Go is an interesting model organism for disempowerment because its practice has both technical and artistic/cultural components. Go AI is indeed disanalogous to coding LLMs from a technical perspective: the Go AI has vastly superhuman competence and the LLM does not. However, as you pointed out, Leela zero and other engines don’t communicate off the board; moreover, their incredible strength actually makes them worse as game and review partners. This results in human-AI Go interactions that are usually hollow and vapid with respect to human-human ones, even if you factor out any cheating. AI is therefore bad at the cultural practice of Go and this shortcoming manifests in similar ways to those of old (maybe even current) coding assistants and LLMs as writers. People gravitate towards using the AI in all of these settings because it does a superficially “good enough” job at replacing them. However, in reality this offloading of cognition to the AI is illegitimate. In your example of coding assistants, the AI is not actually “good enough” on a technical level. At my school, the problem was that the valuable social and cultural exchange between players could not be replaced by their GPUs. Delegating one’s writing to LLMs suffers a bit from both of these problems.
As you pointed out, one antidote to this tendency of cognitive delegation involves being willing to languish in confusion: to continue to think even when it is uncomfortable or frustrating. Sufficiently responsible/thoughtful/robust people can thus benefit from AI usage, which is indeed likely easier with LLMs than with Go AI due to better communication. Relatedly, I think a path to an empowered society would involve system designs that counterbalance the “congitive offloading” tendency.
The patterns of disempowerment described above apply less strongly to professional players because they are selected to enjoy thinking about the game[1]. That’s why I’m a little bit skeptical of how well your example of Chess players floundering straight out of prep applies[2]. Chess players outsource much of their cognition to their memory (this was true before AI), which is a sensible competitive move. I suspect the occasional floundering comes from them dancing on the Pareto frontier between “amount of stuff memorised” and “ability to execute on stuff you memorised” which probably trade off against each other.
though it seems the case for chess play improving is stronger than for go, perhaps?
This is probably downstream of memorisation being relevant in Chess but not in Go.
Perhaps a more important question is, do you plan on writing more history of X posts?
Could you say more on what archetype of post you have in mind? I don’t think I can write a post quite like this one on many other topics because the narrative relies on lived experience. I am slowly cooking a “history of behavioural decision theory”, but that feels rather distinct.
Many AI users at my Go school were stronger (1 Dan to 4 Dan EGF) amateurs who were struggling to keep improving but who were still emotionally invested in watching their rank increase.
Using a computer to learn Chess or Go is like using a calculator to help you learn arithmetic. You can check your answers, but you can’t directly see if your underlying algorithm for generating the answers is any good—it’s like a textbook that has the answers but not the solutions to problems in the back of the book.
Go is an interesting model organism for disempowerment because its practice has both technical and artistic/cultural components. Go AI is indeed disanalogous to coding LLMs from a technical perspective: the Go AI has vastly superhuman competence and the LLM does not. However, as you pointed out, Leela zero and other engines don’t communicate off the board; moreover, their incredible strength actually makes them worse as game and review partners. This results in human-AI Go interactions that are usually hollow and vapid with respect to human-human ones, even if you factor out any cheating. AI is therefore bad at the cultural practice of Go and this shortcoming manifests in similar ways to those of old (maybe even current) coding assistants and LLMs as writers. People gravitate towards using the AI in all of these settings because it does a superficially “good enough” job at replacing them. However, in reality this offloading of cognition to the AI is illegitimate. In your example of coding assistants, the AI is not actually “good enough” on a technical level. At my school, the problem was that the valuable social and cultural exchange between players could not be replaced by their GPUs. Delegating one’s writing to LLMs suffers a bit from both of these problems.
As you pointed out, one antidote to this tendency of cognitive delegation involves being willing to languish in confusion: to continue to think even when it is uncomfortable or frustrating. Sufficiently responsible/thoughtful/robust people can thus benefit from AI usage, which is indeed likely easier with LLMs than with Go AI due to better communication. Relatedly, I think a path to an empowered society would involve system designs that counterbalance the “congitive offloading” tendency.
The patterns of disempowerment described above apply less strongly to professional players because they are selected to enjoy thinking about the game[1]. That’s why I’m a little bit skeptical of how well your example of Chess players floundering straight out of prep applies[2]. Chess players outsource much of their cognition to their memory (this was true before AI), which is a sensible competitive move. I suspect the occasional floundering comes from them dancing on the Pareto frontier between “amount of stuff memorised” and “ability to execute on stuff you memorised” which probably trade off against each other.
This is probably downstream of memorisation being relevant in Chess but not in Go.
Could you say more on what archetype of post you have in mind? I don’t think I can write a post quite like this one on many other topics because the narrative relies on lived experience. I am slowly cooking a “history of behavioural decision theory”, but that feels rather distinct.
Many AI users at my Go school were stronger (1 Dan to 4 Dan EGF) amateurs who were struggling to keep improving but who were still emotionally invested in watching their rank increase.
amateur Chess players were always disempowered with respect to their own prep anyway, though AI probably exacerbates this
Using a computer to learn Chess or Go is like using a calculator to help you learn arithmetic. You can check your answers, but you can’t directly see if your underlying algorithm for generating the answers is any good—it’s like a textbook that has the answers but not the solutions to problems in the back of the book.