year to 66,000. That stands in sharp contrast with his upbeat comments in prepared earnings call remarks, where he claimed that “the fundamental [AI-powered] dynamics of this market are moving in our direction.”

I suspect that artificial intelligence is quickly replacing the type of work commonly sold on Fiverr. Popular Fiverr services include website development, book publishing, and logo design – all things that ChatGPT and its rivals can now do. In fact, Anthropic’s Claude Cowork can build a $10,000 animated website almost instantly.

That spells trouble for the Israeli firm, which hasn’t turned an operating profit in the seven years since going public in 2019. The freelancer marketplace is facing competition from AI chatbots, and growth is stalling out. Why pay a freelancer on Fiverr $100 for a set of logos when you can ask ChatGPT to design thousands of them for no apparent cost?

Fiverr’s overhead costs are also high. Advertising costs eat up $0.31 for every $1 generated, leaving little left over for anything else. Though total disaster hasn’t struck Fiverr’s financials quite yet, insiders don’t seem keen on sticking around to see what happens next.

Stock to Sell No. 2: The SaaS Company That the “SaaSpocalyse” Forgot

It’s been a generally awful year for software-as-a-service (SaaS) companies – the firms that charge monthly fees for access to cloud-based programs. It’s been so bad that one SaaS index, the iShares Expanded Tech-Software Sector ETF (IGV), has fallen 28% since last September on AI disruption fears. Individual names like Monday.com Inc. (MNDY) and Atlassian Corp. (TEAM) have fared even worse, shedding over 75% of their value since their 2025 peaks.

Except one:

EverCommerce Inc (EVCM).

Shares of the $2 billion firm have essentially traded sideways for the past year, sidestepping the massive “SaaSpocalypse” that has consumed its peers. Shares trade for 18X cash flows (within striking distance of its full-history average), and a casual observer might sense nothing wrong with this company that creates software to help small businesses manage operations, billings, payments and marketing.

That could quickly change.

You see, artificial intelligence is upending the SaaS market not because it offers the end-to-end services that EverCommerce or Salesforce Inc. (CRM) provides. That’s a complex task that requires a small army of sales and onboarding staff in addition to a working product.

Instead, AI labs are providing software tools that allow competitors to spring up almost overnight and charge a fraction of what incumbents do. Vibe-coded startups don’t need many engineers, and one AI-powered competitor to Salesforce named “Twenty” reportedly has only 34 employees. They charge between $9 and $19 per month per seat… a tenth of what Salesforce typically asks for.

EverCommerce’s management isn’t waiting around either. Since mid-2025, the company’s top executives (CEO, CFO, CLO) have been selling available shares on the open market. Almost $1 million in stock has been sold across eight transactions.

In addition, CEO Eric Remer has sold stock through 10b5-1 plans faster than he’s been awarded them – a typically bearish sign. In the past month alone, he has sold $674,000 worth of stock this way… more than he earns from a full year of salary.

So, don’t be complacent with SaaS stocks that have yet to fall; if insiders are getting out, it’s often a sign that you should, too.

Stock to Sell No. 3: Can AI Create TV Shows?

Executives at Netflix Inc. (NFLX) have a long history of selling into strength. Co-CEO Gregory Peters made one large sale in July 2020 after the work-from-home craze almost doubled the value of Netflix’s shares. He then made seven more between February 2024 and November 2025 after the stock recovered from a brutal 2022 selloff.

Gregory Peters’ Netflix (NFLX) sales

Source: Tipranks

That’s why a recent round of selling last February should raise eyebrows. These sales were made after shares fell over 40% from their peak, and mark the first time Peters has sold shares after such a deep selloff. Three other executives with similar selling histories did the same.

In fairness, I think these sales are premature. Analysts expect the video streaming firm to hit 14% sales growth this year, powered by rising subscriber numbers and higher monthly fees. Besides, we are entering the most expensive midterm election year in history, which should send Netflix’s advertising revenues to new records.

However, the truth is that artificial intelligence is coming for video creation as well. In fact, I believe we might be just one to three years away from tools that can create entire TV shows from a single prompt.

There are already plenty of signs this can happen. Last February, ByteDance released Seedance 2.0, an astonishingly powerful model that created realistic clips of Hollywood actors. Fearful media firms immediately sent the Chinese firm cease-and-desist orders. (The Netflix insider sales happened right in this period.)

Soon after, New York-based startup Runway began getting traction online after launching real-time intelligent avatars that ranged from the ultra-realistic to the uncanny.

Choose your next company avatar

Source: Runway

“A-AI” will turbocharge this process, allowing AI to not only create plots, scripts, characters, keyframes, and full-length videos, but also give the technology the tools to review its work and iterate until a near-perfect output is created. After all, AI models are increasingly able to evaluate and iterate on their outputs. There’s no reason it can’t do the same with creative work.

That’s why Netflix should start landing on investors’ long-term “Sell” lists. Though shares will likely rise for the next couple of months as election season gets underway (and first-quarter earnings last week topped estimates), insiders selling suggests they might be sensing longer-term trouble ahead from this emerging competitive threat.

The “A-AI” Revolution

Most investors have a bad habit of looking only in the rearview mirror. Value investors take long-term earnings and draw a straight line through them. If a company averaged $1 per share in profits, then this year’s $0.20 disappointment must be temporary, right?

Meanwhile, growth investors and algorithmic traders often take rising revenue numbers or stock prices and draw a line through that.

Both strategies work well in ordinary times. Most value stocks recover, given enough time and capital. And most growth stocks keep going up. Buying both the top-performing S&P 500 companies and cheapest ones routinely yields excellent results.

But technological disruptions tend to cut these trends short. The rise of the internet, for instance, not only destroyed many traditional “value” businesses like brick-and-mortar retailing. It also hurt “growth” businesses like AOL by creating nimbler competitors.

Smartphone apps like Uber Technologies Inc. (UBER) hurt both the traditional taxi medallion market and the upstart car-sharing business like Zipcar.

We’re now facing a new era that threatens the same fate for companies in its path. Hundreds of firms will see their stock prices fall; the recent SaaSpocalypse is only the start.

To make sure you’re on the right side of that split, Eric released a special presentation he calls the Agentic Reckoning. He goes into greater depth of what “A-AI” is… and the stocks that should benefit from this new technological age. 

Plus, he reveals his No. 1 stock to buy before this technology takes off.

Click here to watch Eric’s special presentation now.

Until next week,

Thomas Yeung, CFA

Market Analyst, InvestorPlace

Thomas Yeung is a market analyst and portfolio manager of the Omnia Portfolio, the highest-tier subscription at InvestorPlace. He is the former editor of Tom Yeung’s Profit & Protection, a free e-letter about investing to profit in good times and protecting gains during the bad.

The post 3 Stocks to Sell Before May 19 appeared first on InvestorPlace.

But that was before Lucas Gusher blew…

On the morning of Jan. 10, 1901, crude erupted 150 feet into the sky and kept flowing at 100,000 barrels a day… roughly as much as the rest of the entire U.S. oil industry combined.

Within months, the population of Beaumont quintupled. Pipelines, refineries, and railroad depots sprang up seemingly overnight. The smart money chased every single company and industry that would be needed to move, store, refine, and deploy all that oil.

Fast-forward to this week’s earnings, where the Magnificent Seven put up high-flying numbers that would make the Lucas Gusher blush.

The Mag 7 Earnings Deluge

Microsoft (MSFT), Google (GOOGL), Amazon (AMZN), and Meta (META) collectively committed to spending $725 billion on AI infrastructure in 2026 alone. Three of the four boosted their capital expenditure guidance.

Amazon, the biggest spender of the bunch at $200 billion, maintained its already jaw-dropping commitment. And perhaps most telling of all: none of them so much as hinted at a peak.

Google said 2027 capex would rise significantly over 2026 levels. Amazon pledged to double data center power capacity by the end of 2027. Microsoft implied the company consistently underestimates its own compute needs. Meta echoed the same posture. Every signal pointed the same direction… up.

Those are the kind of numbers that rewrite supply chains. Because every dollar of hyperscaler capex flows downstream like crude from a gusher.

It hits the chip makers…

It hits the construction equipment companies…

Caterpillar (CAT) reported fantastic numbers this week, and why wouldn’t it? You can’t build a data center without heavy machinery. Nvidia (NVDA), Taiwan Semiconductor (TSM), Micron (MU), Seagate (STX), Corning (GLW), Teradyne (TER), KLA Corp (KLAC), and WESCO International (WCC) all sit on the receiving end of that spending river.

The memory stocks deserve special attention.

Micron, SanDisk, Seagate, and Western Digital (WDC) still trade at single-digit or low-double-digit forward price-to-earnings multiples despite sitting squarely in the middle of a massive demand cycle. These aren’t names that have peaked.

Fundamentally, there remains considerable room for both earnings expansion and multiple expansion. SanDisk just launched a two-terabyte memory card retailing for $2,000. That tells you something about where the demand curve is heading.

Stocks to Buy After Earnings

For investors wondering where the next leg of this trade broadens, keep an eye on the industrial complex. Names like Caterpillar, WESCO, Deere (DE), Comfort Systems USA (FIX), and Dycom Industries (DY) are positioned to benefit as AI infrastructure buildout translates into real-world demand for equipment, energy, labor, and land.

These companies still offer discounted valuations relative to the pure semiconductor names (many of which have already sprinted to rich multiples).

Then there’s Qualcomm (QCOM)…

Over the past two years, the AI boom has systematically “reawakened” chip stocks that the market had left for dead. Nvidia was first. Then AMD. Then Intel (INTC), which rallied from the mid-teens to $90-plus in a matter of months. Now Qualcomm announced a new custom silicon business that has already secured a major contract with one of the world’s largest hyperscalers, with shipments expected by year-end.

QCOM stock hasn’t sprinted… yet. But it could follow the Intel pathway over the coming months, which suggests substantial upside from current levels.

Meanwhile, the other side of the economy keeps deteriorating. The Federal Reserve remains locked in a stalemate; a tug-of-war between incoming chair Kevin Warsh and departing chair Jerome Powell is all but guaranteeing that rates stay frozen.

The 10-year Treasury hovering around 4.5%, mortgages elevated, auto loans pinching consumers, credit card balances growing.

Iran’s nuclear standoff keeps oil above $100 per barrel with no resolution in sight.

Wage growth is running below inflation, creating negative real income growth.

The bifurcation is real, and it just got cemented. AI infrastructure is on fire. Everything else is limping.

That’s why Microsoft looks compelling here.

How to Proceed in This Market

A year ago, the market declared Google dead; GOOGL has since rallied 150%. Today, the same narrative is circling Microsoft and its OpenAI partnership. But GPT 5.5 just topped the benchmarks across the board.

OpenAI has $122 billion in fresh funding. And Microsoft remains the primary vehicle for that rebound.

Stop trying to bottom-fish Nike (NKE), Chipotle (CMG), and Disney (DIS). The macro backdrop isn’t built for those recoveries right now.

Ride the momentum where it lives: semiconductors, memory, industrials, and the broadening AI supply chain that’s only getting stronger.

The gusher is still flowing. The question is whether you’re positioned to catch it.

Watch this week’s episode of Being Exponential With Luke Lango to get the full scoop:

The post $725 Billion and Counting: Why the AI Gusher Is Just Getting Started appeared first on InvestorPlace.

One of the main keys to being a smart investor is to avoid staying comfortable and instead adapt to shifting currents – a principle that’s especially relevant in today’s market.

If you don’t believe me, listen to Bob Dylan:

You better start swimmin’ or you’ll sink like a stone,

For the times they are a-changin’.

The times certainly are “a-changin” when it comes to artificial intelligence.

AI is advancing at lightning speed, entering a huge shift away from those useful but ultimately limited chatbots, and towards autonomous systems capable of completing tasks on their own, without human input.

I’m calling this new technology “A-AI.”

And we’ve seen how rapid and disruptive it is. Anthropic’s recent, powerful model releases have already sparked the kind of software-sector volatility that signals a turning point.

The iShares Expanded Tech-Software Sector ETF (IGV) – which tracks North American software firms, heavily weighted toward large-cap leaders – dropped steeply in February when plug-ins from Anthropic’s AI system, Claude Cowork, were released. And with Claude Mythos unveiled in April, turbulence in the sector has continued.

The ETF is now down over 20% year-to-date.

A-AI will only continue to encroach on our lives. There’s not much we can do about that. But we can prepare our portfolios accordingly.

Market shifts tend to trigger three common mistakes every time a breakthrough technology emerges. Millions of portfolios will suffer losses because of them.

So, in this issue, I’ll break down each costly mistake and show you how to stay on the right side of this technology transition. Then, I’ll share the action you need to take now.

Let’s jump in…

Mistake No. 1: Panic Sell

Time and time again, I’ve observed how market shifts can panic investors out of stocks altogether, leaving them to miss out on substantial returns.

Take eBay Inc. (EBAY) during the dot-com bust. When the bubble burst, panic spread through every corner of the tech market. Instead of investors stopping to ask whether a company’s business model was actually broken, they just sold.

eBay was caught in that wave.

Those who held on to the stock were rewarded with a 325% return between 2001 and 2003. It was eBay’s e-commerce operations, its capacity to profit from the internet, that maintained its status as a solid investment.

Of course, it’s important to sell shares when the time is right; but when uncontrollable forces move the markets, your best weapon is to control what you can.

That means to stay calm, strategic, and, thereby, profitable.

Mistake No. 2: Holding On to “Sells”

While you don’t want to panic sell, you also don’t want to hold on to doomed companies past their prime.

During the dot-com boom when the internet was vibrant and new – and Cisco Systems Inc. (CSCO) was soaring to great heights – many investors staked their claim company. Their optimism often blinded them to the inevitable risks of a decline.

The company was working on telecommunications, hardware, and software equipment – the fundamental components of the internet. Cisco was a key “Builder” in the new world of cyberspace.

Eventually, though, the appliers of the internet, like Amazon.com Inc. (AMZN), started gaining on the Builder companies. They eventually overtook them.

Cisco dropped 80% after the dot-com bubble burst and only recently surpassed its 2000 peak 25 years later. An investor who purchased at the peak, or slightly before it and held on, would have seen their significant gains disappear, resulting in over a decade of waiting just to break even.

So, despite its monumental role in creating the internet, the Builders got left behind. And investors that held on too long saw significant losses.

Given the current landscape of the AI market, I believe today’s AI Builders will face similar disadvantages.

So, don’t let the comfort of high-flying names fool you, or you might overlook one of the greatest moneymaking opportunities in history…

Mistake No. 3: Missing Opportunity

The Builders of this AI era – familiar faces like chipmaker Nvidia Corp. (NVDA) – have been the market darlings for the past few years. However, Nvidia and other Magnificent Seven stocks may soon forfeit their reign.  

I believe we’ll see a changing of the guard that favors different, more lowly valued sectors. The companies applying AI, not building it. This is the new opportunity you don’t want to miss.

And it’s why I believe holding on to Nvidia could be one of the most expensive mistakes an investor can make during a technological turning point.

As A-AI becomes more powerful, tomorrow’s gains will go to companies that skillfully use it in their businesses and thrive, much as Amazon did after the dot-com bust.

Many of the high-profile tech companies that are falling right now may not recover for years… or ever. That’s because these companies are facing the serious existential threat of A-AI, which could replace them permanently or damage them beyond repair.

Buying the dip on those stocks would be like buying the dip on Blockbuster the year Netflix Inc. (NFLX) showed up.

Millions of Americans are about to make the classic mistake of assuming the winners of the recent past will also be the winners of the future. Sometimes that’s true. But it is rarely true when a genuinely disruptive technology emerges and reshuffles the market.

Today, we’re in one of, if not the most, consequential technological shifts in history.

And the investors who treat this moment like any other market cycle will be the most caught off guard.

The Bottom Line

The framework here is simple:

Don’t panic out of sound positions when external forces rattle the market. Instead, stay patient in companies that offer a compelling risk-reward profile.

Don’t cling to the AI Builders when the AI Appliers are about to take over.

That means dumping the hyperscalers and pivoting toward smaller firms using AI to improve efficiency, margins, and scalability within existing models.

And don’t miss the new wave of winners by staring in the rearview mirror at the old ones.

In my brand-new presentation, I reveal the number-one stock that I recommend investors get behind instead.

I also explain why A-AI is ushering in market shift that will kick into high gear on May 19.

That’s when tech giant Alphabet Inc. (GOOGL) will announce a radical new autonomous AI platform to 1.8 billion users – and when the market will finally grasp the full scale of what’s coming.

The companies positioned to win the A-AI era aren’t necessarily the ones making the most headlines right now. They are businesses quietly embedding autonomous intelligence into their operations, products, and competitive advantages – like the way Amazon and eBay embedded the internet into retail.

Don’t wait until it’s too late.

Click here to watch my presentation now and learn more about how you can prepare your portfolio for A-AI’s reckoning.

The current is already moving. The only question is whether you’re swimming with it.

Regards,

Eric Fry

The post Don’t Let These 3 AI Investing Mistakes Destroy Your Gains appeared first on InvestorPlace.

The data seemed clear – but that doesn’t mean the conclusions were.

During World War II, U.S. military analysts examined bombers that had safely returned from missions over Europe. They mapped every bullet hole across the aircraft and quickly found that patterns emerged. The wings and fuselage were riddled with damage.

The conclusion felt obvious. Reinforce the areas where the planes were taking the most fire.

Credit: rancho_runner

It was a perfectly rational response, but completely wrong.

Statistician Abraham Wald saw what everyone else had missed. The planes they were studying were the ones that survived.

The data showed where planes could take a hit and still make it home. The truly dangerous spots, the fatal ones, were invisible because those planes never returned.

In other words, the military wasn’t about to act on bad data. They were about to act on a bad lesson.

And that distinction – between what happens and what we think it means – may be one of the most important and most overlooked forces shaping how we understand the world, and the stock market.

Is AI in a Bubble?

OpenAI news sparked a tech selloff this week when The Wall Street Journal reported that the company behind ChatGPT missed on revenue and other key targets in 2025. The report noted that the firm fell short of its goals for new user growth and for hitting 1 billion weekly active users by the end of the year.

In the wake of the report, Advanced Micro Devices (AMD), Intel (INTC) and Qualcomm (QCOM) each fell at least 3%. The VanEck Semiconductor ETF (SMH) also fell about 4%.

OpenAI denied the report and called it “ridiculous.”

But that didn’t stop the AI bears from again speculating that it is all just a bubble.

From Financial Review:

From Medium:

Then the AI hyperscalers reported earnings….

Let’s start with Alphabet (GOOGL).

Google Cloud revenue surged 63% year-over-year to $20.02 billion – well ahead of the $18.05 billion Wall Street expected. Total revenue came in at $109.9 billion versus the $107.2 billion consensus, with net income up 81% from a year ago.

Microsoft (MSFT) revenue came in at $82.89 billion, versus the $81.39 billion expected, with adjusted EPS of $4.27, versus the $4.06 estimate. Azure and other cloud services grew 40% – ahead of the 38.8% to 39.3% analyst range.

Meta (META) beat on revenue – $56.31 billion versus $55.45 billion expected, up 33% from a year earlier and the fastest quarterly growth since 2021. Plus, adjusted EPS of $7.31 topped the $6.79 estimate.

Finally, Amazon (AMZN) had the best report of the group.Amazon Web Services (AWS) grew 28% to $37.59 billion – the fastest pace in 15 quarters, up from 24% last quarter. Total revenue of $181.52 billion crushed the $177.3 billion consensus, and EPS of $2.78 obliterated the $1.64 expectation.

The investment in the buildout has continued to make investors nervous, but the growth, order backlogs, and margin levels should help ease some concerns.

Even if OpenAI is struggling, which they vehemently deny, the rest of the big AI players are still “all in” for the AI trade.

Earnings Cut Through the Noise

But this is exactly how investors get into trouble.

They don’t just react to the data – they react to what they think the data means.

One negative headline becomes “the AI boom is over.”

One selloff becomes “the bubble is bursting.”

But just like those World War II analysts… they’re drawing conclusions from what’s visible but missing what matters.

And in today’s market, that difference can mean the gap between chasing noise and identifying the real winners early.

That’s why market pros like investing legend Louis Navellier don’t rely on headlines or speculation. Instead, they focus on the hard data that actually drives stock prices — earnings, growth, and institutional demand.

And right now, that data is telling a very different story about AI.

Louis positioned his Growth Investor subscribers in the AI trend early, and he continues to find winners today by focusing on companies that can inevitably benefit from hyperscaler spending.

For instance, one key to the AI megatrend is power.

Global data center power demand will climb 50% by 2027 and as much as 165% by the end of the decade, according to analysts at Goldman Sachs. J.P. Morgan analysts forecast that global power demand will grow at a 3.6% compound annual rate from 2026 to 2030, a pace 50% higher than the previous decade.

And while the earnings from the hyperscalers on Wednesday got all the attention, a much smaller player, Quanta Services (PWR),also reported earnings and delivered extraordinary results.

Here is Louis’ summary he sent to Growth Investor subscribers.

The company reported first-quarter adjusted earnings of $2.68 per share and revenue of $7.87 billion, which represented 50.6% year-over-year earnings growth and 26.3% year-over-year revenue growth. The analyst community expected earnings of $2.03 per share on $6.99 billion in revenue, so Quanta Services posted a 32% earnings surprise and a 12.6% revenue surprise.

Quanta Services also noted that it ended the first quarter with a record backlog of $48.5 billion. And thanks to the “exceptional first quarter,” the company increased its outlook for 2026.

For fiscal year 2026, Quanta Services now expects total revenue between $34.7 billion and $35.2 billion and adjusted earnings per share between $13.55 and $14.25. The revised outlook is nicely higher than analysts’ current top- and bottom-line estimates.

The stock surged on Thursday, and is now up 125% in the last 12 months, far outpacing the broader market’s 30% gain.

Louis initially recommended Quanta in 2021, so his subscribers are up more than 660%.

More recently, Louis has been researching a new government project being developed in a hidden government lab in Tennessee, where 40,000 scientists are finishing work on an AI computer 283 trillion times more powerful than today’s data centers – spanning more than 700 miles and built to speed up AI breakthroughs by 36,000%.

When the “Golden Dawn” project launches, it could instantly leapfrog ChatGPT, Gemini, and Grok – and trigger a $100 trillion reset of the AI markets.

Louis reveals the one stock at the center of it in his latest presentation, which you can watch right here.

The Lesson from Those World War II Bombers is Simple but Often Forgotten

Too many headlines focus on missed targets… short-term volatility… and whether the boom has gone “too far.”

But underneath the surface, the biggest companies in the world are still spending billions. Infrastructure is still being built. Demand is still accelerating.

The real opportunity isn’t in reacting to the noise.

It’s in understanding what the noise is distracting you from.

That’s exactly where Louis Navellier is focused right now.

And with breakthrough projects like “Golden Dawn” on the horizon, the next wave of AI winners may already be taking shape — long before most investors even realize it.

Enjoy your weekend,

Luis Hernandez

Editor in Chief, InvestorPlace

The post One Stock to Cut Through the AI Noise appeared first on InvestorPlace.

Leveraged ETFs can make you feel like you’ve found a shortcut. But the problem with shortcuts is that if they really worked as easily as advertised, they wouldn’t be shortcuts — they’d be the way.

I’m sure many of you have traded them before. And if we’re being honest with ourselves… you might even have liked trading them.

They move.

A lot.

And that’s exactly the problem.

Let me explain…

The Product Everyone Loves… for the Wrong Reasons

When you buy a leveraged ETF, you’re not buying the stock.

You’re buying a daily-reset derivative designed to give you a multiple of that stock’s daily move.

Two times. Three times. Sometimes more.

And that word—daily—is everything.

Because every single day:

• The fund resets
• The exposure recalibrates
• The math starts over

It doesn’t care about your thesis, where the stock was last week or what your entry price is.

It just resets.

And then it compounds from there.

Why Professional Traders Don’t Use Them

I started Masters in Trading back in 2015.

Before that, I spent decades on the professional side of this business — CME floor, prop firms, market making at the CBOE. I was managing risk, trading size, thinking in terms of structure, probabilities, positioning.

And here’s the honest truth…

Leveraged ETFs were not a core part of my world.

Not because they didn’t exist — but because the way professionals think about exposure, we don’t need them.

If I want more leverage, I size up. Defined risk? I use options. If I want precision, I build the trade myself.

So when I started working more directly with individual traders, I was surprised to find so many of these poorly structured products getting buzz among my readers.

Why Traders Gravitate Toward Them

It’s not hard to understand.

You pull up your scanner, you see a name like AXT Inc. (AXTI) lighting up with unusual options activity… and then right behind it, someone launches a 2x version of it, enter Tradr 2X Long AXTI Daily ETF (AXTX).

Of course they do. The thought of doing something twice as fast has a lot of appeal, which means people are going to give them what they think is more of a good thing.

If traders are chasing volatility, Wall Street is going to package that volatility and sell it right back to you — with leverage.

And when you trade these products, you’re buying the promise of getting more gratification, faster.

That’s why certain trading cliques love them.

They feel exciting.

But there’s something baked into these products that most people don’t understand…

They leak.

And I don’t mean metaphorically.

I mean structurally.

They are designed in a way where — over time — you are fighting against the math.

The Part They Don’t Want You to Notice

Let me give you the simplest example.

Imagine a stock goes up 10% one day and then gives it right back the next day, down 10%. Most people instinctively think, “Alright, I’m back to even.” But you’re not—you’re actually down a little bit, about 1%. That’s just how percentages work.

Now take that exact same path and apply a 2x leveraged product to it. Instead of up 10%, you’re up 20%. Instead of down 10%, you’re down 20%. And now, instead of being down 1%, you’re down closer to 4%.

Same underlying move. Same two days. Completely different result.

That gap — that widening loss — is what traders call volatility drag or volatility decay.

The more volatile the path — meaning the more frequent and larger the swings up and down — the more those small losses begin to accumulate.

Over time, that creates a measurable gap between the asset’s starting point and its ending value, even if the price appears to be moving sideways overall.

Here’s what that looks like over ten days of back-and-forth movement where the stock itself finishes only slightly lower. It doesn’t look dramatic at first glance…

But the leveraged ETF? It’s down big.

Same underlying move. Wildly different outcome.

That’s not bad trading or poor timing.

That’s the structure of the product quietly working against you the entire time.

Now Layer Options On Top of That…

This is where the situation becomes more complicated. At this point, you’re no longer dealing solely with the standard dynamics of options like time decay and changes in implied volatility —  you’re also working with an underlying product that is structurally losing value over time.

Options have the wonderful ability to amplify small moves in underlying equities. It can turn a small gain into triple-digit returns, and big gains into windfalls.

But when you try to amplify products that are built to lose, you end up amplifying that loss more often than not.

You’ve added another layer of friction to your trade before it even has a chance to work.

So even if you’re right on direction… even if the move you’re anticipating eventually plays out… the trade can still disappoint. And that’s where a lot of traders get tripped up.

It feels like the timing was off, or the market didn’t cooperate, when in reality the vehicle itself is working against you the entire time. You’re not just trying to be right—you’re trying to overcome multiple headwinds at once.

That’s why I say this very clearly: if you’re trading options on leveraged ETFs, you’re pushing a boulder uphill. It doesn’t mean it’s impossible. You can absolutely make money doing it. But you’re making the job harder than it needs to be, and over time, that added difficulty has a way of showing up in your results.

How We Turn Opportunity Into a Structured Trade

A good way to understand this is to look at a setup where you’re not layering complexity on top of complexity — you’re working with a clean underlying and using options the way they’re intended to be used.

In our recent trade on POET Technologies Inc. (POET), we’re dealing with a single stock.

No daily resets, embedded leverage, or structural quirks underneath the surface.

So when we add options to that position, we’re not compounding hidden risks—we’re simply enhancing the exposure.

The options are directly tied to the stock’s movement, which means if the stock moves in our favor, the options respond cleanly to that move. There’s no distortion in the relationship, no extra variable quietly working against us in the background.

We kept things simple: One underlying, one directional thesis, and a defined-risk options structure layered on top.

So instead of fighting multiple headwinds — time decay, implied volatility, and the behavior of the product itself — we’re focused on one thing: whether the stock moves.

And when it does, the structure is designed to pay you, not work against you.

When it was all said and done, our call spread reeled in a 492% return from an 80% move in the stock in 74 days.

That’s what alignment looks like. And that’s why this type of setup tends to produce more consistent, repeatable outcomes over time.

Trades like POET  aren’t one-offs. It’s a repeatable process — from identifying setups, structuring the position, and managing the trade once it starts working. These are the fundamental elements of options trading we focus on inside the Masters in Trading Challenge.

Over the course of just seven days, I lay out the framework you can apply over and over again so that when the market presents an opportunity, you know how to act on it. If you want to see how this comes together in real time—and start applying it yourself—you can learn more about the Masters in Trading Challenge here.

This is the standard I want you using when you evaluate an options trade.

Is the underlying clean? Does it have clear structure? Is the risk defined? Is the trade giving you a direct way to express the thesis?

Because if the answer is no, I’d rather pass.

And that brings me to the names I see traders reach for all the time — the ones that look active, look liquid, look exciting… but are built on exactly the kind of structure we want to avoid.

The ETFs I Want You to Treat Like the Plague

These are some of the most actively traded products I see come across our scanners and conversations.

And I don’t want you touching them—especially with options.

TQQQ — ProShares UltraPro QQQ

3x daily exposure to the Nasdaq.

This one looks incredible in a straight-line rally. But in any kind of chop, the decay adds up quickly. If you’re buying options here, you’re stacking leverage on top of leverage—and the math will catch up to you.

SQQQ — ProShares UltraPro Short QQQ

3x inverse Nasdaq.

Pull up a long-term chart. It’s a slow-motion grind lower with constant reverse splits. That’s decay in action.

SOXL — Direxion Daily Semiconductor Bull 3x

3x semiconductors.

Semis are already volatile. Now you’ve added leverage and daily reset mechanics. Great for intraday movement. Dangerous for anything beyond that.

SOXS — Direxion Daily Semiconductor Bear 3x

3x inverse semis.

Same issue as SOXL, just flipped. You’re not just betting against the sector—you’re betting against the structure holding up over time.

SPXL — Direxion Daily S&P 500 Bull 3X Shares

3x daily exposure to the S&P 500.

This one gives traders triple exposure to the broad market, which can look great when the index is moving straight higher. But the S&P 500 doesn’t move in a straight line. In choppy conditions, the daily reset and leverage create volatility drag, and buying options on top of that means you’re stacking leverage on an already leveraged product.

UVXY — ProShares Ultra VIX Short-Term Futures

Leveraged volatility exposure.

This is one of the cleanest examples of decay in the entire market. It exists to trade short-term volatility spikes—not to hold, and definitely not to build options positions around.

UCO — ProShares Ultra Bloomberg Crude Oil

2x oil exposure via futures.

You’re dealing with leverage and futures roll costs. That combination erodes value over time—even if oil trends correctly.

AGQ — ProShares Ultra Silver

2x silver exposure.

Commodities already have their own quirks. Add leverage and daily resets, and you get tracking issues that make options pricing even more difficult to navigate.

SSO — ProShares Ultra S&P 500

2x S&P 500.

Even in a broad index, the same math applies. In a trending market it can work. In a choppy one, the decay quietly eats away at returns.

UNG — United States Natural Gas Fund

Not leveraged—but still decays.

This is an important one.

Even without leverage, the futures structure creates a long-term drag. Add leverage on top of something like this, and it only gets worse.

The Way I Think About It

If I’m bullish on a name, I’ll buy the name.

If I want leverage, I’ll structure it myself — with options and defined risk.

But I’m not going to rent a decaying product that resets every day and expect it to behave over time.

Because it won’t.

And if you take one thing from this, let it be this:

You’re not just trading direction with these products.

You’re trading against the clock…

Against volatility…

And against the structure itself.

That’s a tough game to win, so don’t play it.

The post Stop Trading These 10 Leveraged ETFs Built to Lose appeared first on InvestorPlace.

Because from here, it won’t be about whether AI wins… it’ll be about who wins.
And that’s where I think a lot of investors are exposed right now.

Many are still concentrated in the “obvious” AI leaders. The companies that built the first wave. But as we’ve seen in past tech cycles, those early winners don’t always lead what comes next.
That’s why I’m sharing this piece from my colleague Eric Fry with you today.

He breaks down a pattern that’s played out from railroads to the internet: The builders get the attention, but the companies that apply the technology often deliver the biggest gains.
Eric believes that same shift is already beginning in AI and that a new phase he calls “A-AI” could accelerate it.

In the essay below, he explains what’s happening – and how to think about your positioning now.

Then, I strongly recommend you watch his new free presentation, where he goes deeper into this trend, including which types of stocks could be at risk, and where the biggest opportunities may be emerging.

*******************

Hello, Reader.

When the very first boxcar full of Campbell’s Condensed Tomato Soup left the company’s Camden, New Jersey, factory in 1898, it was riding atop rails built years earlier by the Philadelphia & Reading Railroad.

While the P&R had already gone bankrupt at that point, The Campbell’s Co. (CPB) went on to become a publicly traded staple worth tens of billions at its peak — with billions of cans sold and a presence in virtually every grocery store in America.

A savory reminder that yesterday’s failed builders often create the foundation for tomorrow’s success.

Every technological boom begins the same way.

A pioneer builds the “rails.” For a brief moment, investors applaud these visionaries.

But then optimistic vision collides with reality. Demand arrives slower than expected, and the builders struggle for survival.

Even as this pioneering group often fails, a follow-on group emerges and capitalizes on those failures. They don’t build the new technology. They use it.

By doing so, these enterprising companies build empires on the infrastructure some unfortunate “first mover” paid for.

And we’ve seen this exact movie in recent history… with the internet.

In the late 1990s, investors poured trillions into building the digital “rails” — telecom networks, fiber optics, and early hardware companies.

Many of those pioneers flamed out. Cisco Systems Inc. (CSCO), the poster child of internet infrastructure, lost roughly 85% of its value after the dot-com bubble burst… and took decades to fully recover.

But the companies that used that internet infrastructure?

They went on to dominate the global economy.

Amazon.com Inc. (AMZN) has surged more than 100,000% from its early days. Alphabet Inc. (GOOGL) has climbed tens of thousands of percent since its IPO.

The builders struggled. The appliers got rich.

And here’s the twist: Many of those same internet winners are now spending aggressively to build the next generation of technology — artificial intelligence.

Which raises a critical question: Will today’s AI builders ultimately follow the same path… while a new class of “AI Appliers” quietly captures the real upside?

In this issue, we’ll show you exactly how that shift is already beginning to play out — including one under-the-radar “applier” we’re giving you free today…

It’s a company already using AI to boost efficiency, expand margins, and quietly pull ahead — with one key metric already surging more than 50% in just the past two years…

The Builders Built It… The Appliers Won

In the late 1800s and early 1900s, railroads were not just transportation. They were a transformational commercial platform. But by the early 1890s, America was neck-deep in rail capacity.

As a result, hundreds of railroads failed to turn a profit.

More than 1,500 railroad companies entered bankruptcy or receivership during the major railroad collapses of the 1870s, 1890s, and early 1900s. One of those early casualties was the Philadelphia & Reading Railroad – a titan of the 19th-century rail industry.

By 1890, the P&R ranked among the most valuable railroad companies on the New York Stock Exchange. But just three years later, it rolled off the rails into bankruptcy court and triggered the national economic depression known as the Panic of 1893.

More than 70 railroads followed the P&R into bankruptcy that year – representing roughly 25% of U.S. railroad mileage at the time.

But even though the P&R and hundreds of other track builders wiped out their shareholders, their expansive iron transportation networks reshaped American commerce. This allowed “second movers” to reach customers nationwide using the established rail networks.

The Campbell Soup Co., now The Campbell’s Co. (CPB), built its empire on the exact rail network the P&R painstakingly assembled over decades.

Campbell’s sat squarely inside the rail geography the P&R helped knit together. The company transported produce by rail from the Midwest to canning facilities in New Jersey, and then rolled boxcars of tomato and cream-of-celery soup across the country.

But the nationwide rail infrastructure was not simply a delivery platform for Campbell’s. It also acted as a catalyst for innovation. Because rail freight rates depended mostly on weight, the company had a major incentive to remove as much weight as possible from its cargo.

In 1897, Campbell’s chemist John Dorrance figured out how to make “condensed” soup by removing most of the water during production. That single innovation spared Campbell’s from transporting worthless “water weight” all over the country.

As a result, it dramatically reduced the effective cost of shipping soup nationwide.

If the rails had not already existed, Dorrance likely would never have bothered trying to condense Campbell’s soup.

Thus, a food empire was born. The invention of condensed soup turned Campbell’s from a small regional food company into an iconic national brand.

But here’s the part most investors miss…

The railroad companies didn’t disappear because their idea was wrong. They disappeared because they built too early, spent too much, and couldn’t capture the value of what they created.

The real fortunes were made by the companies that came next — the ones that used the rails, instead of paying to lay them.

And that same shift is already starting to happen again today…

Today’s AI Builders Are Laying the Same Rails

We know who the P&R companies will be: the hyperscalers building the AI infrastructure – the likes of Alphabet, Amazon, Meta Platforms Inc. (META), and Microsoft Corp. (MSFT) – that are now over-investing.

These are the same companies that once thrived as appliers during the internet boom.

Now, they’re taking on a very different role. They’re the ones building the rails.

The winners of the AI boom won’t be the builders.

They’ll be the Appliers.

These are the “second movers” — companies using AI to cut costs, boost productivity, and expand margins inside already-established businesses.

They don’t look exciting. But historically, they’ve been where the biggest gains are made.

One of the clearest examples is hiding in plain sight.

PayPal Holdings Inc. (PYPL) is a textbook Applier.

