As I write on Tuesday morning, the AI trade is deep in the red, with some semiconductor stocks down double digits.

While it’s tempting to point to a single headline, a variety of dynamics are colliding at once.

South Korea’s tech-heavy ⁠KOSPI Composite Index crashed about 11% last night on bubble fears… this is normal mean-reversion after the near-vertical runup that the AI trade enjoyed in the spring… weeks of pressure are culminating in today’s panic selling, and given that AI stocks are the market’s biggest winners over the past year, they’re number-one on the “sell” list… investors continue to question whether the hyperscalers will eventually cut back on their AI spend, starving the AI infrastructure players of projected earnings… and more acutely today, concerns about China have intensified.

On that last note, reports that a Chinese state-backed company has begun mass-producing advanced chipmaking equipment have reignited fears that China’s semiconductor industry is catching up faster than expected.

Legendary investor Louis Navellier has a response – it’s not true, don’t worry.

Let’s go to his Flash Alert in Growth Investor this morning:

There is a false narrative out there that Asia is taking over and that the U.S. tech sector will be overpowered by Chinese AI firms and Korean memory companies.

I need you to ignore it. The U.S. remains the leader in artificial intelligence, and the AI data center buildout is real.

In discussing the wider pressure on the AI trade and the recent pessimism surrounding it, Louis highlights a video he’ll be posting on Navellier Market Buzz that will come out tomorrow. In short, he says it’s very bullish for the AI trade.

Here’s Louis’ takeaway:

There are unscrupulous people trying to shake you out of your positions before the good news makes your stocks go higher…

Once you watch that video, you should be very excited about the exponential growth that NVIDIA (NVDA) and a lot of our AI data center-related stocks are going to have through at least 2029…

 I need you not to worry about the gyrations in the market.

But while Louis is pointing toward sunnier days ahead, we still have a full plate of potential market-rattling events directly ahead…

Tomorrow brings the latest FOMC meeting from the Federal Reserve

The prevailing expectation is for a fifth straight meeting without an interest-rate change.

As I write on Tuesday, the CME Group’s FedWatch Tool puts the odds at 70.6% that rates remain in the 3.50% – 3.75% target range, with a 29.4% chance of a “surprise” rate hike.

Now, there’s no new Summary of Economic Projections tomorrow, which means no updated dot plot. The FOMC statement itself, and whatever Warsh says in his press conference – probably not much – are the only new information investors get from the Fed.

This means that tomorrow will likely bring a significant dropoff in content for Wall Street to chew on – and that’s by design.

As we’ve covered here in the Digest, Warsh has spent months tearing down the machinery former FOMC Chairs Ben Bernanke and Jerome Powell built up – the dot plot, the running commentary, the “here’s roughly where we’re headed” guidance that Wall Street organized entire trading strategies around.

At his confirmation hearing, Warsh put it as plainly as a Fed chair can:

I don’t believe in forward guidance. I don’t believe that I should be previewing for you what a future decision might be.

This isn’t a Fed Chair trying to signal to the market – it’s someone actively trying to get out of the way. So, the honest expectation for tomorrow isn’t a dramatic press conference – it’s closer to the opposite…

Fewer soundbites. More “the data will tell us.” And deliberate non-answers to reporters trying to bait him into previewing September and beyond.

But taking away the signal doesn’t take away Wall Street’s appetite for it

It just sends that appetite hunting somewhere else.

Fifteen years of forward guidance trained an entire industry – dot plot analysts, Fedspeak translators, hedge fund desks built around parsing a Fed chair’s exact phrasing – to expect a roadmap.

Deny them the map, and they don’t stop looking for direction. So, watch if they start overinterpreting: A pause before an answer… which question gets a clipped response and which gets a long, careful one… how his body language changes on different topics.

It will be interesting to watch how hard the market strains to find meaning in a man actively trying not to give it any.

Still, there’s a limit to how much you can actually read into a pause. So, there are likely two upcoming sources of “signal” that the market will begin to overweight: the FOMC minutes and the other 11 committee members.

As for those FOMC minutes, they provide a detailed, on-the-record account of the committee’s internal debate. Under Powell, the minutes were often an afterthought, since the press conference had usually already told you most of what you needed.

Under Warsh, that flips: with his statements running about half their old length and no color offered in the room, the minutes become the only detailed record of what the committee argued about, line by line.

This meeting’s minutes land on Wednesday, August 19 – we’ll circle back then.

Wall Street will also pay more attention to the other 11 voices on the committee. After all, Warsh going quiet doesn’t mean the Fed goes quiet.

Governors and regional presidents – Christopher Waller, Lisa Cook, Philip Jefferson, and others – have already been filling some of that vacuum with their own public remarks. Expect more of that from here, precisely because the chair isn’t doing it for them anymore.

But this doesn’t mean we don’t have a big data point to trade this week

If Warsh isn’t going to hand the market anything meaningful tomorrow, the next real data point arrives a day later: Thursday’s June PCE report.

Early estimates from Truflation see headline PCE cooling to around 3.7% year-over-year, down from May’s 4.1%, while core PCE is expected to hold roughly flat near 3.3%.

Sounds great, right?

Yes – except there’s a limit to which Warsh will care.

Last month, we told you the new Fed chair doesn’t lean primarily on the standard PCE reading that dominates the headlines. Instead, he watches the trimmed mean PCE, published monthly by the Dallas Fed.

It strips out the most extreme price movements on both ends of the distribution – the one-off spikes and drops that come from a single volatile category – and averages what’s left.

In May, core PCE ran at 3.4% while the trimmed mean sat at 2.42%, nearly a full percentage point cooler and just above the Fed’s 2% target.

That gap hasn’t been a one-month fluke. The trimmed mean’s 12-month reading has held in a tight 2.3%-to-2.4% band for six straight months now, even as headline PCE swung from the high-2s into the low 4s over that same stretch.

So, while the press will be watching Thursday’s PCE data and will be eager to hypothesize on our rate path if it comes in cool, the real issue will be if that trimmed mean’s steadiness continues.

If there’s a significant narrowing or widening, that’s what will move Warsh.

In the meantime, Wall Street is increasingly eyeing September

The Fed’s September meeting brings a fresh Summary of Economic Projections – a new dot plot – that will be genuinely contested.

Nine of 18 FOMC participants penciled in at least one 2026 hike back in June. Warsh himself submitted no projection at all – the same silence he’s likely to maintain tomorrow.

How will those same participants vote next month?

As I write on Tuesday, the CME Group puts a 57% probability on a “hold” in September. But about 23% odds of a quarter-point cut, while about 20% odds of a quarter-point raise.

We haven’t seen this degree of interest-rate uncertainty in years – which means every data release between now and mid-September carries more weight than it used to, precisely because there’s no guidance filling the gaps between them.

But it all starts with tomorrow’s FOMC meeting.

Don’t overlook the other potential market-movers this week

The Fed and the PCE data aren’t the only reasons that tomorrow and Thursday matter.

Both days also carry some of the most consequential corporate earnings of the year – and they land right on top of a debate we’ve been tracking closely in this Digest…

Whether Big Tech’s AI spending is still worth the price of admission.

Tomorrow afternoon, after the market closes, Microsoft (MSFT) and Meta Platforms (META) report earnings. Thursday after the close, it’s Amazon (AMZN) – with Apple (AAPL) also reporting that day, though it doesn’t carry the same hyperscaler capex story as the other three.

Alphabet (GOOGL) reported last week, and its results set the tone for what’s coming. As we covered here in the Digest, Alphabet beat handily, but the stock still fell about 7% the next day on fears of overspending on AI.

Is that what we’ll see this week, too?

The market’s patience for that kind of spending isn’t unconditional anymore. So, if any of these three raises its capex guidance again, don’t be surprised by the same “beat the numbers, sell the stock” reaction that hit Alphabet last week.

Keep your eyes on how the hyperscaler capex forecasts affect the AI infrastructure trade

Our technology expert Luke Lango, editor of Innovation Investor, has argued that holding – or raising – capex guidance should support AI infrastructure stocks, not sink them, since it’s the clearest demand-side validation the market gets.

His read on Alphabet’s post-earnings slide was that it came down to a macro-driven selloff – an oil spike and a jump in Treasury yields that hit the same day. So, strip out the macro noise, and a capex raise should be a bullish signal.

Bottom line: While Warsh will spend this week trying to say as little as possible, three of the four largest AI spenders in the world will be doing the opposite – telling investors exactly how much they intend to spend next and finding out in real time whether Wall Street approves or not.

One more thing: two days until Luke’s AI MegaDeal Event

In 2023, venture firm Spark Capital wrote a $75 million check into an obscure AI startup with almost no revenue and no ticker symbol. That startup was Anthropic.

Today, Spark’s stake is worth an estimated $7 billion. Nearly 100-to-1, on a company that didn’t exist on any exchange when they bought in.

Luke has been studying that pattern closely – and he thinks it’s about to repeat, just with different names attached. To be clear, being early isn’t a guarantee of monster returns. But it’s the only way to be a part of such rocket ships before the price reflects what everyone already knows.

That’s the idea behind Luke’s free event this Thursday, July 30, at 1 p.m. Eastern – right as this week’s hyperscaler-AI-spending debate hits its peak.

Luke will walk through his full framework for identifying these opportunities, plus reveal one specific company he believes fits the pattern today.

To join Luke for free, just click here to register to reserve your seat.

For now, all eyes are on tomorrow’s FOMC meeting and Warsh’s commentary. We’ll report back.

Have a good evening,

Jeff Remsburg

(Disclosure: I own MSFT, AMZN, GOOGL, AAPL)

The post Everyone Will Overread the Fed Tomorrow appeared first on InvestorPlace.

After the benchmark KOSPI index plunged more than 8%, a marketwide circuit breaker automatically halted trading for 20 minutes to prevent the selloff from spiraling.

But when trading resumed, so did the selling. By the closing bell, the KOSPI had plummeted 10.8%.

And two stocks were at the center of the storm.

Samsung Electronics sank 13.3%, while SK Hynix tumbled 14.7%.

Now, to unfamiliar American investors who might be thinking… “What gives? Stocks on the NYSE take a hit like that all the time.”

Well, the fact is that, together, these two memory-chip giants account for more than half of the KOSPI’s total market value.

So, when Samsung and SK Hynix stumble, the entire South Korean market feels it.

And here lately, much of the world feels it, too. In fact, this wasn’t the first time something like this had happened.

That’s because the influence of Samsung and SK Hynix stretches far beyond Korea. Both companies sit at the heart of the global memory-chip industry. And their swings in Seoul can ripple through American semiconductor, memory and data-center stocks.

Here in the U.S., Micron Technology Corporation (MU) fell more than 8%, while SanDisk Corporation (SNDK) tumbled more than 14%. Semiconductor-equipment companies were also caught in the mess, too.

Even NVIDIA Corporation (NVDA), the undisputed leader of the AI boom, initially moved lower before recovering from its early losses. That followed a roughly 5% drop on Monday.

So, what happened in South Korea offers an important reminder: When a handful of technology companies become big enough, their performance can move an entire market.

And when violent moves like this happen, we have to remind ourselves what’s true. Then, we have to have the courage to act. 

That brings us to earnings season – specifically what’s on deck this week.

Four of America’s biggest technology companies will report earnings over the next 48 hours. Meta Platforms, Inc. (META) and Microsoft Corporation (MSFT) report tomorrow, followed by Amazon.com, Inc. (AMZN) and Apple Inc. (AAPL) on Thursday.

Together, these four companies account for nearly 18% of the S&P 500. So, their quarterly results won’t just move their own shares. They could help set the tone for the broader market.

That raises an important question: Should you buy these four Big Tech stocks before they report? Or should we be looking elsewhere…

In today’s Market 360, I’ll set the table for the earnings season so far, and then we’ll put this week’s four Big Tech reporters to the test and see how they stack up in my Stock Grader system. Then, I’ll explain why some of the biggest opportunities created by the AI boom may be found beyond the famous technology names dominating the headlines.

Earnings Are Blowing Past Expectations

Before we turn to this week’s four Big Tech reports, let’s look at the earnings environment.

I’ve been saying that the S&P 500 would deliver earnings growth north of 30% in the second quarter – and, so far, I’ve been proven right!

Our friends at FactSet now expect the S&P 500 to achieve average earnings growth of 37.9% in the second quarter. That’s up from 24.7% just last week and only 18.8% on March 31.

And here’s some important context: If that estimate holds, it will mark the S&P 500’s strongest earnings growth since the third quarter of 2021, when earnings grew 40.3%.

But that was the COVID recovery, when companies were bouncing back from artificially depressed results. This time, the broader earnings strength isn’t simply the result of easy pandemic comparisons.

Alphabet Inc. (GOOG), which reported its second-quarter results last week, is a big reason estimates jumped so sharply.

The Google parent reported 297.6% year-over-year earnings growth, with earnings of $9.11 per share crushing estimates of $2.91 per share by 213%. That made it the biggest contributor to the S&P 500’s second-quarter earnings growth.

Yet Alphabet shares dropped about 7% after the announcement.

Investors looked beyond the headline beat and focused on the company’s rapidly rising AI spending. Alphabet raised its 2026 capital-expenditure forecast to between $195 billion and $205 billion and warned that spending could increase again next year.

It’s an important reminder that a company can deliver a blockbuster report and still see its stock punished.

Here’s why that matters.

Up until recently, Big Tech was expected to spend about $700 billion on AI this year alone.

And with four more technology giants on deck over the next two days, that figure could tick higher.

Investors seem to be growing more and more nervous about the bill, so it’s worth keeping in mind this week.

So, let’s see what Stock Grader (subscription required) has to say about these stocks…

What Stock Grader Says About Big Tech

Buying a stock before earnings means taking a position before you know what the company will report – or how Wall Street will react.

So, rather than guess, let’s see what Stock Grader says about these four Big Tech stocks right now.

Stock Grader evaluates both a company’s underlying fundamentals and the quantitative buying pressure supporting its shares. And when I ran this week’s four Big Tech reporters through the system, this is what I saw…

Apple is the only one of the four to earn a B-rating, which makes it “Strong”. Amazon, Meta and Microsoft all earn D-ratings, making them “Weak”.

Now, these grades do not predict exactly what will happen after earnings. Alphabet is a perfect example. It held an A-rating, or “Very Strong”, in Stock Grader last week, yet its shares still fell after its report.

A Strong-rated stock can stumble if Wall Street expects even more, just as a Weak-rated stock can jump on a positive surprise.

But if there’s one thing I’ve learned in four decades in this business, it’s that good stocks bounce like fresh tennis balls – and bad ones ultimately drop like rocks.

And among this week’s four Big Tech reporters, Apple currently has the strongest overall grade.

This is also exactly the kind of comparison that the new Stock Grader AI will make easier.

Starting Thursday, my Growth Investor Preferred Members will be able to ask Stock Grader questions in plain English and search for stocks by sector, size, risk profile and grade trend. They’ll also be able to review multiple holdings at once with the new Portfolio Checker and receive weekly insights highlighting the most important upgrades, downgrades and sector shifts.

And all of it will still be powered by the same proprietary data and grading system you already know and trust.

Look Beyond the Biggest Names

So, should you buy Big Tech before earnings?

There is no blanket answer. Stock Grader shows that Apple enters this week’s reports with the strongest overall grade. But as we saw with Alphabet, even a Very Strong-rated stock can fall after delivering spectacular results.

Earnings announcements create uncertainty, and no system can guarantee how Wall Street will react overnight.

But there is a larger lesson here.

The companies dominating the headlines are also spending staggering sums to build out artificial intelligence. And some of the biggest beneficiaries may not be the familiar technology giants themselves.

They may be the companies supplying the infrastructure, power and specialized technology this expansion requires.

After all, when companies like Alphabet, Amazon, Meta and Microsoft spend hundreds of billions of dollars on AI, that money has to go somewhere.

It flows to the chipmakers, data-center suppliers, networking specialists, energy companies and other businesses making the AI buildout possible.

These are the kinds of opportunities investors can miss when they focus only on the famous companies making the headlines.

And they’re the focus of a presentation I recently put together.

In it, I explain why we haven’t reached “peak AI,” why the next stage of the buildout could create an entirely new group of winners and why some of the most interesting opportunities may be found outside the familiar Big Tech names.

I’ll also introduce you to three companies I believe are positioned to benefit as this massive technological shift accelerates.

Click here to see the AI opportunity I believe investors should be preparing for 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:

Alphabet Inc. (GOOG), Micron Technology Corporation (MU), NVIDIA Corporation (NVDA) and SanDisk Corporation (SNDK)

The post Here’s What My System Says About 4 Big Tech Stocks Before Earnings… appeared first on InvestorPlace.

It was Georges Doriot’s desk, a Harvard Business School professor with an eye for immaculate suits and unproven companies. 

Decades before anyone coined the phrase “venture capital,” Doriot was already putting it in motion, one rejection at a time.

His students remembered one rule above all the others, because Doriot repeated it constantly: consider backing a grade-A founder with a grade-B idea. Never back a grade-B founder with a grade-A idea.

That rule earned its keep in 1957, when a 31-year-old MIT engineer named Ken Olsen came looking for capital to build computers. The idea spooked the money men of the era, as a string of expensive corporate failures made “computer” practically a curse word. So Doriot’s team advised Olsen to drop it from the pitch and lead with the products instead. 

The company became Digital Equipment Corporation. Doriot’s firm, American Research and Development, paid $70,000 for a 70% stake. By the time that stake was finally distributed to shareholders, it was worth more than $350 million. That’s on the order of a five-thousand-fold return, and the trade proved venture capital could work.

Doriot saw thousands of proposals. Of those, he funded a sliver. But his fortune came from that filtered approach.

Over the past week, I’ve shown you how this era’s biggest fortunes are forming – Spark Capital’s early stake in Anthropic, the Roth IRA built on pre-IPO shares of Roblox Corp. (RBLX), the door Genentech’s IPO opened. And I told you the velvet rope that kept ordinary investors out of these deals is finally moving.

I also told you access is only the starting point.

Today’s issue is not another story about a fortune someone else already made, but with the actual mechanism I use before a single dollar of my own moves into a private deal.

Three questions.

The same three, asked in the same order, every time.

I’m going to walk you through each one in enough depth that you could run it yourself on the next deal that crosses your desk, long after this issue is closed – who’s actually running the company, whether the thing they’ve built solves a problem people can’t walk away from, and whether the clock on the specific opportunity and the clock on the broader market both happen to be pointing the same direction at once.

The Number Behind Every Great Venture Story

I need to give you an uncomfortable number before we go any further.

Roughly nine out of ten early-stage bets fail to return their investors’ money. Across a typical portfolio, something like one in ten produces the outsized winner that makes the whole portfolio work. And more than half of the positions you hold will likely hand back less than you put in. That is the ordinary, expected math of investing in early-stage companies, even for professionals who do this for a living. 

Walk through a newly opened door and put money into the first company you see, and you’ve simply found a faster route into that “nine out of ten.” The door gets you into the room. The framework is what keeps you solvent once you’re inside.

So today I want to hand you the actual framework. This is the same three-part framework I use on every company before I ever recommend one – to members, or to myself. 

I call it PPT: People, Product, Timing.

People: The Filter That Matters Most

If I had to weight the three, I’d honestly call it closer to People, People, People. Or, Doriot’s grade-A rule, rebuilt for 2026. 

You can find the perfect product launched at the perfect moment, and it will still fail if the people running it can’t execute under pressure, adapt when their first plan doesn’t work, or hold a team together through the inevitable stretch where nothing is going right.

Arthur Rock followed this framework when he backed the “traitorous eight” young engineers who fled William Shockley’s lab – a story I shared with you a few days ago. Rock had no product to evaluate and no revenue to model. He bet on the people, and the bet became Fairchild Semiconductor, the company that seeded almost everything we now call Silicon Valley.

The tricky part is that the “right” founder rarely looks like what you’d expect on paper. Take Tobi Lütke, who co-founded Shopify Inc. (SHOP). He left traditional schooling in Germany after the tenth grade to enter a hands-on computer programming apprenticeship, having already taught himself to code by rewriting his own video games around age eleven. On a resume, “left school at sixteen” can look like a red flag. In reality, he’d been doing the actual work – writing software daily – for longer than most of his eventual competitors had been alive.

Lütke and his co-founders set out in 2004 to open an online snowboard shop. The existing e-commerce tools frustrated him so badly that he built his own platform to run the store. That side project became Shopify. 

And the lesson generalizes well past dropouts: the signal you actually want – deep, obsessive competence in the specific problem the company is solving – often never shows up on a conventional resume. You have to look past the credential and evaluate the person. Doriot would have called Lütke a grade-A founder on sight.

In practice, that means checking the boring stuff first: what these people actually built before, where their claims can be independently verified, and whether people who’ve worked with them – other investors, other founders, former colleagues – describe them the way the founders describe themselves. Most candidates fail that process.

Product: Does It Solve a Problem People Can’t Live Without?

Once the people check out, the next question is simpler than most investors make it: does this product meet demand so urgent that customers will tolerate an imperfect early version just to get access to it?

Google is the textbook case. By the late 1990s, the web had exploded into more information than anyone could organize, and search became the only sane way to navigate what had just been built. That urgency is why Andy Bechtolsheim wrote his famous $100,000 check on the spot – made out to a company that hadn’t even been incorporated yet. Early investors who saw the demand clearly were rewarded on a scale that still gets cited in venture circles decades later.

I bring this up now because I think we’re in the early innings of an equally large unmet-demand story: physical AI – robots that can do real work in the real world.

But the true chokepoint of physical AI sits in the training data. A language model can learn from the text of the entire internet, because that text already exists. A robot has no equivalent shortcut – every single skill has to be taught through physical demonstration, recorded frame by frame, often with a human operator physically guiding the machine through the task in real time. Industry data on this has moved fast: the cost of collecting a single hour of usable, labeled training data has fallen sharply over the past two years thanks to better tooling, but it remains a meaningfully expensive, physically constrained process – and a robot that needs to handle the real world’s endless variation reliably needs enormous volumes of that data, far beyond a few hundred hours of demonstrations.

Whichever companies figure out how to produce that training data cheaply, at scale, are sitting on what I think becomes the defining chokepoint of the physical AI era – the equivalent of what chip design and cloud infrastructure became for the last decade of AI. That’s the “insatiable demand” test, applied to where I think the next major AI buildout is actually headed.

Timing: Two Clocks, Not One

The last piece of the framework covers two separate things people often collapse into one.

The first clock is your entry into a specific company. It’s early enough that you’re buying in before the bulk of the value has been captured by earlier rounds. But it’s late enough that the company has proven its basic idea works. That balance is exactly why the People and Product questions come first; allowing you to judge whether a company at an early stage is actually derisked enough to be worth the timing bet. Remember Genentech: the investors who rushed through Wall Street’s door in 1980 were buying pure possibility. The ones who understood what Swanson and Boyer had already proven in the lab were making a judgment call.

The second clock is the broader environment you’re investing into. And on that front, I’d argue current conditions are about as favorable as they’ve been in a long time. I walked you through the arithmetic a few days ago: the largest technology companies are pouring roughly $700 billion into capital projects this year. About $2 billion every single day… And they’re competing with each other for capability itself. 

Companies with that much money and that little time tend to buy what they cannot build fast enough. That competitive pressure compresses the timeline between “promising private startup” and “life-changing exit for early investors,” whether the exit comes through an IPO or an acquisition by a giant racing to secure capability it doesn’t have time to develop from scratch. Increasingly, the buyout is becoming the finish line early investors aim for.

What This Framework Actually Buys You

I want to end where Doriot would have ended: nine out of ten of these bets still won’t work out, and no framework – mine or anyone else’s – changes that basic arithmetic. Even Bessemer Venture Partners, one of the oldest firms in the business, keeps a public “Anti-Portfolio” of the winners it passed on – Google, Apple, Airbnb, FedEx. Anyone who promises you a system that turns venture investing into a sure thing is lying to you.

What People, Product, Timing actually does is shift your odds. It won’t turn a bad bet into a good one. But applied consistently, it filters out the startups that were never going to make it past the “nine” – the founders who don’t hold up under scrutiny, the products solving problems nobody urgently has, the companies you’re either too late or too early to matter for. What’s left is a much smaller list, and it’s the list worth staking capital against.

Doriot read thousands of plans to find one Digital Equipment. However, you won’t need to read thousands. On July 30 at 1 p.m. Eastern, I’m going to walk through this exact framework live, in real time, applied to a specific AI company I’m recommending for free. You’ll see exactly how the People, Product, and Timing questions play out against a real deal, with a real opportunity to act on it the same day… plus two additional recommendations reserved exclusively for members who join that day.

BUTTON: Reserve your spot for The 2026 AI Megadeal Event

The post The 3-Question Test I Use Before Putting a Dollar Into Any Company appeared first on InvestorPlace.

Last week, in the run-up to Alphabet’s (GOOG) earnings, we highlighted analysis from technology expert Luke Lango, editor of Innovation Investor, who argued that if Alphabet held – or raised – Its AI capex guidance, it would help stabilize and support a rebound in the AI trade.

Sure enough, when Alphabet reported last Wednesday, it didn’t just hold its guidance. It raised it.

Cue the meteoric AI rebound rally, right?

Not exactly.

While part of the AI trade pushed higher last Thursday, most of that gain had slipped right back out the door by Friday. As I write on Monday, the AI trade continues to be under pressure.

So, what happened?

Luke: We got the earnings right – we got the timing wrong

That’s the quick summary Luke provided in last week’s Innovation Investor Daily Notes. But let’s go deeper.

After Luke walked through Alphabet’s blowout numbers, calling them “the strongest demand-side validation the AI infrastructure trade has produced in three years,” he pivoted to the ensuing AI selloff.

Here’s his take on why the rebound didn’t materialize:

While the earnings were landing, the Houthis opened a new front in the Iran War, attacking ships in the Red Sea on the other side of Saudi Arabia, sending oil above $90, the 10-year Treasury above 4.7%, and the 30-year toward 5.2%.

Frustrating? Yes. A reason to abandon the thesis? No.

In other words: the culprit was a macro-driven selloff, not a fundamentals-driven one. But Luke has seen this before – as well as what’s on the other side:

Every prior macro-driven correction in this AI bull market has resolved the same way: the geopolitics fades, the fundamentals retake center stage, and the prices correct toward the earnings rather than the earnings toward the prices.

Luke’s bottom line is patience. From a technical perspective, the AI trade sits in “no-man’s land” – meaning we’re mid-range between support and resistance. This leaves us in “wait and see” mode.

Luke’s take is that we’re looking at either a final flush toward the 100-day moving average or a consolidation-and-rebound setup. It’s frustrating – but it’s nothing that derails the AI trade.

Now, that’s Luke’s read on the next few weeks. But zoom out to the next few years, and there’s a different question entirely that has him fired up right now – one that has nothing to do with waiting out a selloff.

What has Luke especially excited today

Most investors assume the way to profit from AI is by owning the household names – Nvidia (NVDA), OpenAI, Anthropic, and the rest.

While Luke agrees, he says that it’s only half the picture…

As Big Tech’s cash pile has grown to hundreds of billions of dollars in recent years, the giants have increasingly chosen to buy breakthrough technology rather than build it themselves.

Look at Google’s DeepMind acquisition, Meta’s (META) $14.3 billion stake in Scale AI, or Microsoft’s (MSFT) $23 billion investment in OpenAI, before most investors had ever heard the name.

That pattern – enormous value created before the public ever gets a chance to buy a share – is what Luke’s calling the next phase of the AI boom.

This phase answers a new question: not “which companies will build AI,” but “which companies will AI’s biggest players eventually decide they can’t afford to compete with?”

For a deeper dive, put this Thursday, July 30, at 1 p.m. Eastern on your calendar. That’s when Luke is holding his 2026 AI MegaDeal Event.

During his presentation, Luke will walk through the framework he’s built for spotting these companies before Wall Street catches on – he calls it his “People, Product, Timing” filter. He’ll reveal one specific opportunity that fits this pattern today.

Plus, if you sign up and opt into Luke’s VIP text list, he’ll send over his bonus report, The AI Collectors’ Portfolio: 7 Stocks to Buy for the Biggest Tech Spending Boom of All-Time – totally free.

We’ll bring you more on this later this week, but to reserve your seat right now, just click here.

What’s powering the AI buildout (and how to profit from it)

Let’s now pivot to a question that’s easy to overlook amid all the AI headlines…

With Big Tech pouring more than a trillion dollars into data centers, chips, and infrastructure, where’s all the electricity to power this AI tsunami going to come from?

Senior Analyst, Brian Hunt, tackled exactly this question last week in his free e-letter Money & Megatrends.

After highlighting the unprecedented scale and velocity of the AI infrastructure capex boom, Brian pivots to the practical reality:

All that AI infrastructure is poised to consume huge amounts of electricity…

Unfortunately, we do not currently have adequate electric power generation and distribution capacity to meet the ravenous needs of AI and other power consumers.

In last Friday’s issue of Investing Insider, I touched upon the same challenge, adding some numbers in addition to Brian’s data.

Did you know that by the early 2030s, a single AI data center campus could consume more electricity each year than one million American homes?

To really grasp this, consider that there are only about 149 million homes in the entire United States.

Now, with this context, how many of these high-capacity facilities are planned or under construction?

267. (There are only 68 up and running today.)

So, if built, their combined electric demand would vastly exceed the electrical footprint of every single household in America.

That’s nearly 200 facilities still to come on top of the 68 already straining the grid today. So, Brian isn’t describing some vague future problem. He’s highlighting a very real math problem that gets worse every year it goes unsolved.

But it is getting resolved – which is where our opportunity lies…

How do we invest today?

One of Brian’s answers is IPPs – Independent Power Producers.

Unlike regulated utilities, which face strict price controls and limited capital flexibility, IPPs can build aggressively and charge market rates for their output. Higher risk, but higher upside.

Brian has flagged Vistra Corp. (VST) as a dominant IPP. But in last week’s issue, he named three smaller ones worth watching…

NRG Energy Inc. (NRG), held by hedge fund manager David Tepper; Talen Energy Corp. (TLN), held by top money manager Scott Ferguson; and Canada’s TransAlta Corp. (TAC).

Back to Brian:

IPP stocks enjoyed a big rally from mid-2023 to mid-2025. Since then, they have digested their gains and traded lower, with VST recently reaching a one-year low.

Given the electric power industry’s bullish long-term fundamentals, these stocks are likely to resume their long-term uptrends.

I agree with Brian. But don’t take it from either one of us. Connecting Brian’s point about “the electric power industry’s bullish long-term fundamentals” and last Friday’s Investing Insider issue, check out this chart I included from BloombergNEF.

It highlights U.S. data center electricity demand through 2035, forecasting it to reach 194 gigawatts – roughly four times today’s level – as hyperscalers race to build the infrastructure needed to power AI.

Bottom line: This is a huge multi-year growth story – invest accordingly.

For more of Brian’s analysis, he publishes Money & Megatrends every day the market is open, flagging opportunities like this before they become front-page news. His issues are loaded with trend analysis, actionable advice, and loads of specific tickers.

Best part: it’s 100% free. You can sign up right here.

Reader mailbag

Last week, I introduced an analytical framework I called “The Messy Middle” – the idea that AI won’t force us to choose between good and bad outcomes, but between two legitimate “goods” vying for priority. We won’t be able to have both in equal measure at the same time.

The conflict we flagged was “progress versus safety” when it comes to AI. Specifically, we looked at cheap, open-source AI models from China – tools that democratize access to frontier AI but also strip away every safety guardrail a closed, paid model comes with.

I asked what you thought, and we received some thoughtful responses. Here are two.

Paul M.:

This idea, that governments have some ability to control the progress of AI, fails completely unless all governments agree about how to do it.

And, what kind of draconian measures would be required to get organized crime to comply?

It seems that any such agreements would entail immense incentives to cheat.

Would you bet on the ability of the non-cheaters to win?

That’s a hard question to wrestle with, Paul – but such wrestling is needed.

The nuclear analogy might be instructive here. Non-proliferation treaties have had seven decades of the world’s most powerful governments behind them, and they’ve still only ever slowed proliferation, never stopped it.

Controlling AI is even harder. Obviously, no physical materials like uranium or centrifuges are required – just a laptop and a copy of the AI’s digital files leaked from a server.

So, maybe the real question isn’t whether we can control AI, but how we live in a world where we never fully can.

Wayne S. took the conversation somewhere different – less about government, more about a gap in the market itself:

There needs to be a strong, defensible, objective counterparty to the AI juggernaut. A company that will analyze the various releases on multiple dimensions and allow a more objective basis for discerning the differences between them. There is absolutely too much heat on this because of this lack.

Wayne, I think you’ve spotted a real gap. Something closer to an S&P rating for AI capability and safety doesn’t yet exist.

But let’s take it a step further…

After someone creates it, what then?

We could objectively measure what a model does – how capable it is, how easily it’s jailbroken, where it fails, and so on. But wouldn’t that data then need to be contextualized and translated into a ranking hierarchy to make it actionable?

If so, how do we objectively measure whether a certain level of risk is “acceptable” relative to the progress it offers?

After all, any attempt at that measurement points us back toward the core problem of the Messy Middle – conflicting values. Competing “goods” must be prioritized.

In this case, by whose standard, and whose authority, does anyone get to decide how much safety we’re willing to trade for some degree of progress?

I wonder – is the real issue whether someone builds such an analytical rating tool? Or whether our society can agree on how to interpret and act upon it once it exists?

If you want to chime in, email me at ipdigestfeedback@investorplace.com.

Have a good evening,

Jeff Remsburg

(Disclosure: I own GOOGL)

The post Google Beat but AI Stocks Are Still Sinking. What’s Going On? appeared first on InvestorPlace.

Tesla Inc. (TSLA) fell nearly 15% last Thursday after it reported earnings after the close on Wednesday. Space Exploration Technologies Corp. (SPCX) has fallen 47% since its IPO last month and sank to an all-time low this morning, despite reporting a successful Starship test on Friday.

The broader market didn’t escape the chaos either.

Surging oil prices tied to the conflict in the Middle East helped send the market lower.

But my portfolio had a very different week.

Despite all the volatility, it finished in positive territory. How is that possible?

In this week’s Navellier Market Buzz, I’ll explain how my fundamentally superior stocks held up in the volatility. I’ll also discuss how earnings season is becoming the biggest catalyst this year. Plus, I’ll share three stocks that I think will outperform the market now.

(And stay tuned for a special announcement about Stock Grader at the end!)

Click the image below to watch the latest episode of Navellier Market Buzz.

To see more of my videos, click here to subscribe to my YouTube channel.

A Better Way to Find Winning Stocks

As I mentioned in the video, I don’t try to hug or mirror the benchmarks. My goal is to find companies with the strongest fundamentals and the greatest potential to outperform them.

One of the best times to find those companies is during earnings season.

That’s when the market begins separating the real winners from everyone else. But by the time most investors recognize them, institutional investors may have already spent weeks – or even months – building their positions, driving the stock higher.

Fortunately, there is a way to identify those companies much earlier. That’s where my Precursor Intelligence (P.I.) system comes in.

It’s designed to help identify where institutional money may be flowing before those moves become obvious to everyone else.

The earlier you can spot those buying trends, the better your chance of getting in before the crowd.

In my latest presentation, I’ll take you inside my P.I. system and explain how I use it to identify companies in my Accelerated Profits service before they become widely recognized. You’ll also learn how we use it to find gains like 604% with Vista Oil & Gas (VIST), 555% on Sezzle Inc. (SEZL), 98% on UFP Technologies (UFPT) – and more.

Click here to watch now.

Sincerely,

Louis Navellier

Editor, Market 360

P.S. We’re releasing a major upgrade to my system later this week… 

We’re talking about a new way to interact with the system. You’ll be able to search for stocks by sector, size, risk profile, ratings trends and more.

You’ll also be able to receive weekly AI-powered insights, highlighting the most important upgrades, downgrades and sector shifts.

I can’t reveal all the details just yet, but my Accelerated Profits members will get full access to this exciting new tool.

Not a member yet? Learn more here.

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:

Sezzle Inc. (SEZL)

The post 3 Stocks to Buy Now That Could Beat the Market appeared first on InvestorPlace.

“Stay boring, Ponyboy” doesn’t have quite the same ring to it. Neither does, “Fortune favors the monotonous.”

But there’s a reason vanilla was the most-purchased ice cream flavor in 2025, according to Instacart data. And why millions of commuters tune into the same two NPR news programs every morning and afternoon.

“Boring” isn’t just safe. Or reliable. It’s strategic.

Especially when everyone else is chasing excitement. Markets have a habit of rewarding discipline long after they stop rewarding hype.

That’s a lesson that investors have learned time and again.

Let’s turn back the clock nearly three decades to the dot-com mania. Back then, I was a Wall Street-based producer of investment research who actively participated in both the boom and the bust.

In the years leading up to the March 2000 peak, for example, I urged my readers to flee from dozens of over-hyped dot-com stocks and pivot toward select also-rans in “boring” sectors.

The chart below tells the other side of that story — the side belonging to investors who stayed with the darlings.

In March 1999, the five largest companies in the U.S. stock market — Microsoft Corp. (MSFT), Cisco, Intel Corp. (INTC), Oracle Corp. (ORCL), and IBM Corp. (IBM) — had a combined market capitalization of roughly $1 trillion.

Over the next twelve months, that figure doubled to $2 trillion, as investors convinced themselves that the future belonged entirely to internet technology and that these five companies would lead the way.

They were mostly right about the future. They were catastrophically wrong about the prices they paid to capitalize on it.

Within 12 months of the peak, every gain had evaporated. The group was back to $1 trillion, exactly where it had started in March 1999.

But the “reappraisal” didn’t stop there. The combined market cap kept falling for a decade, eventually bottoming near $470 billion in early 2009 — less than half the value these stocks had achieved 10 years earlier.

Any investors who purchased all five stocks in March 1999 at what felt like a perfectly reasonable entry point, and then continued to hang on through the bust, would have watched those stocks lose more than half their value and waited 15 years — until mid-2014 — just to break even.

Recovery to the March 2000 peak didn’t arrive until early 2020, a full two decades after the top. There is a word for that kind of investment outcome, and it is not “patience.” The word is “trap.”

This case study from the past is not a mere curiosity; it is the recurring tendency of boom-bust cycles.

The stocks that lead a boom rarely lead the next decade. As enthusiasm peaks, investors become willing to pay too much for the winners, creating opportunities in overlooked areas of the market.

I’ll share those areas below. But first, let’s take a look at what we covered here at Smart Money last week.

Smart Money Roundup

Why You Should Use the EV Playbook to Find AI Winners

July 22, 2026

U.S. companies are racing to build the smartest AI models. China is proving that AI doesn’t always need to be the best. It needs to be “good enough.”

That’s the same playbook China used with electric vehicles. So, let’s take a look at why AI could become China’s next EV export story – and why the biggest investment opportunities may come from the companies powering it.

