Comment by benreesman

Ten years ago it seemed obvious where the next AI breakthrough was coming from: it would be DeepSeek using C31 or RAINBOW and PBT to do Alpha something, the evals would be sound and it would be superhuman on something important.

And then "Large Language Models are Few Shot Learners" collided with Sam Altman's ambition/unscrupulousness and now TensorRT-LLM is dictating the shape of data centers in a self reinforcing loop.

LLMs are interesting and useful but the tail is wagging the dog because of path-dependent corruption arbitraging a fragile governance model. You can get a model trained on text corpora to balance nested delimiters via paged attention if you're willing to sell enough bonds, but you could also just do the parse with a PDA from the 60s and use the FLOPs for something useful.

We had it right: dial in an ever-growing set of tasks, opportunistically unify on durable generalities, put in the work.

Instead we asserted generality, lied about the numbers, and lit a trillion dollars on fire.

We've clearly got new capabilities, it's not a total write off, but God damn was this an expensive ways to spend five years making two years of progress.