[stouset]

When chess engines were first developed, they were strictly worse than the best humans. After many years of development, they became helpful to even the best humans even though they were still beatable (1985–1997). Eventually they caught up and surpassed humans but the combination of human and computer was better than either alone (~1997–2007). Since then, humans have been more or less obsoleted in the game of chess.

Five years ago we were at Stage 1 with LLMs with regard to knowledge work. A few years later we hit Stage 2. We are currently somewhere between Stage 2 and Stage 3 for an extremely high percentage of knowledge work. Stage 4 will come, and I would wager it's sooner rather than later.

[MITSardine]

There's a major difference between chess and scientific research: setting the objectives is itself part of the work.

In chess, there's a clear goal: beat the game according to this set of unambiguous rules.

In science, the goals are much more diffuse, and setting those in the first place is what makes a scientist more or less successful, not so much technical ability. It's a very hierarchical field where permanent researchers direct staff (postdocs, research scientists/engineers), direct grad students. And it's at the bottom of the pyramid where the technical ability is the most relevant/rewarded.

Research is very much a social game, and I think replacing it with something run by LLMs (or other automatic process) is much more than a technical challenge.

[bluecalm]

The evolution was also interesting: first the engines were amazing tactically but pretty bad strategically so humans could guide them. With new NN based engines they were amazing strategically but they sucked tactically (first versions of Leela Chess Zero). Today they closed the gap and are amazing at both strategy and tactics and there is nothing humans can contribute anymore - all that is left is to just watch and learn.

[TGower]

With a chess engine, you could ask any practitioner in the 90's what it would take to achieve "Stage 4" and they could estimate it quite accurately as a function of FLOPs and memory bandwidth. It's worth keeping in mind just how little we understand about LLM capability scaling. Ask 10 different AI researchers when we will get to Stage 4 for something like programming and you'll get wild guesses or an honest "we don't know".

[stouset]

That is not what happened with chess engines. We didn’t just throw better hardware at it, we found new algorithms, improved the accuracy and performance of our position evaluation functions, discovered more efficient data structures, etc.

People have been downplaying LLMs since the first AI-generated buzzword garbage scientific paper made its way past peer review and into publication. And yet they keep getting better and better to the point where people are quite literally building projects with shockingly little human supervision.

By all means, keep betting against them.

[baq]

Chess grandmasters are living proof that it’s possible to reach grandmaster level in chess on 20W of compute. We’ve got orders of magnitude of optimizations to discover in LLMs and/or future architectures, both software and hardware and with the amount of progress we’ve got basically every month those ten people will answer ‘we don’t know, but it won’t be too long’. Of course they may be wrong, but the trend line is clear; Moore’s law faced similar issues and they were successively overcome for half a century.

IOW respect the trend line.

[blt]

And their predictions about Go were wrong, because they thought the algorithm would forever be α-β pruning with a weak value heuristic

[NitpickLawyer]

> With a chess engine, you could ask any practitioner in the 90's what it would take to achieve "Stage 4" and they could estimate it quite accurately as a function of FLOPs and memory bandwidth.

And the same practitioners said right after deep blue that go is NEVER gonna happen. Too large. The search space is just not computable. We'll never do it. And yeeeet...

[guluarte]

so we are going back to physical labor then

[empath75]

We are already at stage 3 for software development and arguably step 4

[zarzavat]

We are at level 2.5 for software development, IMO. There is a clear skill gap between experienced humans and LLMs when it comes to writing maintainable, robust, concise and performant code and balancing those concerns.

The LLMs are very fast but the code they generate is low quality. Their comprehension of the code is usually good but sometimes they have a weightfart and miss some obvious detail and need to be put on the right path again. This makes them good for non-experienced humans who want to write code and for experienced humans who want to save time on easy tasks.

[empath75]

> The LLMs are very fast but the code they generate is low quality.

I think the latest generation of LLM with claude code is not low quality. It's better than the code that pretty much every dev on our team can do outside of very narrow edge cases.