The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch 20VC: NVIDIA vs Groq: The Future of Training vs Inference | Meta, Google, and Microsoft's Data Center Investments: Who Wins | Data, Compute, Models: The Core Bottlenecks in AI & Where Value Will Distribute with Jonathan Ross, Founder @ Groq
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Feb 17, 2025 
Jonathan Ross, Founder and CEO of Groq, shares his insights into the evolving landscape of AI hardware. He discusses the competitive dynamics between training and inference, noting why NVIDIA struggles with inference costs. Ross emphasizes the importance of synthetic data and efficient model performance as key to scaling AI. He also delves into the implications of data center investments by giants like Meta, Google, and Microsoft, asserting that many dollars may be lost in these ventures. Lastly, he touches on China's AI strategy and Europe's potential in the upcoming AI revolution.
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Synthetic Data and Scaling Laws
- Scaling laws assume uniform data quality, but training data varies.
- Synthetic data, generated by advanced models, improves training efficiency.
LLM Limitations with Non-Linear Operations
- LLMs struggle with non-linear operations like multiplication due to needing intermediate steps.
- Training increases memorization, reducing reasoning steps, but can't remove them entirely.
Building for the Future of AI
- Build AI products assuming technology will improve, focusing on significant advancements.
- Position yourself for future waves of innovation, like Groq did with scaled inference.
