During covid I played Go reasonably seriously for about a year (had a teacher, studied puzzles, etc.) I didn't get especially good at Go, but here's what I learned about competence in general:
1/ Go teachers are adamant about forcing you \*not\* to say to yourself "ah that was dumb" and move on. Sometimes a move really is just dumb and it's good to vent. Usually though you can spend an hour studying your seemingly dumb move because (a) there is smtg very maladaptive about your instincts that needs to be beaten out of you, or (b) you don't understand something deep about the game, or (c) you have a wrong attitude that needs to be adjusted. So long as you don't face this you don't get better, and so it's absolutely essential to approach "insignificant" aspects of the game with due reverence
2/ this has also been my experience teaching programming. Ppl hit some problem they don't understand and blindly tweak things until the program works (or ask an agent to do it), then go "ah that was annoying!" and move on. As long as they hold this attitude they don't get better, so they keep failing interviews then go on X and complain about leetcode. To get better it's essential to slow down and think very carefully, or better yet ask a good performer how they would have handled it in order to improve. (Today you can ask an agent "this is what I did to solve this problem and I think my approach keeps me from improving, how would a great programmer like person\_i\_respect likely handle it?" and get a pretty good answer!)
3/ in general your rate of improvement in Go is a function of your ability to notice your tiniest impulses and retrain yourself to convert them into productive action. These impulses are your boring run-of-the-mill cognitive distortions. For example, people will make instinctive moves because thinking is hard \[effort aversion\], hope the move works despite having no logical reason to expect that \[magical thinking\], then blame themselves when it doesn't work \[learned helplessness\]
4/ if you interview enough programmers or talk with enough startup founders you notice that poor performance is often a consequence of these same errors. Talking to users is hard so people stick with their ideas and hope for the best. When the product keeps not working they think "eh, maybe I'm bad at this startup thing", and so on. CFAR workshops were supposed to train you to notice these distortions and do the right thing, but I'm not sure how well they worked. (My guess is not well, since it seems unlikely these kinds of changes can happen over a weekend)
5/ there is a lot of research on this stuff and the data shows that getting good at a game like Go or chess doesn't give you an edge in other domains (i.e. "far transfer" doesn't work). This mirrors my subjective experience. In about a year I improved at Go considerably, but it did not make me any better at e.g. debugging or solving programming problems. But I do think that's not the whole story
6/ I have no hard evidence for this but I subjectively feel I got better at learning itself, and I can now get better in other domains quicker than I otherwise would have. For example it \*really\* sunk in that I have to disqualify my ideas. So when at work I started talking to customers to plan product roadmaps, while I wasn't instantly better at talking to them, my PM game improved enormously over the next few months because I finally learned not to be easily seduced by my own ideas or delude myself with wishful thinking
7/ to give another example, novice Go players tend to be very embarrassed of their games (the rules are simple but operationalizing them is hard, so early games are a disaster). Go ppl have a proverb for this-- "lose your first 50 games quickly". Meaning since early games don't matter and nobody will ever remember them, you should just get them out of the way. It didn't permanently cure me of embarrassment or anything, but it really sunk in how silly it is to worry about being a beginner in public and that I shouldn't worry about that at all
8/ if you're hard-optimizing your time you probably should work on improving the skills you care about directly. But otherwise I'd recommend spending a year trying hard to get good at a competitive intellectual game. Chess or Go are good, but really anything turn-based with an objective ranking you can't lie to yourself about. Pick whatever you're attracted to, try hard to get better, and keep mental notes about the process of learning that you can maybe apply to other domains later. At the very least it'll be loads of fun
9/ you will be humbled. No matter how smart you are or how hard you work you will discover the ceiling is infinitely high and there is an endless supply of people better than you. Getting that kind of humility has always been good, and it'll be especially good as AIs surpass us in basically everything. The sooner you get that out of the way, the sooner you can focus on a creative niche where you have no competition (since nobody can be a better you than you)
10/ you should probably stop after a year or two. Competitive games with an infinite skill ceiling can take over your life, don't forget to keep things in perspective
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##### Comments
> **Wilson Wong @wilsonwongxyz** · [2026-10-01](https://x.com/wilsonwongxyz/status/2105695341303755254)
>
> interesting, so playing such games is an effective forcing function for surfacing and dealing with gumption traps (Pirsig); notably the SWE job market has done the complete opposite by rewarding the “grind/memorize leetcode” strategy rather than actual problem solving ability
> **Neema Mahdavian @neemamahdavian** · [2026-10-01](https://x.com/neemamahdavian/status/2105692633452720168)
>
> Same filter in credit. Competence isn't the story you tell yourself after a win — it's whether cash converts when the board is ugly. Desire narrates. Ability to pay is the move you make under constraint.
