20VC: Mistral's Arthur Mensch: Are Foundation Models Commoditising | How Do We Solve the Problem of Compute | Is There Value in the Application Layer | Open vs Closed: Who Wins and Mistral's Position
The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch - Un pódcast de Harry Stebbings
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Arthur Mensch is the Co-Founder and CEO of Mistral AI. Since its inception in May 2023, Mistral has raised over $520M in funding from investors like Andreeseen Horowitz, General Catalyst, Lightspeed Venture Partners, and Microsoft with a current valuation of $2 billion. Before founding Mistral, Arthur was a research scientist at DeepMind, one of the leading AI institutions in the world. In Today’s Episode with Arthur Mensch We Discuss: From Models to Team Building: Arthur’s Greatest Lessons at DeepMind What were Arthur’s biggest lessons from his time at DeepMind? How did DeepMind shape how Arthur built Mistral? Why does Arthur believe smaller teams are better for AI? Why did Arthur decide to leave DeepMind and start Mistral? Scaling Mistral to $2 Billion Valuation Within a Year What made Mistral 7B so successful? What did Arthur learn from the model release? What are the biggest barriers at Mistral today? How does Arthur balance the sales and research teams at Mistral? What does Arthur know now that he wishes he had known when he started Mistral? How to Win in AI: Open Source, Cost, & Adoption Why did Arthur open-source some models? Why did he close some? How quickly will the cost of compute go down? Why does Arthur believe marginal costs will not go to zero? How will open-sourcing LLMs affect the marginal cost? Does Arthur think open source is ready for enterprise adoption? What questions should enterprises be asking about AI adoption today? What are the biggest challenges to AI adoption today? The Future of LLMs What does Arthur think are the largest bottlenecks of model quality today? Does Arthur think future models will be more generalized or vertical-focused? What does Arthur think about the future of commoditization in models? Why is Arthur optimistic about the profitability of the application layer of AI? How should models differentiate themselves today?
