Knowing What You Don’t Know

A reader corrected a columnist's argument that the AI industry's massive investment in larger models is misguided, pointing to economist William Stanley Jevons' 1865 observation about coal efficiency. Jevons noted that as steam engines became more efficient, coal consumption increased rather than decreased, suggesting that cheaper AI retrieval could similarly drive greater demand for computational power. The exchange highlights a fundamental tension in AI development between building bigger models and creating systems that can acknowledge their own knowledge gaps.

Why the next real breakthrough in AI isn’t a bigger brain — it’s a machine that can admit ignorance. A reader caught me out. Last column I argued that the great AI buildout — the hundreds of billions pouring into data centers and the GPUs that fill them — is aimed at the wrong layer. We are spending as if the bottleneck were the size of the model’s brain, when the real bottleneck is getting the right information in front of it. Cheap retrieval, I said, not expensive cognition. A reader replied, pointing out the name Jevons. In 1865, a young English economist named William Stanley Jevons noticed something strange about coal. As steam engines got more efficient — as they wrung more work out … The post