“How do you use AI?”

That’s my new go-to question when meeting contacts.

Admittedly, it’s self-serving. I get to pick the brains of some of the smartest investors and thinkers in the world about how they’re using the most powerful technology of our lifetimes.

Artificial intelligence (AI) chatbots have transformed my work life. I use Claude, Grok, and ChatGPT at least 50 times daily as my research assistant, writing partner, and instant answer machine.

It’s like having a brilliant intern who never sleeps, never complains, and is always ready to help.

I’ve even taught my kids how to use AI. Now, when they ask me something I don’t know, they immediately say, “Ask the lady on your phone”—their nickname for ChatGPT’s voice mode.

That simple phrase reveals so much about how this technology is becoming woven into our daily lives.

Those who say AI is a fad simply haven’t been paying attention. Or they tried the original ChatGPT two years ago, found it mediocre, and haven’t bothered with the quantum leaps made since.

• Two years ago, I barely used AI.

It was fine for creating silly pictures or asking ChatGPT to write a limerick about Rhodesian Ridgebacks, but it wasn’t truly useful for serious work.

Now? The landscape has completely transformed.

Almost monthly, a cutting-edge AI model debuts that pushes the frontier forward. There are almost too many breakthrough models to keep track of.

This rapid progress isn’t slowing down. Look at this graph showing how AI models have been steadily conquering benchmarks, with OpenAI’s o3 and Grok 3 leading the pack:

Source: Artificial Analysis

What’s driving this improvement?

• It comes down to something called scaling laws.

The two raw ingredients you need to make AI better are:

1. Data (books, essays, Wikipedia pages, etc.)
2. Compute (Nvidia [NVDA] GPUs processing that data)

Scaling laws are brutally simple: Crank up the data and computing power by 10X, and the AI’s quality roughly doubles.

This is why Elon Musk’s xAI built a monster cluster of 100,000 GPUs, triple the size of the previous record. More GPUs processing more data creates better AI.

The new Grok 3 was trained with 3e26 FLOPs of compute. It’s equivalent to running your smartphone continuously for 634,000 years. That astronomical computing power is how Grok 3 smashed benchmarks in record time.

Meanwhile, the amount of data used to train top AI models has increased by a mind-numbing 1,018,766,656% since 2010.

It’s hard to wrap your head around “1 billion percent.” It’s like turning $1 into $10 million.

But that’s the kind of exponential growth AI has achieved in just over a decade.

• More data = better AI

Today’s AI models are pattern recognition systems on steroids.

They identify and learn from text, images, and other data. The more quality data they access, the more they grasp the nuances and complexities of our world.

But if your data is trash, your AI is trash—no matter how sophisticated your model.

For example, GPT-2 (launched in 2019) trained on roughly 3 billion words. The entire English Wikipedia, for comparison, contains about 4.6 billion words.

Just four years later, GPT-4 trained on approximately 9.75 trillion words—over 2,000 times the entire English Wikipedia.

This isn’t some weird academic “who has the bigger AI” contest. Data is the difference between AI being a cool toy and a transformative tool.

When OpenAI upgraded from GPT-3.5 to GPT-4.5, its score on GPQA (a challenging AI benchmark) jumped from 30% to 70%. More data, better AI.

So far, the AI boom has been dominated by infrastructure spending.

Tech giants like Amazon (AMZN), Microsoft (MSFT), Alphabet (GOOG), Meta Platforms (META), and Oracle (ORCL) will spend roughly $300 billion this year on AI infrastructure.

But now, the focus is shifting.

• The biggest investment opportunities are no long in infrastructure alone, but in AI services—companies that feed, deploy, and harness these hungry new machines.

This is a corner of the AI market that’s currently being overlooked. Right now, everyone’s obsessed with flashy AI models and the chips that power them.

They’re missing the critical question: What goes into these machines before they’re even built?

Data is the lifeblood of AI. No data, no AI. It’s that simple.

Feeding data into AI models becomes much more complicated. It’s not a copy-paste job.

In fact, according to S&P Global, a staggering 60% of an AI developer’s time isn’t spent building cool chatbots or features. It’s spent on the grueling work of gathering, preparing, and cleaning data.

• You’ve probably heard the phrase: “Data is the new oil.”

It’s imperfect, as data can be reused infinitely and grows rather than depletes.

But in one crucial respect, the comparison is spot-on: Both must be refined before they’re valuable.

When crude oil comes out of the ground, it’s toxic sludge. It must go through complex refinement processes before becoming the gas that powers your car.

Data is the same. The raw information companies collect is messy, inconsistent, and often unusable for AI systems.

Take a global retailer like Target (TGT). It has reams of customer data—purchase histories, browsing patters, demographic information, in-store traffic, etc. But it’s all scattered across hundreds of systems in various formats.

Refining it into structured, machine-readable data is where the real money is made.

Companies that can clean and structure this data won’t just participate in the AI revolution—they’ll lead it.

• After investing in dozens of megatrends over my career, I’ve learned one crucial lesson…

You have to be nimble.

The best opportunities usually come from small, overlooked corners of the market. Getting into these opportunities early is how true fortunes are made.

Right now, investors only looking at the infrastructure side of the AI megatrend are missing the bigger picture.

Don’t be one of them.

Remember: While everyone else fights for scraps in the crowded AI sector, the firms that can effectively “feed the AI beast” with quality data will quietly build the foundation of trillion-dollar empires.

Now’s the time to prepare for this shift—before the crowd takes notice.

AI is moving fast, and the biggest winners won’t always be the most obvious plays. If you want to stay ahead of the crowd and learn more about the next wave of AI opportunities before they hit the mainstream, sign up for The Jolt—my free investing letter.

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Stephen McBride is Chief Analyst, RiskHedge. To get more ideas like this sent straight to your inbox every Monday and Friday, make sure to sign up for The Jolt, a free investment letter focused on profiting from disruption.