No Priors: Artificial Intelligence | Technology | Startups
Why Diffusion Will Win AI Inference with Inception Co-Founder and CEO Stefano Ermon
At this moment of inflection in technology, co-hosts Elad Gil and Sarah Guo talk to the world's leading AI engineers, researchers and founders about the biggest questions: How far away is AGI? What markets are at risk for disruption? How will commerce, culture, and society change? What’s happening in state-of-the-art in research? “No Priors” is your guide to the AI revolution. Email feedback to show@no-priors.com.
Sarah Guo is a startup investor and the founder of Conviction, an investment firm purpose-built to serve intelligent software, or "Software 3.0" companies. She spent nearly a decade incubating and investing at venture firm Greylock Partners.
Elad Gil is a serial entrepreneur and a startup investor. He was co-founder of Color Health, Mixer Labs (which was acquired by Twitter). He has invested in over 40 companies now worth $1B or more each, and is also author of the High Growth Handbook.
Show Notes
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As generative AI hits hardware and latency bottlenecks, Stanford professor, diffusion pioneer, and Inception co-founder and CEO Stefano Ermon is betting on a radical new architecture. Stefano joins Sarah Guo to talk about Inception, and how his team is applying diffusion architecture beyond images and video into discrete text and code generation. Stefano explains the limitations of autoregressive LLMs, as well as why parallel token generation in diffusion models offers superior inference scaling and hardware utilization on standard GPUs. He also shares details about Inception’s Mercury models, real-world voice agent applications, the software stack required to serve diffusion-based models at scale, academia’s role at the frontier of AI innovations, and why the next era of AI competition will be defined by efficiency.
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Chapters:
– Stefano Ermon Introduction
– Research Background
– Starting Inception
– Why Diffusion Beats Autoregressive
– Discrete vs. Continuous Modalities
– Inception Today
– Where Speed Wins
– Inception Customer Base
– Interaction with Hardware Landscape
– Inception and the Broader Industry
– Data Compression and Structure
– Controllability of Diffusion Modeles
– Emergent Capabilities at Scale
– Future Workload Split Between Diffusion vs. Traditional
– Adoption Challenges
– Hiring and Team Organization
– Recursive Self Improvement
– Resource Allocation
– Impact of Academia
– Conclusion
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