The Nobel Prize-Winning AI That’s Coming for the Stock Market

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Editor’s Note: There are people on Wall Street who say they’ve been using data and quantitative systems to pick stocks their whole career. And then there’s Louis Navellier — who was actually doing it in the 1970s, on a mainframe computer, before most of his peers thought it was even worth trying.

I share that because what Louis is about to tell you isn’t a story about jumping on the AI trend. It’s a story about someone who has spent nearly five decades refining a system — and just found the most powerful upgrade of his career.

Read what he has to say below, and I think you’ll understand why I took notice.

Louis is hosting a free event on June 10 at 10 a.m. Eastern with TradeSmith CEO Keith Kaplan to demonstrate the full system. When you register, you’ll get immediate access to a free tool that lets you check whether any stock you own is currently a short-term buy, hold, or sell — before the event even starts.

Sign up right here.

If you’re under 50 and you stay healthy, you could live to 150.

To you and me, that may sound like science fiction. But to Demis Hassabis, it sounds conservative.

Hassabis is the computer programmer and neuroscientist who founded DeepMind — the pioneer deep learning lab that Google bought in 2014.

Deep Learning is the method of training software to recognize patterns by feeding it enormous amounts of data and letting it learn from its own mistakes. And it’s the core technology behind OpenAI’s ChatGPT, Anthropic’s Claude, Google’s Gemini and most of what people mean when they say “AI” today.

In 2024, Hassabis won the Nobel Prize in Chemistry for building an AI model — called AlphaFold2 — that mapped virtually all 200 million known proteins. This touched off a revolution in drug discovery. 

Most drugs work by binding to a specific protein in your body — much like a key fits into a lock. For 50 years, figuring out the shape of those locks was so slow and expensive that it bottlenecked the entire drug discovery process. 

Thanks to AlphaFold2’s mapping, what used to take researchers years in the lab now happens in hours on a computer.

The progress is so fast that Hassabis estimates we’ll cure ALL disease within 10 years.

I’m 67 — well past the 50-year-old cutoff he’s talking about. But when I look at what’s come out of medical research in just the last two months, he might be right:

• A drug just doubled survival in pancreatic cancer — the deadliest cancer there is.
• A one-time gene-editing infusion permanently cut bad cholesterol by 62% from a single dose.
• A lung cancer pill held back a spreading tumor for five full years — longer than any drug has ever managed.
• The Mayo Clinic built an AI that detects pancreatic cancer on routine CT scans up to three years before a doctor can spot it.
• Eli Lilly’s new anti-obesity drug achieved 30% body weight loss in its Phase 3 trial — and, along the way, cut knee arthritis pain by 76%.

These aren’t random breakthroughs. They were all either discovered, accelerated, or made possible by the kind of deep-learning AI models Hassabis pioneered.

And, folks, these models are only accelerating as AI learns to write code to create more powerful models… which write code for even more powerful models… and so on.

Which brings me to the question that I’ve been thinking about a lot lately.

If AI is rewriting what’s possible in a field as complex as human biology — what is it about to do to financial markets?

I’ve spent 47 years building computer systems to find growth stocks before the crowd catches on. So, I know what it looks like when a new technology changes the game for investors.

In the 1970s, I was one of the few people using a computer to pick stocks. Most of my peers thought it was eccentric at best… and a fool’s errand at worst. Today, computers are responsible for about 80% of daily stock trading volume.

And I believe what’s coming with AI is a change of a far greater magnitude. 

I’ll show you what I mean in a minute — including how adding AI to my own quantitative models could turn a 615% gain on a stock like DXP Enterprises Inc. (DXPE) into a 3,626% winner, or a 292% gain on Broadcom Inc. (AVGO) into 6,284%.

First, though, let me take you back to the early 1970s when I had my first “eureka moment” about how machines could crack the secrets of the stock market. 

The 1970s Eureka Moment That Launched 47 Years of Market-Beating Quant Investing

It was my junior year at Cal State Hayward (now Cal State East Bay), where I was studying finance. 

One of my professors was working for Wells Fargo — using its mainframe computer to build the stock market indexes that were just emerging. He asked me if I could help.

The flashiest technology I’d touched up to that point was a slide rule. Getting access to that mainframe was like an 1800s gold prospector being shown a diesel-powered excavator.

My job was to build a model portfolio that mimicked the S&P 500 using just 320 stocks. But something unexpected happened. Instead of just tracking the market — my version beat it.

That wasn’t supposed to happen. The prevailing theory at the time — which every finance textbook repeated as gospel — was that you couldn’t consistently beat the market. It was impossible. 

My data said otherwise.

So, I dug deeper. I ran the statistical tests. And I found a pattern that would define the next five decades of my career. Some stocks move independently of the broader market and have their own signal. Find them early enough, and the gains can be extraordinary.

Folks on Wall Street call it “alpha.” From that moment on, I was obsessed with building systems to find it.