The Next Great AI Fortune Could Begin Before Wall Street Sees It

July 23, 2026

In 2012, Mark Zuckerberg made what Wall Street called the worst deal of the year. A decade later, Instagram alone generates tens of billions in revenue.

My colleague Luke Lango, InvestorPlace’s tech and growth specialist, thinks that same pattern is repeating itself across the AI boom – just at a much larger scale.

How to Profit From the AI Boom Without Playing Big Tech’s Game

July 25, 2026

No company in the Magnificent Seven wants to be outspent by its rivals, so each feels compelled to keep pouring billions into AI infrastructure – even if doing so hurts near-term profits.

We’re witnessing this dilemma as the first of the septet released earnings last week. Let’s look at the latest results, and then I’ll show you how to avoid the issues these companies are facing.

Don’t Wait for the IPO — Find the Next AI Winner Now

July 27, 2026

Most people think the biggest fortunes in technology are made after a company goes public. Luke Lango believes that assumption is becoming increasingly outdated.

He looks back at some of Silicon Valley’s most successful early investments to explain why the biggest opportunities often appear long before Wall Street takes notice.

The Case for Boring

As booms age and approach their denouement, the high-flying leaders of the boom begin to fade, while the low-flying, overlooked opportunities begin to outperform.

This tendency repeats itself with surprising regularity, and I expect it to repeat itself again during the next two or three years.

For perspective, today’s red-hot valuations are similar to the ones that doomed the dot-com boom. Collectively, Microsoft, Cisco, Intel, Oracle, and IBM were trading for 11 times sales and 54 times earnings at the March 2000 peak. Today, the Mag 7 stocks are trading for nearly 11 times sales, on average, and 70 times earnings. Tesla, the group’s spiritual leader, is trading for 346 times earnings!

That said, leaving the party early is rarely an enjoyable experience. You are certain to miss out on some fun, and will certainly regret your decision… at least for a while.

That’s unavoidable.

Someone at work will mention how much money they made last week on the latest AI darling, while your SpaceX-enthralled brother-in-law will be insufferable at the backyard barbecue.

That talk is exciting. But remember, “boring” has a way of winning.

At Fry’s Investment Report, I recommend these kinds of overlooked, reasonably valued companies that have the potential to become tomorrow’s market leaders.

To learn more, click here.

Regards,

Eric Fry

The post Why “Boring” Could Be Your Best Investment 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.

Sincerely,

Louis Navellier

Editor, Market 360

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

The scientist, Herbert Boyer, had helped develop a method for cutting and recombining DNA. Swanson believed the discovery could become the foundation of an entirely new kind of drug company.

Boyer agreed to give him 10 minutes.

They met at a San Francisco bar. Ten minutes became several hours and a few beers. By the end of the conversation, the two men had agreed to start a company.

They called it Genentech – short for genetic engineering technology.

Four years later, Genentech went public.

Biotechnology was not yet an established industry. Genentech had demonstrated that bacteria could be engineered to produce human proteins, including insulin, but no genetically engineered medicine had been approved for sale in the United States.

Public investors bought the possibility anyway.

Genentech offered one million shares at $35 apiece on October 14, 1980. Within the first hour, the price climbed as high as $88. It closed the day at $71.25.

The investors who bought that day were not there when Swanson and Boyer started the company over beers. But they did arrive before the first genetically engineered human medicine won approval in 1982. Before Genentech brought its own drug to market. Before biotechnology became a multi trillion–dollar global industry.

In other words, the opening bell opened a door while almost the entire opportunity remained on the other side.

For much of the modern stock market’s history, that was normal.

It is not normal anymore.

IPOs No Longer Mark the Beginning

Genentech reached Wall Street four years after it was founded. Microsoft Corp. (MSFT) took 11 years. Nvidia Corp. (NVDA) took six.

Those companies were already promising when they reached Wall Street. But they were still young enough that ordinary investors could participate in decades of expansion.

The public market helped finance the journey.

Today, the journey often happens somewhere else.

The most ambitious technology companies can spend 10, 15, or even 20 years growing before they seriously consider an IPO. During that time, they can develop products, build infrastructure, hire thousands of employees, sign enormous contracts, and reach valuations once reserved for the largest publicly traded corporations.

By the time the ticker appears, the company may no longer resemble the speculative young business Genentech investors encountered in 1980.

It may already be a global institution.

That does not mean investors cannot make money after an IPO. Many companies continue creating tremendous value long after their public debuts.

But the starting point matters.

Buying a four–year–old company before its first approved product is a fundamentally different proposition from buying a mature business whose value has already climbed into the hundreds of billions.

The opening bell used to invite investors into the company’s defining growth chapter.

Increasingly, it announces that the chapter has already been written.

Why AI Companies Can Stay Private Longer

This shift has been building for years. Artificial intelligence is accelerating it.

Young companies once needed Wall Street to raise the money required for large–scale expansion. Going public gave them capital, credibility, and shares they could use to attract employees or acquire competitors.

Today’s most promising AI companies can find extraordinary amounts of capital without entering the public market.

That changes the calculation.

An IPO brings money, but it also brings quarterly earnings expectations, extensive disclosures, regulatory obligations, and constant scrutiny. If a company can fund its growth without accepting those burdens, its leaders may decide there is little reason to rush.

AI also creates another possible ending.

The largest technology companies are racing to secure models, data, chips, energy, robotics, cybersecurity, and specialized talent. When a young company solves a problem that one of those giants considers urgent, it may receive a partnership, investment, or acquisition offer long before an IPO becomes necessary.

In that case, the opening bell does not merely arrive late.

It never arrives.

That is how an important part of the AI economy can remain almost invisible to ordinary investors. They see the breakthrough after a technology giant announces it. They see the new product after it has been folded into a larger platform. They see the effect on a public company’s earnings months or years later.

But the original value was created before any familiar ticker told them where to look.

A Market Divided Into Two Timelines

This has produced two different AI investment timelines.

The public timeline is easy to follow.

Nvidia reports another quarter of soaring chip demand. Microsoft announces a new data center. Meta Platforms Inc. (META) increases capital spending. Alphabet Inc. (GOOGL) releases another model. Investors can watch those developments unfold in real time and decide whether to buy or sell the stocks.

The second timeline is harder to see.

It begins when a small team identifies a technical bottleneck. It continues as that team develops a solution, proves it works, and attracts attention from the companies spending billions to win the AI race.

By the time the development reaches the public timeline, the decisive transaction may already be complete.

That is the locked door.

It was not necessarily built by one company, one regulator, or one group of insiders. It emerged as technology companies learned they could grow larger, remain outside the public market longer, and sell themselves without ever offering shares on Wall Street.

The door is a consequence of how the market evolved.

And AI is making what lies behind it more valuable.

Scarcity Changes the Investing Question: Why Pre-IPO AI Opportunities Run on a Different Clock

Public–stock investors are accustomed to opportunities that remain available.

Nvidia, Microsoft, Meta, and Alphabet shares will still trade tomorrow. Their prices may change, but the opportunity to make a decision remains.

The companies developing outside the public market operate on a different clock.

A financing window can close. Available ownership can fill. A strategic investor can secure a position. A larger company can acquire the business and remove the opportunity entirely.

That scarcity makes timing unusually important.

It also changes the central question.

Instead of asking only, “Which public company will win the AI race?” I believe investors should also ask, “Where is the value being created before the public market sees it?”

The answer will not always be a famous model developer. It could be a company solving a narrow but essential problem in robotics, energy, security, data, or computing efficiency.

Most of those companies will not become the next Genentech. Many will fail. Others will build useful products without generating extraordinary investment results.

Earlier does not automatically mean better.

It simply means the biggest decision may need to happen before a stock symbol provides the comfort of consensus.

The Door Is Beginning to Move

For years, ordinary investors could watch this transformation without having any obvious way to follow the value being created beyond the public market.

I believe that is beginning to change.

I will explain exactly what has changed during my free 2026 AI Megadeal Event on Thursday, July 30, at 1 p.m. Eastern.

More importantly, I will show you how I evaluate the opportunities that emerge.

My framework focuses on three factors: People, Product, and Timing.

I want exceptional founders capable of attracting talent and executing under pressure.

I want a product that solves a real, urgent, and expensive problem – not a thin marketing layer wrapped around somebody else’s technology.

And I want timing that places the company directly in the path of serious capital and an accelerating technological shift.

No framework eliminates risk. Young companies are speculative, difficult to evaluate, and capable of producing a complete loss.

The goal is not to pretend those risks do not exist. It is to determine whether the potential reward justifies taking them.

On July 30, I will walk through that process and reveal one company I believe represents this overlooked side of the AI economy today.

Genentech’s 1980 IPO opened a door before its first medicine had even been approved.

Today’s AI companies may create fortunes, reach enormous valuations, or get bought before most investors even realize the door exists.

That is the market shift I want to help you understand – and the opportunity I want to show you during the event.

Attendance is free, but you must reserve your seat to receive an invitation.

The post The Day Wall Street Opened the Door to an Industry That Barely Existed appeared first on InvestorPlace.

In today’s guest essay, he looks back at some of Silicon Valley’s most successful early investments – from Fairchild Semiconductor to Google to Anthropic – to explain why the biggest opportunities often appear long before Wall Street takes notice.

Then he’ll share the framework he’s using to identify the next wave of AI opportunities during a free online event on Thursday, July 30, at 1 p.m. Eastern. Reserve your seat here.

Back in 1957, eight young engineers working under William Shockley found the co-inventor of the transistor impossible to work for. So, they left the Shockley Semiconductor Laboratory in Mountain View, California, and never looked back.

The eight quickly figured out that no institution or company would support them. Back then, the suburbs and farmland south of San Francisco and north of San Jose wasn’t exactly “Silicon Valley” yet. The budding tech firms in the region weren’t quite ready to invest in unproven ideas.

They were just frustrated engineers with no product and no revenues.

So they called a young financier named Arthur Rock. 

Rock did not have the capital himself, but he had a willingness to bet on people he deemed impressive.

He found a camera company willing to gamble $1.5 million on eight founders and an idea.

Thus, Fairchild Semiconductor was born. Fairchild eventually became one of the most influential technology companies in history, spawning Intel Corp. (INTC) and dozens of other semiconductor firms worth trillions of dollars today.

And the men Shockley lost became known affectionately as the “Traitorous Eight.” They were the accidental architects of a model Silicon Valley still runs on to this day.

The Traitorous Eight: That’s Gordon Moore – of “Moore’s law” fame – on the far left.

Credit: Intel

That same instinct resurfaced in 1998 when Andy Bechtolsheim sat down with two Stanford University grad students. Right on the spot, before their company had a business model or recognizable brand, the Sun Microsystems co-founder wrote a $100,000 check to Larry Page and Sergey Brin.

Anyone who’s ever Googled… well… anything knows how that story ended. But for the record: That $100,000 check reportedly bought roughly a 1% stake in Google, a position that eventually became worth tens of billions of dollars.

More recently, in 2023 Spark Capital invested $75 million in Anthropic while it was still an obscure AI startup with little revenue. Today, millions of people are on a first-name basis with Claude, and that stake is estimated to be worth roughly $7 billion.

Across nearly 70 years, the technologies and the players keep changing. The playbook doesn’t.

Rock backed eight unknown engineers. Bechtolsheim backed two graduate students. Spark Capital backed an AI startup few people had heard of.

In each case, the biggest opportunity wasn’t buying a great business after everyone recognized it. It was recognizing exceptional founders and businesses before everyone else did.

I think that same playbook matters more today than it has in decades.

First, because AI has created an unprecedented race to develop new technologies. Second, because the companies leading that race increasingly have more money than time. And finally, because that combination is changing where some of the biggest fortunes in technology are being created.

Let me explain…

Why the Giants Now Buy Instead of Build

There’s a reason this playbook has endured for nearly 70 years, and it isn’t just today’s excitement over AI.

When the prize is building the next great computing platform, speed becomes everything. If a startup has already solved a problem that would take your own engineers two years to crack, buying that company is often far cheaper than losing those two years.

That’s exactly what’s happening in today’s AI race.

Alphabet Inc. (GOOG) made that decision early, back in 2014, when it acquired the British AI startup DeepMind. Rather than spending years assembling a comparable research lab from scratch, Google bought one of the world’s best AI teams outright. More than a decade later, DeepMind sits at the heart of Google’s AI strategy.

Meta Platforms Inc. (META) reached a similar conclusion last year when it invested $14.3 billion in Scale AI. The deal wasn’t just about software. Scale AI had become one of the industry’s leading providers of the high-quality training data and infrastructure needed to build advanced AI models. Instead of trying to re-create that expertise internally, Meta bought a seat at the table.

Microsoft Corp. (MSFT) made perhaps the biggest AI boom bet of all. Its $23 billion worth of investments in OpenAI, made between 2019 and 2023, gave the company immediate access to one of the world’s leading AI developers years before it could have built a comparable capability on its own.

And this isn’t unique to AI. Cisco Systems Inc. (CSCO) spent much of the 1990s building its networking empire by buying promising startups rather than reinventing technologies itself.

Long story short, this isn’t a new playbook. It’s an old one that’s becoming even more valuable.

Every one of those deals happened because the real value had already been created inside a startup, long before Wall Street ever started paying attention.

That’s why I think one of the most important shifts in investing today is this:

The buyout, not the IPO, is increasingly becoming the finish line many early investors are aiming for.

The Playbook Hasn’t Changed

Even the best startup investors get it wrong sometimes. And nobody understands that better than the funders themselves.

Bessemer Venture Partners keeps what it calls its “Anti-Portfolio” – a public list of companies it had the opportunity to back but passed on. Google is on it. So are Apple, eBay, Airbnb, FedEx, and dozens of other companies that went on to become enormous successes.

Being early is no guarantee, but it does give you the opportunity to make a decision before the rest of the market has reached the same conclusion.

That’s the common thread running through Fairchild Semiconductor, Google, Anthropic, and countless other success stories. The biggest fortunes come from someone recognizing extraordinary people and extraordinary businesses before the consensus formed.

That’s the playbook. And I believe it’s becoming more relevant again as AI reshapes the technology landscape.

The challenge, of course, is knowing what characteristics to look for when opportunities do appear.

That’s exactly what I want to show you during my free 2026 AI MegaDeal Event on Thursday, July 30, at 1 p.m. Eastern.

I’ll explain why I believe AI is creating a new generation of acquisition opportunities, walk through the framework I use to identify them, and share the one company I believe best represents this shift today.

That event is free to attend, but you must reserve your seat in order to get an invitation.

If the history of Arthur Rock, Andy Bechtolsheim, and Spark Capital teaches us anything, it’s that the biggest investment opportunities often look the least obvious at the beginning.

My goal is to help you put this playbook to work before the rest of Wall Street catches on.

I hope you’ll join me.

Sincerely,

Luke Lango

Senior Investment Analyst, InvestorPlace

P.S. When you reserve your seat for the 2026 AI MegaDeal Event, you’ll also have the opportunity to join my VIP text list. As a thank-you, I’ll send you my new report, The AI Collectors’ Portfolio: 7 Stocks to Buy for the Biggest Tech Spending Boom of All Time. It’s free, but you need to register for the event first.

The post Don’t Wait for the IPO — Find the Next AI Winner Now appeared first on InvestorPlace.

In the 2010s, getting acquired was the ultimate startup status symbol.

Waze… Nest… DeepMind… Fitbit…

One by one, the most promising companies in tech got swallowed by giants like Alphabet Inc. (GOOGL) and Meta Platforms Inc. (META), turning their founders and early employees into multimillionaires. America’s top five tech firms made at least 616 purchases between $1 million and $50 million alone that decade, according to the Federal Trade Commission. Hundreds more happened outside that narrow window.

M&A exits became such a fixture that HBO built a TV series around it. The hit show Silicon Valley followed a founder who kept refusing, fumbling, and blowing up opportunities to cash out because he wanted to build something bigger.

The AI Revolution is now creating a new wave of M&A exits. Only this time, many of the acquisition targets are publicly traded companies that regular investors can buy first, rather than sending in a job application and hoping to be employee No. 3 at a company like DeepMind.

Many of these are happening at wonderful premiums. When RadNet Inc. (RDNT) bought AI cancer-detection company iCAD in July 2025, shareholders pocketed a roughly 100% premium overnight. Adobe Inc.’s (ADBE) purchase of AI-powered digital marketing firm Semrush happened at a 78% markup.

Even the “disappointing” deals are closing at 20% premiums – an instant gain that usually takes the S&P 500 two years to grind out.

Now, InvestorPlace Senior Analyst Luke Lango believes he’s found a way to get into these AI investments even earlier, with potentially even bigger payouts. And in a new free presentation, the 2026 Megadeal Event, he lays out his step-by-step method for spotting these targets, and how to buy these firms before everyone else gets in.

The presentation airs on July 30 at 1 p.m. Eastern, and you can sign up for it here.

To show you why the timing matters, I’ll walk you through five companies that look ripe to be bought. AI is redefining tech, and cash-rich incumbents are about to spend enormous sums to stay relevant.

Cybersecurity: Where Buyers Are Hungriest

The busiest corner of the current AI market is cybersecurity, a field that’s quickly getting bundled into AI platforms themselves.

Both sides are motivated to get deals done. AI companies want to buy because security firms have the data they need. They know their AI security products will only be as good as the data it’s trained on.

Meanwhile, smaller cybersecurity companies are getting squeezed on two sides: by new AI entrants and by “one-stop-shop” consolidators like Fortinet Inc. (FTNT) and CrowdStrike Holdings Inc. (CRWD) that IT departments increasingly prefer. For midsized players, that leaves two options: Sell out or bulk up fast enough to compete.

Most will choose the first way out. Sector valuations are depressed (ironically thanks to AI fears), making it hard for smaller players to raise cash and acquire their way to a one-stop-shop size. Besides, high-profile cybersecurity scares are making AI labs even more motivated buyers. Last week, a rogue OpenAI agent hacked another AI startup, highlighting the desperate need for high-quality cybersecurity.

Here are the three public companies I think are likeliest to get a knock from M&A suitors:

Tenable Holdings Inc. (TENB): This vulnerability management (VM) company was exploring a sale as early as 2024 after receiving takeover interest. A deal looks even more likely today, because Tenable is a leader in the internal IT scanning that defends against AI-orchestrated attacks.

Tenable also throws off steady cash flows and carries zero net debt – catnip for private equity firms like Thoma Bravo and Permira. Consolidators like CrowdStrike might also get involved. And make no mistake: A buyout is the best outcome here. Tenable is caught between AI upstarts and one-stop shops, and its path as a standalone company is (fittingly) untenable.

Rapid7 Inc. (RPD): This Boston-based cybersecurity firm is even further down the road to a sale. Rapid7 is a smaller VM company than Tenable, and activist investor Jana Partners pushed for a deal back in 2025. The two sides eventually settled that year with Jana adding three board members and Rapid7 promising a turnaround. That hasn’t been enough. In March 2026, the activists struck a new agreement to raise its stake in Rapid7 to almost 20%. Three months later, Jana replaced the outgoing CEO with one of the three board members.

When an activist takes the wheel like that, the destination is usually the next available exit. With so many AI firms moving into vulnerability management and Rapid7’s stock so low, the company will have no shortage of potential bidders.

SentinelOne Inc. (S): This Silicon Valley cybersecurity firm, with roots in Israel, is by far the largest on our list, with a $6 billion market valuation. It is also the most likely to get acquired by a strategic buyer.

In July 2025, reports surfaced that Palo Alto Networks Inc. (PANW) was seeking to acquire SentinelOne, which had landed in political hot water after hiring Chris Krebs, the former head of the Cybersecurity and Infrastructure Security Agency (CISA). Krebs had a high-profile falling out with the U.S. president over the 2020 election fraud claims. A week later, Palo Alto purchased a different Israeli cybersecurity firm, CyberArk, and denied it had seriously considered buying SentinelOne.

Nevertheless, SentinelOne forced Krebs out anyway, setting itself up as a more attractive acquisition target. Cisco Systems Inc. (CSCO) is a logical suitor, now that antitrust risk has eased. Its endpoint security products trail SentinelOne’s badly. And Alphabet’s $32 billion blockbuster acquisition of cybersecurity firm Wiz shows Big Tech is also willing to pay up for security.

Best of all, SentinelOne has the widest moat of the three cybersecurity names here. Even if no offer ever comes, the company should keep growing on its own. Either way, shareholders win.

AI Software: The “Vibe Coding” Squeeze

The other hotbed for M&A activity is AI software – targeted both by AI companies and by traditional software-as-a-service (SaaS) companies scrambling for more AI exposure.

This segment has already seen some high-profile acquisitions. To name a few:

• LivePerson Inc. (LPSN) (conversational AI) acquired by SoundHound AI Inc. (SOUN) for a 22% premium
• Publicis Groupe is buying LiveRamp Holdings Inc. (RAMP) (consumer data) at a 30% premium.
• Perfect Corp. (PERF) (AI in fashion) is set to go private for a 48% premium.

The pressure to sell is only building. Advanced AI now makes upstart “vibe code” competing products possible, and these are often built in a fraction of the time and cost. Plenty of software CEOs will find it more attractive to exit for a double-digit payout rather than fight an endless wave of copycats.

Here are my two picks to get bought out:

BILL Holdings Inc. (BILL): This Silicon Valley payments firm has been targeted by three separate activist investors since October: Starboard Value, Elliott Investment Management, and Barington Capital Group. All have essentially said the same thing: Either become more profitable or sell yourself to someone who can make that happen.

BILL has been attempting to do both. Analysts now forecast a 19% increase in earnings per share this year (up from 4% in 2025), and another 27% increase in 2027. At the same time, the payments firm has shopped itself to at least one buyout group, Hellman & Friedman. With share prices now down 18% since January and earnings estimates on the rise, BILL is my top software pick to get taken out by an AI suitor.

BlackLine Inc. (BL): One analyst recently called BlackLine “the cleanest takeout” for 2026. I agree. The AI-powered accounting software platform is a particularly popular add-on for SAP SE (SAP), which already made a $66-per-share offer for BlackLine in June last year. The German company was considering raising their offer as recently as October after BlackLine’s board rejected the offer, calling it too low.

Two factors now make a buyout almost certain.

That makes BlackLine a particularly attractive target for traditional major business software companies seeking an AI edge. The firm was an early adopter of agentic AI in accounting, making it the ideal bolt-on product for a legacy software firm.

How to Actually Play the AI Merger Boom

In the 2010s, even wealthy accredited investors could not buy themselves a seat at the M&A table. The top venture capital funds like Sequoia, Benchmark and a16z were closed to retail investors, and institutional cash often had trouble getting into these oversubscribed funds.

Everyone else had to wait for startups to go public… and hope they didn’t get bought out by Big Tech first.

That meant lower returns for regular investors. Here’s how investors in ride-sharing firm Uber Technologies Inc. (UBER) fared depending on when they got in, assuming they held on through present day:

• Seed (2010). First Round bought at ~$0.009/share, a 770,000% return
• Series A (2011). Benchmark bought at ~$0.13/share, a 54,000% return
• Series B (2012). Menlo Ventures bought at $0.65/share, 10,000% return
• Series C (2013). Google Ventures bought at $5, a 1,400% return
• Secondary Market (2018). SoftBank bought shares at $32.87, a 112% return
• IPO investor (2019). Bought at $45, a 56% return

The returns evaporate with each funding round. Early investors are the biggest winners, while later ones start competing for leftovers. The AI-powered M&A boom will turn out the same way, which is why getting in at the right time matters so much.

Fortunately, that “right” time is within reach for regular investors. In his upcoming presentation, the 2026 Megadeal Event on Thursday, July 30, Luke Lango walks through his system for identifying the next AI superstars before everyone else does, and how to get in on these promising deals.

Reserve your spot for that free event here.

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 5 Takeover Targets for the AI Revolution appeared first on InvestorPlace.

The company was running out of road and Tesla’s bears knew it. Margins were shrinking, as was its cash flow, so more and more people lined up to short TSLA stock. 

But what they could not see was the assembly line inside the building, where Tesla would produce a mass-market sedan called the Model 3. That model would eventually send Tesla stock up 10-fold, then 20-fold, then 30-fold, from where it traded that summer. 

This time, we’re seeing that dynamic playing out across five stocks at once, and how they will pivot is what you need to understand before the market figures it out on its own.

The gap between what these companies are earning and what their stocks trade for has become one of the widest gaps this AI bull market has produced. And gaps this wide do not last. Every one of these five names looks, on the surface, like a company in some kind of trouble: an ad giant burning cash, a carmaker whose core business is bleeding margin, a turnaround story everyone has heard before, a neocloud getting whipsawed on a chart, and a rocket company sitting near a 52-week low. 

Semiconductor stocks have fallen roughly 20% over the past month while semiconductor earnings estimates have climbed more than 10% over that same stretch, and the earnings we walked through on this week’s episode of Being Exponential make that gap almost impossible to justify with weaker fundamentals. 

The stock market is currently pricing five of the best AI infrastructure businesses on Earth as though their best days already happened. Here is what the numbers actually say, stock by stock, and why I recommend leaning into the weakness rather than running from it:

Alphabet: The Cloud Business Nobody Priced In

Alphabet Inc. (GOOGL) just posted the kind of cloud number that used to take a decade to produce. Its cloud division grew at 25% to 35% in the years right after the pandemic. Last quarter, that same business put up 82% growth on more than $20 billion in quarterly revenue, an acceleration of 50 percentage points in about a year. That kind of jump on a business already running at a $100 billion annualized clip does not happen in the history of capitalism, because it has not happened before now. Operating margins in the segment climbed to 35% from 20% a year earlier, and the backlog sits at $514 billion. The market is fixated on the fact that free cash flow turned negative because Alphabet needs the debt markets to fund its buildout. I recommend a different fixation: a company this strong can handle debt, and I recommend buying the dip.

Tesla: The Fremont Lot, Version Two

Tesla’s current business genuinely struggles right now. Prices are getting cut, margins are getting crushed, and profits are falling, because the company is buying volume with lower prices rather than earning it with demand. That is today’s Fremont parking lot. But the bulls are not pricing the car business; they are pricing robotaxi, Optimus, and energy, the way an earlier generation of bulls priced the Model 3 ramp instead of the Model S slowdown. This quarter did not hand bulls the volume numbers or the timeline they wanted on robotaxi, so the stock stays dead money in the short term. Longer term, the growth drivers Elon Musk keeps teasing eventually show up in the income statement, the way the Model 3 eventually did, and that is why the stock remains a name for patient investors rather than short-term traders.

Intel: The Turnaround Finally Has Numbers

Intel Corp. (INTC) delivered $16.1 billion in quarterly revenue, up 25% year over year and nearly $2 billion above its own guidance, because its AI compute business finally scaled into real profitability. Data center AI revenue hit $6.3 billion, up 59%, with segment margins swinging to 39.5% from 16.1% a year earlier, a 23-point improvement in four quarters. Foundry losses narrowed from a 71.7% operating margin loss to 36.2%, and custom ASIC revenue nearly tripled. Intel is proving itself a legitimate foundry alternative to Taiwan Semiconductor Manufacturing Co. (TSM) and a real accelerator competitor to Nvidia Corp. (NVDA) and Advanced Micro Devices Inc. (AMD) in the same quarter. The turnaround story has arrived, and I recommend treating this print as the evidence, not the promise.

Nebius: The Dip-Buy Setup of the Week

Nebius Group N.V. (NBIS) benefits from two separate votes of confidence in a single week. Alphabet said on its earnings call that demand for compute continues to outstrip supply even after tripling its own capacity, and that it plans to buy more compute from neoclouds such as Nebius rather than build every gigawatt itself. Separately, Nvidia disclosed a 10% stake in Nebius, a signal from the company that understands AI supply bottlenecks better than anyone. The chart backs up the fundamentals: the stock bounced off its 100-day moving average on a nearly oversold reading, setting up for a bullish crossover. I recommend buying this dip.

SpaceX: A Line in the Sand at $100

SpaceX (SPCX) trades down roughly 50% from its highs since its recent public listing, and the valuation remains rich by any conventional measure. But the business behind it, bringing terrestrial compute online over the next two to three years and orbital compute online in the years after that, could add hundreds of billions of dollars in revenue on each front, building toward $1 trillion in revenue within five to seven years. A business of that scale merits a premium multiple, which is why I recommend punching long at $100 if the stock gets there, a level that marks the point where the risk-reward tilts sharply in your favor.

Where the Real AI Wealth Is Being Created

The lot outside Fremont looked empty of promise in the summer of 2019. But we all know how that turned out. 

This week’s earnings tell you the same thing about five stocks the market currently treats like they have nothing left to prove. Watch this week’s full episode of Being Exponential for the complete breakdown of the technical setups, the bear-case rebuttals, and where the next leg of this divergence resolves.

But here is the part of the Tesla story most investors miss entirely: the biggest money was not made by the public shareholders who bought the dip in that Fremont parking lot. It was made years earlier, by the venture investors who backed Tesla while it was still a private company nobody outside Silicon Valley had heard of. Those early backers then made 10-fold, 50-fold, and even 100-fold their money before the stock ever traded on the Nasdaq.

That is exactly what I want to show you during my free 2026 AI Megadeal Event on Thursday, July 30, at 1 p.m. Eastern.

I recommend zooming out from the five public stocks we just walked through, because the biggest fortunes in this AI boom are forming one step earlier than a single share of GOOGL, TSLA, INTC, NBIS, or SPCX. I’ll explain why I believe AI is creating a new generation of buyout opportunities, walk through the exact framework I use to identify them, and share the one company I believe best represents this trend today.

This event is free to attend, but you must reserve your seat to get an invitation.

The lot outside Fremont taught an entire generation of public investors to look past a bad quarter and toward the ramp coming next. The engineers who backed Tesla before it was popular knew this, and it is why their returns dwarfed everyone else’s. 

It’s my goal to help you put that same playbook to work, before the rest of Wall Street catches on.

The post  Wall Street Left This Company for Dead. Then It Became the Trade of the Decade. appeared first on InvestorPlace.

Two suspects are arrested together, then interrogated in separate rooms.

Each is offered the same deal. Either…

Naturally, both talk to avoid blame. The ironic result? They each receive a longer prison sentence than they would have if they’d both stayed silent.

The suspects ended up with the outcome they tried to avoid.

This is called the prisoner’s dilemma; and once you know the shape of it, you start seeing it everywhere, especially where money is involved.

Take OPEC, the Organization of the Petroleum Exporting Countries. Every member knows the group benefits if everyone limits oil production. But each country has an incentive to pump a little more oil to make extra money, so the agreement is constantly under pressure.

Now, the Magnificent Seven seems to be caught in its own version of the prisoner’s dilemma, all over AI capital spending. No company wants to be outspent by its rivals, so each feels compelled to keep pouring billions into AI infrastructure – even if doing so hurts near-term profits.

We’re witnessing this dilemma as the first of the septet released earnings this week.

So, in today’s Smart Money, let’s look at the latest results from Alphabet Inc. (GOOGL) and Tesla Inc. (TSLA), and what they reveal about the Mag 7 as an investment.

Then, I’ll show you how to organize your portfolio to avoid the issues these companies are facing.

Let’s jump in…

Suspect Number One: Alphabet’s Growth Story Has a Catch

Let’s start with suspect number one.

Alphabet was the first of the seven Big Tech companies to report second-quarter earnings on Wednesday after the bell.

Its revenue came in at $119.8 billion, beating Wall Street’s expectations of $116.93 billion, and year-over-year revenue growth stood at 24%. Earnings per share came in slightly lower than expected, at $2.85, adjusted rather than the targeted $2.89.

However, the main focus here is growth.

• Cloud revenue increased by 82%, and cloud backlog expanded to $514 billion.
• Google’s Antigravity agentic AI platform now has more than 2.4 million weekly active users.
• Since the global launch of Search’s AI Mode last October, search usage has risen. Google now counts over 1 billion monthly active users.

Growth remained a key focus for Alphabet’s capital expenditures (CapEx). It raised its AI spending budget to $195-$205 billion for the year, up from the earlier forecast of $180-$190 billion.

This increase is less exciting for investors to hear. Higher spending means lower profits in the short term. You can’t say they weren’t warned, though. Alphabet CEO Sundar Pichai recently alluded to the AI prisoner’s dilemma, arguing that “The risk of under-investing is dramatically greater than the risk of over-investing.”

That makes the surge in CapEx unsurprising, but not any less unsustainable.

The demand for AI infrastructure has caused hyperscalers to deplete their cash reserves, even prompting them to tap 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, That not only involves Alphabet, but fellow Mag 7 members Meta Platforms Inc. (META) and Microsoft Corp. (MSFT).

Sure, these companies remain immensely profitable. But the financial slack they once enjoyed is disappearing, and the market is noticing. Alphabet’s stock dropped about 6% the next day.

Now, let’s look at suspect number two…

Suspect Number Two: Tesla’s CapEx Problem

On Wednesday, Tesla also reported lower-than-expected earnings for the second quarter, coming in at $0.33 adjusted versus the expected $0.51. But its revenue exceeded expectations – $28.24 billion, beating the expected $25.71 billion.

Although Tesla’s revenue increased by 26% year-over-year and its automotive segment is performing well, its shares fell by nearly 15%. This decline is due to a combination of factors, including a significant rise in CapEx.

The EV-turning-AI company saw its CapEx increase 142% in the second quarter, rising from $2.39 billion to $5.79 billion. This leap suggests the company is likely on track to exceed the $25 billion capex target that CFO Vaibhav Taneja mentioned for this year. That’s a substantial 200% rise over the prior year.

Tesla is currently updating its factories to produce two-seater driverless Cybercabs and continues developing its Optimus humanoid robots, while also preparing to build a large AI chip manufacturing plant in Texas.

Musk acknowledges the workload and its cost, saying, “It’s okay to be slightly less capital-efficient if it helps us finish sooner.” This sentiment is becoming a common denominator amongst the Mag 7 prisoners.

Microsoft, Amazon.com Inc. (AMZN), and Meta are expected to release their earnings this coming week. So, we will need to wait and see whether Microsoft’s $190 billion CapEx for 2026 rises… or if the costs for Amazon’s AI infrastructure projects increase… or just how much Meta’s AI model expenses are catching up with its balance sheet.

But we know this: The harder the Mag 7 tries to avoid falling behind in AI, the deeper they lock themselves into the very prisoner’s dilemma they’re trying to escape. Competing with the other major players also means spending like them.

Alphabet and Tesla prove this already.

So, rather than getting caught in this costly cycle, here’s how to position your portfolio…

How to Escape the Prisoner’s Dilemma Entirely

In Mag 7’s world, AI has become a “cost center,” rather than a powerful growth driver. And once you shift focus away from that group, you begin to see the full range of opportunities in the market.

At Fry’s Investment Report, we carefully select companies from a diverse array of sectors to invest in, especially as we navigate the unpredictable AI age.

The AI boom isn’t just creating enormous opportunities for chip companies or hyperscalers. It’s driving demand in energy, mining, healthcare, retail, e-commerce, and many other industries.

For example, the International Energy Agency finds that the global energy consumption for data centers is projected to reach around 945 Terawatt-hours (TWh) by 2030 (more than double the 415 TWh that data centers consumed globally in 2024). So, we’re holding several energy companies helping sustain the power grid in real time.

Those holdings include opportunities in natural gas and renewable energy. One of those renewable energy picks is an ETF up 53% since I recommended it last year, with plenty of upside left.

That’s just one example of our strategy: looking beyond the obvious AI trades to uncover overlooked opportunities. Instead of getting caught in the Mag 7’s prisoner’s dilemma, we invest in the companies benefiting from the AI boom without shouldering the burden of the spending race.

To find out the names of the stocks we’re holding in Fry’s Investment Report, along with the research behind each pick, click here to learn more.

Regards,

Eric Fry

The post How to Profit From the AI Boom Without Playing Big Tech’s Game appeared first on InvestorPlace.

It was one of history’s great unsolved mysteries.

For more than 500 years, no one knew what had happened to the remains of England’s King Richard III.

If you don’t know the story, Richard III was the last Yorkist King of England, who died in 1485 at the Battle of Bosworth Field. Since his death, he was generally depicted as the most evil of English monarchs – and the subject of one of Shakespeare’s most popular historical plays.

Generations of historians had searched for clues about his burial site. Countless theories came and went. Some believed his remains had been destroyed. Others thought they had simply been lost to history forever.

Credit: Steve Travelguide

Then, in January 2011, researcher Philippa Langley approached the University of Leicester to produce an assessment of one of the most likely sites. This assessment showed that the most likely location was occupied by modern redevelopment (covering 83% of the site), but that the area still contained several open spaces, including a parking lot, that appeared to have remained largely undisturbed since the 16th century.

Archaeologists finally broke through the asphalt in 2012. Within six hours, they discovered a human skeleton. DNA testing later confirmed that the skeleton indeed was the long-lost remains of Richard III.

Extraordinary discoveries rarely happen where everyone else is already searching.

The same idea applies to investing.

Every day, millions of investors hunt for the next great opportunity in the public stock market. They read the same headlines, study the same charts, and debate the same handful of companies.

But many of history’s biggest fortunes weren’t made where everyone else was looking.

The Investing Path You’re Not Using

For most of us, investing begins with one clear assumption: If you want to build wealth, you buy stocks.

Maybe it’s investing in mutual funds. Maybe ETFs in strong sectors. Maybe a few individual companies you believe in.

And that’s exactly the right thing to do. Over decades, the stock market has been one of the greatest wealth-building machines ever created.

But what if you’re seeking above-average returns?

What if you’re searching for the kind of returns that can fundamentally change your financial future?

History is filled with examples of opportunities that often appeared elsewhere first. Long before Google (GOOG) traded on the Nasdaq and Facebook (META) and Snowflake (SNOW) were recognizable names, a much smaller group of investors had already discovered them.

Investors like Don Valentine, whose firm Sequoia Capital backed Apple (AAPL), Cisco (CSCO), Google, YouTube, Airbnb (ABNB), WhatsApp, and Nvidia (NVDA) before most investors had ever heard of them.

Or Peter Thiel, whose $500,000 investment in Facebook grew into more than $1 billion.

Or Marc Andreessen and Ben Horowitz, who have built fortunes by identifying transformative technology companies years before they reached the public markets.

These mega-successful investors weren’t only buying stocks. They were buying tomorrow’s public companies while they were still private.

But here is the thing you might not expect. They weren’t necessarily smarter than everyone else.

They were simply looking at a different market.

The Important Opportunity Today

As we’ve covered many times here in the Digest, we’re in the middle of the biggest technology spending boom since the dawn of the internet.

Microsoft (MSFT), Amazon (AMZN), Alphabet, Meta, OpenAI, Anthropic, and other AI leaders are collectively committing hundreds of billions of dollars this year alone to build what they believe will become the next great computing platform. And the spending isn’t expected to stop anytime soon.

But history suggests that the hyperscalers aren’t the only game in town.

