Jeff Su - DeepSeek: What Actually Matters (for the everyday user)
The discussion focuses on dispelling myths surrounding Deep Seek's AI model, emphasizing its innovative approach within export control constraints. Deep Seek's model, contrary to claims, wasn't built for just $5.6 million; this figure only covers the final training run, excluding significant infrastructure costs. The company creatively optimized its model using less powerful GPUs, showcasing efficiency rather than breaking rules. While Deep Seek's model matches OpenAI's in performance, it leads in efficiency but not overall capability. The video also clarifies that Deep Seek's visible Chain of Thought is a UI choice, not a technical breakthrough. Additionally, Deep Seek's use of model distillation, while controversial, is a common practice in AI development. The video concludes by discussing the implications for users, such as access to advanced AI features without cost and privacy considerations when using Deep Seek's models.
Key Points:
- Deep Seek's model cost exceeds $5.6 million, covering only final training, not infrastructure.
- Deep Seek optimized its model using less powerful GPUs, showcasing efficiency.
- Visible Chain of Thought in Deep Seek's model is a UI choice, not a technical breakthrough.
- Deep Seek's model distillation is common but controversial, breaking OpenAI's terms.
- Users can access advanced AI features for free, with privacy options available.
Details:
1. 🎥 Introduction to Deep Seek Myths
- Deep Seek has been generating significant attention, leading to misinformation, noise, and hype.
- The video aims to debunk the top 10 myths about Deep Seek, providing clarity on its implications for everyday AI users.
- Acknowledgment to Ben Thompson for his comprehensive article that informed the video content.
2. 🧩 Myth 1: Cost Misunderstandings
- The claim that Deep Seek built their model for $5.6 million is misleading because this figure only accounts for the final training run.
- The $5.6 million does not include significant costs such as infrastructure investments.
- Deep Seek reportedly utilizes 50,000 Nvidia Hopper GPUs, which are valued at approximately $1 billion, indicating a much larger scale of investment.
- Comparing the $5.6 million figure to the true cost is like claiming the latest iPhone only costs $500 to manufacture, ignoring other expenses.
3. 🚀 Myth 2: Rule-Breaking Allegations
- Deep Seek gained attention by innovating within export control constraints, specifically using less powerful H800 GPUs due to restrictions on H100 GPUs.
- Their model architecture optimization was driven by the necessity to work within these constraints, potentially leading to a more efficient model despite hardware limitations.
- H800 GPUs, though less powerful, were available for sale to Chinese companies, unlike the restricted H100 GPUs.
- Deep Seek's approach parallels Samsung's strategy of using slightly slower processors in some regions due to licensing agreements, highlighting an industry trend of adaptive innovation.
4. ⚖️ Myth 3: Performance vs. Efficiency
- Deep Seek's reasoning model R1 matches OpenAI's reasoning model 01 in performance, yet OpenAI's model 03 is more powerful, indicating Deep Seek excels in efficiency but not in overall capability.
- OpenAI's release of model 03 mini for free, including search capabilities, demonstrates rapid industry advancement, likely influenced by competitive pressure from Deep Seek.
- The analogy of cost-effective smartphones illustrates that while efficiency can offer significant value at a lower cost, it doesn't equate to surpassing more powerful options in overall performance.
- Efficiency in AI development is crucial for providing accessible solutions without sacrificing core functionalities, but it should not be mistaken for superior performance, which often requires more resources and advanced capabilities.
- The balance between performance and efficiency is strategic, with companies like Deep Seek focusing on delivering efficient solutions that meet specific needs while larger companies like OpenAI push the boundaries of performance with more resource-intensive models.
5. 🔍 Myth 4: Comparability Issues
- Deep Seek models should be compared to other AI models on an 'apple to apple' basis, focusing on their intended purpose and features.
- Deep Seek's base model V3 is equivalent to ChatGPT's 4.0, making them directly comparable in terms of baseline AI functionality.
- Deep Seek's advanced reasoning model R1 surpasses previous versions of ChatGPT with a unique search function, enhancing its reasoning capabilities.
- ChatGPT has responded with the release of 3.0 Mini, now incorporating a search function similar to Deep Seek, leveling the playing field regarding reasoning tasks.
- When comparing AI models, it's crucial to match their functional capabilities to user needs, similar to choosing between a sports car and an SUV based on specific requirements.
6. 🧠 Myth 5: Chain of Thought Misconceptions
- The visibility of the Chain of Thought in deep r1 is primarily a UI choice, not a technical breakthrough, illustrating a common misconception about AI reasoning capabilities.
- Deep seek R1 and open AI 01 possess similar reasoning capabilities, debunking the myth that R1 is inherently more advanced due to its visible reasoning process.
- The apparent transparency in R1's reasoning is due to how it is presented rather than a superior capability, which is a significant point of confusion.
- An analogy of two chefs is used to highlight the difference: it's the presentation of the process, not the actual cooking or results, that differs, emphasizing the role of UI in perceived reasoning advancements.
