The Atomic Unit of the Hospital, Alpha vs Beta Workflows, Post-Training Pipelines
As a paid subscriber since October 2017, Stratechery has greatly influenced my thinking and writing. The latest interview with Palantir CTO Shyam Sankar is a perfect example of why this is: although not explicitly healthcare-focused, it provides deep insights, which translate seemlessly to healthcare. I’ll only put two excerpts below, but feel free to subscribe and listen to the whole thing.
The first point is about whether AI is supposed to substitute people (doctors, nurses, admin and knowledge workers) and how to think about the efficiencies that models create (emphasis mine):
Shyam Sankar: People are being rewarded — and it’s very rational, if you’re a lab, you’ve been in your own reinforcement-learning loop, you do fundamental research, you make a more powerful model, and your valuation doubles or quadruples and every year it seems to keep working. Baked into that is the idea that eventually this stuff will diffuse, eventually there will be economic value.
But one of the conceits I’m not sure people are getting right is, what is the atomic unit of the enterprise? Let’s leave consumer out of this. The presumption, as expressed through the product strategy you see from the labs, is that the atomic unit of the enterprise is the knowledge worker — if I can make every worker more productive, that’s the ROI, and that’s not actually true. The atomic unit of the enterprise is the workflow, you can make a single supply-chain analyst more productive and generate no incremental ROI, you have to make the entire supply-chain workflow measurably more productive to capture the value.
There are some roles where the cardinality collapses — the knowledge worker is the workflow, like coding — so you have perfect product-market fit for those things, and you can chase unbounded productivity. But that’s a very small part of everything we do in the real economy.
The second is about whether all workflows are the same - if they deliver the same value, carry the same risks, need the same output/outcome quality - and what is the type of data that is actually valuable in a world of frontier models (emphasis mine):
Shyam Sankar: No customer I work with is a greenfield where we’re (Palantir) the only thing that exists. If you’re happy with the beta you’re getting but you’re beating your head against the wall trying to figure out why the beta doesn’t let you capture the opportunities in front of you — are you fighting your software stack? Are you fighting decisions made in the past, borne out of an aesthetic desire for something easily governable and maintainable by your IT organization? IT will always yoke toward beta — decisions that are mimetic, closer to best practice, what everyone else uses. The people most interested in alpha are in the line of business — the person who feels frustrated that their strategy is hard to implement, versus “I have a precise steering wheel to do this”.
Ben Thompson: So do models deliver beta or alpha?
Shyam Sankar: Depends how you use them. If you just think about it at the limit, we’re just going to keep betting that these models will get better and I’m going to rent my intelligence from a lab — how are they getting better? They’re somehow compounding all the world’s knowledge into their system, which you’re maybe tacitly helping them do that, maybe you’re not, but that’s the trajectory you’re on. There should be an existential question about, “What is the theory of my firm?”, “What do I do if all the intelligence is compounding into weights I don’t control?”.
A big part of what we’ve been pushing is this idea of Sovereign AI, that our customers have really taken to. It’s not a binary — you don’t need to leave the lab ecosystem. But for what workflows is actually the most valuable asset your post-training pipeline — it’s not about a specific open-weight model, it’s not about a specific post trained open-weight model, it’s actually that you have sovereignty over a post-training pipeline filled with the operational exhaust of your enterprise. The operational decisions that you’re making in the context of the operating system that is our software is generating data that allows you, every month, to train a new checkpoint and evaluate it against everything that came before, in a way where you know that model is yours that you’re going to be relying on.
I’m going to get back to all of those points tomorrow.