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The proliferation of models and harnesses times individual work styles, preferences and use cases creates an exponentially large space, making it a futile endeavor to diagnose the reason for your experience (Opus vs Sonnet, Claude Vs GPT, Claude Code vs Codex? Tool use configuration? Sun spots?) and give any advice. And besides, why engage in big-picture futuristic forecasting? Frontier labs should shill their product on their own dime, and their thesis will be proven right or wrong soon enough.
There's a clear object-level flaw in your writeup, however, and ironically it's the exact sort of confident slop we've come to associate with LLMs when they come short of the standard of human reasoning over novel context. This isn't to dunk: the standard, see, is very high, humans often need conscious effort to match it. That models can ever touch it is miraculous enough.
I mean this part:
You've played the games and you've thought of making it an argument, but you weren't curious enough to read on the actual scoring rule. It's contentious enough that Chollet has to make excuses on Hackernews.
To be clear:
So if the human baseline is 10 actions:
A score of 0.5% (0.005) does not mean that the best AI only solves 1/200th of the problems. Same score is achieved by it being 14.5 times less sample-efficient. But should we care? How much of an opportunity cost comes with wasted AI samples? A white collar professional in the US earns a Claude Max (x10) subscription in 1-2 hours; Claude Max will generate ≈2 OOMs more tokens in a month than said professional can; even if they're 1000 times less useful per token, that's a massive bargain. We already routinely afford AI that's this inefficient. It'll be more efficient soon, though.
Which is not to say it'll be cheaper. Consider that car rentals in American cities go for roughly one monthly Claude Max subscription a day. Sure, the US is a tough place and a car preserves you from being stabbed in the neck on public transport, but we can quantify the millimorts and assign a cost to them; after that, does a car for a day provide as much economic value as a fully exploited Claude Max for a month – tens of millions of Opus-grade output tokens? Seeing how fast OpenAI and especially Anthropic revenues have been growing, what do you think will be their asking price when all dumping from also-rans is rendered irrelevant?
Right now the cost of tokens is suppressed by the lingering user base acquisition phase, hardware gains, rapid competitive model churn and, more importantly, by the threat of cheap open weights models, mostly Chinese, increasingly Nvidia. Should those fall far enough behind, together with other minor competitors, and we enter the territory of a Frontier Cartel, $1000/month subscriptions as baseline expectation. (This, fyi, is implicitly Dario Amodei's theory of victory – see him invoking Cournot equilibrium on Dwarkesh's podcast.) I pray we don't. But people would pay for it. These systems aren't a joking matter, being shut out of them will be quite literally existentially threatening for many businesses soon enough.
The best model+harness scores 36%, by the way. But I'm more impressed by the 12.58% scored by a 4-layer CNN called StochasticGoose. Read this piece, it contains some pretty neat analysis.
Chollet's evals are neat too, but he's pushing a narrative against machine superintelligence from within Deep Learning paradigm, and he's getting embarrassingly biased, with more and more abstract justifications for denying what looks like inevitability.
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