causal claim
Ermon argues conditionally that if a model's perplexity decreases, it can achieve the corresponding compression level, indicating that structure exists in the data and the model has uncovered it.
Stefano Ermon · Why Diffusion Will Win AI Inference with Inception Co-Founder and CEO Stefano Ermon · 18 Sep 2026
In their words
The transcript segments this claim was taken from
“and so to the extent you know the numbers don't lie to the extent that you are able to drive different”
“But if it flexes down,”
“then it means that you can actually build like a compression scheme and that will get you that sort of like level of compression.”
“And so the structure must be there and the model must have been able to uncover it.”24:07Report a misattribution
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