This interview podcast features Lex Fridman discussing China's DeepSeek AI models with Dylan Patel and Nathan Lambert. The conversation begins with an explanation of DeepSeek V3 and R1, including their open-weight nature and the nuances of open-source AI licensing. The discussion then delves into the technical aspects of pre-training and post-training techniques used in these models, highlighting innovations like mixture-of-experts and latent attention that contribute to their efficiency. A comparison of DeepSeek's models with OpenAI's models is made, focusing on performance, cost, and the user experience, including the unique chain-of-thought reasoning showcased by DeepSeek R1. Finally, the geopolitical implications of DeepSeek's advancements and the role of export controls are analyzed, along with the future of AI development and the potential for a technological cold war. For example, the podcast details how DeepSeek R1 achieved significantly lower training and inference costs compared to OpenAI's models through architectural innovations and efficient resource utilization.
Takeaways
Outlines
Q & A
Preview
How to Get Rich: Every EpisodeNaval
#459 – DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast | Podwise
