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Hello, Reader.
Babe Ruth is inarguably one of the most famous baseball players in American history.
He became legendary for his 714 career home runs, especially with the New York Yankees, during his time in Major League Baseball in the 1910s–1930s.
Today, he is widely regarded as the greatest baseball player of all time.
Wall Street has a similar elite player: Nvidia Corp. (NVDA)
Since 2023, Nvidia has reached superstar status with its massive earnings beats, often, like Ruth, knocking it out of the park.
Its first-quarter results, released Wednesday, were another tape-measure shot. Revenue soared 85% year-over-year to $81.6 billion. And first-quarter earnings jumped 140% year-over-year to $45.55 billion, or $1.87 per share.
Thanks to AI demand, data center revenue rose 92% year over year to a record $75.2 billion.
CEO Jensen Huang emphasized that “agentic AI” and AI factories are driving long-term demand, with Nvidia targeting over $1 trillion in AI infrastructure opportunities through 2027.
But the overall market reaction following Nvidia’s earnings release was muted.
This has become a common occurrence with Nvidia earnings: The company delivers huge growth, but the stock already priced in near-perfection beforehand. That means an “excellent” report can lead to a flat or slightly down reaction if investors were hoping for something even bigger.
Even Ruth was so dominant relative to his environment that the environment itself became the constraint. (Because he was so much better than everyone else, pitchers avoided him constantly.)
But the bigger issue is that the AI investment conversation in 2026 has become almost entirely about the “Babe Ruths.”
And while the superstar gets attention, it’s the supporting companies make the ecosystem work.
The AI boom will not be won by franchise players alone. It also depends on quieter companies that reliably enable, distribute, power, and operationalize AI across the economy.
In today’s Smart Money, let’s look past the hype and headlines of the “Babe Ruth” companies.
And I’ll share the type of company that every portfolio needs to create a winning roster…
Why AI Needs More Than Sluggers
Every Babe Ruth needs an Earle Combs.
If you don’t recognize the name, you’re not alone. Combs was the leadoff hitter and center fielder for the 1927 New York Yankees.
His teammates included legends like Babe Ruth and Lou Gehrig. Combs hit .356 that year, led the American League with 231 hits and 23 triples, scored 137 runs, and was later inducted into the Baseball Hall of Fame.
He was, by any objective measure, one of the best players of his era.
But nobody remembers him.
They remember Ruth. Ruth was the reason opposing managers lost sleep. But here is what those managers also knew: The Yankees didn’t beat you with Ruth alone. They beat you because Combs was often on base when Ruth came to the plate.
His job was not to swing for the fences. His job was to get on base, reliably, every day, and let the big guns behind him drive him home.
Similarly, a portfolio that relies only on home run hitters is a fragile lineup. The 1927 Yankees didn’t just have Ruth – they had Combs getting on base almost 40% of the time, quietly and consistently manufacturing the conditions that made Ruth’s power dangerous.
The following company is an “Earle Combs” of the AI era. It is quietly applying AI to real-world business problems in stable, repeatable ways
The Quiet Performers of the AI Revolution
Like every other mining company on Earth, Freeport-McMoRan Inc. (FCX) is continuously striving to boost and optimize its output.
It is integrating AI technologies into its production process, and the results have been remarkable.
Starting with a single aging mine in Bagdad, Arizona, a small joint team of Freeport metallurgists and engineers worked alongside McKinsey data scientists to build and train a machine-learning model from scratch. Instead of running the plant at a single setting all day, Freeport could now adjust settings every hour to maximize production from a given type of ore, quickly boosting output by 5% to 10%.
Freeport’s chief information and innovation officer established a common data infrastructure and architecture to support all processing operations, enabling swift deployment of AI tools across sites with modest tailoring.
The company is determined that scaling AI across its mines could unlock a systemwide production increase yielding 200 million pounds of copper per year – representing $350 million to $500 million in annual operating profit.
The fact that one of the world’s largest copper producers is squeezing meaningful incremental output from existing assets through AI – without the time, capital, or environmental footprint of new construction – demonstrates the hidden power of this technology.
And the hidden power of having an “Earle Combs” on your team.
I recommend another quiet-but-steady performer in my Sell This, Buy That presentation. Unlike Freeport, it does not use AI.
Instead, it enables everyone else to use it. Even the “Babe Ruths.”
And it built that position before most investors understood what the AI buildout would actually require.
It’s the ultimate “set up man.” I reveal the name and ticker of this company here.
The bottom line is that these “Earle Combs” companies are not swinging for the fences. They are applying the established, proven aspects of AI – large language models (LLMs), machine learning, predictive analytics – to problems they understand deeply, with human judgment still in the loop.
They may not generate the headlines like Nvidia does, but they do score runs.
And they’re players you definitely want on your team.
Regards,
Eric Fry
