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Yesterday, I introduced you to what I call the Musk Stampede: the pattern I’ve watched play out when Elon Musk helps prove that a seemingly impossible technology can become a real business.
And I ended with where I believe his attention is taking us next: the intersection of technology and human health.
But Musk isn’t alone. Some of the smartest people and biggest companies in technology are pouring billions of dollars into healthcare. Nvidia (NVDA), Alphabet (GOOGL), and a growing list of pharmaceutical and biotech companies are using artificial intelligence to attack one of the most expensive and frustrating problems in medicine:
Developing a new drug.
For decades, drug discovery has essentially been an extraordinarily sophisticated search problem. Scientists identify a biological target involved in a disease and then begin looking for a molecule or protein capable of affecting it in the right way.
The problem is the number of possibilities is enormous. Researchers can spend years identifying promising candidates, testing them in laboratories, eliminating failures, and eventually moving the survivors into human trials. Even then, many drugs that look promising early in development ultimately fail.
The result is a process that remains slow, expensive, and filled with uncertainty.
AI could change that equation.
Think about what ChatGPT does. It was trained on enormous amounts of information and learned patterns that allow it to generate something new – a sentence, an image, computer code.
Now apply the same basic concept to biology.
Proteins are essentially biological machines built from sequences of amino acids. Their shape and behavior determine how they interact with cells and play critical roles in everything from our immune system to cancer.
AI models can study enormous libraries of existing proteins and learn the relationships between their sequence, structure, and function. Researchers can then use those models to help identify promising drug candidates or even design molecules and proteins with specific characteristics.
Instead of relying solely on searching for the right answer, scientists increasingly have tools that can help design the answer.
And this is already moving beyond the laboratory.
Recursion Pharmaceuticals (RXRX) has built a platform that combines machine learning with enormous biological datasets to identify potential drug candidates and better understand how diseases work. The company has multiple internally developed programs that have advanced into clinical development.
Insilico Medicine (ISLMF) has taken a similar approach. Its AI platform is designed to identify biological targets and generate potential drug molecules, including treatments that have advanced into human clinical trials.
Whether any individual drug succeeds isn’t really my point. Clinical trials will ultimately determine that.
What’s important is that AI is beginning to participate in the actual process of creating medicines, from identifying targets and designing potential treatments to helping researchers decide which candidates are worth pursuing.
That’s a significant change from where we were just a few years ago.
If this were limited to a handful of small biotech companies, I’d be interested.
The fact that some of the world’s largest technology and pharmaceutical companies are moving aggressively into the field makes me pay much closer attention.
Alphabet created Isomorphic Labs out of DeepMind, the AI research organization behind AlphaFold. AlphaFold became famous for its ability to predict the three-dimensional structures of proteins, helping solve a scientific problem researchers had wrestled with for decades.
Isomorphic is now trying to take the next step and use AI to design drugs.
Earlier this year, the company unveiled a system designed to predict how potential drugs interact with biological targets more quickly and efficiently than traditional computational approaches.
The goal is ultimately to move AI-designed medicines into human testing.
Nvidia is moving aggressively into the field as well. It’s partnered with pharmaceutical giants including Eli Lilly (LLY) and Novo Nordisk (NVO), while other technology companies are building their own healthcare AI capabilities.
This is a pattern I’ve seen throughout my investing career.
When enormous amounts of money and talent begin moving toward the same technological problem, I pay attention. It doesn’t guarantee success, but it tells us where some very smart people believe the next major breakthroughs could occur.
We’re still early, and I don’t want to pretend AI has suddenly solved drug development.
A computer can identify or design a promising molecule, but that molecule still has to work inside the extraordinarily complicated human body. Clinical trials remain expensive. Drugs will still fail. And regulatory approval isn’t going away.
In fact, regulators are already preparing for AI to play a larger role.
Earlier this year, the FDA and European Medicines Agency jointly released principles governing the use of AI throughout drug development, another indication of how quickly the technology is moving into mainstream pharmaceutical research.
For investors, that’s where I think this story becomes particularly interesting.
We’ve spent the last few years watching AI learn how to generate words, images, video, and computer code.
Now it’s learning the language of biology.
If AI can eventually help scientists discover better medicines faster, reduce the number of dead ends, or find treatments for diseases that have frustrated researchers for decades, we’re talking about something far more consequential than another productivity tool.
We’re talking about changing how medicine itself is created.
And drug discovery is only one piece of what’s happening. AI, biotechnology, genetics, robotics, and medical devices are beginning to converge at the same time.
Tomorrow, I’ll show you why I believe that convergence could create one of the biggest investment opportunities of the next decade.
