I think enterprise AI has an AlphaFold problem.

Not because the technology is weak. Because we keep treating the solved part like it’s the hard part.

I’ve been circling this in two places for a while: medicine and the enterprise. AI is very good at speeding up work inside a clean, bounded problem. It is much worse at making the messy downstream work disappear.

AlphaFold solved protein folding: sequence in, shape out. A fifty-year problem, gone and deservedly Nobel-Prized. But the shape was never the point. Curing disease was the point. That next gap is stubborn. A molecule still has to work in a human body. Biology is slow. Trials are slow. Compute doesn’t erase that.

Enterprise AI is the same shape. LLMs are excellent at a bounded version of the problem: clean context in, useful output out. But useful output is not the same as changing how a company runs.

The harder question is whether that output survives contact with the real enterprise estate - undocumented dependencies, brittle workflows, old architecture, incentives that were never designed for this pace.

Software engineering makes this visible. In greenfield codebases, AI coding tools move very fast. In mature enterprise codebases, the story is messier. METR has pointed to flat-to-negative gains for experienced engineers in legacy settings. Same model. Different patient.

A clean codebase is a clean patient population. A legacy estate is the complicated one. Same drug, different result. This is also why AI-native companies can look a generation ahead, not because they have better models, but because they carry less baggage. DORA has made the same point: AI doesn’t fix an organization, it amplifies what’s already there.

So here’s my real claim. Enterprise AI adoption isn’t slow because the technology is weak. It’s slow because we keep solving the manageable half, model capability, the fold and acting like the whole problem is solved. The rest is slower and less glamorous: clean data, documented architecture, decoupled systems, trust, redesigned workflows.

That’s how I read the recent moves from Anthropic, OpenAI, Microsoft, and others around forward-deployed engineering teams. The hard part is no longer just building the model. The hard part is getting the model to work inside the patient (enterprise).

DeepMind spun out Isomorphic Labs to own the part AlphaFold couldn’t reach; turning a solved structure into an actual treatment. Anthropic, OpenAI, and Microsoft are now building FDE ventures to get closer to enterprise estates. GSIs have quietly been operating inside those estates for two decades, without the lock-in or constraints that come with being a frontier model or hyperscaler.

Which is why I think the market has this backwards. Services firms are being priced like the fold was what mattered. It isn’t. The clinical trial, “estate readiness” is where the value sits. It gets bought, one embedded engagement at a time. Now you decide!