It’s embedding AI directly into its digital payments infrastructure. It’s putting AI to work in fraud prevention, transaction approvals, customer service, loan underwriting, and even internal functions like marketing and compliance.

This isn’t theoretical. Revenue per employee has surged more than 50% since 2022, a clear sign AI is already driving real efficiency gains.

PayPal is also benefiting from AI’s expanding role in daily commerce. The company’s massive installed base of 435 million users positions it as a critical enabler of global digital commerce, much of which will be AI-influenced in the coming years.

At a recent investor conference, CEO Enrique Lores emphasized plans to accelerate AI-driven changes and serve customers in “new ways” — pointing to an expanding product lineup.

PayPal is riding the rails of already-built AI infrastructure, without risking its balance sheet to construct them.

And if history is any guide…

It’s companies like this, not the ones laying the rails, that stand to capture the biggest gains.

The Next Wave of AI Is Already Here

A new kind of disruptive technology is entering the scene — what I call “A-AI,” or autonomous AI.

This isn’t just AI that responds to prompts.

It’s AI that can act on its own — making decisions, executing tasks, and operating independently.

It has the potential to reshape entire industries — and early signs suggest it’s already putting pressure on some of the most widely held stocks in America.

Some of the market’s biggest winners today could quietly become tomorrow’s biggest disappointments.

Like every major technological shift, this will create winners… and losers.

And once again, the biggest winners are unlikely to be the ones building the technology. They’ll be the ones applying it.

In other words, you don’t want the P&R companies.

You want the Campbell’s.

In my brand-new special broadcast, I break down exactly how this shift is unfolding…

Which popular stocks may be at risk…

And which lesser-known “Appliers” are positioned to benefit.

I also reveal my No. 1 applier stock to buy before this technology takes off.

You can click here to watch it now.

Regards,

Eric Fry

Editor, Smart Money

P.S. Eric has seen multiple tech cycles—and knows where investors tend to get it wrong. His take on “AI Appliers” offers a grounded way to think about this boom. If you want to see how it could impact your portfolio, check out his latest free presentation while it’s still available.

The post Today’s AI Giants Could Be Tomorrow’s Disappointments appeared first on InvestorPlace.

The companies building the breakthrough technology grab the headlines first. Investors pile in. Expectations soar.

But the biggest winners usually come later — from an entirely different group…
Not the companies that build the tech but the ones that use it: the “Appliers.”

My colleague Eric Fry has been researching how this pattern has played out time and again.

He believes that same dynamic is now unfolding in AI — and that a new phase he calls “A-AI” could accelerate it. You can read his full take below… and if you want to go deeper, he’s just released a new presentation detailing how this trend is playing out — and which companies could benefit most. Check that out right here.

When the very first boxcar full of Campbell’s Condensed Tomato Soup left the company’s Camden, New Jersey, factory in 1898, it was riding atop rails built years earlier by the Philadelphia & Reading Railroad. 

While the P&R had already gone bankrupt at that point, The Campbell’s Co. (CPB) went on to become a publicly traded staple worth tens of billions at its peak — with billions of cans sold and a presence in virtually every grocery store in America.

A savory reminder that yesterday’s failed builders often create the foundation for tomorrow’s success.

Every technological boom begins the same way.

A pioneer builds the “rails.” For a brief moment, investors applaud these visionaries. 

But then optimistic vision collides with reality. Demand arrives slower than expected, and the builders struggle for survival.

Even as this pioneering group often fails, a follow-on group emerges and capitalizes on those failures. They don’t build the new technology. They use it.

By doing so, these enterprising companies build empires on the infrastructure some unfortunate “first mover” paid for.

And we’ve seen this exact movie in recent history… with the internet.

The Internet Boom Followed the Same Pattern 

In the late 1990s, investors poured trillions into building the digital “rails” — telecom networks, fiber optics, and early hardware companies.

Many of those pioneers flamed out. Cisco Systems Inc. (CSCO), the poster child of internet infrastructure, lost roughly 85% of its value after the dot-com bubble burst… and took decades to fully recover.

But the companies that used that internet infrastructure?

They went on to dominate the global economy.

Amazon.com Inc. (AMZN) has surged more than 100,000% from its early days. Alphabet Inc. (GOOGL) has climbed tens of thousands of percent since its IPO.

The builders struggled. The appliers got rich.

And here’s the twist: Many of those same internet winners are now spending aggressively to build the next generation of technology — artificial intelligence.

Which raises a critical question: Will today’s AI builders ultimately follow the same path… while a new class of “AI Appliers” quietly captures the real upside?

In this issue, we’ll show you exactly how that shift is already beginning to play out — including one under-the-radar “applier” we’re giving you free today…

It’s a company already using AI to boost efficiency, expand margins, and quietly pull ahead — with one key metric already surging more than 50% in just the past two years…

AI Builders vs. AI Appliers: Who Really Wins? 

In the late 1800s and early 1900s, railroads were not just transportation. They were a transformational commercial platform. But by the early 1890s, America was neck-deep in rail capacity. 

As a result, hundreds of railroads failed to turn a profit.

More than 1,500 railroad companies entered bankruptcy or receivership during the major railroad collapses of the 1870s, 1890s, and early 1900s. One of those early casualties was the Philadelphia & Reading Railroad – a titan of the 19th-century rail industry.

By 1890, the P&R ranked among the most valuable railroad companies on the New York Stock Exchange. But just three years later, it rolled off the rails into bankruptcy court and triggered the national economic depression known as the Panic of 1893. 

More than 70 railroads followed the P&R into bankruptcy that year – representing roughly 25% of U.S. railroad mileage at the time.

But even though the P&R and hundreds of other track builders wiped out their shareholders, their expansive iron transportation networks reshaped American commerce. This allowed “second movers” to reach customers nationwide using the established rail networks.

How ‘Second Movers’ Capture the Upside 

The Campbell Soup Co., now The Campbell’s Co., built its empire on the exact rail network the P&R painstakingly assembled over decades. 

Campbell’s sat squarely inside the rail geography the P&R helped knit together. The company transported produce by rail from the Midwest to canning facilities in New Jersey, and then rolled boxcars of tomato and cream-of-celery soup across the country.

But the nationwide rail infrastructure was not simply a delivery platform for Campbell’s. It also acted as a catalyst for innovation. Because rail freight rates depended mostly on weight, the company had a major incentive to remove as much weight as possible from its cargo.

In 1897, Campbell’s chemist John Dorrance figured out how to make “condensed” soup by removing most of the water during production. That single innovation spared Campbell’s from transporting worthless “water weight” all over the country. 

As a result, it dramatically reduced the effective cost of shipping soup nationwide. 

If the rails had not already existed, Dorrance likely would never have bothered trying to condense Campbell’s soup.

Thus, a food empire was born. The invention of condensed soup turned Campbell’s from a small regional food company into an iconic national brand. 

But here’s the part most investors miss…

The railroad companies didn’t disappear because their idea was wrong. They disappeared because they built too early, spent too much, and couldn’t capture the value of what they created.

The real fortunes were made by the companies that came next — the ones that used the rails, instead of paying to lay them.

And that same shift is already starting to happen again today…

Today’s AI Stocks Are Repeating the Same Pattern 

We know who the P&R companies will be: the hyperscalers building the AI infrastructure – the likes of Alphabet, Amazon, Meta Platforms Inc. (META), and Microsoft Corp. (MSFT) – that are now over-investing. 

These are the same companies that once thrived as appliers during the internet boom.

Now, they’re taking on a very different role. They’re the ones building the rails.

The winners of the AI boom won’t be the builders.

They’ll be the Appliers.

These are the “second movers” — companies using AI to cut costs, boost productivity, and expand margins inside already-established businesses.

They don’t look exciting. But historically, they’ve been where the biggest gains are made.

One of the clearest examples is hiding in plain sight.

A Real Example of an AI ‘Applier’ Stock 

PayPal Holdings Inc. (PYPL) is a textbook Applier. 

It’s embedding AI directly into its digital payments infrastructure. It’s putting AI to work in fraud prevention, transaction approvals, customer service, loan underwriting, and even internal functions like marketing and compliance.

This isn’t theoretical. Revenue per employee has surged more than 50% since 2022, a clear sign AI is already driving real efficiency gains.

PayPal is also benefiting from AI’s expanding role in daily commerce. The company’s massive installed base of 435 million users positions it as a critical enabler of global digital commerce, much of which will be AI-influenced in the coming years.

At a recent investor conference, CEO Enrique Lores emphasized plans to accelerate AI-driven changes and serve customers in “new ways” — pointing to an expanding product lineup.

PayPal is riding the rails of already-built AI infrastructure, without risking its balance sheet to construct them.

And if history is any guide…

It’s companies like this, not the ones laying the rails, that stand to capture the biggest gains.

The Next Wave of AI Stocks: Autonomous AI 

A new kind of disruptive technology is entering the scene — what I call “A-AI,” or autonomous AI.

This isn’t just AI that responds to prompts.

It’s AI that can act on its own — making decisions, executing tasks, and operating independently.

It has the potential to reshape entire industries — and early signs suggest it’s already putting pressure on some of the most widely held stocks in America.

Some of the market’s biggest winners today could quietly become tomorrow’s biggest disappointments.

Like every major technological shift, this will create winners… and losers.

And once again, the biggest winners are unlikely to be the ones building the technology. They’ll be the ones applying it.

In other words, you don’t want the P&R companies. 

You want the Campbell’s.

In my brand-new special broadcast, I break down exactly how this shift is unfolding…

Which popular stocks may be at risk…

And which lesser-known “Appliers” are positioned to benefit.

I also reveal my No. 1 applier stock to buy before this technology takes off.

You can click here to watch it now.

The post The Real Winners of the AI Boom Aren’t Who You Think appeared first on InvestorPlace.

The U.S. Interstate Highway System took 35 years to build. It stretches 48,000 miles, from Maine to California, with every on-ramp and overpass in between.

That’s enough asphalt to circle the globe nearly twice.

The total cost in today’s dollars? About $630 billion.

Now think about this… Big Tech is spending $725 billion on the artificial intelligence buildout.

And that’s just this year.

That’s such a gob-smacking amount of money, it’s hard to even wrap our brains around it.

To stack that much cash in $100 bills, you’d need 7,250 pallets. Laid end to end, that’s a line of money stretching from New York to Los Angeles and back – three times.

Now, Wall Street wants to know what they’re getting for all of that spending.

That’s why all eyes have been glued to the Big Tech reports this earnings season – especially the Magnificent Seven stocks.

So, in today’s Market 360, we’ll review three of them: Alphabet Inc. (GOOGL), Amazon.com, Inc. (AMZN) and Microsoft Corporation (MSFT). We’ll also see whether their AI spending is actually paying off.

Then, I’ll show you what this means for the next phase of the AI boom, and where the next investment opportunities are emerging.

Alphabet Beats – and Keeps Spending

Alphabet reported $5.11 earnings per share on $109.9 billion in revenue, crushing expectations on both counts.

The biggest standout was the Google Cloud unit, which reported $20.03 billion in revenue – up 63% from a year ago and well ahead of the $18.4 billion analysts expected. That is a clear sign that Alphabet’s AI investments are translating into real growth.

Investors noticed, sending shares up by about 10% yesterday.

However, that growth won’t come cheap. Alphabet raised its full-year spending outlook to between $180 billion and $190 billion. It also said that spending will rise “significantly” again through 2027.

Amazon Beats Big, but Gets Punished…

Next up, we have Amazon. The company reported $2.78 earnings per share on $181.5 billion in revenue in the first quarter, easily beating expectations for $1.62 earnings per share on $177.2 billion in revenue.

In other words, a massive beat.

AWS, its cloud business, generated $37.6 billion in revenue – up 28% year-over-year and its fastest growth rate in 15 quarters.

That should have been enough, but it wasn’t. Investors were looking for AWS growth closer to 30%.

But Amazon made it clear that it isn’t pulling back on spending, projecting nearly $200 billion in capex spending in 2026 alone – the same as previous projections.

Despite the stellar results, Amazon only rose about 1% on Thursday.

Microsoft: Incredible AI Numbers, Terrible Reaction

And finally, we turn to Microsoft. It may have delivered the clearest proof yet that AI is driving real revenue – even if it came with a catch.

For its third quarter, the company reported $4.27 earnings per share on $82.89 billion in revenue, topping expectations for $4.04 and $81.46 billion.

Azure Cloud grew 40% year-over-year, ahead of the 38% analysts expected. Its AI segment generated $37 billion – up 123% year-over-year.

So, Microsoft is converting all that AI spending into meaningful revenue. But the company is still spending heavily to keep up.

But Microsoft told investors to expect $40 billion in capex next quarter alone, and $190 billion for all of 2026 – up sharply from an earlier estimate of $150 billion. Next quarter’s revenue guidance came in just below Wall Street’s consensus.

That combination of softer guidance and rising capex was enough to send Microsoft’s shares down 4% on Thursday.

Should You Buy These Stocks?

So, what did we learn from this week’s earnings?

AI demand is real, and these companies are seeing strong growth from it, thanks to their booming cloud revenues. But investors are clearly becoming fickle about the results.

They’re also nervous about all that AI spending – and for understandable reasons.

Big Tech has already spent about $1 trillion on AI infrastructure. Throw in the $725 billion from this year alone, plus the projected $3 trillion in future spending… it’s mind-boggling.

We’re talking about the largest collective investment in history.

So, is all this spending actually worth it? Only time will tell. But more importantly, the question is… should you buy these stocks today?

And if not, which stocks are more deserving of your money?

To answer that, I ran all three through my Stock Grader system (subscription required). As you can see in the table below, the results are mixed…

In the case of Amazon and Microsoft, strong revenue doesn’t automatically make a strong stock. Right now, my system is flagging real headwinds for both – and this earnings season helps explain why.

Alphabet earns a Total Grade of B, making it a “Strong” stock. If you own it, I’m not telling you to sell. But honestly, I think there’s more money to be made elsewhere…

The AI Boom Isn’t Slowing Down – but the Winners Are Changing

Let’s go back to that $725 billion number for a second. Think about it. Somebody is supplying all of that infrastructure.

The data centers. The servers. The cooling systems.

Those companies – the ones enabling the boom – are the real winners right now.

But there’s something else none of these earnings calls addressed.

There’s a ceiling on what today’s AI can actually do. Cloud revenue can grow 63%. AI segments can surge 123%. But the underlying technology powering all of these systems has a fundamental limitation that more spending alone won’t fix.

And if we want to really unleash the full potential of AI and do things that actually matter – cure diseases like cancer, build energy sources that never run out or answer some of the deepest questions about the universe – today’s AI simply isn’t going to get us there.

That’s where a new class of AI computing comes in.

Right now, it’s being built quietly behind the scenes to break through that ceiling.

That’s what I break down in my new AI Reset briefing.

In it, I’ll show you exactly which companies I believe are best positioned as this shift unfolds – including one that recently signed a deal with the Trump administration to build what could be the most powerful AI computing system ever assembled.

The AI cloud race is real. But the next race has already started. And it looks nothing like what you saw in this week’s earnings reports.

Click here now to watch my full “AI Reset” briefing now.

Sincerely,

Louis Navellier

Editor, Market 360

The post Is the $725 Billion AI Spending Boom Paying Off? appeared first on InvestorPlace.

In today’s Friday Digest takeover, our macro investing expert Eric Fry revisits a pattern that’s played out across multiple technological revolutions. The early builders capture the spotlight, but the companies that use that new infrastructure often end up capturing the real long-term wealth.

Eric argues we’re seeing that same shift begin in AI right now.

While Big Tech is pouring billions into building the “rails,” a different class of companies is emerging – businesses quietly using AI to cut costs, boost efficiency, and expand margins, with results already showing up in the numbers.

Below, Eric breaks down how this transition is unfolding – and why the biggest winners of the next phase of AI may look very different from the leaders of today.

He also goes into far greater detail in a new presentation that includes the specific “AI Applier” stocks he believes are best positioned as this shift accelerates. You can check that out right here.

This is one of those ideas that can reshape how you think about the entire AI trade – and your portfolio.

I’ll let Eric take it from here.

Have a good evening,

Jeff Remsburg

************

Hello, Reader.

When the very first boxcar full of Campbell’s Condensed Tomato Soup left the company’s Camden, New Jersey, factory in 1898, it was riding atop rails built years earlier by the Philadelphia & Reading Railroad.

While the P&R had already gone bankrupt at that point, The Campbell’s Co. (CPB) went on to become a publicly traded staple worth tens of billions at its peak — with billions of cans sold and a presence in virtually every grocery store in America.

A savory reminder that yesterday’s failed builders often create the foundation for tomorrow’s success.

Every technological boom begins the same way.

A pioneer builds the “rails.” For a brief moment, investors applaud these visionaries.

But then optimistic vision collides with reality. Demand arrives slower than expected, and the builders struggle for survival.

Even as this pioneering group often fails, a follow-on group emerges and capitalizes on those failures. They don’t build the new technology. They use it.

By doing so, these enterprising companies build empires on the infrastructure some unfortunate “first mover” paid for.

And we’ve seen this exact movie in recent history… with the internet.

In the late 1990s, investors poured trillions into building the digital “rails” — telecom networks, fiber optics, and early hardware companies.

Many of those pioneers flamed out. Cisco Systems Inc. (CSCO), the poster child of internet infrastructure, lost roughly 85% of its value after the dot-com bubble burst… and took decades to fully recover.

But the companies that used that internet infrastructure?

They went on to dominate the global economy.

Amazon.com Inc. (AMZN) has surged more than 100,000% from its early days. Alphabet Inc. (GOOGL) has climbed tens of thousands of percent since its IPO.

The builders struggled. The appliers got rich.

And here’s the twist: Many of those same internet winners are now spending aggressively to build the next generation of technology — artificial intelligence.

Which raises a critical question: Will today’s AI builders ultimately follow the same path… while a new class of “AI Appliers” quietly captures the real upside?

In this issue, we’ll show you exactly how that shift is already beginning to play out — including one under-the-radar “applier” we’re giving you free today…

It’s a company already using AI to boost efficiency, expand margins, and quietly pull ahead — with one key metric already surging more than 50% in just the past two years…

The Builders Built It… The Appliers Won

In the late 1800s and early 1900s, railroads were not just transportation. They were a transformational commercial platform. But by the early 1890s, America was neck-deep in rail capacity.

As a result, hundreds of railroads failed to turn a profit.

More than 1,500 railroad companies entered bankruptcy or receivership during the major railroad collapses of the 1870s, 1890s, and early 1900s. One of those early casualties was the Philadelphia & Reading Railroad – a titan of the 19th-century rail industry.

By 1890, the P&R ranked among the most valuable railroad companies on the New York Stock Exchange. But just three years later, it rolled off the rails into bankruptcy court and triggered the national economic depression known as the Panic of 1893.

More than 70 railroads followed the P&R into bankruptcy that year – representing roughly 25% of U.S. railroad mileage at the time.

But even though the P&R and hundreds of other track builders wiped out their shareholders, their expansive iron transportation networks reshaped American commerce. This allowed “second movers” to reach customers nationwide using the established rail networks.

The Campbell Soup Co., now The Campbell’s Co. (CPB), built its empire on the exact rail network the P&R painstakingly assembled over decades.

Campbell’s sat squarely inside the rail geography the P&R helped knit together. The company transported produce by rail from the Midwest to canning facilities in New Jersey, and then rolled boxcars of tomato and cream-of-celery soup across the country.

But the nationwide rail infrastructure was not simply a delivery platform for Campbell’s. It also acted as a catalyst for innovation. Because rail freight rates depended mostly on weight, the company had a major incentive to remove as much weight as possible from its cargo.

In 1897, Campbell’s chemist John Dorrance figured out how to make “condensed” soup by removing most of the water during production. That single innovation spared Campbell’s from transporting worthless “water weight” all over the country.

As a result, it dramatically reduced the effective cost of shipping soup nationwide.

If the rails had not already existed, Dorrance likely would never have bothered trying to condense Campbell’s soup.

Thus, a food empire was born. The invention of condensed soup turned Campbell’s from a small regional food company into an iconic national brand.

But here’s the part most investors miss…

The railroad companies didn’t disappear because their idea was wrong. They disappeared because they built too early, spent too much, and couldn’t capture the value of what they created.

The real fortunes were made by the companies that came next — the ones that used the rails, instead of paying to lay them.

And that same shift is already starting to happen again today…

Today’s AI Builders Are Laying the Same Rails

We know who the P&R companies will be: the hyperscalers building the AI infrastructure – the likes of Alphabet, Amazon, Meta Platforms Inc. (META), and Microsoft Corp. (MSFT) – that are now over-investing.

These are the same companies that once thrived as appliers during the internet boom.

Now, they’re taking on a very different role. They’re the ones building the rails.

The winners of the AI boom won’t be the builders.

They’ll be the Appliers.

These are the “second movers” — companies using AI to cut costs, boost productivity, and expand margins inside already-established businesses.

They don’t look exciting. But historically, they’ve been where the biggest gains are made.

One of the clearest examples is hiding in plain sight.

PayPal Holdings Inc. (PYPL) is a textbook Applier.

It’s embedding AI directly into its digital payments infrastructure. It’s putting AI to work in fraud prevention, transaction approvals, customer service, loan underwriting, and even internal functions like marketing and compliance.

This isn’t theoretical. Revenue per employee has surged more than 50% since 2022, a clear sign AI is already driving real efficiency gains.

PayPal is also benefiting from AI’s expanding role in daily commerce. The company’s massive installed base of 435 million users positions it as a critical enabler of global digital commerce, much of which will be AI-influenced in the coming years.

At a recent investor conference, CEO Enrique Lores emphasized plans to accelerate AI-driven changes and serve customers in “new ways” — pointing to an expanding product lineup.

PayPal is riding the rails of already-built AI infrastructure, without risking its balance sheet to construct them.

And if history is any guide…

It’s companies like this, not the ones laying the rails, that stand to capture the biggest gains.

The Next Wave of AI Is Already Here

A new kind of disruptive technology is entering the scene — what I call “A-AI,” or autonomous AI.

This isn’t just AI that responds to prompts.

It’s AI that can act on its own — making decisions, executing tasks, and operating independently.

It has the potential to reshape entire industries — and early signs suggest it’s already putting pressure on some of the most widely held stocks in America.

Some of the market’s biggest winners today could quietly become tomorrow’s biggest disappointments.

Like every major technological shift, this will create winners… and losers.

And once again, the biggest winners are unlikely to be the ones building the technology. They’ll be the ones applying it.

In other words, you don’t want the P&R companies.

You want the Campbell’s.

In my brand-new special broadcast, I break down exactly how this shift is unfolding…

Which popular stocks may be at risk…

And which lesser-known “Appliers” are positioned to benefit.

I also reveal my No. 1 applier stock to buy before this technology takes off.

You can click here to watch it now.

Regards,

Eric Fry

P.S. Eric has seen multiple tech cycles—and knows where investors tend to get it wrong. His take on “AI Appliers” offers a grounded way to think about this boom. If you want to see how it could impact your portfolio, check out his latest free presentation while it’s still available.

The post Why the Biggest AI Winners May Not Be AI Companies appeared first on InvestorPlace.

It doesn’t announce itself. It doesn’t show up in the headlines. It shows up first in the data —pressure building beneath the surface of stocks that everyone assumes are safe.

Right now, I’m seeing that pressure building inside the business models of some of Wall Street’s most widely held software and AI companies.

The math changed before the narrative did at Enron.

It changed before the narrative did at Lehman. At Silicon Valley Bank. At every major blowup I’ve tracked across four decades of building quantitative models.

The stock charts looked fine. But the numbers underneath told a completely different story.

Right now, my models are picking up that same kind of stress again.

Not in the credit markets. Not in the broader economy. But inside the business models of some of the most widely held software and AI stocks on Wall Street; companies that most investors still think are bulletproof.

Most investors aren’t seeing it yet. The stocks still look fine and the narrative is still bullish. But the underlying dynamics are shifting in a big way.

My colleague Thomas Yeung has been tracking this more carefully than anyone I know. In the essay below, Tom explains what is driving this divergence — specifically, a new class of AI that operates without waiting for instructions, and what that means for the companies most investors still consider untouchable.

He also points you to a free presentation from Eric Fry, who has been studying this transition for months. Eric’s conclusion: this isn’t just volatility. It’s the early innings of a major rotation — one that could separate the next generation of big winners from the companies quietly being left behind.

I’d encourage you to read Tom’s essay carefully, and then watch Eric’s full presentation here.

The window to act is still open. But these windows have a habit of closing faster than anyone expects…

Imagine waking up one morning to find your bank account drained… your phone locked… and your passwords no longer work.

At the same time, systems you rely on every day — payments, communications, even parts of the power grid — start to glitch or go dark.

All this with no warning, no explanation, and no obvious point of entry.

Just chaos.

This is what could happen if hackers armed with AI exploited “zero-day” vulnerabilities: hidden flaws in software that no one knows exist and, therefore, has had zero days to fix.

On April 7, Anthropic released a limited version of Claude Mythos, an AI system so capable that the company immediately restricted access to it.

Mythos uncovered zero-day vulnerabilities in every major operating system, including one that had gone undetected for 27 years.

These hidden weaknesses can be exploited to steal data, seize control of computer systems, cripple critical infrastructure, and more.

Anthropic didn’t program Mythos to do this. The hacking capabilities emerged on their own.

As the company explained: “We did not explicitly train Mythos to have these capabilities. Rather, they emerged as a downstream consequence of general improvements in code, reasoning, and autonomy.”

The reaction at the highest levels was immediate. Federal Reserve Chair Jerome Powell and Treasury Secretary Scott Bessent held a closed-door meeting with top bank CEOs to discuss risks to the global financial system. Shares of major cybersecurity firms fell by double digits.

Most investors missed it entirely. The usual noise — Middle East tensions, gas prices, tariffs — drowned out what may be the single most consequential technological development of our generation.

Because Mythos isn’t just a more powerful chatbot.

It’s a signal that AI has crossed a threshold that I’ve been watching for, and writing about, for months now. We’ve moved from AI as a tool that responds to instructions… to AI that can act, adapt, and solve complex problems entirely on its own.

In my work tracking hypergrowth opportunities across decades of market cycles, shifts like this don’t just change the technology landscape. They reshuffle the entire investment landscape with them.

The companies on the right side of this shift could see the kind of explosive, compounding growth that defined the early cloud winners and the best AI infrastructure plays of the last three years.

The companies on the wrong side may not survive it.

Which side your portfolio is on right now matters more than almost anything else.

This Shift Is Already Underway

To understand why Mythos matters, you need to understand what’s been building underneath it.

A new kind of AI that doesn’t just respond to prompts… but can execute complex tasks on its own.

A year ago, a Chinese startup called Manus AI introduced a system that could analyze financial transactions, screen job candidates, and navigate complex digital workflows without step-by-step human input. Retired New York Times writer Craig S. Smith called it a “game-changer.”

That forced every major Western AI company to respond. Within months, OpenAI and Anthropic released similar systems capable of handling multistep tasks, managing workflows, and making decisions with minimal oversight.

Then last November came OpenClaw, a free, open-source platform that exploded to 30 million monthly users. At Nvidia Corp.’s (NVDA) GTC conference, CEO Jensen Huang called it “probably the single most important release of software… probably ever.”

These aren’t chatbots. They’re digital workers – handling emails, moving files, managing information, writing code, reviewing contracts… and doing it around the clock without asking for a raise.

I’ve seen this firsthand. With Claude Code, I can now give an AI assistant raw financial data and ask it to build a quantitative model. It runs off by itself to write thousands of lines of code. Then it tests the model… critiques it… asks for more data… and suggests improvements. It’s no longer a robotic mecha-suit that needs a human pilot. It’s the whole machine, replacing entire teams of analysts and coders.

And if I can do that as one analyst, imagine what Anthropic’s 1,500-person engineering team came up with when they used these tools for themselves…

So even if Mythos isn’t the endpoint, it’s a clear step-change in what these systems can do. New generations of AI models typically appear six to 12 months after a major launch, and I wouldn’t be surprised if a “Mythos V2” arrives by December.

Why Your “Safe” AI Stocks May Be the Most Exposed

Here’s where things get uncomfortable.

The same technology behind Mythos is now dismantling the business models behind some of Wall Street’s most popular stocks.

On Feb. 4, Anthropic released a legal plug-in for Claude Cowork. The effect on Wall Street was immediate.

• Shares of Thomson Reuters Corp. (TRI) gapped down 19%.
• LexisNexis parent RELX Plc (RELX) dropped 15%.
• LegalZoom.com Inc. (LZ) crashed 20%.

Wall Street has been calling this the “SaaSpocalypse,” a rolling collapse in software-as-a-service (SaaS) stocks that has now spread far beyond legal tech.

Will AI replace customer service platforms?

Real estate brokerages?

Financial services?

Business automation?

That fear isn’t misplaced. For 15 years, the SaaS profit machine worked like this: Build a dashboard, connect it to a database, charge companies $30 to $100 per month per employee to use it. The more workers a client hired, the more money software companies made. No one questioned the 95%-plus gross margins these firms routinely earned.

But agentic AI doesn’t need dashboards. It connects directly to underlying systems, pulls data, updates records, and triggers next steps automatically. When one AI agent can do the work of five junior analysts or paralegals, companies don’t just need fewer employees. They need fewer software licenses.

And if these systems get powered by a model as powerful as Mythos, the pressure on SaaS business models could accelerate very quickly.

Meanwhile, the companies you’d expect to benefit – the pure-play AI names – are trading at valuations that assume perfection.

We saw this movie before during the dot-com hysteria. Many sought-after internet darlings like Cisco Systems Inc. (CSCO), Lucent, and AOL failed to deliver… and so did firms like Borders and Circuit City that were disrupted by the internet era.

So, the question isn’t whether AI is a big deal.

That debate is over.

The question is: As investors, how can we profit?

The Coming AI Reckoning

My InvestorPlace colleague Eric Fry believes the big profit opportunities will be in the “Appliers.” These aren’t the firms building AI. They’re the ones using it to transform entire industries.

Think sensors, robotics, industrial systems, and security infrastructure. Companies with hard-to-replicate data edges and real-world integration that can’t be vibe-coded away.

He sees this “AI Reckoning” as a major inflection point. In the coming months, he believes we’re going to see a wealth shift from those holding the wrong stocks to those positioned in AI Applier companies that connect this digital technology to the physical world.

He’s put together a free presentation that goes far deeper than I can here – naming the specific stocks he believes are most at risk, and the ones positioned to capture the upside as this shift accelerates.

The scenario we started with may sound extreme.

But the forces behind it are already here—and they’re beginning to reshape which companies win, and which ones don’t.

If you own any AI-adjacent stocks (and at this point, who doesn’t?), it’s worth seeing what he found – especially before this shift becomes more obvious to the broader market.

Thomas Yeung, CFA

Market Analyst, InvestorPlace

P.S. A lot of investors think the biggest AI gains are already behind us. Eric Fry believes the opposite may be true… but only for a specific group of companies that most people aren’t watching. In his latest presentation, he explains why some of today’s biggest winners could struggle from here, and how a lesser-known group could deliver outsized gains in the next phase of the cycle. It’s worth a look if you haven’t seen it yet.

FAQ

What is agentic AI and why does it matter for investors?

Agentic AI refers to artificial intelligence systems that can act autonomously — executing complex tasks, making decisions, and solving problems without step-by-step human input. Unlike traditional AI chatbots that respond to prompts, agentic AI operates more like a self-directed digital worker. For investors, it matters because it threatens the business models of widely held SaaS companies while simultaneously creating a new class of winners among companies that deploy it effectively.

What is the “SaaSpocalypse” and which stocks are most at risk?

The “SaaSpocalypse” refers to the rolling collapse in software-as-a-service stocks triggered by agentic AI. For 15 years, SaaS companies charged businesses per employee per month to access software dashboards — a model that produced 95%+ gross margins. Agentic AI bypasses those dashboards entirely, connecting directly to underlying systems and automating the work those licenses supported. Companies most at risk are those whose value proposition is access rather than irreplaceable data or deep workflow integration.

What are “AI Appliers” and why does Eric Fry believe they represent the next big opportunity?

AI Appliers are companies that use artificial intelligence to transform physical industries — think sensors, robotics, industrial systems, and security infrastructure — rather than companies building the underlying AI models themselves. Eric Fry believes these companies represent the next phase of the AI wealth transfer because they combine hard-to-replicate data advantages with real-world integration that can’t easily be automated away. Many are still under the radar while the market remains fixated on richly valued AI builders.

What is Claude Mythos and what makes it different from previous AI systems?

Claude Mythos is an AI system released by Anthropic in April 2025 that was so capable the company immediately restricted access to it. What made it significant wasn’t just its power — it was the fact that it autonomously discovered zero-day cybersecurity vulnerabilities in every major operating system, including one that had gone undetected for 27 years. Anthropic confirmed it never programmed Mythos to do this. The capabilities emerged on their own as a byproduct of advances in reasoning and autonomy — a signal that AI development has crossed an important threshold.

How should I position my portfolio for the AI Reckoning?

The dot-com era offers the clearest roadmap. When the internet arrived, it minted a new generation of winners — while destroying companies that most investors assumed were untouchable. The same dynamic is playing out now. The key is distinguishing between companies that will be disrupted by agentic AI and those positioned to deploy it as a competitive weapon. Eric Fry’s free presentation identifies specific stocks on both sides of that divide — including names most investors aren’t watching yet.

The post The AI Nobody’s Talking About Is Already Picking Winners appeared first on InvestorPlace.

We are compute constrained in the near term.

Our cloud revenue would have been higher if we were able to meet the demand.

That was Alphabet’s CEO Sundar Pichai on last night’s earnings call – and it speaks directly to the question we posed in Monday’s Digest.

The numbers that followed were genuinely impressive – across the board. But as with most things in this AI story, there’s more to it than the headlines suggest.

On Monday, we presented last night’s cluster of Big Tech earnings not as a test of individual companies, but as a diagnostic on the AI trade itself. We said there were two issues to watch: revenues, as a signal of whether AI-driven demand is actually materializing, and capex, as a signal of whether conviction in that demand is holding.

Now that the numbers are in, here’s what we learned…

Massive revenue growth across the board

Let’s start with Alphabet (GOOGL).

Google Cloud revenue surged 63% year-over-year to $20.02 billion – well ahead of the $18.05 billion Wall Street expected. Total revenue came in at $109.9 billion versus the $107.2 billion consensus, with net income up 81% from a year ago.

It was a blockbuster performance, putting the Pichai quote above in an important context. He wasn’t just spouting the usual CEO earnings-call optimism – he was highlighting a real supply constraint.

Google Cloud, acting as the company’s primary AI revenue engine, didn’t merely beat expectations – it was demand-limited.

From Pichai:

Our enterprise AI solutions have become our primary growth driver for cloud for the first time in Q1.

That’s a stronger enterprise AI demand signal than generic cloud growth – but I’ll note that it still sits closer to the infrastructure layer than a pure end-user monetization signal. I’ll get more into this distinction shortly.

Shifting to Microsoft (MSFT), its revenue came in at $82.89 billion, versus the $81.39 billion expected, with adjusted EPS of $4.27, versus the $4.06 estimate. Azure and other cloud services grew 40% – ahead of the 38.8% to 39.3% analyst range.

Another strong performance.

Meanwhile, the enterprise adoption numbers are striking. GitHub Copilot now has over 26 million users, and over 90% of the Fortune 500 is using Microsoft 365 Copilot. Commercial bookings jumped 112% year-over-year, driven by Azure commitments from OpenAI.

Turning to Amazon (AMZN), it delivered the most dramatic acceleration of the group.

Amazon Web Services (AWS) grew 28% to $37.59 billion – the fastest pace in 15 quarters, up from 24% last quarter. Total revenue of $181.52 billion crushed the $177.3 billion consensus, and EPS of $2.78 obliterated the $1.64 expectation.

One number to highlight is Amazon’s chip business. Graviton, Trainium, and Nitro combined have now exceeded a $20 billion annual revenue run rate, growing triple digits year-over-year.

Finally, Meta (META) beat on revenue – $56.31 billion versus $55.45 billion expected, up 33% from a year earlier and the fastest quarterly growth since 2021. Plus, adjusted EPS of $7.31 topped the $6.79 estimate.

Still, shares are down about 10% as I write on Thursday because daily active people came in at 3.56 billion, below the 3.62 billion Wall Street projected and more than 5% below Q4.

The company partly attributed the decline to internet disruptions in Iran, which is a genuine factor. Whether that fully explains the miss or masks something more structural is a question worth watching over the next quarter or two.

Enterprise growth is real, but here’s what it does and doesn’t tell us

Across all four companies, demand is strong and accelerating – but it’s worth being precise about the nature of these revenues.

Much of it is enterprise AI deployment: companies paying to build, train, and run AI systems. That is genuinely encouraging and more advanced than it was even two quarters ago.

But enterprise demand is also a messy signal…

Some of what’s driving these numbers is durable deployment. Some is still experimentation, pilot programs, and capacity being built ahead of demand that hasn’t fully arrived yet. At this stage, the earnings don’t cleanly separate those outcomes.

And either way, the “end user” here is overwhelmingly corporate. That’s an entirely different story from ordinary people opening their wallets for AI products in their daily lives.

That market – the Regular Joe who decides every month whether an AI subscription is worth keeping – is still largely unproven at scale.

So, what’s our best window into that demand?

OpenAI remains the clearest window into the consumer side

It doesn’t report earnings, but as we covered on Tuesday, OpenAI has reportedly missed internal usage and revenue targets due to massive compute costs and rising competition.

It has a real and growing enterprise business – ChatGPT Enterprise, API revenue, and deep integration with Microsoft’s product suite. But consider the scale of its infrastructure commitments: OpenAI has obligated itself to $250 billion in Azure spending alone – and that figure doesn’t even include the broader compute costs its own operations require.

