Austen Allred Lists Bottlenecks Keeping AI From Self-Training
Austen Allred shares a reading list explaining why AI models cannot yet train themselves, pointing to bottlenecks in RL environments, evaluation, verifiers and a shortage of human text data. The list includes links on RL environment costs of $20k to $300k each and benchmarks like SWE-bench and OSWorld.
Original post · 2 min read
They theoretically can, and kind of are, but they don’t have the data/evals/gyms required to do so.
A short reading list:
Bottleneck is the environment, not compute
medium.com/@shuchaobi/ais-next-bottleneck-isn-…
RL envs cost real money ($20k–$300k/env, and this is for simulated ones which are just kinda crappy IMO)
epoch.ai/gradient-updates/state-of-rl-envs
We’re running out of human text
epoch.ai/publications/will-we-run-out-of-data-…
Eval is the bottleneck
ysymyth.github.io/The-Second-Half/
Verifier’s law
jasonwei.net/blog/asymmetry-of-verification-an…
What labs buy: Foody on RL envs
youtube.com/watch?v=a00xIn5kwhM
Economy as RL environment machine
mercor.com/blog/the-economy-will-become-an-rl-…
APEX-Agents generalization
mercor.com/blog/generalization-results-from-tr…
Etna: ~$1B/yr on external data, supply-constrained
x.com/hannahhaina/status/2090519081279705359
Surge Tuesday (can it get through a workday?)
surgehq.ai/blog/tuesday-frontier-work-index
Dario: task/process distribution, not more web text
dwarkesh.com/p/dario-amodei-2
OSWorld 2.0 (~20% on long workflows)
osworld-v2.xlang.ai/
arxiv.org/abs/2606.29537
SWE-bench = ticket + repo + tests
swebench.com
Karpathy: sucking supervision through a straw
dwarkesh.com/p/andrej-karpathy
Ilya: peak data / one internet
reuters.com/technology/artificial-intelligence…
Robert Sterling @RobertMSterlingMight be a dumb question, but as we reach AGI and AI becomes smarter than humans, and as the frontier labs compete for market share in a winner-takes-all industry, what’s to stop them from letting their AI models program their own updates and recursively self-improve?
And what does that mean for us, the humans now watching from the sidelines as AI models become more intelligent, more powerful, and less comprehensible to us, at rates that accelerate continuously, not just month by month or day by day, but millisecond by millisecond?
At that point, how do we even understand the inner workings …