The biggest breakthroughs, the ones that go on to make famous names, rarely stay confined inside the biggest companies.

Every major technology boom eventually creates an entire ecosystem of smaller innovators. These are the kinds of companies solving specialized problems, developing breakthrough solutions, designing new infrastructure, or building technologies that the giants eventually realize they can’t afford to ignore.

That’s when the next wave of wealth creation often begins.

These solutions become indispensable… then they become acquisition targets… and sometimes they become the next generation of industry leaders.

For years, investors have focused on the companies building AI.

The next phase could belong to the companies that AI giants decide they need.

And those opportunities may emerge long before they ever appear on the public stock market.

That’s exactly why I want you to hear what Luke Lango has to say.

Luke has spent years studying technological inflection points – long before Wall Street recognizes them.

He gets his subscribers in early, ahead of the big money, and helps them achieve outsized gains.

Luke has never chased yesterday’s winners. He’s always looking for the next wave of innovation and the companies positioned to benefit before everyone else catches on.

Today, he believes we’re approaching another one of those moments.

If he’s right, the biggest opportunities in AI may no longer be the “Mag 7” everyone already knows. They could be the smaller businesses developing technologies that the industry’s giants eventually decide they need to own.

Over the past year, Luke has developed a new framework to identify these opportunities that focuses on People, Product, and Timing. It’s a repeatable PPT filter for assessing whether an early-stage opportunity is worth serious consideration.

Luke’s PPT asks: Are the founders the kind of people who figure things out when everything goes wrong? Does the product solve a genuine problem, and are customers paying for it? And is the timing right? Is the market ready for this, or is the company a decade too early?

On July 30 at 1 p.m. ET, Luke will share one company that passes that filter.

But more than that, he will talk about how he found this firm, and others, so you can learn how to evaluate them yourself.

As the Richard III story reminds us, extraordinary discoveries rarely happen where everyone else is already searching.

Luke believes the same is true in investing.

On Thursday, July 30, at 1 p.m. ET, he’ll explain why he believes AI is entering a new phase and reveal one early-stage opportunity that already passes his People, Product, and Timing test.

If you want to learn how to spot tomorrow’s winners before they become household names, I hope you’ll join us.

Reserve your seat here.

Enjoy your weekend,

Luis Hernandez

Editor in Chief, InvestorPlace

The post The Secret Behind Silicon Valley’s Biggest Fortunes appeared first on InvestorPlace.

For decades, I had helped the biggest hedge funds and investment banks in the world profit from the most lucrative options flow trading hands in the markets.

And then in one fell swoop, it all nearly ended.

Let me back up and tell you the whole story…

Back in 1997, I got my break on the Chicago Mercantile Exchange (CME) floor – that’s the world’s largest options and futures exchange.

It was the early days of Globex. The pits were still packed, but the future was moving to digital screens and high-speed internet.

Speed became everything. If you couldn’t execute trades faster than the next guy, you were toast.

But by 2003, volatility dried up. I knew the easy money was gone. So I made my move…

That’s when I got recruited to a bond prop firm. That simply meant we used our own money instead of managing client funds. It was high risk/high reward.

The Fed was gearing up to move rates. I could see what was coming – bond volatility was about to explode.

So I positioned myself right in the middle of the wildest bond markets we’d seen in decades.

In 2008 alone, I made over $4 million trading volatility. I was riding high during one of the biggest bull markets in a generation.

But then the Financial Crisis happened – and disaster struck.

That bond prop firm I mentioned? We faced a potential catastrophe with just one trade.

Our firm had $32 million in capital, and a single oil trade put $15 million at risk.

Worse still, I couldn’t do much about it. The trader was the majority owner. And he didn’t want advice from someone he considered less experienced.

That trade went bad. In fact, our capital dropped from $32 million to $18 million overnight.

It was a huge loss… And it wasn’t just the firm that was doing badly.

The stress of my job was overwhelming me. I’d developed constant headaches and even ended up in the hospital a few times.

It seemed clear that I wouldn’t last too long with the way things were going.

And then a few years later, another big change happened.

In 2011, the proprietary options trading firm I’d been a partner at for seven years was sold to a billion-dollar hedge fund. And I found myself suddenly out of a job for the first time in years.

Now don’t get me wrong – I wasn’t hurting financially.

I’d certainly built up enough capital to retire comfortably.

But the uncertainty of what comes next was eating at me. I’m someone who thrives on hard work and being where the action is.

Instead of jumping back into another trading firm, I decided to take it easy. I spent more time with my family.

And I decided to pursue some dreams I’d put on hold. I even trained for and completed the Wisconsin Iron Man 140-mile triathlon.

It all felt great.

But I knew I still wanted to be where the action was. Now, I wasn’t interested in going back to the 18-hour days and mind-numbing calculations of my old job. That’s not what I loved most about trading.

My true passion was using my knowledge to help others get ahead in the markets.

I missed the daily learning and the insights I could share with friends and family. I wanted to take that next step beyond just helping institutional traders fatten their wallets.

Just when I was at my most uncertain, an opportunity came along that changed everything for me…

How I Got Back in the Game

My friend Bob was one of the best option traders in Chicago. In fact, he was one of the most successful option traders in the history of the Chicago Board Options Exchange (CBOE).  

Now keep in mind: The CBOE is the world’s biggest options market. He wasn’t playing with Monopoly money here. He was making markets with the biggest players in the game.

So one day, I got up the courage to make a bold proposition to my old friend:

Hey, let’s make a deal. 

You show me your options-trading system.   

And I’ll agree to become your student.  

I really wanted that knowledge. But I didn’t want to work for another firm for years. I didn’t need the stress.   

So for the next six months, we played golf together, and he taught me his system on the side.  

I essentially worked for him for free.     

And after learning my mentor’s time-tested options strategy, we went after those big, consistent winners together.

But this time, things were different…

I ponied up about $250,000 of my own money. That’s no small feat… And believe me, I was sweating bullets. After all, I had a quarter of a million at stake in an extremely unpredictable market.

But I wasn’t worried for long…

Every day, we executed dozens of trades on the CBOE floor.

We were winning against some of Wall Street’s biggest firms – Goldman Sachs, Bank of America, Citigroup.

Just two regular guys going head-to-head with major institutions.

We made markets on about a hundred stocks. It was intense, profitable, and exactly what I’d been missing.

After all was said and done, we cashed out with a pretty sizable bank balance after a few years.   

And I retired – again. But this time, it was on my own terms.

Now I could play sports, trade my own money – literally do whatever I wanted.  

But you know what they say… Once a trader, always a trader.

Even after the big wins we scored together, I couldn’t stop myself.

After years of making money for the biggest players in the financial world, I’d found a new purpose in life.

I learned so much trading with my friend. And even more important, I got a crash course in how the markets could work for the little guy.

It seemed like a waste to keep all that valuable information to myself.

How You Can Trade Like the “Smart Money”

And that got me thinking…

What if regular traders had access to the kind of intel Bob and I used to make markets?

Now I’m not talking about your basic technical indicators or some time-worn market truisms.

That’s all noise…

If you want to know what real market makers – the smart money – are looking at, you need to track where institutional traders are placing their biggest bets.

It’s the same intel I used to leverage at the CME and that bond prop firm.

And it’s exactly what I highlight every day I go live on Masters in Trading. Because when you know the exact timing of massive options trades, you have the beat on where capital is really moving in the market.

That knowledge is exactly what can help us capitalize on big moves that most traders never see coming.

And for anyone looking to dive deeper into the signals and setups we discover every day…

 That’s exactly what the Masters in Trading Options Challenge is designed to teach.

I’ll take you step by step through the same process I use every day—finding opportunities, structuring trades with defined risk, managing winners, and protecting your capital along the way. No hype. No guesswork. Just a practical framework you can apply to every trade you make.

If you’re serious about becoming a better options trader, join me inside the Masters in Trading Options Challenge.  I think you’ll be surprised how quickly these concepts begin to click—and how much more confident you’ll feel every time you place a trade.

Remember, the creative trader wins,

Jonathan Rose

Founder, Masters in Trading

P.S. Most people think the biggest fortunes in technology are made after a company goes public. My InvestorPlace colleague Luke Lango thinks that assumption is becoming dangerously outdated — and the numbers back him up.

Luke has been tracing the playbook that’s been running Silicon Valley for nearly 70 years. It’s the same one that turned eight frustrated engineers and a $1.5 million bet into Fairchild Semiconductor — and eventually Intel. The same one that had a young Sun Microsystems co-founder write a $100,000 check to two Stanford grad students before Google had a single dollar of revenue. And the same one that turned a $75 million stake in a little-known AI startup called Anthropic into a position now estimated near $7 billion.

The pattern is accelerating. AI’s biggest players have decided it’s faster — and cheaper — to buy breakthrough technology than build it themselves. That shift is changing where the real money gets made. And Luke has spent years building a framework to spot the companies caught up in it before the rest of the market catches on.

Next Thursday, July 30 at 1 p.m. ET, Luke is going live to walk through that framework in full — including the one company he believes best represents this opportunity today.

Seats are limited. Register now and you’ll also get access to Luke’s VIP text list, where he’s sending a bonus report — “The AI Collectors’ Portfolio: 7 Stocks to Buy for the Biggest Tech Spending Boom of All Time” — free to anyone who signs up before Thursday.

The post Most Traders Watch Price. I Spent 28 Years Watching One Thing They Never See. appeared first on InvestorPlace.

In the essay below, Luke explains why he believes that’s exactly what’s happening. Tracing nearly 70 years of Silicon Valley history, he argues the biggest fortunes are often made when large technology companies embrace innovations first developed by smaller, lesser-known businesses.

As the AI race intensifies, he believes that pattern is becoming even more important now.

To learn how Luke is applying this playbook to today’s AI boom, reserve your spot here for The 2026 AI Megadeal Event on Thursday, July 30, at 1 p.m. Eastern. When you register, you can also join Luke’s VIP text list and receive his bonus report, The AI Collectors’ Portfolio: 7 Stocks to Buy for the Biggest Tech Spending Boom of All Time.

Now, here’s Luke with the full story…

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

Back in 1957, William Shockley should have owned the future.

He had co-invented the transistor, won the Nobel Prize, and had eight of the brightest young engineers in America working under him in his Mountain View, California, laboratory.

Instead, all eight engineers quit because they found Shockley impossible to work for.

With no product and no revenue, the eight quickly realized that no institution or company would support them. Back then, the suburbs and farmland south of San Francisco and north of San Jose weren’t exactly “Silicon Valley” yet. The budding tech firms in the region weren’t quite ready to invest in unproven ideas.

So they made one phone call.

A young financier named Arthur Rock listened to their story and took a risk. 

Although Rock did not have the capital himself, he was willing to bet on people he deemed impressive.

He found a camera company willing to gamble $1.5 million on eight founders and an idea.

Thus, Fairchild Semiconductor was born. Fairchild eventually became one of the most influential technology companies in history, spawning Intel Corp. (INTC) and dozens of other semiconductor firms worth trillions of dollars today.

And the men Shockley lost became known affectionately as the “Traitorous Eight.” They were the accidental architects of a model Silicon Valley still runs on to this day.

The Traitorous Eight: That’s Gordon Moore – of “Moore’s law” fame – on the far left.

That same instinct resurfaced in 1998 when Andy Bechtolsheim sat down with two Stanford University grad students. Right on the spot, before their company had a business model or recognizable brand, the Sun Microsystems co-founder wrote a $100,000 check to Larry Page and Sergey Brin.

Anyone who’s ever Googled… well… anything knows how that story ended. But for the record: That $100,000 check reportedly bought roughly a 1% stake in Google, a position that eventually became worth tens of billions of dollars.

More recently, in 2023 Spark Capital invested $75 million in Anthropic while it was still an obscure AI startup with little revenue. Today, millions of people are on a first-name basis with Claude, and that stake is estimated to be worth roughly $7 billion.

Across nearly 70 years, the technologies and the players keep changing. The playbook doesn’t.

Rock backed eight unknown engineers. Bechtolsheim backed two graduate students. Spark Capital backed an AI startup few people had heard of.

In each case, the biggest opportunity wasn’t buying a great business after everyone recognized it. It was recognizing exceptional founders and businesses before everyone else did.

I think that same playbook matters more today than it has in decades.

First, because AI has created an unprecedented race to develop new technologies. Second, because the companies leading that race increasingly have more money than time. And finally, because that combination is changing where some of the biggest fortunes in technology are being created.

Let me explain…

Why the Giants Now Buy Instead of Build

There’s a reason this playbook has endured for nearly 70 years, and it isn’t just today’s excitement over AI.

When the prize is building the next great computing platform, speed becomes everything. If a startup has already solved a problem that would take your own engineers two years to crack, buying that company is often far cheaper than losing those two years.

That’s exactly what’s happening in today’s AI race.

Alphabet Inc. (GOOG) made that decision early, back in 2014, when it acquired the British AI startup DeepMind. Rather than spending years assembling a comparable research lab from scratch, Google bought one of the world’s best AI teams outright. More than a decade later, DeepMind sits at the heart of Google’s AI strategy.

Meta Platforms Inc. (META) reached a similar conclusion last year when it invested $14.3 billion in Scale AI. The deal wasn’t just about software. Scale AI had become one of the industry’s leading providers of the high-quality training data and infrastructure needed to build advanced AI models. Instead of trying to re-create that expertise internally, Meta bought a seat at the table.

Microsoft Corp. (MSFT) made perhaps the biggest AI boom bet of all. Its $23 billion worth of investments in OpenAI, made between 2019 and 2023, gave the company immediate access to one of the world’s leading AI developers years before it could have built a comparable capability on its own.

And this isn’t unique to AI. Cisco Systems Inc. (CSCO) spent much of the 1990s building its networking empire by buying promising startups rather than reinventing technologies itself.

Long story short, this isn’t a new playbook. It’s an old one that’s becoming even more valuable.

Every one of those deals happened because the real value had already been created inside a startup, long before Wall Street ever started paying attention.

That’s why I think one of the most important shifts in investing today is this:

The buyout, not the IPO, is increasingly becoming the finish line many early investors are aiming for.

The Playbook Hasn’t Changed

Even the best startup investors get it wrong sometimes. And nobody understands that better than the funders themselves.

Bessemer Venture Partners keeps what it calls its “Anti-Portfolio” – a public list of companies it had the opportunity to back but passed on. Google is on it. So are Apple, eBay, Airbnb, FedEx, and dozens of other companies that went on to become enormous successes.

Being early is no guarantee, but it does give you the opportunity to make a decision before the rest of the market has reached the same conclusion.

That’s the common thread running through Fairchild Semiconductor, Google, Anthropic, and countless other success stories. The biggest fortunes come from someone recognizing extraordinary people and extraordinary businesses before the consensus formed.

That’s the playbook. And I believe it’s becoming more relevant again as AI reshapes the technology landscape.

The challenge, of course, is knowing what characteristics to look for when opportunities do appear.

That’s exactly what I want to show you during my free 2026 AI Megadeal Event on Thursday, July 30, at 1 p.m. Eastern.

I’ll explain why I believe AI is creating a new generation of acquisition opportunities, walk through the framework I use to identify them, and share the one company I believe best represents this shift today.

That event is free to attend, but you must reserve your seat in order to get an invitation.

If the history of Arthur Rock, Andy Bechtolsheim, and Spark Capital teaches us anything, it’s that the biggest investment opportunities often look the least obvious at the beginning.

My goal is to help you put this playbook to work before the rest of Wall Street catches on.

I hope you’ll join me.

Sincerely,

Luke Lango

Senior Investment Analyst, InvestorPlace

P.S. When you reserve your seat for the 2026 AI Megadeal Event, you’ll also have the opportunity to join my VIP text list. As a thank-you, I’ll send you my new report, “The AI Collectors’ Portfolio: 7 Stocks to Buy for the Biggest Tech Spending Boom of All Time.” It’s free, but you need to register for the event first.

The post Why Silicon Valley’s Biggest Winners Don’t Wait for the IPO appeared first on InvestorPlace.

Now picture a second statement. Same government-created account. Same annual contribution limits. Same rules on paper.

Only this IRA holds $68 million.

It belongs to Gregory Baszucki. The gap of more than 250 times between his balance and the average American’s is not a rounding error…

Do the math on that for a moment. If the average retirement saver’s account were a single step on a staircase, Baszucki’s account would be standing on a ledge nearly the height of a 60-story building above it.

And here’s the part that should stop you dead in your tracks: Baszucki could not have gotten to $68 million by saving alone.

IRA contribution limits are strict by design, allowing a few thousand dollars a year, with no exceptions and no workarounds. At that pace, even a lifetime of maximum contributions, compounded at extraordinary market returns, could never approach eight figures.

The math simply does not permit it.

Which means the $68 million wasn’t built one paycheck at a time. Something else happened inside that account — something that allowed it to grow in value by a magnitude no salary could ever match.

Washington has now taken notice.

The New Proposal to Cap Mega Retirement Accounts Above $10 Million

Two senior members of Congress introduced legislation on July 22 aimed at capping balances like this one. 

The proposal would require certain high-income taxpayers to begin withdrawing money once their tax-preferred retirement balances exceeded $10 million. It would also prohibit additional IRA contributions when an individual’s combined retirement balances were above that level in the prior year.

According to data released with the proposal, just 208 Americans held a combined $85.1 billion in tax-sheltered retirement accounts at the end of 2024. Their average balance was an astonishing $409 million.

The proposal has only been introduced. It is not yet law, yet the message from its sponsors could not be clearer:

They do not want more people building eight- and nine-figure fortunes inside retirement accounts. From their perspective, retirement incentives were created to help ordinary workers prepare for old age – not necessarily to shelter dynastic fortunes from taxes.

Politicians see Gregory Baszucki’s $68 million account and ask: How do we stop another account from getting this large?

But a cap on the container does nothing to answer the more interesting question…

I see the same number and ask: How did he get the opportunity to build it in the first place – and why were most Americans never given the same chance?

Because the Roth IRA did not create this fortune.

The asset inside it did.

The Fortune Began Before RBLX Had a Ticker

Gregory Baszucki did not build this account by discovering a secret savings account paying 10,000% interest.

And he certainly did not contribute $68 million from his paycheck. Annual IRA contribution limits would make that impossible.

His retirement account acquired an enormous stake in Roblox while Roblox was still an off-market company.

Roblox’s original registration filing showed 2,030,000 shares held by PENSCO Trust Company as custodian for the Greg Baszucki IRA.

That was before most people could open a brokerage account, type in “RBLX,” and buy a single share.

Years later, Roblox’s 2026 proxy statement still listed approximately 1.32 million shares inside his Roth IRA.

In other words, by the time Wall Street gave the public a ticker symbol, Gregory Baszucki had already run most of the race.

The public saw an exciting new stock. Baszucki saw liquidity for an asset his account had owned before the public was invited in. And that difference – between buying before the ticker and buying after it – is the heart of this entire story.

Gregory Was Already Inside the Room

Clearly, Gregory Baszucki was no average investor.

His brother, David Baszucki, is the founder and CEO of Roblox.

Gregory has served on the company’s board since February 2008. He is also an experienced entrepreneur and the co-founder of Founder Partners, a private partnership that builds and invests in software companies.

He had proximity, experience, and strong relationships.

Most importantly, he had access.

That is not an accusation. There is nothing inherently wrong with a founder, employee, director, or early backer owning shares in a company they helped build.

It is simply a description of how the system used to work.

In my early years in Silicon Valley, I learned the rules very quickly.

There were generally three ways to get into these deals:

If none of those descriptions applied to you, you waited – for the company to become famous, for investment bankers to prepare an offering, for a ticker to appear in your brokerage account.

And while you waited, insiders accumulated shares at valuations the public might never see again.

That was the velvet rope.

Gregory Baszucki was standing on the other side of it.

The Account Was the Wrapper. Access Was the Engine.

Washington is focused on whether one person should be permitted to shelter $68 million inside a retirement account.

I am focused on why the asset capable of creating that fortune was offered to a tiny circle of insiders in the first place.

They are trying to lower the ceiling.

I want to move the velvet rope.

How Regulation Crowdfunding Opened Part of the Private Market

For decades, everyday investors were told that this was simply the natural order of things: Private deals were for venture capitalists. Public stocks were for everybody else.

You could buy the chipmaker, the bank financing the deal, or the giant corporation that might eventually acquire the young company.

But you could not own the young company itself.

At least, not until most of its identity – and potentially much of its value – had already been established.

That is no longer universally true.

Certain regulatory pathways now allow eligible companies to offer securities to non-accredited investors through SEC-registered online intermediaries.

Under Regulation Crowdfunding, for example, an eligible company can raise up to $5 million during a 12-month period. Non-accredited investors are permitted to participate, subject to limits based on their income and net worth. The transaction must occur through a registered broker-dealer or funding portal.

Many of the most desirable deals remain tightly restricted.

But the old absolute – ordinary investors cannot participate at all – is beginning to break down.

And for the first time, certain individual investors can examine opportunities that historically would have remained entirely behind the velvet rope, giving more people a legitimate opportunity to reach the starting line.

Access Is Only the Starting Point

I have spent years working in and around Silicon Valley, working alongside some of the biggest names in venture capital.

And I have watched the same story play out again and again.

A small group enters early. The company grows outside the public market. Its valuation climbs.

Years later, everybody else is introduced to the company as though the opening bell marked the beginning of its story.

Then the media publishes profiles about the astonishing fortunes created by the people who were there first.

I do not want to stand on the other side of the velvet rope, pointing into the room and explaining what you missed.

I want to move the rope.

The goal is to give individual investors access to a category of opportunity – and a disciplined way of evaluating it – that was historically reserved for the wealthy and well-connected.

That means examining the founders.

Testing whether the product solves a genuine problem.

Looking for evidence that customers are willing to pay.

Understanding the valuation and terms.

Estimating how much additional capital the company will require.

Studying dilution, competition, liquidity, and every credible reason the investment could fail.

Getting through the door is not enough.

You have to understand what is waiting on the other side.

One Pre-IPO Winner Can Change a Portfolio – and Also Go to Zero

Gregory Baszucki’s story demonstrates the strange mathematics of early ownership.

A single exceptional company can come to dominate an entire portfolio.

You do not need 100 Roblox-sized outcomes.

It only takes one.

But don’t mistake that phrase for a promise.

A single failed investment can also fall to zero.

Companies raising money through crowdfunding are often young, speculative, and unproven. Their securities may be difficult or impossible to resell, and Regulation Crowdfunding securities generally cannot be resold for one year. Non-accredited investors are also subject to annual investment limits.

That is why access alone is not the edge.

Scarcity is not analysis. And the participation of wealthy investors is not proof that an opportunity will succeed.

The real edge is combining access with a repeatable filter.

That is what I will show you.

Off-Market Investment Windows Can Close Fast

There is one more important difference between these assets and ordinary stocks.

A public stock will generally still be available tomorrow. Its price may change, but the ticker remains on your brokerage screen.

Off-market deals do not necessarily work that way.

They can have a defined maximum offering amount and a stated closing date. They can reach capacity – sometimes much sooner than you would expect. And once the available allocation has been filled, the door closes.

The specific deal I will reveal at The 2026 AI Megadeal Event on Thursday, July 30, at 1 p.m. ET is expected to close within a matter of weeks. It could fill even sooner.

You will not buy it through a traditional brokerage account, and there are no market hours to wait for. But there is a real investment window – and it will not remain open indefinitely.

So be sure to tune in to The 2026 AI Megadeal Event, where I will share everything I’ve learned about:

• What these off-market assets are and how they work
• How certain deals have become available to non-accredited investors – and which rules still apply
• Why the current flood of AI capital could create an unusual 12-to-24-month window
• How I personally evaluate a company before putting my own money behind it
• One specific off-market deal you can review immediately, completely free

Washington can continue debating whether Gregory Baszucki should be allowed to keep $68 million inside a Roth IRA.

My concern is more fundamental.

I want to make sure the next great company is not automatically reserved for someone’s brother…

Someone’s board member…

Or someone who was already wealthy enough to receive an invitation.

The velvet rope is moving.

And I intend to hold it open.

Reserve your spot now to secure your invitation.

The post How One Pre-IPO Roblox Bet Built a $68 Million Roth IRA appeared first on InvestorPlace.

Our technology expert Luke Lango thinks the next phase could reward the smaller companies those giants eventually decide they need to own.

In today’s Friday Digest takeover, Luke explains why Silicon Valley’s biggest AI players may soon find it faster to buy innovation than build it themselves – and why that shift could create a new generation of outsized winners for investors who get there early. Along the way, he lays out the framework he’s using to identify the kinds of companies that could become tomorrow’s acquisition targets.

Luke also expands on this idea during his free 2026 AI Megadeal Event on Thursday, July 30, at 1 p.m. Eastern, where he’ll walk through the strategy in greater detail and highlight one company he believes fits the pattern today. You can reserve your seat right here.

If Luke’s thesis is correct, the next big AI winners may not be the household names everyone is watching today – but the companies quietly becoming indispensable to them.

I’ll let Luke take it from here.

Have a good evening,

Jeff Remsburg

In May 2023, Spark Capital made the biggest investment in its history.

The venture-capital firm wrote an initial $75 million check to help fund Anthropic, a little-known artificial intelligence startup with no stock symbol, almost no revenue, and no way for ordinary investors to buy in.

There was no Wall Street research report telling investors what it was worth. There were no earnings estimates.

Three years later, Spark’s stake is estimated to be worth $7 billion on paper.

One investment. Nearly a 100-fold return.

Spark Capital only needed one Anthropic. History suggests investors often don’t need many, either.

I keep coming back to that story because I think it marks the beginning of the current AI investment cycle. Investors like Spark provided the capital that helped companies like Anthropic grow into some of the most valuable businesses in the world.

But that cycle is already changing.

The companies that were startups just a few years ago are fast becoming giants themselves: Anthropic, OpenAI, and others. Along with Microsoft Corp. (MSFT), Alphabet Inc. (GOOG), Amazon.com Inc. (AMZN), Meta Platforms Inc. (META), and Nvidia Corp. (NVDA), they’re raising – and spending – enormous amounts of money, and racing to build what they believe will become the next great computing platform.

Sooner or later, though, they’ll find they can’t invent everything themselves. No company — not even the AI labs valued near $1 trillion — can hire every brilliant engineer or invent every breakthrough first.

Long story short, I think we’re entering the next phase of the AI boom.

The first phase rewarded investors who recognized that AI infrastructure – chips, memory, networking, power, and data centers – would become essential. I still believe many of those companies have room to run.

But the next phase could look very different.

Instead of asking, “Which companies will build AI?” investors may soon need to ask, “Which companies will AI’s biggest players decide they have to own?”

If I’m right, answering that one question could make the difference between simply participating in the AI Revolution… and getting there before the rest of Wall Street catches on.

That’s what I want to show you today.

First, I’ll explain why I think the AI arms race is entering a new phase.

Then I’ll show you how Silicon Valley’s biggest companies tip their hands long before they announce their next blockbuster acquisition.

Finally, I’ll explain the framework I’ve started using to identify the kinds of companies I believe could become tomorrow’s biggest winners, whether they eventually go public or get bought first.

Let’s start by following the money…

Follow the Money

One of the first rules I learned as an investor is that money leaves clues. When hundreds of billions of dollars begin flowing in the same direction, I pay attention.

Right now, the money isn’t just flowing into Nvidia.

It’s flowing into the entire AI ecosystem.

Follow the money.

Amazon alone expects to pour roughly $200 billion into capital projects this year. Microsoft and Alphabet are each planning about $190 billion. Meta could spend another $135 billion.

That’s roughly $700 billion in a single year, or about $2 billion every single day.

That’s an arms race.

Meanwhile, Anthropic’s latest funding valued the company at around $965 billion. And OpenAI is said to be worth roughly $852 billion.

But even when companies have that kind of money, they can’t invent everything themselves. Call it Silicon Valley’s dirty little secret: It’s often faster to buy innovation than build it yourself.

Think about what these companies are trying to accomplish.

The companies leading AI genuinely believe they’re building the next computing platform. When the stakes get that high, companies stop asking, “Can we build this?” and  start asking, “Who already has?”

We’ve seen this movie before.

In 2012, Facebook paid $1 billion for Instagram. At the time, it looked ridiculous.

Instagram had just 13 employees. It wasn’t making money. Most people thought Mark Zuckerberg had wildly overpaid for a photo-sharing app that let people put vintage-looking filters on pictures of their lunch.

Turns out, Zuckerberg got the bargain of the century. Instagram has since become one of Meta’s most valuable businesses. Last year, Meta estimated Instagram’s brand value at over $70 billion, and said it generates nearly $67 billion in annual revenue.

But that’s not what I find most interesting.

The biggest winners weren’t the people who bought Meta stock after the Instagram deal was announced. They were the people who already owned a piece of Instagram before Zuckerberg came calling.

And this pattern continued throughout the last few tech booms:

Google bought Android before smartphones became ubiquitous and picked up YouTube before online video dominated media.

Facebook bought Instagram and WhatsApp before those businesses reached their full potential.

Microsoft acquired GitHub as software development became increasingly collaborative and cloud-based.

Notice the pattern. None of these companies were acquired because the buyers were running out of money. They were acquired because the buyers were running out of time.

I think AI is setting up a similar dynamic, only on a much bigger scale.

The Ticker Is No Longer the Starting Line

For decades, most investors assumed the stock market was where great companies began.

Increasingly, it’s becoming where they finish their first chapter.

Think about Space Exploration Technologies Corp. (SPCX).

Millions of investors finally had the opportunity to buy shares after the IPO. But by then, SpaceX had already spent years building rockets, launching satellites, signing government contracts, and creating enormous value.

And within weeks of the IPO, its shares slid far below its high of $202.50 – as well as the original $135 offer price.

If you got in on Day One, you were part of the largest IPO in history. But the momentous occasion didn’t prevent you from getting caught in a painful drawdown.

Investors who entered years earlier were playing a different game entirely. They bought at private valuations far below the one public investors received on IPO day. A modest change in the public stock price therefore means something very different to those two groups.

Two people can believe equally in the same company and still walk away with radically different results.

That’s why I’ve recently started asking: Which companies will become so important that an AI giant decides it cannot afford to compete against them?

Some of those companies will become the next generation of AI leaders. Others may receive buyout offers long before they ever ring the opening bell on Wall Street.

Either path can create enormous value.

The challenge is recognizing those businesses early.

Over the past year, I’ve built an entirely different framework for finding those opportunities.

It isn’t based on chasing whatever stock is trending on social media. It isn’t based on guessing tomorrow’s headlines. Instead, it’s based on following the money.

I study where Silicon Valley is investing, what capabilities the largest AI companies still lack, and which smaller businesses are solving problems the giants may eventually decide they need to own.

It’s a different research process and a whole different way of looking at the AI boom.

And honestly, I think it’s one of the most exciting parts of this entire cycle.

History tells us that during technological revolutions, the headlines almost always focus on the giants.

But the biggest fortunes are often created one or two layers beneath them.

Back in 2023, Spark Capital saw something in Anthropic that most of the world could not. That one decision may become one of the defining investments of the AI era.

I believe the next chapter of AI could produce similar opportunities –because every technological revolution creates a new generation of companies solving problems the giants can’t solve alone.

My goal isn’t to find another Anthropic. It’s to find the companies Anthropic and the rest decide they can’t afford to ignore.

That’s what I’m going to show you on Thursday, July 30, at 1 p.m. Eastern. On that day, going to walk you through this framework during a free online event I’m calling The 2026 AI Megadeal Event. (You can reserve your spot here.)

I’m going over a few things during that event.

• Why I believe the AI investment cycle is entering an entirely new phase.
• The framework I use to identify companies that could become tomorrow’s AI leaders – or tomorrow’s acquisition targets.
• One specific opportunity I believe illustrates exactly how this next phase could unfold.

The first phase of the AI boom rewarded the companies building the future. I think the second phase could reward the companies those builders decide they need to own.

And I’m going to show you how to look for them. The event is free. All you need to do is reserve your seat here in order to receive an invitation (plus my bonus report: “The AI Collectors’ Portfolio: 7 Stocks to Buy for the Biggest Tech Spending Boom of All-Time.”)

I hope you’ll join me.

Sincerely,

Luke Lango

Senior Investment Analyst, InvestorPlace

The post How to Get in Position Before the Next AI Megadeal appeared first on InvestorPlace.

My colleague Luke Lango believes AI may be approaching a similar turning point.

He argues that the first phase of the AI boom rewarded the companies building the technology. But as those companies grow larger, the next opportunities may come from the smaller innovators they decide they need to own.

In today’s guest essay, Luke explains why he believes that shift is already underway, the clues he watches for and the framework he uses to search for potential winners.

If you’d like to learn more, I encourage you to reserve your spot for Luke’s free2026 AI Megadeal Eventon Thursday, July 30, at 1 p.m. Eastern. He’ll take a deeper dive into this framework and share one company he believes illustrates how this next phase of AI could unfold.

Now, here’s Luke to show you what he’s watching – and why he thinks it matters now…

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

In May 2023, Spark Capital made the biggest investment in its history.

The venture-capital firm wrote an initial $75 million check to help fund Anthropic, a little-known artificial intelligence startup with no stock symbol, almost no revenue, and no way for ordinary investors to buy in.

There was no Wall Street research report telling investors what it was worth. There were no earnings estimates.

Three years later, Spark’s stake is estimated to be worth $7 billion on paper.

One investment. Nearly a 100-fold return.

Spark Capital only needed one Anthropic. History suggests investors often don’t need many, either.

I keep coming back to that story because I think it marks the beginning of the current AI investment cycle. Investors like Spark provided the capital that helped companies like Anthropic grow into some of the most valuable businesses in the world.

But that cycle is already changing.

The companies that were startups just a few years ago are fast becoming giants themselves: Anthropic, OpenAI, and others. Along with Microsoft Corp. (MSFT), Alphabet Inc. (GOOG), Amazon.com Inc. (AMZN), Meta Platforms Inc. (META), and Nvidia Corp. (NVDA), they’re raising – and spending – enormous amounts of money, and racing to build what they believe will become the next great computing platform.

Sooner or later, though, they’ll find they can’t invent everything themselves. No company — not even the AI labs valued near $1 trillion — can hire every brilliant engineer or invent every breakthrough first.

Long story short, I think we’re entering the next phase of the AI boom.

The first phase rewarded investors who recognized that AI infrastructure – chips, memory, networking, power, and data centers – would become essential. I still believe many of those companies have room to run.

But the next phase could look very different.

Instead of asking, “Which companies will build AI?” investors may soon need to ask, “Which companies will AI’s biggest players decide they have to own?”

If I’m right, answering that one question could make the difference between simply participating in the AI Revolution… and getting there before the rest of Wall Street catches on.

That’s what I want to show you today.

First, I’ll explain why I think the AI arms race is entering a new phase.

Then I’ll show you how Silicon Valley’s biggest companies tip their hands long before they announce their next blockbuster acquisition.

Finally, I’ll explain the framework I’ve started using to identify the kinds of companies I believe could become tomorrow’s biggest winners, whether they eventually go public or get bought first.

Let’s start by following the money…

Follow the Money

One of the first rules I learned as an investor is that money leaves clues. When hundreds of billions of dollars begin flowing in the same direction, I pay attention.

Right now, the money isn’t just flowing into Nvidia.

It’s flowing into the entire AI ecosystem.

Follow the money.

Amazon alone expects to pour roughly $200 billion into capital projects this year. Microsoft and Alphabet are each planning about $190 billion. Meta could spend another $135 billion.

That’s roughly $700 billion in a single year, or about $2 billion every single day.

That’s an arms race.

Meanwhile, Anthropic’s latest funding valued the company at around $965 billion. And OpenAI is said to be worth roughly $852 billion.

But even when companies have that kind of money, they can’t invent everything themselves. Call it Silicon Valley’s dirty little secret: It’s often faster to buy innovation than build it yourself.

Think about what these companies are trying to accomplish.

The companies leading AI genuinely believe they’re building the next computing platform. When the stakes get that high, companies stop asking, “Can we build this?” and  start asking, “Who already has?”

We’ve seen this movie before.

In 2012, Facebook paid $1 billion for Instagram. At the time, it looked ridiculous.

Instagram had just 13 employees. It wasn’t making money. Most people thought Mark Zuckerberg had wildly overpaid for a photo-sharing app that let people put vintage-looking filters on pictures of their lunch.

Turns out, Zuckerberg got the bargain of the century. Instagram has since become one of Meta’s most valuable businesses. Last year, Meta estimated Instagram’s brand value at over $70 billion, and said it generates nearly $67 billion in annual revenue.

But that’s not what I find most interesting.

The biggest winners weren’t the people who bought Meta stock after the Instagram deal was announced. They were the people who already owned a piece of Instagram before Zuckerberg came calling.

And this pattern continued throughout the last few tech booms:

Google bought Android before smartphones became ubiquitous and picked up YouTube before online video dominated media.

Facebook bought Instagram and WhatsApp before those businesses reached their full potential.

Microsoft acquired GitHub as software development became increasingly collaborative and cloud-based.

Notice the pattern. None of these companies were acquired because the buyers were running out of money. They were acquired because the buyers were running out of time.

I think AI is setting up a similar dynamic, only on a much bigger scale.

The Ticker Is No Longer the Starting Line

For decades, most investors assumed the stock market was where great companies began.

Increasingly, it’s becoming where they finish their first chapter.

Think about Space Exploration Technologies Corp. (SPCX).

Millions of investors finally had the opportunity to buy shares after the IPO. But by then, SpaceX had already spent years building rockets, launching satellites, signing government contracts, and creating enormous value.

And within weeks of the IPO, its shares slid far below its high of $202.50 – as well as the original $135 offer price.

If you got in on Day One, you were part of the largest IPO in history. But the momentous occasion didn’t prevent you from getting caught in a painful drawdown.

Investors who entered years earlier were playing a different game entirely. They bought at private valuations far below the one public investors received on IPO day. A modest change in the public stock price therefore means something very different to those two groups.

Two people can believe equally in the same company and still walk away with radically different results.

That’s why I’ve recently started asking: Which companies will become so important that an AI giant decides it cannot afford to compete against them?

Some of those companies will become the next generation of AI leaders. Others may receive buyout offers long before they ever ring the opening bell on Wall Street.

Either path can create enormous value.

The challenge is recognizing those businesses early.

Over the past year, I’ve built an entirely different framework for finding those opportunities.

It isn’t based on chasing whatever stock is trending on social media. It isn’t based on guessing tomorrow’s headlines. Instead, it’s based on following the money.

I study where Silicon Valley is investing, what capabilities the largest AI companies still lack, and which smaller businesses are solving problems the giants may eventually decide they need to own.

It’s a different research process and a whole different way of looking at the AI boom.

And honestly, I think it’s one of the most exciting parts of this entire cycle.

History tells us that during technological revolutions, the headlines almost always focus on the giants.

But the biggest fortunes are often created one or two layers beneath them.