At that scale, even strong enterprise growth may not be enough to close the loop. Consumer monetization isn’t a nice-to-have – it’s a necessity.

The evidence that it’s arriving at scale remains thin. That’s the gap last night’s strong enterprise numbers didn’t resolve.

Shifting to our capex question: the canary didn’t just survive – it sang louder

On Monday, we said the “canary” worth watching was whether any hyperscaler quietly signaled doubt by dialing back investment. This wasn’t our prediction for last night – we flagged the capex canary as a longer-term watch item, not an imminent threat.

But was there not even the faintest glimmer of that signal: three of the four raised their spending commitments, and the fourth held firm on a $200 billion full-year plan.

Microsoft’s quarterly capex actually came in below estimates – $31.9 billion versus the $35.3 billion consensus – but paired that undershoot with a full-year guide of $190 billion, well above the $154.6 billion analysts had expected. The message: more conviction overall, just on a more deliberate quarterly cadence.

Alphabet raised its full-year range to $180 to $190 billion, up from $175 to $185 billion previously, and CFO Anat Ashkenazi told analysts to expect 2027 capex to “significantly increase” from there.

Meta raised its 2026 range to $125 to $145 billion, up from $115 to $135 billion, citing higher component pricing.

Finally, Amazon is holding firm on its $200 billion commitment.

Add it up, and you’re looking at somewhere north of $700 billion in combined 2026 capex across these four companies.

That’s not just a vote of confidence in AI’s potential, it’s a multi-hundred-billion-dollar bet that demand is real and growing.

Here’s a chart our hypergrowth expert Luke Lango sent me that summarizes the numbers:

Bottom line: the capex picture is increasingly clear. The returns picture is where it gets more nuanced.

So where does all this leave us?

In a better place than the skeptics expected, but a more nuanced place than the bulls may admit.

The infrastructure layer delivered genuinely good news last night. Cloud demand is real, accelerating, and in some cases supply-constrained – even if it doesn’t yet tell us cleanly how much of that demand will translate into durable end-user monetization.

But what we didn’t learn last night is whether the current wave of enterprise AI adoption is primarily a cost-cutting story from Corporate America or the beginning of a genuine new revenue-generation story.

Right now, the evidence leans toward efficiency: companies using AI to do more with fewer people. That’s valuable. But cost-cutting has a ceiling.

What we’d rather see is AI generating entirely new revenue streams rather than trimming existing costs – something that hasn’t yet shown up clearly in the numbers.

Bottom line: Last night’s numbers were strong. They confirmed the infrastructure bet is real. What they didn’t do is prove that the returns will match the investment.

Which brings us to a question that the $700 billion capex number raises…

If the builders are spending at this scale, who actually captures the return?

Our global macro expert Eric Fry of The Speculator has spent months studying how major technology booms play out – and his conclusion adds a layer of complexity to last night’s numbers.

His research shows that in every major tech cycle, from railroads to the internet, it’s rarely the infrastructure builders who capture the lasting gains.

As Eric puts it:

The builders struggled. The appliers got rich.

He points to Cisco – the poster child of internet infrastructure — which lost roughly 85% of its value after the dot-com bubble burst and took decades to recover.

The company that best used Cisco’s wreckage?

Amazon – up more than 100,000% from its early days.

Eric believes that same rotation – from AI Builders to AI Appliers – is beginning now. In his new free presentation, he names the stocks he’d sell and the overlooked companies he believes capture the next phase.

You can check it out right here.

Coming full circle, we’ll end on this…

The AI trade is real. Whether it’s as profitable as it is powerful is the question that will define the next few years.

Have a good evening,

Jeff Remsburg

The post AI’s $700 Billion Question appeared first on InvestorPlace.

That’s how Jerome Powell concluded the press conference after his final Federal Open Market Committee meeting as Federal Reserve Chair on Wednesday.

It sounded like a clean goodbye.

But like a lot of things around the Fed these days, the reality is a little more complicated.

Powell’s final meeting as Fed Chair ended with no rate cut, no clear consensus and no easy answers.

Right now, the Fed is more divided than it has been in decades. Inflation is still sticky. Energy prices are muddying the picture. And even though rate cuts may be coming, nobody seems to agree on how soon they should begin.

As for Powell, his term as Fed Chair ends on May 15. Kevin Warsh is moving closer to taking over the gavel after the Justice Department dropped its criminal investigation into Powell and the Senate Banking Committee advanced Warsh’s nomination. A vote is expected soon.

But Powell has also said he plans to remain on the Fed Board after his Chair term ends. So, Warsh may take over as Chair while Powell remains in the room.

That is unusual. Most modern Fed Chairs have left once they handed over the gavel. I’ll have more to say about this changing of the guard next week, but in today’s Market 360, I want to focus on what happened at the latest meeting, what the latest inflation data tells us about future rate cuts – and why the bigger opportunity may have nothing to do with the Fed at all.

A New Fed Regime Is Coming

On Wednesday, the Fed held its benchmark interest rate steady at 3.5% to 3.75%. That marks the third-straight meeting where the central bank decided to sit tight.

Fed Governor Stephen Miran wanted to cut rates immediately. But three Fed hawks supported holding rates steady and did not want to telegraph future cuts. In plain English, one group wants to start easing now, while another group does not want Wall Street assuming rate cuts are guaranteed.

I understand why the Fed is hesitant. Cut too soon, and it risks feeding inflation. Wait too long, and it risks choking off growth. That is the box Powell has been in for months.

But I think what they’re missing is the fact that a lot of the inflation we are dealing with right now is energy-related, which was evident in this morning’s Personal Consumption Expenditures (PCE) reading.

Core PCE rose 0.3% in March, pushing the annual rate to 3.2% – its highest level since November 2023. Headline PCE rose 0.7% for the month and 3.5% over the past year, as oil prices pushed consumers’ costs higher.

The good news: These numbers matched expectations, so this was not an inflation shock.

The bad news: Inflation is still too hot for the Fed to declare victory.

Personally, I do think rate cuts are coming, because the reality is that the Fed cannot control energy prices. But if the conflict in the Middle East can wrap up soon, then energy prices should cool, and inflation could start looking better quickly.

And if inflation looks better, the new Fed regime will have more cover to cut.

Warsh Could Bring a Different Fed

What people need to understand is Kevin Warsh is not some unknown. He was at the Fed during the 2008 financial crisis, so he knows what it means to act when markets are under stress.

But he also has a different view of monetary policy than Powell. We’ll get into that more next week. For now, I would just say that a Warsh-led Fed is much more likely to act quickly on interest rates, especially if inflation cools and energy prices stop pressuring the data.

Lower rates make it cheaper for companies to borrow and expand. They also tend to push investors back toward stocks, especially growth stocks.

But I do not want you to make the mistake of thinking rate cuts solve everything.

There is still risk in the bond market. If bond vigilantes keep pushing long-term yields higher, investors can collect a nice yield but still watch their principal erode. That is a problem Treasury Secretary Scott Bessent will have to deal with.

Don’t Wait on the Fed

So, the biggest fortunes aren’t made by waiting for the Fed to give the all-clear. They are made by getting positioned before the next great market reset becomes obvious to everyone else.

And right now, I believe that reset is happening in AI.

The first phase of the AI boom was about chatbots, graphics chips and data centers. It produced some incredible winners.

But the next phase could be much bigger. It will be the phase where you’ll hear about things like scientific AI breakthroughs, AI agents, national-security AI infrastructure, quantum computing, nuclear fusion, and more.

That is why I recently sat down for an urgent presentation about what I call the AI Reset.

When a reset that big happens, the old winners do not always remain winners.

And in my special presentation, I explain why some of today’s most popular AI stocks could be vulnerable… which new class of AI stocks I believe could benefit as this technology comes online… and the AI stocks I believe investors should buy – and avoid – before this reset hits.

Click here now to watch my presentation and see how to position your portfolio before this story breaks wide open.

Sincerely,

Louis Navellier

Editor, Market 360

The post Don’t Let a Divided Federal Reserve Distract You From the Next Wave of AI appeared first on InvestorPlace.

Hello, Reader.

When the very first boxcar full of Campbell’s Condensed Tomato Soup left the company’s Camden, New Jersey, factory in 1898, it was riding atop rails built years earlier by the Philadelphia & Reading Railroad.

While the P&R had already gone bankrupt at that point, The Campbell’s Co. (CPB) went on to become a publicly traded staple worth tens of billions at its peak — with billions of cans sold and a presence in virtually every grocery store in America.

A savory reminder that yesterday’s failed builders often create the foundation for tomorrow’s success.

Every technological boom begins the same way.

A pioneer builds the “rails.” For a brief moment, investors applaud these visionaries.

But then optimistic vision collides with reality. Demand arrives slower than expected, and the builders struggle for survival.

Even as this pioneering group often fails, a follow-on group emerges and capitalizes on those failures. They don’t build the new technology. They use it.

By doing so, these enterprising companies build empires on the infrastructure some unfortunate “first mover” paid for.

And we’ve seen this exact movie in recent history… with the internet.

In the late 1990s, investors poured trillions into building the digital “rails” — telecom networks, fiber optics, and early hardware companies.

Many of those pioneers flamed out. Cisco Systems Inc. (CSCO), the poster child of internet infrastructure, lost roughly 85% of its value after the dot-com bubble burst… and took decades to fully recover.

But the companies that used that internet infrastructure?

They went on to dominate the global economy.

Amazon.com Inc. (AMZN) has surged more than 100,000% from its early days. Alphabet Inc. (GOOGL) has climbed tens of thousands of percent since its IPO.

The builders struggled. The appliers got rich.

And here’s the twist: Many of those same internet winners are now spending aggressively to build the next generation of technology — artificial intelligence.

Which raises a critical question: Will today’s AI builders ultimately follow the same path… while a new class of “AI Appliers” quietly captures the real upside?

In today’s Smart Money, we’ll show you exactly how that shift is already beginning to play out — including one under-the-radar “applier” we’re giving you free today…

It’s a company already using AI to boost efficiency, expand margins, and quietly pull ahead — with one key metric already surging more than 50% in just the past two years…

The Builders Built It… The Appliers Won

In the late 1800s and early 1900s, railroads were not just transportation. They were a transformational commercial platform. But by the early 1890s, America was neck-deep in rail capacity.

As a result, hundreds of railroads failed to turn a profit.

More than 1,500 railroad companies entered bankruptcy or receivership during the major railroad collapses of the 1870s, 1890s, and early 1900s. One of those early casualties was the Philadelphia & Reading Railroad – a titan of the 19th-century rail industry.

By 1890, the P&R ranked among the most valuable railroad companies on the New York Stock Exchange. But just three years later, it rolled off the rails into bankruptcy court and triggered the national economic depression known as the Panic of 1893.

More than 70 railroads followed the P&R into bankruptcy that year – representing roughly 25% of U.S. railroad mileage at the time.

But even though the P&R and hundreds of other track builders wiped out their shareholders, their expansive iron transportation networks reshaped American commerce. This allowed “second movers” to reach customers nationwide using the established rail networks.

The Campbell Soup Co., now The Campbell’s Co. (CPB), built its empire on the exact rail network the P&R painstakingly assembled over decades.

Campbell’s sat squarely inside the rail geography the P&R helped knit together. The company transported produce by rail from the Midwest to canning facilities in New Jersey, and then rolled boxcars of tomato and cream-of-celery soup across the country.

But the nationwide rail infrastructure was not simply a delivery platform for Campbell’s. It also acted as a catalyst for innovation. Because rail freight rates depended mostly on weight, the company had a major incentive to remove as much weight as possible from its cargo.

In 1897, Campbell’s chemist John Dorrance figured out how to make “condensed” soup by removing most of the water during production. That single innovation spared Campbell’s from transporting worthless “water weight” all over the country.

As a result, it dramatically reduced the effective cost of shipping soup nationwide.

If the rails had not already existed, Dorrance likely would never have bothered trying to condense Campbell’s soup.

Thus, a food empire was born. The invention of condensed soup turned Campbell’s from a small regional food company into an iconic national brand.

But here’s the part most investors miss…

The railroad companies didn’t disappear because their idea was wrong. They disappeared because they built too early, spent too much, and couldn’t capture the value of what they created.

The real fortunes were made by the companies that came next — the ones that used the rails, instead of paying to lay them.

And that same shift is already starting to happen again today…

Today’s AI Builders Are Laying the Same Rails

We know who the P&R companies will be: the hyperscalers building the AI infrastructure – the likes of Alphabet, Amazon, Meta Platforms Inc. (META), and Microsoft Corp. (MSFT) – that are now over-investing.

These are the same companies that once thrived as appliers during the internet boom.

Now, they’re taking on a very different role. They’re the ones building the rails.

The winners of the AI boom won’t be the builders.

They’ll be the Appliers.

These are the “second movers” — companies using AI to cut costs, boost productivity, and expand margins inside already-established businesses.

They don’t look exciting. But historically, they’ve been where the biggest gains are made.

One of the clearest examples is hiding in plain sight.

PayPal Holdings Inc. (PYPL) is a textbook Applier.

It’s embedding AI directly into its digital payments infrastructure. It’s putting AI to work in fraud prevention, transaction approvals, customer service, loan underwriting, and even internal functions like marketing and compliance.

This isn’t theoretical. Revenue per employee has surged more than 50% since 2022, a clear sign AI is already driving real efficiency gains.

PayPal is also benefiting from AI’s expanding role in daily commerce. The company’s massive installed base of 435 million users positions it as a critical enabler of global digital commerce, much of which will be AI-influenced in the coming years.

At a recent investor conference, CEO Enrique Lores emphasized plans to accelerate AI-driven changes and serve customers in “new ways” — pointing to an expanding product lineup.

PayPal is riding the rails of already-built AI infrastructure, without risking its balance sheet to construct them.

And if history is any guide…

It’s companies like this, not the ones laying the rails, that stand to capture the biggest gains.

The Next Wave of AI Is Already Here

A new kind of disruptive technology is entering the scene — what I call “A-AI,” or autonomous AI.

This isn’t just AI that responds to prompts.

It’s AI that can act on its own — making decisions, executing tasks, and operating independently.

It has the potential to reshape entire industries — and early signs suggest it’s already putting pressure on some of the most widely held stocks in America.

Some of the market’s biggest winners today could quietly become tomorrow’s biggest disappointments.

Like every major technological shift, this will create winners… and losers.

And once again, the biggest winners are unlikely to be the ones building the technology. They’ll be the ones applying it.

In other words, you don’t want the P&R companies.

You want the Campbell’s.

In my brand-new special broadcast, I break down exactly how this shift is unfolding…

Which popular stocks may be at risk…

And which lesser-known “Appliers” are positioned to benefit.

I also reveal my No. 1 applier stock to buy before this technology takes off.

You can click here to watch it now.

Regards,

Eric Fry

P.S. Eric has seen multiple tech cycles—and knows where investors tend to get it wrong. His take on “AI Appliers” offers a grounded way to think about this boom. If you want to see how it could impact your portfolio, check out his latest free presentation while it’s still available.

The post Don’t Buy the Railroads. Buy the Campbell’s. appeared first on InvestorPlace.

That’s because it was not just a product. It was a platform. And the investment universe reorganized around it. 

Apple (AAPL) became the most valuable company on Earth. Amazon (AMZN) transformed from a bookstore into the world’s largest retailer. Facebook reinvented itself as Meta (META), the kingpin of social media. Google cemented its grip on how the world finds information. All of it ran through the same chokepoint: the smartphone screen. 

But now we’re catching glimpses of a future no longer ruled by the traditional smartphone — because it is no longer the obvious endpoint of consumer computing.

The OpenAI-Qualcomm Partnership Signals a New Computing Era 

According to reports leaked earlier this week, OpenAI has tapped Qualcomm (QCOM) and MediaTek to develop chips for an AI-first consumer device, with Luxshare —a major player in Apple’s supply chain —“filling the role of exclusive system co-design and manufacturing partner.”

The device isn’t expected to reach mass production until 2028. But the signal here is still enormous.

OpenAI is trying to build the next consumer computing interface — and it’s already picking its manufacturing partners. 

That is how platform shifts usually begin. 

A new device starts as a curiosity, then becomes a companion, then becomes the default interface. At first, people ask why anyone needs it. A few years later, they question how anyone lived without it. The smartphone did that to the PC. AI can do that to the traditional smartphone.

The Rise of Ambient AI Devices

The traditional smartphone era is centered around on-screen intelligence. You pull out your ‘pocket computer,’ open an app, type something, tap something, scroll something – buy something – then usually repeat that ritual hundreds of times per day.

The AI era is different. It’s all about ambient intelligence: intelligence that can meet the user in the real world, see what they see, hear what they hear, understand context, and act on their behalf.

That requires a different kind of device — which means the ubiquitous consumer electronic device of the future will be an AI-native device designed around continuous context. It may look like glasses, earbuds, a pendant, a wearable, or a phone-like companion device. 

Eventually, it will evolve into something even more ambient. But whatever the shape, the AI-native device will be designed around agents.

The user gives intent. The agent does the work. The system handles execution.

That kind of computing works best when freed from an app grid, present, contextual, multimodal, and persistent. It wants ‘eyes,’ ‘ears,’ local processing, connectivity, and cloud access. 

That brings us to Qualcomm.

Why Qualcomm Could Win the AI Device Era 

The irony here is that Qualcomm — the company that made its fortune on the back of the smartphone boom – may be one of the biggest winners of the ambient-AI era. Because the thing replacing the traditional smartphone will still need almost everything Qualcomm is good at — and probably more of it.

An AI-native mobile device needs low power consumption, local AI inference, memory efficiency, wireless connectivity, camera/audio/sensor integration, and thermal discipline. It needs to run small models on-device while handing bigger reasoning workloads to frontier models in the cloud.

That is not a side quest for Qualcomm. That is Qualcomm’s resume – and it’s why the OpenAI report matters so much. 

It reframes Qualcomm from a mature handset chip supplier into a potential platform company for the next device era. 

That is the investment opportunity.

The bear case on Qualcomm has always been that smartphones are mature. Unit growth is slow. Apple is trying to bring more silicon in-house. The Android market is cyclical. Margins can be pressured. 

Wall Street has treated Qualcomm like a company tied to the last platform shift. But what if it’s actually tied to the next one?

The AI-native device thesis says Qualcomm’s future is not fewer phones but more intelligent devices.

Phones, glasses, earbuds, wearables, robots, drones, vehicles, industrial machines, smart home devices – all will become AI endpoints. All will require efficient edge AI compute, connectivity, and the ability to sense the physical world in real time.

In that world, Qualcomm is a toll road.

And if the ambient-AI mobile device category grows faster than the legacy smartphone category declines, Qualcomm’s growth profile could actually accelerate. 

This is why QCOM stock is the most obvious buy for this theme.

But it is not the only one…

AI Stocks Positioned for the Post-Smartphone Era 

If AI is about to transform the smartphone era, the entire hardware stack will get repriced.

• Coherent (COHR) and Lumentum (LITE) are worth watching. Ambient AI devices will need optical systems, lasers, and sensing components that go well beyond what data center optics demand — and these two are among the few pure-play suppliers of that hardware. 
• Taiwan Semiconductor (TSM) is a natural toll road. The advanced foundry king remains deeply embedded in the global AI semiconductor supply chain — and every advanced chip in this new device category still runs through its fabs. 
• Arm (ARM) matters because the future of ambient AI will be built around power-efficient compute. The device needs efficient architectures, and Arm remains central to that universe.
• Meta is a major platform play because it is already pushing AI glasses into the market through Ray-Ban Meta. If AI glasses become the first successful post-smartphone form factor, Meta may own one of the earliest mainstream distribution channels for ambient AI.
• Google’s Gemini, Android, Maps, Search, YouTube, Gmail, and Android XR give it a natural path into AI-native devices. The opportunity is enormous — but so is the risk. If agents replace search as the default interface, Google’s core business gets abstracted away. If Google wins the agent layer instead, it becomes the operating system for the physical world. 
• Apple is the giant question mark. It has the consumer trust, design talent, installed base, wearables franchise, silicon capability, and retail distribution to win the next era. But it also has the most to lose if the moat shifts from hardware plus apps to hardware plus agents. Right now, OpenAI has the agent mindshare.

Every one of these companies is circling the same question: who owns the interface when the screen is no longer the center? The OpenAI-Qualcomm report is the first concrete answer. That is why this reported deal feels so important.

It is not really about one future device in 2028. It is about the direction of travel.

The Bottom Line: AI Is Moving Off the Screen 

AI is moving from the cloud into the physical. From apps into agents. From screen-based into ambient.

The first phase of the AI boom was about training giant models in giant data centers. Nvidia (NVDA) won because intelligence needed a cloud-based brain.

The next phase is about deploying intelligence into billions of real-world devices. Qualcomm can win because intelligence now needs a body.

OpenAI is trying to build the next great consumer device. Qualcomm may be supplying the silicon foundation for it. And investors who still think of QCOM as just a boring old smartphone chip stock may be missing one of the most important AI hardware pivots hiding in plain sight.

The difference between those two eras isn’t just growth — it’s the difference between supplying one device category and supplying every intelligent endpoint on the planet. 

Be early to the next choke point.

For nearly 20 years, the smartphone controlled the flow — of information, attention, and trillions of dollars. The companies that owned that screen captured the upside.

Now that control layer is shifting.

As computing moves off the screen and into the real world, the winners won’t just be the companies building new devices but the ones that control what those devices do — especially when it comes to money…

Because every new interface needs a financial backbone.

That’s exactly what Elon Musk is building inside X: a system designed to move, store, and deploy your money instantly, without the friction of traditional banks or apps.

Two massive shifts are colliding at once: a new way to interact with technology and a new way money moves through it.

If you can see how those pieces connect, you’re already ahead of the market.

Click here to see how we’re positioning for it — and the key opportunities we’re tracking before they go mainstream.

The post OpenAI Is Building What Comes After the iPhone appeared first on InvestorPlace.

The Federal Reserve wrapped up its April FOMC meeting today, voting to hold its benchmark federal funds rate steady at 3.5%-3.75%.

This marks the third consecutive meeting in which the committee chose to stand pat, following three straight cuts to close out 2025.

The hold was fully expected. What wasn’t entirely expected was just how divided the committee turned out to be.

The vote split 8-4, the most dissents at a Fed meeting since October 1992.

To be clear, three of the four dissenters – Cleveland Fed President Beth Hammack, Minneapolis Fed President Neel Kashkari and Dallas Fed President Lorie Logan – agreed with the hold on rates. Their objection was to the “easing bias” language retained in the statement, specifically the phrase referencing “additional adjustments to the target range,” which implies the next move is more likely down than up. They don’t want to signal that anymore.

That’s not to be confused with a preference for a rate hike. As Powell said in his press conference, answering a question on this: “No one’s calling for a hike.”

The fourth dissenter, Fed Governor Stephen Miran, went the other direction – he pushed for a quarter-point cut today.

So, the primary disagreement inside the Fed isn’t about the immediate decision. It’s about what posture the central bank should project heading into an environment where the inflation picture remains genuinely murky.

On that front, Powell was direct about why the inflation picture is murky: the Middle East conflict

The FOMC’s statement acknowledged that the war is “contributing to a high level of uncertainty about the economic outlook,” with elevated inflation tied to the “recent increase in global energy prices.”

Powell’s core point about this in the press conference was straightforward – no one knows how long this conflict will last, so the Fed can’t confidently model what will happen to energy prices and related inflation.

This resulted in Powell’s usual tap-dance routine with the press. For example, when asked about high oil prices and the risk of elevated core inflation readings, Powell said:

We’re just going to have to wait and see.

But he did say that inflation is “already kind of misbehaving,” though it’s too soon to see the full extent of it.

For doves wanting rate cuts, Powell didn’t offer much encouragement. He said that the energy shock “hasn’t even peaked yet,” and that the Fed would want to see the back side of it before even thinking about reducing rates.

Overall, as usual, “wait and see” was Powell’s bottom line for interest rate policy in light of all the uncertainty today:

We’re in a good place to wait and let things develop.

As for Powell himself, he confirmed he will remain on the Board of Governors for a time “to be determined,” serving out his term as governor, which runs through January 2028.

But he was clear about saying that he will not be a “shadow chair”:

I plan to keep a low profile as a governor.

There’s only ever one chair of the Federal Reserve Board. When Kevin Warsh is confirmed and sworn in, he will be that chair.

Overall, there were no major curveballs today – and the market seemed to agree.

Stocks were mixed but largely held their ground, with most of Wall Street’s attention focused on the Magnificent Seven earnings due after the bell.

We’ll report on these tomorrow.

Speaking of Kevin Warsh, he got one step closer today to being the new Fed Chair

While Powell was preparing for his press conference this afternoon, the Senate Banking Committee was voting on his replacement.

In a vote that fell along party lines, the Committee advanced Warsh’s nomination to be the new Fed chair. The full Senate vote is expected the week of May 11.

So, who is Warsh, and what kind of Fed will he run?

Warsh served on the Fed’s Board of Governors from 2006 to 2011. His tenure overlapped with the 2008 financial crisis, during which he helped manage the central bank’s response under then-Chair Ben Bernanke.

During that period, he earned a reputation as a rate hawk – someone who generally preferred higher interest rates as a tool for maintaining price stability.

Since leaving the Fed, he has become one of its most vocal critics. He was an early proponent of the bond-buying programs that expanded the Fed’s balance sheet during the financial crisis but grew increasingly skeptical of the practice over time – ultimately tendering his resignation over the central bank’s continued purchases.

President Donald Trump nominated him with the expectation that he would cut interest rates, but the President may not get what he bargained for.

At his confirmation hearing last week, Warsh tried to thread a difficult needle. On one hand, he voiced support for Fed independence:

Monetary policy independence is essential. Monetary policymakers must act in the nation’s interest.

On the other hand, he defended the right of elected officials to weigh in on rates – a position Democrats hammered as cover for political interference.

When pressed directly by Senator Elizabeth Warren on whether he would be Trump’s “sock puppet” at the Fed, Warsh pushed back:

I’m honored the president nominated me for the position, and I’ll be an independent actor if confirmed as chairman of the Federal Reserve.

Whether that independence holds under political pressure will be the central question of the Warsh era.

Warsh is not the pure hawk his reputation suggests. He favors rate cuts, though paired with a meaningful reduction in the Fed’s balance sheet.

And yet, this is the part of the Warsh story that most financial coverage has missed. And it matters – directly – to your mortgage, your borrowing costs, and your portfolio.

Let’s talk about why…

The potential for stealth tightening under Warsh

Most of the Warsh coverage has centered on one question…

Will he cut the fed funds rate sooner than Powell would have, or later?

While that question matters in the short term, another question has far more impact on the long-term outlook.

The more consequential issue is Warsh’s interest in reducing the Fed’s balance sheet – a $6.7 trillion portfolio of Treasury bonds and mortgage-backed securities that has ballooned from less than $900 billion before the 2008 financial crisis.

Consider what that massive expansion actually did…

When the Fed buys bonds, it floods the financial system with cash. All that cash has to go somewhere – and it did.

It flowed into stocks, real estate, corporate debt, and venture capital. It suppressed long-term interest rates far below where a free market would have set them. And it made borrowing artificially cheap for corporations, homebuyers, and the federal government alike.

In short, it inflated the price of nearly every asset you can name.

That was the point – at least initially. In the depths of the 2008 crisis and again during COVID, the Fed used its balance sheet as an emergency tool to prevent financial collapse.

The problem is that the emergency never fully ended, at least not on the Fed’s books. The balance sheet stayed swollen long after the crisis passed.

But a decade-plus of artificially suppressed rates has consequences: a housing market that’s priced out of reach for first-time buyers… a stock market trading at the second highest CAPE valuation in more than 140 years… and today’s federal debt that has crossed $39 trillion, financed for years at rates that never reflected the true cost of borrowing – a bill that gets far more expensive to service the moment those artificial supports are removed.

Warsh argues that this distortion is still baked into the system. At $6.7 trillion, the Fed’s footprint in the bond market remains enormous.

But if we remove the Fed as a perpetual bond buyer, demand likely falls. And that can mean the Treasury will have to offer higher yields to attract other buyers. Mortgage rates follow. Corporate borrowing costs follow. And the whole long end of the curve faces upwardrepricing pressure – not because of anything Warsh does with the fed funds rate, but because the artificial support is being withdrawn.

That’s the mechanical effect of removing a major price-insensitive buyer from the market – regardless of policymakers’ intentions.

That is why the balance sheet story is bigger than the rate story. The fed funds rate is the number everyone watches. But the balance sheet is the lever that moves the rates everyone actually pays.

To be clear, this is not necessarily Warsh’s goal. His argument is that if done credibly, this mix could actually lower inflation expectations and allow long-term rates to stabilize or even fall.

Still, “if” and “could” are doing a lot of work there.

Warsh has been explicit about his intentions

On the Larry Kudlow program last summer, he said:

You could take down that balance sheet a couple trillion dollars over time, in concert with the Treasury secretary.

That’s a big rate cut that could come, and what you would do then is turbo-charge the real economy, where things are somewhat tougher, and ultimately the financial markets would be fine.

Let’s make sure we’re on the same page about this…

When the Fed lowers rates, it loosens economic conditions. But when it shrinks its balance sheet, it tightens them – because pulling cash out of the system has the same basic effect as making that cash more expensive to borrow.

Warsh’s strategy is to execute both simultaneously – not explicitly to steepen the curve, but to normalize how it’s set.  

Cut the fed funds rate to give the economy some relief on short-term borrowing costs – meanwhile, shrink the balance sheet and withdraw the liquidity that has been artificially suppressing long-term yields for over a decade.

The expected outcome? The short end comes down. But lhe long end faces upward pressure – even if Warsh hopes it doesn’t rise materially. The curve risks steepening.

Regardless, from a policy standpoint, it would be a win for Warsh – he can tell the White House he delivered rate cuts, and he can tell inflation hawks he kept overall financial conditions tight.

But if market mechanics outweigh policy intentions, the net effect on the real economy could be tighter than the headline rate cut implies.

Whether the fed funds rate gets cut in the second half of 2026 or not, a Warsh-led push to reduce the balance sheet would likely exert independent upward pressure on long-term yields.

That means mortgage rates staying elevated… corporate borrowing costs staying elevated… and the valuation math on growth stocks staying pressured – all without Warsh touching the fed funds rate dial once.

Bottom line: The market is watching the rate decision – but it should be watching the balance sheet, because that’s where Warsh’s intentions and market realities are most likely to diverge.

We’ll keep tracking this as the Warsh era begins.

Have a good evening,

Jeff Remsburg

P.S. The next evolution of AI is arriving…

Eric Fry just released a new presentation that explains why a different kind of AI — one that can act on its own — could reshape entire industries and trigger a major market shift.

He walks through why some of today’s most popular stocks may be at risk… and how a lesser-known group of companies could benefit. If you haven’t watched yet, it’s worth your time.

The post The “Powell Era” Ends with a Divided Fed appeared first on InvestorPlace.

Tom Yeung here with today’s Smart Money.

Imagine waking up one morning to find your bank account drained… your phone locked… and your passwords no longer work.

At the same time, systems you rely on every day – payments, communications, even parts of the power grid – start to glitch or go dark.

No warning. No explanation. No obvious point of entry.

Just chaos.

This is what could happen if hackers armed with AI exploited “zero day” vulnerabilities – hidden flaws in software that no one knows exist and, therefore, has had zero days to fix.

On April 7, Anthropic released a limited version of Claude Mythos, an AI system so powerful that the company immediately restricted access to it.

Mythos uncovered these zero-day vulnerabilities in every major operating system, including one that had gone undetected for 27 years.

These hidden weaknesses can be exploited by hackers to steal data, take control of computers, cripple critical infrastructure, and more.

Now, Anthropic didn’t program Mythos to do this. The hacking skills emerged on their own.

As the company itself explained: “We did not explicitly train Mythos to have these capabilities. Rather, they emerged as a downstream consequence of general improvements in code, reasoning, and autonomy.”

The reaction at the highest levels was immediate. Federal Reserve Chair Jerome Powell and Treasury Secretary Scott Bessent held a closed-door meeting with top bank CEOs to discuss risks to the global financial system. Shares of major cybersecurity firms fell by double digits.

And yet, most people missed the story. War in the Middle East… gas prices… tariffs… the usual noise drowned out what may be the single most important technological development of our generation.

Because Mythos isn’t just a better chatbot. It’s a signal that AI has crossed an important threshold, moving from a tool that responds to instructions… to one that can act, adapt, and solve problems on its own.

And that changes everything for your portfolio…

This Shift Is Already Underway

To understand why Mythos matters, you need to understand what’s been building underneath it.

A new kind of AI that doesn’t just respond to prompts… but can execute complex tasks on its own.

A year ago, a Chinese startup called Manus AI introduced a system that could analyze financial transactions, screen job candidates, and navigate complex digital workflows without step-by-step human input. Retired New York Times writer Craig S. Smith called it a “game-changer.”

That forced every major Western AI company to respond. Within months, OpenAI and Anthropic released similar systems capable of handling multistep tasks, managing workflows, and making decisions with minimal oversight.

Then last November came OpenClaw, a free, open-source platform that exploded to 30 million monthly users. At Nvidia Corp.’s (NVDA) GTC conference, CEO Jensen Huang called it “probably the single most important release of software… probably ever.”

These aren’t chatbots. They’re digital workers – handling emails, moving files, managing information, writing code, reviewing contracts… and doing it around the clock without asking for a raise.

I’ve seen this firsthand. With Claude Code, I can now give an AI assistant raw financial data and ask it to build a quantitative model. It runs off by itself to write thousands of lines of code. Then it tests the model… critiques it… asks for more data… and suggests improvements. It’s no longer a robotic mecha-suit that needs a human pilot. It’s the whole machine, replacing entire teams of analysts and coders.

And if I can do that as one analyst, imagine what Anthropic’s 1,500-person engineering team came up with when they used these tools for themselves…

So even if Mythos isn’t the endpoint, it’s a clear step-change in what these systems can do. New generations of AI models typically appear six to 12 months after a major launch, and I wouldn’t be surprised if a “Mythos V2” arrives by December.

Why Your “Safe” AI Stocks May Be the Most Exposed

Here’s where things get uncomfortable.

The same technology behind Mythos is now dismantling the business models behind some of Wall Street’s most popular stocks.

On February 4, Anthropic released a legal plug-in for Claude Cowork. The effect on Wall Street was immediate.

• Shares of Thomson Reuters Corp. (TRI) gapped down 19%.
• LexisNexis parent RELX Plc (RELX) dropped 15%.
• LegalZoom.com Inc. (LZ) crashed 20%.

Wall Street has been calling this the “SaaSpocalypse,” a rolling collapse in software-as-a-service (SaaS) stocks that has now spread far beyond legal tech.

Will AI replace customer service platforms?

Real estate brokerages?

Financial services?

Business automation?

That fear isn’t misplaced. For 15 years, the SaaS profit machine worked like this: Build a dashboard, connect it to a database, charge companies $30 to $100 per month per employee to use it. The more workers a client hired, the more money software companies made. No one questioned the 95%-plus gross margins these firms routinely earned.

But A-AI doesn’t need dashboards. It connects directly to underlying systems, pulls data, updates records, and triggers next steps automatically. When one A-AI can do the work of five junior analysts or paralegals, companies don’t just need fewer employees. They need fewer software licenses.

And if these systems get powered by a model as powerful as Mythos, the pressure on SaaS business models could accelerate very quickly.

Meanwhile, the companies you’d expect to benefit – the pure-play AI names – are trading at valuations that assume perfection.

We saw this movie before during the dot-com hysteria. Many sought-after internet darlings like Cisco Systems Inc. (CSCO), Lucent, and AOL failed to deliver… and so did firms like Borders and Circuit City that were disrupted by the internet era.

So, the question isn’t whether AI is a big deal.

That debate is over.

The question is: As investors, how can we profit?

The Coming AI Reckoning

My InvestorPlace colleague Eric Fry believes the big profit opportunities will be in the “Appliers.” These aren’t the firms building AI. They’re the ones using it to transform entire industries.

Think sensors, robotics, industrial systems, and security infrastructure. Companies with hard-to-replicate data edges and real-world integration that can’t be vibe-coded away.

He sees this “AI Reckoning” as a major inflection point. In the coming months, he believes we’re going to see a wealth shift from those holding the wrong stocks to those positioned in AI Applier companies that connect this digital technology to the physical world.

He’s put together a free presentation that goes far deeper than I can here – naming the specific stocks he believes are most at risk, and the ones positioned to capture the upside as this shift accelerates.

The scenario we started with may sound extreme.

But the forces behind it are already here—and they’re beginning to reshape which companies win, and which ones don’t.

If you own any AI-adjacent stocks (and at this point, who doesn’t?), it’s worth seeing what he found – especially before this shift becomes more obvious to the broader market.

Thomas Yeung, CFA

Market Analyst, InvestorPlace

P.S. A lot of investors think the biggest AI gains are already behind us. Eric Fry believes the opposite may be true… but only for a specific group of companies that most people aren’t watching. In his latest presentation, he explains why some of today’s biggest winners could struggle from here, and how a lesser-known group could deliver outsized gains in the next phase of the cycle. It’s worth a look if you haven’t seen it yet.

The post The Software Crash That’s Creating the Next 1,000% Winners appeared first on InvestorPlace.

But the constraint that is becoming the ceiling on AI buildout isn’t silicon… it’s watts.

A single next-generation AI training cluster (the kind that trains frontier models like GPT-5 or Claude 4) can consume 1 to 2 gigawatts of electricity. That is enough power to run a mid-sized American city. The hyperscalers, which include Meta (META), Microsoft (MSFT), Google (GOOGL), Amazon (AMZN), are each trying to build dozens of these clusters simultaneously around the globe.

