Interesting points made in the video.

But models did not become good at coding just because coding is replayable. It’s because there are countless repos, issues, Stack Overflow threads, and Reddit posts/comments/questions where a solution is clearly marked as “solved” or “that helped,” and AI can learn from that feedback.

Being replayable does play a role because a solution can be tested against a compiler, and the resulting errors or lack of errors/warnings can reveal whether it worked.

This becomes much harder in fields like fitness, where changes take much longer and cause and effect relationships are not straightforward to establish.

Your muscle gain increased but was it because you increased protein intake? Or was it because you started eating more carbs, which added more energy to the system?

Once protein needs are already met, calories may become the limiting factor. In that case, the additional gains may come primarily from increased calorie intake rather than the higher protein intake itself.

AI is bad at fitness, evidently.

Many people forget, conversation with a model also generates training data. This is how your problems, algorithms, solutions end up in training data and end up right at your competitors without your competitor trying to actively steal your code.

I simply do not expose core algorithms which improve my product to AI agents.

I think this is an interesting thing that will happen once the rate of change slows down a little bit - imagine a world where there's more or less a couple base models and everyone trains on top of them, and the bitter lesson is defunct just via sheer physics (maybe we have the best models we can physically run in reasonable energy density substrates, or something), then it becomes "your personal model" with your overlay, training, or feedback on top.

I'm glad you're asking because I've seen it too and don't get it either. I assumed initially it was alpha as a typo, then I Googled it and got even more confused.