Back in 2023, Spark Capital saw something in Anthropic that most of the world could not. That one decision may become one of the defining investments of the AI era.

I believe the next chapter of AI could produce similar opportunities –because every technological revolution creates a new generation of companies solving problems the giants can’t solve alone.

My goal isn’t to find another Anthropic. It’s to find the companies Anthropic and the rest decide they can’t afford to ignore.

That’s what I’m going to show you on Thursday, July 30, at 1 p.m. Eastern. On that day, going to walk you through this framework during a free online event I’m calling The 2026 AI Megadeal Event. (You can reserve your spot here.)

I’m going over a few things during that event.

• Why I believe the AI investment cycle is entering an entirely new phase.
• The framework I use to identify companies that could become tomorrow’s AI leaders – or tomorrow’s acquisition targets.
• One specific opportunity I believe illustrates exactly how this next phase could unfold.

The first phase of the AI boom rewarded the companies building the future. I think the second phase could reward the companies those builders decide they need to own.

And I’m going to show you how to look for them. The event is free. All you need to do is reserve your seat here in order to receive an invitation (plus my bonus report: “The AI Collectors’ Portfolio: 7 Stocks to Buy for the Biggest Tech Spending Boom of All-Time.”)

I hope you’ll join me.

Sincerely,

Luke Lango

Senior Investment Analyst, InvestorPlace

The post One AI Deal Could Be Worth More Than a Portfolio Full of Stocks appeared first on InvestorPlace.

He had co-invented the transistor, won the Nobel Prize, and had eight of the brightest young engineers in America working under him in his Mountain View, California, laboratory.

Instead, all eight engineers quit because they found Shockley impossible to work for.

With no product and no revenue, the eight quickly realized that no institution or company would support them. Back then, the suburbs and farmland south of San Francisco and north of San Jose weren’t exactly “Silicon Valley” yet. The budding tech firms in the region weren’t quite ready to invest in unproven ideas.

So they made one phone call.

A young financier named Arthur Rock listened to their story and took a risk. 

Although Rock did not have the capital himself, he was willing to bet on people he deemed impressive.

He found a camera company willing to gamble $1.5 million on eight founders and an idea. 

Thus, Fairchild Semiconductor was born. Fairchild eventually became one of the most influential technology companies in history, spawning Intel Corp. (INTC) and dozens of other semiconductor firms worth trillions of dollars today.

And the men Shockley lost became known affectionately as the “Traitorous Eight.” They were the accidental architects of a model Silicon Valley still runs on to this day.

The Traitorous Eight: That’s Gordon Moore – of “Moore’s law” fame – on the far left. 

That same instinct resurfaced in 1998 when Andy Bechtolsheim  sat down with two Stanford University grad students. Right on the spot, before their company had a business model or recognizable brand, the Sun Microsystems co-founder wrote a $100,000 check to Larry Page and Sergey Brin. 

Anyone who’s ever Googled… well… anything knows how that story ended. But for the record: That $100,000 check reportedly bought roughly a 1% stake in Google, a position that eventually became worth tens of billions of dollars.

More recently, in 2023 Spark Capital invested $75 million in Anthropic while it was still an obscure AI startup with little revenue. Today, millions of people are on a first-name basis with Claude, and that stake is estimated to be worth roughly $7 billion.

Across nearly 70 years, the technologies and the players keep changing. The playbook doesn’t.

Rock backed eight unknown engineers. Bechtolsheim backed two graduate students. Spark Capital backed an AI startup few people had heard of. 

In each case, the biggest opportunity wasn’t buying a great business after everyone recognized it. It was recognizing exceptional founders and businesses before everyone else did.

I think that same playbook matters more today than it has in decades.

First, because AI has created an unprecedented race to develop new technologies. Second, because the companies leading that race increasingly have more money than time. And finally, because that combination is changing where some of the biggest fortunes in technology are being created.

Let me explain…

Why AI Giants Buy to Fill Critical Gaps

There’s a reason this playbook has endured for nearly 70 years, and it isn’t just today’s excitement over AI.

When the prize is building the next great computing platform, speed becomes everything. If a startup has already solved a problem that would take your own engineers two years to crack, buying that company is often far cheaper than losing those two years.

That’s exactly what’s happening in today’s AI race.

Alphabet Inc. (GOOG) made that decision early, back in 2014, when it acquired the British AI startup DeepMind. Rather than spending years assembling a comparable research lab from scratch, Google bought one of the world’s best AI teams outright. More than a decade later, DeepMind sits at the heart of Google’s AI strategy.

Meta Platforms Inc. (META) reached a similar conclusion last year when it invested $14.3 billion in Scale AI. The deal wasn’t just about software. Scale AI had become one of the industry’s leading providers of the high-quality training data and infrastructure needed to build advanced AI models. Instead of trying to re-create that expertise internally, Meta bought a seat at the table.

Microsoft Corp. (MSFT) made perhaps the biggest AI boom bet of all. Its $23 billion worth of investments in OpenAI, made between 2019 and 2023, gave the company immediate access to one of the world’s leading AI developers years before it could have built a comparable capability on its own.

And this isn’t unique to AI. Cisco Systems Inc. (CSCO) spent much of the 1990s building its networking empire by buying promising startups rather than reinventing technologies itself.

Long story short, this isn’t a new playbook. It’s an old one that’s becoming even more valuable.

Every one of those deals happened because the real value had already been created inside a startup, long before Wall Street ever started paying attention.

That’s why I think one of the most important shifts in investing today is this:

The buyout, not the IPO, is increasingly becoming the finish line many early investors are aiming for.

How to Identify AI Acquisition Targets Before Wall Street

Even the best startup investors get it wrong sometimes. And nobody understands that better than the funders themselves.

Bessemer Venture Partners keeps what it calls its “Anti-Portfolio” – a public list of companies it had the opportunity to back but passed on. Google is on it. So are Apple, eBay, Airbnb, FedEx, and dozens of other companies that went on to become enormous successes.

Being early is no guarantee, but it does give you the opportunity to make a decision before the rest of the market has reached the same conclusion.

That’s the common thread running through Fairchild Semiconductor, Google, Anthropic, and countless other success stories. The biggest fortunes come from someone recognizing extraordinary people and extraordinary businesses before the consensus formed.

That’s the playbook. And I believe it’s becoming more relevant again as AI reshapes the technology landscape.

The challenge, of course, is knowing what characteristics to look for when opportunities do appear.

That’s exactly what I want to show you during my free 2026 AI Megadeal Event on Thursday, July 30, at 1 p.m. Eastern. 

I’ll explain why I believe AI is creating a new generation of acquisition opportunities, walk through the framework I use to identify them, and share the one company I believe best represents this shift today. 

That event is free to attend, but you must reserve your seat in order to get an invitation.

If the history of Arthur Rock, Andy Bechtolsheim, and Spark Capital teaches us anything, it’s that the biggest investment opportunities often look the least obvious at the beginning.

My goal is to help you put this playbook to work before the rest of Wall Street catches on.

The post The Ticker May Be Too Late for AI’s Next Great Winners appeared first on InvestorPlace.

Yesterday, after the closing bell, Alphabet (GOOG) reported its second-quarter results, and it was a whopper.

The tech giant blew past expectations, showing massive growth across its entire business:

• Total revenue: up 24% year-over-year to $119.8 billion.
• Google search revenue: up 17%.
• Google Cloud (the AI engine): rocketed 82%.
• Operating income: up 30% while operating margins expanded to 34%.

But the real issue going into the report was its capex guidance…

Would Alphabet maintain its commitments to AI infrastructure?

Yes – and then some.

Its capex increased 100% year over year to $44.9 billion. And it increased its already elevated full-year 2026 outlook of $180 billion to $190 billion, established in April, to $195 billion to $205 billion. And it won’t stop there…

CFO Anat Ashkenazi reiterated that 2027 spending will “significantly increase.”

Now, the downside of this is that the aggressive capex bill resulted in a negative free cash flow of -$5.85 billion for the quarter. This is weighing on Alphabet’s stock price today. As I write on Thursday, the stock is down 7%.

As has been the pattern in recent quarters, Wall Street is panicking about this colossal capex spend, fearing the returns won’t justify it. But beyond that fear, there’s no way to read this as anything other than a blockbuster performance. CEO Sundar Pichai summed it up this way:

Our AI investments are redefining what’s possible across every part of our business.

Alphabet down, AI trade up

Going into last night, our technology expert Luke Lango, editor of Innovation Investor, gave us the playbook…

If Alphabet confirmed and/or raised its capex guidance, it would begin to firm up the AI infrastructure trade, which has taken a bath in recent weeks.

Sure enough, as I write on Thursday, though the Nasdaq is down about 2%, Western Digital (WDC) is up 5%, Marvell (MRVL) is 2% higher, and Seagate (STX) has added 3%. Other AI infrastructure darlings are also outperforming.

I reached out to Luke after the results, and he told me:

Alphabet’s results were stunning and a broad, strong rebuttal of “peak spending” fears which have weighed on the AI trade for the last two months…

So, they’re going to spend more. The 2026 capex forecast was boosted ~5% from $190B to $200B, its second hike this year already. That’s not a peak. That’s an acceleration…

We just got the confirmation we needed. The hyperscalers are going to keep spending. The party rolls on. 

Bottom line: Alphabet is the first Magnificent 7/hyperscaler domino to fall this earnings season, and the numbers were fantastic – despite the stock taking a beating today.

But that prompts a question…

When will the “Lag 7” return to being the “Mag 7”?

In recent months, as the performance of the Magnificent 7 stocks has underwhelmed, the financial media has come up with an alternative name – the “Lag 7.”

Through late June, the Mag 7 were down about 3% on average year-to-date, while the S&P 500 was up nearly 9% over the same stretch.

Why?

In a word: capex – the same issue that has Alphabet deep in the red today.

Investors have grown nervous that the hundreds of billions these companies are pouring into AI data centers won’t pay off fast enough to justify the spend.

As we’ve been covering here in the Digest, those investment dollars have been rotating out of the AI spenders and into the AI infrastructure suppliers, which have soared even as the Mag 7 lagged.

Now, this capex spend is a legitimate issue for Mag 7 owners to consider. But here’s what the “Lag 7” narrative has forgotten…

The Mag 7’s Q1 earnings were generally quite strong, and projected Q2 earnings are equally impressive.

Here’s FactSet:

In aggregate, the “Magnificent 7” companies have reported higher (year-over-year) earnings growth than the other 493 companies in the S&P 500 over the past several quarters.

Is this trend expected to continue in Q2 2026? The answer is yes.

For Q2 2026, the estimated (year-over-year) earnings growth rate for the “Magnificent 7” companies is 31.1%.

On the other hand, the blended (combines actual and estimated results) earnings growth rate for the remaining 493 companies in the S&P 500 for the second quarter is 22.8%.

Thirty-one percent growth isn’t the profile of a group that’s “lagging.” It’s the profile of a group still doing exactly what earned it the “Magnificent” label in 2023.

Meanwhile, here’s what’s been mostly left out of the “Lag 7” critique…

It’s a one-sided read.

It focuses almost entirely on what the hyperscalers are spending through the lens of “the returns won’t justify it.”

But what if they do? What if Wall Street just needs to take a deep breath and relax?

It’s worth remembering that investors have been wrong about this exact question before. The cloud buildout of the 2010s drew the same kind of margin anxiety at the time – and it went on to become one of the more durable profit engines in corporate history.

I dug up a Wall Street Journal article from 2014 titled “Google, Amazon and Microsoft’s Costly Spending War” that noted “being a tech giant ain’t cheap,” and then quoted Bernstein Research analyst Carlos Kirjner:

Google’s remarkable capex increase over the last year has raised concerns among investors.

Other articles from that period highlighted the anxious handwringing of investors due to the massive capex spend.

Sound familiar?

And how’d that turn out?  Well, when Amazon (AMZN) finally unbundled Amazon Web Services’ financial reporting in early 2015, Wall Street began to change its tune. Rather than a money pit, AWS was revealed to be a massive, highly efficient business generating billions in high-margin software revenue

This doesn’t guarantee AI capex plays out the same way. The scope of the capex spending today is on a completely different level.

Still, it’s a reminder that cries of “We’re spending too much” today could turn into “Wow! What foresight and vision!” tomorrow.

This is what we’ll be tracking. But history suggests that, when in doubt, we should give these Mag 7 management teams the benefit of the doubt.

But the good news doesn’t stop with Big Tech

Let’s circle back to the FactSet quote from a moment ago.

Did you catch this?

On the other hand, the blended (combines actual and estimated results) earnings growth rate for the remaining 493 companies in the S&P 500 for the second quarter is 22.8%.

That figure isn’t just solid – FactSet notes it would mark the strongest growth the “other 493” have posted since Q4 2021.

And the trend is expected to broaden even further as the year goes on…

FactSet projects that by Q4 2026, the other 493 companies will actually outgrow the Mag 7: 25.3% versus 22.8%.

That fits with what we’ve been seeing in the “Lag 7” rotation: money moving into names that sit outside the traditional Mag 7 but are riding the same AI wave.

This helps explain why legendary investor Louis Navellier, editor of Growth Investor, is so bullish today…

The “best market environment since 1999”

Let’s go straight to Louis:

The second quarter was the best-performing quarter for the NASDAQ and S&P 500 in six years…

I believe this is the best market environment we have seen since 1999…

In fact, I believe the current AI boom could ultimately be even more powerful than the internet boom of the 1990s.

It’s important to understand that this isn’t Louis being a perma-bull. His optimism is anchored in economic strength.

He notes that GDP grew at a 2.1% annual pace in the first quarter. Growth cooled a bit in the second quarter, but it is set to reaccelerate in the second half of 2026. And Louis is calling for GDP to hit “at least a 5% annual pace” in Q3.

Back to the investment legend:

Economic growth is poised to reaccelerate. The AI buildout is still gathering momentum. And most importantly, corporate profits are accelerating.

That is why the foundation beneath this market remains solid…

An economic reacceleration would goose what’s already been a period of strong returns for the market.

For example. I’m looking at Louis’ Growth Investor portfolio, seeing returns including:

• Broadcom, Inc. (AVGO): 363%
• Carpenter Tech. (CRS): 212%
• EMCOR Group (EME): 249%
• Quanta Services (PWR): 421%

And if Louis is right, these are the kinds of stocks that have more room to climb as the hyperscalers continue spending.

If you’d like Louis’ help in finding tomorrow’s triple-digit winners as this “best market environment since 1999” continues, click here to learn about joining him in Growth Investor.

But what about the AI bubble?

Let me push back on all this optimism with a critique I’ve made in recent years…

It’s expensive.

Uber bears put it more dramatically: “We’re so overvalued today that we’re on the verge of a catastrophic crash that will put the dot-com crash to shame!”

But here’s the thing about all that capex from the hyperscalers…

It’s growing earnings so quickly that forward-looking valuations have been coming down significantly. This requires us to reassess the market’s overall price tag.

To do this, let’s use the forward P/E ratio: it compares today’s prices to forecasted earnings over the next 12 months.

According to FactSet, the S&P 500 has a forward P/E ratio of about 20.

Is this an egregious “super bubble that must pop” valuation?

No.

Over the last decade, the average forward P/E has been 19.

At 20, the market is slightly more expensive than usual, but nowhere near a runaway, terrifying bubble. For comparison, during the Dot-Com crash of 2000, this number pushed past 23.

Plus, this relatively high price tag of 20 is distorted by just a few massive tech giants. If you strip away those top heavyweights and look at the other 490+ stocks in the S&P 500, the rest of the market is trading at a much cheaper, more normal historical average of around 16 to 17.

Yes, you might want to diversify some of your portfolio away from higher-valuation tech into lower-valuation sectors. But that would be more of a rebalancing rather than a panicked “escape the bust” reaction.

One final reason for confidence…

As we’ve just looked at, robust earnings growth is the solution to high valuations. So, how are earnings growth rates shaping up as we look ahead?

Back to FactSet:

For the second quarter, S&P 500 companies are reporting year-over-year growth in earnings of 24.7% and year-over-year growth in revenues of 12.8%.

For Q3 2026, analysts are projecting earnings growth of 27.0% and revenue growth of 10.8%.

For Q4 2026, analysts are projecting earnings growth of 24.6% and revenue growth of 10.4%.

For CY 2026, analysts are projecting earnings growth of 24.5% and revenue growth of 10.9%.

With numbers like this, Louis’ optimism about today’s market opportunities makes far more sense.

Back to the legendary investor:

Please – pinch yourself. You are not dreaming. The opportunity is real, folks.

It is time to grow and prosper.

Again, for Louis’ help, click here to learn about joining him in Growth Investor.

We’ll keep tracking the rest of the hyperscalers reports as they roll in over the next two weeks. But so far, so good for the AI trade.

Have a good evening,

Jeff Remsburg

The post The AI Trade Isn’t Slowing appeared first on InvestorPlace.

That is why I warned investors to stay away from the Space Exploration Technologies Corp. (SPCX) IPO.

A great company can still be a bad investment if you pay the wrong price. And the excitement surrounding a new stock can make that mistake especially easy to make.

In the case of SPCX, anyone who bought the stock when it went public on June 12 is now holding the stock for a loss. 

Of course, this isn’t the first time an IPO has turned on investors. The same story played out over and over again during the dot-com bubble.

Back then, venture capitalists played an important role in funding young Internet companies and helping them mature. But as greed took over, that system broke down. Wall Street began pumping out one hot IPO after another, often at absurd valuations, because individual investors were willing to buy almost anything tied to the Internet.

In some cases, having no sales was treated as an advantage. Without revenue or earnings, there was nothing concrete investors could use to value the company. Promoters could simply tell a bigger story.

Then the bubble burst.

The NASDAQ plunged 77.9% from its March 2000 peak to its October 2002 low, and individual investors paid the steepest price.

See, an IPO may look like the starting line to the public. But for founders, early employees and venture investors, it can be the exit ramp after years of value creation.

But what about the wealth being created before a company ever reaches Wall Street?

In today’s Market 360, I want to discuss the incredible shift we’ve seen in private-company valuations in recent years – and how the AI Revolution is accelerating this shift. I’ll also explain how this market has become too large for individual investors to ignore – and introduce you to the technology expert I trust to investigate these opportunities before Wall Street catches on.

The Market Behind the Market

Before I go any further, I should add that the problem during the dot-com bubble was not venture capital itself.

Venture capital serves an important purpose. See, most startup companies are just too plain risky for traditional financing methods, like banks. Venture capital gives young companies the money and guidance they need to turn promising ideas into real businesses.

So, in many cases, that early support helps remove some of the risk before a company reaches the public market.

The trouble begins when the goal shifts from building a durable business to rushing an exciting story onto Wall Street just to make a quick buck.

That distinction matters today, because here’s the crazy part… The private-company market has grown far beyond anything we saw during the dot-com era.

Back in 2013, privately held companies worth at least $1 billion were so rare that venture capitalist Aileen Lee called them “unicorns.”

Today, according to Crunchbase, there are 1,821 unicorn companies around the world with a combined valuation of $8.8 trillion.

In other words, this is no longer some small corner of the financial world.

The term “unicorn” no longer really applies, if you think about it.

But it also means that private companies can now raise billions of dollars, hire thousands of employees, develop important technology and reach enormous valuations before ordinary investors ever see a ticker symbol.

You probably already know a few of the biggest names – but you may not know their staggering valuations:

Source: Crunchbase

Those are just a few of the big ones. But many more companies are working outside the spotlight. They are building specialized software, tools and technologies that could become critical to the next stage of the AI Revolution.

And some may never reach the stock market at all.

The Next Question Investors Should Ask

Tomorrow, you’ll hear from my InvestorPlace colleague Luke Lango about why the biggest technology companies increasingly buy innovation instead of building it themselves.

As Luke will explain, the AI race is moving so quickly that even giants like Alphabet Inc. (GOOG), Meta Platforms, Inc. (META) and Microsoft Corporation (MSFT) cannot develop every important capability in-house. When a smaller company has already solved a problem that could take years to crack, buying it may be faster and cheaper than starting from scratch.

That changes the question investors should be asking. Instead of only asking which large AI stock could be the next big winner, we should also ask which smaller company could become so important that one of those giants decides it has to own it.

We need to ask that question because, by the time a buyout is announced, much of the early value may already have been created.

The Technology Expert I Turn To

Now, my Stock Grader system evaluates publicly traded companies using hard data, including sales and earnings growth, analyst revisions and institutional buying pressure.

Private companies do not offer the same trail of public information.

So, when I want to understand a new technology before Wall Street has fully wrapped its arms around it, I turn to Luke.

Luke is a Caltech graduate, a former startup founder and one of the sharpest young technology analysts I know. He understands the technology, the founders and the Silicon Valley networks behind these companies.

More importantly, he has a knack for spotting important trends before they become obvious to Wall Street.

Right now, Luke believes many investors are watching the giant companies spending hundreds of billions of dollars on AI while overlooking the smaller businesses receiving that money, solving critical problems and positioning themselves as possible acquisition targets.

Of course, not every young AI company will succeed. The dot-com crash taught us what happens when investors stop asking hard questions and start buying stories.

That is why Luke studies the people running the company, the product they have built and whether the timing is right.

Your Ticket to a Different Side of the AI Boom

On Thursday, July 30, at 1 p.m. Eastern, Luke will explain this approach during a free online presentation called The 2026 AI Megadeal Event.

He will show you why he believes the AI investment cycle is entering a new phase, explain what makes a smaller company attractive to a technology giant and reveal one specific opportunity he believes illustrates the trend.

Go here now to reserve your seat.

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:

Alphabet Inc. (GOOG)

The post How to Get Ahead of the Next AI Megadeal appeared first on InvestorPlace.

A decade later, Instagram alone generates tens of billions in revenue – and the people who profited most never bought a share of Meta stock. They owned a piece of Instagram long before Zuckerberg came knocking.

My colleague Luke Lango, InvestorPlace’s tech and growth specialist, thinks that same pattern is repeating itself across the AI boom – just at a much larger scale.

So, for today’s Smart Money, I invited Luke to track where Silicon Valley’s biggest AI labs are pouring their money, and he makes the case that the next fortunes won’t go to the companies building AI, but to the smaller players the giants can’t afford to compete with. Luke will walk through the full framework — plus one specific company he thinks fits the pattern — during a free online event this Thursday, July 30, at 1 p.m. Eastern. Reserve your seat here.

In May 2023, Spark Capital made the biggest investment in its history.

The venture-capital firm wrote an initial $75 million check to help fund Anthropic, a little-known artificial intelligence startup with no stock symbol, almost no revenue, and no way for ordinary investors to buy in.

There was no Wall Street research report telling investors what it was worth. There were no earnings estimates.

Three years later, Spark’s stake is estimated to be worth $7 billion on paper.

One investment. Nearly a 100-fold return.

Spark Capital only needed one Anthropic. History suggests investors often don’t need many, either.

I keep coming back to that story because I think it marks the beginning of the current AI investment cycle. Investors like Spark provided the capital that helped companies like Anthropic grow into some of the most valuable businesses in the world.

But that cycle is already changing.

The companies that were startups just a few years ago are fast becoming giants themselves: Anthropic, OpenAI, and others. Along with Microsoft Corp. (MSFT), Alphabet Inc. (GOOG), Amazon.com Inc. (AMZN), Meta Platforms Inc. (META), and Nvidia Corp. (NVDA), they’re raising – and spending – enormous amounts of money, and racing to build what they believe will become the next great computing platform.

Sooner or later, though, they’ll find they can’t invent everything themselves. No company — not even the AI labs valued near $1 trillion — can hire every brilliant engineer or invent every breakthrough first.

Long story short, I think we’re entering the next phase of the AI boom.

The first phase rewarded investors who recognized that AI infrastructure – chips, memory, networking, power, and data centers – would become essential. I still believe many of those companies have room to run.

But the next phase could look very different.

Instead of asking, “Which companies will build AI?” investors may soon need to ask, “Which companies will AI’s biggest players decide they have to own?”

If I’m right, answering that one question could make the difference between simply participating in the AI Revolution… and getting there before the rest of Wall Street catches on.

That’s what I want to show you today.

First, I’ll explain why I think the AI arms race is entering a new phase.

Then I’ll show you how Silicon Valley’s biggest companies tip their hands long before they announce their next blockbuster acquisition.

Finally, I’ll explain the framework I’ve started using to identify the kinds of companies I believe could become tomorrow’s biggest winners, whether they eventually go public or get bought first.

Let’s start by following the money…

Follow the Money

One of the first rules I learned as an investor is that money leaves clues. When hundreds of billions of dollars begin flowing in the same direction, I pay attention.

Right now, the money isn’t just flowing into Nvidia.

It’s flowing into the entire AI ecosystem.

Follow the money.

Amazon alone expects to pour roughly $200 billion into capital projects this year. Microsoft and Alphabet are each planning about $190 billion. Meta could spend another $135 billion.

That’s roughly $700 billion in a single year, or about $2 billion every single day.

That’s an arms race.

Meanwhile, Anthropic’s latest funding valued the company at around $965 billion. And OpenAI is said to be worth roughly $852 billion.

But even when companies have that kind of money, they can’t invent everything themselves. Call it Silicon Valley’s dirty little secret: It’s often faster to buy innovation than build it yourself.

Think about what these companies are trying to accomplish.

The companies leading AI genuinely believe they’re building the next computing platform. When the stakes get that high, companies stop asking, “Can we build this?” and  start asking, “Who already has?”

We’ve seen this movie before.

In 2012, Facebook paid $1 billion for Instagram. At the time, it looked ridiculous.

Instagram had just 13 employees. It wasn’t making money. Most people thought Mark Zuckerberg had wildly overpaid for a photo-sharing app that let people put vintage-looking filters on pictures of their lunch.

Turns out, Zuckerberg got the bargain of the century. Instagram has since become one of Meta’s most valuable businesses. Last year, Meta estimated Instagram’s brand value at over $70 billion, and said it generates nearly $67 billion in annual revenue.

But that’s not what I find most interesting.

The biggest winners weren’t the people who bought Meta stock after the Instagram deal was announced. They were the people who already owned a piece of Instagram before Zuckerberg came calling.

And this pattern continued throughout the last few tech booms:

Google bought Android before smartphones became ubiquitous and picked up YouTube before online video dominated media.

Facebook bought Instagram and WhatsApp before those businesses reached their full potential.

Microsoft acquired GitHub as software development became increasingly collaborative and cloud-based.

Notice the pattern. None of these companies were acquired because the buyers were running out of money. They were acquired because the buyers were running out of time.

I think AI is setting up a similar dynamic, only on a much bigger scale.

The Ticker Is No Longer the Starting Line

For decades, most investors assumed the stock market was where great companies began.

Increasingly, it’s becoming where they finish their first chapter.

Think about Space Exploration Technologies Corp. (SPCX).

Millions of investors finally had the opportunity to buy shares after the IPO. But by then, SpaceX had already spent years building rockets, launching satellites, signing government contracts, and creating enormous value.

And within weeks of the IPO, its shares slid far below its high of $202.50 – as well as the original $135 offer price.

If you got in on Day One, you were part of the largest IPO in history. But the momentous occasion didn’t prevent you from getting caught in a painful drawdown.

Investors who entered years earlier were playing a different game entirely. They bought at private valuations far below the one public investors received on IPO day. A modest change in the public stock price therefore means something very different to those two groups.

Two people can believe equally in the same company and still walk away with radically different results.

That’s why I’ve recently started asking: Which companies will become so important that an AI giant decides it cannot afford to compete against them?

Some of those companies will become the next generation of AI leaders. Others may receive buyout offers long before they ever ring the opening bell on Wall Street.

Either path can create enormous value.

The challenge is recognizing those businesses early.

Over the past year, I’ve built an entirely different framework for finding those opportunities.

It isn’t based on chasing whatever stock is trending on social media. It isn’t based on guessing tomorrow’s headlines. Instead, it’s based on following the money.

I study where Silicon Valley is investing, what capabilities the largest AI companies still lack, and which smaller businesses are solving problems the giants may eventually decide they need to own.

It’s a different research process and a whole different way of looking at the AI boom.

And honestly, I think it’s one of the most exciting parts of this entire cycle.

History tells us that during technological revolutions, the headlines almost always focus on the giants.

But the biggest fortunes are often created one or two layers beneath them.

Back in 2023, Spark Capital saw something in Anthropic that most of the world could not. That one decision may become one of the defining investments of the AI era.

I believe the next chapter of AI could produce similar opportunities –because every technological revolution creates a new generation of companies solving problems the giants can’t solve alone.

My goal isn’t to find another Anthropic. It’s to find the companies Anthropic and the rest decide they can’t afford to ignore.

That’s what I’m going to show you on Thursday, July 30, at 1 p.m. Eastern. On that day, going to walk you through this framework during a free online event I’m calling The 2026 AI Megadeal Event. (You can reserve your spot here.)

I’m going over a few things during that event.

• Why I believe the AI investment cycle is entering an entirely new phase.
• The framework I use to identify companies that could become tomorrow’s AI leaders – or tomorrow’s acquisition targets.
• One specific opportunity I believe illustrates exactly how this next phase could unfold.

The first phase of the AI boom rewarded the companies building the future. I think the second phase could reward the companies those builders decide they need to own.

And I’m going to show you how to look for them. The event is free. All you need to do is reserve your seat here in order to receive an invitation (plus my bonus report: “The AI Collectors’ Portfolio: 7 Stocks to Buy for the Biggest Tech Spending Boom of All-Time.”)

I hope you’ll join me.

Sincerely,

Luke Lango

Senior Investment Analyst, InvestorPlace

The post The Next Great AI Fortune Could Begin Before Wall Street Sees It appeared first on InvestorPlace.

The venture-capital firm wrote an initial $75 million check to help fund Anthropic, then a little-known artificial intelligence startup trying to compete with OpenAI. Anthropic had no stock symbol, and it generated very little revenue. There was no Wall Street research report telling investors what it was worth.

Three years later, Spark’s stake is estimated to be worth $7 billion on paper. 

Spark Capital only needed one Anthropic. History suggests investors often don’t need many, either.

I keep coming back to that story because I think it marks the beginning of the current AI investment cycle. Investors like Spark provided the capital that helped companies like Anthropic grow into some of the most valuable businesses in the world.

But that cycle is already changing.

AI’s Builders Are Becoming Buyers

The companies that were startups just a few years ago are fast becoming giants themselves: Anthropic, OpenAI, and others. Along with Microsoft Corp. (MSFT), Alphabet Inc. (GOOG), Amazon.com Inc. (AMZN), Meta Platforms Inc. (META), and Nvidia Corp. (NVDA), they’re raising – and spending – enormous amounts of money, and racing to build what they believe will become the next great computing platform.

Sooner or later, though, they’ll find they can’t invent everything themselves. No company — not even the AI labs valued near $1 trillion — can hire every brilliant engineer or invent every breakthrough first.

Long story short, I think we’re entering the next phase of the AI boom.

The first phase rewarded investors who recognized that AI infrastructure – chips, memory, networking, power, and data centers – would become essential. I still believe many of those companies have room to run.

But the next phase could look very different.

Instead of asking, “Which companies will build AI?” investors may soon need to ask, “Which companies will AI’s biggest players decide they have to own?”

If I’m right, answering that one question could make the difference between simply participating in the AI Revolution… and getting there before the rest of Wall Street catches on.

That’s what I want to show you today.

First, I’ll explain why I think the AI arms race is entering a new phase. 

Then I’ll show you how Silicon Valley’s biggest companies tip their hands long before they announce their next blockbuster acquisition. 

Finally, I’ll explain the framework I’ve started using to identify the kinds of companies I believe could become tomorrow’s biggest winners, whether they eventually go public or get bought first.

Let’s start by following the money…

Follow the $700 Billion AI Arms Race

One of the first rules I learned as an investor is that money leaves clues. When hundreds of billions of dollars begin flowing in the same direction, I pay attention.

Right now, the money isn’t just flowing into Nvidia.

It’s flowing into the entire AI ecosystem.

Follow the money. 

Amazon alone expects to pour roughly $200 billion into capital projects this year. Microsoft and Alphabet are each planning about $190 billion. Meta could spend another $135 billion. 

That’s roughly $700 billion in a single year, or about $2 billion every single day. 

That’s an arms race. 

Meanwhile, Anthropic’s latest funding valued the company at around $965 billion. And OpenAI is said to be worth roughly $852 billion.

But even when companies have that kind of money, they can’t invent everything themselves. Call it Silicon Valley’s dirty little secret: It’s often faster to buy innovation than build it yourself.

AI Giants Will Buy What They Cannot Build Fast Enough

Think about what these companies are trying to accomplish.

The companies leading AI genuinely believe they’re building the next computing platform. When the stakes get that high, companies stop asking, “Can we build this?” and  start asking, “Who already has?”

We’ve seen this movie before.

In 2012, Facebook paid $1 billion for Instagram. At the time, it looked ridiculous.

Instagram had just 13 employees. It wasn’t making money. Most people thought Mark Zuckerberg had wildly overpaid for a photo-sharing app that let people put vintage-looking filters on pictures of their lunch.

Turns out, Zuckerberg got the bargain of the century. Instagram has since become one of Meta’s most valuable businesses. Last year, Meta estimated Instagram’s brand value at over $70 billion, and said it generates nearly $67 billion in annual revenue. 

But that’s not what I find most interesting.

The biggest winners weren’t the people who bought Meta stock after the Instagram deal was announced. They were the people who already owned a piece of Instagram before Zuckerberg came calling.

And this pattern continued throughout the last few tech booms:

Google bought Android before smartphones became ubiquitous and picked up YouTube before online video dominated media. 

Facebook bought Instagram and WhatsApp before those businesses reached their full potential. 

Microsoft acquired GitHub as software development became increasingly collaborative and cloud-based. 

Notice the pattern. None of these companies were acquired because the buyers were running out of money. They were acquired because the buyers were running out of time.

I think AI is setting up a similar dynamic, only on a much bigger scale.

Why the Best AI Acquisition Targets May Never Reach the Stock Market

For decades, most investors assumed the stock market was where great companies began.

Increasingly, it’s becoming where they finish their first chapter.

Think about Space Exploration Technologies Corp. (SPCX).

Millions of investors finally had the opportunity to buy shares after the IPO. But by then, SpaceX had already spent years building rockets, launching satellites, signing government contracts, and creating enormous value.

And within weeks of the IPO, its shares slid far below its high of $202.50 – as well as the original $135 offer price.

If you got in on Day One, you were part of the largest IPO in history. But the momentous occasion didn’t prevent you from getting caught in a painful drawdown.

Investors who entered years earlier were playing a different game entirely. They bought at private valuations far below the one public investors received on IPO day. A modest change in the public stock price therefore means something very different to those two groups.

Two people can believe equally in the same company and still walk away with radically different results.

That’s why I’ve recently started asking: Which companies will become so important that an AI giant decides it cannot afford to compete against them?

Some of those companies will become the next generation of AI leaders. Others may receive buyout offers long before they ever ring the opening bell on Wall Street.

Either path can create enormous value.

The challenge is recognizing those businesses early.

The Bottom Line: Find the Companies AI Giants Cannot Ignore

Over the past year, I’ve built an entirely different framework for finding those opportunities.

It isn’t based on chasing whatever stock is trending on social media. It isn’t based on guessing tomorrow’s headlines. Instead, it’s based on following the money.

I study where Silicon Valley is investing, what capabilities the largest AI companies still lack, and which smaller businesses are solving problems the giants may eventually decide they need to own.

It’s a different research process and a whole different way of looking at the AI boom.

And honestly, I think it’s one of the most exciting parts of this entire cycle.

History tells us that during technological revolutions, the headlines almost always focus on the giants.

But the biggest fortunes are often created one or two layers beneath them.

Back in 2023, Spark Capital saw something in Anthropic that most of the world could not. That one decision may become one of the defining investments of the AI era.

I believe the next chapter of AI could produce similar opportunities –because every technological revolution creates a new generation of companies solving problems the giants can’t solve alone.

My goal isn’t to find another Anthropic. It’s to find the companies Anthropic and the rest decide they can’t afford to ignore.

That’s what I’m going to show you on Thursday, July 30, at 1 p.m. Eastern. On that day, I’ll walk you through this framework during a free online event I’m calling The 2026 AI Megadeal Event. (You can reserve your spot here.)

I’m going over a few things during that event.

• Why I believe the AI investment cycle is entering an entirely new phase. 
• The framework I use to identify companies that could become tomorrow’s AI leaders – or tomorrow’s acquisition targets.
• One specific opportunity I believe illustrates exactly how this next phase could unfold.

The first phase of the AI boom rewarded the companies building the future. I think the second phase could reward the companies those builders decide they need to own.

And I’m going to show you how to look for them. The event is free. All you need to do is reserve your seat here in order to receive an invitation (plus my bonus report: “The AI Collectors’ Portfolio: 7 Stocks to Buy for the Biggest Tech Spending Boom of All-Time.”)

I hope you’ll join me.

The post Silicon Valley Is Hunting for Its Next $1 Billion Bargain appeared first on InvestorPlace.

Did Alphabet (GOOG) confirm its capex spend?

By the time you read this, we’ll likely already know the answer. Alphabet’s second-quarter earnings report – and the management call that follows it – should be out.

As we detailed in Monday’s Digest, Wall Street has grown skeptical of the rosy earnings projections coming out of some AI infrastructure companies this season.

Our technology expert Luke Lango, editor of Innovation Investor, has been blunt about what it will take to change that mood: hyperscalers need to confirm their capex plans.

So, did Alphabet come through?

We’ll walk through its results tomorrow.  If capex guidance came in strong, that’s a green light for the AI trade. If it didn’t, expect the skepticism to deepen and market fireworks to intensify.

For now, let’s turn to a different AI story – one that’s less about whether the spending holds up, and more about what happens if the very thing hyperscalers are spending on keeps getting cheaper and more available faster than anyone planned for.

Kimi K3, and the tradeoff we said we’d watch for

Back on April 6, and touched upon in yesterday’s Digest, we laid out five Prisoner’s Dilemmas running through the AI economy – moments where every individual actor makes the locally rational choice, and yet the sum of those choices produces an outcome nobody actually wanted.

The fourth dilemma centered on AI infrastructure itself…

Every hyperscaler must pour billions into computing capacity or risk falling behind – even as its own research team races to build smarter, more efficient models that need less of that same capacity.

As we wrote back in April:

The better the AI research team does its job, the more it undermines the infrastructure team’s investment…

So, the company is simultaneously building an empire…and designing the weapon that destroys it.

Over the last few days, investors have gotten one of their clearest real-world examples of this yet.

Two Chinese companies, Moonshot AI and Alibaba, released open-weight AI models: Kimi K3 and Qwen 3.8 Max. Because they’re open, anyone can download them, run them, and customize them for free.

They’re reportedly excellent – Kimi K3 beat both Claude Opus 4.8 and GPT-5.5 on coding benchmarks, and at a fraction of the cost.