The U.S. electrical grid was not designed for this. Utility interconnection queues – the waiting list to plug a new facility into the grid – now stretch 5 to 10 years in many regions. Environmental permitting adds more time. Transmission infrastructure is decades behind where it needs to be. And the laws of physics don’t care how much money you have: you cannot run a 2-gigawatt data center if there are only 500 megawatts available on the local grid.

This is what we call the resource-bound problem. And the companies that figure out how to route around this constraint will win the AI infrastructure race. Meta just showed you one of the most audacious routes imaginable.

[The resource-bound problem isn’t unique to energy, by the way. It’s showing up in payments, too – where legacy banking infrastructure is the grid, and a handful of tech companies are racing to route around it. Elon Musk’s X Money is one of the most ambitious attempts yet. We broke down the full investment story here.]

One company isn’t waiting for Washington to fix the grid, or hoping some utility builds a new transmission line in time. It went over all of it… literally.

That company is Meta, which earlier this week, announced something that would have sounded like science fiction five years ago.

The company struck a commercial agreement with a startup called Overview Energy to power its artificial intelligence data centers using solar energy collected in space and beamed back to Earth. Overview plans an initial orbital demonstration in 2028, with commercial power delivery beginning in 2030.

The world’s fifth-largest company by market cap is now contractually committed to receiving electricity from orbit.

Let that land for a second.

This is no simple corporate press release designed to make the ESG crowd feel warm and fuzzy but rather a commercial deal with a delivery schedule signed by the same company that runs Facebook, Instagram, and WhatsApp, and that is currently spending $60 billion to $65 billion on AI capital expenditure in 2025 alone. Meta does not sign orbital energy contracts for fun. Meta signed this contract because its engineers ran the numbers, looked at the global power grid, and concluded that Earth – the entire planet – cannot supply enough electricity to support their AI ambitions.

That is an extraordinary thing to admit. And it has profound investment implications.

Enter Orbital Compute: The Bull Thesis from the Ground Up

The core insight behind what we call the Orbital Compute thesis is elegantly simple:

Terrestrial infrastructure is resource-bound. Orbital infrastructure is technology-bound.

Resource-bound systems hit ceilings that don’t scale with innovation. You cannot Moore’s Law your way past a decade-long utility interconnection queue. But technology-bound systems – where cost and capability are driven by engineering progress – fall in price and improve in performance over time, just like semiconductors did.

Space infrastructure is technology-bound. Launch costs have fallen approximately 90% over the past decade, almost entirely due to SpaceX’s development of reusable rockets. Starship, SpaceX’s next-generation launch vehicle currently in testing, promises to drop costs another order of magnitude. As launch becomes cheaper and more reliable, putting infrastructure in orbit stops being a moonshot and starts being a business decision.

Space is actually a remarkable place to generate power. Solar panels in orbit face the sun 24 hours a day with no clouds, no night, no weather; producing roughly 8 to 10 times more electricity per panel than the same hardware on the ground. Cooling is easier too: heat simply radiates into the cold of space, no giant air conditioning units required. And for the heavy-duty AI jobs that don’t need to happen in real time – training a model, crunching a massive dataset – it doesn’t matter where the computer physically sits. Why not run those jobs in orbit, where the power is essentially free and unlimited?

Meta’s Overview Energy deal validates Phase One of this thesis: orbital power generation beamed back to terrestrial data centers. Phase Two – actual compute infrastructure in orbit – is the logical next step once the orbital energy and launch infrastructure matures. We are somewhere in the early innings of a multi-decade infrastructure buildout that eventually moves meaningful AI compute off-planet entirely.

The 5 Orbital Pure-Play Stocks to Buy

NASA ETF (NASA): The most direct way to own SpaceX in the public markets. The NASA ETF holds a basket of space-economy companies with meaningful SpaceX exposure embedded in the portfolio. SpaceX is the keystone of the entire orbital economy – it dominates launch, is developing Starship to push costs lower, and operates Starlink, the world’s most commercially successful satellite constellation. Nothing in the orbital compute thesis works without cheap, reliable access to orbit, and SpaceX owns that chokepoint more completely than any company in history. The NASA ETF is the cleanest public-market proxy for that monopoly.

Rocket Lab (RKLB): If SpaceX is the Boeing of orbital infrastructure, Rocket Lab is the Airbus – a credible, well-capitalized second player in the launch market with a track record of successful missions and an expanding product line. Rocket Lab’s Electron rocket handles small-payload launches reliably and affordably, and its larger Neutron rocket is in development to compete for medium-payload contracts. Beyond launch, the company is building out a full-stack space systems business – designing and manufacturing satellites, not just launching them. As orbital infrastructure demand scales, Rocket Lab is positioned to capture a disproportionate share of the second-tier launch market while building a recurring revenue base in satellite manufacturing.

Planet Labs (PL): Planet operates the world’s largest constellation of Earth-observation satellites, capturing daily imagery of the entire planet’s landmass. That sounds niche until you realize what it enables: real-time monitoring of supply chains, agricultural yields, infrastructure construction, military positioning, and environmental change at a scale no terrestrial system can match. Planet is essentially building a persistent, AI-queryable visual feed of Earth from orbit. As AI models get better at extracting signal from imagery, Planet’s data becomes dramatically more valuable. Think of it as a picks-and-shovels play on the intersection of orbital infrastructure and AI data.

AST SpaceMobile (ASTS): One of the more speculative but potentially transformative names in the space, AST SpaceMobile is building a constellation of large, phased-array satellites designed to deliver broadband connectivity directly to standard mobile phones – no specialized hardware required. The commercial implications are enormous: billions of people in underserved markets gaining reliable internet access, with telco partners including AT&T and Verizon already signed. Early satellites are in orbit and operational. This is a high-risk, high-reward name for investors who want direct exposure to the next phase of satellite infrastructure buildout.

Redwire Space (RDW): A less well-known name but an important one. Redwire manufactures specialized hardware for space environments – solar arrays, deployable structures, in-space manufacturing systems. As orbital infrastructure scales from a handful of satellites to a genuine off-planet industrial economy, companies that build the physical components of that infrastructure become critical suppliers. Redwire is the kind of name that looks small and obscure today and looks indispensable in 2035.

The 7 Overlapping AI Infrastructure Stocks to Buy

Here’s the important thing to understand about the orbital compute thesis: it doesn’t replace the terrestrial AI infrastructure trade. It extends it.

The buildout happening on Earth right now – hundreds of billions of dollars in data center construction, chip fabrication, power infrastructure, and networking – is the bridge that carries us to the orbital era. While Overview Energy prepares for its 2028 orbital demo, the hyperscalers are still building as fast as humanly possible on Earth. Meta’s deal confirms the long-term direction, but the near-term dollars are still flowing into terrestrial infrastructure.

That means the classic AI infrastructure basket remains as relevant as ever.

Nvidia (NVDA): Needs no introduction. Nvidia supplies the GPUs that power virtually every major AI training and inference workload on the planet. The Blackwell architecture extends the company’s dominance into the next hardware generation, and the insatiable demand from hyperscalers pouring capital into AI infrastructure means the order book stays full. Nvidia is the one stock in this basket where the bull case requires the least imagination.

Broadcom (AVGO): The sleeper hit of the AI infrastructure trade. Broadcom designs custom AI accelerators – XPUs – for hyperscaler customers including Google, Meta, and ByteDance. These custom chips are purpose-built for specific AI workloads and offer dramatically better efficiency than general-purpose GPUs for those tasks. Broadcom’s AI chip revenue has been growing explosively, and the company has guided for over $100 billion in AI-related revenue in fiscal 2027. It also dominates the networking silicon that connects AI clusters together, making it a dual beneficiary of compute and interconnect spending.

Marvell Technology (MRVL): Marvell’s story is similar to Broadcom’s but earlier in the earnings ramp. The company designs custom AI accelerators and high-speed networking chips for Amazon, Google, and Microsoft, among others. Its fiscal 2028 AI revenue target of $15 billion implies roughly 5x growth from current levels. Marvell is the higher-beta AI infrastructure play for investors who want more upside leverage on the custom silicon buildout.

Eaton Corporation (ETN): Every data center – terrestrial or, eventually, orbital – requires power management and distribution equipment. Eaton is the dominant supplier of that equipment globally. It makes the switchgear, busways, uninterruptible power supplies, and electrical distribution systems that sit between the grid and the servers. As AI data centers get bigger and more power-hungry, Eaton’s addressable market expands in direct proportion. It is the least glamorous name in this basket and among the most reliable.

Coherent (COHR) and Lumentum (LITE): As AI clusters grow to consume multiple gigawatts and process exabytes of data, moving information between chips and between servers at the speed of light stops being optional and becomes the central engineering challenge. Coherent and Lumentum manufacture the optical transceivers and components that make high-speed data transmission inside data centers possible. The shift from copper to optical interconnects at shorter and shorter distances – driven by AI’s insatiable bandwidth demands – is a multi-year secular tailwind for both companies.

Astera Labs (ALAB): The newest name on this list and one of the most compelling. Astera Labs designs semiconductor connectivity solutions – CXL memory expansion, PCIe retimers, and optical interconnect controllers – that solve the data bottleneck between processors and memory inside AI servers. As AI models get larger and memory bandwidth becomes the binding constraint at the chip level, Astera’s products become increasingly mission-critical. It’s a pure-play on the AI interconnect buildout with a differentiated product portfolio and hyperscaler customers already in production.

The Bottom Line

Meta’s Overview Energy deal is a data point of extraordinary significance, and investors who dismiss it as a curiosity are missing the forest for the trees.

This is not a company experimenting with speculative technology to look innovative. This is a company that has already committed over $60 billion to AI infrastructure spending this year alone, looked at its power options, and concluded that going to space was the rational choice. When the hyperscalers start sourcing electricity from orbit, the old investment frameworks for AI infrastructure need to expand.

The orbital compute bull thesis rests on a simple, durable premise: Earth is running out of room to build AI, and the companies solving that problem from orbit will be among the most important infrastructure investments of the next two decades. Meta just handed us the clearest confirmation signal yet that the thesis is not only correct, it’s already being acted on at the highest levels of corporate capital allocation.

The question, as always, is whether you’re positioned before the rest of the market notices. The companies solving that problem from orbit are still flying under most investors’ radar, but there’s another infrastructure revolution unfolding right now… one that could be just as transformative, and just as underfollowed.

I just released a full presentation on X Money — Elon Musk’s long-awaited attempt to turn X into America’s first super-app bank. The same way SpaceX cracked open the orbital economy, X Money could crack open the payments economy. And just like orbital compute, the real opportunity isn’t in the headline name — it’s in the public companies powering it behind the scenes.

Click here to watch it now.

The post 12 Stocks to Buy After Meta’s Deal to Beam Energy From Space appeared first on InvestorPlace.

This morning, the United Arab Emirates (UAE) announced it will exit OPEC on May 1, ending nearly six decades of membership and dealing one of the most significant blows to the cartel in its history.

The UAE was OPEC’s third-largest producer before the Iran war disrupted Gulf shipping. Its departure strips the group of roughly 12% of its overall supply. More critically, it removes one of only two members – alongside Saudi Arabia – with meaningful spare production capacity.

As to the impact, here’s Jorge Leon, head of geopolitical analysis at Rystad Energy and a former OPEC secretariat official:

The longer-term implication is a structurally-weaker OPEC.

Outside the group, the UAE would have both the incentive and the ability to increase production, raising broader questions about the sustainability of Saudi Arabia’s role as the market’s central stabilizer.

The timing of this morning’s news doesn’t appear accidental for two reasons…

One, the announcement comes just days before a scheduled OPEC+ meeting this coming Sunday, where members are set to begin negotiating next year’s production quotas. Getting out now means the UAE won’t be bound by a new quota framework heading into 2027 – which dovetails into the second reason…

The war in Iran has closed the Strait of Hormuz to most exports, but the UAE can route more than half its oil overland, bypassing the blockade entirely.  So, while Saudi Arabia, Iraq, and others are watching their exports get throttled by the war and can’t easily get oil to market regardless of their quotas, the UAE could potentially export more – but OPEC’s rules won’t let it.

The exit frees Abu Dhabi to pursue its ambition to reach 5 million barrels per day of capacity by 2027 without asking Riyadh for permission.

As to the impact on oil prices, here’s veteran trader Jonathan Rose of Masters in Trading: Live:

The UAE’s decision represents a fracturing of the world’s oil export order at one of the most fragile moments in years.

Volatility will rise quickly as traders reprice supply expectations, regional alliances, and the next move in crude.

Refiners have remained strong so far. But that leadership may not hold if the market starts rotating into a new energy narrative.

In this morning’s Masters in Trading LIVE episode, Jonathan walked through the implications of the UAE decision, how it could reshape the energy trade, and the key stocks and sectors best positioned for the coming wave of volatility. You can watch it for free right here.

Bottom line: a structurally weaker OPEC is a long-term headwind for oil price management. We’ll keep tracking the implications as the energy picture evolves.

Now, let’s shift gears but stay with Jonathan…

While he spent this morning flagging new opportunities in energy, yesterday, his subscribers were reminded that knowing when to exit matters just as much as knowing when to enter…

“Take your profits, or someone else will take them for you”

American financier Bernard Baruch is credited with that line. And yesterday, some traders who ignored that advice learned it the hard way.

The stock in question is POET Technologies Inc. (POET) – a small semiconductor company that designs optical engines to move data using light rather than copper wiring.

As AI data centers grow larger and faster, copper’s heat and power limitations are creating a bottleneck. POET has been building technology to solve it, putting the company squarely in the path of one of the most powerful demand forces in the market.

Regular Digest readers know this story well…

In our February 12 Digest, we profiled POET as a trade idea from veteran Jonathan Rose, editor of Masters in Trading: Live. By late last week, that trade had returned almost 170% since we highlighted it.

But also last week, we noted that Jonathan had recommended his subscribers take profits.

From his Trade Alert on Friday:

POET Technologies has now pushed into levels the stock hasn’t seen in more than a decade, capping off a move that’s been nothing short of vertical.

As much as I like this stock in the longer term, when a move like this falls in your lap, you don’t ask questions, you take profits. 

We’ll look for more opportunities for POET down the line. 

And then came yesterday…

POET imploded 47% after the management reported that Marvell Technology (MRVL) had canceled all purchase orders from Celestial AI – a company owned by Marvell and POET’s anchor customer.

Marvell accused POET of violating confidentiality obligations by publicly disclosing the order. With its most important customer relationship gone, POET collapsed by nearly half in a single session.

The momentum-chasing traders who bought from Jonathan’s subscribers last Friday are now sitting on losses that require almost a 100% gain just to break even.

This is an important reminder about how the pros manage trades – even ones that appear unstoppable.

Jonathan saw the signs – a narrative-driven spike, sentiment outrunning fundamentals, shares extended – and he protected his subscribers’ gains before the story unraveled. The traders who chased POET into the frenzy without a plan got caught.

Being part of a vertical move is exhilarating. But discipline is what gets you out before you become someone else’s buying opportunity.

Bottom line: Take your profits, or someone else will take them for you.

To follow along with Jonathan’s daily commentary and market analysis, click here to join him for his free Masters in Trading: Live videos, each day the market is open.

Finally, the AI monetization gap just got harder to ignore

Regular Digest readers know we’ve been tracking a question that most of Wall Street has been happy to defer…

Will end-user spending on AI ever justify the staggering sums being poured into the buildout?

Back in March, we pointed to the sudden shutdown of OpenAI’s video-generation model Sora as an early warning…

Downloads had collapsed 75% from their peak. OpenAI was burning $15 million a day to run it. And Bill Peebles, OpenAI’s own head of Sora, even admitted, “the economics are currently completely unsustainable.”

We called it Exhibit A in the gap between building AI and monetizing it.

Then, in yesterday’s Digest, we framed what to watch in this week’s Big Tech earnings – specifically, whether hyperscaler revenue signals would show consumer and enterprise AI spending finally catching up to the infrastructure bets being made on its behalf.

Well, the Wall Street Journal just published a report that lands right in the middle of everything we’ve been tracking.

According to the WSJ, OpenAI missed its internal goal of reaching one billion weekly active users for ChatGPT by year-end… missed its yearly revenue target after Google’s Gemini ate into its market share… and missed multiple monthly revenue targets earlier this year after losing ground to Anthropic in coding and enterprise.

That’s a lot of misses for a company sitting on $600 billion in future spending commitments.

Meanwhile, the company has also struggled with subscriber defection rates.

But the more unsettling detail is what’s happening internally.

Here’s the WSJ:

Chief Financial Officer Sarah Friar has told other company leaders that she is worried the company might not be able to pay for future computing contracts if revenue doesn’t grow fast enough.

The article reports that OpenAI has signed up for so much computing power that it expects to burn through $122 billion in the next three years, assuming that it meets ambitious revenue targets.

But those revenue targets are already being missed.

This is precisely the dynamic we’ve been flagging. The AI infrastructure layer is generating real revenue today. But the consumer-facing application layer – where the math ultimately has to close – remains stubbornly unproven.

To be clear: none of this means the AI trade is over, or that AI in general won’t generate revenues. But the question was never whether AI will work or have plenty of customers. It’s whether it will work and be profitable at a scale and speed that justifies the spending – before the money runs out.

Right now, the people closest to the numbers at OpenAI appear to be asking the same question we are.

We’ll keep tracking this – and bring you the key takeaways from this week’s Big Tech earnings later this week.

Have a good evening,

Jeff Remsburg

P.S. Want to know how to turn $100,000 into $1.3 million?

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As part of it, Keith explained how this AI turned $100,000 into $1.3 million in its 5-year backtest through bull and bear markets. Last year alone, it booked a 124% total gain.

All it takes is just 10 minutes a week, Keith says.

Today is the final day we’re making the free relay of Keith’s presentation available.

For all the details, click here.

The post The Cartel Cracks: What the UAE Exit Means appeared first on InvestorPlace.

You’ve got geopolitical uncertainty in the background and a Federal Reserve decision on deck.

But it’s the surge of major earnings about to roll in that could really move the needle.

And that’s where we could see fireworks on Wall Street.

Over the next couple of days, five of the Magnificent Seven stocks will report results – and their updates could go a long way in determining the market’s next move.

We already got an early signal last week.

As I discussed in last Thursday’s Market 360, Tesla Inc. (TSLA) was one of the first major companies to report – and it came in ahead of expectations, reinforcing what we’ve been starting to see more broadly.

Because so far, this earnings season isn’t just holding up…

It’s coming in even stronger than expected.

That’s why, in today’s Market 360, I’ll walk you through what’s driving this earnings momentum and what the latest data tells us about where the market could be headed next.

Earnings Season Is Gaining Momentum

Earnings season is still in its early stages, but things are about to accelerate in a big way.

Roughly one-third of S&P 500 companies are set to report this week alone, making it one of the most important stretches of the entire earnings season.

And so far, the results have been better than expected.

According to FactSet, about 28% of S&P 500 companies have announced quarterly results, and 84% of them have exceeded analysts’ expectations.

But the real strength lies in technology.

The average earnings surprise for tech is 21.1% in the first quarter. Revenue is coming in 5.8% higher than expectations, too.

That’s an important detail.

When companies are beating on both the top and bottom line, it tells us demand is holding up.

And analysts are starting to take notice.

The S&P 500 is now anticipated to achieve 15.1% average earnings growth – that’s up from 13.1% on March 31.

That’s not what you typically see in a fragile market.

It’s what you see when momentum is building beneath the surface.

What’s Driving the Strength

So, what’s driving this earnings momentum?

It comes down to one key trend: data centers.

Right now, companies are pouring billions into building out the infrastructure needed to support artificial intelligence. And that demand is starting to show up clearly in the numbers.

Order backlogs for data center-related companies are climbing sharply, giving these businesses strong visibility into future revenue.

That’s critical. Because it tells us this isn’t a short-term spike in demand – it’s sustained.

At the same time, major tech companies continue to invest heavily in expanding data center capacity across the U.S. In fact, estimates show that global data spending will be somewhere around $750 billion this year, a trend that shows no signs of slowing down.

Put it all together, and the message is clear: This earnings strength is driven by a powerful, long-term investment cycle tied directly to AI.

What the Data Is Telling Me

When markets get noisy like this, I don’t chase headlines… I follow the data.

That’s where my Stock Grader system (subscription required) comes in.

Every week, it analyzes thousands of stocks based on fundamental and quantitative factors that have historically driven outperformance – such as earnings growth, sales momentum, analyst revisions and overall financial strength.

And I’ve just updated it with the latest data.

So, let’s take a closer look at my latest Stock Grader ratings for 120 big blue-chip stocks. Of those 120 stocks…

• Twelve stocks were upgraded from Strong (B-rating) to Very Strong (A-rating).
• Twenty-three stocks were upgraded from Neutral (C-rating) to Strong (B-rating).
• Fifteen stocks were upgraded from Weak (D-rating) to Neutral.
• Five stocks were upgraded from Very Weak (F-rating) to Weak.
• Seventeen stocks were downgraded from Very Strong to Strong.
• Twenty-seven stocks were downgraded from Strong to Neutral.
• Eighteen stocks were downgraded from Neutral to Weak.
• And three stocks were downgraded from Weak to Very Weak.

I’ve listed the first 10 stocks rated as Very Strong below, but you can find a more comprehensive list – including all 120 stocks’ Fundamental and Quantitative Grades – here. Chances are that you have at least one of these stocks in your portfolio, so you may want to give this list a skim and adjust accordingly.

Put simply, this is a more selective market, where strength is being rewarded and weakness is being left behind.

And that selectivity is happening for a reason…

The Bigger Shift Now Underway

This kind of divergence isn’t random… It’s being driven by a much bigger shift now underway in the market.

We’re entering a new phase of the AI boom.

The first phase was driven by excitement, big ideas and the companies most closely tied to them.

Now that AI is moving from concept to execution, the market is starting to reward something very different.

And that’s exactly what we’re seeing in the earnings data.

Behind the scenes, companies are spending aggressively to build out the systems that enable AI. And that demand is now showing up in results.

That’s why this market is becoming more selective.

The companies delivering real results are starting to separate themselves from the rest. That’s the reset.

And it’s already in motion.

That’s why I put together a special presentation breaking down what I call the AI Reset, where this shift is headed next and which types of companies are best positioned to benefit.

If you want to stay ahead of what’s coming, I strongly encourage you to watch it now.

Sincerely,

Louis Navellier

Editor, Market 360

The Editor hereby discloses that as of the date of this email, the Editor, directly or indirectly, owns the following securities that are the subject of the commentary, analysis, opinions, advice, or recommendations in, or which are otherwise mentioned in, the essay set forth below:

Baker Hughes Company Class A (BKR), EMCOR Group, Inc. (EME) and KLA Corporation (KLAC)

The post Quant Ratings Updated on 120 Stocks appeared first on InvestorPlace.

Oil spiked while futures dropped amid headlines that screamed “escalation in the Middle East.” In a nut shell, the macro narrative looked fragile, possibly even broken…

And then earnings week started.

What we got wasn’t confirmation of a weakening economy.

We got proof that there are two economies now… and they’re moving in completely different directions.

For months, we’ve argued that the global economy is quietly splitting in two.

On one side: companies building and powering the artificial intelligence revolution.

On the other: everyone else.

Last week, that idea started showing up consistently and overwhelmingly in earnings.

To understand what’s happening, we need to zoom out…

The Winners: A Stack-by-Stack Victory Lap

The first thing you need to understand is what “AI infrastructure” actually means.

Building an AI data center is not just buying a bunch of Nvidia (NVDA) GPUs and plugging them in. It requires a complete physical stack: specialized chip-making equipment, memory chips, processors, power management semiconductors, optical fiber connections, and — at the very foundation — physical buildings filled with HVAC systems, electrical wiring, and cooling infrastructure.

Each layer of that stack reported earnings this past week and every single one broke records.

• Chip equipment firms like ASM International (ASMIY) and Lam Research (LCRX) reported surging demand and backlogs stretching years into the future.
• SK Hynix — the dominant supplier of high-bandwidth memory — posted revenue up nearly 200% year-over-year, with margins that more closely resemble a luxury goods stock than semiconductors.
• Texas Instruments (TXN), the most diversified chip company in the world, saw data center revenue jump 90%.
• Intel (INTC) revealed that AI now drives 60% of its business and is accelerating.
• Optical and connectivity players like MaxLinear are seeing triple-digit growth tied directly to expanding AI clusters.
• Power and thermal infrastructure providers like Vertiv (VRT) and GE Vernova are raising guidance as hyperscalers build out capacity at unprecedented scale.
• And at the ground level, Comfort Systems (FIX) — the company physically constructing data centers — is growing revenue more than 50% with a record backlog that keeps rising.

Translation: buyers are no longer negotiating price. They’re just asking for volume. That is a strategic reorientation.

“Customers are prioritizing securing volume over pricing, which is sustaining the current strength.” — SK Hynix CFO, Q1 2026 Earnings Call

This is synchronized strength across an entire industrial ecosystem.

And most importantly, demand is outrunning supply… everywhere.

Customers aren’t negotiating price anymore.

They’re fighting for volume…

The Losers: A Guided Tour of the Other Side

Now let’s cross the divide.

Consumer-facing and non-AI-exposed companies told a completely different story.

• Packaging giant Sonoco (SON) cut guidance as energy and supply chain costs rise.
• United Airlines (UAL) warned that higher oil prices are squeezing margins and weakening demand.
• ServiceNow (NOW) flagged longer sales cycles as enterprise buyers hesitate.
• Pegasystems (PEGA) dropped to new lows after weak results.
• Lululemon (LULU) replaced its CEO amid slowing growth.

These businesses are tied to consumer demand, input costs, and discretionary spending cycles.

And right now, those forces are soft.

Why This Is Structural

Skeptics will argue that this is just a boom cycle… that AI infrastructure spending will eventually slow, that the winners will give back their gains, and that the bifurcation will narrow as the economy normalizes. History suggests otherwise, and the earnings data makes the case explicitly.

Consider what we heard from management teams across the winning cohort: SK Hynix said customers’ demand for memory over the next three years “far exceeds current supply capacity.”

Intel said server CPU demand has “improved over the last 90 days” and momentum extends into 2027.

MaxLinear said backlog is already building into 2027.

Comfort Systems said their backlog rose sequentially even as their burn rate accelerated — meaning they are signing new contracts faster than they can complete existing ones.

These are not companies talking about a hot quarter. These are companies describing structural supply constraints that are measured in years.

The reason is simple: the physical infrastructure required to run AI at scale — fabs, fabs equipment, memory capacity, power plants, fiber networks, data center buildings — takes two to five years from initial investment decision to operational capacity. The demand is here today.

The supply is not.

That gap does not close quickly, which means the pricing power, the margin expansion, and the earnings growth will persist far longer than a traditional cyclical recovery would suggest.

There is also a demand-side structural argument that emerged clearly from the Intel and SK Hynix transcripts this week.

AI is evolving from the training phase — where massive models are built in specialized data centers — into the inference and agentic phase, where AI actively processes real-world requests at scale, continuously, across millions of concurrent users.

Every agentic AI task generates intermediate data that must be stored and processed. Every inference call requires a CPU to orchestrate it alongside a GPU to compute it. The more AI is deployed, the more of every layer of the infrastructure stack is required. The demand loop is self-reinforcing.

What This Means for Your Portfolio

The bifurcation thesis has now graduated from analytical framework to documented business reality.

It is showing up in quarterly revenue lines, gross margin percentages, backlog figures, and earnings-per-share numbers across the entire AI infrastructure stack simultaneously. That is not a coincidence. It is a structural shift in where economic value is being created.

The companies building, powering, connecting, and computing for the AI era are operating in an environment of structurally constrained supply against structurally growing demand… the most favorable economic condition that exists for sustained earnings growth.

The companies outside that world are operating in an environment where consumer confidence is shaky, input costs are elevated, and discretionary spending is vulnerable.

The data has spoken. The AI divide isn’t coming….

It’s already here.

If you want the picks-and-shovels play on the most ambitious financial product launch of our generation, you need to see our latest research.

I just launched a full presentation on X Money – Elon Musk’s long-awaited attempt to turn X into the “Bank of Elon” – and the public companies powering it behind the scenes.

In it, I break down the full story on how you could benefit as X Money rolls out.

Click here to watch it now.

The post Earnings Season Just Proved the AI Economy Is Splitting in Two appeared first on InvestorPlace.

As I write on Monday, oil is trading higher in the wake of this weekend’s failed peace negotiations.

Over the weekend, President Donald Trump scrapped plans to send envoys Steve Witkoff and Jared Kushner to Islamabad for face-to-face talks, citing “tremendous infighting and confusion” within Tehran’s leadership.

However, this morning, Reuters is reporting that Iran has proposed a new plan: end the war first, resolve the Strait of Hormuz dispute second, and only then address Iran’s nuclear program.

That sequencing is almost certainly a non-starter in Washington. The Trump administration has been explicit that nuclear issues must be on the table from the outset. As Trump put it Sunday:

They cannot have a nuclear weapon; otherwise, there’s no reason to meet.

As I write, the two sides appear to remain far apart – and the energy market is pricing in the distance. Brent crude (the European benchmark) trades at nearly $109 a barrel, while West Texas Intermediate Crude is up to $97.

Goldman Sachs has raised its Brent forecast to $90 by late 2026 – up from $80 previously – citing persistent Gulf disruptions and a supply drawdown it estimates is running at a record 11 to 12 million barrels per day here in April.

Invesco puts $80 as a likely floor for Brent this year absent a full normalization of Hormuz flows. Even if the Strait eventually reopens, the inventory hole being dug right now will take months to refill.

Bottom line: the longer oil prices stay at these elevated levels, the more pressure they put on the U.S. consumer, the greater the risk of inflation, and the harder it becomes for the Fed to justify the rate cut the market wants. We’ll keep tracking this as well as its economic consequences.

Now, let’s zero in on one of the biggest potential market movers this week.

This week’s Big Tech earnings aren’t really about Big Tech

This week, four of the five most valuable companies in the world report quarterly results within 48 hours. Microsoft (MSFT), Alphabet (GOOGL), Meta Platforms (META) and Amazon.com (AMZN) all drop their numbers on Wednesday. Apple (AAPL) follows on Thursday. That’s roughly $16 trillion in market cap reporting inside 48 hours.

Although earnings beats and misses will generate headlines, this week’s reports are less about any individual hyperscaler’s bottom line and more about the ongoing verdict on the entire AI trade.

So, before Wednesday’s numbers hit, let’s frame what you should really be looking for and why…

The billions of dollars flowing into AI infrastructure have been enormously profitable – for select chip companies like Nvidia (NVDA), data center operators, power companies, and for the whole ecosystem of picks-and-shovels plays that have driven much of the AI bull market. That spending is real. Those revenues are real. Those stock gains are real.

But that spending by hyperscalers is not the same as consumer and enterprise demand for AI products.

The hyperscalers have been building – aggressively, almost frantically – on the assumption that end users will eventually pay for AI at scale.

Microsoft, Alphabet, Meta, and Amazon spent a combined $410 billion on capex last year, nearly triple what they spent in 2022. Wall Street expects as much as $674 billion from those four companies this year alone – the third consecutive year of combined growth exceeding 60%.

Chart: Source Visible Alpha / WSJ

That’s a staggering bet on future demand.

And while the AI infrastructure trade has been pricing in the win, the revenue side will demonstrate if the win is real, which brings us back to this week’s earnings.

The immediate diagnostic to watch this week: revenues

Three years into this arms race, the monetization question is becoming harder and harder to push off…

Are consumer-facing AI products (whether to individuals or companies) making serious money?

So, this week, we need to watch the revenue signals closely in hyperscalers’ earnings calls.

Are cloud revenues accelerating in ways that can be credibly tied to AI workloads? Are enterprise customers moving from pilots into full deployments? Are any of these companies beginning to break out AI-specific revenue in ways they haven’t before?

Strong signals here don’t prove the bull case permanently, nor do weak ones kill it. But we’re three years in now, and each earnings cycle without clear monetization progress shortens the runway for investors’ patience.

The market has been willing to fund the build. But at some point, the build has to fund itself. This week is one more weight on the balance, and the balance is getting harder to ignore.

But this week’s revenue picture is just the start…

The longer-term vital sign: capex

Revenues tell us what’s already happened. But capex guidance tells us what these companies believe is coming – and reveals whether conviction in AI’s ROI is holding or fading.

Over the last few years, we’ve seen Wall Street panic about how much money the hyperscalers are spending to build out AI. But now, the market has fully bought the “you have to spend to win” argument. So, aggressive spending is the baseline.

But I’d take it one step further…

With many AI infrastructure stocks soaring thanks to the hyperscaler spending boom, aggressive spending is now the desired strategy. But this means that the far greater risk today isn’t overspending, but underspending.

If a major hyperscaler quietly revises its capex outlook downward, softens its data center expansion timeline, or uses language suggesting a “reassessment” of its investment pace, Wall Street won’t just react to the reduced spending. It would start asking a more unsettling question…

What does this company know that we don’t?

Reduced spending wouldn’t read as fiscal responsibility, but as lost conviction.

And that fear wouldn’t stay contained to one hyperscaler. It would ripple across the entire AI complex.

To be clear, nobody is expecting that signal this week. So, we raise this issue not because Wednesday is the moment to worry about it, but because it’s the canary worth knowing about now – and tracking – well before it becomes obvious.

Because when/if it does become obvious to the masses – and the end-user AI revenue side hasn’t picked up the slack by then – get ready for fireworks.  

We’ll be watching closely and will bring you the key takeaways as results roll in. For now, all eyes on revenues.

The Mythos story just got more urgent

Two weeks ago, we covered a story that rattled the cybersecurity world…

AI company Anthropic had created Claude Mythos, a model its own developers described as “currently far ahead of any other AI model in cyber capabilities.” Mythos is allegedly capable of finding and exploiting vulnerabilities in every major operating system and browser.

In response, Treasury Secretary Bessent and Federal Reserve Chairman Jerome Powell summoned the CEOs of major U.S. banks to the Treasury Building to discuss the threat Mythos posed to financial infrastructure.

Anthropic launched Project Glasswing the same day, giving eleven partners – among them Amazon, Apple, Google, Microsoft and Nvidia – early access to stress-test their own systems.

Meanwhile, Wall Street’s verdict on what that meant for the cybersecurity industry was swift and brutal: Palo Alto Networks (PANW) fell roughly 10%, CrowdStrike (CRWD) dropped 11% and Zscaler (ZS) shed 14%.

But the story isn’t over…

Last week, a small group of unauthorized users from the online community Discord gained access to Mythos. They got in through a mix of insider contractor access, web-scouring bots, and educated guesses about the model’s online location – and they’ve been using it regularly since.

Anthropic says it’s investigating and has found no evidence the breach was widespread. But the damage to confidence is already done.

Here’s Fortune:

If a group of AI nerds could get into Mythos – allegedly without malicious intent – imagine the fallout if the next ones to slide through that door were actual criminals.

But the breach confirmed something bigger

AI is now accelerating the discovery of online vulnerabilities faster than organizations can patch them. Here’s Fortune:

AI can now find flaws and exploit them so quickly that defenders may be the ones left truly exposed…

That’s the new reality Project Glasswing was designed to address. Whether it can move fast enough is a different question.

We’ll keep tracking this as its outcome is critical not only for cybersecurity stocks, but for the safety of our sensitive data online.

But there is a slight silver lining here…

The same capability, pointed at markets

Viewed through a “tech” lens, the Mythos story is about AI crossing a threshold in pattern recognition. As TradeSmith CEO Keith Kaplan put it this week:

Mythos finds patterns in computer programs that no human could see.

It reads millions of lines of code, identifies the specific combinations of conditions that point to a vulnerability. Then it acts on them.

Our friends at TradeSmith have spent years doing exactly this: building a system that applies this exact kind of AI-powered pattern recognition – but to financial markets.

Back to Keith:

The stock market contains similar kinds of hidden structures. Buried inside decades of data for every stock are “signals” — specific combinations of conditions that have consistently preceded big moves.

For most of market history, they were invisible. The data existed… only nobody had the tools to read it.

But today, TradeSmith’s system has identified 30,000 of these signals across nearly 2,500 stocks, each with historical accuracy rates of 75% or better.

Their flagship three-stock model portfolio – always three S&P 500 positions, rotated algorithmically when exit signals fire – produced a backtested compounded annual return of 54% from January 2020 through January 2026, versus roughly 15% for the S&P 500 over the same period.

Backtests aren’t guarantees, but the underlying logic is sound: the same AI leap that makes Mythos alarming to cybersecurity professionals is the one making tools like this possible for regular investors.

Last week, Keith walked nearly 9,000 people through the full system in his live AI Signals Trading Event. The replay is still available – trade examples, strategy details, and his current model portfolio picks included. The replay is available for a limited time right here.

Have a good evening,

Jeff Remsburg

The post What This Week’s Earnings Really Tell Us About AI appeared first on InvestorPlace.

One reason is the Federal Reserve. The central bank will announce its key interest rate decision on Wednesday. And while it is widely expected to remain steady at the 3.5% – 3.75% range, Wall Street will be looking for clues to the Fed’s thinking on the economy and inflation in light of the ongoing conflict in the Middle East.

On top of that, earnings season is kicking into high gear, with five of the Magnificent Seven set to report this week: Alphabet Inc. (GOOG), Amazon.com, Inc. (AMZN), Apple Inc. (AAPL), Meta Platforms, Inc. (META) and Microsoft Corporation (MSFT).

What’s interesting about this earnings season is that, so far, companies have to beat on all key metrics across the board and guide higher, just to survive.

We saw this last week when GE Vernova (GEV) announced earnings that beat expectations and raised guidance, sending the stock up 14%. Tesla, Inc. (TSLA) also beat on earnings, but it fell about 4% as investors worried about its outlook and AI spending plans.

