Matt Perault
Frontier Insights
Thesis: a16z anchors its long-term fund economics on open AI innovation and broad market adoption, championing a “Little Tech” agenda that rejects heavy upfront development controls in favor of downstream accountability.
Strategy: Regulate malicious use—not model creation—via existing laws. Advocate for revenue-based regulatory thresholds over compute or training caps to prevent entrenching trillion-dollar incumbents, while leveraging federal preemption, open-source access, and targeted national action plans to protect agile startups.
Risks: Premature compute-based licensing risks cementing an oligopoly. Conversely, downstream enforcement leaves ecosystems vulnerable to irreversible catastrophic risks or laboratory accidents occurring before legal remedies can act.
Key Views & Dialogues
The Little Tech Agenda for AI
a16z’s Little Tech thesis is that AI rules built for trillion-dollar incumbents could compound their existing capital, compute, and talent advantages. The operative comparison is “five people and you’re in a garage” versus “thousand-person compliance teams”; licensing, audits, impact assessments, and disclosure regimes could entrench a small number of frontier developers. For investors, policy design…
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- a16z’s Little Tech thesis is that AI rules built for trillion-dollar incumbents could compound their existing capital, compute, and talent advantages. The operative comparison is “five people and you’re in a garage” versus “thousand-person compliance teams”; licensing, audits, impact assessments, and disclosure regimes could entrench a small number of frontier developers. For investors, policy design may determine whether startups remain credible challengers or the market consolidates by construction.
- Matt Perault and Collin McCune reject zero regulation in favor of “regulate use, do not regulate development.” Existing consumer-protection, civil-rights, and criminal laws already reach many harmful AI uses, while development controls burden every builder before a violation occurs. Perault ties that position to venture’s 10-year fund horizon: unsafe, scammy products and public distrust cannot produce the durable ecosystem investors need.
- The pair argues that the 2023 safety panic nearly normalized an unprecedented permission-to-build regime for software. CEO testimony, existential-risk advocacy, and a social-media-policy “do-over” produced proposals for frontier licenses, nuclear-style oversight, FLOPs-based disclosures, and open-source bans. Perault’s warning is concrete: U.S. nuclear policy yielded only “2 or 3 new nuclear power plants in a 50-year period”; applying that model to AI would suppress breakthroughs and, in his categorical view, mean “you lose to China.”
- Colorado is their clearest case that compliance process can substitute for actual protection. Its framework asks resource-constrained startups to classify high-risk uses and conduct assessments or audits that might identify bias but will not “end racism in our society.” Perault prefers direct liability: codify that using AI to violate anti-discrimination law is illegal, then let the attorney general enforce against the observable harm.
- They concede that existing law may not be the endpoint if AI creates genuinely incremental risk. Asked whether terrorism, cybercrime, or other capabilities could become “10,000x” more powerful, Perault calls that conceivable and endorses policy responsive to marginal risk. His resistance is to speculative ex-ante surveillance—predicting who might offend and intervening before conduct occurs—which may be both invasive and ineffective.
- The National AI Action Plan marks a thesis-level shift from “safety with a splash of innovation” toward winning while keeping people safe. The speakers highlight support for open source, right-sized startup regulation, worker retraining, labor-market monitoring, and clearer federal-state roles. On China, McCune sees a hard tradeoff: restrictions must keep powerful technology from the PLA and CCP, but locking down U.S. open-source models invites Chinese products to become the world’s default platform.
- The failed federal moratorium exposed political execution risk, not consensus for a 50-state AI patchwork. McCune says its perceived 10-year ban on all state AI law was a misreading, but “perception is reality”; a partisan reconciliation vehicle and one or two Republican senators were enough to defeat it. The next push is narrower federal preemption for model regulation, backed by a more organized coalition and Leading the Future PAC—even as Perault expects big and little tech may diverge again on the details.