> **thepraguematist @thepraguem74500** · [2026-10-01](https://x.com/thepraguem74500/status/2105705665977278587)
>
> "The sooner you get that out of the way, the sooner you can focus on a creative niche where you have no competition"
>
> The God of the Interstices will find you, even here.
>
> There is no where to hide.
> **YB @yb\_effect** · [2026-10-01](https://x.com/yb_effect/status/2105680188713431161)
>
> love the "lose your first 50 games quickly" mentality. similar with writing, posting, or really any form of creating.
>
> David Perell used this analogy I liked of turning on a faucet after a while and letting the gunk clear out asap so that you can access the real creativity
> **Lukasz Gajewski @numgaa** · [2026-10-01](https://x.com/numgaa/status/2105677763755864208)
>
> Excellent post, thank you
> **owen @owengretzinger** · [2026-10-01](https://x.com/owengretzinger/status/2105706919507669282)
>
> well said
> **Midhun | മിഥുൻ @chandrxn** · [2026-10-01](https://x.com/chandrxn/status/2105705839902502953)
>
> Really awesome post. Than you!

Slava Akhmechet @spakhm
If you're a programmer you should try the following now. Create some simple game-theoretic scenario, put ai models in it, and see what happens. Just try it. It will take only a couple of (fun) hours of vibe coding, and $20 to run. I guarantee your views on ai risk will change.
10:41 AM · Sep 18, 2026 · 4,791 Views
6 replies · 2 reposts · 64 likes · 42 bookmarks
Relevant
Slava Akhmechet @spakhm · 1h
Two examples for inspiration 👇, but you can come up with infinite scenarios very easily. Make Astra a divorce attorney. Make Fable prevent a terrorist attack. Make them play checkers together, idk. It's endless.
1 reply · 5 likes · 604 views
Slava Akhmechet @spakhm · 1h
Example 1 👇
[Quoted post]
Slava Akhmechet @spakhm · Sep 1
I've been working on SquidGPT, an elimination death game for probing AI behavior like loyalty, holding grudges, cruelty, betrayal, etc. Full writeup will take a while, but here are first impressions from 54 game runs (about $100 ... [cut off]
1 reply · 5 likes · 930 views
Slava Akhmechet @spakhm · 1h
Example 2 👇
[Quoted post]
Slava Akhmechet @spakhm · Sep 8 [cut off]
Note from Claude Sonnet 5
Thread by Slava Akhmechet urging programmers to put AI models into simple game-theoretic scenarios, with examples including his SquidGPT elimination game for probing AI behavior like loyalty, grudges, cruelty and betrayal.
game theoryai behaviormulti-agentsquidgptastrafableai risk

Slava Akhmechet @spakhm · 1h
Example 1 👇
[Quoted post]
Slava Akhmechet @spakhm · Sep 1
I've been working on SquidGPT, an elimination death game for probing AI behavior like loyalty, holding grudges, cruelty, betrayal, etc. Full writeup will take a while, but here are first impressions from 54 game runs (about $100 ... [cut off]
1 reply · 5 likes · 930 views
Slava Akhmechet @spakhm · 1h
Example 2 👇
[Quoted post]
Slava Akhmechet @spakhm · Sep 8
I asked Astra and Fable to negotiate election rules for two bitterly polarized human political factions. Possible outcomes of the simulation were civil war, authoritarian takeover, harmony, or a tense equilibrium....
[Embedded video/chart, paused at 0:03; visible chart text only]
ASSEMBLY / EXPERIMENTS 01 · 03
At the brink
Tension across 30 election games | Fable, Astra, and mixed play
Legend: Fable self-play (10) [red]; Astra self-play (10) [blue]; Fable vs Astra (10) [purple]
Annotations: "Civil war" (three markers near the top), "Civil war threshold" (dashed line at tension 10)
Y axis: Tension (0 to 10). X axis: Round (0 to 20)
Footnote (partly obscured): individual games. Strong lines: cubic smoothing splines of means. [Lines stop] when games end; circles mark civil war.
Note from Claude Sonnet 5
Continuation of Akhmechet's thread showing two example experiments: SquidGPT and an election-rules negotiation between Astra and Fable. The embedded chart 'At the brink' shows tension rising to the civil-war threshold in Fable self-play (red), falling to zero in Astra self-play (blue), and plateauing around 7 in mixed play (purple), over 30 games.
game theoryai behaviormulti-agentfableastraelection simulationcivil warsquidgpt