Wikipedia's overview: <https://en.wikipedia.org/wiki/Cyc>
Project / company homepage: <https://cyc.com/>
It's failure is no shade against Doug. Somebody had to try it, and I'm glad it was one of the brightest guys around. I think he clung on to it long after it was clear that it wasn't going to work out, but breakthroughs do happen. (The current round of machine learning itself is a revival of a technique that had been abandoned, but people who stuck with it anyway discovered the tricks that made it go.)
The goal was that in a decade it would become self-sustaining. It would have enough knowledge that it could start reading natural language. And it just... didn't.
Contrast it with LLMs and diffusion and such. They make stupid, asinine mistakes -- real howlers, because they don't understand anything at all about the world. If it could draw, Cyc would never draw a human with 7 fingers on each hand, because it knows that most humans have 5. (It had a decent-ish ontology of human anatomy which could handle injuries and birth defects, but would default reason over the normal case.) I often see ChatGPT stumped by simple variations of brain teasers, and Cyc wouldn't make those mistakes -- once you'd translated them into CycL (its language, because it couldn't read natural language in any meaningful way).
But those same models do a scary job of passing the Turing Test. Nobody would ever have thought to try it on Cyc. It was never anywhere close.
Philosophically I can't say why Cyc never developed "magic" and LLMs (seemingly) do. And I'm still not convinced that they're on the right path, though they actually have some legitimate usages right now. I tried to find uses for Cyc in exactly the opposite direction, guaranteeing data quality, but it turned out nobody really wanted that.