In this week’s Navellier Market Buzz, I explain what investors are actually rewarding this earnings season. Then, I reveal Palantir Technologies Inc.’s (PLTR) biggest rival, a company that’s challenging Palantir’s dominance in defense and AI. The name might surprise you.

Click the image below to watch now.

To see more of my videos, click here to subscribe to my YouTube channel.

Plus, the grades in Stock Grader (subscription required) have been updated this week! Click here to plug in your own stocks and see how they’re rated.

What the Market Is Really Responding To

What we’re seeing during this earnings season isn’t random.

When one company beats expectations and surges, it’s a sign the market is reacting to more than the headlines.

The challenge is that by the time most investors understand what’s really driving a big move, the biggest gains are already gone.

That’s where TradeSmith’s Signals system comes in.

It scans thousands of stocks every day, looking for specific patterns that have historically led to outsized moves, identifying them before they happen.

In fact, in a five-year backtest, this approach uncovered more than 30,000 signals and produced a model portfolio with a 12X return and a 73% win rate.

That kind of consistency suggests these patterns aren’t random.

For you, that could mean a chance to spot opportunities earlier, position yourself ahead of the crowd and avoid chasing moves after they’ve already played out.

My colleague Keith Kaplan explains how it works in his recent presentation, The AI Signals Trading Event.

But I should warn you – this presentation will be closed tomorrow at midnight, so you’ll want to watch it now before it’s taken down.

Click here to watch the full presentation.

Sincerely,

Louis Navellier

The Editor hereby discloses that as of the date of this email, the Editor, directly or indirectly, owns the following securities that are the subject of the commentary, analysis, opinions, advice, or recommendations in, or which are otherwise mentioned in, the essay set forth below:

GE Vernova (GEV) and Palantir Technologies, Inc. (PLTR)

The post Palantir’s Biggest Rival Revealed appeared first on InvestorPlace.

This Week’s Ratings Changes:

Upgraded: Strong to Very Strong

Downgraded: Very Strong to Strong

Upgraded: Neutral to Strong

Downgraded: Strong to Neutral

Upgraded: Weak to Neutral

Downgraded: Neutral to Weak

Upgraded: Very Weak to Weak

Downgraded: Weak to Very Weak

To stay on top of my latest stock ratings, plug your holdings into Stock Grader, my proprietary stock screening tool. But, you must be a subscriber to one of my premium services.

To learn more about my premium service, Growth Investor, and get my latest picks, go here. Or, if you are a member of one of my premium services, you can go here to get started.

Sincerely,

Louis Navellier

Editor, Market 360

The post Intel Upgraded, Toyota Downgraded: Updated Rankings on Top Blue-Chip Stocks appeared first on InvestorPlace.

It’s about who sees patterns first.

And the tools once reserved for elite hedge funds – massive data processing, pattern recognition, and machine learning – are becoming more accessible to everyday investors.

As technology changes how opportunities are identified, the edge is moving away from pure speed and toward smarter, data-driven insight.

Understanding and applying that shift will be key to navigating what comes next.

TradeSmith CEO Keith Kaplan is joining us to share how AI is turning massive data sets into predictive signals. He explains how his team is using AI to identify specific “signals” in the market before they fully play out.

To learn the ins and outs of this system, click here to watch Keith’s AI Signals Trading Event.

The goal isn’t to chase hype; It’s to spot real, lasting advantages and use them wisely.

In a forest near the Arctic Circle, a Russian-born mathematician is building the future of trading.

The site is in Kajaani, a small city in northern Finland. The first of five data centers there is roughly the size of three football fields. It’s set to go live this year. A second one, next door, is nearly finished and will come online in 2027.

One of XTX’s giant data centers in Finland (Source: XTX)

The man behind them is Alex Gerko. He’s worth an estimated $12 billion. And his hedge fund, XTX Markets, doesn’t employ a single human trader.

Gerko spent the 2000s trading at Deutsche Bank. But he’d come to believe the race to shave microseconds off trade execution – the obsession of Wall Street’s high-frequency firms – was a dead end. He wanted to compete on intelligence, not speed.

So he moved to London and founded XTX in 2015. Its employees weren’t traders. They were coders and researchers who built AI models to predict price moves milliseconds, minutes, and hours into the future. Every decision was made by an algorithm.

Last year, XTX’s UK business generated $5.3 billion in revenue and $2.3 billion in profit. It traded an average of $250 billion a day across stocks, bonds, currencies, and crypto – with just 250 employees.

That’s more profit per head than quant giants Citadel or Jane Street.

Which is why Gerko is doubling down. The Finland data centers are his own, built from scratch – an unusual approach in finance. To fuel them, he has amassed 25,000 AI chips, mostly from Nvidia. The northern location helps keep the machines from overheating.

He isn’t alone.

Earlier this month, Jane Street invested $1 billion in the AI-infrastructure company CoreWeave and signed a $6 billion deal to use its computing power. Bridgewater, the world’s largest hedge fund, has launched an AI-based investment fund to find trading patterns uncorrelated to human strategies.

Wall Street’s smartest, best-funded firms have all reached the same conclusion: the future of trading belongs to machines that can read patterns in market data that humans cannot.

Until recently, that kind of edge was unreachable for anyone outside an elite hedge fund.

Today, thanks to advances in AI, that’s changing. Not by outspending these hedge fund titans. But by applying the same underlying principle to finding short-term signals on liquid stocks that a regular investor can actually trade in their brokerage accounts.

That’s what my team and I at TradeSmith have spent the last 12 months developing. It’s an AI-powered trading system that finds repeating signals inside market data that point to future moves in stocks, many with historical accuracy rates of 90% or better.

We’ve built a new AI-powered trading tool that’s unearthed more than 30,000 signals across nearly 2,500 stocks.

As I showed the nearly 9,000 viewers who joined my AI Signals Trading Event last week, in a six-year backtest, a model portfolio of these signals trades delivered a 12x return.

And in 2022 — the worst year for stocks in half a century — they produced an average backtested gain of 16.6% while the S&P 500 fell nearly 20%.

If you missed it, the replay is still online. It’s packed full of trade examples, strategy details, and on-screen demos. Go here to watch it now.

Today, I want to share something I didn’t have time to cover last week – how much work and ingenuity went into building our new system.

It’s been our No. 1 priority for the development team over the past 12 months. But the seed was planted more than two decades ago, when our chief developer started asking questions about repeating market patterns.

Can the Weather in Paris Move the Stock Market?

Mike Carr has been writing code for 40 years.

He spent 20 years in the U.S. Air Force – coding nuclear missile paths, working on cryptography for the National Security Agency, and helping install an early version of the internet at the Pentagon.

When he left the military, he also became a Chartered Market Technician – a credential only about 4,500 people in the world hold. And as an investor he managed more than $200 million in client funds.

Two years ago, Mike joined TradeSmith to help us develop new analytics and strategies. And he brought with him the kernel of an idea he’d been working on for more than 20 years.

In 2003, he started collecting signal studies. Every time he spotted a repeating pattern in the data that tended to precede a move in a stock, he  recorded it. He traded this way for years, constantly testing what worked and what didn’t.

Then in 2016, he read a profile of Jim Simons’ hedge fund, Renaissance Technologies. One detail stuck with him. Simons had once found a tradable signal involving the weather in Paris.

If he could find a signal in Parisian weather, the signals hiding in ordinary market data had to be almost limitless. That was the moment Mike decided to stop hunting for signals himself and build a system to hunt them at scale – one ordinary investors could actually use.

Last year, we started feeding signals he’d collected into an AI system to generate more like them. We processed more than 1 trillion database rows, running every stock through 847 individual calculations. We tested every combination of price patterns, technical indicators, and calendar conditions we could find.

Then we hit a problem we hadn’t anticipated.

Our AI signals generator delivered more 30,000 trade setups with historical accuracy rates of 75% or better. It was far too much for any trader to handle, no matter their level of experience.

So we spent the next six months solving a different problem: How do you take that many high-quality signals and deliver something every investor can use?

The answer Mike came up with was the Quality Score. It’s a 0-to-100 rating created by two machine learning models that grade each signal’s track record and assess how well today’s market conditions favor it.

Pair that with a focused model portfolio, and the flood became an actionable shortlist.

It holds three S&P 500 stocks — each one selected by an algorithm based on its Quality Score and other key factors. When an exit signal fires on one, a new trade signal takes its place.

Each trade is selected not because a human liked a chart or a story… but because an algorithm chose a mathematically optimal opportunity.

Stress Testing in an Unfriendly Environment

We backtested data from January 1, 2020 through January 30, 2026.

We covered the COVID crash, the 2022 bear market, two years of historic inflation, rising interest rates, and two wars. But here’s what the Signals Master Portfolio produced:

• A 54% compounded annual return — versus about 15% for the S&P 500 over the same six years
• A 73.4% win rate across hundreds of trades
• A maximum drawdown of 18.1% — less than the S&P 500’s maximum drawdown of 25.4%

The maximum drawdown is worth pausing on.

A lot of trading systems can generate a high compounded return in a backtest. Few can produce one that holds up better than the S&P 500 during its worst stretch. That’s the test of whether a system is managing risk effectively or just riding luck.

Which is the point of what we’ve spent the last 12 months – and, in Mike’s case, more than 20 years – developing. Hedge funds have been doing this kind of work for decades – pattern recognition, machine learning, and disciplined rotation. But until now, nothing like it has existed for regular investors.

I went into all the details during last week’s launch event. So if you haven’t already, make sure to check it out while it’s still online.

And if you were there for the event, a big thank you. You helped make it one of the most successful in TradeSmith history.

Regards,

Keith Kaplan
CEO, TradeSmith

The post Inside the Arctic Data Center That Could Change How Wall Street Trades appeared first on InvestorPlace.

They rely on pattern recognition — systems that sift through enormous amounts of market data looking for repeatable setups with a statistical edge.

That shift is already reshaping how money is made on Wall Street.

That’s the idea my colleague Keith Kaplan explores in the essay below. He shows how his team built an AI-driven system to uncover repeatable signals across thousands of stocks — and turn them into a simple, actionable strategy.

You can dig into the full framework — and see the system in action — in his Signals presentation, available here.

“There is an entity that cannot be defeated.”

That’s how Lee Sedol – the world’s greatest Go player – described AI after losing a five-game contest against Google’s AlphaGo AI model in Seoul in March 2016.

Go was invented in China more than 2,500 years ago. Two players place black and white stones on a grid, trying to capture each other’s pieces.

It looks easy. But it contains more possible positions than there are atoms in the observable universe. The best players win by feel and intuition rather than brute calculation.

So, Sedol thought he was safe. The game was too creative – too deeply human – for a machine to crack. Before the match, he predicted he’d win every game.

Then came Move 37 in the second game.

Google’s AlphaGo placed a stone in a position no normal player would make – a move so strange that one commentator blurted out, “That’s not a human move.”

He was right.

AlphaGo’s analysis showed that a human player would make that move less than 1 in 10,000 times. But it turned the tide in AlphaGo’s favor, and Sedol lost.

He went on to lose the contest 4–1.

The machine wasn’t smarter. But it could see patterns no human eye could. And that was enough to defeat the world’s best player.

From Go Boards to the Stock Market

It’s a cautionary tale for investors today. AI models are way better at recognizing patterns now than they were a decade ago. And there’s no domain richer in hidden patterns – or more ruthlessly efficient at rewarding those who spot them and punishing those who miss them – than the stock market.

That’s why my team and I at TradeSmith have spent the last 12 months building a new application of AI for self-directed investors like you.

It uses the same principles that defeated the world’s greatest Go player to spot patterns that flag the day’s most promising trades – 90 minutes before they happen.

I’ll show you how it works today – and pass on the link where you can watch it in action. First, let me give you some background on TradeSmith and what we’re all about.

Inside TradeSmith’s AI Trading Research Lab

As TradeSmith’s CEO, I run a team of 65 people, and an annual budget of $8 million, to develop hedge-fund-level analytical systems for self-directed investors.

More than 134,000 people in 86 countries use our software to manage more than $29 billion in assets.

Inside our Research Lab, we’re like modern-day prospectors panning for gold – only we use data and computers, not pans and pickaxes.

We’re constantly testing trading strategies, financial metrics, and data patterns to uncover profitable systems and indicators.

That’s what’s gotten us featured in Forbes, The Wall Street Journal, and The Economist.

Our risk-management software, TradeStops, put us on the map among financial technology firms. It takes the emotions out of investing by showing you the ideal time to sell your stocks.

We’ve also created software that finds hidden seasonality patterns in stocks… spots undervalued options plays… and uses AI to forecast stock moves up to 21 trading days out.

We’re always innovating. But we’ve never gone this deep.

Our new AI-powered system doesn’t look at balance sheets… read earnings reports… or follow news headlines. Instead, it detects tiny anomalies in stocks’ historical data. Then it finds statistical connections between them that a human analyst would never find.

Think of it like a “thumbprint.” Every great trade has one. A unique combination of factors – technical indicators, price patterns, market conditions – that has lined up before.

When those factors align again, our system flags a high-probability setup. Some with historical accuracy rates of 90% or more.

Here’s what happens when we tested it out.

Real Trades Powered by AI Trading Signals

In January, our system flagged a trade in Qnity Electronics Inc. (Q) – a company I’d never heard of before.

It identified three factors that had aligned only four times over the past decade. Every time those same factors lined up for Q, the stock went up.

On January 28, this signal fired again – and over the next 30 days Q shares jumped 26%.

Or take AI chipmaker Advanced Micro Devices Inc. (AMD). On a recent signal, our system forecast an 8.4% move in 14 days, based on a pattern with 95% historical accuracy.

The result was an 8.1% gain in 48 hours.

Or take another popular AI stock, Palantir Technologies Inc. (PLTR). Our system signaled a 5.8% move in nine days, again with a historical accuracy rate of 95%. The result was a 15.1% gain in seven days – nearly three times the forecasted return.

These returns are from trading our signals with stocks. Here’s what those same signals produced using options:

• Caterpillar Inc. (CAT): 126% in 72 hours
• Nvidia Corp. (NVDA): 129% in 5 days
• Lockheed Martin Corp. (LMT): 365% in 30 days
• HCA Healthcare Inc. (HCA): 461% in 13 days
• Generac Holdings Inc. (GNRC): 1,082% in 33 days

Now, you’ve seen some of what the signals can do. Let’s look at what’s happening under the hood…

How AI Stock Predictions Work Under the Hood

The technology behind this new trading system is similar to what powers ChatGPT and other AI models.

They soak up massive samples of language, find statistical relationships between words, and predict what comes next. Our system uses the same principle – but for numbers.

We fed it data on 2,467 stocks going back 10 years – including interest rates, Treasury yields, Relative Strength Index readings, Bollinger Bands, and intraday trading ranges.

It also looks at indicators most traders have never heard of. Things like:

• Kaufman Efficiency Ratio: It measures how cleanly a stock is trending versus how much it’s just drifting sideways.
• Williams %R: It’s a momentum oscillator that measures where price sits relative to its recent high-low range.
• DMI+/DMI-: They measure the strength of upward versus downward movement independently, rather than just looking at price direction.

No one knows exactly why each signal works so well. Frankly, it doesn’t matter. Like AlphaGo, our AI finds the what… not the why.

It runs each stock through 847 individual calculations daily, compiling more than 2 million trade evaluations every 24 hours.

It’s looking for combinations that have worked before – regardless of whether there’s an obvious reason why.

The result is a trading system that doesn’t care if we’re in a bull or a bear market. It doesn’t need a strong economy or a calm geopolitical environment. It just needs the ingredients to align.

Given what we’ve seen in 2026 – a wipeout in software stocks, oil above $100 a barrel, and rising volatility – that kind of neutrality matters more than ever.

Now You Can Try This Breakthrough for Yourself

This is unlike anything we’ve released in our firm’s 21-year history.

The 30,000 (and counting) signals our system has discovered for 2,467 stocks give you the kind of edge that would otherwise be off-limits for most investors.

Find out more about it by tuning into my AI Signals Trading Event.

I’ll walk you through how it works in more detail – including the signals it’s tracking right now and the trades it’s flagging for the weeks ahead.

Click here to learn more.

The post Inside the Machines That Now Run Wall Street appeared first on InvestorPlace.

History is full of moments when the right insight was hiding in plain sight, buried in data no one yet knew how to interpret. The investors who recognize those shifts early are often the ones best positioned to benefit.

Today, we may be entering another one of those moments.

Markets generate an overwhelming amount of information every second, but raw data alone has never created an advantage. The real opportunity lies in identifying the patterns that matter – and acting before the rest of the market catches on.

Today, TradeSmith CEO Keith Kaplan is joining us to share how that process is changing, why new tools are making once-invisible signals easier to detect, and what that could mean for investors searching for an edge in an increasingly data-driven market.

He explains how his team is using AI to identify specific “signals” in the market before they fully play out.

To learn the ins and outs of this system, click here to watch Keith’s AI Signals Trading Event, where he walks you through how it works and what it’s flagging in the market today.

Take it away, Keith…

Tycho Brahe’s mission in life was to be the first to explain how the planets really moved.

So this obsessive 16th century Danish astronomer spent more than two decades building the most precise record of planetary motion the world had ever seen — and then guarded it so jealously almost no one was allowed to see it.

In Brahe’s time, there were no telescopes. Every measurement he took was with the naked eye, using instruments he designed and built himself on a small island off the coast of Denmark.

 It was a data set unlike anything that had ever existed — page after page of handwritten figures and precise planetary positions.

He couldn’t interpret all that data alone. So he brought on a brilliant young German mathematician named Johannes Kepler. But Brahe, afraid Kepler might make the discovery first, handed his apprentice only just enough data to be useful and locked the rest away.

That arrangement lasted barely a year. In October 1601, Brahe died suddenly. Kepler inherited his notebooks and studied them intensely for the next four years. 

What he found was proof that everything astronomers had assumed since ancient Greece was wrong.

Kepler realized that planets didn’t move in perfect circles. They moved in ellipses — slightly flattened ovals, with the sun off to one side rather than in the direct center.

Almost a century later, Isaac Newton read Kepler’s laws of elliptical motion and worked out the force that explained them. He called it gravity.

One of the most important ideas in the history of science was hiding in Brahe’s notebooks for decades. The data had always been there. All it needed was someone who could make it legible.

I’m telling you this because the stock market has a Tycho Brahe problem, too.

It generates more data in a single trading day than Brahe recorded in a lifetime. The problem is, for most of its existence, only a tiny fraction of it has been readable.

But today, thanks to AI, it’s possible to find “signals” inside that data — repeating patterns that point to future moves in stocks, many with historical accuracy rates of 90% or better.

I know because my team and I at TradeSmith have created a new AI-powered trading tool that’s unearthed more than 200 of these signals across nearly 2,500 stocks.

As I showed the nearly 9,000 people who joined my AI Signals Trading Event on Wednesday, in a six-year backtest, a model portfolio of these signals trades delivered a 12x return.

And in 2022 — the worst year for stocks in half a century — they produced an average backtested gain of 16.6% while the S&P 500 fell nearly 20%.

If you missed it, the replay is still online. It’s packed full of trade examples, strategy details, and on-screen demos. Go here to watch it now.

Today, I want to share something I didn’t have time to cover on Wednesday — how much work and ingenuity went into building our new system.

The answer is: more than I expected.

Can the Weather in Paris Move the Stock Market?

Our chief developer, Mike Carr, has been writing code for 40 years.

He spent 20 years in the U.S. Air Force – coding nuclear missile paths, working on cryptography for the National Security Agency, and helping install an early version of the internet at the Pentagon.

When he left the military, he went on to manage more than $200 million in client funds. He also became a Chartered Market Technician — a credential only about 4,500 people in the world hold.

Two years ago, he joined TradeSmith to help us develop new analytics and strategies. And he brought with him the kernel of an idea he’d been working on for more than 20 years.

In 2003, Mike started doing rudimentary signal studies. Every time he spotted a repeating pattern in the data that tended to precede a move in a stock — he noted it down. He traded this way for years, constantly testing what worked and what didn’t.

Then in 2016, he read a Bloomberg profile of Jim Simons’ storied hedge fund, Renaissance Technologies. One detail stuck with him: Simons had once found a tradable signal involving the weather in Paris.

If he could find a signal in Paris weather, Mike realized, the signals hiding in ordinary market data had to be almost limitless. That was the moment he decided to stop hunting for signals manually and start building a system that could hunt them at scale — one ordinary investors could actually use.

Last year, we started feeding the 150 or so signals he’d collected into an AI system and prompted it to generate more like them. We processed more than 1 trillion database rows, running every stock through 847 individual calculations. We tested every combination of price patterns, technical indicators, and calendar conditions we could find.

Then we hit a problem we hadn’t anticipated.

On any given day, our AI-powered signals generator was delivering a flood of 697 trade setups – all with historical accuracy rates of 75% or better. It was far too much for any trader to handle, no matter their level of experience.

So we spent the next six months solving a different problem: How do you take that many high-quality signals and deliver something an investor can actually use?

The answer Mike came up with was the Quality Score. It’s a 0-to-100 rating that factors each signal’s win rate and average returns and uses machine learning to figure out how effective it was during similar market conditions in the past.

Pair that with a focused model portfolio, and the flood became an actionable shortlist.

Introducing Our Three-Stock Strategy

At any given moment, it holds three S&P 500 stocks — each one selected by an algorithm based on its Quality Score and other key factors. When an exit signal fires on one of the three, a new trade recommendation takes its place.

That’s the whole strategy. Three positions, always live. Each one selected not because a human liked a chart or a story… but because an algorithm chose a mathematically optimal trade.

We backtested it from January 1, 2020 through January 30, 2026 – a stretch that covered the COVID crash, the 2022 bear market, two years of historic inflation, rising interest rates, and two wars.

It wasn’t a friendly period to stress-test a trading system against. But here’s what the Signals Master Portfolio produced:

• A 54% compounded annual return — versus about 15% for the S&P 500 over the same six years
• A 73.4% win rate across hundreds of trades
• A maximum drawdown of 18.1% — less than the S&P 500’s maximum drawdown of 25.4%

The model portfolio’s maximum drawdown is worth pausing on.

A lot of trading systems can generate a high compounded return in a backtest. Few can generate one that also held up better than a benchmark like the S&P 500 during its worst stretch. That’s the litmus test of whether a system is managing risk effectively or just riding luck.

Which is the point of what we’ve spent the last 12 years (and in Mike’s case more than 20 years) developing. Hedge funds have been doing this kind of work for decades — pattern recognition, machine learning, disciplined rotation in and out of short-term trades. But until now, nothing like it has existed for regular investors.

I went into all the details during Wednesday’s launch event. So if you haven’t already, make sure to check it out while it’s still online.

Keith Kaplan
CEO, TradeSmith

The post Humans Can’t Spot These Stock Market Signals – But AI Can appeared first on InvestorPlace.

Last week here, I flagged three compelling companies to buy: 

• Reddit Inc. (RDDT) 

• Arm Holdings Plc (ARM) 

• AeroVironment Inc. (AVAV) 

Our colleagues at TradeSmith had just released their most advanced AI-powered stock-selection system yet. And these three picks made the cut into their Signals Master Portfolio, the system’s highest conviction stocks at any given time. 

I hope you tuned in. 

Since then, the trio has returned 12% on average, buoyed by a 40% surge in Arm. 

Not bad for a single week of trading. 

Even better, one of these picks is still in the Signals Master Portfolio. That means it’s not too late to buy in. 

There’s also still time for you to learn more about TradeSmith’s AI-powered trading system. If you missed out on Keith Kaplan’s AI Signals Trading Event from Wednesday, you can still catch the full replay – including live demos and trade examples – right here. 

I’ll feature two new top stocks in today’s update. And as a bonus, I’ll let you know (at the end) which of the three picks from last week continues to remain one of the Signals Master Portfolio’s top picks… 

The “Safer” Investment 

On Wednesday, the Signals Master Portfolio offered a surprisingly safe bet: 

Nu Holdings Ltd. (NU). 

This holdings company is the parent of Nubank, the largest digital bank in Brazil. Sixty percent of all Brazilian adults have an account with the bank, and the company’s Colombian and Mexican expansions are seeing exponential growth. 

There are three fundamental stories to watch. 

First, Nubank has an incredible business model. The bank has no physical branches, and its efficiency ratio (a measure of costs) sits at just 27.9% – well below Banco Bradesco’s (BBD) 47% and Itau Unibanco Holding SA’s (ITUB) 53%. (In banking, lower efficiency ratios are better.) 

That’s allowed Nubank to offer better rates, landing them an enormous number of new customers in a short stretch. The company now serves at least 112 million Brazilians, and revenues have more than doubled since 2023. Profits are also incredible. The firm earns 30% returns on equity (3X to 4X higher than rivals), and net income is projected to rise another 40% in fiscal 2026. These high returns are crucial, because they allow the bank to plow capital back into the business and generate even more returns. 

Second, Nubank has a long growth runway. The company is rapidly expanding across Latin America, and its success in Mexico shows it can compete even against well-entrenched players like BBVA Mexico. In addition, the company currently offers a very narrow line of lending products, giving it room to expand into auto loans and mortgages at home in Brazil. 

Most importantly, shares are extremely cheap relative to growth rates. Shares have declined 20% since January, pricing the stock at just 17X forward earnings. Markets are assuming just two more years of growth, which is clearly too conservative. 

Now, it’s important to note that Nu Holdings still owns a bank, which is a risky business by nature. Default rates in Latin America are particularly high, and Nubank had a 9.6% net charge-off rate in 2025. It carries a 15.4% allowance for loan losses – almost 10 times higher than a typical U.S. bank. 

Nevertheless, the company has maintained a conservative balance sheet and reminds me greatly of SoFi Technologies Inc. (SOFI) and Dave Inc. (DAVE) – two digital banks in America that have also become wildly successful. Many younger savers no longer want to pay for physical bank branches they never use, and digital options offer a low-cost solution that traditional banks cannot match.  

To me, it’s no surprise that NU landed in TradeSmith’s top-stock portfolio. 

The “Moonshot” Bet 

The Signals Master Portfolio also flagged a far riskier pick this week: 

Rocket Lab Corp. (RKLB) 

Now, conservative investors might want to stay far away from this one. As its name suggests, Rocket Lab operates a relatively lumpy business that launches things into space. The firm lost $200 million last year, and analysts expect more losses until at least 2027. 

As the bankruptcy of Virgin Orbit in 2023 highlighted, rockets are a difficult business. 

Yet, Rocket Lab is more than a space launch company. 

Since 2020, the Los Angeles area-based firm has made five significant acquisitions that have transformed it from a launch-only firm into a broader “space as a service” company that designs and builds spacecraft components as well. This includes composite structures, star trackers, solar power panels, space-grade batteries, and the occasional fully assembled satellite. In 2025, Rocket Lab generated twice as many sales from its Space Systems segment as from Launch Services. 

Now, there are four investment cases that make Rocket Lab so interesting… 

1. Proven Launch Business (Electron). Unlike many competitors, Rocket Lab has an operating launch business with a long history of success. Its flagship “small launch” service, known as Electron, had 75 successful missions and more than 200 spacecraft deployed in 2025. These launch systems focus on research satellites and weather sensors that are too small for larger rockets like SpaceX’s Falcon 9. 

2. Proven Systems Business. Rocket Lab is a trusted name in satellite production and design. In December 2025, they signed an $816 million contract with America’s Space Development Agency to build 18 satellites for the military. These will be part of the Tracking Layer Trance 3 (TRKT3) program, an advanced missile warning designed to alert the U.S. military of incoming threats. This enormous project helped increase RKLB’s backlog by 73% to $1.85 billion, and should generate further revenue as the company sells additional payloads, components, and repairs. 

3. New Launch Business (Neutron). While Electron is excellent for small satellites, the “medium lift” market has quickly become far more important. These are the kind of payloads that Starlink and the U.S. military need. In this area, Rocket Lab is making substantial progress in its Neutron class of rockets. Its partially reusable rocket is due to be tested in late 2026 and would provide a compelling alternative to SpaceX’s Falcon 9. The project was delayed earlier this year, but it appears to be due to a manufacturing defect, rather than a design problem. 

4. New Systems Business. RKLB’s defense exposure is increasing right as the U.S. military is rearming itself for 21^st-century conflict. Earlier this week, the Pentagon proposed a $1.5 trillion 2027 defense budget (a 42% annual increase) that more than doubles allocation to the U.S. Space Force to $71.2 billion. It also includes $1.5 billion for a space data network. This spending lands squarely in Rocket Lab’s backyard, given the firm’s history of building the type of sensors and materials needed for threat detection and data transfers. 

There’s also a tactical reason to buy shares: 

SpaceX is going public. 

Highly publicized blockbuster listings often generate a stampede of retail investors seeking a piece of the pie. For instance, the $4.4 billion listing of Lucid Group Inc. (LCID) in 2021 created a frenzy in electric vehicle stocks. Robinhood Markets Inc. (HOOD), Beyond Meat Inc. (BYND), and Coinbase Global Inc. (COIN) made similar waves during their listings. 

SpaceX’s IPO could create the largest tsunami yet. Its owner, Elon Musk, is a master at stealing headlines. And as the billionaire drums up attention for the upcoming IPO – and his attempt to reach trillionaire status – don’t be surprised if Rocket Lab gets swept up in the mania as well. 

And One to Hold 

As promised, here’s the pick from last Sunday’s Digest that the Signals Master Portfolio held onto this week: 

AeroVironment Inc. (AVAV) 

According to the system, shares of the drone maker still have significant upside after its 6% rally. 

Since last Sunday, AeroVironment has announced another $14.6 million production contract with the U.S. Army and said it had successfully demonstrated its LOCUST Laser Weapons System aboard the USS George H.W. Bush, a Nimitz-class aircraft supercarrier. During the live fire event, the high-energy laser “tracked, engaged and neutralized multiple target drones,” according to AeroVironment’s press release. 

This achievement validates that the LOCUST LWS is truly platform-agnostic, seamlessly transitioning from fixed-site and land-based mobile platforms, such as the Joint Light Tactical Vehicle (JLTV) and Infantry Squad Vehicle (ISV), to the dynamic and demanding environment of a maneuvering aircraft carrier. 

In plain English, it’s a system that the Navy can use on both land and sea – reducing complexity and training for these anti-drone weapons. 

More gains could be on the way. The $1.5 trillion Pentagon budget mentioned earlier also included “drone dominance” in addition to “space superiority” as goals, and it earmarked $53.6 billion for autonomous drone platforms alone.  

Once again, I encourage you to catch the replay of TradeSmith CEO Keith Kaplan’s AI Signals Trading Event if you haven’t yet. In it, he explains how the system came together, how it’s helping investors turn overwhelming amounts of data into a simple, actionable strategy – and how you can access it too and start using it for yourself. 

Click here for more details.

Until next week, 

Thomas Yeung, CFA 

Market Analyst, InvestorPlace 

Thomas Yeung is a market analyst and portfolio manager of the Omnia Portfolio, the highest-tier subscription at InvestorPlace. He is the former editor of Tom Yeung’s Profit & Protection, a free e-letter about investing to profit in good times and protecting gains during the bad.

The post Two More Stocks to Buy Immediately, According to AI  appeared first on InvestorPlace.

Every time a major tech company reports a marginally-less-than-perfect quarter, the ‘AI is a bubble’ crowd gets louder. They cite ‘diminishing returns’ and ‘overcapacity’ — while the largest capital expenditure cycle in history keeps accelerating around them.

And then, in the span of a single week, the bears got handed two more data points they’ll spend months trying to explain away.

Amazon (AMZN) and Anthropic just inked a $100 billion partnership — the kind of capital commitment that doesn’t get unwound when consumer confidence dips or oil prices spike.

This wasn’t an outlier.

Alongside that deal, OpenAI just closed a $122 billion investment round. That is more capital flowing into a single startup than the entire market cap of most legacy Blue Chip companies.

While the ‘Magnificent Seven’ usually hog the headlines, the real story is happening one layer beneath them — in the infrastructure that keeps the whole establishment standing.

AI’s ‘brain’ has already been built. Now the buildout is moving into the nervous system.

The AI Chip Supply Chain Is Breaking Under Demand

For the past few years, the entire bull case revolved around Nvidia (NVDA) and the cutting-edge chips built on Taiwan Semiconductor‘s (TSM) 3nm and 5nm nodes that power its GPUs.

But the boom has now gotten so massive that it is breaking the capacity of the entire semiconductor ecosystem.

According to earnings reports from this past week alone:

• GE Vernova (GEV) reported order growth north of 70% year-over-year, with data center demand driving a surge in backlog.
• Teledyne Technologies (TDY) and Lam Research (LCRX) both delivered record or near-record results, with demand for imaging systems and wafer-fab equipment hitting cycle highs.
• Even Intel (INTC) — the sector’s long-standing cautionary tale — posted its sixth consecutive earnings beat, with foundry yields running ahead of schedule and a landmark customer commitment from Elon Musk’s Terafab project finally giving its manufacturing ambitions a real anchor.

But the most interesting signal came from the ‘mature’ part of the market — the elements that run on 28nm, 40nm, and 90nm processes. These are the kinds of chips that go into AI power management systems, sensors, automotive hubs, and connectivity modules. Every AI server needs a support staff of dozens, if not hundreds, of mature-node chips to manage its power, its heat, and its data throughput.

That demand is now translating into pricing power. Reports suggest United Microelectronics (UMC) is raising prices in the second half of 2026 due to strong demand for mature-node chips — something almost unthinkable just a year ago.

We are entering an era where the ‘cheap’ chips are becoming the most expensive bottlenecks.

Inside the AI ‘Nervous System’

If NVIDIA is the rockstar performing to a sold-out stadium, GlobalFoundries (GFS) is the one building the stage, wiring the speakers, programming the light show, monitoring the sound levels… They are the ‘tech crew’ running the show behind the scenes — unsexy, unloved, and until recently, completely ignored by the crowd.

For the better part of a year, GFS and its peers have been trading at dirt-cheap multiples. While the rest of the tech world was seeing 40X-plus forward earnings, GFS was languishing in the valuation basement, between 19X and 20X forward earnings.

But GlobalFoundries has pivoted into high-margin ‘specialty’ nodes. It is now the world’s largest pure-play foundry in Silicon Photonics — the technology that uses light to move data rather than electricity. 

As data center speeds push toward 800Gbps and beyond, traditional copper interconnects simply can’t move data fast enough without becoming a bottleneck. Photonics eliminates that ceiling because light doesn’t degrade the way electrons do across distance. GFS is the one making the light-speed connectivity modules that will keep the AI training clusters from hitting bandwidth and latency limits.

Why AI Infrastructure Stocks Like This Have Asymmetric Upside

While everyone was racing to build 3nm capacity, almost no one expanded 28nm or 40nm production. Supply is effectively fixed. Demand, meanwhile, is exploding — because robotics, industrial AI, and automotive systems all depend on these mature, specialized chips.

That imbalance creates pricing power. And pricing power in a historically cheap part of the market is what drives sudden revaluations — like the move we’re starting to see in names like GFS.

On one side is accelerating, vertical growth driven by the AI trickle-down. On the other, a group of stocks trading at significant discounts to their intrinsic value. Those two forces are now converging — and the impact is just starting to show up in stock prices.

The beauty of this particular trade is the ‘Double-Whammy.’ You aren’t just betting on earnings growth — you’re betting on multiple expansion. If GFS grows earnings by 20% but its P/E ratio also expands from 15x to 25x as the market recognizes it as an AI infrastructure play, the stock doubles.

Few setups combine that kind of earnings catalyst with that kind of valuation gap. This is one of them.

The Bottom Line: The AI Boom Is Moving Down the Stack

The AI Boom has conquered the cloud. Now it’s working its way down the stack — into the power systems, the interconnects, the mature-node chips that make the whole machine run.

The companies building that layer have been ignored long enough that they’re still priced like they don’t matter. The earnings are beginning to say otherwise.

That gap between price and reality is where the market is mispricing the present.

But the bigger opportunity comes from what it’s still missing about the future.

Because as this buildout continues, value will move on from the hardware layer as well, consolidating elsewhere in the AI stack.

And now is the time to get positioned for that.

The post The AI Infrastructure Stocks No One Is Watching appeared first on InvestorPlace.

Earlier this week, multiple roads were closed in Albany, N.Y., when a black bear climbed a tree and spent hours lounging on a branch.

The bear was eventually shot with a tranquilizer dart and caught in a net held below the tree.

Credit: Albany Police Department

The roads were reopened. The bear was turned over to the New York Department of Environmental Conservation and eventually released away from population centers.

For drivers, this was just an inconvenience. Traffic was rerouted temporarily, and it took them a little longer to reach their destination.

But if investors get their portfolios rerouted by the threat of a bear market, the potential risks to their retirement can be far greater than just an “inconvenience.”

That’s how folks should think about the bear market talk we often hear in the media.

From MarketWatch:

From Barron’s:

Of course, Operation Epic Fury in Iran and the accompanying gyrations in oil prices have certainly led to market volatility. But anyone scared out of the market by this talk is being done a disservice.

Money Is Flowing to These Stocks

Investing legend Louis Navellier has been vocal that anyone not bullish on the market is making a big mistake. Just this week, earnings on some of his top stocks proved his point.

Here’s Louis from his Growth Investor podcast to subscribers:

We’re seeing more proof that the data center boom is alive and well.

GE Vernova (GEV) is surging after reporting first-quarter orders that were double its sales. That tells you everything you need to know about how large the backlogs of these AI infrastructure companies have become.

These companies book the orders first, and then those orders flow through to sales and earnings later. And that is why the winners in this infrastructure buildout continue to act so well.

GEV is up 90% since Louis’ initial recommendation, but just as importantly, it’s up 76% YTD, compared to the 4% gain in the S&P.

But it isn’t just data center stocks that are surging.

One of Louis’ recommendations develops and supplies advanced electronics for defense, homeland security, and commercial applications.

