Jeff Morgan
Showing 1–2 of 2 interviews.
- Y Combinator1h 51m
Why Inference Matters 12x More Than Training | YC Paper Club
Dan Fu, Jeff Morgan, Joe Fioti, Lianmin Zheng, Francois
DeepSeek, Together AI, and other industry leaders are transforming AI economics by shifting focus from training to inference optimization, where compute costs now dominate due to high token volumes and long model lifespans. Through innovations in kernel compilers, disaggregated system architectures, and model-specific techniques like Mixture of Experts and quantization, providers are achieving gross margins near 80% while delivering open models at a fraction of the cost of proprietary alternatives. Strategic advancements in runtime scheduling, KV cache retention, and hardware-at-compiler levels address critical bottlenecks in decode throughput, enabling robust, low-latency inference across hybrid local-cloud environments.
- Sequoia Capital20 min
Will Open Source AI Overtake Closed Models? Ft. Olama, Fireworks and Open Router
Jeff Morgan, Dmytro Dzhulgakov, Alex Atallah, Dima, Matthew Walkerman
Industry leaders from Ollama, Fireworks, and OpenRouter advocate for a balanced future where open-source and closed-source models each command 50% of inference tokens, driven by the strategic advantages of decentralized innovation and lower capital barriers. While current open adoption sits at 30%, panelists predict this share will expand as foundational models improve through reinforcement learning and enterprises prioritize ownership of proprietary fine-tuned artifacts. This shift relies critically on sustainable decentralized inference infrastructure to compete against the entrenched advantages of large, closed-source providers.