All-In Podcast - AI Czar David Sacks Explains the DeepSeek Freak Out
The release of a Chinese open-source AI model, Deep Seek, has sparked significant discussion in the AI community. This model, comparable to OpenAI's reasoning models, has been surprising due to its origin and the fact that it was open-sourced. The conversation highlights the geopolitical implications of a Chinese company advancing in AI technology, challenging the US's dominance. Additionally, the debate over the model's development cost, claimed to be $6 million, is scrutinized. Experts argue that this figure only represents the final training run and not the total investment, which includes substantial compute resources. The discussion also emphasizes the innovative approaches taken by Deep Seek, such as using alternative algorithms and bypassing traditional software constraints, which were driven by necessity and resource limitations. This scenario suggests that constraints can lead to significant innovation, challenging the Western approach of abundant funding in AI development.
Key Points:
- Deep Seek's release of an open-source AI model challenges US dominance in AI.
- The model's development cost is debated, with claims of $6 million being misleading.
- Innovative approaches by Deep Seek were driven by resource constraints.
- The geopolitical aspect of AI development is highlighted, with China closing the gap.
- Constraints in resources can lead to significant innovation, as seen with Deep Seek.
Details:
1. 🗣️ Engaging with AI Experts
- Engagement with AI experts facilitates widespread communication and interest within the professional community, offering opportunities for dynamic collaboration.
- This role enables professionals to discuss and exchange ideas with a diverse group of experts, which enhances professional growth and expands networks.
- Interactive job roles in this field allow for the sharing of innovative solutions and ideas, contributing to the evolution of AI technologies.
- Examples of engagement activities include panel discussions, workshops, and collaborative projects that drive innovation and knowledge sharing.
- Challenges in engagement may include staying updated with rapidly evolving AI technologies and managing diverse viewpoints, which require strategic approaches.
2. 🌟 The Explosive Model Release
- The release of the new AI model caused a significant global reaction, becoming a major news story overnight.
- There was a trillion-dollar decline in market capitalization within one day, with technology and finance sectors being the most affected, highlighting the model's profound impact.
- AI experts are divided, with some praising its technological advancements while others warn of potential ethical and economic implications, underscoring its controversial nature.
- The development of this model involved unprecedented collaboration between leading tech companies and academic institutions, contributing to its groundbreaking capabilities.
- The model's release is expected to drive future innovations in AI, but it also raises concerns about regulatory challenges and the need for updated policies.
3. 🌍 China vs. US: AI and Open Source
- The competition between China and the US in AI development is intensifying, with significant attention on a Chinese company's open-source approach.
- This approach contrasts with the more closed models typical of US companies, highlighting a strategic divergence in AI development.
- There is growing international interest and debate over whether the US might lose its leading position in AI to China.
- Open-source models are gaining traction as a cost-effective alternative, potentially offering solutions at '1/20th the cost' of proprietary offerings like those from OpenAI.
- This open-source movement is supported by many who view it as a democratizing force in AI development, challenging the traditional, expensive models.
4. 🔍 Unpacking the AI Narrative
- The second company to release a reasoning model akin to OpenAI's GPT-3 was a Chinese company, a move that surprised many industry observers due to the rapid advancement and competitive market positioning it represented.
- This unexpected release highlights the increasing global competition in AI development, emphasizing China's growing influence in the technology sector.
- The release not only showcases technical prowess but also challenges the dominance of traditionally leading AI companies, prompting a reassessment of competitive strategies within the industry.
5. 🧠 Evolution of AI Models
- There are two major kinds of AI models currently: Bas llm models like Chachi P40 and V3, and new reasoning models based on reinforcement learning.
- Bas llm models function like a smart PhD, providing direct answers to questions, making them suitable for straightforward queries.
- Reasoning models do not provide snap answers but break down complex problems into smaller sub-problems, solving them sequentially, a process known as 'Chain of Thought.'
- Open AI was the first to release a reasoning model, with Google also developing similar models, showcasing industry-wide adoption.
- The new generation of AI models can sequentially perform tasks and solve more complex problems compared to previous models, enhancing their applicability in complex decision-making scenarios.
6. 🇨🇳 China's Leap in AI Development
- China is making significant strides in AI with the development of Gemini 2.0, a leading AI model, and its prototype Deep Research 1.5, showcasing the country's technological capabilities.
- Deep Seek, another Chinese AI initiative, stands out for releasing a fully public reasoning model, positioning it ahead of some Western counterparts.
- The open-sourcing of Deep Seek was unexpected, with API access offered at a highly competitive price, significantly lower than the market average, enhancing its accessibility and potential for widespread adoption.
7. 💰 The $6 Million Myth Debunked
7.1. Funding and Training Costs
7.2. AI Development Timelines
8. 💻 Unveiling Compute Investments
- The final training run for AI models cost tens of millions of dollars, challenging previous claims of a $6 million cost, and this was around nine or 10 months ago.
- The frequently quoted billion-dollar figure includes the cost of all hardware purchases and years of development, not just the final training run.
- Deep Seek's compute resources are estimated to include 50,000 high-performance units, comprising 10,000 H100s, 10,000 H800s, and 30,000 H20s, indicating a substantial investment.
- A compute cluster with over 50,000 units likely costs over a billion dollars, contradicting lower cost claims.
- Acquisition of these units occurred before export controls, showing strategic foresight by the founder.
- The founder uses AI for algorithmic trading via a hedge fund, highlighting a dual-purpose investment strategy.
- Differentiating between the fully loaded cost of AI development and isolated training run expenses is crucial for fair comparisons.
9. 🔍 Deep Dive into Deep Seek's Strategy
- Deep Seek's strategy is shrouded in some uncertainty regarding the full extent of its capabilities, which is compounded by market speculation.
- Semiconductor analysts who are bullish on Nvidia may have biases that influence their interpretation of Deep Seek's training cost claims, affecting industry perceptions.
- Deep Seek's approach is markedly different from conventional methods, emphasizing a unique strategic pathway that distinguishes it from competitors.
- The company has actively published multiple papers to refine and communicate their approach, demonstrating a commitment to evolving their strategy in response to market needs.
- Deep Seek's strategic approach could involve leveraging unique technological advancements or methodological innovations to gain a competitive edge.
- The impact of Deep Seek's strategy could potentially reshape industry standards or introduce new paradigms in its field.
10. 💡 Innovation Through Constraints
- The development of the GRPO algorithm demonstrates how constraints in computing resources can lead to innovative solutions, utilizing less memory while maintaining high performance.
- By opting for PTX over Nvidia's CUDA, developers gained direct hardware control similar to assembly language, highlighting innovation by circumventing industry-standard limitations.
- In the Western world, the lack of constraints due to financial abundance may impede creative problem-solving, as seen in the failure to develop similar innovations.
- Suggesting that startups begin with smaller initial funding (e.g., $2 million rather than $200 million) could foster deeper, more innovative solutions by imposing resource constraints.
- The rapid commoditization of AI models indicates that future value creation might shift upstream in the value chain, focusing on user interaction or economic integration rather than the models themselves.