He recommended Elbit Systems, Ltd. (ESLT) long before Operation Epic Fury. Since the initial recommendation, the stock is up more than 128%, but year to date, the stock is up 48% compared to the market’s 4% gain.

Louis’ focus has always been on fundamentally superior stocks – those with growing revenue and earnings. And GEV and ESLT certainly fit the bill.

Here Louis’ summary of Elbit’s quarterly report from March.

Fourth-quarter revenue rose 11.3% year-over-year to $2.15 billion, and earnings jumped 42.4% year-over-year to $169.9 million, or $3.56 per share. The consensus estimate called for fourth-quarter earnings of $3.14 per share and revenue of $2.09 billion, so Elbit Systems posted a slight revenue surprise and a 13.4% earnings surprise.

Elbit Systems also noted that it ended the year with an order backlog of $28.1 billion. For fiscal year 2025, the company achieved total revenue of $7.94 billion and earnings of 598.0 million, or $12.75 per share. That represented 16.3% annual revenue growth and 52.7% annual earnings growth. These results also topped estimates for revenue of $7.9 billion and earnings of $12.32 per share.

The stock is trading below Louis’ buy below price, so he is still bullish on the pick.

There are still many exciting opportunities left in 2026. For example, Louis has been researching a new government project, code name: “Golden Dawn.”

It’s being developed in a hidden government lab in Tennessee, where 40,000 scientists are finishing work on an AI computer 283 trillion times more powerful than today’s data centers — spanning more than 700 miles and built to speed up AI breakthroughs by 36,000%.

When Golden Dawn launches, it could instantly leapfrog ChatGPT, Gemini, and Grok – and trigger a $100 trillion reset of the AI markets.

Louis reveals the one stock at the center of it in his latest presentation, which you can watch right here.

Stocks Accelerating Through the Volatility

The AI megatrend has kept marching forward, regardless of negative headlines related to the U.S./Iran war. Tech investing expert Luke Lango reminded his readers that this trend is inevitable and no amount of uncertainty in the Middle East will stop it.

The economy has split into two large groups. Luke told his readers:

On one side: AI infrastructure stocks posting blowout results, raising guidance, and struggling to keep up with demand.

On the other: traditional companies cutting forecasts under the weight of rising costs.

Same economy. Radically different realities.

The AI Boom is not just surviving the current macro turbulence. It is accelerating through it. And everything tethered to old-economy inputs — energy costs, consumer discretionary spending, rate-sensitive balance sheets — is getting crushed under the weight of them.

As proof, one need only look at the stocks Luke bought after the start of Epic Fury (Feb. 28).

Not long after the start, Luke went on a buying spree for his Innovation Investor subscribers, taking advantage of the market downturn that followed the onset of hostilities.

One of Luke’s picks is a name you might not associate with AI Infrastructure – Corning (GLW). Here is Luke’s explanation of how it fits into the AI megatrend.

GLW is the picks-and-shovels play on the optical interconnect revolution. The company is the dominant global manufacturer of optical fiber, and as AI datacenter networking shifts from copper to optical — a transition already underway and accelerating — GLW’s optical fiber business should see a multi-year demand surge.

As the market has been meandering close to even since the beginning of Operation Epic Fury, GLW has risen 31% since Luke’s recommendation.

Same as Louis, Luke is seeing abundant market opportunities today, well beyond GLW and fiber optics stocks.

One relates to a bold prediction he made for 2026: AI leader OpenAI will go public THIS YEAR – and that this IPO will shatter ALL previous Silicon Valley records and create thousands of new millionaires.

Investors should remember that Google’s IPO instantly created 900 millionaires.

Nvidia’s IPO created more than 27,000 just among its employees.

Luke has found a way for retail investors to participate, and he shares it in a new presentation you can view for free right now.

The media likes to trade on bear market fears. But bear talk this year has probably scared a lot of investors into missing the bull markets that are going on behind the headlines.

Make sure you’re not taking an unnecessary detour that delays or damages your financial goals.

Enjoy your weekend,

Luis Hernandez

Editor in Chief, InvestorPlace

The post Three AI Stocks Surging As the Market Meanders appeared first on InvestorPlace.

It might be surprising to learn that the market has its own doctor, sometimes more trusted than Wall Street analysts.

And it’s made of metal.

Copper has earned the nickname “Dr. Copper” for good reason: Because the metal is essential to construction, manufacturing, power generation, and more, its price moves like a real-time health barometer for the global economy.

Rising copper demand – and rising prices – is a good sign for the economy. On the other hand, falling prices often predict a slowdown.

But right now, copper is sending a signal as messy as a doctor’s handwriting.

The market is currently navigating a period of mixed signals. Copper’s price closed at $6.03 yesterday, not far off its all-time high of $6.50 on January 29 of this year. Long-term structural demand is driving prices higher. But weak near-term demand out of China is creating volatility.

So, let’s make Dr. Copper our patient today and examine its recent movements. After all, demand-driven and supply-driven spikes lead to very different outcomes for investors.

Then, I’ll show you exactly which stocks are positioned to profit from both sides of the story.

Let’s jump in…

The AI Economy Runs on Copper

The demand for copper has surged to near-record levels, driven by several factors – a major one being artificial intelligence.

Copper runs through virtually every component of AI infrastructure. Data centers, in particular, are essentially a copper-and-aluminum exoskeleton wrapped around racks of silicon. These facilities consume enormous volumes of copper wiring, busbars, and switchgear.

Therefore, as data centers pop up around the world, copper demand will jump accordingly. S&P Global projects worldwide copper demand will rise from approximately 28 million metric tons today to 42 million metric tons per year by 2040.

Data centers also have an immense thirst for power. The International Energy Agency (IEA) expects U.S. electricity demand to grow around 2% per year from 2026 to 2030, as AI data centers, semiconductor fabs, and other large industrial loads – involving copper – connect to the grid.

The headlines reflect this.

This week alone, Alphabet Inc. (GOOGL) announced it is developing a second data center in Georgia, pledging to cover the power and infrastructure costs for the site – bringing Google’s total Georgia investment to $1.2 billion. Dallas-based data center developer DataBank secured a $2 billion loan from a group of banks led by Mitsubishi UFJ Financial Group to build Oracle Corp. (ORCL) data centers. And two former DirecTV data center facilities in Washington and Minnesota hit the market for a combined $27 million.

Data centers are being built, bought, and sold at a record pace… meaning copper’s demand is only going up.

But copper can only give as much as it has.

Supply Is Where Things Get Complicated

Copper prices can rise significantly due to supply disruptions – and currently, two major ones are at play.

The first is shipping. Disruptions around the Strait of Hormuz are rerouting and slowing tankers and cargo ships, creating supply-chain bottlenecks and delaying copper shipments.

When supply temporarily shrinks, traders price in scarcity fast, which in turn spikes spot premiums, especially in import-heavy regions – and that kind of price jump is a very different signal from a healthy global-growth rally.

The second is China. Starting next Friday, May 1, China – a major driver of copper – will restrict exports of sulfuric acid, a chemical essential to copper mining. When production inputs are unavailable, costs spike before actual shortages occur. Traders immediately price in future supply risk, constraining supply at the production level, which is the hardest to fix quickly.

So yes, copper is rallying, but it’s not entirely “healthy.”

It appears that copper’s rising demand is putting it on a solid path, but when supply risk is in the mix, it causes copper’s vital signs to go up – due to stress, not strength.

Adding to copper’s trajectory, Goldman Sachs maintained its forecast for the copper price to average $12,650 per metric ton this year and its estimate of a 490,000-ton surplus in 2026. In the longer term, the International Energy Agency (IEA) sees a 30% deficit by 2035.

Copper is simultaneously abundant and scarce, depending on where you look. But for investors, that tension is creating opportunity…

How to Play Both Sides of Copper

Because copper’s rally is being driven by two distinctive forces – genuine demand growth and supply risk stress – the best approach is to invest in stocks that win in either scenario.

On the demand side, the winners are energy and raw materials companies that benefit as copper gets consumed at record rates by AI infrastructure, power grids, and industrial expansion. On the supply side, the winners are copper mines, whose profit margins expand when prices spike faster than their production costs.

The stock that sits squarely at the intersection of both is a pure-play copper miner and Fry’s Investment Report holding Freeport-McMoRan Inc. (FCX).

It hit an all-time high of $70.97 on Monday after delivering strong earnings this week – a move that illustrates both its underlying strength and volatile environment.

When demand spikes, FCX sells more copper into a strong market. When supply shock hits, copper prices jump faster than FCX’s costs do, expanding margins either way.

Beyond Freeport-McMoRan, my Fry’s Investment Report portfolio includes several other positions built for this demand and supply risk. For example…

• A precious metals miner that’s up 286%…
• A key energy company that’s gained 50%…
• An oil & gas company with 46% returns…
• And an independent energy producer up 25%

The bottom line: Dr. Copper’s diagnosis is complicated – but it makes for healthy returns for investors.

Click here for more details on how to join my Fry’s Investment Report service.

Regards,

Eric Fry

P.S. As we just discussed, copper’s rising prices have much to do with artificial intelligence’s growing prevalence and infrastructure needs – and that’s not changing anytime soon.

AI is entering its next phase as more companies join the agentic AI market, but the names that delivered solid returns may not be the same ones profiting from this new form of technology. That’s why I’m creating a free presentation to alert investors about the details of AI’s upcoming shift, which could cost you years of gains if ignored…

Keep an eye on your inbox next week for a direct link to that broadcast.

The post Copper’s Mixed Signals Are Your Next Big Opportunity appeared first on InvestorPlace.

Specifically, it was about a team that had successfully mapped the entire brain of a microscopic worm — every neuron, every connection. 

Then it laid out what it would take to scale that process from a worm… to a mouse… and eventually, to a human.

Now, here’s where things get interesting for us.

For decades, mapping the human brain has been one of science’s biggest challenges. And while we’re still years away from a definitive solution, we’re starting to see the missing piece fall into place that could break the whole field wide open: brain-computer interfaces.

These are systems that allow the brain to communicate directly with machines — turning thought into action and speech.

Right now, the headlines focus on major players like Elon Musk’s Neuralink— buffeted by twelve patients, a $9B valuation, and IPO rumors.

Sam Altman also committed $250 million to his own brain-chip startup earlier this year.

The reporting surrounding these companies often leans toward the dramatic: the merging of mind and machine, the next frontier of artificial intelligence, the possibility of restoring lost functions or even augmenting human cognition. 

It is a story that lends itself to headlines. But it is also, in some ways, a misleading one. The visible pieces of this emerging field — the implants, the interfaces — represent only its final layer.

What they’re hiding is a much larger system that must exist before they can function in any meaningful way.

This is where the real opportunity lies for us. And in order to understand, we need to dig deeper than the headlines…

The Critical Systems Holding This Breakthrough Back

Once I got over my initial excitement after reading the report and considering what it might mean, I did what traders do: I moved straight to the supply chain. 

The MIT thesis, perhaps unintentionally, offers a map of this growing system. 

It identifies several areas that must advance together: structural imaging, which captures the physical wiring of the brain; functional imaging, which records activity; molecular analysis, which explains how neurons behave; and computational infrastructure, which integrates and simulates these layers. 

Each of these parts has made significant progress in isolation. What’s new is the recognition that they are dependent on each other so that major progress in one without the others is unlikely.

This interdependence means that what we’re really looking at is a set of bottlenecks that are overlooked in the broader narrative. 

This is exactly the kind of shift we focus on inside the Masters in Trading Challenge — learning how to move past the obvious story and identify where the real edge sits before it becomes consensus.

It’s not about predicting the headline outcome. It’s about understanding what has to happen underneath it—and positioning early. If you want to see exactly how we approach setups like this, you can learn more about the Masters in Trading Challenge here.

One of the clearest examples of this shows up in imaging. Imaging a brain at sufficient resolution is not simply a matter of improving a single machine. It requires scaling entire systems — microscopes, data pipelines, processing algorithms — by orders of magnitude.

The thesis suggests that even mapping a mouse brain would require dozens of high-throughput electron microscopes operating continuously for years.

And for a human brain? That demands far more extensive infrastructure that’s currently lacking.

The Next “Genome Project” Is Already Taking Shape

Building these systems are not incremental challenges. They resemble, in scale and coordination, the kinds of efforts more commonly associated with large public works or scientific “moonshots.” 

The Human Genome Project – the effort to map all human DNA that turned biology into a data-driven science and made genetic research dramatically faster and cheaper – is often cited as an analogy. 

When it was all said and done, that project required more than a decade and billions of dollars to complete. 

Brain emulation, if pursued at a similar scale, would likely demand comparable levels of investment and collaboration.

If the history of technological change offers any guidance, it is that the most transformative shifts rarely occur where attention is first directed. 

They unfold, instead, in the spaces beneath the surface, where small advances accumulate until they alter what is possible.

The sensors. The chips. The imaging systems.

That’s where capital is starting to flow.

Right now, I’m tracking 14 names tied to this theme — 10 public, 4 private.

In today’s essay, I’m breaking down all fourteen names across three buckets:

• Two to start with today
• Eight to build deeper exposure
• And four private companies to watch as they approach public markets.

Let’s dive in…

Bucket 1 – Start Here

If you only buy two names from this basket, start here.

Butterfly Network (BFLY): ~$500M market cap

BFLY produces handheld, chip-based ultrasound devices used in brain imaging. And unlike some of the other names on this list, this early-stage player is already bringing in sound business.

Last quarter, BFLY turned profitable for the first time, with revenue growing 41% year-over-year to $31.5M.

That momentum is only increasing as BFLY secures more contracts around its signature tech. Its ultrasound chip is licensed into Forest Neurotech, an Eric Schmidt-backed brain-computer interface project – and that’s just one key partnership among many.

At a $500M market cap – with real revenue and BCI exposure – the risk/reward looks cleaner than most.

Start a position and add on pullbacks to support. This isn’t a moonshot—it’s a functioning business with an underappreciated catalyst.

Quantum-Si (QSI): ~$194M market cap

This is the asymmetric bet. It offers the only commercial single-molecule protein sequencer currently available on the market.

The MIT research identifies protein sequencing as a gating technology for brain mapping. Right now, this is the only U.S.-listed way to access it. With revenue at just $2.4 million, this is a company firmly in its earliest stages with massive growth ahead.

Keep position size small. Treat it like a call option, not a core holding. If the thesis works, the upside could be significant. If not, downside risk is real.

Bucket 2 – Add These to Go Deeper

These names round out the basket if you want broader exposure. It’s a mix of small-cap volatility and large-cap stability.

Hyperfine (HYPR): $127M market cap

HYPR provides the only FDA-cleared portable brain MRI system on the market – its Swoop device, which brings imaging directly to the bedside.

HYPR’s reach is beginning to expand beyond the U.S. Just this year, the startup received approval in India to sell its devices, opening up a large new market.

HYPR is one of the smallest small-caps on this list. And it always trades on news.

With that approval and a surge in value since March, HYPR remains one of the best early land-grab opportunities in this basket.

Add HYPR for direct brain imaging exposure. Watch upcoming guidance for signs of international traction.

NVIDIA (NVDA): $5.6T market cap

Most investors own NVIDIA for AI, but few connect it to the brain race. That’s about to change.

NVIDIA’s Holoscan is an important application in the brain imaging race. For those who don’t know, it’s an AI-enabled sensor processing platform designed for real-time edge computing that can be deployed in various use cases – from security applications to the medical field.

Many of the top BCI players are building their tech on top of NVIDIA’s Holoscan. BCI manufacturer Synchron’s interface runs on NVIDIA Holoscan. And another BCI startup, Merge Labs, is expected to train models on its chips.

Regardless of which platform wins, NVIDIA sits upstream.

If you already own it, you have exposure. If not, this is another reason to consider it.

Micron (MU): $560B market cap

This is the memory bottleneck trade. Compute has scaled far faster than memory over the last three decades. That gap is becoming critical for both AI and brain emulation.

Micron is the clearest U.S.-listed play on high-bandwidth memory. And with so many clients for its chips already being participants in the modern brain race, this stock is one of the best ways to gain early exposure today.

It’s cyclical. Look to buy on weakness.

Medtronic (MDT): $100B market cap

Medtronic partnered with Precision Neuroscience, gaining exposure to BCIs without building the technology internally.

That’s a massive edge in a market where the biggest BCI makers are still dealing with major cost overruns and costly implementation failures.

Medtronic is already a giant in the space. With this partnership in place, MDT represents a more conservative way to trade the theme.

Useful for portfolios seeking income and lower volatility with some upside optionality.

Nautilus Biotechnology (NAUT): $331.6M market cap

This stock is a complementary play to QSI. NAUT approaches protein sequencing differently but targets the same bottleneck. And a recent partnership with Baylor College adds early validation.

Smaller position than QSI. Treat both as a paired bet.

Bruker (BRKR): $5.56B market cap

This is a “picks and shovels” name that’s been setting off my UOA Monitor for weeks.

In fact, I just recently recommended the trade on Masters in Trading LIVE  as the perfect way to gain early exposure to the BCI trend.

This stock already has massive penetration in the space. Its microscopy systems are used across leading brain-mapping labs. It’s a medium-sized player with a lot of room to run.

A more stable position relative to smaller biotech names.

Thermo Fisher (TMO): $174.5B market cap

One of only two companies globally producing the high-throughput electron microscopes used in connectomics research. A long-term compounder.

A steady, long-duration hold with lower volatility.

Broadcom (AVGO): $2.01T market cap

AVGO provides custom AI chips and networking infrastructure for hyperscalers. While not a direct BCI play, it supports the systems that make brain-scale computation possible.

Similar to NVIDIA—core infrastructure exposure.

Bucket 3 – Watching, But Not Yet Public

These are private companies to monitor for IPO activity. When one files, expect ripple effects across the entire basket.

Neuralink
Private: ~$9B valuation

Elon Musk’s BCI company has twelve patients implanted. So far, it’s one of the most regulatorily compliant and well capitalized names in the space, already backed by FDA Breakthrough Device designation.

Not publicly tradable yet. When it files, expect rapid repricing across related equities.

Synchron
Private: Pre-IPO

Synchron offers a less invasive interface delivered via the jugular vein – all backed by Jeff Bezos and Bill Gates. As I alluded to at the top, Synchron also has several major partnerships in place with companies like NVIDIA and Apple Vision Pro.

Watch for IPO filing. Potentially lower-risk than Neuralink due to its approach.

Precision Neuroscience
Private: Pre-IPO

Precision is working on a surface-level brain interface developed by a former Neuralink engineer. And it just recently received FDA clearance and partnered with Medtronic – all great signs as it works its way to a potential IPO.

Indirect exposure exists through Medtronic. Consider direct exposure if it goes public.

Colossal Biosciences
Private: Pre-IPO

Colossal is connected to Harvard geneticist George Church. While it’s not a pure BCI play, the company is part of a broader biotech ecosystem that could intersect with neural research.

Highly speculative. Monitor for developments.

One last note: I’m long QSI. I do not currently hold the other names listed here. This reflects my research and watchlist—not a recommendation to buy or sell any security. Small-cap stocks in this space can be highly volatile. Do your own research and consult a licensed professional before investing.

What Happens Next

History shows that when a complex problem becomes a matter of engineering—when it can be broken down into discrete constraints—capital flows to the solutions. And that all happens often before the broader narrative fully takes hold.

Today, much of this ecosystem remains underfollowed and, in some cases, mispriced relative to its potential role in the broader shift.

That won’t last.

As progress in these underlying technologies becomes more visible — through partnerships, breakthroughs, and eventually public listings — the market will connect the dots.

When it does, the repricing is unlikely to be gradual.

For investors, the takeaway is straightforward: Focus less on the outcome and more on what must happen for that outcome to exist.

That’s where the opportunity is today.

And if you’re interested in learning more about the system that discovered all these names…

That knowledge is waiting for you inside the Masters in Trading Options Challenge.

The Challenge is where we take everything you’ve learned in my articles and daily LIVEs — fixed risk, thesis-driven exits, laddered entries, defined-duration trades, and emotional discipline — and put it into practice in a structured, step-by-step environment.

For seven days, we walk through the foundations of real options trading the way I learned them on the trading floor. You’ll learn exactly how I think, exactly how I build trades, and exactly how I manage both the winners and the losers.

Just click here to check out what the Masters in Trading Options Challenge has in store for you.

Remember, the creative trader wins.

Jonathan Rose,

Founder, Masters in Trading

P.S. In a recent beta test, TradeSmith CEO Keith Kaplan’s new signals-based approach produced gains in just days — not months. Now, for a limited time, you can explore the same system yourself and see what it’s flagging right now across the market. To get a firsthand look at the same AI Signals system Keith uses — including the top signals of the day and detailed data on any stock you search — watch the full presentation here.

The post Forget Neuralink: The Real Brain Tech Trade Has 10 Names You’ve Never Heard appeared first on InvestorPlace.

Today, Keith Kaplan takes that idea a step further.

He points to a new initiative called Project Glasswing, where advanced AI systems are being used to find hidden weaknesses in complex systems, problems that went unnoticed for years.

That matters because it shows what AI can really do. It can work through massive amounts of data and uncover patterns humans miss. The same approach can be applied to the stock market.

In a five-year backtest, Keith’s team applied this approach across thousands of stocks and built a model portfolio that found 30,000 hidden signals. It delivered a 12X return, with a 73% win rate, and in 2022, it gained 16.6% while the S&P 500 fell nearly 20%.

Results like that are hard to ignore.

Below, Keith explains how this approach works and how it could help investors spot opportunities early. He also walks through it in more detail during his AI Signals Trading Event.

If you haven’t seen it yet, you can watch the replay here.

Now, here’s Keith…

*******************

The most important story in the world right now isn’t the Strait of Hormuz…

…or the price of oil…

…or the Epstein files.

It’s 100x more important than all of that.

It’s a crash program called Project Glasswing that brings together top finance officials from the federal government and the CEOs of some of America’s most powerful corporations.

What sparked it is a new frontier model called Mythos from Anthropic, the private firm behind Claude AI. By every measure, Mythos is the most capable AI model ever built – and what it can do is genuinely alarming.

It can read the source code of the software running your bank, your hospital, even your power grid… and find security flaws that human experts missed for decades.

As just one example, Mythos discovered a flaw in OpenBSD – a system that runs sensitive firewalls, government networks, and critical infrastructure – that its human developers had missed over 27 years of detailed security audits.

Then it wrote the code to exploit it… autonomously… on the first try.

As Anthropic put it in a press release on April 7, Mythos reveals a stark fact about the state of AI in 2026…

AI models have reached a level of coding capability where they can surpass all but the most skilled humans at finding and exploiting software vulnerabilities.

When Washington leaders caught wind of this, they knew they had to move quickly.  

Treasury Secretary Scott Bessent and Fed Chair Jerome Powell summoned the CEOs of Citigroup, Morgan Stanley, Bank of America, Wells Fargo, and Goldman Sachs to the Treasury Building at 1500 Pennsylvania Avenue — steps away from the White House’s East Wing.

Anthropic announced Project Glasswing the same day. Eleven launch partners — Amazon, Apple, Google, Microsoft, JPMorgan Chase, Cisco, NVIDIA, Broadcom, CrowdStrike, Palo Alto Networks, and the Linux Foundation — would get early access to Mythos.

Anthropic committed up to $100 million in usage credits. The mission: find the flaws before someone else weaponized a model like this one.

This may sound like the plot of a Hollywood movie. But Glasswing is very real. And what it tells us about AI’s capabilities has implications that reach well beyond cybersecurity — into every portfolio in America.

Decoding the Market’s Hidden Structure

Mythos finds patterns in computer programs that no human could see.

It reads millions of lines of code, identifies the specific combinations of conditions that point to a vulnerability. Then it acts on them.

Look deep enough, and you’ll find that the stock market contains similar  kinds of hidden structures. Buried inside decades of data for every stock are “signals” — specific combinations of conditions that have consistently preceded big moves.

Only for most of market history, they were invisible. The data existed… only nobody had the tools to read it.

But today, thanks to AI, it’s possible to find signals with historical accuracy rates of 90% or better.

I know because my team and I at TradeSmith have created a new AI-powered trading tool that’s unearthed 30,000 of these signals across nearly 2,500 stocks.

As I showed the nearly 9,000 people who joined my AI Signals Trading Event on Wednesday, in a five-year backtest, a model portfolio of these signals trades delivered a 12x return.

And in 2022 — the worst year for stocks in half a century — they produced an average backtested gain of 16.6% while the S&P 500 fell nearly 20%.

If you missed it, the replay is still online. It’s packed full of trade examples, strategy details, and on-screen demos. Go here to watch it now.

Today, I want to share something I didn’t have time to cover on Wednesday — how much work and ingenuity went into building our new system.

The answer is: more than I expected.

Can the Weather in Paris Move the Stock Market?

Our chief developer, Mike Carr, has been writing code for 40 years.

He spent 20 years in the U.S. Air Force – coding nuclear missile paths, working on cryptography for the National Security Agency, and helping install an early version of the internet at the Pentagon.

When he left the military, he went on to manage more than $200 million in client funds. He also became a Chartered Market Technician — a credential only about 4,500 people in the world hold.

Two years ago, he joined TradeSmith to help us develop new analytics and strategies. And he brought with him the kernel of an idea he’d been working on for more than 20 years.

In 2003, Mike started doing rudimentary signal studies. Every time he spotted a repeating pattern in the data that tended to precede a move in a stock — he noted it down. He traded this way for years, constantly testing what worked and what didn’t.

Then in 2016, he read a Bloomberg profile of Jim Simons’ storied hedge fund, Renaissance Technologies. One detail stuck with him: Simons had once found a tradable signal involving the weather in Paris.

If he could find a signal in Paris weather, Mike realized, the signals hiding in ordinary market data had to be almost limitless. That was the moment he decided to stop hunting for signals manually and start building a system that could hunt them at scale — one ordinary investors could actually use.

Last year, we started feeding the 150 or so signals he’d collected into an AI system and prompted it to generate more like them. We processed more than 1 trillion database rows, running every stock through 847 individual calculations. We tested every combination of price patterns, technical indicators, and calendar conditions we could find.

Then we hit a problem we hadn’t anticipated.

On any given day, our AI-powered signals generator was delivering a flood of 30,000 trade setups – all with historical accuracy rates of 75% or better. It was far too much for any trader to handle, no matter their level of experience.

So we spent the next six months solving a different problem: How do you take that many high-quality signals and deliver something an investor can actually use?

The answer Mike came up with was the Quality Score. It’s a 0-to-100 rating that factors each signal’s win rate and average returns and uses machine learning to figure out how effective it was during similar market conditions in the past.

Pair that with a focused model portfolio, and the flood became an actionable shortlist.

Introducing Our Three-Stock Strategy

At any given moment, it holds three S&P 500 stocks — each one selected by an algorithm based on its Quality Score and other key factors. When an exit signal fires on one of the three, a new trade recommendation takes its place.

That’s the whole strategy. Three positions, always live. Each one selected not because a human liked a chart or a story… but because an algorithm chose a mathematically optimal trade.

We backtested it from January 1, 2020 through January 30, 2026 – a stretch that covered the COVID crash, the 2022 bear market, two years of historic inflation, rising interest rates, and two wars.

It wasn’t a friendly period to stress-test a trading system against. But here’s what the Signals Master Portfolio produced:

• A 54% compounded annual return — versus about 15% for the S&P 500 over the same six years
• A 73.4% win rate across hundreds of trades
• A maximum drawdown of 18.1% — less than the S&P 500’s maximum drawdown of 25.4%

The model portfolio’s maximum drawdown is worth pausing on.

A lot of trading systems can generate a high compounded return in a backtest. Few can generate one that also held up better than a benchmark like the S&P 500 during its worst stretch. That’s the litmus test of whether a system is managing risk effectively or just riding luck.

Which is the point of what we’ve spent the last 12 years (and in Mike’s case, more than 20 years) developing. Hedge funds have been doing this kind of work for decades — pattern recognition, machine learning, disciplined rotation in and out of short-term trades. But until now, nothing like it has existed for regular investors.

I went into all the details during Wednesday’s launch event. So if you haven’t already, make sure to check it out while it’s still online.

Keith Kaplan
CEO, TradeSmith

The post These AI Patterns Are 100x More Important Than the Strait of Hormuz appeared first on InvestorPlace.

They’re buried in price action, timing, volatility, and dozens of other factors — and consistently show up before major moves.

The challenge has always been finding them. 

That’s where AI is starting to change the game.

In the guest essay below, Keith Kaplan shares how his team is using it to surface hidden market signals — the same kind of pattern recognition that powered some of the most successful hedge funds ever.

You can see exactly how it works — and what it’s finding right now — in his Signals presentation.

Tycho Brahe’s mission in life was to be the first to explain how the planets really moved.

So this obsessive 16th century Danish astronomer spent more than two decades building the most precise record of planetary motion the world had ever seen — and then guarded it so jealously almost no one was allowed to see it.

In Brahe’s time, there were no telescopes. Every measurement he took was with the naked eye, using instruments he designed and built himself on a small island off the coast of Denmark.

It was a data set unlike anything that had ever existed — page after page of handwritten figures and precise planetary positions.

He couldn’t interpret all that data alone. So he brought on a brilliant young German mathematician named Johannes Kepler. But Brahe, afraid Kepler might make the discovery first, handed his apprentice only just enough data to be useful and locked the rest away.

That arrangement lasted barely a year. In October 1601, Brahe died suddenly. Kepler inherited his notebooks and studied them intensely for the next four years.  

What he found was proof that everything astronomers had assumed since ancient Greece was wrong. 

Kepler realized that planets didn’t move in perfect circles. They moved in ellipses — slightly flattened ovals, with the sun off to one side rather than in the direct center. 

Almost a century later, Isaac Newton read Kepler’s laws of elliptical motion and worked out the force that explained them. He called it gravity.

One of the most important ideas in the history of science was hiding in Brahe’s notebooks for decades. The data had always been there. All it needed was someone who could make it legible.

From Hidden Data to Actionable Signals

I’m telling you this because the stock market has a Tycho Brahe problem, too.

It generates more data in a single trading day than Brahe recorded in a lifetime. The problem is, for most of its existence, only a tiny fraction of it has been readable.

But today, thanks to AI, it’s possible to find “signals” inside that data — repeating patterns that point to future moves in stocks, many with historical accuracy rates of 90% or better.

I know because my team and I at TradeSmith have created a new AI-powered trading tool that’s unearthed more than 200 of these signals across nearly 2,500 stocks.

As I showed the nearly 9,000 people who joined my AI Signals Trading Event on Wednesday, in a six-year backtest, a model portfolio of these signals trades delivered a 12x return. 

And in 2022 — the worst year for stocks in half a century — they produced an average backtested gain of 16.6% while the S&P 500 fell nearly 20%.

If you missed it, the replay is still online. It’s packed full of trade examples, strategy details, and on-screen demos. Go here to watch it now.

Today, I want to share something I didn’t have time to cover on Wednesday — how much work and ingenuity went into building our new system.

The answer is: more than I expected.

The ‘Paris Weather’ Insight That Changed Everything

Our chief developer, Mike Carr, has been writing code for 40 years.

He spent 20 years in the U.S. Air Force – coding nuclear missile paths, working on cryptography for the National Security Agency, and helping install an early version of the internet at the Pentagon.

When he left the military, he went on to manage more than $200 million in client funds. He also became a Chartered Market Technician — a credential only about 4,500 people in the world hold. 

Two years ago, he joined TradeSmith to help us develop new analytics and strategies. And he brought with him the kernel of an idea he’d been working on for more than 20 years.

In 2003, Mike started doing rudimentary signal studies. Every time he spotted a repeating pattern in the data that tended to precede a move in a stock — he noted it down. He traded this way for years, constantly testing what worked and what didn’t. 

Then in 2016, he read a Bloomberg profile of Jim Simons’ storied hedge fund, Renaissance Technologies. One detail stuck with him: Simons had once found a tradable signal involving the weather in Paris.

If he could find a signal in Paris weather, Mike realized, the signals hiding in ordinary market data had to be almost limitless. That was the moment he decided to stop hunting for signals manually and start building a system that could hunt them at scale — one ordinary investors could actually use.

Last year, we started feeding the 150 or so signals he’d collected into an AI system and prompted it to generate more like them. We processed more than 1 trillion database rows, running every stock through 847 individual calculations. We tested every combination of price patterns, technical indicators, and calendar conditions we could find.

The Problem With Too Many Trading Signals

Then we hit a problem we hadn’t anticipated.

On any given day, our AI-powered signals generator was delivering a flood of 697 trade setups – all with historical accuracy rates of 75% or better. It was far too much for any trader to handle, no matter their level of experience. 

So we spent the next six months solving a different problem: How do you take that many high-quality signals and deliver something an investor can actually use?

The answer Mike came up with was the Quality Score. It’s a 0-to-100 rating that factors each signal’s win rate and average returns and uses machine learning to figure out how effective it was during similar market conditions in the past. 

Pair that with a focused model portfolio, and the flood became an actionable shortlist.

The AI Trading Strategy: Just Three Stocks at a Time

At any given moment, it holds three S&P 500 stocks — each one selected by an algorithm based on its Quality Score and other key factors. When an exit signal fires on one of the three, a new trade recommendation takes its place.

That’s the whole strategy. Three positions, always live. Each one selected not because a human liked a chart or a story… but because an algorithm chose a mathematically optimal trade.

We backtested it from January 1, 2020 through January 30, 2026 – a stretch that covered the COVID crash, the 2022 bear market, two years of historic inflation, rising interest rates, and two wars. 

It wasn’t a friendly period to stress-test a trading system against. But here’s what the Signals Master Portfolio produced:

• A 54% compounded annual return — versus about 15% for the S&P 500 over the same six years
• A 73.4% win rate across hundreds of trades
• A maximum drawdown of 18.1% — less than the S&P 500’s maximum drawdown of 25.4%

Lower Drawdowns Than the Broader Market

The model portfolio’s maximum drawdown is worth pausing on.

A lot of trading systems can generate a high compounded return in a backtest. Few can generate one that also held up better than a benchmark like the S&P 500 during its worst stretch. That’s the litmus test of whether a system is managing risk effectively or just riding luck.

Which is the point of what we’ve spent the last 12 years (and in Mike’s case more than 20 years) developing. Hedge funds have been doing this kind of work for decades — pattern recognition, machine learning, disciplined rotation in and out of short-term trades. But until now, nothing like it has existed for regular investors.

I went into all the details during Wednesday’s launch event. So if you haven’t already, make sure to check it out while it’s still online.

The post AI Trading Signals Are Solving the Market’s Data Problem appeared first on InvestorPlace.

The data was there all along – it just took the right lens to make sense of it.

That same idea is at the heart of today’s Friday Digest takeover from TradeSmith CEO Keith Kaplan.

Below, Keith draws a fascinating parallel between that breakthrough moment and what’s happening in today’s stock market. Investors now have more data than ever to analyze, but until recently, most of it has been impossible to interpret in any meaningful way.

Now, thanks to AI, that’s starting to change.

Keith and his team have been using machine learning to uncover repeatable “signals” buried inside massive datasets – patterns that have historically pointed to high-probability trades, many with striking consistency across very different market environments.

In today’s essay, Keith explains how this system came together, and how it’s turning an overwhelming amount of information into a surprisingly simple, actionable strategy.

And if you missed Keith’s AI Signals Trading Event from Wednesday, you can still catch the full replay – including live demos and trade examples – right here.

I’ll let Keith take it from here.

Have a good evening,

Jeff Remsburg

Tycho Brahe’s mission in life was to be the first to explain how the planets really moved.

So this obsessive 16th century Danish astronomer spent more than two decades building the most precise record of planetary motion the world had ever seen — and then guarded it so jealously almost no one was allowed to see it.

In Brahe’s time, there were no telescopes. Every measurement he took was with the naked eye, using instruments he designed and built himself on a small island off the coast of Denmark.

 It was a data set unlike anything that had ever existed — page after page of handwritten figures and precise planetary positions.

He couldn’t interpret all that data alone. So he brought on a brilliant young German mathematician named Johannes Kepler. But Brahe, afraid Kepler might make the discovery first, handed his apprentice only just enough data to be useful and locked the rest away.

That arrangement lasted barely a year. In October 1601, Brahe died suddenly. Kepler inherited his notebooks and studied them intensely for the next four years. 

What he found was proof that everything astronomers had assumed since ancient Greece was wrong.

Kepler realized that planets didn’t move in perfect circles. They moved in ellipses — slightly flattened ovals, with the sun off to one side rather than in the direct center.

Almost a century later, Isaac Newton read Kepler’s laws of elliptical motion and worked out the force that explained them. He called it gravity.

One of the most important ideas in the history of science was hiding in Brahe’s notebooks for decades. The data had always been there. All it needed was someone who could make it legible.

I’m telling you this because the stock market has a Tycho Brahe problem, too.

It generates more data in a single trading day than Brahe recorded in a lifetime. The problem is, for most of its existence, only a tiny fraction of it has been readable.

But today, thanks to AI, it’s possible to find “signals” inside that data — repeating patterns that point to future moves in stocks, many with historical accuracy rates of 90% or better.

I know because my team and I at TradeSmith have created a new AI-powered trading tool that’s unearthed more than 200 of these signals across nearly 2,500 stocks.

As I showed the nearly 9,000 people who joined my AI Signals Trading Event on Wednesday, in a six-year backtest, a model portfolio of these signals trades delivered a 12x return.

And in 2022 — the worst year for stocks in half a century — they produced an average backtested gain of 16.6% while the S&P 500 fell nearly 20%.

If you missed it, the replay is still online. It’s packed full of trade examples, strategy details, and on-screen demos. Go here to watch it now.

Today, I want to share something I didn’t have time to cover on Wednesday — how much work and ingenuity went into building our new system.

The answer is: more than I expected.

