Stefano Ermon
@StefanoErmon · Nov 6, 2025
When we began applying diffusion to language in my lab at Stanford, many doubted it could work.
That research became Mercury diffusion LLM: 10X faster, more efficient, and now the foundation of @_inception_ai.
Proud to raise $50M with support from top investors.
Inception@_inception_ai· Nov 6, 2025Today’s LLMs are painfully slow and expensive. They are autoregressive and spit out words sequentially. One. At. A. Time.
Our dLLMs generate text in parallel, delivering answers up to 10X faster. Now we’ve raised $50M to scale them.
Full story from @russellbrandom in
Elon Musk
@elonmusk
Diffusion will obviously work on any bitstream.
With text, since humans read from first word to last, there is just the question of whether the delay to first sentence for diffusion is worth it.
That said, the vast majority of AI workload will be video understanding and generation, so good chance diffusion is the biggest winner overall.
Also means that the ratio of compute to memory bandwidth will increase.
11:47 AM · November 7, 2025 · 583.4K views
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