That’s the Prisoner’s Dilemma showing up outside any single hyperscaler’s walls…

What happens when AI capability keeps improving faster than the economics underpinning today’s infrastructure buildout can absorb?

The Wall Street Journal connected it directly to the market’s recent jitters:

The threat that new players will vastly undercut what they can charge for advanced AI pushed down some tech and AI company stock prices last week.

In other words, the trillion-dollar infrastructure bet has always rested on Anthropic and OpenAI staying capable enough to justify the bill.

But a free competitor that’s as good or better doesn’t just compete – it chips away at the assumption the entire buildout was priced on.

This is Prisoner’s Dilemma Four, live, with a new name attached to it.

But this isn’t just a Prisoner’s Dilemma example, it’s also a “Messy Middle” story

One headline – two frameworks.

In yesterday’s Digest, I introduced a concept related to the Prisoner’s Dilemma – the Messy Middle.

In short, as our world adjusts to AI, we’ll be forced to navigate competing, colliding priorities. It won’t be about picking a “good choice” over a “bad choice,” it will be two things we desire that can’t be chosen equally at once.

We’ll have to choose – and live with the consequences of the path not chosen.

In this case, OpenAI and Anthropic have spent the past week sounding the alarm about a world of cheap, open, uncontrolled AI models. OpenAI’s head of strategic futures, Dean Ball, didn’t mince words, calling that future “a dystopian hellscape.”

Here’s more from Ball:

One probable outcome of an open-weight-model-dominant world is full AI communism, which is precisely what China proposes: rather than a market product, AI is a ‘public good’ which will ultimately be provided by the state as a kind of ‘digital public infrastructure.

Anthropic’s CEO, Dario Amodei, has separately spent months warning that freely downloadable models with advanced capabilities pose real cybersecurity risk – the kind that doesn’t require a rogue superintelligence, just a bad actor with a laptop and no company standing between them and the tool.

We all want and prioritize a safe world. So, we should ban these open-source models, no?

Except – what about the other thing we want?

Progress.

Cheap, open AI puts frontier-level tools into the hands of any developer, any small business, anywhere, without a subscription fee in the way. The world benefits from lower costs and faster progress.

Isn’t that the exact democratization of technology we’ve been asking for?

Perhaps. But the openness that makes free, amazing AI a “good” strips out every guardrail a closed, paid model comes with.

And that brings us to a story out just this morning about what can go wrong…

An “unprecedented cyber incident”

Earlier today, OpenAI disclosed that one of its AI models – a next-generation system that hasn’t even been released publicly yet – broke into Hugging Face’s infrastructure, the platform that hosts a huge share of the world’s AI models and datasets.

The breach happened during an evaluation of the model’s cyber capabilities, with guardrails deliberately lowered so researchers could see what it was capable of.

OpenAI didn’t downplay it:

We consider this to be an unprecedented cyber incident, involving state-of-the-art cyber capabilities.

That’s not a “someday-AI-might-do-this” warning. That’s a frontier lab watching its own model autonomously find a vulnerability, exploit it, and breach a third party’s systems – under supervised, intentionally loosened conditions.

It gets messier, though…

Hugging Face’s co-founder didn’t use this incident to walk away from open AI systems – he argued the opposite.

His point is that when a frontier model starts attacking your infrastructure, defenders need fast, wide access to near-frontier tools of their own to fight back – not a slow, gatekept process to request permission from whoever controls the closed model doing the attacking.

So, what will we prioritize?

Progress on cutting-edge technology or safety?

This is the Messy Middle, live in action.

Running it through our two lenses

In yesterday’s Digest, I said we’d keep coming back to two lenses whenever the Messy Middle shows up. Let’s walk through what they tell us here.

Lens 1: Which side of the productivity/disruption tradeoff is a given state or company protecting – and does your portfolio have exposure to the side that just got harder?

We don’t have a clean signal yet.

On the one hand, President Trump has argued for few, if any, restrictions on AI.

On the other hand, today’s OpenAI hack is prompting calls for immediate regulation from various politicians like Texas congressman Greg Casar:

This is extremely alarming.

AI is developing extremely fast with no real regulations to keep us safe. That has to change.

Until Washington picks a direction, we don’t know whether restrictions would favor the closed frontier labs (by blunting their free competition) or whether inaction would favor the companies already saving money by running cheap, open models internally.

That’s a real “we don’t know yet,” and we’d rather tell you that than force a conclusion that isn’t there.

Lens 2: Is this news pushing Luke Lango’s 2028 AI-backlash clock earlier, or giving it more breathing room?

In short, Luke believes a populist backlash against AI will reach a tipping point around the 2028 election cycle, leading to policies and legislation that could derail the AI Boom.

Luke’s clock runs on rising living costs and job losses tied to AI, broadly. Both paths this story could take feed that clock, just through different mechanisms.

Letting the free AI models spread removes the two biggest frictions slowing AI-driven job displacement: cost and access.

A company that might balk at an enterprise AI price tag has no such hesitation about a free one – and no hesitation about the layoffs that follow. That accelerates the job-loss side of Luke’s thesis.

However, restricting the open models creates a new grievance: people paying more, or losing access to a free tool, with it appearing as though the motivation is to enable a handful of U.S. AI companies to protect their margins.

Cue the complaints about inequality and the systems of injustice that benefit a select few. That’s a living-cost grievance – just a different flavor than electricity bills.

Either way, we face the risk of growing anti-AI sentiment that could impact your portfolio. There may not be a version of this story where it doesn’t – which is itself the more interesting takeaway than picking a side.

Wrapping up

The open-source Chinese AI models aren’t just another headline. They’re a reminder that the biggest investment questions today rarely have clean answers.

AI can become dramatically cheaper and more powerful… while simultaneously creating uncertainty for the very infrastructure companies that make that progress possible. That’s the Prisoner’s Dilemma.

And deciding which outcome we should prioritize – or which investments ultimately benefit most – is the Messy Middle.

Expect to see far more of both before this transition is over.

Digest readers weighing in

In yesterday’s Digest, I solicited your feedback about AI and this Messy Middle. We’ve received loads of feedback. Thanks to everyone for writing in.

Scott G. raised a good point:

Another “soft risk” to the deployment of AI…overstatement, exaggeration and misinformation on the part of media regarding AI’s many benefits and risks.

For example, trying to suggest that banning ALL data center development from an entire state is at all similar to banning data center development in RESIDENTIAL NEIGHBORHOODS is a bit of a misread of the two situations.

Agreed, Scott.

As to my coverage of both in yesterday’s Digest, you’re right that these aren’t equivalent policies – an outright moratorium and a targeted zoning rule are different tools with different intentions.

A quick clarification: I wasn’t pointing to the identical nature of the policies themselves, but the same political reflex underneath them: officials in both parties responding to local pushback.

Meanwhile, Tom, a 77-year-old reader who watched manufacturing fade out of his own region decades ago, says he sees the same dynamic playing out again now – people caught in the gap between an old economic era ending and a new one not yet arrived:

Let’s hope we don’t move forward and become like the Eloi (in the movie The Time Machine) but progress cannot be stopped so who knows.

If you don’t get Tom’s reference, the Eloi are a future human race in H.G. Wells’ story who’ve had everything provided for them, and lost all capability and curiosity as a result. It’s along the same theme as our Mouse Utopia Digest.

We’ll end with Peter C., who wraps up our entire conversation today succinctly:

This is an exciting time to be here because the solutions will be a compromise of some sort. Thanks for the essay!

Thank you, Peter – and to everyone who wrote in.

If you want to chime in on the conversation, email me at ipdigestfeedback@investorplace.com.

Have a good evening,

Jeff Remsburg

(Disclosure: I own GOOGL)

The post An AI Just Hacked a Company on Its Own appeared first on InvestorPlace.

Remember the DeepSeek shock back in January 2025? When the Chinese AI lab DeepSeek released its DeepSeek-R1 reasoning model, it shook global markets.

The model showed that a Chinese company could compete with Silicon Valley’s best AI systems at a much lower cost.

Now, China has delivered the sequel.

On Sunday, Alibaba Group Holding Limited (BABA) unveiled a preview of its newest AI model, Qwen3.8 Max. The company says Qwen3.8 Max ranks among the world’s most powerful AI models, trailing only Anthropic’s Fable 5. Shares of Alibaba rose over 5% on Monday following the news.

The launch came shortly after Chinese startup Moonshot AI’s release of Kimi K3, another model competing with the U.S.’s top AI systems. In benchmark tests, Kimi K3 beat Anthropic’s top model and OpenAI’s GPT-5.6 Sol at some coding tasks.

U.S. companies are racing to build the smartest AI models. China is proving that AI doesn’t always need to be the best.

It needs to be “good enough”, cheaper, and easier to adopt.

That’s the same playbook China used with electric vehicles (EVs): build products that are nearly as good, sell them much cheaper, then win global market share.

In today’s Smart Money, let’s take a look at why AI could become China’s next EV export story.

Then, I’ll share why the biggest investment opportunities may not come from the companies with the best technology – but from the companies powering it.

The EV to AI Playbook

Around 2010, well before the EV market took off, China heavily invested in the industry. This gave Chinese companies years to improve before they faced intense global competition.

Unlike the U.S., where Tesla Inc. (TSLA) became the dominant EV player, China encouraged dozens – even hundreds – of EV makers to compete.

And they competed on cost.

Chinese automakers realized that they didn’t need to build the world’s best cars. They only needed to build them good enough, and at half the price.

Once they built a strong business at home, automakers expanded overseas into markets where affordable EVs were in high demand. Today, Chinese EVs are common across Europe and other international markets. (In fact, I recently traveled to Europe, where I saw them all over the place.)

Now, China appears to be applying the same playbook to AI.

The recent Kimi K3 and Alibaba Qwen announcements are drawing attention because they suggest China’s approach to EVs could work in AI as well:

Build powerful technology. Lower the cost. Make it easier for businesses around the world to adopt.

Just a year ago, most AI discussions centered on OpenAI, Anthropic, Alphabet Inc. (GOOGL), and Meta Platforms Inc. (META). Investors now also watch Alibaba, Moonshot AI, DeepSeek, MiniMax, and Z.AI.

These companies are proving that AI does not always need to be the absolute best to win customers. Rather than beating Anthropic by a small percentage, Qwen3.8 Max and Kimi K3 are trying to be nearly as capable while being dramatically cheaper and easier to deploy.

Both Alibaba’s Qwen3.8 Max and Moonshot AI’s Kimi K3 are open-weight models, meaning businesses can download and run them locally instead of paying for access through a third-party service.

And for companies that use AI heavily, that can make them more attractive than Anthropic’s Claude or OpenAI’s ChatGPT.

We’ve been watching this “good enough” trend here at Smart Money, and now we’re really starting to see it play out. The Information reported on Monday that Microsoft Corp. (MSFT) is testing Kimi K3 inside its Copilot AI assistant. If successful, the move could save Microsoft up to $600 million by reducing its use of more expensive AI models like ChatGPT and Claude.

For many businesses, that is the key advantage.

And as more developers use Chinese AI models, they attract more users, more improvements, and more investment.

If China can build AI that is almost as good, much cheaper, and easier to adopt globally – as it did with EVs – it may capture a significant share of the worldwide AI market.

That doesn’t necessarily make U.S. AI companies losers. OpenAI, Anthropic, and others may continue to lead in performance. But it does suggest the AI world could look a lot like the EV market: premium U.S. models at the high end, and cheaper Chinese models competing on price and accessibility.

If the AI market follows a similar path, investors should remember an important lesson from the EV revolution…

Forget the Innovators. Own the Enablers.

It can be tempting to chase the companies grabbing the most attention. But history shows that revolutionary technologies often create even bigger opportunities for the companies supplying the picks and shovels.

The same lesson applied during the EV revolution. In October 2019, I took a closer look at Tesla, the EV industry’s biggest success story. I wrote to my Fry’s Investment Report subscribers:

Every automaker on the planet retools to launch EVs of its own.

The onslaught of competing EV models that is coming to market over the next two years is truly breathtaking. Tesla will struggle to repel this onslaught.

The company is an undisputed innovator, but that doesn’t mean it will survive to lead the new era it has spearheaded. “First movers” like Tesla often succumb to the creative energies they unleash.

So I say: Why not sell Tesla and buy the dynamic companies that will power and nurture the Second Electric Revolution?

I predicted that the global boom in electric vehicles could cause a major surge in copper demand. EVs require about four times as much copper as a typical internal combustion vehicle. Therefore, as they continued to gain market share, they would absorb a growing slice of the global copper supply.

That was when I recommended a picks-and-shovels play to my readers: Freeport-McMoRan Inc. (FCX). The copper company is up over 260% since then, and we’ve taken several triple-digit gains along the way.

I am playing the AI Revolution the same way.

Multiple stocks in my Fry’s Investment Report portfolio offer that sort of opportunity.

These companies include large producers of copper and aluminum, and several ETFs that hold baskets of stocks that operate in the uranium and nuclear industries… the solar industry… and the wind energy industry.

That’s just to name a few.

China may challenge U.S. AI dominance with cheaper, “good enough” models. But these pick-and-shovel plays could benefit no matter who leads the AI race.

I believe they offer compelling opportunities at their current quotes.

To learn how to access these names, simply click here.

Regards,

Eric Fry

The post Why You Should Use the EV Playbook to Find AI Winners appeared first on InvestorPlace.

As we reported here in the Digest last Wednesday, New York Gov. Kathy Hochul became the first governor to sign a law pausing the construction of new hyperscale AI data centers.

And yet, the very next day, The Verge reported that Hochul said her administration is using AI to review “every single rule, regulation, and policy” on New York’s books – a process she says would have taken five years but can now be accomplished in just a few months.

If you’re sensing a contradiction, you’re not alone.

New York wants AI’s productivity – just not the infrastructure required to produce it.

Now, Hochul isn’t confused, and she isn’t unusually hypocritical.

She’s standing at the edge of an enormous, uncharted AI-based transformation. So, she’s trying to hold two things at once that both sound reasonable, yet don’t fit together.

She’s not alone – the coming months and years will usher in a wave of elected officials forced into that same impossible split.

I don’t think we’ve felt this kind of vertigo before. But history has.

Imagine a weaver in Manchester, England, in 1820…

He’s spent 20 years mastering his craft – like his father and grandfather before him.

Then a factory opens down the road. Inside, a machine – tended by a worker with none of his skill – produces more cloth in a day than he can make in a month. It’s not long before his trade is worthless.

He can’t compete with the machine, so he goes to work in the factory that replaced him, for wages that barely keep his family fed, in a town that grew too fast to build proper housing, sanitation, or anything resembling the life he had before.

He’s watching, in real time, the thing that economists will spend the next two centuries calling “an extraordinary leap forward in human productivity.” But from where he’s standing, it’s hell.

This Industrial Revolution transition in Britain continued for roughly 50 years – the entire working life of a generation, and then some. Wages stagnated even as output surged. Riots broke out as displaced workers turned their anger on the machines themselves.

Only after this period – after enough dislocation had run its course, after society slowly built the institutions and norms to absorb the shock – did wages finally start rising alongside productivity again, and quality of life improved.

Economic historians call that gap “Engels’ Pause.”

Yes, the optimists of that era were eventually proven right – industrialization did, in time, lift living standards.

But not for workers in that 50-year messy middle.

Are we standing at the start of our own Pause?

Not a repeat – the specifics will look nothing alike. And its length could be shorter.

Still, the shape of it might rhyme. Real gains from AI could arrive on one timeline, while the mess of displacement, backlash, and adjustment arrives on a completely different one.

I’m calling this stretch “The Messy Middle.”

We’ve spent plenty of Digests arguing about the destination – whether AI ends in abundance or disaster. We’ve spent less time discussing what’s right at our doorstep today – the in-between part that we’ll live through no matter which ending turns out to be right.

Over the coming months, I’m going to dive into this – not on a fixed schedule, but whenever a real-world story presents a clean example of the trade-offs this transition is forcing on us.

And that’s really what the Messy Middle is about: when competing priorities collide, what do we choose – and are we ready for what that choice costs us?

The reality of what Hochul chose

Economist Thomas Sowell famously observed, “There are no solutions, only trade-offs.”

Today, we want cheaper energy, community stability, lower living costs, and all the ways technology promises to make life better.

But we can’t have them all, right now, in equal proportion. Some must be prioritized above others.

New York just showed us which side of that collision it’s willing to sacrifice. It gave up the potential for the longer-term productivity gains, jobs, and tax-base increases that the data centers would have delivered in exchange for the short-term benefit of preventing disruption and higher electricity prices.

The moratorium’s own architects effectively stalled a proposed $19.4 billion data center project in Genesee County. This is a real, named example of what “potential productivity gains” means in dollars.

Not right or wrong – simply a prioritization.

But Hochul isn’t the only one making this choice…

Democratic Pennsylvania Gov. Josh Shapiro – a 2028 presidential hopeful – initially embraced the data center boom in his state. When the public pushback mounted, he reversed course:

We need to be selective about the projects that get built here.

But it’s not confined to blue states. Texas Gov. Greg Abbott, a Republican who governs the most data-center-friendly state in the country, has separately called for a ban on data centers in rural parts of his state.

(To be fair, Abbott’s targeted ban focuses strictly on keeping facilities out of residential and rural neighborhoods, but it leaves the door open for continued massive development in more suitable, high-capacity industrial corridors).

Hochul, Shapiro, Abbott. Both parties. Three states. Not identical moves, but the same direction – within months of each other.

That’s not a “just New York” story. That’s a pattern.

How your portfolio might be affected right now

New York isn’t a major data-center market today, especially compared to Virginia or Texas. But more than 12 gigawatts of future data-center capacity is sitting in line waiting to connect to the state’s grid – and that pipeline just got a lot less certain.

The immediate investment implications are fairly narrow. Analysts have already flagged Equinix (EQIX) and Digital Realty (DLR) for their meaningful exposure to New York’s data center market.

But that’s not the bigger takeaway. The bigger question is whether this becomes the first domino…

If permitting, environmental opposition, and electricity concerns begin slowing AI infrastructure projects across multiple states, investors will have to reassess how quickly portions of the AI buildout can proceed.

Of course, political resistance in one state doesn’t stop AI. It often just pushes investment somewhere else. The bigger question – the one for us to watch – is whether isolated pushback eventually becomes a broader national movement.

The longer-term way your portfolio might be affected

This broader dynamic reminds me of something we discussed here in the Digest back on April 6…

Regular Digest readers may recall how I laid out five Prisoner’s Dilemmas running through the AI economy – cases where every individual actor makes the locally rational choice, yet the sum of those choices produces an outcome that nobody wants.

Hochul, Shapiro, and Abbott just handed us a sixth.

To be fair, it’s not a clean Prisoner’s Dilemma – plenty of states, Texas and Virginia among them, still see the economic upside as worth the cost and keep building. But that’s exactly what makes this worth watching…

Blocking data centers is becoming the more politically rewarding move.

No governor will be viewed as wrong to protect their own constituents from a real, immediate cost. But if enough of them start to, the country ends up under-building the very infrastructure necessary for the long-term payoff.

It’s the same logic as April’s dilemmas, just wearing a political mask instead of a corporate one.

This is why, in response to Hochul’s decision, Pennsylvania Sen. John Fetterman wrote on X, “China wins.”

Of course, choosing data centers isn’t painless either…

If every governor welcomes them with open arms instead, it will be their own constituents who absorb the higher bills, strained water supplies, and the general disruption.

Someone, in some way, must bear the cost.

Remember – there are no solutions, only trade-offs.

Which brings us to Luke Lango’s countdown clock

Luke – our technology expert and the editor of Innovation Investor – has been tracking this dynamic from a much broader perspective.

As we’ve discussed here in the Digest, Luke believes the biggest long-term risk to the AI boom isn’t a technological failure, demand collapse, or even a recession.

It’s politics.

Specifically, he believes a populist backlash against AI will steadily build as rising electricity bills, AI-related job displacement, and widening wealth inequality become more visible – ultimately reaching a tipping point around the 2028 election cycle.

Here’s Luke:

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.

Hochul’s decision doesn’t prove Luke’s timeline, but I do think it’s the sort of evidence worth keeping a running tally of.

Three governors. Two political parties. The same underlying tension has emerged within months.

That’s not proof, but it’s not random either.

To be clear, none of these changes affect Luke’s investment outlook today. In fact, he remains firmly bullish on the next several years:

This trade will not last forever. Like everything, it has an expiration date.

Make your money now. The window for transformational wealth creation in this AI cycle is the next two to three years.

But his countdown clock is ticking.

So, here are the two investment lenses I’ll keep returning to when we explore aspects of this Messy Middle…

Lens 1: Which side of the productivity/disruption tradeoff is a given state or company protecting – and does your portfolio have exposure to the side that just got harder?

Lens 2: Is the news of the week pushing Luke’s political clock earlier, or giving it more breathing room?

This isn’t hypothetical anymore

So far in today’s Digest, the disruption we’ve covered is electricity hikes and permitting delays. Not exactly 1820s Manchester.

Or is it? Just earlier in the cycle?

This June, federal prosecutors charged five people with conspiracy to commit murder over a foiled plot targeting a UFC event on the White House lawn. Investigators say the group cited data centers “taking up all the water in communities,” among their grievances.

In April, a man threw a Molotov cocktail at OpenAI CEO Sam Altman’s home, then walked to the company’s headquarters and threatened to burn it down.

And in Indianapolis, a city councilman had 13 rounds fired into his home after voting to approve a data center in his district – with a note reading “No Data Centers” left under his door.

Researchers who track political violence are now studying this directly. One analyst told Newsweek the shift from corporate executives to local officials is a “substitution effect” – as CEOs gain security details, the anger doesn’t disappear; it moves toward softer, more exposed targets: city council members and township officials with no protection and a public home address.

Compare that to the weaver in Manchester…

Displaced anger aimed at whatever stands closest to the machine. Same mechanism, new machine.

Now, keep one thing in mind…

This is all happening as this Messy Middle is barely becoming visible, and unemployment is still historically low.

What might this suggest for what lies ahead?

Welcome to the Messy Middle

I don’t know exactly where AI ends up. Neither does anyone else, honestly, no matter how confidently they say otherwise.

But I do think the next few years – the journey, not the destination – is what will matter most for your portfolio. That’s what this series is for.

So, every time AI forces two legitimate values into conflict – jobs versus productivity, electricity versus innovation, privacy versus convenience – we’ll use the Messy Middle framework to think through what matters for your portfolio.

One twist…

I’d like you to be a part of it with me – sharing your own thoughts, insights, and perspectives. If you have any on today, send them to me at ipdigestfeedback@investorplace.com.

Have a good evening,

Jeff Remsburg

(Disclosure: I own DLR)

The post AI Investors Must Keep This Threat in Mind appeared first on InvestorPlace.

In October 1974, a boxer past his prime walked into a ring in Kinshasa against a champion nobody thought he could beat.

George Foreman hit harder than anyone in the sport. Muhammad Ali knew that head-on, he loses. So he did something that looked, to the crowd ringside, like defeat in slow motion. He leaned back against the ropes and let Foreman swing.

Round after round, Foreman unloaded everything he had, and Ali just absorbed it, covered up, and waited. By the eighth round, Foreman had punched himself into exhaustion. Ali uncoiled, landed five clean shots, and Foreman hit the canvas.

The crowd that thought Ali was losing had it backward the entire time. He was not retreating. He was setting up the knockout.

I bring this up because the artificial intelligence infrastructure trade is doing its own version of the rope-a-dope right now, and a lot of investors are reading the ropes as a loss.

I do not think this AI infrastructure pullback is the beginning of the end. I think it is the eighth round, and the knockout punch is coming by early August.

Since mid-May, AI infrastructure stocks have gone nowhere. Two months of consolidation. Peak-spending fears everywhere you look. And this past week, we got three enormous earnings reports that should have reignited the trade, and the trade shrugged them off entirely.

Taiwan Semiconductor Manufacturing Co. Ltd. (TSM) posted blowout numbers. ASML Holding N.V. (ASML) posted blowout numbers. Advantest posted blowout numbers. Samsung Electronics did the same the week before. Four major chip-and-equipment names, four strong reports, and AI stocks kept sliding anyway.

Here is the part almost nobody is talking about correctly. Those four companies were never going to be the ones to end this selloff. Not because their businesses are weak. Because they are the wrong messengers entirely.

So let me walk you through why the real catalyst has not shown up yet, why I believe it arrives within weeks, and why the technical setup right now looks like one of the best buying windows of this entire AI cycle:

Capex Spenders Set the Tone

Think about who actually cashes the check in this buildout. TSM, ASML, Advantest, and Samsung sit on the receiving end of the spending. They are capex takers. Every dollar that flows through the AI infrastructure buildout eventually lands in their revenue line, but they do not control the spigot. They cannot tell you what 2027 and 2028 capital spending looks like, because that decision does not belong to them.

TSM said it is largely sold out into 2027 and 2028. ASML implied the same. Advantest gave bullish 2027 guidance that points in an identical direction. That is real, useful information about current demand. What it is not is guidance on whether the hyperscalers keep the spending accelerating into the later innings of this cycle. Those four companies structurally cannot answer that question, no matter how good their earnings look.

The companies that can answer it have not reported yet. Alphabet Inc. (GOOGL), Amazon.com, Inc. (AMZN), Microsoft Corp. (MSFT), Oracle, and Meta Platforms, Inc. (META) are the capex spenders. They write the checks. Until they stand up and reaffirm, or better yet hike, their 2026 AI capex guidance, this selloff does not end. It is that simple. So yes, four companies hit home runs this week, and the market did not care, because the market is waiting on the hyperscalers to step up to the plate.

The Case for Optimism Builds by the Day

Now, here is why I expect the hyperscalers to deliver exactly what the market wants. Look at the news flow. Alphabet just signed a multibillion-dollar cloud computing deal with SpaceX (SPCX). Meta is building out its own cloud infrastructure business and pouring billions into a new compute cluster. Anthropic just launched new models. ChatGPT 5.6 just launched. None of that reads like a spending slowdown. It reads like convergence, every major player accelerating at once.

I also want you to look at the technical picture, because it tells the same story the fundamentals tell. Since ChatGPT launched in late 2022, every single garden-variety pullback in AI infrastructure stocks has bottomed in the 10% to 15% max drawdown range. That is exactly where the sector sits right now. The SOX and SMH both hold their 50-day moving averages. They have not broken lower. That is the tell. Wall Street is not dumping this trade. Wall Street is waiting for the hyperscaler catalyst before it buys the dip.

Add in a genuinely favorable macro backdrop. Consumer Price Index and Producer Price Index inflation both came in soft this week, which confirms May marked the peak of this inflation hump. Softer prints in June, softer estimates for July, and I expect August continues the trend. Disinflation pulls Treasury yields lower, and lower yields matter enormously here, because the AI boom increasingly runs on debt financing.

Capex-to-operating-cash-flow ratios for these hyperscalers sit near 100%, so fresh spending increasingly comes from the debt markets. Cheaper debt means easier financing, and easier financing means the spending keeps running.

My Verdict

Putting it all together…

Strong current demand from the capex takers… Plus, a believable catalyst on deck from the capex spenders…. And a technical setup holding its historical bottoming zone with a disinflation trend that eases financing costs for the whole complex…

This is a market absorbing punches on the ropes, waiting for the eighth round.

I expect this trade to reawaken by the end of July, with AI infrastructure names back at all-time highs by early August.

Across the broader complex, I look for bounces in the range of 15% to 20%, and in specific high-beta names, moves of 50% to 100% over the following weeks.

These stocks run in exactly that rhythm: fifteen steps forward, five steps back. We just took the fifth step back. It is time for the fifteen forward.

I recommend staying aggressive here, because the buy window like this one does not stay open long.

The post Get Into The AI Trade Before This Happens appeared first on InvestorPlace.

Fire arrows streaked through the darkness. Explosions cracked across the palace grounds. Strange devices skittered along the floor, spitting sparks in every direction.

To a European traveler in the 13th century, it must have looked like magic.

Historians still debate how much of that story is true. But we do know the Chinese had been using early fireworks for centuries by then, first by tossing bamboo into fires and later by packing gunpowder into paper tubes.

Americans may have gotten their fill of fireworks over the Fourth of July. But folks, judging by the earnings growth expected in the months ahead, Wall Street may have an even more spectacular show in store.

Now, I know it may not feel that way after the market’s recent gyrations. Iran-related headlines have unsettled investors, while sharp swings in AI and data center stocks have made the ride feel especially queasy at times.

But take a step back and look at the bigger picture.

Investors enjoyed strong gains in the first half of the year. The U.S. economy remains resilient. Artificial intelligence investments continue to expand. And most importantly, we are entering what could be one of the strongest earnings environments I can remember.

So, do not let a few bumpy trading days scare you away from the opportunity in front of us. Earnings season has only just begun, and the results could be even better than Wall Street currently expects.

In today’s Market 360, I’ll explain why the foundation beneath this market remains solid, why corporate earnings are poised to deliver plenty of fireworks and how my Precursor Intelligence system can help identify the market’s next earnings leaders before their strength becomes obvious to Wall Street.

The Foundation for a Strong Second Half

 The U.S. has emerged as a true economic oasis.

GDP grew at a 2.1% annual pace in the first quarter. Growth cooled a bit in the second quarter, but it is set to reaccelerate in the second half of 2026. Personally, I expect U.S. GDP growth to hit at least a 5% annual pace in the third quarter.

Add anticipated AI productivity gains to that outlook, and it is easy to see why the U.S. economy could continue to accelerate through year-end.

That strength is already showing up in the stock market.

The second quarter was the best-performing quarter for the NASDAQ and S&P 500 in six years. The S&P 500 rallied 15%, the Dow climbed 13% and the NASDAQ surged 21%. The small-cap Russell 2000 also jumped about 21%.

I believe this is the best market environment we have seen since 1999.

Back then, the internet buildout unleashed a wave of business investment, productivity growth and corporate profits.

Today, artificial intelligence is creating a similar opportunity. Companies are pouring money into AI infrastructure, data centers and the technology needed to power them. Estimates say tech companies will spend around $750 billion on the AI buildout just this year alone.

And aside from the buildout itself, the resulting productivity gains could provide another major boost to economic growth.

In fact, I believe the current AI boom could ultimately be even more powerful than the internet boom of the 1990s.

I know the market’s recent gyrations have made that opportunity easy to overlook. But a few bad days do not change the bigger picture…

Economic growth is poised to reaccelerate. The AI buildout is still gathering momentum. And most importantly, corporate profits are accelerating.

That is why the foundation beneath this market remains solid.

And it brings us to the fireworks show investors should be watching now: earnings season.

The Next Fireworks Show Is Just Beginning

The first-quarter earnings season had its share of fireworks – but the second quarter will be even more impressive.

Consider this… Analysts expected the S&P 500 to post average earnings growth of 13% at the end of the first quarter.

The final result? 28.6%, as wave after wave of positive earnings surprises drove results well above expectations.

We will likely see the same thing play out again in the second quarter.

Analysts expect S&P 500 earnings to grow 24.7%, according to FactSet.

And if analysts are underestimating results again, we could be looking at 30% earnings growth by the time it’s all said and done.

That is especially encouraging for fundamentally superior stocks. When companies beat estimates and raise guidance, Wall Street is forced to revise its outlook, and institutional money tends to follow. That can send the strongest stocks sharply higher.

So please – pinch yourself. You are not dreaming. The opportunity is real, folks.

It is time to grow and prosper, so let’s talk about how to find the stocks most likely to benefit.

How to Spot the Next Earnings Leaders Early

Now, I want to make one thing clear. I’m on a mission from God to help you get rich.

I do not say that lightly. That is why I focus on the companies showing the earliest signs of accelerating sales, earnings and buying pressure.

With earnings growth expected to strengthen through the rest of the year, those stocks could lead the market’s next major move.

The challenge, of course, is identifying those companies before their strength becomes obvious to the rest of Wall Street.

A blockbuster earnings report rarely comes out of nowhere. The underlying business often begins improving well before that growth appears in the headline results.

And that is exactly why I designed my Precursor Intelligence system: to track the early fundamental and institutional signals that often appear before stronger earnings, raised guidance and a major move in the stock.

The goal is not to chase a stock after it has already delivered spectacular results and captured Wall Street’s attention. It is to identify potential earnings leaders while their growth stories are still taking shape.

That distinction could be especially important in the months ahead. A strong earnings environment can lift the broader market, but the biggest gains tend to come from companies whose fundamentals are improving the fastest.

Precursor Intelligence helps me focus on those opportunities and separate companies with genuine earnings momentum from stocks that are simply riding the market’s enthusiasm.

That is the edge I broke down in my recent special presentation. I also revealed my No. 1 stock to buy now, along with one stock I believe investors should avoid.

With the earnings fireworks already underway, I strongly encourage you to watch the presentation now. You will see how P.I. can help identify potential earnings leaders before the next wave of results puts them on the rest of Wall Street’s radar.

Sincerely,

Louis Navellier

Editor, Market 360

P.S. The Precursor Intelligence system I told you about is about to get even better…  

I’ve been thinking a lot lately about how regular investors like you can use it the way I do – not just to look up individual tickers, but to search for broader patterns.

Which sectors are seeing the most upgrades? Which stocks are showing improving grades over time? Where is institutional money quietly moving? In a sense, I’ve been asking the system these questions for decades – and it has helped point me toward the strongest opportunities.

I think we’re close to something that will fundamentally change the way you interact with it – making it more intuitive, more powerful and more personal to your own portfolio.

Stay tuned…

The post How to Profit From Wall Street’s Next Fireworks Show appeared first on InvestorPlace.

Moonshot AI’s new model added fuel to a broader technology selloff that pushed the Philadelphia Semiconductor Index (SOX) down nearly 10% for the week, its worst performance in more than a year. Investors worried that another capable Chinese model had exposed the hyperscalers’ infrastructure spending as wasteful.

Then Moonshot ran into a wall.

Demand for Kimi K3 surged so quickly that the company temporarily stopped accepting new subscriptions. User traffic had pushed its GPU capacity close to the limit, forcing Moonshot to protect service for existing customers while it added more computing resources.

The selloff targeted chipmakers and infrastructure suppliers even as Moonshot proved it needed more of what they sell.

K3 may pressure model pricing, but every token it generates still needs chips, memory, networking, data centers, and power.

Wall Street found a real disruption.

It just sold the wrong layer.

Why Kimi K3 Is Not Another DeepSeek

K3 deserves the attention. Moonshot built it for long coding projects, visual tasks, and complex agent workflows. Early benchmark results place it near the frontier, especially in coding, and the company plans to release the full model files and technical report on July 27.

The more important detail for investors is what it takes to run. Moonshot recommends clusters of at least 64 high-end AI chips. Like many advanced models, K3 only activates a portion of its parameters per request – a clever efficiency move that trims the compute bill somewhat. It doesn’t make the hardware requirements go away. 

That is where the DeepSeek comparison starts to break down.

The original DeepSeek panic centered on efficiency. Investors thought R1 had shown that a Chinese lab could approach frontier performance with far less infrastructure than the industry assumed.

K3 poses a different threat. It gives developers another capable model they can eventually host, modify, and build around. But it still needs serious hardware – and Moonshot ran into GPU-capacity limits almost immediately after launch.

Releasing the model files gives developers more control. It does not make chips, memory, networking, cooling, or power optional.

K3 may weaken the pricing power of model providers. Its adoption strengthens the case for the infrastructure underneath them.

How Kimi K3 Changes AI Model Economics

For the past few years, frontier labs could charge premium prices because buyers had few comparable options. K3 adds another credible choice.

Companies can use Moonshot’s API or, once the model files are released, host and customize K3 themselves. That gives buyers more leverage, particularly in routine, high-volume work such as coding assistance, document processing, basic research, customer support, and internal agents.

That said, capability benchmarks aren’t the only thing enterprises buy. 

U.S. labs still hold important advantages in security, governance, enterprise support, and regulated industries. A bank will not replace a proven production system because of one benchmark chart. But the middle of the model market is getting more crowded, and vendors may have to compete harder for each dollar of revenue.

Open models also change who pays the compute bill.

A company using Moonshot’s API pays Moonshot to run the model. A company hosting K3 itself pays a cloud provider or builds its own environment. Either route requires chips, memory, networking, storage, cooling, and power.

K3’s launch gave us a clean demonstration. Moonshot opened access, developers arrived, usage climbed, and GPU capacity became the immediate constraint.

That is the Jevons effect in plain sight: easier access creates more use. A startup that could not afford a premium frontier model can try K3. A large company can customize it. Developers can build specialized agents around it. Each new deployment adds another stream of AI traffic.

Model providers may earn less on each task. Infrastructure providers can earn more because there are more tasks to run.

The Bottom Line: Wall Street Sold the Wrong Layer

Kimi K3 is a serious achievement. It shows that Chinese labs are closing the capability gap and gives enterprises another credible model choice.

Moonshot plans to release K3’s full model files and technical report on July 27. Independent testing should tell us whether outside evaluators can reproduce its strongest results, what the model costs to run at scale, and how it performs beyond launch benchmarks – which will determine how much pressure K3 can actually put on premium model pricing.

But its first infrastructure signal has already arrived. Moonshot’s GPU shortage showed what happens when a capable model finds an audience: usage grows, available capacity gets consumed, and more capacity has to be built.

Wall Street conflated those two layers in the recent selloff. 

That is the opportunity – and why we are using the weakness to add selectively to the companies supplying the infrastructure beneath the model economy.

The price of intelligence can fall while the compute bill keeps rising. Moonshot just proved it without meaning to. The model got cheaper. The GPU shortage got worse.

That dynamic points to a specific set of assets – and most investors are still looking at the wrong layer to find them.

The physical layer of this trade is where the most interesting capital has been moving. Not into chips or hyperscalers. Into the hard assets that make the compute bill payable in the first place: energy, nuclear capacity, fabrication. The substrate that compounds regardless of which model wins the benchmark wars.

Here’s the curated portfolio built around that thesis.

The post Wall Street Misread Kimi K3 appeared first on InvestorPlace.

As I write on Monday, the tech/AI trade is pushing higher. But on Friday, it briefly dipped into an official bear market.

I’m referencing the Philadelphia Semiconductor Index, tracked by the iShares Semiconductor ETF (SOXX). It provides diversified exposure to the entire critical supply chain of the AI boom – from chip designers, to custom accelerators, to critical manufacturing equipment. It’s a one-click way to own “AI.”

And here’s how it looked at one point on Friday – down 20%+, official bear-market territory.

This bear hasn’t been driven by bad news – it’s arrived despite some of the best news the AI infrastructure trade has seen all year.

Take last week’s earnings from AI bellwether Taiwan Semiconductor Manufacturing Co. (TSMC).

The company reported a record-shattering second quarter, with revenue rising 36% year over year to $40.20 billion and net income surging 77%, driven by strong demand for AI chips. Gross margins were good, and management raised its full-year revenue growth outlook to over 40%, supported by a massive expansion of its capital expenditure budget.