Can the Weather in Paris Move the Stock Market?

Our chief developer, Mike Carr, has been writing code for 40 years.

He spent 20 years in the U.S. Air Force – coding nuclear missile paths, working on cryptography for the National Security Agency, and helping install an early version of the internet at the Pentagon.

When he left the military, he went on to manage more than $200 million in client funds. He also became a Chartered Market Technician — a credential only about 4,500 people in the world hold.

Two years ago, he joined TradeSmith to help us develop new analytics and strategies. And he brought with him the kernel of an idea he’d been working on for more than 20 years.

In 2003, Mike started doing rudimentary signal studies. Every time he spotted a repeating pattern in the data that tended to precede a move in a stock — he noted it down. He traded this way for years, constantly testing what worked and what didn’t.

Then in 2016, he read a Bloomberg profile of Jim Simons’ storied hedge fund, Renaissance Technologies. One detail stuck with him: Simons had once found a tradable signal involving the weather in Paris.

If he could find a signal in Paris weather, Mike realized, the signals hiding in ordinary market data had to be almost limitless. That was the moment he decided to stop hunting for signals manually and start building a system that could hunt them at scale — one ordinary investors could actually use.

Last year, we started feeding the 150 or so signals he’d collected into an AI system and prompted it to generate more like them. We processed more than 1 trillion database rows, running every stock through 847 individual calculations. We tested every combination of price patterns, technical indicators, and calendar conditions we could find.

Then we hit a problem we hadn’t anticipated.

On any given day, our AI-powered signals generator was delivering a flood of 697 trade setups – all with historical accuracy rates of 75% or better. It was far too much for any trader to handle, no matter their level of experience.

So we spent the next six months solving a different problem: How do you take that many high-quality signals and deliver something an investor can actually use?

The answer Mike came up with was the Quality Score. It’s a 0-to-100 rating that factors each signal’s win rate and average returns and uses machine learning to figure out how effective it was during similar market conditions in the past.

Pair that with a focused model portfolio, and the flood became an actionable shortlist.

Introducing Our Three-Stock Strategy

At any given moment, it holds three S&P 500 stocks — each one selected by an algorithm based on its Quality Score and other key factors. When an exit signal fires on one of the three, a new trade recommendation takes its place.

That’s the whole strategy. Three positions, always live. Each one selected not because a human liked a chart or a story… but because an algorithm chose a mathematically optimal trade.

We backtested it from January 1, 2020 through January 30, 2026 – a stretch that covered the COVID crash, the 2022 bear market, two years of historic inflation, rising interest rates, and two wars.

It wasn’t a friendly period to stress-test a trading system against. But here’s what the Signals Master Portfolio produced:

• A 54% compounded annual return — versus about 15% for the S&P 500 over the same six years
• A 73.4% win rate across hundreds of trades
• A maximum drawdown of 18.1% — less than the S&P 500’s maximum drawdown of 25.4%

The model portfolio’s maximum drawdown is worth pausing on.

A lot of trading systems can generate a high compounded return in a backtest. Few can generate one that also held up better than a benchmark like the S&P 500 during its worst stretch. That’s the litmus test of whether a system is managing risk effectively or just riding luck.

Which is the point of what we’ve spent the last 12 years (and in Mike’s case more than 20 years) developing. Hedge funds have been doing this kind of work for decades — pattern recognition, machine learning, disciplined rotation in and out of short-term trades. But until now, nothing like it has existed for regular investors.

I went into all the details during Wednesday’s launch event. So if you haven’t already, make sure to check it out while it’s still online.

Keith Kaplan
CEO, TradeSmith

The post This AI Spots Winning Trade Patterns Before You Can appeared first on InvestorPlace.

In my experience, the investors who do best are the ones who can cut through that noise and focus on the signals that tend to repeat.

Today, my colleague Keith Kaplan shares a similar approach. He and his team have been using AI to scan through massive amounts of market data, looking for patterns that have shown up again and again over time.

He recently walked through the full strategy during his AI Signals Trading Event, including how the system selects trades and manages risk.

You can watch the full presentation here.

In the essay below, Keith explains how this system works – and how it helps uncover patterns in market data that have been there all along, but are only now becoming visible.

******************

Tycho Brahe’s mission in life was to be the first to explain how the planets really moved.

So this obsessive 16th century Danish astronomer spent more than two decades building the most precise record of planetary motion the world had ever seen — and then guarded it so jealously almost no one was allowed to see it.

In Brahe’s time, there were no telescopes. Every measurement he took was with the naked eye, using instruments he designed and built himself on a small island off the coast of Denmark.

 It was a data set unlike anything that had ever existed — page after page of handwritten figures and precise planetary positions.

He couldn’t interpret all that data alone. So he brought on a brilliant young German mathematician named Johannes Kepler. But Brahe, afraid Kepler might make the discovery first, handed his apprentice only just enough data to be useful and locked the rest away.

That arrangement lasted barely a year. In October 1601, Brahe died suddenly. Kepler inherited his notebooks and studied them intensely for the next four years. 

What he found was proof that everything astronomers had assumed since ancient Greece was wrong.

Kepler realized that planets didn’t move in perfect circles. They moved in ellipses — slightly flattened ovals, with the sun off to one side rather than in the direct center.

Almost a century later, Isaac Newton read Kepler’s laws of elliptical motion and worked out the force that explained them. He called it gravity.

One of the most important ideas in the history of science was hiding in Brahe’s notebooks for decades. The data had always been there. All it needed was someone who could make it legible.

I’m telling you this because the stock market has a Tycho Brahe problem, too.

It generates more data in a single trading day than Brahe recorded in a lifetime. The problem is, for most of its existence, only a tiny fraction of it has been readable.

But today, thanks to AI, it’s possible to find “signals” inside that data — repeating patterns that point to future moves in stocks, many with historical accuracy rates of 90% or better.

I know because my team and I at TradeSmith have created a new AI-powered trading tool that’s unearthed more than 200 of these signals across nearly 2,500 stocks.

As I showed the nearly 9,000 people who joined my AI Signals Trading Event on Wednesday, in a six-year backtest, a model portfolio of these signals trades delivered a 12x return.

And in 2022 — the worst year for stocks in half a century — they produced an average backtested gain of 16.6% while the S&P 500 fell nearly 20%.

If you missed it, the replay is still online. It’s packed full of trade examples, strategy details, and on-screen demos. Go here to watch it now.

Today, I want to share something I didn’t have time to cover on Wednesday — how much work and ingenuity went into building our new system.

The answer is: more than I expected.

Can the Weather in Paris Move the Stock Market?

Our chief developer, Mike Carr, has been writing code for 40 years.

He spent 20 years in the U.S. Air Force – coding nuclear missile paths, working on cryptography for the National Security Agency, and helping install an early version of the internet at the Pentagon.

When he left the military, he went on to manage more than $200 million in client funds. He also became a Chartered Market Technician — a credential only about 4,500 people in the world hold.

Two years ago, he joined TradeSmith to help us develop new analytics and strategies. And he brought with him the kernel of an idea he’d been working on for more than 20 years.

In 2003, Mike started doing rudimentary signal studies. Every time he spotted a repeating pattern in the data that tended to precede a move in a stock — he noted it down. He traded this way for years, constantly testing what worked and what didn’t.

Then in 2016, he read a Bloomberg profile of Jim Simons’ storied hedge fund, Renaissance Technologies. One detail stuck with him: Simons had once found a tradable signal involving the weather in Paris.

If he could find a signal in Paris weather, Mike realized, the signals hiding in ordinary market data had to be almost limitless. That was the moment he decided to stop hunting for signals manually and start building a system that could hunt them at scale — one ordinary investors could actually use.

Last year, we started feeding the 150 or so signals he’d collected into an AI system and prompted it to generate more like them. We processed more than 1 trillion database rows, running every stock through 847 individual calculations. We tested every combination of price patterns, technical indicators, and calendar conditions we could find.

Then we hit a problem we hadn’t anticipated.

On any given day, our AI-powered signals generator was delivering a flood of 697 trade setups – all with historical accuracy rates of 75% or better. It was far too much for any trader to handle, no matter their level of experience.

So we spent the next six months solving a different problem: How do you take that many high-quality signals and deliver something an investor can actually use?

The answer Mike came up with was the Quality Score. It’s a 0-to-100 rating that factors each signal’s win rate and average returns and uses machine learning to figure out how effective it was during similar market conditions in the past.

Pair that with a focused model portfolio, and the flood became an actionable shortlist.

Introducing Our Three-Stock Strategy

At any given moment, it holds three S&P 500 stocks — each one selected by an algorithm based on its Quality Score and other key factors. When an exit signal fires on one of the three, a new trade recommendation takes its place.

That’s the whole strategy. Three positions, always live. Each one selected not because a human liked a chart or a story… but because an algorithm chose a mathematically optimal trade.

We backtested it from January 1, 2020 through January 30, 2026 – a stretch that covered the COVID crash, the 2022 bear market, two years of historic inflation, rising interest rates, and two wars.

It wasn’t a friendly period to stress-test a trading system against. But here’s what the Signals Master Portfolio produced:

• A 54% compounded annual return — versus about 15% for the S&P 500 over the same six years
• A 73.4% win rate across hundreds of trades
• A maximum drawdown of 18.1% — less than the S&P 500’s maximum drawdown of 25.4%

The model portfolio’s maximum drawdown is worth pausing on.

A lot of trading systems can generate a high compounded return in a backtest. Few can generate one that also held up better than a benchmark like the S&P 500 during its worst stretch. That’s the litmus test of whether a system is managing risk effectively or just riding luck.

Which is the point of what we’ve spent the last 12 years (and in Mike’s case more than 20 years) developing. Hedge funds have been doing this kind of work for decades — pattern recognition, machine learning, disciplined rotation in and out of short-term trades. But until now, nothing like it has existed for regular investors.

I went into all the details during Wednesday’s launch event. So if you haven’t already, make sure to check it out while it’s still online.

Keith Kaplan
CEO, TradeSmith

The post What AI Sees in the Market That You Don’t appeared first on InvestorPlace.

But most of it hasn’t shown up in the stock market yet.

Because the companies driving the most important breakthroughs are still private.

Think about the last time you used AI. Maybe you asked ChatGPT a question, got Claude to help edit a document, or read Grok’s take on the news. Those models are among the most powerful AI tools in the world… and you can’t invest in them directly.

OpenAI, Anthropic, xAI… not a share available on any exchange. 

And those are just the consumer-facing names. Beneath the surface, the opportunity is even bigger. For example, a little-known company called Anduril is building the future of defense, centered on advanced autonomous systems. And, yes, it is private, too. 

AI is changing the world but you don’t really own the AI that matters. 

But the insiders, founders, and venture capitalists… They have the field-level seats. They are the ones who will get phenomenally rich when these companies – the real AI pioneers like OpenAI, Anthropic, xAI, Anduril, etc. – go public.

And for most investors, there’s never really been a way in.

But that’s starting to change fast…

AI IPO 2026: The Biggest Tech Listings In a Generation

2026 is shaping up to be one of the most consequential years for technology IPOs in decades. Not because one great company is going public – but because several of them are.

Leading the charge is OpenAI, which is preparing for an IPO that could potentially value it near the trillion-dollar mark – making it one of the largest technology IPOs ever attempted.

The company generates over $20 billion in annualized revenue, growing at triple-digit rates, with 810 million monthly active users and 1 million enterprise customers. It just closed a funding round valued at $730 billion with backing from Amazon, SoftBank (SFTBY), Nvidia, and Microsoft. 

OpenAI is targeting a listing as early as Q4 2026. Close behind, Anthropic – the AI safety-focused lab backed by Google and valued at $380 billion – is also widely expected to explore a public listing in that same window.

The SpaceX–xAI Mega IPO and the Rise of AI Defense

But OpenAI and Anthropic are only the beginning.

Elon Musk has assembled the most audacious corporate structure in modern tech. In February, he merged SpaceX (his aerospace company) with xAI to create a trillion-dollar conglomerate that combines the world’s leading orbital launch provider, a frontier AI lab, and the social media platform X. The combined entity was originally targeting an IPO as early as June, at a valuation some sources put as high as $1.5 trillion. 

Now that timeline may be accelerating.

According to recent reporting from CNBC, SpaceX could file IPO paperwork as soon as this week – with Bloomberg indicating the company may seek a valuation north of $1.75 trillion, potentially making it the first 10-figure IPO in market history.

And if you’re investing in the SpaceX IPO, you’re also buying xAI and X. It is, by design, the most vertically integrated technology company ever to approach public markets.

Then there’s the sleeper in this lineup: Anduril Industries. 

Founded in 2017 by Palmer Luckey – the same wunderkind entrepreneur who founded Oculus and sold it to Facebook at age 21 – Anduril builds systems traditional defense primes struggle to replicate: AI-native, software-first autonomous systems. Its Lattice OS platform serves as the operating system for autonomous military operations, integrating sensor data across every domain and coordinating weapons systems in real time. 

Revenue is racing toward $2 billion. Its valuation has jumped from $14 billion to more than $60 billion in under two years.

With a $1 billion advanced manufacturing facility coming online in Ohio – and a CEO who has publicly said an IPO is ‘definitely’ coming – Anduril’s debut looks less like a question of if, and more a question of when.

The 2026 AI IPO Bonanza is imminent. And it is going to be one of the most talked-about investment moments of our lifetimes.

But there’s a critical dimension to this story that most investors haven’t heard yet… one that makes getting in early not just attractive but urgent.

The $48 Billion IPO Squeeze Wall Street Isn’t Thinking About

New reporting from Bloomberg Intelligence fundamentally changes the calculus here. 

S&P Global, FTSE Russell, and Nasdaq are all actively considering “fast-track” rules that would add SpaceX, OpenAI, and Anthropic to their major indices within days of their IPO – bypassing the traditional 12-month seasoning requirement that currently blocks newly public companies from immediate index inclusion.

If those rules are adopted – and Bloomberg’s analysts suggest they’re being taken seriously – the implications are massive.

Here’s the math. Roughly $12 trillion in index-tied assets – passive funds mirroring the S&P 500, Russell 1000, and Nasdaq 100 – would effectively become forced buyers of these IPOs within days of listing. Bloomberg estimates $24- to $48 billion in automatic passive demand representing approximately 20% of shares offered. If active fund managers benchmarked to those same indices simultaneously move to neutral weights, index inclusion could require buying up to 55% of the public float within five trading days of the IPO.

Now consider the supply side. These companies are expected to go public with free floats of just 5- to 10% of total market value – deliberately tiny, to avoid flooding the market with shares. A 5% float of a $1.5 trillion SpaceX means about $75 billion in publicly available shares absorbing tens of billions in forced institutional demand within the first week.

That is a structural supply/demand imbalance of historic proportions.

The Bloomberg report also identified 37 publicly listed funds already holding SpaceX exposure. Of those, the ERShares Private-Public Crossover ETF topped the rankings by portfolio weight at nearly 37% SpaceX exposure – more than Baron Capital, Fidelity’s Contrafund (which holds over $6 billion in SpaceX in dollar terms), ARK Invest, and Neuberger Berman. Bloomberg’s independent analysis confirms what we’ve been telling you: concentrated pre-IPO vehicles exist, they are accessible right now, and they are already sitting on extraordinary unrealized gains.

On that note: Bloomberg’s data on estimated SpaceX returns by fund shows Baron sitting on gains of approximately 864%, Fidelity at 715%, and Neuberger Berman at 733% from their initial entry prices. ARK, which was slower to build its position, shows an estimated 291% return. The message is unambiguous: time of entry is everything, and the gap between early investors and late ones is measured not in percentage points but in multiples.

Why IPO Day Is Usually Too Late for the Biggest Gains

When these companies arrive, the combination of genuine investor enthusiasm and $48 billion in mechanically forced passive buying will almost certainly produce one of the most violent opening-day pops in stock market history.

But there’s a darker flip side to this golden coin. We’ve seen this all before.

Think back to the first wave of internet IPOs in the late 1990s. It produced some of the most spectacular opening-day pops ever recorded. 

Yet, for most post-IPO investors, the years that followed were brutal. The insiders and venture capitalists who invested at pre-IPO valuations captured the overwhelming majority of the gains. The retail investors who piled in after the bell, swept up in the excitement, often held stocks that subsequently fell 50%, 70%, 90%.

The lesson wasn’t about the technology. It was about when you got in.

Now apply that pattern here, and add the Bloomberg dynamic: if $48 billion in forced passive buying hits a 5% float in the first five trading days, the post-IPO price could reflect an extraordinary one-time structural premium that has nothing to do with fundamental value. Once that forced buying is absorbed, what happens next? 

Pre-IPO holders get to sell into the most structurally bid-up IPO market in history. Post-IPO buyers are the ones providing the exit liquidity.

How to Invest In AI Companies Before Their IPO

The investment landscape has genuinely changed over the last few years.

Now, a new category of investment vehicle has emerged. And it allows ordinary investors – not just hedge funds, accredited millionaires, or Silicon Valley venture insiders – to gain pre-IPO exposure to the world’s most transformational private companies. 

These vehicles trade like stocks. All you need are a ticker symbol and a brokerage account – no $250,000 minimum check, VC connections, three-year lockup period, or complex special purpose vehicle (SPV) paperwork required.

And most importantly, there are specific vehicles in this category that provide direct exposure to OpenAI, xAI, SpaceX, and Anduril right now, before they go public. 

These are not futures bets or derivatives or synthetic products. They are investment funds with actual positions in these private companies, wrapped in publicly traded structures and available to any investor with a standard account.

The venture capitalists who bet on these companies early are preparing to cash out at valuations that will make them unimaginably wealthy. And the founders are about to see their net worth go vertical. 

For the first time, ordinary investors have a legitimate way to stand alongside them.

Before the IPO circus arrives, institutional allocations are spoken for, and the opening-day pop happens without you.

The 2026 AI IPO Bonanza is the financial story of the decade. And Bloomberg makes one thing clear: this is not a “wait and see” moment. The time to get positioned is before the index funds are forced to act – not after.

If you want to get in before these IPOs hit – and before billions in forced buying distorts prices – you need to see this. 

I just put together a full presentation on this topic, including a deep-dive analysis of each vehicle, the risks every investor needs to understand, and our specific recommendations. 

Click here to watch it now.

Editor’s note: “The $48 Billion AI IPO Squeeze Wall Street Isn’t Warning You About” was previously published in March 2026 with the title, “IPO Day Is When Most Investors Get It Wrong.”It has since been updated to include the most relevant information available.

The post The $48 Billion AI IPO Squeeze Wall Street Isn’t Warning You About appeared first on InvestorPlace.

In the horse race between gold and silver, which metal is poised to take the lead – and which should you be betting on right now?

For more than two years, we’ve urged Digest readers to own both gold and silver.

But as relative value has shifted, we’ve also been clear about which metal should lead at different points in the cycle.

To navigate these shifts, we’ve frequently referenced the gold-to-silver ratio – a long-standing measure of relative value between the two metals that has historically been mean-reverting, though its baseline has shifted over time.

• In the 20th century, it averaged roughly 47:1
• From 2000 through 2020, it averaged closer to 60:1

In the first half of 2025, fear and a preference for gold sent the ratio above 105 – just shy of its COVID-panic peak. At that point, silver was deeply undervalued relative to gold.

When the ratio began to roll over, we flagged silver’s asymmetric upside. In our July 25 Digest, we wrote:

We don’t anticipate a meaningful decline in gold’s price beyond normal profit-taking and healthy “two steps forward, one step back” market action.

Therefore, if the gold/silver ratio is to continue normalizing to the more recent average of around 60, it’ll be up to silver to do the heavy lifting – meaning silver’s price gains must outpace those of gold.

That’s exactly what we expect.

Sure enough, between that July 25 Digest and January 15, gold climbed a respectable 37%, but silver exploded 137%.

I mention January 15 because that’s when we updated our analysis and our forecast

In our January 15 Digest, we argued that after silver’s blistering rally, it was time to bet on gold for outperformance:

Silver’s explosive move has dramatically reset the gold-to-silver ratio. It sits at about 51 – its lowest level since 2012.

Practically speaking, it means silver has gone from undervalued to relatively expensive versus gold in a very short period.

From here, history argues that gold is more likely to outperform silver as the ratio works its way back toward equilibrium, which we’d peg in the mid-to-upper 50s, with 58 to 60 as a reasonable medium-term target.

As you can see below, that’s exactly what’s happened.

While gold has managed to eke out a 2% climb, silver has fallen 17% as the gold-to-silver ratio climbed back to where it sits as I write – nearly 61.

So, where should you put your money now?

With the gold-to-silver ratio back to equilibrium, there’s no lopsided imbalance that tips the odds squarely in one camp.

That said, our technology expert Luke Lango of Innovation Investor is looking for silver to make a sharp move higher for one primary reason…

Its critical role in the AI rollout.

Silver is one of the most overlooked heroes in the AI story.

It’s not just a precious metal; it’s the best natural conductor of electricity we have. That makes it indispensable for wiring, switches, and contacts inside data centers where AI models are trained, as well as for the chips that run those models.

The more AI scales, the greater the demand for components that can move massive amounts of energy and data at lightning speed – and silver’s conductivity makes it irreplaceable in those applications.

So, are you bullish on AI? Then you’re also bullish on silver.

Now, in Luke’s Innovation Investor Daily Notes from earlier this week, he explained how the VanEck Semiconductor ETF (SMH) has been a very good proxy for general global economic strength and AI-driven demand over the last 2.5 years.

Here he is explaining the relationship:

When SMH is rising, global economic strength and demand is improving, so silver (the offensive precious metal with tons of AI applications) outperforms gold (the defensive precious metal with limited AI applications).

When SMH is falling, global economic strength and demand is weakening, so silver underperforms. 

Luke notes that while semiconductors have had a massive breakout rally over the last few weeks, silver has underperformed.

Here’s how that looks: SMH is up 24% so far in April, while silver has climbed just 3.5%.

Back to Luke:

This implies that silver is due for a massive short-term melt-up over the next few weeks.

Meanwhile, semis will likely keep surging higher amidst all this positive AI infrastructure buildout and funding news, so silver should keep pushing higher over the next few quarters.

The set-up for silver looks quite attractive over the short and long term.

To get Luke’s latest analysis, as well as his specific silver and AI picks, click here to learn about joining him in Innovation Investor.

But you don’t want to overlook gold either

While Luke is bullish on silver, our macro investing expert, Eric Fry of Fry’s Investment Report, believes gold has plenty of upside ahead.

Nearly a year ago, Eric made a formal bullish argument for gold and gold stocks. To frame it, he borrowed a line from the esteemed financial writer James Grant:

Gold is a bet on monetary disorder – indeed, on other kinds of disorder too, including fiscal, geopolitical and presidential.

Eric’s bet has since more than doubled the S&P 500’s returns over the same period. Not bad for a “bet on disorder.”

But with gold still sharply lower than its January all-time high, Eric is asking the natural follow-up question…

Is it time to take profits and walk away, or add to the gold bet?

Here’s Eric:

I recommend the latter, simply because the four disorders Grant identifies have not dissipated.

If anything, they have grown more disorderly over the past year.

The one disorder Eric thinks is most underappreciated – and most consequential – is fiscal disorder.

Fiscal disorder is an elegant term for soaring government deficits and debts. When a nation’s finances spiral out of control and its creditors back away, that nation loses a significant portion of its freedom, its self-determination and its power.

A debt crisis quickly becomes a currency crisis, and a currency crisis can destroy an entire economic foundation.

The U.S. is not yet on the threshold of a currency crisis, but it is inching toward the kind of fiscal disorder that could put sustained downward pressure on the dollar’s value.

The stories about our government’s fiscal disorder are never-ending

As we’ve profiled in the Digest last month, the U.S. national debt stood at $39 trillion – growing by roughly a trillion dollars every three months.

That number alone is striking. But the more important story is what’s happening around it.

Last week, the IMF issued a warning that should have commanded far more attention than it did:

The increase in the U.S. Treasury security supply is compressing the safety premium that U.S. Treasuries have traditionally commanded—an erosion that pushes up borrowing costs globally.

In plain English: the world’s most trusted financial instrument is losing its status as the world’s most trusted financial instrument.

It gets worse…

The IMF also found that the international “convenience yield” of Treasuries – the premium investors pay for liquidity and the perceived “safety” of U.S. Treasuries – has now turned negative.

From the report:

Treasuries now offer a higher yield than the synthetic-dollar equivalents for hedged G10 sovereign bonds.

In other words, investors aren’t paying up for safety anymore. They’re demanding compensation for the risk.

And it doesn’t stop there…

Also last week, Henry Paulson, Treasury secretary during the George W. Bush administration, issued a stark warning.

According to Fortune, Paulson believes the country’s debt problem is beginning to erode the long-standing reliability of Treasury securities – and could eventually cause demand to collapse.

Here’s Paulson:

We need an emergency break-the-glass plan, which is targeted and short-term, on the shelf, so it’s ready to go when we hit the wall.

The former treasury secretary isn’t saying this crisis is at our doorstep, but he does warn that when it arrives, “It will be vicious.”

Back to Eric as we refocus on gold’s role in all this:

America’s rising indebtedness – and the waning appetite of foreign creditors to finance it – provides plenty of raw material for the “bet on gold” to keep paying off.

That is why we continue to hold several positions in our Fry’s Investment Report portfolio that provide exposure to the gold market.

The U.S. government’s financial predicament is not a crisis today. But it is a slow-motion reckoning that is becoming harder to ignore and more expensive to defer.

For investors looking to act on Eric’s analysis, he’s put together a list of three gold stocks he currently recommends – including one he considers the lowest-risk entry point for investors who want to increase their precious metals exposure at current levels. You can find his full breakdown here.

Coming full circle, what’s our portfolio action step – gold or silver?

The case for owning both is strong.

Gold is a bet on disorder.

Silver is a bet on AI.

Silver might move up faster in the short term as it plays catch-up to SMH as Luke flagged… but gold’s longer-term payoff remains enormous due to our government’s fiscal excesses.

So, which wins?

Both.

Invest accordingly.

Before we sign off…

Our friends over at TradeSmith are getting phenomenal feedback about CEO Keith Kaplan’s AI Signals Trading Event presentation yesterday morning – and for good reason.

He explained how his team is using AI to detect what they call “signals”—specific data combinations that have historically led to profitable trades, regardless of the underlying story.

Using TradeSmith’s platform, you can detect the morning’s best trades, 90 minutes before they occur – with 90% historical accuracy. And if you prefer more guidance, Keith has created a model portfolio that returned 124% in his backtest last year.

Click here for the free replay of the full presentation – including the AI’s #1 pick today.

Have a good evening,

Jeff Remsburg

The post Gold or Silver – Which Do You Buy Right Now? appeared first on InvestorPlace.

In the film, one team couldn’t compete with the biggest spenders in baseball. So, instead of chasing star players, they changed the way they evaluated talent.

They focused on overlooked metrics that actually drove wins – and in doing so, found value where others weren’t looking.

That shift in thinking changed everything.

And we’re seeing that same dynamic play out in the market right now, especially in the race to dominate artificial intelligence.

Right now, most investors are still focused on the obvious names – the ones grabbing headlines and driving the broader AI narrative. But behind the scenes, a different group of companies is quietly delivering the real results.

These are the businesses enabling the AI boom – supplying the infrastructure, power and systems that make it all possible. Unlike the headline-grabbers, their growth is driven by real, measurable demand.

In other words, while investors are still focused on the “flash,” the biggest opportunities are being built on function.

And two companies that reported earnings this week make that contrast crystal clear.

Tesla, Inc. (TSLA) and Vertiv Holdings Co. (VRT) both released results yesterday – and in doing so, revealed exactly where investors are still getting AI wrong.

In today’s Market 360, we’ll break down what they reported, which company is the better opportunity right now… and how to tell the difference between hype and real AI opportunity.

Tesla, Inc.

Tesla doesn’t need much of an introduction. It’s one of the most widely followed companies in the market – and one of the names most closely tied to the AI story.

While best known for its electric vehicles, Tesla has increasingly positioned itself as an AI company, with a long-term vision centered on autonomous driving, robotaxis and robotics.

That vision has kept Tesla at the forefront of investor attention, as reflected in the company’s latest results.

In the first quarter, Tesla reported revenue of $22.39 billion, a 16% increase from a year ago and slightly ahead of expectations.

Adjusted earnings came in at $0.41 per share, also topping analyst estimates.

The results show Tesla’s core business is beginning to stabilize, even as the company shifts more focus toward its AI-driven future.

The company plans to spend roughly $25 billion this year developing autonomous Cybercab vehicles, Optimus humanoid robots and other AI-driven initiatives.

That includes early work on its planned “Terafab” project. This is a projected 100-million-square-foot facility – roughly ten times the size of Tesla’s Gigafactory Texas. It aims to produce 70% of the world’s current chip output by generating one terawatt of computing capacity annually.

The project is a massive joint venture between Tesla, SpaceX, xAI, and Intel. And if the full vision comes to fruition, it could ultimately cost up to $5 trillion.

For Tesla’s part, the facility will be designed to produce next-generation A15 chips and computing infrastructure, which will power both its vehicle and robotics ambitions.

This project is still in its early stages. In the first quarter, Tesla spent less than $2.5 billion toward those efforts. But spending is expected to ramp significantly in the years ahead.

At the same time, the company acknowledged that some of these initiatives will take time to scale. CEO Elon Musk noted that robotaxi revenue is not expected to be “super material” this year, even as the company expands its service. Tesla’s robotaxi platform is already active in cities like Austin, Dallas and Houston, with additional launches planned for Phoenix, Miami, Orlando, Tampa and Las Vegas. Meanwhile, production of its Optimus robot is still ramping gradually.

Looking ahead, management pointed to a stable demand environment while acknowledging growing competition and pricing pressure across the EV market.

Vertiv Holdings Co.

Vertiv Holdings isn’t a household name. But it plays a critical role in the AI boom.

The company is a global leader in digital infrastructure for data centers, communication networks and industrial environments. Its products include power systems, thermal management, racks and monitoring software.

In simple terms, Vertiv helps keep data centers powered, cooled and fully operational.

And that role is becoming more important by the day.

As artificial intelligence workloads expand, they require more computing power. That, in turn, drives higher energy demand and greater heat output. Without the right infrastructure in place, those systems simply don’t work.

That’s where Vertiv comes in.

The company supports more than 750,000 customer sites across over 130 countries, providing the backbone that enables AI systems to run at scale. This isn’t a “nice to have” part of the AI story – it’s essential.

And that demand is now clearly showing up in the company’s results.

In the first quarter, Vertiv’s sales grew 30% year-over-year to $2.65 billion, in line with analysts’ expectations. Adjusted earnings jumped 83.4% to $1.17 per share, easily beating expectations of $1.01 per share.

Looking ahead, the company expects second-quarter sales between $3.25 billion and $3.45 billion, adjusted earnings per share between $1.37 and $1.43. That compares to sales of $2.64 billion and earnings of $0.95 per share in the second quarter of 2025.

For the full year, Vertiv raised its outlook and now expects sales between $13.5 billion and $14 billion, alongside earnings per share of $6.30 to $6.40.  That represents 29% to 31% annual sales growth and 50% to 52.4% annual earnings growth.

In other words, this is a company seeing strong demand from one of the most important trends in the market today – and its results are beginning to reflect that.

Which Is the Better Pick?

If you go back to the Moneyball story, you’ll remember the entire edge came down to using the right metrics.

And that’s exactly how we should be evaluating these two companies today.

On the surface, Tesla is the more dominant story. With a market capitalization of roughly $1.2 trillion, it commands enormous attention and sits firmly at the center of the AI conversation.

Vertiv Holdings, by comparison, is far smaller, with a market cap of roughly $115 billion – just a fraction of Tesla’s size. It doesn’t receive the same level of attention, but it plays a critical role in powering the on which that AI depends.

So how do you separate the two?

That’s where my Stock Grader system (subscription required) comes in.

Instead of focusing on headlines or narrative, Stock Grader evaluates companies based on the factors that have historically driven outperformance – things like earnings growth, sales momentum and overall financial strength.

And when you run both companies through that system, the difference becomes clear. Vertiv Holdings earns an “A” rating, meaning “Very Strong,” while Tesla earns a “C” rating or “Neutral.”

This isn’t about opinions or hype. It’s about what the data is telling us.

Vertiv is delivering strong, accelerating growth tied directly to one of the most important trends in the market today. Its business is benefiting from real, measurable demand.

And this is exactly the kind of opportunity I look for.

In fact, I recommended Vertiv to my Growth Investor subscribers back in 2024, well before the current wave of enthusiasm around AI infrastructure.

Since then, the stock has surged nearly 356%.

Over that same time, Tesla is up about 85%.

That’s a massive performance gap – especially given how much more attention Tesla continues to receive.

And it highlights exactly what we’ve been talking about.

One company is being driven by what’s happening right now – real demand, real growth and real results.

The other is still largely tied to what investors expect could happen next.

Hype vs. Real AI Opportunity

What we just walked through with Tesla and Vertiv isn’t a one-off situation. It’s a clear example of how the AI story is shifting.

The first wave of AI rewarded visibility. Big ideas and companies with compelling narratives.

And for a while, that was enough.

But that’s beginning to change.

As AI moves from concept to execution, the market is starting to reward something very different – real demand, real growth and the infrastructure that makes it all possible.

That’s the difference between hype and real opportunity.

And it’s exactly what we’re starting to see play out across the market right now.

That raises an important question: If the AI story is evolving… are you positioned for what comes next?

Because, as we just saw, the way investors need to approach AI is already starting to change.

That’s why I recently put together a new presentation on what I’m calling the AI Reset.

In it, I break down where this shift is headed next, which types of companies are best positioned to benefit and how to get ahead of it before the crowd fully catches on.

I strongly encourage you to watch this presentation now while this opportunity is still developing.

Sincerely,

Louis Navellier

Editor, Market 360

The Editor hereby discloses that as of the date of this email, the Editor, directly or indirectly, owns the following securities that are the subject of the commentary, analysis, opinions, advice, or recommendations in, or which are otherwise mentioned in, the essay set forth below:

Vertiv Holding Co. (VRT)

The post Tesla Vs. Vertiv – Which One Should You Buy After Earnings? appeared first on InvestorPlace.

We are all well aware of the risks artificial intelligence adds to investing. Just look at the recent “SaaSpocalypse” that rocked the software sector earlier this year.

But it’s also important to focus on the investing benefits of artificial intelligence. 

This world-changing technology has already helped America’s economy become remarkably efficient, especially relative to those less focused on software. Median household incomes in America are now roughly 60% higher than those in Germany.

Over the coming years, we believe AI will continue to help humanity in ways we never thought possible.

Safer self-driving vehicles…

New scientific discoveries…

Breakthrough drug therapies…

Not to mention the hours of custom-made video made by AI. (Personally, I’m looking forward to a 2001: A Space Odyssey remake with HAL 9000 played by MS Word’s “Clippy.”)

So today, I’d like to detail a perhaps unexpected winner of the AI Revolution.

It’s a cyclical sector that investors often hate for its unpredictable returns – but that is performing remarkably well in today’s wild markets…

AI Is Changing the Way We Shop

New technologies have long upended retail business models:

• The rise of railroads in the 1890s enabled national mail-order firms like Sears and Montgomery Ward, which ended up breaking a system of cozy local monopolies.
• Refrigeration and national freight networks (plus regulatory changes) allowed The Great Atlantic & Pacific Tea Co. (A&P) to trounce local grocers in the mid-20th century.
• The internet established e-commerce giants like Amazon.com Inc. (AMZN), eventually turning Borders, Circuit City, and, yes, Sears into corporate footnotes.

The development of AI shopping agents is now changing retail once again… and in ways that are surprising.

On April 16, Adobe Analytics released a new report showing that web traffic to retailers from AI sources surged 393% year-over-year in the first quarter.

In a study of 1 trillion visits to U.S. retail sites and a 5,000-person companion survey, the firm found that 39% of consumers now say they have used AI to help with online shopping. Eighty-five percent of them said AI helped improve their experience.

Even better, Adobe found that AI traffic improved outcomes for retailers themselves. This included a wide variety of metrics, including:

• Conversion rates: 42% better than non-AI traffic.
• Time spent on websites: Increased by 48%.
• Pages per visit: 13% more viewed.
• Revenue per visit: 37% higher sales.

In other words, AI bots have become so good at understanding user intent that e-commerce websites are now gaining from better-screened customers. This stands in contrast to last year, when AI traffic converted 38% worse.

AI conversion rates are now higher than non-AI rates

That’s having a stabilizing effect on retailers with strong brand recognition.

Take Birkenstock Holding plc (BIRK).

Birkenstock has already announced some excellent financial figures. In February, the footwear seller said revenue grew 18% on a constant currency basis, ahead of its full-year guidance range of 13%-15%.

Lower inventory levels likely helped boost wholesale B2B (business-to-business) sales, which rose 24%.

In Asia-Pacific – where Birkenstock has fewer physical stores – direct-to-consumer (DTC) sales surged 37%, growing about twice as fast as wholesale.

This matters because DTC allows brands to sell directly to customers, bypassing middlemen. And that model is becoming even more powerful as AI changes how people shop – shifting users from passive consumers of ads to active seekers of information.

As Adobe puts it:

Consumers are engaging with large language models (LLMs) to find specific products or locate the best deals, while receiving links to relevant retail websites.

AI-powered consumers are simply more motivated than those seeing the same online ads for the 20th time. (Studies have found that ad retargeting gets less effective over time.)

There’s also an “intelligence” element.

AI Turns Trust Into Profits

AI has become far smarter… not to mention better trained on user data.

That means conversion rates are naturally rising; Alphabet Inc. (GOOGL) notes how advertisers using AI in its Google search campaigns see 14% more conversions at similar costs.

Ultimately, AI may transform how people shop, but it does not change what they are looking for: products and brands they can trust.

