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And thanks to LLM coding, what took a whole team of research scientists at DeepMind and millions of dollars of research and compute can now be done for a few thousand dollars of rented compute.
So if you plot out how much compute it took to build various iterations of strong GoBots over the years, you can see that in 2020, there was a open source project called Katago by David Wu from Jane Street, who basically achieved a 40x reduction in compute needed to train a really strong GoBot tablet rasa. I'm not certain if it's stronger than AlphaGo 0 or Alpha 0 or Mu 0, but it's very, very strong. And this is what most Go practitioners today train against when they're playing an AI.
When I saw the kind of early breakthroughs on AlphaGo in 2014, 2015, 2016, and so forth, it was just profound to see how smart AI systems could become and the kind of computational complexity class that they could tackle with deep learning. This is a problem that has long been understood to be kind of intractable for a search, and yet it was solved through deep learning.