Is 50% good? It's what you'd expect by posing "will AI do X by 2026?" and then flipping a coin to get an answer.

Are they integrating it well with AI overviews then? It looks like they haven't found a way to monetize it yet.

That is to say, its main product is its human users.

It's a bit nit picky because what most users use is theirnai search , well I'm guessing that's what most of us interact with

Yes, thank you.

This is a weird point because the Bitter Lesson spends a meaningful chunk of its short length talking about chess specifically.

People sort of generally refer to the Bitter Lesson as if its key argument is "Large Transformer models are best", but this is directly from the essay:

"One thing that should be learned from the bitter lesson is the great power of general purpose methods, of methods that continue to scale with increased computation even as the available computation becomes very great. The two methods that seem to scale arbitrarily in this way are search and learning."

The second paragraph of the essay is specifically about chess:

"In computer chess, the methods that defeated the world champion, Kasparov, in 1997, were based on massive, deep search. At the time, this was looked upon with dismay by the majority of computer-chess researchers who had pursued methods that leveraged human understanding of the special structure of chess. When a simpler, search-based approach with special hardware and software proved vastly more effective, these human-knowledge-based chess researchers were not good losers. They said that ``brute force" search may have won this time, but it was not a general strategy, and anyway it was not how people played chess. These researchers wanted methods based on human input to win and were disappointed when they did not."

The "brute force search" he's talking about (Deep Blue) used tree search with alpha-beta pruning on top of a ton of human crafted heuristics and a database of game-specific information it could access. Stockfish has historically been an even more "brute force" approach in that it is much more focused on optimizing search--which is one of "arbitrarily scalable" techniques listed in the Bitter Lesson essay--and it has increasingly replaced handwritten human heuristics with neural networks over the life of its development.

At no point in the Bitter Lesson is there a mention of LLMs driving cars, or a suggestion that large transformer models will be the dominant class of model for every single task. It is specifically reflecting on the fact that approaches that leverage search and learning, combined with large amounts of compute, consistently prove to be most effective. Stockfish would qualify as evidence of that, under his criteria.

For now. But the trend remains that bigger, smarter, general purpose AI beats smaller specific purpose models. This is why GPT 5 and 6 remain better at OCR than anything Mistral has put out.

> but because society rejected or didn’t adopt that technology

OK, so imagine that in 1980, I predicted that we'll colonize Mars by the year 2020. Today, I'm arguing that I was right in my prediction - in broad strokes, we have the technology! Nothing is stopping us. It's just that politicians have different priorities.

Of course, that's a load of bull. You can't decouple predictions about the future of mankind from what said mankind actually decides.

I looked up the predictions on Wikipedia and I'd say that most of them are general enough that they'd hard to disagree with back in 1999 (there will be bioengineering, there will be portable computers, there will be robots, there will be AI), but the timing or the specifics are off on almost every single one of them.

Go to Dan Luu's evaluation (https://danluu.com/futurist-predictions/), search for "Appendix: detailed information on predictions". Seems like Kurzweil's has a huge number of awful predictions. This is from four years ago, so a few things are closer to being real now. But many things he predicted for the early 2000s still haven't arrived.

I think it is largely domain dependent. He's had solid predictions around computing, but for biology (and longevity) he seems to be way off.

For instance, by his prediction, we're already many years past a 100 year average life expectancy (with 120 years expected by the end of the decade).

I think his mistake was tightly coupling exponential compute with exponential progress in biology - which has so far not panned out.

> but because society rejected or didn’t adopt that technology

I would argue that society would likely have been glad to adopt, but the profit-seeking motive got in the way (again).

The real survivorship bias is the selection of forecasters.