By 1989, Digital Equipment Corporation had fifty-nine technical staff assigned to maintaining the infrastructure and rule base behind its internal expert systems, at that point the most widely publicised application of AI anywhere.

The obvious objection is that fifty-nine people was a bargain. XCON, the configurator most of those rules served, was reckoned to be saving DEC around $25 million a year, and against a number like that a headcount is rounding error. Fair enough. The trouble was never the size of the bill, it was the shape of it: recurring, rising with the rule base, and quoted to nobody at the point of purchase. A system sold on the promise of bottling up scarce expertise turned out to need a permanent staff to keep the bottle from going off.

The winters get told as a story about capability. The machines couldn't do what was claimed, so the money left. Thomas Haigh's reading of the record is less tidy: the famous first winter of the 1970s largely didn't happen, and the real slump was the two-decade one following the 1980s bubble. What collapsed in 1987 was a hardware market rather than a technology. Cheap Unix workstations from Sun ran the same software the specialised Lisp machines ran, and the dedicated machines stopped making sense. Plenty of the expert systems kept running for years afterwards on ordinary computers. The field concluded the idea had failed, which was a larger conclusion than the evidence supported.

Nobody can claim they weren't told. Drew McDermott used the word at an AAAI panel in 1984, at the top of the boom, on a bill called The Dark Ages of AI, borrowing it from the nuclear winter argument then going on. He described a deep unease that the expectations being set would end badly, and he was four years early.

So the lesson everybody agrees on is don't overpromise. Ted Senator puts it plainly in his AAAI paper on what the bust should teach this boom: be measured about strengths and limitations even when the excitement is genuine. That's the cheap lesson, though. No one has ever been talked out of a funding round by their own caution.

The expensive lesson is the fifty-nine. The hardware business died in 1987 for reasons of its own, but what stopped companies replacing their expert systems was never the cost of building them. It was the cost of keeping them correct while the world they described moved underneath. That problem hasn't gone away, it has been renamed. Engineers air it constantly, as complaints about evaluation suites going stale and retrieval indexes rotting quietly. Where it doesn't appear is the investment case, which is still written in training runs and inference margins, as though correctness were a fixed cost you pay once.

The cold periods, on whichever count you accept, arrived when the money behind AI was overwhelmingly governmental, including much of what looked from outside like a commercial hardware market. A handful of decisions could switch it off: the Lighthill report in Britain, the Strategic Computing Initiative cancelling new AI spending in 1988. Henry Kautz argues a third winter is unlikely, and he may be right that the floor sits higher now. Commercial money tied to renewals isn't obviously safer, though. It fails differently, as erosion rather than a freeze, and erosion has no announcement date anyone can point at afterwards.

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