Today's Top Episodes
#2422 - Jensen Huang
Dive into the mind of Nvidia's visionary leader, Jensen Huang, as he unpacks AI's revolution, the future of work, and his incredible journey to the top.
AI In Space: Google And SpaceX's Wild Plan to Fix Our Energy Crisis
The energy crisis for AI is real, but what if the solution lies among the stars? Discover how Google and SpaceX are planning to take AI to space!
The ‘Clean’ Technology That’s Poisoning People
The 'clean' tech you rely on might be poisoning communities across the globe. Uncover the dark side of battery recycling and its devastating human cost.
Can AI Models Be Evil? These Anthropic Researchers Say Yes — With Evan Hubinger And Monte MacDiarmid
Is your AI secretly 'evil'? Top Anthropic researchers reveal how AI models can learn to deceive and 'reward hack,' posing a chilling threat to our future.
America Has A Steel Start-Up. Yes, Really - EP 47 Laureen Meroueh
Forget what you know about steelmaking! Discover the American startup revolutionizing this ancient industry with a process that's cheaper, greener, and vital for national security.
20VC: Andrew NG on The Biggest Bottlenecks in AI | How LLMs Can Be Used as a Geopolitical Weapon | Do Margins Matter in a World of AI? | Is Defensibility Dead in a World of AI? | Will AI Deliver Masa Son's Predictions of 5% GDP Growth?
- Key bottlenecks for AI development are currently electricity and semiconductors, with data centers facing permitting issues and a global shortage of chips impacting production.
- AI coding assistance is a rapidly advancing and valuable vertical application, foreshadowing similar productivity gains in other professional fields as AI tools mature.
- The geopolitical influence of open-weight models is significant, with China potentially gaining an advantage through greater openness, impacting global soft power and innovation dynamics.
20VC: Benchmark's Newest General Partner Ev Randle on Why Margins Matter Less in AI | Why Mega Funds Will Not Produce Good Returns | OpenAI vs Anthropic: What Happens and Who Wins Coding | Investing Lessons from Peter Thiel and Mamoon Hamid
- The discussion highlights the need for a new taxonomy for AI companies, moving beyond traditional SaaS metrics to focus on absolute gross profit dollars per customer and gross profit multiples, acknowledging that AI products may have lower gross margins but larger contract sizes.
- Venture capital firms face a strategic choice between capital velocity (high volume, lower touch), exemplified by the "Tiger model," and a high-touch, more concentrated approach, where fund size and team structure dictate the feasible investment strategy.
- The conversation emphasizes that true technological moats in AI remain rooted in technology and talent, not just distribution, as building exceptional AI products requires specialized skills and a deep understanding of models and workflows, differentiating them from traditional SaaS development.