The errors are correlated, not random. Lumps are usually benign cysts. If you start cutting people open for every cyst on a scan, you’d kill a lot more people than you’d save.

This isn’t something you can solve with more scans because the tests test for data that is indistinguishable. They look the same on a scan, there’s an overlap in the assay with some random protein with the same binding sites that is only present in 1% of the population, the coding gene in one person gets repeated in a noncoding region in another, and so on. The “more data” that works is a doctor applying professional judgement (which they’re also famously bad at because biology is a fickle mistress).

Certainly there's still a business there, I'm not saying they won't exist. But it's not going to be a monopoly-esque business with so many players in the ring, OpenAI, Anthropic, Google, Meta, Deepseek, Alibaba, GLM, Kimi etc. It will be cutthroat and a race to the bottom on price. And the difference from today -> 6 months ago intelligence will not be very meaningful.

Investors are largely treating these as future monopolies though.

We can already do so much with existing models. Harness improvements are probably more meaningful at this point.

e.g. say most image recognition can get saturated by a model of size xB parameters, so your tool for that can handoff to a smaller model. Document text extraction can use a model of size yB parameters. A model of size zB for summarizing text.

We are starting to get to a point where you can reasonably scope out an upper bound of required size/effort for many common tasks, and if you string these together, the frontier will largely act as an intelligent invoker of more efficient models.

Up until now there have been meaningful gains to each of those types of workstreams by using newer models, but that is starting to no longer be the case.

Yes, I do believe token consumption will rise exponentially from here in the near term. But cost of switching is low, and substantial profitability will be difficult.

Something like curing cancer (more realistically, curing a specific kind of cancer) has to interact with much slower real-world processes. The most expensive part of drug development is Phase 3 clinical trials in humans. Even the smartest model in the world can't accelerate that meaningfully. Even much earlier when drugs are just testing in cell cultures, it's a lot slower to run lab tests than to run software tests or mathematical proof checkers.

Or to put it another way, there's enough natural variation in real-world bottlenecks that no pharma company can assume they'll beat competitors to market by using a smarter model.

A really smart model could significantly improve the pharma business if it could identify promising approaches to cancer treatment that are less likely to fail in clinical trials, but I don't think that the frontier labs have data to make that work yet. Much of the biomedical literature is poorly reproducible ("replication crisis") and much of the drug-development-specific data is proprietary, never published in the first place.

I do have hopes that general laboratory automation will go faster with LLM assistance, even if all the LLM does is write Python glue scripts to enable custom workflows and instrument integrations.