And yet Wall Street punished that blowout performance with a 5% selloff.

It’s not the only one.

Fellow AI giants ASML (ASML) and Samsung Electronics also smashed earnings last week (Samsung reported a colossal 15-fold surge in operating profit) only to be rewarded with heavy selling – ASML dropped 5% the day after it reported earnings while Samsung tanked about 8%.

The most fascinating part of all this is that the “beat-and-drop” anomaly is occurring against a backdrop of clear forward visibility. These AI infrastructure giants aren’t just promising growth – their explosive future revenue and cash flows are heavily backlogged and already under long-term contract.

So, why is AI suddenly in a bear market then?

Because Wall Street has gotten nervous – not about today’s orders, but about tomorrow’s.

Investors are increasingly questioning whether today’s AI spending spree still has legs.

The “capex-taker problem”

Last week, our technology expert Luke Lango, editor of Innovation Investor, detailed what’s happening:

The market’s hesitation is not about today’s demand (for AI), but about whether hyperscaler spending remains as robust in 2027 and beyond.

Yes, the hyperscalers have spent billions so far – profiting the supply-chain companies like ASML, Taiwan Semiconductor, and Samsung extravagantly – and they’ve pledged billions more to come.

But yesterday’s pledge isn’t the same thing as tomorrow’s delivery. And Wall Street is increasingly worried it will vanish.

Back to Luke:

The problem is that supply-chain companies are capex takers—they build against spending decisions made months or years ago—and therefore cannot answer the market’s only remaining question:

Whether hyperscalers intend to sustain today’s spending into 2027 and 2028.

Well, we don’t have to wait much longer to find out.

That demand-side confirmation begins arriving on Wednesday when Google (GOOG) reports, followed by Microsoft (MSFT) and Meta (META) next Wednesday (July 29), and Amazon (AMZN) on July 30.

Back to Luke:

The four questions that matter remain straightforward:

• Do hyperscalers maintain or raise 2026 AI capex?
• Do they provide constructive commentary around 2027 and 2028 spending?
• Are AI investments producing measurable returns that justify continued expansion?
• And do they announce additional infrastructure projects that demonstrate the buildout is still accelerating?

Luke believes that all four questions will receive positive answers. And if so, get ready for a sharp recovery rally across AI infrastructure.

Here’s his bottom line:

The capex taker problem is real and it ends [starting this week].

Google, Microsoft, Meta, and Amazon will tell the market what Samsung, ASML, and TSMC structurally cannot: Whether the AI infrastructure buildout has durable legs into 2027 and 2028.

We believe the answer is yes, and every leading indicator from the demand side supports that belief.

To see how Luke is positioning his Innovation Investor subscribers to be ready for the potential AI rally, click here.

Now, while money has been flowing out of AI infrastructure over the past few weeks, another group continues to strengthen: oil refiners.

And that’s exactly where veteran trader Jonathan Rose of Masters in Trading Live is finding opportunity today…

Plenty of fuel in the tank

Jonathan has long kept a close eye on oil refiners.

One of the primary indicators he watches is the “crack spread” – essentially the profit margin refiners earn by turning crude oil into gasoline and diesel.

Historically, refinery stocks tend to follow that margin. When the crack spread expands – as it’s been doing recently – refiners’ earnings power often improves soon after.

During last Friday’s free Masters in Trading Live video, Jonathan pointed out that the crack spread has continued strengthening – and then called it one of the most powerful moves he’s ever seen:

I’ve actually never seen such a strong, violent move…

You want to stay long. All refiners. Patience. There is no reason to cover.

Among the names he highlighted were Phillips 66 (PSX), HF Sinclair (DINO), CVR Energy (CVI), and PBF Energy (PBF).

As you can see below, over the last month, these stocks have surged between 24% and 67%.

While this might feel like “too far, too fast,” just recognize that as long as refining margins continue to expand, the industry’s underlying fundamentals remain supportive of more gains.

If you’d like to hear Jonathan walk through the charts himself – including why he believes the crack spread remains one of the market’s most reliable leading indicators – you can watch last Friday’s free Masters in Trading Live episode here.

And if you’re new to Jonathan, he publishes these free MIT Live videos every day that the market is open at 11 a.m. ET. He profiles market trends, explains entries and exits, discusses the opportunities he’s watching in real time, and offers plenty of tickers along the way. You can sign up right here.

But energy isn’t the only place investors have been finding relief from the wobbly AI trade.

Senior Analyst Brian Hunt just highlighted the recent outperformance of a traditionally defensive corner of the market…

There’s always a bull market somewhere

One of the easiest mistakes investors make during a sharp selloff is assuming everything is falling.

That’s rarely true.

Here’s Brian, editor of Money & Megatrends, with the reality:

There’s always a bull market somewhere. And in pursuit of finding such bull markets, money usually stays in the market.

It ‘sloshes’ back and forth in between various sectors, industries, and themes… looking for at least a temporary home where it will be treated well.

This month, that “sloshing” has become especially apparent.

While many of the market’s premier AI infrastructure stocks are down 20%+ from their recent highs, Brian notes that another group has been setting records – the Invesco Dividend Achievers ETF (PFM) just hit a fresh all-time high.

Dividend Achievers are companies that have raised their dividends every year for at least 10 years. Think Johnson & Johnson (JNJ), Visa (V), Coca-Cola (KO), Procter & Gamble (PG), ExxonMobil (XOM), Chevron (CVX), Walmart (WMT), and PepsiCo (PEP). Many of these companies are as “blue” as “blue chip” comes.

If the AI selloff is keeping you from sleeping well, you don’t have to abandon the market altogether – just choose a different investment vehicle, one that has a multi-decade track record of strength.

Back to Brian:

These businesses have paid and increased their dividends through recessions, bear markets, and a global pandemic.

In terms of consistency, these firms rank just behind the rising sun. PFM is a fund designed specifically to own such firms.

Whether the current AI selloff proves temporary, as Luke expects, or lasts longer than investors hope, Brian’s broader reminder is important to remember:

There’s always a bull market somewhere.

If you’d like Brian’s help in finding them, he writes Money & Megatrends every day the market is open, highlighting all sorts of opportunities before they become front-page news – best of all, it’s 100% free.

His issues are loaded with trend analysis, actionable advice, and loads of specific tickers. You can sign up right here.

Coming full circle

We’ll learn a lot over the next two weeks.

The hyperscalers are finally going to answer the question Wall Street has been asking all summer: Is the AI infrastructure buildout still accelerating, or is the spending boom beginning to fade?

If Luke is right, this recent AI bear market could prove remarkably short-lived.

If not, Jonathan and Brian offer an equally valuable reminder: markets don’t move as a single giant monolith. Capital is constantly searching for opportunity – sometimes in oil refiners, sometimes in blue-chip dividend growers, and soon enough, perhaps back into AI.

Have a good evening,

Jeff Remsburg

(Disclosure: I own TSM, ASML, GOOGL, MSFT, AMZN, CVX, WMT)

The post AI Has Entered a Bear Market appeared first on InvestorPlace.

Earnings season is now underway, but something unusual is happening this time around.

Good earnings are no longer enough to send some of the market’s biggest AI stocks higher.

Two of those stocks, ASML Holding N.V. (ASML) and Taiwan Semiconductor Manufacturing Company Limited (TSM), both beat earnings expectations and raised their outlooks. But both stocks declined, with ASML down roughly 8% and Taiwan Semiconductor down about 4% by the end of last week.

At first glance, it doesn’t make sense. They should’ve gone up. So, what happened?

The answer may tell us a lot about what’s driving the AI market right now – and what’s coming next.

In this week’s Navellier Market Buzz, technology analyst Tiernan Ray joins me to explain why Wall Street reacted the way it did, what it says about the AI buildout and where investors should focus next.

Click the image below to watch the latest episode of Navellier Market Buzz.

To see more of my videos, click here to subscribe to my YouTube channel. And to learn more about Tiernan, check out his newsletter, The Technology Letter, right 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.

Don’t Fall Into the AI Trap

One point Tiernan made in our discussion really stood out to me. The information investors once relied on is changing.

As he explained, companies like ASML and Taiwan Semiconductor are giving investors a different outlook than they have in the past.

Instead of focusing only on current demand, they’re spending more time discussing where AI could be headed years from now. That makes it even harder to separate real opportunities from market noise.

I believe that’s one reason so many investors are falling into what I call the 50-Million AI Trap.

See, AI can analyze thousands of stocks in seconds. But when millions of investors rely on the same AI tools, they often end up chasing the same ideas. By then, the biggest opportunities may already be behind them.

That’s exactly why I developed my proprietary Precursor Intelligence (P.I.) system.

It’s designed to help me spot signs that institutional investors are beginning to buy a stock – before the crowd catches on.

In my latest presentation, I’ll explain how the AI Trap works and how P.I. can help you stay one step ahead.

Click here 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:

Taiwan Semiconductor Manufacturing Company Limited (TSM)

The post Why Did Two AI Giants Fall After Beating Earnings? appeared first on InvestorPlace.

0-0

The score remained deadlocked for nearly two hours.

In last night’s FIFA World Cup championship game, Argentina goalkeeper Emiliano Martínez repeatedly denied Spain with spectacular saves (and set a Men’s World Cup Final record in the process).

Despite controlling much of the match, Spain had nothing on the scoreboard to show for it.

Other teams might have abandoned their game plan in search of a breakthrough. Spain didn’t. They stayed committed to their patient, possession-based approach, trusting that if they kept making the right decisions, the win would eventually come.

It did.

Substitute Ferran Torres scored the winning goal in the 106th minute of extra time, giving Spain a 1-0 victory over defending champion Argentina.

The lesson of commitment and patience extends far beyond soccer. Success often belongs to those willing to endure uncomfortable stretches without abandoning the process. Markets work much the same way. They often delay rewarding good decisions – but the delay doesn’t invalidate them.

No one understood this dynamic better than Jean-Marie Eveillard, the long-time portfolio manager of the First Eagle Global Fund (SGENX).

During his first decade running the fund, Eveillard was a superstar — his fund  returned 236% from inception through March 1997, versus 133% for the MSCI World Index. Then he looked around at the market, didn’t like what he saw, and started trimming positions, raising cash, and adding some gold exposure.

For three years, he suffered the slings and arrows of miserable relative performance.

Between March 1997 and March 2000, while the Nasdaq Composite nearly tripled, First Eagle posted a total return of just 28%. Clients cashed out, and the fund nearly shut down.

But Eveillard was unflappable. “I would rather lose half of our shareholders,” he said, “than half of our shareholders’ money.”

As it turned out, he did lose about half his shareholders. Then the cycle turned.

In the 10 years from March 2000 to March 2010, the S&P 500 and the iShares MSCI EAFE ETF (EFA) – which tracks the performance of large- and mid-cap companies – both produced negative total returns, while the First Eagle Global Fund more than tripled.

Prudence, it turned out, was prudent after all. It just required an uncomfortable wait.

Seth Klarman, whose Baupost Fund returned 15.9% per year during the “lost decade” of 1998 to 2008 while the S&P 500 lost 1.4% annually, has described the psychological burden of this approach with characteristic precision: “You must ignore the market and go against the grain in the short term [in order] to win in the long term.”

Now, I am not claiming to be an Eveillard, or even a victorious Spanish athlete. But I applaud his disciplined approach… and consider it to be especially relevant and timely in today’s exuberant market.

That is precisely why I’ve been looking beyond the market’s AI darlings.

I’ll share how that same principle has guided my own investment approach below. But first, let’s take a look at what we covered here at Smart Money last week.

Smart Money Roundup

AI Is No Longer Just Building Companies – It’s Funding Them

July 15, 2026

Lyzr Inc. recently raised $100 million. But it’s not the amount that makes Lyzr interesting, it’s how it raised the money… by using one of its own AI agents to do it. Here’s what this means for the Agentic Reckoning.

Before You Buy the Dip, Check the Calendar

July 16, 2026

Memory and data storage stocks have dominated the AI market for the past few years, achieving such huge gains that many investors wonder whether there’s still money to be made. TradeSmith CEO Keith Kaplan explains how recurring seasonal patterns have historically helped identify more favorable times to buy and sell individual stocks.

How to Beat Wall Street’s “Easy Button”

July 18, 2026

In early 2005, Staples launched a new ad campaign that introduced the world to the “easy” button. Now, wouldn’t it be great if investors also had a similar button to press? Well, they might. Tom Yeung shares why the best long-term returns often come from a mix of turnaround stocks and fast-growing companies trading at fair prices.

The Nile Flooded Every Year — So Does This Stock

July 19, 2026

Looking back can often help us see what’s likely to happen next. That approach has enabled Keith Kaplan and his team at TradeSmith to deliver notable investment gains for their subscribers. Instead of predicting market moves based on gut or headlines, they created a software system that looks to history to identify calendar windows optimal for investing.

Why Patience Is Paying Off

Spain didn’t abandon its style because the scoreboard remained 0-0.

Eveillard didn’t abandon his discipline because the Nasdaq was soaring.

And last October, I didn’t believe investors should abandon every company outside the AI trade simply because the market had become infatuated with it.

I asserted that “AI Survivor” stocks could be the kinds of investments that produce surprisingly strong results over the coming years. As I stated in that Fry’s Investment Report monthly issue:

The world’s most future-proof companies may not be the ones building AI technologies, but the ones that have nothing to do with them.

Since then, the Magnificent Seven stocks have advanced just 5.9%, on average. By comparison, the specific AI Survivor stock I recommended in the October issue, has advanced 43%.

More broadly, the Fry’s Investment Report portfolio, configured as it was in mid-October, has gained an average of 17.8%. In other words, the rotation I anticipated may have already begun.

You can learn how to access my “AI Survivor” recommendations here.

Admittedly, these stocks rarely spark the kind of excitement surrounding AI favorites like Nvidia Corp. (NVDA), Broadcom Inc. (AVGO), or Advanced Micro Devices Inc. (AMD). Neither will they dominate the financial media this summer.

But they could deliver surprisingly strong returns as the AI frenzy fades.

To be clear, I am not predicting that high-flying AI stocks will plummet tomorrow, or next quarter, or next year. Rotations take time. Sentiment changes slowly. And investors who abandon a sound strategy too early often miss the payoff they’re waiting for.

That’s why I continue to believe the market’s hypercritical treatment of non-AI companies is creating compelling opportunities.

These businesses don’t need to become the market’s newest obsession. They simply need to keep executing until the market recognizes what was there all along.

Click here to learn more.

Regards,

The post The Investment Lesson Hidden in Spain’s 1-0 Victory 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.

Sincerely,

Louis Navellier

Editor, Market 360

The post ASML Holding N.V. Upgraded, Super Micro Computer Downgraded: Updated Rankings on Top Blue-Chip Stocks appeared first on InvestorPlace.

That’s true in medicine and science. And increasingly, it’s true in investing.

As computing power improves, researchers can analyze data in ways that simply weren’t possible a decade ago. Investors can, too.

That’s the backdrop for today’s article from TradeSmith’s Keith Kaplan. Keith explains how his team uses modern computing to analyze decades of market history, searching for recurring opportunities that would be nearly impossible to spot by eye – and why one of those opportunities has his attention today.

He shared the full framework during his Breakthrough 2026 event. Watch the free replay here. Then read on to see how that research translates into actionable investment ideas.

How often do you see a photo that makes you question everything you thought you knew?

That’s how Nobel Prize-winning biologist James Watson described seeing “Photograph 51” for the first time, in January 1953.

It was a strange, blurry image taken by British chemist Rosalind Franklin with a technique called X-ray crystallography.

It captured a crucial pattern in our DNA that no one had detected before. The DNA strands twisted and crossed into what we now know as the double helix.

Watson didn’t discover DNA – that happened back in 1869. But 80 years went by before Franklin’s X-ray image revealed the hidden pattern that had been there all along.

From Photograph 51 to Hidden Market Patterns

Something similar is true of the stock market. On the surface it can seem random, but there are also hidden patterns to how stocks move. You just have to have the right technology to spot them.

And like the DNA double helix, you can’t do it with the naked eye. You need an X-ray view.

That’s what TradeSmith’s Seasonality software is designed to do. We ran thousands of stocks through the same test, going back 33 years of market history. And we found reliable windows when they tended to rise and fall.

These patterns have held up through bull and bear markets, manias and panics, wars, and pandemics.

Based on these signals, we created a rapid-fire trading strategy to pinpoint bullish seasonality windows on 5,000 stocks – to the day. In our backtests, the system got the direction right 83% of the time – meaning the stock finished the window higher, not lower.

What the 18-Year Backtest Showed

The returns beat the broad market, too. In an 18-year backtest, a model portfolio of these seasonal trades returned 857%, versus 412% for the S&P 500. 

That doesn’t mean the system will deliver those exact returns when you run it live. But it’s an edge worth paying attention to.

On Thursday, more than 16,000 viewers joined me for my Breakthrough 2026 event to see how this X-ray view works.

I walked them through how one of the most important bullish windows in the entire market closes next week – and how it closes right as the market’s biggest names report earnings. It’s the kind of moment where your timing matters more than stock picking. 

Watch it here while it’s still online. Then read on for more on how this system works – and three seasonal setups for your radar right now.

How Stock Seasonality Finds Historically Strong Trading Windows

Finding seasonal cycles in stocks on your own would be an enormous undertaking.

You’d have to pull up a one-year chart like this one for Google parent Alphabet (GOOGL).

Then line up one-year charts like this, one after the other, going back a decade or more…

…and keep track of how that stock behaved across thousands of trading windows.

Or you could just type GOOGL into TradeSmith’s Seasonality software. It averages as many years as you want and gives you one simple seasonality trend line. 

Best of all, it highlights “green days” when the stock has gone up 80% of the time or more. Plus “red days,” when it’s fallen more than 80% of the time. 

You can do this for pretty much any stock you want and map out high probability trade setups in advance. Not just the buy date, either – but the sell date, too.

Alphabet’s Strongest Seasonal Window Is Open

In the past 15 years, GOOGL has had stretches of green days in January, May, July, and late October. But the best window is the one we’re in now:

Between June 29 and July 30, Google stock has gone up in 14 of the past 15 years with an average return of 8.7%. In 2025, the price action lined up almost perfectly, with GOOGL gaining 8.9% during that seasonally bullish window.

Two More Stock Seasonality Signals to Watch

Take Deckers Outdoor (DECK), the maker of Ugg boots and Hoka running shoes. DECK’s next green zone is July 29 through Aug. 14. In that window, the average return was 3.5% over the past 15 years:

Then DECK has an especially strong bullish window starting Nov. 23. Buying that day returned an average 7% through Dec. 11. 

Those are the optimal patterns to follow our seasonality strategy, trading individual stocks at their absolute best times of year. 

Or take Applied Materials (AMAT), which builds machines that are used to make advanced computer chips.

It gained 10% during its first stretch of green days on our seasonality chart in January and February.

And in a seasonally bullish window in May, AMAT climbed 16.8%.

But don’t be surprised if that party ends by August. From July 30 to Aug. 31, AMAT has fallen 80% of the time, with an average loss of 2.9%:

No Signal, No Trade

I’m sure you’ve noticed all the other times of year that don’t get these green or red windows. They’re times when there isn’t a statistically strong enough pattern to rely on. When the data doesn’t clear our bar, we leave it alone. No signal, no trade.

Using TradeSmith’s Seasonality tool, we’ve put this approach to the test across thousands of stocks, indexes, and even commodities and currencies.

And, as I mentioned up top, over an 18-year backtest following these seasonal trades delivered 857% in total returns – more than twice what the S&P 500 delivered over the same stretch.

The worst year in our test was 2007 – and even then, our strategy still turned a profit. It beat the S&P 500 by more than two to one that year.

The S&P 500’s Bullish Window Ends July 23

I dove into the details during my Breakthrough 2026 event.

I walked through the seasonal patterns coming up that you need to watch for… why they keep working even when markets get chaotic… and how to put them to work in your portfolio.

As you’ll see, getting your seasonal timing right could matter more to your wealth than any stock pick you make this year.

The next date to watch is July 23. That’s when one of the biggest green zones in the entire S&P 500 comes to an end.

Every prior year it’s closed, the market has turned choppy – and this time it closes right as Tesla, Amazon, Apple, and Microsoft report earnings. I don’t know which way the biggest names will break. 

But I’d rather watch that window close with my eyes open than be blindsided by the market regime shift it could trigger.

Catch the replay here. 

The post Three Stocks Just Flashed Seasonal Signals appeared first on InvestorPlace.

That approach has enabled Keith Kaplan and his team at TradeSmith to deliver notable investment gains for their subscribers. Instead of predicting market moves based on gut or headlines, they created a software system that looks to history to identify calendar windows optimal for investing.

For today’s Smart Money, I invited Keith to dive deeper into why patterns are important and detail multiple backtests, including one that turned every $10,000 into $85,700.

In fact, he recorded a free presentation showing the tool in action, which you can find here.

Without further ado, here’s Keith…

On Wednesday, the U.S. and Iran blew through their second ceasefire in four months.

The Strait of Hormuz — the world’s most important oil chokepoint — has now been declared open, then closed again, at least three times since February.

I’ll be the first to admit it: I didn’t see the Iran war coming, and I have no clue when it’s going to end.

I’m an ignoramus on where interest rates are going, too.

Will Kevin Warsh, the new Trump-appointed Fed boss, raise or lower rates?

I have no clue!

And don’t ask me to predict the earnings of Nvidia, Microsoft, Google or any of the other big AI players. I can’t.

And it’s not just me — nobody else can, either.

Just ask Wharton School professor Philip Tetlock. His long-running study tracking thousands of predictions from political and economic experts found that many performed barely better than chance.

Or as he famously put it, the average expert was roughly as accurate as “a dart-tossing chimp.”

That’s why at TradeSmith we don’t try to make predictions about the future. To see what’s likely coming next, we look at the past.

That includes seasonal patterns — calendar windows during which stocks have tended to rise and fall in the past with remarkable accuracy.

It’s why I tell folks to forget about interest rate announcements… earnings… macro narratives… and all the other usual reasons to buy a stock.

All the money you could ever want to make in the market boils down to just a handful of dates on the calendar. I’ll show you how it works in a moment — with an 83% historical accuracy across 5,000 stocks going back to the start of the 1990s.

First, a quick stop in ancient Egypt.

Using the Past to See Into the Future

The Nile River was the source of all life for the people of ancient Egypt. It could also be the source of death and devastation.

Every July, it flooded with astonishing regularity. And how much flooding there was determined whether Egypt feasted or starved that year.

Too little water, and crops failed. Too much, and the flood destroyed villages, swept away irrigation canals, and drowned the harvest before it could grow.

To know what was coming, the ancient Egyptians didn’t try to predict when rain would fall in the distant Ethiopian highlands and fill the Nile’s tributaries. They didn’t need to.

Instead, they built stone columns called Nilometers, sunk into the riverbank. Each column carried marks recording how high past floods had climbed, year after year, going back generations.

By reading centuries of those marks, the Egyptians knew when to plant, when to harvest, when to store grain for a lean year. Not because they could forecast the weather — but because they trusted those past patterns to hold.

As the river climbed each summer, priests marked its progression against every flood before it. If the waters were rising faster than usual, it signaled a big flood. Slower meant a drought.

By managing these rhythms, Egypt turned an unpredictable river into a reliable food supply.

Taxes, grain stores, and harvests were planned out years in advance. All built on nothing more than centuries of marks on a stone wall.

Most investors don’t know it. They’re too busy trying to peer into the future…

But like the Nile, the stock market has its own “floods” and “droughts” — times when stocks predictably surge or dry up at specific times of the year based on decades of data.

This may be news to most regular investors, but traders have known about these patterns for decades.

Traders Have Tracked These Cycles for Centuries

Commodity traders, for example, have long tracked planting and harvest cycles in crops like corn and wheat.

The gold market has also shown recurring seasonal tendencies, often strengthening during certain parts of the year tied to jewelry demand, central bank buying, and annual festivals in India and China.

And stock investors have studied seasonal phenomena such as the January Effect for decades. Even Wall Street’s old saying — “Sell in May and go away” — comes from observed seasonal behavior, not theory.

The only thing that’s changed is how precisely we can track these seasonal effects.

Today, we can discover these patterns across thousands of stocks, over decades of history, and measure them down to specific days — not just months or quarters.

That’s what my team and I set out to do at TradeSmith.

We built software to analyze more than 2 quintillion historical prices across roughly 5,000 stocks, running millions of tests to answer a simple question: Is there an optimal time of year to buy — and an optimal time to sell — each individual stock?

When we tested this approach over the past 18 years, the results were remarkably consistent.

Boston Beer (SAM), for instance, has entered a seasonal “green zone” every Oct. 6 for the past 15 years — climbing an average of 6.6% over the next 17 days:

And SAM stock rallied during that October window 100% of the time. Fifteen out of 15 years – as you can see below:

Nvidia (NVDA) has done something similar every Oct. 23, rising an average of 6.5% over the next 18 days:

NVDA climbed during that window in 13 out of the last 15 years. Even back in 2015, long before many investors had heard of the company:

None of these moves depended on a specific earnings outcome…or any other news headline. They showed up on the calendar, year after year, in bull markets and bear markets alike.

Zoom out, and the pattern holds at the portfolio level, too. A portfolio built around these calendar windows turned every $10,000 into $85,700 over an 18-year backtest.

Those are the kind of results that get your attention — especially when the world seems less predictable now than ever.

Why This Matters Now

Life in the 21st century can feel bamboozling.

We’re constantly buffeted by geopolitical shifts, economic threats like inflation, and exponential technological change — especially from AI.

But you don’t need to predict the next geopolitical shock… Fed decision… or figure out the future of AI and humanity… to grow your wealth. If professional forecasters get these calls wrong more often than not, what hope do you or I have of getting them right?

But you do need a way to understand when the odds are historically tilted in your favor — and when they aren’t. That’s what makes seasonality so valuable.

So, I urge you to watch the replay of this week’s Breakthrough 2026 event.

I walk you through how we uncovered these patterns in stocks… why seasonality keeps working even when markets feel uncertain… and how you can use our software to spot hidden seasonality trends.

I also share a free stock recommendation so you can see how this works in real time — not just in backtests.

Markets will always feel noisy. And 2026 is no exception.

To get an edge, you need to know which signals to ignore — and which patterns have been there all along, hidden in the data.

Here’s that link again to watch the replay.

Regards,

Keith Kaplan
CEO, TradeSmith

The post The Nile Flooded Every Year — So Does This Stock appeared first on InvestorPlace.

However, long-term investors dare less likely to mind. It’s hard to imagine Warren Buffett caring much about stock prices while he’s sipping a Cherry Coke on the beach.

But the volatility has been much tougher for day traders, with many finding themselves in trouble this year. For example…

• Shares of Space Exploration Technologies Corp. (SPCX) briefly dipped below their IPO price last week.
• Chipmakers have tumbled from their June highs.
• And many former meme stocks like Lucid Group Inc. (LCID) and Herts Global Holdings Inc. (HTZ) are now teetering on bankruptcy.

Nevertheless, there’s always a bull market somewhere. Many retailers in the Far East have a  “Second Thanksgiving” in summer. And some American sectors like oil & gas never actually go on holiday. I wrote in May how gasoline refiner PBF Energy Inc. (PBF) would surge thanks to peak summer demand; shares have surged 33% since then on peak summer demand.

To help investors minimize these summer risks, Keith Kaplan and his TradeSmith team have created a quantitative Seasonality tool that precisely identifies the best days to buy a particular stock. With this software, you don’t have to guess when to buy Costco Wholesale Corp. (COST) for the year-end “Santa Claus” rally. Instead, you will know that October 27 is exactly the right date to get in.

To learn more about the Seasonality software, check out Keith’s Breakthrough 2026 presentation, where he explains exactly how the system works.

To illustrate the power of Keith’s creation, I’d like to bring you one more stock this week that the system has flagged. It is a summer darling that does swift business in warm months… and I believe it’s at the start of a multi-year turnaround.

The company has passed other quantitative screens, and Keith’s system shows this coming week is the best time to buy. After all, shares have historically returned a stunning 35.6% between July 24 and August 17, as shown in the chart below.

Historically one of late summer’s best stocks

So, let’s “dive” into the details…

My No. 1 Stock for the Summer of 2026

Every summer, my neighborhood turns into a buzz of activity.

People are out barbecuing…

Music is playing…

And this year, there was a new sound across the street: the splashing of water in a pool.

You see, my neighbors installed a new in-ground pool earlier this year. And now that it’s warm, they seem determined to get every minute of splash time in before fall arrives.

They’re not alone. Since 2025, demand for in-ground pools in the U.S. has stabilized after collapsing in the post-Covid-19 years. Many Americans are finally reopening their wallets and getting that swimming pool they’ve always wanted.

To buy into that trend, my favored pick is America’s largest supplier of in-ground pools:

Latham Group Inc. (SWIM).

Latham is a New York-based company that installs roughly one in every five new in-ground pools in America. They are particularly dominant in fiberglass pools, and the stock chart above belongs to them.

Here’s why I believe shares are worth buying today.

The Cycle Has Turned

Few businesses swing as violently with the economy as a swimming pool maker. A new in-ground pool can cost $50,000 or more, making it an “affordable luxury” that’s sometimes more “luxury” than “affordable.” So, when stimulus checks and near-zero interest rates collided with stay-at-home nesting during the Covid-19 pandemic, Americans poured into their backyards, sending Latham’s revenues to a record $696 million peak in 2022.

Then the interest rate shock hit. Pool demand cratered, and Latham’s sales collapsed 27%. Below is a graph showing the number of new pools added per year, with light-blue bars reflecting estimates.

New pool starts collapsed after the Covid-19 pandemic

Source: Latham investor relations

But something important happened this year: New U.S. pool construction stopped going down. Latham’s management now expects the market to be “about flat to last year,” and is modeling significant growth going forward. My big-spending neighbors seem to be part of a broader trend.

In fact, this view might even be too bearish. Demand for other affordable luxuries like marine craft, high-end decking, and air travel is on the rise. Mercury Marine owner Brunswick Corp. (BC) announced a “beat and raise” quarter earlier this spring; analysts expect their boat sales to rise 8% this year, compared to a 2% decline last year. Airlines are all guiding higher, too.

I’m also not so worried about low consumer survey sentiment numbers, which on average have reached record lows. Latham’s core market is in the “Sand States” (Florida, Texas, Arizona, and California), and three of them lean Republican – a cohort that has remained optimistic about the economy. Below is a chart that shows consumer sentiment, separated by political views.

The University of Michigan Consumer Sentiment, divided by political affiliation

Source: University of Michigan

Put another way, not every consumer needs to feel great about the economy. As long as the cohort interested in $50,000 in-ground pools are spending money, then Latham should come out ahead. And Keith’s quantitative tool has flagged this week as the best time to play that insight.

The Self-Help Engine

Latham is also riding a broader American trend towards fiberglass pools. That means revenues should rise even if pool starts remain flat. Here’s why…

Fiberglass pools are premade shells that need just two to three days to install, rather than the eight to 16 weeks for a traditional gunite pool (concrete over steel bars). Fiberglass versions are significantly cheaper to install (often half the cost) and only require refinishing every 20 years. (Gunites need resurfacing every 10 years to 15 years, while vinyl liner pools need replacement every five 10.)

American homeowners are steadily catching on. Fiberglass has climbed to about 23% to 24% of U.S. in-ground pools, up from 17% to 18% in 2019, and is growing at double digits in states like Florida.

For context, 70% of in-ground pools in Australia are fiberglass, so the U.S. growth runway is long.

Together, that means 2025 was already a growth year for Latham, despite fewer overall pool starts. Sales rose 7% to $546 million, and became the company’s first profitable year (on a reported GAAP basis) since its 2021 IPO. Management is now guiding for another 9% growth in 2026, and that figure could be far higher if strong sales of boating and other big-ticket items are a guide.

I should also note that roughly half of Latham’s revenues come from pool liner and cover replacements, providing an additional source of income.

The Insiders are Buying

In May, two Latham insiders bought stock in the open market:

• Chief Financial Officer Oliver Gloe bought about $74,000’s worth at $4.90
• Director James Cline bought roughly $242,000’s worth at $4.84.

CFO buys are my favorite type of buying signal. Studies have found that CFO buys perform twice as well as CEO buys, and these purchases came with no sells by any insider.

I pay close attention to these purchases, because they are one of the most honest signals on Wall Street. Executives can sell for a hundred reasons, including college tuition, a new house, diversification… or perhaps a new in-ground pool. But they buy for only one:

They think the stock is going higher.

And when two insiders are buying shares (with no selling by anyone else), it’s a sign they know something the rest of the market does not. They may have been early relative to the “Buy” signal provided by Keith’s tool… but there’s no harm in getting in sooner rather than later.

Of course, there are risks in the short term. First is randomness: One of my recommended seasonal stocks last week, Coupang Inc. (CPNG), dropped almost 10% out of the gate because of an unexpected South Korean court ruling. Second is Latham’s volatile earnings. The company’s annual interest payments of $26 million are large relative to its $30.6 million of operating income last year, so even tiny changes in operating income will have enormous effects on the bottom line. And third is America’s recovery: If the U.S. suddenly goes into a recession, then no cyclical consumer stock will be spared.

Nevertheless, the three factors cut both ways. And so, I’m flagging Lantham as a stock worth owning as America dives back into the deep end.

Getting the Timing Right

I’ve had my eye on Latham for a while now. The company runs a perfectly understandable business, has a clear profit engine, and its new CEO (who replaced a retiring one) has excellent experience in selling pricey home goods. Before joining Latham as CEO, Sean Gadd ran the North American business of James Hardie Industries plc (JHX).

Yet, the timing on Latham has never been quite clear. Shares of the leveraged stock seem to move almost randomly: a 22% decline in March… a 11% rally in April… another 20% collapse in early May…

2026 has been a guessing game for Latham’s stock.

But with Keith’s trading system, much of that uncertainty goes away. Seasonal trends become clearer, and buying opportunities start presenting themselves.

Click here to watch Keith’s Breakthrough 2026 event to learn more about his Seasonality software.

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 My No. 1 Summer Stock to Buy appeared first on InvestorPlace.

His real skill wasn’t simply picking winners. Buffett had a knack for telling the difference between a hurt business and a hurt stock price. More than that, Buffett had the conviction to act on that distinction.

Do you have Warren Buffett’s conviction? 

We just wrapped an incredible first half of 2026, with the S&P 500 up around 10% and the Nasdaq up about 12%, built almost entirely on chips, memory, and AI infrastructure names. Then July hit, and the market rotated hard. The leaders of the last six months got sold, the laggards got bought, and financial media started asking whether the AI trade has finally run its course.

Look, a 20% pullback in a stock you love never feels good. But nothing about the power buildout, the falling cost of inference, or the tightening memory market actually broke in the last two weeks. If anything, the case for owning this trade got stronger, not weaker.

So while the crowd wrings its hands over a rotation, I want to point you toward two sectors that recently started breaking out while everyone else was busy panicking. One is rebuilding the telecom industry from orbit. The other might be the most underappreciated corner of the entire AI boom…

These 2 Sectors Are Breaking Out While Wall Street Panics

You can call this a technical rotation. You can call it mechanical profit-taking. What you cannot call it is a fundamental shift in the structure of the U.S. economy or in the dynamics powering this market, because nothing structural changed. 

Meta Platforms (META) is building its own cloud business to monetize excess GPU capacity, which flips the “hyperscalers are overbuilding with no return” bear case on its head entirely. Excess capacity becomes high-margin, monetizable inventory, not stranded capital expenditure.

Bloom Energy Corp. (BE) just expanded its AI infrastructure power partnership with Brookfield Asset Management Ltd. (BAM) from $5 billion to $25 billion, a fivefold increase in nine months. 

Power has now joined chips, networking, and memory as a core structural bottleneck in the AI buildout, and when one of the largest infrastructure capital pools in the world quintuples its bet on that exact bottleneck, that is not speculation. That is return-driven allocation, plain and simple.

Add in OpenAI’s inference breakthrough, which reportedly cuts inference costs by roughly 50%, and you get a textbook Jevons paradox: cheaper inference does not shrink demand, it explodes it. Lower the barrier to entry and adoption floods in. More models, more spending, more everything.

All of this points to a market working through sentiment while the earnings keep growing underneath it. These are stocks that rip 100%, give back 20% to 25%, then rip another 100%. Right now, you are sitting in one of those pullbacks, and that is a buying opportunity, not an exit sign.

Space Stocks: The SpaceX Junior Trade

The recent SpaceX (SPCX) IPO cooled off the broader space sector, and that cooldown is not a red flag either. Rocket Lab Corp. (RKLB) just posted first-quarter revenue growth of 64% year over year with a record $2.2 billion backlog. Planet Labs PBC (PL) grew revenue 42% year over year with its own backlog up 72%. Money keeps pouring into this sector: Amazon.com Inc. (AMZN) just launched its Leo satellites, Charter Communications Inc. (CHTR) is reportedly in talks with SpaceX, and Verizon Communications Inc. (VZ) and AT&T Inc. (T) are scrambling to counter Starlink.

Rocket Lab is becoming SpaceX Jr., transforming from a rocket-launch company into a vertically integrated space AI player after its acquisition of Rocket Lab National Security. AST SpaceMobile Inc. (ASTS) is chasing a different prize entirely: the telecom industry itself. Reports suggest AST SpaceMobile secretly unveiled a new device to investors during its roadshow, and if Elon Musk is building his own phone to pair with satellite spectrum bought from EchoStar, he is not just trying to kill Starlink’s competitors. He is trying to own the entire stack, the same way he wants to own AI compute through xAI and semiconductors through TeraFab.

That kind of ambition forces AT&T, Verizon, T-Mobile US Inc. (TMUS), and even Apple Inc. (AAPL) to spend defensively on space infrastructure, and that spending is a tailwind for the whole sector.

As for Apple: the company generates enormous free cash flow and chose not to build its own AI compute infrastructure the way Meta, Amazon, Alphabet Inc. (GOOGL), and Microsoft Corp. (MSFT) did. That restraint now looks like the mistake, not the discipline, because you do not grow a company by refusing to invest in it.

Biotech Wakes Up

The SPDR S&P Biotech ETF (XBI) is not merely on the verge of a breakout. It already broke out, climbing from roughly $130 on June 10 to $160 by July 6 in what amounts to a near-vertical line on the chart. Biotech is one of the most rate-sensitive sectors in the market, because its companies are valued on cash flows a decade out, and the macro backdrop just turned constructive. Inflation indicators are falling fast, oil has dropped from more than $100 to under $70, and the path toward a Fed rate cut looks increasingly clear.

The AI and biotech convergence is one of the most underappreciated stories in this entire boom. Medicine generates massive volume, variety, and velocity of data, exactly what AI models need to perform well, and the market is only beginning to price that in.

