[ankit219]

(Author here)

I address that in part right there itself. Programming has parts like chess (ie bounded) which is what people assume to be actual work. Understanding future requiremnts / stakeholder incentives is part of the work which LLMs dont do well.

> many domains are chess-like in their technical core but become poker-like in their operational context.

This applies to programming too.

[notnullorvoid]

My bad, re-read that part and it's definitely clear. Probably was skimming by the time I got to the section and didn't parse it.

[pixl97]

>Programming has parts like chess (ie bounded)

The number of legal possible boards in chess is somewhere around 10^44 based on current calculation. That's with 32 chess pieces and their rules.

The number of possible permutations in an application, especially anything allowing turing completeness is far larger than all possible entropy states in the visible universe.

[ankit219]

Bounded domains require scaling reasoning/compute. Two separate scenarios - one where you have hidden information, other where you have high number of combinations. Reasoning works in second case because it narrows the search space. Eg: a doctor trying to diagnose a patient is just looking at number of possibilities. If not today, when we scale it up, a model will be able to arrive at the right answer. Same goes with Math, the variance or branching for any given problem is very high. But LLMs are good at it. and getting better. A negotiation is not a high variance thing, and low number of combinations, but llms would be repeated bad at it.