This Lex Fridman podcast features Dylan Patel (SemiAnalysis) and Nathan Lambert (Allen Institute for AI) discussing the DeepSeek AI models, their implications, and the broader AI landscape. The conversation covers DeepSeek's model architecture, training costs, geopolitical aspects (US-China relations, export controls), the race to AGI, and the future of AI.
DeepSeek's Models: DeepSeek-V3 is a large language model, while DeepSeek-R1 is a reasoning model. DeepSeek-R1 is notable for its chain-of-thought reasoning, which is displayed in the output, making the model's thought process transparent. Both models are relatively "open," with DeepSeek-R1 having a permissive MIT license.
DeepSeek's Efficiency: DeepSeek achieved low training costs through a mixture-of-experts model and a novel multi-head latent attention mechanism. These techniques optimize parameter usage and memory during training and inference. They even went below the CUDA layer for further optimizations.
Geopolitical Implications: The DeepSeek moment highlights the geopolitical competition between the US and China in AI. US export controls on GPUs aim to limit China's AI development, particularly regarding AGI. However, China's industrial capacity and talent pool pose significant challenges to these restrictions. The situation could escalate to a cold war or even military conflict.
The Future of AI: The podcast discusses the ongoing race to AGI, with various companies investing heavily in large-scale data centers and advanced hardware. While chat applications are a current focus, the future lies in AI agents capable of performing complex, autonomous tasks. Open-source AI models present both opportunities and risks, including potential subversion or embedding of biases.
Cost of Inference: The cost of inference is significantly affected by the complexity of the task (e.g., reasoning vs. simple chat). Reasoning models, which require more compute at inference time, increase the cost proportionally to the length of the output.
