@adlrocha - AI inequality: from GPU-poor to token-poor
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@adlrocha - AI inequality: from GPU-poor to token-poor<br>Fable’s release and on strengthening the case for AI independence<br>adlrocha<br>Jun 14, 2026
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Last week I ended this newsletter questioning if the people inside the labs actually see the same end-of-cycle signals the rest of us are reading from the outside, or are they rushing their IPOs because they can see something coming that the market hasn’t priced in yet.<br>This week, Anthropic offered something resembling an answer, at least from a technical standpoint. Fable is here, and it reinforces my argument from last week: model technical capabilities are not actually plateauing and the winter will come from a shortage of funds and adoption.
What Fable actually is…
Fable 5 is the first model in Anthropic’s new Claude 5 family, specifically the first model in a new tier they’re calling Mythos-class (whatever that means), which sits above Opus in capability. The key thing to understand about the release is that Fable 5 and Mythos 5 are essentially the same underlying model. The difference in Fable is not its underlying architecture, but its embedded guardrails.<br>On benchmarks, Fable tops Cognition’s FrontierCode evaluation for coding, is the first model to break 90% on Anthropic’s core analytics benchmarks (a ten-point leap over Opus) and leads Hebbia’s senior-level finance reasoning evaluation. In biology, it accelerated protein design by roughly 10x, generated 9 of 14 strong drug candidates in a molecular design task, and produced scientific hypotheses that domain experts preferred over Opus-class outputs about 80% of the time. Fable also completed Pokémon FireRed using only vision input, which is either impressive or unsettling depending on your sense of humour (it took me a few dozen hours to finish it when I was a kid, and to get that legendary Pokémon I wanted. Spoiler alert: it was Zapdos). In short, this model is a beast!
But obviously, this beast is expensive: $10 per million input tokens, $50 per million output tokens. Less than half the price of Mythos Preview, but still a price that I don’t know if I would pay for my daily tasks.<br>Ok, that’s Anthropic’s PR machine, but what does the crowd say about this model? One of the people in the community that is trying to solve a hard problem in computer science and tries to get the most out of these models is Victor Taelin (who has made an appearance on this newsletter a few times already). He is building the HVM programming language, built on interaction networks. He is using agents extensively for his work (I highly recommend following how he journals it in his X account).<br>He’d already thrown everything at the problem before: a fleet of 32 GPT-5 agents running for 20 hours each, then Opus 4.8 and GPT-5.5 optimising for 8 hours. The best result was a 6–34% speedup, and the code quality had deteriorated after each iteration and he had to clean things up manually (as described here). Then he asked Fable.<br>Two hours later: a 1,770% speedup in one case, over 100% in four others, 22% on average. He immediately assumed it was hardcoding the benchmarks, a reflex he calls ‘GPT trauma’ 😆 . So he decided to dig deep into the implementation to confirm it. What Fable had found was that HVM5 was wasting time garbage-collecting unused branches of pattern-match nodes. Taelin had already optimised this for static matches, but not dynamic ones. Fable figured out how to do it for the dynamic case and implemented it correctly.<br>Then, as he was preparing to audit the solution, Fable interrupted him to report a bug in the code Taelin himself had written, a subtle pointer aliasing error in the garbage collection logic that was so specific, Taelin estimated he’d have needed hours or days to find it himself, if he ever had. Fable found it as a side note, while finishing the optimisation.<br>His dramatic conclusion was: “this isn’t about Anthropic or OpenAI, this is about our collective future as a species.”<br>So here’s the answer to my capability question from last week. The technology is still accelerating. This confirms that the AI winter framing I offered last week was about diffusion and adoption, and about companies that hadn’t yet learned what to do with the tools is feasible. Fable clarifies one thing on this framing (that may make this winter more dramatic): the ceiling may still be going up fast.
… and why is controversial
This release came with terms and controversy.<br>The most discussed one was that Fable will silently limit its own capabilities when it detects you’re using it for frontier AI development. Not an explicit refusal, i.e. you won’t see an error message. The model may quietly reduce its effectiveness through prompt modification, steering vectors, or fine-tuning adjustments. The list of affected work includes: building large-model pretraining pipelines, designing data pipelines for training frontier LLMs,...