"The Founders Playbook: What AI Founders
Need To Know Before Raising & Scaling" | dAGI Summit 2025

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.