Interesting comparison, thanks for sharing! It reminds me of this post about how machine learning and encryption have some fundamental similarities: https://reiner.org/neural-net-ciphers
> I can certainly imagine LLMs taking a similar path.
Maybe it's useful to think about what fundamental differences could contribute to LLMs taking a very different path. What comes to mind is the scaling hypothesis, implying that the best LLMs will require enormous capital investment.
That seems largely incompatible with open source barring a fundamental change. There's open weights, but I can't think of a clean historical analogy there and find it extremely difficult to even guess how the future will go
My guess is that they liked the status quo with Project Glasswing and didn't want Fable to be public, especially if anyone is jailbreaking it into Mythos and using it for cyber
But then it backfired spectacularly and now it seems they can't use Mythos currently
It's certainly worse news for Anthropic than other labs since it's not completely random, and there's people in the administration (e.g. David Sacks) who don't like Anthropic -- perhaps seeing them as an enemy
Seems like estimates are that 70-85% of their revenue comes from API usage/pricing, so some users switching from Opus to Fable for that would've had a big impact
Then there's people switching from GPT 5.5 or upgrading their subscriptions, and Fable being scheduled for removal from subscriptions on the 23rd
Fable 5 on medium is amazing. It's handling everything I throw at it
I had _one_ instance where for some obscure reason it decided to fall back to Opus 4.8 and Opus IMMEDIATELY fucked it up and implemented a super obvious feature in a slightly-wrong way.
I find high+ unusable, it's way too slow and "thorough" on 99% of mundane task.
Sure it's better at vibecoding whole tasks, it's clearly good at it, but give it a simple one, and it will still do way more than needed.
It's way too fixated on validating even the simplest things, I find it an unproductive model unless you're implementing whole tasks and doing other things in the meantime.
Why are you deploying a bleeding edge, incredibly expensive, model to do the simplest things? Use Sonnet, hell, use Haiku, they'll get the job done and won't set fire to several rainforests in order to achieve the task.
To assess where a event betting platform has placed their lines is in no way useful here. Perhaps you are only fishing for people to pile onto the other side of a bet you made or helping you to feel well about this bet you chose to make which would be an inappropriate use of this platform.
I'm on the $100/m plan and used $300 at API billing yesterday (according to ccusage)
Seems like one session is >$100 and I can get 10 full sessions per week
The $200/m plan is supposed to be 4x that in usage, so with 2 of those you could use 4*2*100*10=$8000 in just a week
Using Simon's numbers here as a bare minimum https://simonwillison.net/2026/May/27/product-market-fit/#en... you'd get 1200*4*2=$9600 a month
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