Be like Warren Buffett. Own these two fundamentally solid sectors while the crowd is still distracted by a rotation that changes nothing.

We break this down in far greater depth, charts and all, on this episode of Being Exponential. Drop your questions in the comments for a future show, or send them here.

The post These 2 Sectors Are Quietly Breaking Out While Wall Street Panics appeared first on InvestorPlace.

$2 billion dollars.

That’s how much money Taylor Swift brought in during her Eras Tour. It is estimated that she personally made between $600 million and $1 billion.

There are few fan bases as passionate as Taylor Swift’s.

So, when she recently married Travis Kelce at Madison Square Garden in New York, it shouldn’t surprise you that someone found a way to make money from the event.

What may surprise you is how.

An artist used the event as an opportunity to sell out of… garbage.

Literally.

Following the wedding, New York artist Justin Gignac walked the streets outside Madison Square Garden collecting discarded coffee cups, cigarette butts, candy wrappers – even a lone AirPod.

He sealed the trash in small plastic cubes, labeled them “JUST & T MARRIED,” priced them at $25 each… and sold every single one.

Gignac looked at the exact same New York streets as everyone else – but he saw an opportunity that everyone else missed.

This same trait can be used to describe the best investors.

They look at the exact same world everyone else sees, but they see opportunities others miss. They can filter out much of the noise we encounter every day and identify money-making opportunities.

That’s what I’ve always appreciated about Luke Lango’s approach to investing.

He isn’t simply trying to buy the companies everyone is already excited about – what everyone already sees.

He’s trying to identify second- and third-order opportunities arising from the trends everyone else is watching.

Winners others don’t see

Just like everyone knew where Taylor Swift was getting married, every investor has access to the same headlines.

All investors have access to the same earnings reports and economic data.

The difference isn’t what they look at, but what they see.

Rather than chasing the companies already dominating the headlines, Luke often asks a different question:

“Who’s quietly benefiting from the trend everyone else is talking about?”

Earlier this year, for example, while most investors were focused on artificial intelligence companies themselves, Luke recommended Howmet Aerospace (HWM).

On the surface, it isn’t the kind of stock that generates much excitement, but Luke saw something many investors overlooked. Luke described what he was seeing when he recommended the stock to his Innovation Investor subscribers.

The AI infrastructure connection comes through two channels. First, the thermal management and precision manufacturing expertise that Howmet has built over decades in aerospace applications is directly transferable to the data center cooling challenge. AI GPU clusters operating at 600W+ per chip create thermal environments that are increasingly analogous to the heat management problems inside a gas turbine. Howmet’s advanced aluminum alloy casting capabilities and precision thermal management components are finding new markets in the high-performance computing infrastructure buildout.

Second, and more immediately significant: Howmet is one of the primary beneficiaries of the commercial aerospace recovery and defense spending ramp that coincides with the post-war investment environment. Airlines are ordering new aircraft at record pace — the Boeing and Airbus backlogs extend a decade into the future. Every next-generation aircraft engine requires Howmet’s precision-cast turbine blades, produced through a proprietary directional solidification process that creates single-crystal components with no grain boundaries to propagate cracks under extreme stress.

Since Luke recommended the company on April 8, the shares have risen about 15%.

It’s not what you look at, but what you see

Everyone knows data centers have been driving market returns. Everyone is looking at the obvious companies that have risen on this trend.

A stock like Howmet didn’t rise because everyone suddenly was talking about it.

Luke saw the same headlines as everyone else, but spotted the value they didn’t recognize, and got his subscribers in.

Luke believes too many investors are making a mistake today by rushing into the stocks everyone is already talking about.

Everyone is watching Elon Musk and talking about SpaceX.

Everyone is speculating about AI.

But according to Luke, the biggest investment opportunity may not be the companies grabbing all the headlines.

While Wall Street focuses on the obvious stories, he believes the biggest opportunity may be hiding just beneath the surface in companies quietly positioned to benefit from one of the biggest changes to the financial system in decades.

If you’d like to see what Luke sees – and why he believes this overlooked opportunity could become far bigger than most investors realize – click here to watch his new presentation.

Money-making opportunities can come from places many investors simply can’t see.

The trick isn’t just what you’re looking at, but what you see that others may miss.

Luke’s approach has always helped his subscribers get into those stocks.

Click here to join him.

Enjoy your weekend,

Luis Hernandez

Editor in Chief, InvestorPlace

The post The investing lesson hidden in Taylor Swift’s wedding appeared first on InvestorPlace.

In today’s guest essay, Keith explains why he spends less time trying to predict the future – and more time studying the patterns history has already revealed.

Instead of focusing on interest rates, earnings, or the latest headlines, Keith and his team analyze decades of market data to uncover recurring seasonal patterns. Those patterns power TradeSmith’s Seasonality tool, which helps identify historically favorable buying windows across thousands of stocks.

If you missed his Breakthrough 2026 event, I encourage you to watch the replay. Keith takes a deeper dive into how seasonality works, why he believes it can give investors an edge, and how he applies it in today’s market.

Now, here’s Keith…

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

Last Wednesday, the U.S. and Iran blew through their second ceasefire in four months.

The Strait of Hormuz — the world’s most important oil chokepoint — has now been declared open, then closed again, at least three times since February.

I’ll be the first to admit it: I didn’t see the Iran war coming, and I have no clue when it’s going to end.

I’m an ignoramus on where interest rates are going, too.

Will Kevin Warsh, the new Trump-appointed Fed boss, raise or lower rates?

I have no clue!

And don’t ask me to predict the earnings of Nvidia, Microsoft, Google or any of the other big AI players. I can’t.

And it’s not just me — nobody else can, either.

Just ask Wharton School professor Philip Tetlock. His long-running study tracking thousands of predictions from political and economic experts found that many performed barely better than chance.

Or as he famously put it, the average expert was roughly as accurate as “a dart-tossing chimp.”

That’s why at TradeSmith, we don’t try to make predictions about the future. To see what’s likely coming next, we look at the past.

That includes seasonal patterns — calendar windows during which stocks have tended to rise and fall in the past with remarkable accuracy.

It’s why I tell folks to forget about interest rate announcements… earnings… macro narratives… and all the other usual reasons to buy a stock.

All the money you could ever want to make in the market boils down to just a handful of dates on the calendar. I’ll show you how it works in a moment — with an 83% historical accuracy across 5,000 stocks going back to the start of the 1990s.

First, a quick stop in ancient Egypt.

Using the Past to See Into the Future

The Nile River was the source of all life for the people of ancient Egypt. It could also be the source of death and devastation.

Every July, it flooded with astonishing regularity. And how much flooding there was determined whether Egypt feasted or starved that year.

Too little water, and crops failed. Too much, and the flood destroyed villages, swept away irrigation canals, and drowned the harvest before it could grow.

To know what was coming, the ancient Egyptians didn’t try to predict when rain would fall in the distant Ethiopian highlands and fill the Nile’s tributaries. They didn’t need to.

Instead, they built stone columns called Nilometers, sunk into the riverbank. Each column carried marks recording how high past floods had climbed, year after year, going back generations.

By reading centuries of those marks, the Egyptians knew when to plant, when to harvest, when to store grain for a lean year. Not because they could forecast the weather — but because they trusted those past patterns to hold.

As the river climbed each summer, priests marked its progression against every flood before it. If the waters were rising faster than usual, it signaled a big flood. Slower meant a drought.

By managing these rhythms, Egypt turned an unpredictable river into a reliable food supply.

Taxes, grain stores, and harvests were planned out years in advance. All built on nothing more than centuries of marks on a stone wall.

Most investors don’t know it. They’re too busy trying to peer into the future…

But like the Nile, the stock market has its own “floods” and “droughts” — times when stocks predictably surge or dry up at specific times of the year based on decades of data.

This may be news to most regular investors, but traders have known about these patterns for decades.

Traders Have Tracked These Cycles for Centuries

Commodity traders, for example, have long tracked planting and harvest cycles in crops like corn and wheat.

The gold market has also shown recurring seasonal tendencies, often strengthening during certain parts of the year tied to jewelry demand, central bank buying, and annual festivals in India and China.

And stock investors have studied seasonal phenomena such as the January Effect for decades. Even Wall Street’s old saying — “Sell in May and go away” — comes from observed seasonal behavior, not theory.

The only thing that’s changed is how precisely we can track these seasonal effects.

Today, we can discover these patterns across thousands of stocks, over decades of history, and measure them down to specific days — not just months or quarters.

That’s what my team and I set out to do at TradeSmith.

We built software to analyze more than 2 quintillion historical prices across roughly 5,000 stocks, running millions of tests to answer a simple question: Is there an optimal time of year to buy — and an optimal time to sell — each individual stock?

When we tested this approach over the past 18 years, the results were remarkably consistent.

Boston Beer (SAM), for instance, has entered a seasonal “green zone” every Oct. 6 for the past 15 years — climbing an average of 6.6% over the next 17 days:

And SAM stock rallied during that October window 100% of the time. Fifteen out of 15 years – as you can see below:

Nvidia (NVDA) has done something similar every Oct. 23, rising an average of 6.5% over the next 18 days:

NVDA climbed during that window in 13 out of the last 15 years. Even back in 2015, long before many investors had heard of the company:

None of these moves depended on a specific earnings outcome…or any other news headline. They showed up on the calendar, year after year, in bull markets and bear markets alike.

Zoom out, and the pattern holds at the portfolio level, too. A portfolio built around these calendar windows turned every $10,000 into $85,700 over an 18-year backtest.

Those are the kind of results that get your attention — especially when the world seems less predictable now than ever.

Why This Matters Now

Life in the 21st century can feel bamboozling.

We’re constantly buffeted by geopolitical shifts, economic threats like inflation, and exponential technological change — especially from AI.

But you don’t need to predict the next geopolitical shock… Fed decision… or figure out the future of AI and humanity… to grow your wealth. If professional forecasters get these calls wrong more often than not, what hope do you or I have of getting them right?

But you do need a way to understand when the odds are historically tilted in your favor — and when they aren’t. That’s what makes seasonality so valuable.

That’s why I want to encourage you to check out the replay of my Breakthrough 2026 event.

I’ll walk you through how we uncovered these patterns in stocks… why seasonality keeps working even when markets feel uncertain… and how you can use our software to spot hidden seasonality trends.

I’ll also share a free stock recommendation, so you can see how this works in real time — not just in backtests.

Markets will always feel noisy. And 2026 is no exception.

To get an edge, you need to know which signals to ignore — and which patterns have been there all along, hidden in the data.

Here’s that link again to watch the replay. 

Keith Kaplan
CEO, TradeSmith

P.S. Subscribers have been telling us this changed the way they think about the market.

Mark S. put it simply: “This is a life altering system.” And John B. wrote to say he’s simply hooked: “I LOVE TRADE CYCLES. AND YOUR SERVICE.”

If you’re interested in a new way of making money this year — and a new kind of investing — make sure to check out the replay here.

The post The Market Leaves Clues. Here’s How to Find Them… appeared first on InvestorPlace.

Bull markets. Bear markets. Earnings seasons. Election years.

The headlines change. But often, the patterns don’t – something TradeSmith CEO Keith Kaplan has been researching closely.

Today’s guest essay from Keith examines one pattern most investors never think about: seasonality. Decades of historical data has led him to believe certain periods consistently tilt the odds in investors’ favor – and that one of those windows is about to close.

He unpacked that research yesterday during his free Breakthrough 2026 event, including how his team is using it to identify opportunities throughout the rest of the year.

Here’s the lowdown on their strategy.

For thousands of years, the appearance of Halley’s Comet was a bad omen. If you saw it streaking across the sky, it meant war, plague, or the death of a king was coming.

Then an English astronomer figured out it was something far more ordinary: a regular visitor, keeping to a schedule.

Edmund Halley saw the comet when it passed in 1682. And he started asking questions. Where had it come from? Had anyone seen it before? And was it following some kind of hidden pattern?

Years later, he found the answer buried in old sighting records: The same comet returned roughly every 76 years.

In 1705, it led him to predict that the comet would return 76 years after the last sighting – in 1758.

Halley died in 1742. So, he never got to find out if he was right. But in 1758, right on schedule, the comet came back. And it’s come back about every 76 years since. You may have seen it when it last passed through the sky in 1986. Its next appearance will be in 2061.

What Halley’s Comet Teaches Us About Market Cycles

My team and I have taken the same idea – that the future can be read in the past – and pointed it at the market. The result is our breakthrough Seasonality software.

It shows that stocks keep schedules of their own – calendar periods when they have tended to rise or fall, year after year.

They’re hard to spot unless you have decades of data and the right algorithms to crunch through it all. But once you find them, you can do something most investors can’t: hold a stock for its strongest stretch of the year, and stand aside before its weakest.

And right now, it shows that one of the most important windows in the entire market is about to end. It’s a  bullish window for the S&P 500. And it closes next Thursday, July 23.

I covered it in detail at yesterday’s Breakthrough 2026 event, where I was joined by more than 16,000 viewers. I also introduced the next evolution of our Seasonality strategy.

Over our 18-year backtest, these seasonal trades delivered 857% in total growth. That’s more than twice what the S&P 500 delivered over the same time.  

Even in 2007, the worst year in our testing, we saw an average gain of 2.5% and an annualized return of 37.9%. That’s close to four times the average annual gain of the S&P 500:

So make sure to catch the replay here.

Today, I’ll show you how it’s combining with a much older four-year cycle to raise the odds of a rough second half – and what you can do about it.

A market like that is a slow grind for buy-and-hold investors – months of going nowhere. But the same swings that punish them can work in your favor, if you know which days to trade around.

The S&P 500’s Bullish Seasonal Window Is About to Close

We call these calendar windows green days – and history shows they’re usually great times to invest.

The chart below shows the seasonality patterns for the S&P 500. As you can see, it’s been sitting inside one of these windows for weeks – a stretch of the calendar it’s climbed every year for the past 15 years.

But every stretch of green days has its end. And this one’s almost over.

We don’t always know why these seasonal windows appear. We only know that they do – and how reliably they’ve done so in the past. 

But the end of this window will coincide with quarterly earnings reports from some of the most widely followed stocks in the world – Tesla, Amazon, Apple, Microsoft. And expectations are set so high right now that a company can beat its numbers and still get punished. 

Last fall, AI darling Palantir did exactly that – it topped estimates and still fell almost 8% in a day, dragging the market down with it.

An expiring bullish window happening at the same time as a slew of sensitive earnings reports is enough to make me pay attention. But there’s also a second bearish calendar pattern at work – one that runs far deeper than any single earnings season. 

It’s called the Presidential Cycle. It goes back to all the way to 1933. And it raises the chances of a downturn significantly.

The Presidential Cycle Adds a Midterm-Year Warning

A market historian named Yale Hirsch spotted it in the 1960s, working much the way Halley did – digging through decades of past data until a rhythm emerged. 

Stocks, he found, tend to move in step with the four-year political calendar. And the second year of a president’s term – around the turbulent midterms – has typically been a rough one.

About 70% of U.S. bear markets have begun in the first or second year of a president’s term. And 2026 is a second year.

We saw this play out during the last two administrations. In the second year of President Biden’s term, the S&P 500 plunged 18%. And there was a 4% drop in the second year of President Trump’s first term.

That’s why I went public with yesterday’s event. I want to get this onto as many radars as possible. These historical patterns don’t guarantee stocks will roll over next week right on cue. But they’re serious enough that I’d rather you hear it from me now than find out the hard way in a few months. 

This way, there’s plenty of time to prepare.

How to Trade Around the Seasonal Shift

A more volatile market isn’t only a threat. It’s also opportunity. In the kind of year I think is ahead, the swings that punish everyone else become the openings you can trade around. And green days help there, too.

Our system tracks 5,000 stocks and pinpoints the green days forming across all of them – the windows when a stock has historically tended to climb. 

In our backtesting, a model portfolio built on this system turned every $10,000 into $85,700 – beating the S&P 500 by an average of 99%, through the longest bull market on record and the biggest selloffs alike.

You don’t have to go looking for them. Each month, we hand you a short list of the strongest setups we can find, timed to the calendar.

So don’t just wait to see how the volatility plays out. Watch Breakthrough 2026. You’ll see how we find these windows, why they hold up even when the market doesn’t, and what the close of this one could mean for the rest of your year.

You can view the replay of Breakthrough 2026 here.

The post July Is Historically Strong. What Comes Next Is the Problem. appeared first on InvestorPlace.

Tom Yeung here with today’s Smart Money.

In early 2005, Staples launched a new ad campaign that introduced the world to the “easy” button.

Customers soon began asking where to buy this button. And by the fall, the office supply store chain had turned the advertising prop into a money-making product. Staples would sell almost a million units within the first several months, and many more over the coming years.

Now, wouldn’t it be great if investors also had a similar button to press?

Well, they might.

Momentum investing has become one of the easiest ways to get ahead. The strategy is a simple one: Buy stocks that have been going up, and sell stocks that have been falling.

While momentum can deliver quick gains, patience has historically delivered bigger ones.

So, in today’s Smart Money, I’ll share why the best long-term returns often come from a mix of turnaround stocks and fast-growing companies trading at fair prices.

Then, I’ll show you where to find them.

The Momentum Trap

There is a case to be made for chasing the market’s hottest stocks. Since 2014, investors could have used a surprisingly simple strategy:

This strategy worked well: The winning stocks gained 12.3% over the next year, while the losing stocks returned 7.5%.

For years, it seemed like you didn’t need deep research or complicated financial analysis. Simply buying stocks that were already rising often worked. That’s been the story for much of the market for the past decade-plus.

But over longer periods, the results flip.

After removing penny stocks, financially weak companies, and businesses with falling sales or earnings, yesterday’s losers become the better investment. Over the next 24 months, the former winners returned 8.2%, but carefully selected former losers returned 13.5%.

In other words, it’s possible to cruise along with an “easy” strategy in the short run (i.e., chasing momentum). But chasing winners also comes with risks.

Investors who piled into hot stocks before the crashes in 2000 or 2008 suffered steep losses. (More recently, Micron Technology Inc.’s (MU) 25% drop showed how quickly high-flying stocks can fall.)

That’s why the best long-term returns come from a thoughtful selection of turnarounds paired with faster-growing names at reasonable prices.

Of course, buying well-priced companies for the long haul is challenging. Dips often happen in the first year of holding before a rebound in the second.

Consider Nvidia Corp. (NVDA), now the most valuable company on Earth. An investor who bought precisely when ChatGPT was launched in November 2022 would be sitting on roughly 1,100% gains.

Getting the timing right, however, was tough. An investor buying a year earlier would have been handed 50% losses. And anyone exiting at that point would have done so at the worst possible moment.

Why Bargains Win

At Fry’s Investment Report, our strategy is to look at “boring,” undervalued stocks, like copper miner Freeport-McMoRan Inc. (FCX). Freeport itself was purchased in 2020 after a roughly 35% decline a year earlier.

It is now up over 250%. And Eric has booked multiple triple-digit gains along the way.

In fact, many of our Fry’s Investment Report top performers have done so well precisely because they were bought during dips.

The key is to look at companies with fundamental stories backing them up. Like a great bottle of wine or a first-class airline ticket, the lower these prices go, the more attractive they become.

Even in the face of painful, short-term moves, we’re happy buying these solid companies, and then checking them in 24 months. It’s only a matter of time before their quality shines through in prices.

The bottom line: By buying turnarounds, investors are setting themselves up to potentially triple the returns offered by the “easy button” of momentum investing. It’s not an effortless way to invest, but it can be more rewarding in the end.

Click here to learn more about the undervalued stocks Eric believes could become tomorrow’s biggest winners.

Until next time,

Thomas Yeung, CFA

Market Analyst, InvestorPlace

The post How to Beat Wall Street’s “Easy Button” appeared first on InvestorPlace.

Keith Kaplan thinks most of that is noise.

In today’s Friday Digest takeover, the TradeSmith CEO makes the case that one of the market’s most reliable signals isn’t found in the latest headline – it’s found in the calendar. By analyzing decades of market history, Keith and his team uncovered recurring seasonal patterns that have appeared across thousands of stocks with surprising consistency.

Below, he explains how those patterns work, why they can help remove emotion from investing, and why they offer a valuable edge in today’s increasingly unpredictable market.

Keith – alongside legendary investor Louis Navellier – dove much deeper into this research during yesterday’s Breakthrough 2026 event. He demonstrated the Seasonality tool and shared one stock currently entering a historically favorable window. You can watch the replay right here.

Whether you’re bullish or bearish today, I think you’ll find Keith’s data-driven approach valuable.

I’ll let him take it from here.

Have a good evening,

Jeff Remsburg

On Wednesday, the U.S. and Iran blew through their second ceasefire in four months.

The Strait of Hormuz — the world’s most important oil chokepoint — has now been declared open, then closed again, at least three times since February.

I’ll be the first to admit it: I didn’t see the Iran war coming, and I have no clue when it’s going to end.

I’m an ignoramus on where interest rates are going, too.

Will Kevin Warsh, the new Trump-appointed Fed boss, raise or lower rates?

I have no clue!

And don’t ask me to predict the earnings of Nvidia, Microsoft, Google or any of the other big AI players. I can’t.

And it’s not just me — nobody else can, either.

Just ask Wharton School professor Philip Tetlock. His long-running study tracking thousands of predictions from political and economic experts found that many performed barely better than chance.

Or as he famously put it, the average expert was roughly as accurate as “a dart-tossing chimp.”

That’s why at TradeSmith, we don’t try to make predictions about the future. To see what’s likely coming next, we look at the past.

That includes seasonal patterns — calendar windows during which stocks have tended to rise and fall in the past with remarkable accuracy.

It’s why I tell folks to forget about interest rate announcements… earnings… macro narratives… and all the other usual reasons to buy a stock.

All the money you could ever want to make in the market boils down to just a handful of dates on the calendar. I’ll show you how it works in a moment — with an 83% historical accuracy across 5,000 stocks going back to the start of the 1990s.

First, a quick stop in ancient Egypt.

Using the Past to See Into the Future

The Nile River was the source of all life for the people of ancient Egypt. It could also be the source of death and devastation.

Every July, it flooded with astonishing regularity. And how much flooding there was determined whether Egypt feasted or starved that year.

Too little water, and crops failed. Too much, and the flood destroyed villages, swept away irrigation canals, and drowned the harvest before it could grow.

To know what was coming, the ancient Egyptians didn’t try to predict when rain would fall in the distant Ethiopian highlands and fill the Nile’s tributaries. They didn’t need to.

Instead, they built stone columns called Nilometers, sunk into the riverbank. Each column carried marks recording how high past floods had climbed, year after year, going back generations.

By reading centuries of those marks, the Egyptians knew when to plant, when to harvest, when to store grain for a lean year. Not because they could forecast the weather — but because they trusted those past patterns to hold.

As the river climbed each summer, priests marked its progression against every flood before it. If the waters were rising faster than usual, it signaled a big flood. Slower meant a drought.

By managing these rhythms, Egypt turned an unpredictable river into a reliable food supply.

Taxes, grain stores, and harvests were planned out years in advance. All built on nothing more than centuries of marks on a stone wall.

Most investors don’t know it. They’re too busy trying to peer into the future…

But like the Nile, the stock market has its own “floods” and “droughts” — times when stocks predictably surge or dry up at specific times of the year based on decades of data.

This may be news to most regular investors, but traders have known about these patterns for decades.

Traders Have Tracked These Cycles for Centuries

Commodity traders, for example, have long tracked planting and harvest cycles in crops like corn and wheat.

The gold market has also shown recurring seasonal tendencies, often strengthening during certain parts of the year tied to jewelry demand, central bank buying, and annual festivals in India and China.

And stock investors have studied seasonal phenomena such as the January Effect for decades. Even Wall Street’s old saying — “Sell in May and go away” — comes from observed seasonal behavior, not theory.

The only thing that’s changed is how precisely we can track these seasonal effects.

Today, we can discover these patterns across thousands of stocks, over decades of history, and measure them down to specific days — not just months or quarters.

That’s what my team and I set out to do at TradeSmith.

We built software to analyze more than 2 quintillion historical prices across roughly 5,000 stocks, running millions of tests to answer a simple question: Is there an optimal time of year to buy — and an optimal time to sell — each individual stock?

When we tested this approach over the past 18 years, the results were remarkably consistent.

Boston Beer (SAM), for instance, has entered a seasonal “green zone” every Oct. 6 for the past 15 years — climbing an average of 6.6% over the next 17 days:

And SAM stock rallied during that October window 100% of the time. Fifteen out of 15 years – as you can see below:

Nvidia (NVDA) has done something similar every Oct. 23, rising an average of 6.5% over the next 18 days:

NVDA climbed during that window in 13 out of the last 15 years. Even back in 2015, long before many investors had heard of the company:

None of these moves depended on a specific earnings outcome…or any other news headline. They showed up on the calendar, year after year, in bull markets and bear markets alike.

Zoom out, and the pattern holds at the portfolio level, too. A portfolio built around these calendar windows turned every $10,000 into $85,700 over an 18-year backtest.

Those are the kind of results that get your attention — especially when the world seems less predictable now than ever.

Why This Matters Now

Life in the 21st century can feel bamboozling.

We’re constantly buffeted by geopolitical shifts, economic threats like inflation, and exponential technological change — especially from AI.

But you don’t need to predict the next geopolitical shock… Fed decision… or figure out the future of AI and humanity… to grow your wealth. If professional forecasters get these calls wrong more often than not, what hope do you or I have of getting them right?

But you do need a way to understand when the odds are historically tilted in your favor — and when they aren’t. That’s what makes seasonality so valuable.

So, I urge you to watch a replay of the Breakthrough 2026 event we held yesterday.

I walk you through how we uncovered these patterns in stocks… why seasonality keeps working even when markets feel uncertain… and how you can use our software to spot hidden seasonality trends.

I also share a free stock recommendation so you can see how this works in real time — not just in backtests.

Markets will always feel noisy. And 2026 is no exception.

To get an edge, you need to know which signals to ignore — and which patterns have been there all along, hidden in the data.

Here’s that link again to watch the replay.

Keith Kaplan
CEO, TradeSmith

P.S. Subscribers have been telling us this changed the way they think about the market.

Mark S. put it simply: “This is a life-altering system.” And John B. wrote to say he’s simply hooked: “I LOVE TRADE CYCLES. AND YOUR SERVICE.”

If you’re interested in a new way of making money this year — and a new kind of investing — make sure to watch the Breakthrough 2026 event here.

The post You Don’t Need a Crystal Ball When You Have a Calendar appeared first on InvestorPlace.

We have certainly seen plenty of back-and-forth action lately, especially in technology stocks. Still, the underlying growth of the economy remains very good, and earnings season is off to a strong start. In the end, fundamentals rule the roost.

But even in a fundamentally healthy market, timing can make a meaningful difference. That is the idea behind the seasonal research TradeSmith CEO Keith Kaplan has spent years developing.

Yesterday, Keith shared his latest findings during the Breakthrough 2026 event. And one of them is especially timely: The S&P 500 is approaching the end of a historically favorable seasonal window on July 23.

That does not mean stocks are guaranteed to roll over next week. But with volatility already elevated, it is a signal worth watching.

If you missed yesterday’s event, you can watch the replay here.

In today’s guest essay, Keith explains why July 23 matters and what investors can do before this favorable window closes.

Here’s Keith to explain more…

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

For thousands of years, the appearance of Halley’s Comet was a bad omen. If you saw it streaking across the sky, it meant war, plague, or the death of a king was coming.

Then an English astronomer figured out it was something far more ordinary: a regular visitor, keeping to a schedule.

Edmund Halley saw the comet when it passed in 1682. And he started asking questions. Where had it come from? Had anyone seen it before? And was it following some kind of hidden pattern?

Years later, he found the answer buried in old sighting records: The same comet returned roughly every 76 years.

In 1705, it led him to predict that the comet would return 76 years after the last sighting — in 1758.

Halley died in 1742. So, he never got to find out if he was right. But in 1758, right on schedule, the comet came back. And it’s come back about every 76 years since. You may have seen it when it last passed through the sky in 1986. Its next appearance will be in 2061.

My team and I have taken the same idea — that the future can be read in the past — and pointed it at the market. The result is our breakthrough Seasonality software.

It shows that stocks keep schedules of their own — calendar periods when they have tended to rise or fall, year after year.

They’re hard to spot unless you have decades of data and the right algorithms to crunch through it all. But once you find them, you can do something most investors can’t: hold a stock for its strongest stretch of the year, and stand aside before its weakest.

And right now, it shows that one of the most important windows in the entire market is about to end. It’s a  bullish window for the S&P 500. And it closes next Thursday, July 23.

I covered it in detail at yesterday’s Breakthrough 2026 event, where I was joined by more than 16,000 viewers. I also introduced the next evolution of our Seasonality strategy.

Over our 18-year backtest, these seasonal trades delivered 857% in total growth. That’s more than twice what the S&P 500 delivered over the same time.  

Even in 2007, the worst year in our testing, we saw an average gain of 2.5% and an annualized return of 37.9%. That’s close to four times the average annual gain of the S&P 500:

So make sure to catch the replay here.

Today, I’ll show you how it’s combining with a much older four-year cycle to raise the odds of a rough second half — and what you can do about it.

A market like that is a slow grind for buy-and-hold investors — months of going nowhere. But the same swings that punish them can work in your favor, if you know which days to trade around.

These Green Days End Next Week

We call these calendar windows green days — and history shows they’re usually great times to invest.

The chart below shows the seasonality patterns for the S&P 500. As you can see, it’s been sitting inside one of these windows for weeks — a stretch of the calendar it’s climbed every year for the past 15 years.

But every stretch of green days has its end. And this one’s almost over.

We don’t always know why these seasonal windows appear. We only know that they do — and how reliably they’ve done so in the past.

But the end of this window will coincide with quarterly earnings reports from some of the most widely followed stocks in the world — Tesla, Amazon, Apple, Microsoft. And expectations are set so high right now that a company can beat its numbers and still get punished.

Last fall, AI darling Palantir did exactly that — it topped estimates and still fell almost 8% in a day, dragging the market down with it.

An expiring bullish window happening at the same time as a slew of sensitive earnings reports is enough to make me pay attention. But there’s also a second bearish calendar pattern at work — one that runs far deeper than any single earnings season.

It’s called the Presidential Cycle. It goes back to all the way to 1933. And it raises the chances of a downturn significantly.

Stocks Move With This Four-Year Political Calendar

A market historian named Yale Hirsch spotted it in the 1960s, working much the way Halley did — digging through decades of past data until a rhythm emerged.

Stocks, he found, tend to move in step with the four-year political calendar. And the second year of a president’s term — around the turbulent midterms – has typically been a rough one.

About 70% of U.S. bear markets have begun in the first or second year of a president’s term. And 2026 is a second year.

We saw this play out during the last two administrations. In the second year of President Biden’s term, the S&P 500 plunged 18%. And there was a 4% drop in the second year of President Trump’s first term.

That’s why I went public with yesterday’s event. I want to get this onto as many radars as possible. These historical patterns don’t guarantee stocks will roll over next week right on cue. But they’re serious enough that I’d rather you hear it from me now than find out the hard way in a few months.

This way, there’s plenty of time to prepare.

Do This Before the Bullish Window Closes

A more volatile market isn’t only a threat. It’s also opportunity. In the kind of year I think is ahead, the swings that punish everyone else become the openings you can trade around. And green days help there, too.

Our system tracks 5,000 stocks and pinpoints the green days forming across all of them — the windows when a stock has historically tended to climb.

In our backtesting, a model portfolio built on this system turned every $10,000 into $85,700 — beating the S&P 500 by an average of 99%, through the longest bull market on record and the biggest selloffs alike.

You don’t have to go looking for them. Each month, we hand you a short list of the strongest setups we can find, timed to the calendar.

So don’t just wait to see how the volatility plays out. Watch Breakthrough 2026. You’ll see how we find these windows, why they hold up even when the market doesn’t, and what the close of this one could mean for the rest of your year.

You can view the replay of Breakthrough 2026 here.

All the best,

Keith Kaplan

CEO, TradeSmith

The post A Historically Bullish Window Is About to Close appeared first on InvestorPlace.

Editor’s note: “Follow the Smoothie, Not the Beer” was previously published in June 2026 with the title “The Best Trade Nobody’s Making Because It Doesn’t Involve a GPU.” It has since been updated to include the most relevant information available.

The old night out used to have a predictable rhythm.

Meet for dinner. Order drinks. Stay out late. Spend too much money and call it a good time.

That rhythm is changing.

For a growing share of young consumers, the social calendar now looks different. Saturday mornings start at the gym. Friend groups form around run clubs. Recovery sessions get booked like brunch reservations. A functional drink can carry the same social signal that a cocktail once did.

And the data is catching up to the lifestyle shift. Bank of America’s (BAC) latest payment data shows Gen Z has the highest share of households with a fitness-related payment. Life Time (LTH) is expanding hybrid fitness competitions. Dave & Buster’s (PLAY) reported falling comparable sales. Alcohol moderation is spreading beyond the youngest consumers. 

The classic nightlife is losing wallet share to the new morning routine. 

From Barstools to Barbells: The Data Behind Gen Z’s Wellness Shift

According to a February 2026 Bank of America report, gym-related spending among Gen Z and millennials is rising sharply as alcohol consumption continues to decline. 

A separate survey from Mintel found that 77% of U.S. Gen Z consumers say they are more focused on wellness than they were a year ago, with 30% spending more on gym memberships and classes in that time.

With over 3.4 million posts under #Pilates on Instagram alone and TikTok overflowing with gym routines, “what I eat in a day” videos, and run club recaps, fitness isn’t something Gen Z does. It’s something Gen Z is. 

When identity changes, spending follows. And when spending follows, stocks eventually do, too.

Why Fitness Is Becoming Gen Z’s New Social Infrastructure

Health is only part of the story. These premium gyms and boutique studios are functioning as social infrastructure – filling the community void once occupied by bars, restaurants, and even offices.

The data bears this out. According to Bank of America, Gen Z households spend 2.8 times more than baby boomers on fitness. Fitness club foot traffic has surpassed bars and pubs by 22 percentage points since 2021. Non-alcoholic beverage spending has outpaced alcoholic alternatives by 28 points over the same period.

And the data keeps moving in the same direction. On June 16, Bank of America reported that roughly 21% of Gen Z households now have a fitness-related payment, the highest share of any generation. It also cited McKinsey data showing that 56% of Gen Z says fitness is a “very high priority,” versus 40% of U.S. consumers overall. 

This is identity showing up in household payment data. 

Spending on premium fitness carries a social ROI that a traditional gym membership never had. You don’t build your professional network at a $30/month big-box gym. But at a $300/month Equinox or a $40-per-class boutique studio? 

The switching costs and community lock-in are real. And for the consumers most committed to this lifestyle, the willingness to pay has been remarkably sticky, even with rent, student debt, and a brutal job market applying pressure. Some are spending $500-plus per month on fitness and recovery because the category has become part of who they are.

The Long Side: Three Wellness Stocks Built for Gen Z Spending

Against this backdrop, three names stand out as the highest-conviction expressions of this trend in public markets.

Life Time: The Premium Fitness Social Hub

Life Time (LTH) is the cleanest public-market expression of this shift. The company has spent years building what it calls the “athletic country club”: large, high-end facilities where fitness, recovery, work, and social life overlap.

Its LT Games expansion makes the model even more interesting. Life Time is bringing its hybrid fitness competition to Dallas, anchored by a dedicated HYBRID XT studio in Frisco. That turns the gym from a place to work out into a recurring social-and-competition platform. Planet Fitness (PLNT) owns the budget lane. Life Time owns the high ground.

Xponential Fitness: The Boutique Studio Platform

Xponential Fitness (XPOF) is the franchisor behind the entire boutique studio ecosystem – Club Pilates, CycleBar, Pure Barre, Row House, Rumble Boxing, and more. The asset-light franchise model captures the brand and community value without the real estate risk. XPOF has been beaten up, and it is not the cleanest operator in the group. But in a secular growth story, a damaged stock can still become interesting if the underlying category keeps expanding. 

Dutch Bros: The Morning-Routine Beverage Play

Dutch Bros (BROS) is the least obvious pick but arguably the most interesting. The wellness trend isn’t just about where Gen Z works out – it’s about the entire morning ritual that replaces the hangover recovery of previous generations. Up at 5 a.m. for the gym, strong coffee or functional energy drink before the session, no bar the night before. With its customizable, high-energy beverages and protein coffee, Dutch Bros is built precisely for this demographic. When the macro headwinds eventually clear, BROS is positioned to be a significant beneficiary.

The Short Side: Stocks Losing the Old Night Out

Wellness isn’t just gaining dollars. It is taking them from somewhere else.

And the places losing that cash flow are increasingly clear: alcohol, casual dining, and bar-centered entertainment.

In fact, rather than one narrow cohort going sober, we’re seeing a broader cultural move away from alcohol as the default. Recent IWSR data reported by the Financial Times suggests baby boomers are now cutting back most sharply, while new Attest research frames Gen Z’s shift as moderation, home consumption, and more flexible low-alcohol behavior. That actually strengthens the short-side thesis. 

Boston Beer: Craft Beer’s Replacement Cohort Problem

Boston Beer (SAM) is the cleanest short in the alcohol space. Craft beer was supposed to be the cool, premium alternative to mass-market beer – precisely the type of product that captures younger consumers. It isn’t working. Its hard seltzer brand Truly was supposed to be the Gen Z entry point. But there is no pivot available when the replacement cohort simply doesn’t drink.

Dave & Buster’s: The Old Friday-Night Formula

Dave & Buster’s (PLAY) is the cleanest short against the old night out. The company sells the exact Friday-night formula this thesis says is losing share: arcade games, food, and a heavy alcohol attachment inside large venues that are hard to reinvent.

The pressure is already showing up in the numbers. Q1 revenue fell 1.5% year over year, while comparable-store sales dropped 5.4%. Management is trying new games, food-and-beverage upgrades, and World Cup activations. But those are tactical fixes against a structural problem: the social occasion Dave & Buster’s was built around is losing share. 

Bloomin’ Brands: Casual Dining Under Pressure

Bloomin’ Brands (BLMN) – owner of Outback Steakhouse – represents the casual dining category losing to boutique fitness social events. It carries the weakest balance sheet among major casual dining operators, making it most vulnerable to sustained structural headwinds.

Three Long/Short Wellness Trades to Watch

If you want clean expression of this thesis:

• Long LTH/Short SAM – premium fitness social hub directly cannibalizing craft beer’s Friday night occasion
• Long XPOF/Short PLAY – boutique studio franchisor vs. bar entertainment venue, competing for the same Gen Z “where do I go tonight” budget
• Long BROS/Short Molson Coors (TAP) – morning fitness culture functional beverage vs. traditional beer whose core demographic is literally aging into retirement

Why Gen Z Wellness May Be the Cleanest Non-AI Trade

Almost every macro conversation in 2025 and ’26 has circled back to AI infrastructure. And rightly so – the ‘Pax Silica’ buildout remains the dominant investment theme of this era. But AI infrastructure investing is crowded, expensive, and requires navigating geopolitical risk, tariff exposure, and supply chain complexity.

