The most profitable part of the drug business

When Novartis launched Kymriah, a groundbreaking cancer treatment approved by the FDA in 2017, Novartis shareholders did well. But the biggest gains went to a company most people have never heard of.

Oxford Biomedica makes a specialized ingredient called a lentiviral vector, a critical component in certain cell therapies. When Kymriah launched, Oxford Biomedica’s stock surged 17,751%. That is 178 times your money, enough to turn every $10,000 into $1.78 million, without ever owning the drugmaker.

This is the core insight of Josh Baylin’s Stansberry Research presentation, and it is the part of the thesis that holds up best under scrutiny. As we noted in our full review of the SIR pitch, the supply chain logic is the most defensible part of the medical AI investment thesis.

The pattern repeats

Josh walks through several examples, and the pattern is consistent. The drug maker gets the press. The supply chain company gets the gains.

When bluebird bio launched Skysona, a treatment for a serious neurological disease, the biggest gains did not come from bluebird bio. They came from Lonza, the Swiss company that actually manufactured the medicine at a facility in Houston, Texas. Lonza’s stock rose 13 times.

When Amtagvi entered patient trials, BioLife Solutions, which provides the cryogenic freezing and storage technology that keeps the medication viable, saw its stock rise more than 27 times. Cryoport, which ships these medications in specialized temperature-controlled containers, rose 30 times.

The logic is simple. Every new drug needs to be manufactured, stored, and shipped. The drug maker takes the clinical risk. The supply chain company takes the order. If the drug works, the supply chain company benefits. If the drug fails, the supply chain company still has other customers. The risk is lower, and the leverage comes from volume.

Why AI accelerates the supply chain thesis

Here is where the medical AI thesis gets interesting for supply chain investors. Traditional drug discovery produces a small number of new drugs over long timelines. The supply chain grows slowly because the pipeline grows slowly.

AI drug discovery changes that math. As we explore in our guide to AI drug discovery, the MIT lab that discovered a new class of antibiotic using AI did it in hours, not years. A lab in Scotland screened 4,000 molecules for aging-reversal candidates in five minutes.

If AI can produce drug candidates at 10,000 times the traditional pace, the bottleneck shifts from discovery to production. More drugs in the pipeline means more manufacturing, more cold-chain shipping, more specialized ingredients, and more demand for the companies that provide them.

Josh makes this point explicitly in the presentation: “What used to be a once-in-a-decade event is about to start firing dozens of times each year.” If that is accurate, the supply chain companies face a demand surge that the current system is not built to handle.

The specific supply chain categories

Based on the examples Josh cites, there are several categories of supply chain companies that stand to benefit from accelerated drug discovery:

Contract manufacturing. Companies like Lonza that physically produce the drugs. Every new drug needs a manufacturer, and specialized therapies like cell and gene treatments require specialized facilities.

Cold-chain logistics. Companies like Cryoport and BioLife Solutions that handle temperature-sensitive shipping and storage. Many new therapies, particularly cell and gene treatments, require cryogenic preservation. If more drugs are discovered faster, more of them need to be shipped cold.

Specialized ingredients. Companies like Oxford Biomedica that produce specific components used in advanced therapies. These are often the highest-leverage positions because the ingredient may be proprietary or technically difficult to produce.

Life-sciences software. Companies like Veeva Systems that provide the data infrastructure for clinical trials, regulatory compliance, and drug development. More drugs in the pipeline means more software needed to track them.

The iPhone analogy

Josh draws a parallel to the iPhone that is worth considering. When the iPhone launched, Apple was the obvious investment. But Lam Research, which makes a critical component inside every iPhone, rose 393,352%. You could have turned $1,000 into $3 million without ever buying Apple stock.

Nvidia is the same story. It sells the base layer of AI chips, and an early $1,000 investment could have turned into $5.8 million. You did not need to know which AI company would win. You just needed to know AI would grow.

Medical AI could follow the same pattern. The drug makers are the obvious investments, but the supply chain companies may offer the better risk-adjusted returns. They benefit from every drug, not just the ones that succeed. And as the pace of discovery accelerates, their volumes accelerate with it.

The honest caveat

The supply chain thesis is genuine, but it is not a guarantee. Supply chain companies face their own risks: manufacturing capacity constraints, regulatory approvals, competition from larger players, and the possibility that AI drug discovery does not accelerate as fast as projected.

But the logic is sound. Every drug needs a supply chain. If AI produces more drugs faster, the supply chain grows with the pipeline. And the historical evidence Josh cites, from Lonza to Oxford Biomedica to BioLife Solutions, suggests that the supply chain companies can sometimes produce returns that rival or exceed the drug makers themselves.

For investors who want exposure to medical AI without betting on a single drug, the supply chain is the diversified path. As Josh says, “you just need to back the base technology, and the suppliers and partner companies that make it possible.”

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