If anything, a world filled with fake AI-generated content will make that trust more valuable than ever. And for companies like Birkenstock, its logo is not just a branding advantage, but also a lasting competitive strength.

In Fry’s Investment Report, Eric recommends several other retailers whose earnings have steadied in recent months, despite continued risks of tariffs and a consumer slowdown.

That ends a downward trend that began in early 2025 after the White House began warning of broad import taxes.

And a lot of that is thanks to AI.

To learn more about these companies, click here.

Regards,

Thomas Yeung, CFA

Market Analyst, InvestorPlace

P.S. The retailers we just covered – and dozens of others quietly thriving right now – are what I call AI Appliers. They’re not building the technology… they’re using it to grow faster, operate smarter, and pull ahead while others fall behind. I’m putting together a free broadcast soon to break down exactly how this shift is creating a new class of winners — and the specific event on May 19 that could send them even higher in the months ahead. Keep an eye on your inbox next week for a direct link to that broadcast.

The post The AI Shopping Shift Separating Retail Winners From Losers appeared first on InvestorPlace.

On one side: AI infrastructure stocks posting blowout results, raising guidance, and struggling to keep up with demand.

On the other: traditional companies cutting forecasts under the weight of rising costs.

Same economy. Radically different realities.

The AI Boom is not just surviving the current macro turbulence. It is accelerating through it. And everything tethered to old-economy inputs — energy costs, consumer discretionary spending, rate-sensitive balance sheets — is getting crushed under the weight of them.

A great “AI Bifurcation” is beginning to split the economy — and market — in two. And the gap between them is widening fast.

AI Infrastructure Stocks Just Delivered Blowout Earnings

Four companies. Four different corners of the AI infrastructure stack. One unmistakable signal. 

Vertiv: Data Center Demand Is Outpacing Capacity

Vertiv (VRT) is an essential supplier within the AI infrastructure stack. It makes the cooling and power management systems that keep AI data centers humming smoothly. And in the company’s first-quarter earnings call, CEO Giordano Albertazzi put it plainly: the urgency from customers has increased, the scale of deployments is larger, and the technical complexity is greater than it was six months ago. 

The numbers back it up. Vertiv posted organic sales growth of 23% in Q1. The Americas — where the hyperscaler buildout is most concentrated — was up 44% organically. Free cash flow more than doubled, up 147% year-over-year to $653 million. At $1.17, earnings per share (EPS) beat guidance by $0.19 and was up 83% from a year ago. 

The numbers are strong. The guidance is stronger.

Vertiv raised its full-year EPS guidance to $6.35 — up 51% from 2025 — and its full-year revenue guidance to $13.75 billion, reflecting 34% growth. Its adjusted operating profit is now expected to hit $3.2 billion, up 53%.

Vertiv is running out of room to grow faster. GE Vernova, which supplies the power that makes Vertiv’s cooling systems necessary, has a similar problem.

GE Vernova: The Power Behind the AI Boom

CEO Scott Strazik opened his latest quarterly remarks by noting that the company’s total backlog has grown from $116 billion at its spin-off to $163 billion today. And it now expects to hit $200 billion in backlog by 2027 — a full year ahead of schedule.

In Q1 alone, GE Vernova (GEV) booked $18.3 billion in orders — up 71% year-over-year — at a book-to-bill ratio of approximately 2X. That means that for every dollar of product it shipped, customers ordered two dollars more. 

Free cash flow in the quarter was $4.8 billion — more than the company generated in all of 2025. Likewise, the company’s electrification segment’s data center orders reached $2.4 billion in Q1 alone — a full year’s worth of 2025 volume, in a single quarter.

Gas turbine pricing is now running 10 to 20 percentage points higher on new orders than where the backlog was sitting in Q4 of last year. GE Vernova raised its full-year free cash flow guidance to $6.5- to $7.5 billion, up from $5- to $5.5 billion previously. 

Its April order intake alone has already exceeded all of Q1.

That’s no ordinary growth rate. It’s the product of a massive — and growing — wave of demand.

Teledyne: Defense and AI Demand Converge

The Iran conflict that’s strangling United Airlines is, for Teledyne Technologies (TDY), a revenue catalyst.

The firm makes sensors, imaging systems, drones, and defense electronics. And on its latest earnings call, executive chairman Robert Mehrabian reported record Q1 sales, record EPS, and record operating margins — the company’s 10th consecutive quarter of a book-to-bill ratio above 1.0. 

Defense orders are surging, with the U.S. government actively investing in response to the Iran conflict and broader geopolitical pressures. Counter-drone orders came in the “tens of millions” in Q1 alone. And the company’s Black Hornet nano-drone — the smallest autonomous combat drone on the market — is shipping to customers worldwide. Their total defense-related business is approaching $2 billion in annual revenue, growing at high-single-digit organic rates, and the pipeline is building further.

Geopolitical instability and AI infrastructure demand are, it turns out, not competing forces. For Teledyne, they’re the same order book.

ASM International: Chip Demand Is Maxing Out Supply

And then there’s ASM International (ASM), the Dutch semiconductor equipment maker whose atomic layer deposition (ALD) tools are essential to manufacturing the chips that run AI models. 

ASM posted Q1 revenue of €863 million (~$1.01 billion USD) — at the high end of guidance — with a record operating margin of 33.1%. Its Q2 guidance calls for €980 million (~$1.15 billion USD), approaching the €1 billion (~$1.17 billion USD) quarterly run rate. 

CEO Hichem M’Saad wasn’t shy: “We are fully booked for this year.” 

The company confirmed it will outgrow the broader wafer fabrication equipment market in 2026 — a market that Gartner now estimates is tracking toward 25% growth.

Every segment — logic, memory, power — is growing simultaneously, a rare setup in semis. The reason? Chipmakers are moving to increasingly complex transistor designs that require more of ASM’s tools at every new generation. The current leading-edge chips are already in volume production. The next generation begins pilot production in the second half of this year — and ASM has already locked in key equipment wins there. 

The demand runway isn’t measured in quarters. It’s measured in chip generations.

The Old Economy Is Getting Hit by Energy and Cost Pressures

While companies across the AI infrastructure stack were posting blowout results, the past quarter proved to be a slog for firms mired in the “old economy.”

Sonoco, for instance, cut its full-year profit guidance, citing elevated costs it can’t fully pass through to customers. Similarly, United Airlines revised its 2026 outlook downward, grappling with softer demand signals and higher fuel costs largely driven by elevated oil prices.

These disappointing results aren’t the result of strategic errors. These companies are just exposed to the wrong inputs at a time when the Iran War has driven up energy costs. And in a world of razor-thin consumer discretionary margins, higher costs translate directly into lower earnings.

This is the “Hormuz toll” thesis playing out in real time.

Higher oil prices function as a structural tax on energy-intensive, consumer-facing businesses like airlines, packaging, logistics, and shipping. 

For hyperscalers and AI infrastructure builders, that tax simply doesn’t apply. Their contracts are priced in compute demand, not jet fuel.

The Bottom Line: AI Infrastructure Stocks Are Pulling Away

The divergence we’re noting between the “old” and “new” economy is not a one-quarter anomaly driven by backlog timing or easy year-over-year comparisons. 

AI infrastructure requires years of continued construction; and orders placed today won’t be delivered until 2027 or 2028. The backlog is locked in. The capex commitments from hyperscalers are growing. And the demand from sovereign governments, data center developers, and enterprise customers alike is accelerating.

The old economy, meanwhile, is navigating a world of higher energy costs, softer consumer demand, and the lingering uncertainty of a geopolitical conflict that has permanently altered oil price floors. Anyone whose business model touches physical goods, fuel, or the rate-sensitive consumer is feeling real pressure.

We’re watching two movies on a split screen. And AI has a better plot, a bigger budget, and — for now — all the action.

The structural tailwinds behind AI infrastructure — the power, the chips, the grid, the defense electronics — aren’t waiting for the macro to settle. They’re compounding through it.

Position accordingly.

Today, capital is flowing into infrastructure.

But the biggest gains tend to show up in the next phase, when the platforms built on top of that infrastructure take over.

That shift is already underway.

And one company sits right at the center of it — still private, but moving steadily toward what could be the most important AI IPO of this decade.

I’ve found a way to position ahead of that moment — before it becomes obvious.

I break it down in detail here.

The post AI Infrastructure Stocks Are Leaving the ‘Old Economy’ Behind appeared first on InvestorPlace.

As I write on Wednesday, stocks are rallying on last night’s news that President Donald Trump has extended the U.S.-Iran ceasefire that was set to expire today.

Trump said the move was warranted because Tehran’s government is “seriously fractured.” The truce will now continue until Iran’s leaders submit a unified proposal to end the war with the U.S. and Israel.

The extension came after a planned second round of peace talks in Islamabad fell apart. Vice President J.D. Vance had been set to travel to Pakistan for negotiations, but Iranian officials refused to attend, citing what they called unreasonable American demands.

Trump said Pakistan’s leadership – specifically Prime Minister Shehbaz Sharif and Field Marshal Asim Munir – asked the U.S. to hold off on resuming strikes while Iran works toward a unified position.

In the meantime, Iranian gunboats have fired on three ships in the Strait of Hormuz today, seizing two and escorting them into Iranian waters – so, travel through the world’s most critical oil chokepoint remains limited. Iran says it won’t reopen the Strait or resume talks until the U.S. naval blockade ends. Yet the blockade stays in place.

Despite the lingering Hormuz drama, U.S. stocks are focusing on the indefinite ceasefire as I write – all three indexes are up. We’ll keep tracking the latest developments.

Shifting from geopolitics, one trade that Digest readers had a chance to act on back in February has turned into a triple-digit winner…

Jonathan Rose’s POET trade has exploded

114%.

That’s roughly what you’d be sitting on if you’d acted on our February 12^th Digest when we profiled a trade on POET Technologies Inc. (POET) – a small semiconductor company positioned at the center of the AI infrastructure boom.

It came courtesy of veteran Jonathan Rose, who recommended it to his Masters in Trading: All Access subscribers.

Here’s Jonathan with a quick recap of the trade’s rationale:

Copper creates heat. Heat demands power. Power is becoming the constraint inside modern data centers.

The solution the industry is racing toward is optical interconnect — moving information with light instead of electricity. Faster, dramatically more efficient, and far cheaper at scale.

That’s where POET Technologies Inc. (POET) comes in.

POET designs optical engines that move data using light rather than traditional electrical signals. As AI clusters grow larger and faster, the connections linking thousands of processors together are becoming a bottleneck. POET provides a solution.

Now, as you can see in the chart above, most of the gains have come in recent days.

Here’s Jonathan from earlier this week to explain:

Shares of POET Technologies are ripping higher again…

The recent move is being fueled by social media chatter linking POET’s photonics platform into the Nvidia-Marvell AI supply chain, along with commentary from a former Sivers Semiconductor executive suggesting a deeper role in that ecosystem.

If you missed this move, Jonathan remains very bullish on POET – it’s one of his favorite stocks – but for the moment, taking profits is the play, not adding more exposure.

Here’s his note to subscribers:

Sentiment has rallied sharply after the company pushed back on a short-seller report, with the stock now one of the most actively discussed names in the media and momentum building quickly.

This is exactly the kind of environment where we want to get on prints and take risk off the table.

The move here is being driven more by narrative expansion than fundamentals, and with shares now extended after their strongest run in months.

We’ll keep tracking this trade, but for now, a big congratulations to all of Jonathan’s subscribers who are taking profits this week, as well as any Digest readers who jumped into this trade.

The next potential triple-digit winner that Jonathan is eyeing

In Monday’s Masters in Trading: Live video, Jonathan dug into the AI water economy. This is a $1 trillion investment ecosystem that no one is taking about yet.

Here’s Jonathan from the video:

Every hyperscaler needs water to cool AI chips. That creates demand not just for the machines doing the cooling, but also for the infrastructure that moves, treats, and recycles that water.

There are two key categories here: the cooling systems themselves — things like heat exchangers, chillers, and immersion tanks — and the water infrastructure that feeds those systems.

Jonathan has been building a watchlist to trade both opportunities.

On the infrastructure side, one stock he likes is Modine Manufacturing Company (MOD). It’s a global leader in thermal management, engineering, and manufacturing heating, cooling, and ventilation solutions.

But it’s the water side that’s more interesting…

Morgan Stanley is projecting AI data center water usage to grow 16x by 2028. Back to Jonathan:

When you see a new industry scaling like that, the opportunity is getting positioned early — before it’s fully priced in.

We’ve already seen strong performance in the cooling names. Some of those stocks are up significantly. But the water infrastructure side hasn’t moved as much yet — and that’s where I think opportunity still exists.

In Monday’s Masters in Trading: Live video, Jonathan flagged one of his favorite stocks related to this opportunity:

It’s a smaller-cap name — around $600 million — which means it can be volatile, but it also has significant upside potential if the theme plays out.

For the name of the stock and additional details, you can check out the free episode right here.

Bottom line: If you’re looking for an opportunity that Wall Street hasn’t yet bid up, give the AI water economy a look.

You can get Jonathan’s latest trade ideas – totally free – every day the market is open at 11 a.m. ET in his Masters in Trading: Live broadcasts. You can sign up right here.

The wiring that powers the AI age

Let’s turn to another electricity-driven opportunity – one that runs parallel to everything Jonathan just laid out but plays it from a different angle.

While the AI water economy focuses on cooling the machines, there’s an equally urgent problem on the other side…

Getting enough power to them in the first place.

And that problem runs straight into one of the most neglected pieces of infrastructure in America.

Brian Hunt, editor of Money & Megatrends, has been urging readers to get in position on this opportunity for months. Here’s how he frames it:

All the electric power generation in the world isn’t worth much if you can’t get it to consumers.

The U.S. power grid is often called the world’s largest machine — a giant network of power stations, transmission lines, substations, and underground wires.

Most people barely know it’s there. But without it, your lights don’t turn on, there’s no Netflix, and your iPhone doesn’t charge.

The problem?

That “machine” is aging – badly. Brian points out that the American Society of Civil Engineers gave the energy sector a D+ in its 2025 Infrastructure Report Card, citing surging electricity demand, crumbling infrastructure and a dangerous lack of transmission capacity.

And atop that crumbling grid, we’re now layering on AI and next-gen technologies with their unprecedented electricity demands.

It’s like connecting a fire hose to a garden spigot. The pressure is coming. The pipe isn’t ready.

Every hyperscale data center that gets built, every EV that gets plugged in, every semiconductor fab that comes online – all of it runs on electricity.

Here’s Tech Insider to give you a sense for how big this problem is:

The International Energy Agency now projects that global data center electricity consumption will exceed 1,000 TWh by the end of 2026, an amount equivalent to Japan’s entire annual electricity usage.

The AI economy doesn’t just need more electricity. It needs a whole Japan’s worth more.

But for investors who recognize the problem, this is an enormous opportunity. Back to Brian:

Soaring electricity demand… a grid badly in need of an upgrade… the global race for AI supremacy… and trillions of dollars of economic output on the line. This is a recipe for a bull market in companies that build, repair, and upgrade our power grid.

So, how do you play it?

Here are two of Brian’s ideas:

Monolithic Power Systems (MPWR) – a power management semiconductor company whose chips sit at the heart of efficient energy delivery inside data centers. It’s up 58% since Brian’s original analysis.

Quanta Services (PWR) – the largest specialty contractor in North America, doing the boots-on-the-ground work of actually building and upgrading the grid. It’s climbed 41% since the original note as well.

You can get the rest in Brian’s picks in his issue here.

Here’s his bottom line:

Big business and technology trends that change the world tend to play out over years, not months. The Power Grid Upgrade is one such trend. Money & Megatrends is still bullish.

You can access Brian’s full Power Grid Upgrade analysis – including his complete buy list – for free by signing up for Money & Megatrends right here.

We’ll keep you updated on all these stories here in the Digest.

Have a good evening,

Jeff Remsburg

P.S. Want to know how to turn $100,000 into $1.3 million?

Then you’ll want to catch a free replay of this morning’s AI Signals Trading Event with Keith Kaplan, CEO of TradeSmith.

Keith walked through how a new form of AI could double your portfolio by detecting the most profitable trades, 90 minutes before they occur – including how it turned $100,000 into $1.3 million in its 5-year backtest through bull and bear markets. Last year alone, it booked a 124% total gain.

It could crush conventional investing this year by spotting the best place to put your cash each morning, with 90% historical accuracy.

All it takes is just 10 minutes a week, Keith says.

For all the details, click here.

The post Three Stocks for the AI Infrastructure Boom appeared first on InvestorPlace.

More and more, the edge favors investors who can identify these patterns early — or, even better, employ systems that can detect them automatically.

That’s why we wanted to share today’s guest essay from TradeSmith CEO Keith Kaplan. In it, he explains how his team is using AI to identify specific “signals” in the market — patterns that have historically led to profitable trades — often before they fully play out.

To learn the ins and outs of this system, click here to watch Keith’s AI Signals Trading Event, where he walks you through how it works and what it’s flagging in the market today.

Take it away, Keith…

“There is an entity that cannot be defeated.”

That’s how Lee Sedol – the world’s greatest Go player – described AI after losing a five-game contest against Google’s AlphaGo AI model in Seoul in March 2016.

Go was invented in China more than 2,500 years ago. Two players place black and white stones on a grid, trying to capture each other’s pieces.

Stones on a Go board (Source: Wikimedia Commons)

It looks easy. But it contains more possible positions than there are atoms in the observable universe. The best players win by feel and intuition rather than brute calculation.

So, Sedol thought he was safe. The game was too creative – too deeply human – for a machine to crack. Before the match, he predicted he’d win every game.

Then came Move 37 in the second game.

Google’s AlphaGo placed a stone in a position no normal player would make – a move so strange that one commentator blurted out, “That’s not a human move.”

He was right.

AlphaGo’s analysis showed that a human player would make that move less than 1 in 10,000 times. But it turned the tide in AlphaGo’s favor, and Sedol lost.

He went on to lose the contest 4–1.

The machine wasn’t smarter. But it could see patterns no human eye could. And that was enough to defeat the world’s best player.

It’s a cautionary tale for investors today. AI models are way better at recognizing patterns now than they were a decade ago. And there’s no domain richer in hidden patterns – or more ruthlessly efficient at rewarding those who spot them and punishing those who miss them – than the stock market.

That’s why my team and I at TradeSmith have spent the last 12 months building a new application of AI for self-directed investors like you.

It uses the same principles that defeated the world’s greatest Go player to spot patterns that flag the day’s most promising trades – 90 minutes before they happen.

I’ll show you how it works today – and pass on the link where you can watch it in action. First, let me give you some background on TradeSmith and what we’re all about.

Modern-Day Prospectors

As TradeSmith’s CEO, I run a team of 65 people, and an annual budget of $8 million, to develop hedge-fund-level analytical systems for self-directed investors.

More than 134,000 people in 86 countries use our software to manage more than $29 billion in assets.

Inside our Research Lab, we’re like modern-day prospectors panning for gold – only we use data and computers, not pans and pickaxes.

We’re constantly testing trading strategies, financial metrics, and data patterns to uncover profitable systems and indicators.

That’s what’s gotten us featured in Forbes, The Wall Street Journal, and The Economist.

Our risk-management software, TradeStops, put us on the map among financial technology firms. It takes the emotions out of investing by showing you the ideal time to sell your stocks.

We’ve also created software that finds hidden seasonality patterns in stocks… spots undervalued options plays… and uses AI to forecast stock moves up to 21 trading days out.

We’re always innovating. But we’ve never gone this deep.

Our new AI-powered system doesn’t look at balance sheets… read earnings reports… or follow news headlines. Instead, it detects tiny anomalies in stocks’ historical data. Then it finds statistical connections between them that a human analyst would never find.

Think of it like a “thumbprint.” Every great trade has one. A unique combination of factors – technical indicators, price patterns, market conditions – that has lined up before.

When those factors align again, our system flags a high-probability setup. Some with historical accuracy rates of 90% or more.

Here’s what happens when we tested it out.

Turning Patterns Into Profits

In January, our system flagged a trade in Qnity Electronics Inc. (Q) – a company I’d never heard of before.

It identified three factors that had aligned only four times over the past decade. Every time those same factors lined up for Q, the stock went up.

On January 28, this signal fired again – and over the next 30 days Q shares jumped 26%.

Or take AI chipmaker Advanced Micro Devices Inc. (AMD). On a recent signal, our system forecast an 8.4% move in 14 days, based on a pattern with 95% historical accuracy.

The result was an 8.1% gain in 48 hours.

Or take another popular AI stock, Palantir Technologies Inc. (PLTR). Our system signaled a 5.8% move in nine days, again with a historical accuracy rate of 95%. The result was a 15.1% gain in seven days – nearly three times the forecasted return.

These returns are from trading our signals with stocks. Here’s what those same signals produced using options:

• Caterpillar Inc. (CAT): 126% in 72 hours
• Nvidia Corp. (NVDA): 129% in 5 days
• Lockheed Martin Corp. (LMT): 365% in 30 days
• HCA Healthcare Inc. (HCA): 461% in 13 days
• Generac Holdings Inc. (GNRC): 1,082% in 33 days

Now, you’ve seen some of what the signals can do. Let’s look at what’s happening under the hood…

Finding the What, Not the Why

The technology behind this new trading system is similar to what powers ChatGPT and other AI models.

They soak up massive samples of language, find statistical relationships between words, and predict what comes next. Our system uses the same principle – but for numbers.

We fed it data on 2,467 stocks going back 10 years – including interest rates, Treasury yields, Relative Strength Index readings, Bollinger Bands, and intraday trading ranges.

It also looks at indicators most traders have never heard of. Things like:

• Kaufman Efficiency Ratio: It measures how cleanly a stock is trending versus how much it’s just drifting sideways.
• Williams %R: It’s a momentum oscillator that measures where price sits relative to its recent high-low range.
• DMI+/DMI-: They measure the strength of upward versus downward movement independently, rather than just looking at price direction.

No one knows exactly why each signal works so well. Frankly, it doesn’t matter. Like AlphaGo, our AI finds the what… not the why.

It runs each stock through 847 individual calculations daily, compiling more than 2 million trade evaluations every 24 hours.

It’s looking for combinations that have worked before – regardless of whether there’s an obvious reason why.

The result is a trading system that doesn’t care if we’re in a bull or a bear market. It doesn’t need a strong economy or a calm geopolitical environment. It just needs the ingredients to align.

Given what we’ve seen in 2026 – a wipeout in software stocks, oil above $100 a barrel, and rising volatility – that kind of neutrality matters more than ever.

Now You Can Try This Breakthrough for Yourself

This is unlike anything we’ve released in our firm’s 21-year history.

The 30,000 (and counting) signals our system has discovered for 2,467 stocks give you the kind of edge that would otherwise be off-limits for most investors.

Find out more about it by tuning into my AI Signals Trading Event.

I’ll walk you through how it works in more detail – including the signals it’s tracking right now and the trades it’s flagging for the weeks ahead.

Click here to learn more.

Keith Kaplan

CEO, TradeSmith

The post You Can Now See What Wall Street Can’t appeared first on InvestorPlace.

For the past three years, every major AI company — Alphabet (GOOGL), Meta (META), Amazon (AMZN), Microsoft (MSFT) — has relied on its GPUs to train and run models at scale.

That worked — until the economics changed.

Today, the real cost isn’t training. It’s inference — the billions of times those models are used every day. 

At that scale, even small inefficiencies become massive, recurring expenses.

So Big Tech isn’t just buying chips anymore.

They’re building their own.

Why Custom AI Chips Are Replacing Nvidia GPUs

Nvidia’s GPUs are general-purpose chips. They’re powerful and flexible — they can train AI models, run video games, render 3D animations, and simulate physics. That versatility made them the backbone of the AI boom.

But versatility has a cost. A chip designed to do everything isn’t optimized for any one task. 

As AI has scaled into mass consumer and enterprise adoption, inference has become the dominant — and fastest-growing — compute cost in the entire industry. 

That’s the opportunity that custom chips — Application-Specific Integrated Circuits (ASICs), or XPUs — are built to capture. Instead of doing everything, these chips are built for a single task. Less flexible, yes. But the payoff is better performance-per-watt and significantly lower operating costs at the scale these hyperscalers operate at.

Broadcom (AVGO) and Marvell (MRVL) are the two leading designers of these custom chips — what we’ve previously called “The Builders” in the custom silicon investment stack. They sit in the middle of the value chain: Big Tech brings the specifications, Broadcom and Marvell do the engineering, and Taiwan Semiconductor (TSM) manufactures the final product.

And in just the last two weeks, the news coming out of those Builders has been staggering — the kind of deal flow that, in our view, Wall Street hasn’t fully woken up to yet…

Inside the Avalanche of Recent Custom Chip Deals

The drumbeat of recent announcements is telling a very clear story.

Let’s walk through what happened.

Broadcom Secures Google Through 2031

On April 6, Broadcom filed an 8-K with the SEC — the kind of routine regulatory document most people skip past. But this one contained something remarkable: a new five-year agreement with Google to develop and supply custom AI chips for future generations of its Tensor Processing Units (TPUs) — custom chips optimized for AI workloads — through 2031.

This isn’t a new relationship. Broadcom and Google have been co-designing TPUs since 2016. But extending the partnership through 2031 is a strong statement of intent.

Google is building its entire next-generation AI infrastructure stack around custom silicon, and it’s telling the world it plans to do so through the end of the decade, at least.

Anthropic Bets on TPU-Based Infrastructure

Buried in that same announcement was a second important revelation: Broadcom has struck a separate deal giving Anthropic — the maker of Claude and one of the hottest AI startups in the world — access to approximately 3.5 gigawatts of TPU-based computing capacity, with delivery starting in 2027.

Just months ago, Anthropic was consuming around 1 gigawatt of compute. The new commitment nearly quadruples that before the year is even out. And the reason Anthropic needs so much more power? Its growth has been parabolic — annualized revenue reportedly crossed $30 billion in 2026, up from roughly $9 billion at the end of 2025.

Anthropic has committed to investing $50 billion in U.S. computing infrastructure. The decision to build that infrastructure on Google’s custom TPUs rather than Nvidia’s GPUs is a significant strategic bet — one that validates the entire custom silicon thesis.

Meta Broadens Its Custom Silicon Strategy

On April 15, Meta and Broadcom jointly announced an extension of their existing partnership through 2029, with Meta committing to using Broadcom’s technology for future generations of Meta’s Training and Inference Accelerator (MTIA) chips — the company’s proprietary custom AI processor.

Meta’s commitment here is not small. The company has already paid Broadcom $2.3 billion for AI chip design and related services in just the past year. And with Meta planning to spend up to $135 billion on AI infrastructure in 2026 alone, custom silicon from Broadcom is a central pillar of that strategy.

Meta’s reasoning for going custom mirrors those of every other hyperscaler: cost control (Nvidia chips are expensive), performance optimization (custom ASICs outperform GPUs on targeted workloads), and supply chain independence. When the world’s largest social media company is building a competitive moat in AI, it doesn’t want that moat to be controlled by someone else’s chip.

OpenAI Begins Moving Beyond Nvidia

Perhaps the most symbolically powerful development is this: OpenAI — the company that essentially launched the modern AI era and has been almost entirely dependent on Nvidia — is now developing its first custom AI chip with Broadcom, targeting deployment in 2027 with over 1 gigawatt of compute capacity.

The company that made Nvidia the most important chipmaker in the world is now building its own silicon to reduce its dependence on Nvidia. 

Google Expands to Marvell for Next-Gen Chips

Then, on April 19, came another flashing signal. The Information reported that Google is in talks with Marvell to develop not one but two new AI chips: a Memory Processing Unit designed to work alongside existing TPUs and a brand-new TPU built specifically for inference.

The talks came just days after the Broadcom deal was announced — meaning Google isn’t content with one world-class custom silicon partner. It’s diversifying its design relationships, building redundancy, and optimizing for the emerging inference economy.

Marvell’s stock jumped on the news and reached a fresh all-time high of $151.44. The market understood the implication immediately: Marvell is on the verge of landing one of the most significant chip design relationships in the industry.

How to Invest In the Custom AI Chip Boom

In our view, the custom silicon revolution is a structural shift with significant room to run. The deals announced in the past two weeks merely represent the first few chapters of a novel-length book.

Here’s our framework for playing it.

The Highest-Conviction Names: Broadcom and Marvell

According to TrendForce, custom AI chip sales are projected to grow 45% in 2026. GPU shipments? 16%. By 2033, the custom ASIC market is expected to hit $118 billion. Broadcom is on track to capture roughly 60% of that market by 2027. Marvell is targeting 20- to 25%. Together, these two companies are positioning to divide the majority of a category growing nearly three times faster than the GPU market they’re displacing.

Broadcom reported $8.4 billion in AI semiconductor revenue for Q1 FY2026, representing 106% year-over-year growth. The company had previously guided for AI revenue to reach $100 billion by 2027. 

Let that sink in for a moment. Mizuho analysts estimate that the Anthropic deal alone could generate $21 billion in revenue for Broadcom in 2026 — and $42 billion in 2027. That’s just one client. Now multiply that logic across Google, Meta, and OpenAI. Suddenly, Broadcom’s $100 billion AI revenue target by 2027 looks increasingly conservative. And the client base underpinning it — spanning search, social media, the world’s hottest AI lab, and the biggest cloud platforms on earth — is not a moat any competitor can easily replicate.

Marvell, meanwhile, expects roughly 30% year-over-year revenue growth in fiscal 2027 — before accounting for any new Google deals. In December 2025, the company acquired Celestial AI for up to $5.5 billion, gaining photonic interconnect technology that could further solidify its position at the intersection of custom silicon and high-bandwidth connectivity. Interestingly, Nvidia also invested $2 billion in Marvell at the end of March — a tacit acknowledgment that even the GPU king wants a seat at the custom silicon table.

The Infrastructure Layer

Arm Holdings (ARM) – Every custom chip in this ecosystem is built on ARM’s instruction set architecture. Whether it’s Google’s TPU, Meta’s MTIA, or Amazon’s Graviton, all pay ARM a royalty. It’s like the toll road of the custom silicon revolution.

Synopsys (SNPS) — If Broadcom and Marvell are the architects, Synopsys makes the drafting software. Every custom chip starts as a design in an EDA tool, and Synopsys dominates that market.

Cadence Design Systems (CDNS) — Synopsys’ closest peer in EDA, with the same structural tailwind. More custom chip designs, more licenses for both.

The Manufacturing Backbone

Taiwan Semiconductor (TSM) — Every custom chip designed by Broadcom or Marvell is manufactured by TSM. There is no custom silicon revolution without TSMC’s advanced fabs. Google’s TPU Ironwood, for example, is built on TSM’s 3nm node.

The Bottom Line: Custom AI Chips Are the Next Phase of the AI Boom

The original thesis was simple: given enough resources and motivation, Big Tech would eventually stop buying compute and start owning it.

That shift is happening now. And the companies positioned at the center of that buildout — the Broadcoms and Marvells and ARMs and TSMs of the world — are looking at a combined addressable market north of $700 billion a year.

The last time the semiconductor industry shifted this decisively, it minted a generation of winners. Nvidia was one of them. The next chapter is being written right now — and the pen is in different hands.

The shift here isn’t just about custom silicon. It’s about what that silicon enables.

As inference becomes the dominant cost in AI, the companies that control the platforms — the ones answering billions of queries a day — gain the most leverage.

That’s where the next wave of value accrues.

And right now, one company sits at the center of that shift.

It isn’t public yet. But as AI moves from training models to deploying them at scale, its position only strengthens.

I’ve been looking for a way to get exposure before that becomes obvious…

And I’ve found one.

The post The Rise of Custom AI Chips Is Breaking Nvidia’s Grip appeared first on InvestorPlace.

Much like Steve Jobs in his prime at Apple Inc. (AAPL), Huang has a rare ability to see where technology is headed and explain it in a way that captures the market’s imagination.

And investors who listened – and positioned themselves early – have made fortunes.

That is why I’m surprised that one of Huang’s most remarkable comments from NVIDIA’s recent GTC conference has not received more attention.

In fairness, the timing helps explain it. The headlines have been dominated by rising tensions in the Middle East, especially between the United States and Iran.

But I do not think investors can afford to miss what Huang said.

Because when he described one new software release as “probably the single most important release of software, you know, probably ever,” we should all pay attention.

He was not talking about a better chatbot.

He was pointing to what I believe is the next major phase of the AI Revolution.

I’m talking about AI agents.

In today’s Market 360, I want to explain what AI agents really are, how they emerged so quickly and why they could become far more disruptive than the first phase of the AI boom. Then, we’ll wrap things up by discussing how you can position yourself to profit.

From Chatbots to Agents

Most people still think of AI as a tool for generating answers. You ask a question. It responds. Maybe it writes an email, summarizes a document, generates an image or helps you clean up a bit of code.

That is the version of AI most people know.

But that is not where this story ends.

The next phase of AI is about agents.

An AI agent is not just a chatbot that gives you an answer. It is a system that can actually carry out tasks. It can follow instructions, handle multi-step assignments, solve problems, write code, test results and keep working toward a goal.

That is a very big leap.

It means AI is moving beyond conversation and into execution.

And this is not some far-off concept that belongs in a science-fiction script. Anthropic publicly rolled out its “computer use” capability in October 2024, allowing Claude to move a cursor, click on-screen locations and type into applications. Then, in July 2025, OpenAI introduced ChatGPT agent, saying it could “think and act” by choosing from a toolbox of skills and using its own computer to complete tasks.

In other words, the groundwork has already been laid.

The chatbot era taught millions of people how to interact with AI through prompts. Then developers began giving these systems tools. Access to browsers, files,  software and workflows. Once that happened, it was only a matter of time before AI moved from answering questions to actually doing the work.

This is where OpenClaw enters the picture.

Why OpenClaw Changes Everything

See, OpenClaw is what Jensen Huang specifically referred to at the GTC conference. It’s been all the rage in Silicon Valley, yet I bet nine out of 10 people on the street have no clue about it.

To put it simply, OpenClaw is an open-source project created by Austrian developer Peter Steinberger. It went viral early this year with a simple but powerful pitch: This was the AI that actually does things.

It can write code, manage a calendar, book flights, handle online tasks and work across other systems on a user’s behalf. Think of it like having a digital employee or personal assistant.

The project was an instant hit. In fact, it was so popular with developers that it racked up 150,000+ GitHub stars in 72 hours.

You can see why Huang would be so struck by that. In fact, NVIDIA recently announced it would launch a product called NemoClaw that would add guardrails to OpenClaw and related open-source AI agents to make them safe for business use.

For years, the AI story has been dominated by models that talk better, search better and generate better.

Useful? Absolutely. Revolutionary? In many ways, yes.

But agents are different. Agents do not just sound intelligent. They can become economically useful.

A chatbot might help you draft a memo. An agent could gather the research, organize the notes, draft the first version, schedule the follow-up meeting, pull the relevant files and keep refining the work until it is done.

To put it simply, a chatbot might help you think, but an agent can help you execute.

And once AI begins doing real work, the implications spread far beyond Silicon Valley.

Think about what that means for a small business owner. An agent could handle customer follow-ups, schedule appointments, update records, prepare invoices and track supplies.

Or how about what it means for a family. It could help pay bills, organize travel, manage schedules, renew subscriptions and stay on top of all the little digital chores that eat up time.

Think about what it means inside a large company. Agents could support legal teams, finance departments, software engineers, sales staffs, call centers and back-office operations. They could work around the clock. They could eventually work in teams. And they could allow one skilled employee to become dramatically more productive.

In short, once AI agents are mainstreamed, we’re essentially talking about a full-blown productivity boom on a scale we’ve never seen.

How to Profit From the AI Reset

The first wave of AI changed how we access information.

This next wave could change how work gets done.

Barely a week seems to go by now without some fresh reminder that the market is beginning to understand that.

In February, fears around Anthropic’s Claude Cowork and related AI automation tools helped trigger a broad software selloff.

Bloomberg reported a $285 billion rout across software, financial services and asset-management stocks after Anthropic released new automation capabilities, while Fast Company said updates to Claude Cowork threatened to replace software tools already embedded in professional workflows.

Now, some of the stocks that were affected by that initial panic have recovered. Some haven’t.

But the point is, this is just a preview of things to come.

That matters because the first phase of the AI boom rewarded almost any company with a halfway believable AI story.

And for a while, it worked.

But I do not think that will be good enough in this next phase. Because once AI starts doing real work, investors will begin to ask tougher questions, like:

• Who really benefits as AI agents become more capable?
• Who has the computing power, the infrastructure and the business model to thrive as AI moves beyond conversation and into execution?
• And just as important, which companies are still living off yesterday’s AI story?

That is where this story gets interesting.

Some companies will adapt beautifully and become even more valuable.

On that side of the equation, I’m specifically looking at the chips, platforms and infrastructure that make the whole agent economy possible.

But others will get exposed – like some of the legacy software names, for example.

That is why I have been paying such close attention to this story – and it’s one of the reasons I recently released a new presentation about what I’m calling the AI Reset.

Because if Jensen Huang is right, and AI agents really are the next major phase of the AI Revolution, then the biggest winners of the next cycle may not be the names most investors expect.

And some stocks that look perfectly safe today may not be nearly as safe as Wall Street assumes.

If you want to see where I believe this is headed, and how I recommend positioning before the crowd fully catches on, I encourage you to watch my latest presentation now.

Sincerely,

Louis Navellier

Editor, Market 360

The Editor hereby discloses that as of the date of this email, the Editor, directly or indirectly, owns the following securities that are the subject of the commentary, analysis, opinions, advice, or recommendations in, or which are otherwise mentioned in, the essay set forth below:

NVIDIA Corporation (NVDA)

The post The AI Story Is Changing – And So Will the Market Leaders appeared first on InvestorPlace.