AI is adopted fastest not where models perform best, but where their output is cheapest to check. That one sentence explains most of the adoption map. New essay on why. In 2016 Hinton told people to stop training radiologists. 2026: radiology has more FDA-cleared AI than any specialty, roughly 3/4 of all cleared medical AI. Radiologist demand held up. The AI finds the lesion. A radiologist verifies and signs. That is the pattern everywhere. AI collapsed the cost of producing plausible work. It did nothing to the cost of trusting it. Those used to be bundled. The skilled human who made something also certified it, tacitly, for free. AI unbundles them. So why did coding go first? Not because models are best at code. Because code arrives wrapped in the deepest stack of cheap automatic verification ever built. Compiler. Types. Linters. Tests. CI. Runtime. Errors caught in milliseconds, long before anyone expensive A credit decision has none of that. No compiler. No test suite. The first check is also the most expensive one: a qualified human reading the whole thing. Same models. Same capability. Wildly different cost of trust, and therefore wildly different adoption. The strategic consequence. Models are commoditising. What a frontier lab ships this quarter reaches your competitor soon after. The machinery for judging whether output can be trusted does not commoditise, because it encodes your firm's policy and risk appetite. Rent the intelligence. Own the judgment. Full essay, with the economics (Spence, Polanyi, Holmstrom, Kremer) and the predictions it commits to: modelsandmarkets.com/p/the-v…

Companies have driven the cost of producing work to almost zero, and most of them are seeing no matching gain. More output, no more done. The bottleneck didn't disappear, it moved. Here's the economics of why: when one input to a production process becomes cheap and abundant, value migrates to whichever complementary inputs stay scarce. AI made the input of plausible cognitive output nearly free. However, it did nothing for the other inputs: specifying what should be built, verifying the result is correct, and standing behind it. Those didn't get cheaper. One of them, the cost of catching a confident, plausible error, arguably got more expensive (as anyone who has had to deal with reading/reviewing/responding to "workslop" knows). So the value moved to these inputs. I call the return on those scarce complementary factors the Coordination Premium. AI has landed first and hardest in software, and the usual explanation is that models are good at code. The real reason, in my view, is that code is uniquely cheap to check: compilers, types, tests, CI, linters, and a runtime that either works or doesn't. A legal memo has no such machinery, so the first line of verification is also the most expensive one. i.e. an expert reading the whole contract or law or regulation. Same model, same capability, wildly different cost of trust, and therefore wildly different adoption. As such, I believe AI diffuses in order of verification cost, not model quality. This then points at where the next moat sits. Models are commoditising and anything a frontier lab gives you this quarter reaches your competitor next quarter. What doesn't commoditise as easily is the firm-specific machinery for judging output of the model. In other words, rent the intelligence but own the judgement. This new essay works the whole argument through, with Coase, Baumol, Akerlof and one very instructive train crash. modelsandmarkets.com/p/the-c…