[mattnewton]

I think algorithms is a unique limit because it changes how much data or compute you need. For instance, we probably have the algorithms we need to brute force solving more problems today, but they require infeasible compute or data. We can almost certainly train a new 10T parameter mixture of experts that continues to make progress in benchmarks, but it will cost so much to train and be completely undeployable with today’s chips, data, and algorithms.

So I think the truth is likely we are both compute limited and we need better algorithms.

[aaronblohowiak]

There are a few "hints" that suggest to me algorithms will bear a lot more fruit than compute (in terms of flops):

1) there already exist very efficient algorithms for rigorous problems that LLMs perform terribly at! 2) learning is too slow and is largely offline 3) "llms aren't world models"

[PUSH_AX]

General intelligence exists in this world, the inability to transfer it to a machine does seem like an algorithm problem. When it’s here we don’t even know if it will be an llm, no one knows the computer requirements.