Originally published at vinpatel.com
Everyone reads Garry Tan's push for US open-weight labs to distill frontier models as cheerleading. It's an admission: the word "too" means everyone else already does this, and the US doesn't.
The tempo of that admission matters more than the ask itself.
In 2023, the open-weight movement treated distillation as a workaround for labs without frontier-scale compute — a way to compress a bigger model's behavior into something smaller you could actually run.
By 2025, distillation had stopped being a workaround and become the default strategy. Compressing a frontier reasoning model into a smaller student turned "train small on a big teacher" into the playbook that open-weight labs without a frontier training budget started copying wholesale.
On September 11, 2026, Garry Tan, Y Combinator's president, told US open-weight labs to do the same thing everyone else already had. Not as encouragement. As a correction.
The through-line: distillation went from a cost-saving trick to the only viable lane for labs that can't afford to pretrain frontier models from scratch. Everyone building outside that frontier tier — in the US and out of it — is now choosing between two paths. Raise enough capital to compete on pretraining, or admit you're in the distillation business and get good at it fast. Tan's post reads like someone watching the second group get outbuilt by labs elsewhere that made peace with that choice earlier.
That's the part worth sitting with if you're shipping on top of an open-weight model right now. The lab whose weights you depend on isn't really choosing between "open" and "closed." It's choosing between distilling someone else's frontier model well, or falling behind labs that already did. Which strategy your model's lab picked is now a more useful signal than its benchmark scores.
Here's the falsifiable version: if Tan's push actually moves anyone, expect at least one US open-weight lab to publicly announce a distilled model built on a named frontier teacher before the end of 2026. If none does, the "too" in his post was accurate — and stayed that way.
For the broader arc of how the field got from raw scaling to this kind of triage, the three years that changed everything is worth the read. And for a sense of how uneven "frontier" performance actually is once you stop trusting the benchmark, this look at where a flagship model actually fails is the companion piece.
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