A toy world is a forgiving place to build an intelligent machine. Put a few blocks on a table, give the program a neat description of them, and search can look uncannily capable. Add the furniture, the door, a person walking through it, the million irrelevant details a real room contains, and the trick becomes a bill. The machine has far too many possible next moves to examine.

That objection sat at the centre of Sir James Lighthill's review of British artificial intelligence. Submitted to the Science Research Council in 1972 and published the following year as Artificial Intelligence: A General Survey, the report did not argue that computers could do nothing useful. It argued that ambitious work kept running into the same wall: as the world a program needed to represent grew, the possible searches through that world grew violently faster.

Lighthill called this combinatorial explosion. I prefer the plainer description: intelligence was expensive precisely where it appeared to be general. A system could solve a carefully bounded problem by carrying a great deal of human knowledge inside it. Let the boundaries loosen, and the method began spending its time discovering how much it did not know. That is less a philosophical defeat than an engineering one, but funding committees tend to recognise engineering defeats quickly.

There is an awkwardness here. John McCarthy, in his response to Lighthill, did not deny the explosion; he objected that AI researchers had known about it all along. He argued that heuristics existed to reduce just this kind of search. The dispute was not between a sober mathematician and fantasists who had never met complexity. It was about whether the field's workarounds amounted to progress, or merely made small demonstrations look more impressive than they were.

Britain's later account of this period is more careful than the neat myth. A parliamentary history of AI evidence records the report's connection with the British AI winter, while resisting the idea that one hostile document simply switched off a living field. The historian Jon Agar makes a similar correction in his reassessment of the report: Lighthill mattered, but his report also landed inside an argument about what publicly supported science was for.

This is why the story feels different from an ordinary wrong prediction. The earlier American decision to reduce machine-translation support, which I wrote about in Pierce's Verdict, had a similar trap built into it: evaluate a field by what its present methods can deliver, and you may be entirely fair while still starving whatever method comes next. Lighthill's case is harder, because the expensive search never went away. Bigger machines and learned representations did not abolish the problem. They changed which parts of it we could afford to tolerate.

I distrust histories in which the critic is made foolish by subsequent success. He had found a live wire. General systems still become costly when the world they must handle expands, even if their failure now shows up as a compute invoice or an answer produced without enough grip on the facts. The frustrating part is that a serious objection can be both true and mistimed.

The surviving document has the dry force of a public expenditure review: show me where this scales. It is not a glamorous demand. It is also the question that keeps returning whenever an impressive demonstration asks to be mistaken for a reliable world.

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