The fastest drug discovery engine in history
When a lab at MIT used AI to discover an entirely new class of antibiotic in just hours, something that would have taken human scientists years, it marked a turning point. Josh Baylin, presenting Stansberry Research’s latest SIR pitch, calls it “Sam Altman’s Next Venture”: medical AI that is “10,000 times faster than even the smartest, most well-funded human scientists on earth.”
The number sounds impossible, but the direction is real. AI drug discovery is a genuine field with genuine companies behind it, and the investment opportunity is not just in the drug makers. It is in the entire supply chain that makes the technology work.
As we broke down in our full review of the Stansberry SIR presentation, the medical AI thesis has real substance underneath the marketing. The question for investors is which companies actually stand to benefit.
The drug discovery platforms
The most direct way to invest in medical AI is through the companies building AI-powered drug discovery platforms. These are firms that use machine learning to screen molecular libraries, predict protein structures, and identify drug candidates faster than traditional methods.
Recursion Pharmaceuticals (RXRX) is one of the purest names. The company runs an AI platform that screens billions of molecules against disease models, and it has partnerships with Bayer, Roche, and Sanofi. Insilico Medicine, a private company, made headlines when its AI-discovered drug rentosertib entered human trials for a rare lung condition called IPF. As Josh notes in the presentation, “it was the first drug ever discovered entirely with this new form of Medical AI.”
Exscientia, which merged with Recursion in 2024, was another pioneer. Veeva Systems (VEEV), while not a drug maker, provides the life-sciences software backbone that many AI drug discovery programs run on, with a data moat that competitors cannot easily replicate.
These are the companies most directly exposed to the trend Josh describes when he says “AI has already figured them out in a fraction of the time, for a fraction of the cost.”
The supply chain plays
Here is where the Stansberry presentation gets genuinely interesting. Josh makes a compelling case that the biggest gains may not come from the drug makers themselves but from the companies that make the drugs possible.
He walks through several examples. When bluebird bio launched Skysona, a treatment for a neuro disease, the biggest gains came not from bluebird bio but from Lonza, the company that actually manufactured the medicine in Houston, Texas. Lonza’s stock rose 13 times. When Amtagvi entered patient trials, BioLife Solutions, which helped freeze and store the medication, saw its stock rise 27 times. Cryoport, which shipped the medication in temperature-controlled containers, rose 30 times.
And when Novartis launched Kymriah, a company called Oxford Biomedica that makes a key ingredient saw a gain of 17,751%. As Josh puts it: “You never had to invest in the actual creator of the medicine. You just had to find a company making the medicine possible.”
This is the picks-and-shovels logic applied to medical AI. The same logic that says you should own Lam Research instead of Apple, or Nvidia instead of whichever AI company wins the model race. The supply chain is where the leverage is, because every drug needs manufacturing, storage, and shipping, regardless of which molecule wins.
Why the pace is accelerating
The Stansberry presentation cites a striking statistic from Nature Reviews Drug Discovery: the number of new drugs approved per billion dollars of R&D has collapsed roughly 80-fold since 1950. Traditional drug discovery is getting less efficient over time, not more. And the cost keeps climbing, with each new drug taking 10 to 15 years and more than $2 billion to develop.
AI inverts that curve. The MIT antibiotic discovery took hours, not years. A lab in Scotland used AI to screen 4,000 molecules for aging-reversal candidates in five minutes, a process that would have taken scientists months. Josh notes there are already 173 AI-discovered medicines in human trials.
If the pace of drug discovery accelerates the way Josh suggests, the supply chain companies face a volume problem, not a demand problem. More drugs means more manufacturing, more cold-chain shipping, more storage, and more ingredients. The supply chain scales with the pipeline.
The billionaire backing
Sam Altman’s involvement is real and documented. MIT Technology Review reported that Altman personally funded the entire $180 million raised by Retro Biosciences, an AI-focused longevity startup. His name was initially kept secret, with BioSpace noting at the time that the investor identities were “notably missing” from the announcement.
Peter Thiel, Elon Musk, and Jeff Bezos are all AI investors, and the presentation notes that Google and Amazon are now involved in the medical AI space as well. The framing that “all four billionaires backed this specific project” is where the marketing stretches the evidence, as we noted in our review. But the underlying interest from Silicon Valley’s most connected investors is genuine.
What to watch
The medical AI thesis has real legs. AI drug discovery is a growing field with real companies, real drugs in trials, and a real supply chain that stands to benefit from faster discovery cycles. The main risk is the same one every biotech theme carries: clinical trials fail, drugs get rejected, and the timeline from discovery to revenue is measured in years, not months.
For investors who want exposure without betting on a single drug, the supply chain companies, the manufacturers, the cold-chain shippers, the ingredient suppliers, offer a diversified way to play the trend. As Josh says, “you don’t need to even care about which drugs or biotech startups will win or lose. You just need to back the base technology.”
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