This VC panel from the dAGI Summit explores venture capital's evolving landscape amid AI's transformative surge. The discussion tackles whether venture remains attractive (Sequoia's Roloff Beha argues it's "return-free risk"), examines talent consolidation toward major labs offering $10M+ salaries, and debates open-source versus centralized AI futures. Key tensions emerge: enterprise security requirements favoring closed models while advocates push permissionless innovation; the challenge of building decentralized systems when speed and capital naturally favor oligopolies. Panelists agree the power law will intensify—most funds lose money while winners capture trillion-dollar outcomes—but disagree on whether decentralized approaches can compete commercially beyond niche use cases.

Key takeaways

▸ Venture's extreme bifurcation: ~95% of funds will deliver sub-1x returns, but trillion-dollar outcomes are now plausible—creating unprecedented power law concentration where top funds massively outperform.

▸ Talent consolidating to labs: Major AI labs pay extraordinary compensation ($10M cash offers to 24-year-olds mentioned), creating negative selection for startups—though counterbalanced by smaller teams achieving more (cited: 2 people, $1M ARR).

▸ 1999 analogy breaks down: Unlike dot-com bubble, leading labs have real revenue (Anthropic at 35x revenue, 5x ARR growth)—though froth exists in oversubscribed seed rounds with 24-hour term sheet timelines.

▸ Open source paradox: Distributed AI progress disappoints despite philosophical appeal; ironically, China and Meta's commoditization strategy drive open-source advancement more than decentralized crypto projects.

▸ Decentralization handicapped: Startups require rapid iteration; decentralization excels at immutability (Bitcoin, DeFi)—fundamental mismatch for early-stage companies needing governance flexibility.

▸ Enterprise blocks open adoption: Security, liability, and procurement bureaucracy favor centralized labs; open/decentralized projects must solve compliance or target consumer first.

▸ Multipolar AI emerging: 10+ reasonably-sized labs now exist versus 2-3 two years ago—but open models still lag frontier capabilities significantly.

▸ Companions achieve PMF: AI companion apps showing strong product-market fit (0 to $2.5M revenue in 6 months cited); addresses loneliness crisis (average American has 1.3 friends versus 7 needed).

▸ Progress slowdown enables open-source: Open models become compelling when enterprises optimize for cost over cutting-edge; currently "AI curious" phase keeps everyone chasing frontier.

▸ Safety as structural advantage: Security/interpretability aren't just cost centers—they're deployment prerequisites and potential moats (insurance products, secure compute, model evaluation).

▸ Third-party evaluation essential: Labs can't grade own homework on capabilities/risks; independent evaluators necessary even as labs internalize safety work.

▸ AI transforming VC: Partners using AI extensively for decisions; one fund running parallel "AI portfolio" to test if AI outperforms human selection—humans becoming "data collectors" for AI decision-making.

▸ Bot performance advantage: Like poker bots that performed worse than players' peak but better than average (no tilt, bad days)—AI may outperform VCs across entire decision distribution, not just at peak.

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