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 diseases 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.

My Eureka Moment

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.

Nearly 700 Gains of 100% or More

That discovery launched a career I could never have predicted.

Over the next five decades, I built quant models that powered some of the most successful investment newsletters in America.

My system has identified 676 stocks that went on to double – including recommendations like Microsoft Corp. (MSFT) in 1987, Nike Inc. (NKE) and Apple Inc. (AAPL) in 1988, and Nvidia Corp. (NVDA) a full 17 years before most people had ever heard of ChatGPT.

That last one alone would have turned $1,000 into more than $1 million.

None of those wins came from hunches or gut feelings. They came from what I discovered with the help of that Wells Fargo mainframe in the 1970s — a systematic, data-driven process for finding fundamentally superior stocks backed by powerful institutional buying pressure.

The process got more refined over the decades. The data got richer. The models got more powerful.

In other words, I’ve spent my career looking for the crème de la crème of the stock market. But I never had access to a technology as powerful as what I’m about to show you.

The Difference Is Extraordinary

As I like to say, good stocks bounce like fresh tennis balls, while bad stocks fall like rocks. The key is knowing the difference before the market starts shaking.

That’s why, for the past year, I’ve been working with the team at TradeSmith on something I’ve never attempted before.

If you don’t know them already, it’s the financial technology company behind some of the most sophisticated portfolio tools available to individual investors today.

Together, we’ve built a new form of AI that takes my Stock Grader system and adds a layer it didn’t have before: a precise, data-driven signal for when to get in and when to get out of the stocks I recommend.

It includes a layer of the same kind of pattern-recognition AI technology that’s diagnosing cancer three years earlier and designing drugs in hours instead of years.

The difference it makes is extraordinary.

Take AppFolio Inc. (APPF), a stock I recommended in 2017.

Anyone who acted on that recommendation has enjoyed an annualized gain of 20%. Compounded over time, that’s excellent. But according to our backtesting, this new AI-enhanced system would have delivered a 74% annualized gain.

Or take Nexstar Media Group (NXST), which I recommended in 2013. A 23% average yearly gain becomes 173%.

Same stock over the same stretch of time. Just smarter timing.

Across the board, backtesting suggests that pairing this new AI with my Stock Grader ratings could generate up to 20 times more money than following Stock Grader alone.

That’s why I say this is the biggest edge I’ve seen in my 47 years as a professional investor. It’s not a new stock picking system – it’s a new layer of intelligence on top of what I’ve already built.

And if we get more stock market gyrations this summer, I believe that kind of intelligence could be more valuable than ever.

The Biggest Edge I’ve Seen

Back in the 1970s, the idea of using a computer to pick stocks seemed absurd to most people on Wall Street. I did it anyway. The results spoke for themselves.

Today, the idea that AI can reliably improve on a 47-year track record might seem equally hard to believe. I get that skepticism. I felt it myself. But then I looked at the testing and had to admit that AI plus my system works like gangbusters.

I’ve been hunting for edges in this market for 47 years. I’ve never seen one like this.

To see exactly how it works – and get the full list of stocks it’s flagging as urgent buys and sells – join me for my online event with TradeSmith CEO Keith Kaplan next Wednesday, June 10, at 10 a.m. Eastern.

When you register your interest, you’ll get access to TradeSmith’s Short-Term Health indicator.

While Stock Grader’s main focus is on what stocks to buy, Short-Term Health is all about when to buy them.

It allows you to type in any ticker to see if a stock is a short-term buy or sell based on a simple traffic light system. Green means buy. Yellow means hold. And Red means sell.

Here’s that link again to access the unlocked version.

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:

Broadcom Inc. (AVGO) and NVIDIA Corporation (NVDA)

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