AI Czar David Sacks Explains the DeepSeek Freak Out

Summary

The transcript discusses the rise of a Chinese AI company, Deep Seek, that surprised the industry by releasing an open-source reasoning model at a fraction of the cost of competitors like OpenAI. The conversation highlights the differences in AI model types, with reasoning models being more complex and efficient. Deep Seek’s cost-effective approach, fueled by innovative workarounds and necessity-driven solutions, challenges Western AI giants. The conversation also touches on broader implications, suggesting that value creation in AI may shift upstream or in other sectors as AI technology becomes commoditized.

Takeaways

• 😀 The speaker highlights how the job allows them to converse with key figures in the AI field, offering a unique perspective on AI developments.
• 😀 The release of a new AI model by a Chinese company caused a global news stir, drawing attention due to the intersection of China vs. US competition and open-source vs. closed-source debates.
• 😀 A large part of the attention around the new model stems from its open-source nature, which contrasts with proprietary models like OpenAI's.
• 😀 The emergence of reasoning models, such as 01, represents a significant shift in AI development, moving beyond basic LLM models to more complex problem-solving models using Chain of Thought.
• 😀 OpenAI was the first to release a reasoning model, with other companies like Google and Anthropic working on similar models, but Deep Seek was the second to release a public version.
• 😀 Deep Seek's open-source release and affordable pricing (12th of the cost of competitors) drew significant attention and changed expectations about China's AI capabilities.
• 😀 The cost of training the model, claimed to be $6 million, has been widely debated. Comparing costs is difficult due to differences in what is included (e.g., hardware, R&D costs).
• 😀 The speaker stresses the importance of comparing costs for the final training runs rather than total R&D expenses, which may include hardware purchases and experiments over time.
• 😀 Deep Seek's approach involved clever innovations to reduce costs, such as creating a new algorithm for reinforcement learning and bypassing proprietary software like CUDA in favor of PTX.
• 😀 The speaker suggests that constraints can lead to innovation, drawing parallels between Deep Seek's success and the value of operating within limitations to foster creative solutions.

Q & A

What makes Deep Seek's AI model launch surprising?

The surprise lies in Deep Seek being a Chinese company, which contrasts with the general expectation that the next big AI breakthrough would come from American companies. Additionally, Deep Seek's release of an open-source reasoning model at a fraction of the cost compared to U.S. companies caught attention.

What are reasoning models in AI, and how are they different from traditional models?

Reasoning models, like Deep Seek's R1, perform tasks through a step-by-step approach, solving complex problems by breaking them into smaller problems. Unlike traditional large language models (LLMs), they don’t provide immediate answers but work through processes. These models utilize reinforcement learning rather than pre-training.

How does the cost of Deep Seek's model compare to OpenAI's?

While Deep Seek reportedly trained their model for $6 million, this figure is misleading when compared to the full operational costs of U.S. companies. OpenAI’s full costs, including R&D, hardware, and previous model training, are in the tens of millions, making a direct comparison between the costs of training runs inaccurate.

What role does Deep Seek's open-source approach play in its success?

Deep Seek’s decision to open-source their model has contributed to significant attention. This contrasts with the more closed-source approaches of companies like OpenAI, creating excitement around the idea of freely accessible AI and raising the stakes in the open-source vs. closed-source AI debate.

What is the controversy around the reported $6 million cost for Deep Seek's training run?

The $6 million figure likely underestimates the actual costs involved. The true cost of AI development includes hardware, years of research, and various previous training runs. This number, if accurate, pertains only to the final training run, not the overall development, making the comparison to a billion-dollar figure from U.S. companies misleading.

What is the significance of Deep Seek's hardware setup?

Deep Seek reportedly has a significant hardware infrastructure, including 50,000 Hoppers and 10,000 H100s. This setup, valued at over a billion dollars, was crucial in training their models, challenging the notion that they achieved their results with minimal investment.

How did Deep Seek overcome limitations in compute power during model development?

Deep Seek developed innovative solutions to work around their hardware constraints. They created a new reinforcement learning algorithm (GRPO) that uses less memory and is highly efficient, and bypassed Nvidia's Cuda by developing a method called PTX, which allows for more direct control over hardware.

Why did Deep Seek's approach to AI development differ from Western companies?

Necessity played a major role. Due to hardware limitations, Deep Seek had to innovate and find new, more efficient ways to build their models. Western companies, with more funding and hardware resources, didn’t face the same constraints, potentially limiting the motivation to develop alternative solutions.

What is the potential impact of cheaper AI models on the future of the industry?

As AI models become cheaper and more competitive, the value may shift away from just model development to other areas in the AI value chain, such as user applications and broader economic integration, similar to how electricity production shifted its value away from just power generation.

How does Deep Seek’s success influence perceptions of China's position in the AI race?

Deep Seek’s breakthrough has reshaped perceptions of China's role in AI development, moving it from being viewed as 6 to 12 months behind to only 3 to 6 months behind the U.S. The rapid development and success of their reasoning model have accelerated timelines in the global AI race.