Interconnects
Nathan Lambert on post-training and open models; rigorous technical judgment
11:44Meta and Anthropic at the same valuation: one is bound to be vastly wrong
Meta's current valuation sits under $2 trillion; Anthropic, heading toward IPO, prices at comparable levels. One builds profit from advertising; the other from AI models alone. The author argues one of these valuations will face substantial revision.
9:12Most cyberattacks on record stem from closed models, not open-source ones
Public evidence shows most cyberattacks to date stem from closed models, not open-source ones; by the cyber-risk logic used to justify banning open models, you must also ban closed models' public APIs—otherwise, you only widen the offense-defense gap.
1:05:29The US-China AI gap is six to eight months: complete distillation bans won't slow much
Epoch AI researcher JS Denain argues the leading-model capability gap between the US and China is only six to eight months, and even under a complete distillation ban, the gap would expand merely from six months to eight or nine months within half a year.
17:59Chinese open-weight models lead, and distillation explains only one to two months of the gap
Chinese models lead across the board on downloads and benchmarks, but distillation explains only one to two months of the gap; the real reasons are release cadence and task focus, and US companies are already building products on Chinese models.
9:32True RSI Isn't Here Yet; the Bottleneck Is Peak Intelligence, Not Compute
In-lab automation is concentrated on measurable tasks like software engineering and log monitoring, while scaling laws demand exponential compute for linear intelligence gains—RSI is more likely to make models cheaper than to make intelligence explode.
10:59One resignation turned the embers of AI fear into a wildfire
AI risk discussion was pushed to an extreme by a single resignation, but the thing actually worth worrying about isn't extinction — it's that the labs haven't even hardened their own infrastructure.
8:31AI Is a Rounding Error for Ordinary People: Diffusion Takes Fifty Years, Backlash Won't Wait
The Industrial Revolution gave ordinary people cheap clothes and sewing machines; AI so far gives them fun images and better search — the benefits are too indirect, and the political backlash is coming too fast.
12:32AI Writing Has Stalled: Long-Form Nonfiction Is the Touchstone of Model Capability
Models are improving slowly at long-form nonfiction writing, exposing a defect in their ability to organize knowledge. This is more alarming than progress in code and math, because it bears on whether AI can solve open scientific problems.
49:05Open Source Catching Closed Source Isn't About Distillation: Nobody Knows How to Make Distillation Useful
Nathan Lambert dismantles the narrative that Chinese open models are just distilled: using Fable/GPT 5.6 as a judge during RL doesn't pencil out on cost, and even if you were handed the strongest model's reasoning traces for free, the field still doesn't know how to turn them into capability gains.
20:03Open weights are only 3-5 months behind closed — and that time gap is the entire safety margin
Kimi K3 compresses the open-versus-closed gap from a contested 6-9 months to 3-5 months, and that gap is the only risk buffer there is — heavy-handed regulation of open weights would only delay the inevitable while voiding the buffer itself.