> There was never any plausible explanation for why this wouldn’t happen.

What a nice post hoc revision of history. Distillation is still an active area of research, that you can distill models as easily as you can it genuinely interesting and absolutely not something that was taken for granted even 12 months ago.

Even 6 months ago this idea that 'using model outputs as training examples' was listed as the reason that all models would fail in the near future due to some spooky circular training catastrophe.

Don't pretend like this was so obvious.

I think you’re being overly combative. It’s intuitively quite obvious that it’s incredibly easy to implement and the circular training catastrophe was only ever a conjecture. It’s kind of like releasing a crypto primitive without knowing a proof. Like… maybe it works, but you can’t assume that just because you don’t know how to break it. You have to remember that 100s of billions of enterprise valuation rely on frontier models being moats. The burden of proof is on those raising valuations assuming they will capture the full market.

I agree that hindsight is doing work here, but DeepSeek R1 from Jan 2025 seemed to heavily leverage distillation, and 18 months is an eternity in this climate.