7. 🛠️ Myth 6: Ground-Up Misconception
- Deep Seek employed Model Distillation by training on ChatGPT's outputs, a method prevalent in the AI industry, although it breaches OpenAI's terms of service.
- Despite an ongoing investigation by Microsoft and OpenAI into Deep Seek's practices, Microsoft has included Deep Seek's R1 in its Cloud offerings, showing a conflicting stance.
- Deep Seek's approach is akin to a phone maker studying iPhone photos to mimic its image processing, illustrating how companies can replicate functions without direct code copying.
- This practice raises ethical and legal questions about the boundaries of innovation and imitation in AI development.
8. 🔒 Myth 7: Security Concerns
- Using the native Deep Seek app requires sending and storing data in China, which raises privacy concerns for users worried about data sovereignty.
- To mitigate these concerns, users can opt for platforms like Perplexity or Venice AI that access Deep Seek's models while keeping data within the US.
- Deep Seek models are gaining popularity due to their cost-effectiveness, evidenced by their recent integration into platforms like Cursor.
- For maximum privacy, users have the option to run Deep Seek models locally on personal devices, completely avoiding data transmission to external servers.
- Implementing local models ensures full control over data, addressing privacy concerns effectively.
9. 📈 Myth 8: Impact on Nvidia
- Tech analysts and the CEO of Microsoft support Jevon's Paradox, which posits that increased efficiency in AI, such as that seen with Deep Seek, will lead to higher overall demand for AI solutions.
- As AI solutions become more cost-effective, their usage is expected to rise, which could result in increased demand for Nvidia's chips.
- An analogy is drawn with the smartphone industry, where the reduction in smartphone costs has led to higher demand for premium phone processors like those from Qualcomm. This suggests a similar potential outcome for Nvidia.
10. 🇺🇸 Myth 9: Effect on US Tech Companies
- Amazon benefits from serving high-quality open-source models like DeepMind's at lower costs, reducing its dependency on proprietary models, which could lead to significant cost savings and increased flexibility in AI applications.
- Apple can leverage the Apple Silicon chip advantages for Edge inference, enhancing performance on local devices. This positions Apple to offer superior AI-powered functionalities directly on devices, reducing latency and improving user experience.
- Meta stands as the biggest winner as AI enhances every aspect of its business, particularly advertising, by enabling cheaper and more effective product monetization. This positions Meta to vastly improve its ad targeting and engagement metrics, driving revenue growth.
- The effect of cheaper AI is likened to the impact of cheaper smartphones and faster internet, potentially enabling new products and services. This positions US tech companies to capitalize on new market opportunities and drive innovation.
11. 🚀 Myth 10: China's AI Milestone
- The segment compares China's AI advancements to historical technological milestones, specifically the USSR's Sputnik moment.
- Unlike the secrecy of the USSR's methods, China's AI methods, as demonstrated by deep seek, have been openly published.
- Deep seek achieved expected efficiency improvements within existing technological frameworks, rather than groundbreaking innovations.
- Industry experts liken China's AI milestone to Google’s 2004 moment, where Google demonstrated efficient infrastructure building without expensive mainframes.
- Deep seek's approach shows that achieving competitive AI performance does not necessarily require the most powerful chips.
12. 🔑 Implication 1: Accessible AI Features
- Users can access advanced AI features without cost, providing opportunities for those who have not paid for AI tools.
- Access is available to two powerful reasoning models: DeepSeeks R1 and ChatGPT's 03 Minium.
- These reasoning models excel in complex math problems and programming challenges, making them highly valuable for users needing support in these areas.
- The availability of these models at no cost democratizes access to complex problem-solving tools, potentially increasing user engagement and skill development.
- To access these features, users simply need to download the associated app or visit the platform's website, enhancing ease of use and accessibility.
13. 🔐 Implication 2: Data Privacy Options
- Users concerned about data privacy have two main options with Deep Seek.
- Option 1: Users can choose platforms like Perplexity, Venice, and Cursor that integrate Deep Seek, providing an alternative to using Deep Seek's native apps, thus potentially offering enhanced privacy controls.
- Option 2: For users wanting maximum data security, they can run Deep Seek's models locally using tools like LM Studio or o Lama, ensuring data is not stored externally. This method requires users to set up and manage the software environment independently, which may involve technical expertise.
14. 💡 Implication 3: Smart Adoption Choices
- Avoid switching tools or technologies solely based on trends unless the new option provides a significant improvement in your workflow.
- Consider the 'switching tax' and potential future updates to current tools before making a change.
- For developers aiming to minimize costs, adopting new tools with clear benefits is advisable.
- Everyday users paying for existing services should evaluate data storage preferences before switching.
- The success of new technologies like Deep Seek may influence market dynamics and lead to competitive offerings, such as free access to certain features.
- Understanding the implications of new tech before adoption is crucial to avoid unnecessary changes driven by hype.