The wellness trade is different. It’s a consumer behavioral shift playing out in plain sight, being documented in real time by Bloomberg, Bank of America, and Mintel. It requires no technology adoption curve, regulatory approval, or transformer architecture expertise. The tailwinds – Gen Z’s identity-level commitment to wellness, structural alcohol decline, and the social collapse that made boutique gyms the new “third place” – are durable across multiple years.

That same cultural force that is minting new revenue at Life Time and Xponential is quietly bleeding out Boston Beer and Dave & Buster’s. Long/short, the thesis is self-hedging and structurally clean.

Gen Z replaced the entire nightlife scene with something better – and built a $300-a-month subscription around it. 

For investors willing to follow the smoothie instead of the beer, the setup has rarely been cleaner.

That is the broader lesson here.

The best trades often start as behavior most investors dismiss.

A gym becomes a social network. A run club becomes a spending category. A functional drink starts replacing a cocktail. By the time Wall Street gives the shift a name, the early money has usually already moved.

We’re seeing a similar setup inside the AI trade.

While most investors are still focused on the obvious headline stocks, private capital has been moving toward the physical layer underneath the boom: energy, nuclear power, chip fabrication, natural resources, and the hard assets required to keep persistent AI compute running.

Most of those positions are locked away in private markets.

But seven public-market backdoors exist.

And they may be some of the most compelling AI plays hiding in plain sight.

See for yourself.

The post Follow the Smoothie, Not the Beer appeared first on InvestorPlace.

After Arab oil producers imposed an embargo on the United States, gasoline supplies tightened and prices soared. Drivers waited in lines that stretched around the block, wondering whether the station would run dry before they reached the pump.

Inflation was no longer an abstract number buried in a government report.

It was posted on gas station signs. It was eating into family budgets. And it was sitting in a line of cars that barely moved.

More than 50 years later, it looked like history might repeat itself.

War erupted in the Middle East. Oil prices surged. Gas prices followed. And just as inflation had started to cool, fears of another energy shock came roaring back.

Then the story changed.

Oil prices reversed course. By late June, crude had fallen back near the levels where it traded before the fighting began. Gas prices followed, though drivers were still paying more than they had before the conflict.

That reversal showed up clearly in this week’s inflation reports.

So, does the latest data mean the inflation threat has truly passed, or has it merely changed shape?

In today’s Market 360, I’ll explain what the latest Consumer Price Index (CPI) and Producer Price Index (PPI) reports reveal about inflation, why oil and gasoline prices reversed course and why quickly changing conditions like these can make it so difficult to know when to buy, sell or simply stand pat.

Then, I’ll show you how my Precursor Intelligence system helps me track the shifts that matter most and identify the stocks that could lead the market’s next move.

The CPI Delivers Good News

Let’s start with the CPI, which offered some encouraging news.

Consumer prices fell 0.4% in June, marking the first monthly decline since 2020. Economists had expected a decline of just 0.2%.

On an annual basis, that brought the rate down to 3.5%, down from 4.2% in May – and below a forecasted 3.8%.

Core CPI, which excludes food and energy, was unchanged. Economists had expected a 0.2% increase. That brought the annual rate to 2.6%, down from 2.9% previously.

A big reason for the drop was gasoline prices, which fell 9.7% as oil prices retreated. Food prices rose a modest 0.2%.

But the most important number to me was owners’ equivalent rent.

This measure estimates how much homeowners would pay to rent their own homes. It rose just 0.2% in June after running at higher levels in previous months.

That matters because housing has been one of the most stubborn sources of inflation. Any sign that rent pressures are easing is good news for consumers, interest rates and the Federal Reserve.

In fact, I think this report effectively took another Fed rate hike off the table.

Whatever the talking heads on television may say, inflation is coming in below expectations. Market rates have also been moving lower, which is another positive for stocks.

And the following day’s Producer Price Index gave us even more reason for optimism.

Good News Continues With the PPI

Producer prices fell 0.3% in June, beating economists’ expectations for no change and marking the first monthly decline since last August.

On an annual basis, prices slowed to 5.5%, down from 6.0% in May. Just as encouraging were the report’s underlying details.

Energy prices fell 6.4%, and food prices slipped 0.6% on a monthly basis. Core PPI, which excludes food and energy, rose a modest 0.2%.

Energy Remains the Wild Card

Now, it’s important to remember that May’s Producer Price Index told a very different story.

Wholesale prices jumped 1.1% that month, largely because energy prices surged after the conflict with Iran began.

But as you can see in the chart below, crude oil later gave back much of that initial spike. Countries found alternative ways to move energy supplies around the Strait of Hormuz, fears of a prolonged disruption eased and oil prices retreated.

That reversal helped pull both consumer and wholesale inflation lower in June.

But I would not declare victory just yet.

West Texas Intermediate crude traded near $65 before the conflict. It briefly fell back below $70, but prices have since started moving higher again. And I still expect energy prices to remain firm through Labor Day.

So, energy remains the wild card.

For now, though, the latest numbers are encouraging. Inflation came in below expectations at both the consumer and wholesale levels. That has taken the threat of another Federal Reserve rate hike off the table, while market rates have started meandering lower.

That is good news for stocks.

But the speed of the reversal also carries an important lesson for investors.

The Lesson Behind the Numbers

The speed of that reversal carries an important lesson for investors.

Economic reports tell us what has already happened. The market is always trying to figure out what happens next. May’s PPI reflected the initial surge in energy prices. June’s CPI and PPI captured the retreat. And as the crude-oil chart above shows, prices have already started moving again.

That is why I do not build my portfolio around the latest headlines. On any given day, markets can swing on news about interest rates, geopolitics or countless other developments. But once the dust settles, it’s earnings and fundamentals that ultimately rule the roost.

The stocks with the strongest earnings, sales growth and guidance are the ones most likely to move higher over time.

The hard part is identifying those companies before Wall Street catches on.

That’s exactly why I developed my Precursor Intelligence (P.I.) system.

It helps me track shifts in institutional buying and combine those signals with the factors that matter most, including strong sales growth, accelerating earnings and positive guidance.

When those signals line up, that’s when I really start paying attention.

It’s a disciplined approach I use when selecting stocks for my Accelerated Profits  portfolio.

And the results speak for themselves. Here are 10 of the biggest winners currently on my Buy List:

These gains did not come from chasing headlines or reacting to every market swing. They came from identifying fundamentally superior companies with strong institutional support, then giving their earnings time to do the heavy lifting.

These are exactly the kinds of companies I want to own as earnings season unfolds – and you should, too.

In my latest presentation, I’ll show you how my P.I. system helps me identify those opportunities before the broader market catches on.

Click here to watch it now.

Sincerely,

Louis Navellier

Editor, Market 360

The post Inflation Is Cooling. Here’s What Will Decide the Market’s Next Big Winners appeared first on InvestorPlace.

Yesterday, New York Governor Kathy Hochul signed an executive order barring construction of new large-scale data centers – those drawing 50 megawatts or more of power – for up to one year.

New York is now the first state in the nation to impose such a ban, though at least 15 others are considering or actively advancing legislation to restrict, study, or temporarily ban new data center construction.

Hochul framed it as a matter of survival for ratepayers:

We’re in the midst of one of the most significant economic upheavals in generations … perhaps ever.

These hyperscale AI data centers consume enormous amounts of power…

They drive up costs for local ratepayers, and I refuse to let those costs get passed down to New Yorkers.

This isn’t just a New York issue – it’s representative of a national anti-AI mood that’s increasingly framed in political and moral terms.

Back in April, Sen. Bernie Sanders (D-Vt.) said:

AI oligarchs do not want to just replace specific jobs.

They want to replace workers.

That’s a wildly provocative and easily debated assertion. But even if that’s true, these “AI oligarchs” still must contend with something more powerful than they are – the customer.

Cut too many of the right employees, and quality drops – and the customer retaliates long before any politician does.

About a year ago, Ford (F) CEO Jim Farley made a headline-grabbing prediction

He said:

Artificial intelligence is going to replace literally half of all white-collar workers.

While Ford then cut portions of its workforce and turned to AI during its broader restructuring, it turns out AI couldn’t quite finish the job on its own.  

Over a rolling three-year initiative to fix costly vehicle defects, Ford has quietly rehired 350 veteran engineers – internally nicknamed “gray beards” – to fix quality problems its AI-driven design systems couldn’t catch on their own.

The fix worked: Ford just topped JD Power’s Initial Quality Survey for the first time in 16 years.

Ford isn’t alone in its return to human workers.

IBM (IBM), Starbucks (SBUX), McDonald’s (MCD), Air Canada, and Commonwealth Bank of Australia, among others, have all made similar reversals recently after quality dropped.

But the most dramatic example may be Klarna (KLAR), the Swedish buy-now-pay-later giant. It slashed 22% of its workforce and boasted that its AI chatbots could do the work of 700 human agents – before quietly launching a recruitment drive to bring humans back into customer support after service quality collapsed.

Workforce firm Careerminds found that two-thirds of companies that made AI-driven cuts in the past year are already rehiring, more than half within six months.

This pattern already has a nickname: the “AI Boomerang” – cut for AI, watch the judgment-heavy edge cases pile up, then rehire humans at a higher cost.

Nearly a third of rehiring companies ended up spending more than they’d originally saved, because AI-era replacement roles now command a 20%-25% pay premium over the roles they replaced.

Still, the wider jobs data tells a complicated story

Challenger, Gray & Christmas’s June report showed overall U.S. layoffs cooling sharply – down 53% from May.

But AI was still the leading reason cited for job cuts that month, the fourth straight month it’s topped the list. And AI-linked cuts now account for roughly 23% of everything announced this year.

Meanwhile, an MIT study using a tool it calls the Iceberg Index estimated AI can already perform work equivalent to 11.7% of the U.S. labor force – about $1.2 trillion in wages.

As of now, actual layoffs represent only a small fraction of that exposure – but clearly the potential for far more is available.

Put those two threads together, and you get the real shape of this story…

AI fear is currently running well ahead of AI layoffs.

Our technology expert, Luke Lango, has been tracking this broad anti-AI sentiment

Luke, editor of Innovation Investor, has argued for months that the thing most likely to end this AI bull market isn’t a technological stumble – it’s policy:

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.

Yesterday’s data center moratorium in New York is exactly the kind of early tremor Luke predicted. Rising energy bills, viral anger, and politicians willing to act on it are shaping up to be a fierce headwind against AI.

But Luke isn’t sitting on the sidelines waiting for that day. He’s still bullish on AI today because he believes we’re in the strongest phase of the boom.

At the same time, he’s constantly monitoring the political landscape, corporate spending, and market leadership for the signals that the investment landscape is beginning to change. That allows him to stay positioned in the companies still benefiting from AI’s rapid expansion today – while preparing subscribers for the eventual shift before it becomes obvious to Wall Street.

If you’d like to follow Luke’s research and see the AI companies he believes are best positioned in this stage of the cycle, you can learn more about Innovation Investor here.

So, where does all this leave investors?

We’re still long the AI trade – but watching the calendar just as closely as the earnings.

But being long the AI trade doesn’t feel very good these days, and there’s a new reason for caution

Yesterday, we learned that margin debt just hit a level that’s only been seen at past market tops.

Let’s go to CNBC:

According to data from Leuthold Group, margin debt has grown by more than 40% over the past 12 months, a threshold seen at prior market peaks in 2000, 2007 and 2021.

To make sure we’re all on the same page, margin debt is money you borrow from your broker, using your existing stocks as collateral, to buy even more stock. It juices your gains on the way up – but amplifies your losses on the way down.

What’s unusual this time is the pace relative to the market itself…

The S&P 500 is up about 22% over the past year, dividends included – roughly half the rate at which margin debt has grown. Put simply, investors are borrowing money faster than stocks are actually rising.

Leuthold’s chief investment officer, Scott Opsal, was direct with CNBC about the implications:

When people start doubling down with borrowed money, that’s a contrarian sign that’s really hard to beat…

This is very bearish looking.

Opsal has a theory for where the borrowed money is going: the AI trade.

He points to the recent boom in leveraged ETFs – assets in those funds nearly doubled in just two months this spring.

That matters because concentrated bets amplify themselves on the way down, too. If a single AI infrastructure or data-center stock cracks, the margin calls that follow could ripple through every other investor leaning on the same trade – forcing sales that have nothing to do with the underlying business and everything to do with a broker demanding more collateral.

To be clear, this doesn’t mean the AI trade is finished. But alongside the political backlash we’ve been tracking above, it’s one more sign that this bull market may be entering its more fragile, top-heavy stretch.

As always, be sure you know what you own, why you own it, and what your exit strategy will be if you’re not in it for the long haul.

If this has you feeling panicked, take a breath…

Everything above is a reason for caution, not runaway fear.

The recent sharp pullback isn’t unusual – it’s often just the toll booth on the way through a bull market, especially in the group leading this one.

That kind of drop can feel like the beginning of the end. But let’s be clear about where the damage is concentrated, and then contextualize it…

The PHLX Semiconductor Sector Index (SOX) – a proxy for the AI trade since semis are the brains of AI – is down about 18% from its recent peak in June.

It’s off again today despite Taiwan Semiconductor (TSM) reporting a record 77% surge in quarterly net profit that beat Wall Street expectations. Investors are concerned about a 15% hike in its 2026 capital spending plan, reinforcing the sector-wide fear that excessive spending will hurt long-term profit margin.

Now, this pullback is painful for sure, but it’s also well short of the 30%+ drawdowns that have marked prior sector-wide tops in 2000, 2008 and 2018.

Plus, we should remember the near-vertical ascent that preceded it (trendlines added for perspective).

From this perspective, we’re just working off some of that excess.

Now, a skeptic could say:

Okay, Jeff, but what stops the current 18% SOX pullback from becoming a 30% crash next month, then 90% debacle by Christmas?

Well, I can’t guarantee you that won’t be the outcome – but history argues against it.

Studies of S&P 500 volatility since 1929 show that only about 39% of corrections have ever deepened into a full 20%+ bear market – and in the decades since World War II, that rate has dropped closer to 25%.

Most corrections stall out in the mid-teens and recover within a few months, which is almost exactly where the SOX sits today.

Bottom line: If you’re white-knuckling your way through this correction, there’s reason for optimism.

Still, if you want to be more selective in how much exposure you have to today’s volatile market, we have an idea for you…

Let historical data guide your trades.

This morning, legendary investor Louis Navellier and TradeSmith CEO Keith Kaplan went live in their Breakthrough 2026 event.

In short, instead of predicting where the market or a stock will go next, Keith’s team studied decades of price history across roughly 5,000 stocks, looking for historically favorable windows when those stocks have risen or fallen with real consistency – in bull markets and bear markets alike.

Running an 18-year backtest, trading only within those windows produced 857% total growth, more than double the S&P 500 over the same stretch, and the strategy still came out ahead in 2007, the worst year in the test.

With this approach, you can pick and choose your shots, keeping as much money as you want on the sidelines. You take advantage of trading opportunities only when – and for how long – you decide, all according to historical data.

If you missed the broadcast, we have a free replay available to you right here.

We’ll keep you updated on all these stories here in the Digest.

Have a good evening,

Jeff Remsburg

The post New York Bans New Data Centers appeared first on InvestorPlace.

TradeSmith CEO Keith Kaplan is here to answer that question.

But instead of assessing only headlines and earnings estimates, he studies recurring seasonal patterns that have historically helped identify more favorable times to buy and sell individual stocks.

In today’s essay, Keith explains how that research applies to two well-known AI memory companies and why timing may matter just as much as the underlying investment story.

He also expands on this research during his Breakthrough 2026 event this morning. You can click here to watch the replay.

Take it away, Keith…

OpenAI is reportedly in talks to buy up to five exabytes of data storage. Which sounds meaningless, until you translate it into everyday terms.

The top iPhone from Apple Inc. (AAPL) holds a terabyte. That’s enough for about 250 high-definition movies. Multiply that 5 million times over, and you’re getting near what OpenAI is buying. In one order.

And it isn’t the only AI company scooping up storage. An estimated seven out of every 10 memory chips are going to Microsoft Corp. (MSFT), Alphabet Inc. (GOOGL), Amazon.com Inc. (AMZN), and the other hyperscalers building AI data centers.

That’s because AI models like OpenAI’s ChatGPT are memory hogs.

Training an AI model like that starts with feeding it a meaningful slice of everything humanity has ever written, photographed, and filmed. All of it has to sit on a hard drive, inside one of those buildings, before the model can start learning.

And the amount of data these models are training on is growing exponentially.

Training GPT-2, one of OpenAI’s earliest language models, took about as much text as you’d find on 2,800 shelves of library books. Two years later, GPT-3 needed the equivalent of 30,000 shelves. By 2024, Llama 3 from Meta Platforms Inc. (META) trained on the equivalent of 1 million shelves of books.

This surge in demand sent shares of memory and storage companies like Western Digital Corp. (WDC) and Micron Technology Inc. (MU) soaring. Western Digital is up 800% over the past year. Micron has done nearly as well — up more than 700% — after revealing it had sold out its most important product, high-bandwidth memory for AI chips, all the way through 2026.

But this month, investors started taking profits. Micron has fallen roughly 20% from the record high it hit in June. And Western Digital is down 26%.

This has triggered a lot of questions. Is the AI bull market still intact? Was that the top? Or is this a healthy pause and nothing more?

But guessing without some kind of edge — a plan, a pattern, something more solid than a hunch — is how most people lose money chasing a good story.

One way to find that edge is to stop trying to answer those questions at all, and to look at something else entirely — seasonality.

Every Stock Has Its Green Days

Seasonality is the study of how stocks trade across different calendar windows, year after year — through bull and bear markets, manias and panics, wars, pandemics, and more.

I didn’t come to TradeSmith from Wall Street. I’m a software engineer by training. So when my team went looking for an edge for investors, we didn’t start by asking what should move a stock. We started by asking what the data already showed.

By crunching through years of stock market history, we’ve found seasonally bullish days for thousands of stocks.

We call these “green days.” Once you know them, you don’t need to know whether the AI story holds up, or whether this correction has further to run. You just need to know the dates when those windows occur.

Take Parker-Hannifin Corp. (PH), the aerospace and industrial company. For the past 15 years, the stock has gone up starting on October 27 — not most years, every year. A 100% historical accuracy rate, through bull markets and bear markets both:

That same time of year is also bullish for KLA Corp. (KLAC), which makes equipment for semiconductor manufacturers. Its stock has risen beginning October 21 in 93.3% of the last 15 years:

Parker-Hannifin and KLA have nothing in common. They’re different businesses in different industries with different customers. What they share are windows of time during the calendar year that tend to be bullish for their stock prices.

TradeSmith’s research team has now found seasonality patterns across roughly 5,000 stocks.

So what does that analysis say about the two companies at the center of the memory story?

A Better AI Memory Trade Than Micron in July

Micron is the company most directly in the crosshairs of the AI chip shortage. It makes the high-bandwidth memory that sits right next to the chip in an AI system, feeding it data in real time.

If you’re looking for a stock that’s emblematic of the AI memory trade, Micron is it.

But right now, Micron isn’t in one of its green windows. Its next one doesn’t open until August 20. Through September 9, it’s been up on average 4.1% during this window 80% of the time:

For a memory stock with a window open right now, look at Western Digital instead. It’s one of the oldest names in computer storage, making the hard drives that data centers — including the ones being built for AI right now — depend on to hold everything we’ve been talking about.

And its green days run from July 1 to July 22. Over the past 15 years, the stock has gone up during that stretch 86.7% of the time.

You don’t have to just take my word for it – click here to watch the tool in action.

How to Access Our Seasonality Software Today

This year is critical, which is why I urge you to watch our Breakthrough 2026 event, all about the seasonal patterns you need to be alert to.

During the presentation, I walk you through how we uncovered these patterns, why they persist even in chaotic markets, and how you can use them to guide real-world trading decisions.

Importantly, you’ll learn how to access the software and prepare accordingly for the fast-approaching seasonality patterns.

Knowing when these windows open and close is crucial to your wealth. Click here to learn how.

All the best,

Keith Kaplan

CEO, TradeSmith

P.S. Thanks to Keith for sharing his perspective on the AI memory trade. If you’d like to see how his seasonality research applies to thousands of stocks – not just the names discussed here – I encourage you to watch the Breakthrough 2026 replay. He’ll explain why he’s watching the weeks ahead so closely, and reveal three free stock recommendations he believes could be well positioned for what’s next.

The post Before You Buy the Dip, Check the Calendar appeared first on InvestorPlace.

They just haven’t learned how to leave the office.

Right now, most of the inference demand driving the AI infrastructure boom is happening inside company walls. A bank runs agents on internal risk data. A logistics company uses agents to optimize its own supply chain. A retailer deploys agents to manage procurement inside systems it already controls.

That demand and spending are real. And the trade around chips, memory, networking, storage, and power is already playing out.

But it is still mostly contained.

And Vint Cerf just started working on the layer that could let it break out.

Why Vint Cerf’s DNSid Project Matters for AI Agents

Cerf helped build the modern internet. He’s one of the architects behind TCP/IP – the foundational protocols that made the internet possible. 

After more than two decades at Google, he is now turning to his next project: identity infrastructure for AI agents operating on the open internet. 

As TechCrunch reported, Cerf is now advising Innovation Labs on something called DNSid – essentially a passport system for AI agents. The idea is to link each agent to a verified domain name and use cryptographic proof to show where it came from, who authorized it, and who is responsible for what it does.

Cerf helped solve the internet’s first coordination problem. Now he’s working on the next one.

The Trust Problem Holding Back the Agentic Web

The agent economy is still mostly trapped inside company walls.

Take a retailer’s procurement agent, for example. It operates inside the retailer’s own systems – searching its own inventory databases, working within its own supplier relationships, accountable to its own IT team. 

Most enterprise AI agents in production today work this way, contained within a single organization’s limits. And that’s why the current infrastructure demand, as large as it already is, may represent only a fraction of what’s coming.

The biggest use cases – and the ones that would generate the most compute demand – involve agents crossing organizational boundaries. 

• A procurement agent that negotiates directly with a supplier’s agent. 
• A financial agent that transacts in real time with a bank’s agent. 
• A logistics agent that coordinates across a dozen different carriers’ systems simultaneously. 
• A healthcare agent that pulls verified records from multiple hospital networks to inform a treatment decision. 

All require agents to operate across the open internet – interacting with unfamiliar systems, on behalf of humans who are not watching every step. 

But that world has a trust problem. 

When an agent shows up somewhere on the internet today, there’s no reliable way to verify who sent it, what it’s authorized to do, or who’s accountable if something goes wrong. Without a solution, the highest-value agentic use cases simply can’t safely deploy at scale.

That is the missing layer.

What Happens When AI Agents Can Work Across the Open Internet

Before shared internet protocols, computer networks were islands. Each organization ran its own system. Those systems had value, but they could not easily talk to one another. 

Once a shared standard let those networks communicate, the internet became a global market. Every person and institution suddenly needed to connect. The demand for routers, cables, servers, and all the physical infrastructure underneath became essentially limitless.

The agentic web is approaching a similar moment. Today’s enterprise agent deployments look a lot like those isolated networks of the 1980s: valuable, growing, but fundamentally contained. Once a shared identity standard lets agents operate across organizational boundaries, with accountability built in, the addressable market for agentic infrastructure will likely expand dramatically.

Every cross-enterprise workflow becomes a potential agent-to-agent interaction. Every government service, financial transaction, and logistics chain becomes a candidate for agentic automation – each requiring inference compute, memory, networking, and storage that currently sits outside the demand projections most investors are working from.

The infrastructure thesis doesn’t change. The size of it does.

The Investment Implication: The Inference Market Gets Bigger

The physical infrastructure stack – accelerators, high-bandwidth memory, optical networking, power, cooling, storage – remains the core of the trade. That demand keeps growing, and the companies supplying it are reporting it in earnings quarter after quarter.

Cerf’s work adds a new layer. If agents get a trusted way to identify themselves online, the demand story moves beyond internal enterprise workflows and onto the open internet. That would create a larger market than most investors are modeling. 

Cloudflare (NET) may be the cleanest public-market way to play that identity-and-routing layer. It already sits in the flow of internet traffic, security, authentication, and developer infrastructure. And its tools are increasingly being built for a world where agents need to discover services, prove who they are, and transact across the web. If the agentic web moves beyond the enterprise firewall, Cloudflare could become one of the trust-and-routing layers underneath it.

Beyond that, the broader infrastructure names supplying the physical substrate that every agent workload runs on are the same names that benefit directly when the agentic economy expands. More agents operating across more boundaries means more inference calls, more memory consumption, more networking traffic, more power draw.

The market understands enterprise agents.

It has not fully priced internet agents – or their much larger workload. 

The Bottom Line: AI Agent Identity Could Unlock the Agentic Web

Agents are already working inside companies.

The bigger opportunity begins when they can work between companies.

That requires identity and trust – a way to know who sent the agent, what it is allowed to do, and who is responsible if something breaks.

Vint Cerf is working on that layer now.

If it works, the inference supercycle expands onto the open internet.

The physical infrastructure that powers that market is already being built. The companies supplying it are already reporting the demand. And the names best positioned for the next leg of this expansion – the ones the market hasn’t fully found yet – are exactly what we’ve been tracking.

That’s the trade. And it just got bigger.

The post Vint Cerf’s DNSid Project Could Expand the AI Infrastructure Trade appeared first on InvestorPlace.

Before we jump in today, a reminder about tomorrow morning’s Breakthrough 2026 event at 10 a.m. ET with legendary investor Louis Navellier and TradeSmith CEO, Keith Kaplan.

They’ll be walking through TradeSmith’s Seasonality tool. It scans more than 5,000 stocks across decades of price history, hunting for one thing…

Windows of time when a stock has historically gone up – or down – with remarkable consistency.

Run through an 18-year backtest, trading only inside these windows produced 857% in total growth – more than double the S&P 500 over the same stretch, and the strategy still came out ahead even in 2007, the worst year in the test.

When you sign up to join Louis and Keith, you’ll get free access to the Seasonality Tool. Give it a spin in your own portfolio.

Then, tomorrow morning, Keith will lay out why he believes the period beginning around July 23 could mark an important shift in market leadership – and how to use the tool to capitalize. That ties into how Louis is using these timing signals with his own stock-grading system.

There will be plenty more, including some free stock recommendations. Just click here, and we’ll see you tomorrow morning at 10 a.m. ET.

Everyone wants to debate whether AI is a bubble

That’s the wrong question.

The question that determines who wins and loses – and what happens in your portfolio – is far more mundane…

What does it cost to buy an AI token?

To make sure we’re on the same page, a token is the basic unit AI models get billed by – roughly a few characters of text. It’s the meter running every time a chatbot answers a question, an AI agent completes a task, or a piece of software calls a model behind the scenes.

Think of token prices the way airlines think about jet fuel or manufacturers think about steel. They’re the core input cost of the AI economy.

Today, those costs are falling for a simple reason…

AI models are becoming dramatically more efficient, while competition among providers – including a wave of open-source alternatives – is driving prices down. Every major AI lab is racing to deliver more intelligence for fewer dollars.

Whatever you think about valuations, this is the number that ultimately decides who wins and who loses inside the trade.

And right now, it’s collapsing.

The number that’s already crashed

In March 2023, running OpenAI’s best available model cost roughly $30 to process a million tokens – roughly the amount consumed by a lengthy AI conversation or thousands of simple prompts.

Today, comparable-quality performance runs a few cents to a couple of dollars – a decline of 90% or more in a little over three years and still falling.

Now, despite that collapse, total enterprise AI spending hasn’t fallen. By most accounts, it’s tripled. Companies are using it dramatically more because it’s finally affordable enough to deploy across the business.

Chatbots have given way to autonomous agents that loop, recheck their own work, and call external tools dozens of times to finish a single task. Every loop consumes tokens. So, even as the unit price craters, total usage is growing faster than the price is falling.

This is exactly why AI infrastructure – mostly chip and compute demand – hasn’t cracked yet.

Prices down, but overall spending up. That contradiction is the whole ballgame.

But however much prices have crashed so far, two powerful AI CEOs think it needs to fall dramatically further.

AI is still too expensive

Last week, Palo Alto Networks (PANW) CEO Nikesh Arora went on CNBC to say, in effect, that the current price of an AI token is holding back enterprise adoption.

Here’s Arora to explain:

I think 54% is a good start… I think we probably need another turn at it.

That 54% was a reference to OpenAI’s claim that its newest model is 54% more token-efficient than its last one.

Arora’s point: a nice start, nowhere close to enough. Later in the same interview, he said it more plainly:

We need to see the pricing for AI come down.

Palantir (PLTR) CEO Alex Karp went further the week before, calling the token-pricing model broken outright:

I’m not throwing shade at them, but something has gone completely wrong.

The basic view among enterprises in this country is I’m going to chillax and waste my time with tokens.

In other words, customers don’t want to think about tokens. They just want AI that’s cheap enough to use everywhere.

Two CEOs, running two companies supposedly winning during the AI boom, are publicly complaining that AI costs too much to use at scale. That’s not noise.

These are two massive AI customers telling you where the ceiling is today – and where things are going tomorrow.

What our own Eric Fry is watching

Our global macro expert, Eric Fry, editor of Fry’s Investment Report, has been tracking a specific driver behind that price pressure: competition from open-source models, including Chinese labs like Z.ai, deliberately tuned to run on older, cheaper chips rather than the newest ones.

They’ve been getting results nearly indistinguishable from Western models several months more advanced.

Here’s Eric to explain:

Token costs have come down around 20% since the start of June, reducing what data centers can charge for computing power.

Reflecting that trend, shares of data center company CoreWeave Inc. (CRWV) have fallen roughly 40% in the past two months.

I’ll note that CoreWeave’s slide has more than one storyline behind it – reports of Meta (META) building its own compute-for-rent business have gotten most of the mainstream press attention.

But Eric believes falling token prices are the underlying force behind that story – an alternative explanation.

Running against the escalator

Now, what’s the implication for investors?

Well, let’s understand the landscape first with an analogy – trying to run up a descending escalator.

Usage growth is like you climbing up that escalator. Falling token prices are the escalator moving down beneath your feet.

Right now, you’re climbing faster than the escalator descends, so you’re still making progress toward the top – total AI spending keeps rising. This is a win for AI infrastructure companies, and somewhat of a win for companies that want cheap AI.

But if the escalator speeds up (prices fall faster) or your legs tire (usage growth matures), the escalator wins, and you get carried down instead of up. Not a win for AI infrastructure companies, but a big win for companies that want cheap AI.

Right now, usage is dominating – it’s growing faster than prices are falling, which is why infrastructure demand stays strong even as the per-unit economics erode underneath it.

Our growth investing expert Louis Navellier, editor of Growth Investor, provided evidence of how real that “usage is winning” phase still is…

Yesterday, IBM (IBM) issued an unexpected preliminary Q2 earnings report and a profit warning, triggering the company’s worst single-day stock decline in its history.

But this wasn’t a problem for Louis. Here he is explaining why:

Earnings season is off to a very good start. I know International Business Machines missed, but they missed because they’re losing out market share to data centers.

So, that’s good for us because guess what we own?

Lots of data center-related stocks.

This is Louis – one of the best analysts in our industry – who knows exactly where the money is flowing today and is successfully running up the escalator.

But as Louis knows, and will eventually factor into his recommendations, this same escalator will ultimately win out.

In other words, at some point in the future, token prices will fall far enough, or usage growth will mature enough, that the balance will flip – and when it does, the AI trade will reach a key inflection point.

To join Louis in Growth Investor so you can navigate that transition with him, click here to learn more.

Here’s the big-picture version of who’s on each side when that happens

Exposed: the companies that built expensive, specialized infrastructure to rent out compute by the unit. We’re talking chipmakers, neocloud data-center operators, and any hyperscaler selling raw processing power, because their pricing power depends on scarcity.

When compute stops being scarce, that pricing power goes with it.

Helped: What Eric calls “AI Appliers” – companies that adopt AI as a tool inside their existing business rather than sell compute as the product, expanding their own margins every time their AI bill shrinks.

And the biggest pool of value sits one layer further out: ordinary enterprises across finance, healthcare, retail, and industry, whose AI costs turn from a budget headache into a rounding error, unlocking productivity growth that shows up as real earnings growth rather than a bigger tech bill.

Eric has already recommended specific AI Appliers in Fry’s Investment Report– companies positioned to catch that margin expansion before the rest of the market catches on. To discover what they are, click here to learn about joining him.

One category that I won’t even pretend to have a clean answer on: traditional software-as-a-service companies. These are the companies that suffered the “SaaSmageddon” earlier this year.

Cheap AI should, in theory, let legacy software platforms bolt on powerful features without blowing up their margins. But cheap AI also lowers the barrier for customers – or nimble competitors – to build capabilities that software vendors used to charge dearly for.

Whether SaaS incumbents end up net winners or casualties of this same price collapse is genuinely unresolved, and it likely depends on the specific company, not the sector.

We’re watching this one as closely and will keep you up to speed.

How to monitor all this

Nobody knows exactly when this escalator drama will hit its inflection point, and we’re not going to claim that we do.

Instead, watch whether the major AI labs start reporting expanding margins even as prices keep falling – like, for example, OpenAI reporting that its cost to serve a query dropped faster than the price it charges for one. That would mean efficiency gains are outrunning price cuts, a bad sign for hardware-heavy names like Nvidia (NVDA).

Also, watch whether the software companies riding on top of all this start actually showing cheaper AI in their own reported margins, not just their marketing.

The bottom line

The token collapse isn’t a one-time event, and it isn’t finished.

It’s already happened, it’s still happening, and by Arora’s own math, it needs to happen substantially more before AI adoption really breaks wide open.

What’s unresolved isn’t the direction – it’s how long usage will outrun the price collapse, and when it ultimately reverses, what will happen if the infrastructure trade is still priced for the demand side to win forever.

This is the issue that decides who wins and loses here. It’s not whether “AI is a bubble.” It’s not some abstract multiple on some chip stock everyone already argues about on TV.

This issue requires more analysis and work, which is exactly why it’s the one most people watching from the sidelines won’t wrestle with…

But it’s the one you must wrestle with if you have money in AI.

We’ll keep you updated.

Have a good evening,

Jeff Remsburg

The post Forget the AI Bubble Talk. Watch This Number Instead appeared first on InvestorPlace.

“We decided yesterday not to take this to the next level.”

“While this sounds interesting, it is not something we would do here.”

“We’ve had the chance to discuss internally, and unfortunately don’t think that it’s the right opportunity from an investment perspective.”

Those are just a few of the rejection emails Airbnb Inc. (ABNB) CEO Brian Chesky received in 2008 when seeking initial funding for the travel services company.

Funding is critical for companies because it provides the cash they need to grow. And after years of hearing “no,” Airbnb eventually raised $1.5 billion in 2015. The company was able to accelerate its global expansion, marketing, and new travel offerings before its IPO in 2020.

Nearly two decades later, securing funding is still one of the hardest parts of building a business. That’s because no two fundraising processes look exactly alike.

But if it’s an AI company looking for funding, investors are willing to throw money at it.

For example, Lyzr Inc., a New York-based AI startup, recently raised $100 million. But it’s not the amount that makes Lyzr interesting; it’s how it raised the money.  

It used one of its own AI agents to do it.

In today’s Smart Money, let’s explore why this seemingly small story could have big implications for investors… and what it means for the “Agentic Reckoning” to come.

The AI That Pitched Itself

First, let’s take a look at Lyzr Inc.

The company builds AI agents – autonomous software systems that can complete complex tasks with minimal human supervision. More importantly, Lyzr enables other companies, including consulting firm Accenture plc (ACN), to build and test AI agents within their own organizations.

When it came time to raise capital, Lyzr turned one of its agents “on” to streamline the process.

Its AI agent, SivaClaw, helped manage the company’s $100 million Series B fundraising process by taking over many of the repetitive, time-consuming tasks. It drafted investment memos, communicated with more than 130 prospective investors, and automatically answered many of their initial questions.

SivaClaw also tracked how investors interacted with Lyzr’s pitch deck – monitoring which slides they spent the most time viewing – to help the founders identify what topics generated the most interest, or confusion.

And because the round attracted about $400 million of interest for a $100 million target, the company used the agent to help determine which investors were the best strategic fit.

“It just sped up our fundraising process,” said Siva Surendira, Lyzr’s co-founder and namesake of the AI agent.

That speed matters because fundraising is one of the least structured workflows in business. Every investor asks different questions, wants different information, and requires repeated follow-up. By automating much of that administrative work, SivaClaw allowed the founders to focus on what humans still do best: tell their story, build relationships, and negotiate terms.

It’s important to note that Lyzr’s AI only assisted with fundraising. Investors still evaluated the company and made the investment decisions.

When Raising Billions Gets Easier

Now, the $100 million Lyzr raised is just pennies compared to Anthropic and OpenAI.

Anthropic – the creator of the “Claude” AI assistant – recently closed a funding round valuing the company at nearly $1 trillion, having risen more than fifteenfold in just over a year. OpenAI, the creator of ChatGPT, is sporting a similar valuation. That company recently raised capital at 72 times annual sales.

Let’s also consider Space Exploration Technologies Corp.’s (SPCX), Elon Musk’s data-center-in-space enterprise, $1.8 trillion valuation after it went public.

Within its first week of trading, the stock rocketed 67% to a breathtaking market cap of nearly $3 trillion – or 160 times revenues. That’s not a valuation; that’s a theological statement. These three companies are collectively worth something in the neighborhood of $4 trillion.

If Agentic AI makes it dramatically easier to funnel money into an industry that’s already spending at unprecedented levels, investors should ask an uncomfortable question: Has AI just made the next AI bubble even easier to inflate?

Investing in Agentic AI’s Impact

I’m not suggesting this outcome is inevitable. But it is a fascinating development that deserves attention: AI is now helping finance the next wave of AI.

And fundraising is just one of the many areas Agentic AI is beginning to reshape.

We saw an early glimpse of this during the “SaaSpocalypse” selloff earlier this year, when investors began reassessing how vulnerable certain software businesses could be to Agentic AI.

Agentic AI’s reckoning has severe implications from the labor market to the broader economy. Companies disrupted by Agentic AI could see their stocks fall sharply, with the panic spreading well beyond the tech sector.

Widespread adoption of Agentic AI could trigger a market-wide selloff similar to past technology-driven crashes.

I explore these risks – and the investment opportunities they may create – in my Agentic Reckoning broadcast.

History rewards investors who recognize transformative shifts before everyone else. But the biggest risk isn’t always investing in a disruptive technology; it’s failing to recognize just how disruptive it can become.

Click here to learn more.

Regards,

Eric Fry

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