How Should AI Be Regulated? Use vs. Development

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Jan 20, 2026

Martin Casado, a general partner at a16z and a former CTO of Nicira, shares his insights on AI regulation. He emphasizes the importance of focusing on how AI is used rather than how it's developed, drawing parallels with past tech shifts. Casado warns that existing laws should apply when AI is used for wrongdoing. He highlights the risks of hasty regulations and the need for evidence-based policies. The discussion touches on the significance of open-source AI for U.S. competitiveness and stresses learning from historical tech governance to create effective regulations.

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INSIGHT

Focus Regulation On Uses, Not Development

Regulate uses, not core development, because you can’t foresee emergent risks at invention time.
Focusing on development creates loopholes and hampers innovation without effectively stopping bad actors.

INSIGHT

Marginal Risk Determines Smart Regulation

You must study marginal risk to know what new regulations, if any, are appropriate.
Without evidence of distinct marginal risks, regulatory change may be misdirected or counterproductive.

ADVICE

Strengthen Enforcement Tools, Not Blanket Bans

Build enforcement and investigative capabilities alongside legal frameworks to address misuse.
Expand forensic and cyber skills in law enforcement as technologies evolve to keep pace with new harms.

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To Regulate AI Effectively, Focus on How It’s Used

A conversation with Martin Casado on learning from past computing platform shifts, understanding marginal risk in AI, and why open source matters for US competitiveness.

One of the core pillars of our roadmap for federal AI legislation makes clear AI should not excuse wrongdoing. When people or companies use AI to break the law, existing criminal, civil rights, consumer protection, and antitrust frameworks should still apply. Enforcement agencies should have the resources they need to enforce the law. If existing bodies of law fall short in accounting for certain AI use cases, any new laws should be evidence-based, clearly defining marginal risks and the optimal approach to target harms directly.

In this conversation, we go deeper on what that principle means in practice with Martin Casado, general partner at a16z where he leads the firm’s infrastructure practice and invests in advanced AI systems and foundational compute. Martin has lived through multiple platform shifts–as a researcher where he worked on large-scale simulations for the Department of Defense before working with the intelligence community on networking and cybersecurity, a pioneer of software-defined networking at Stanford, and the cofounder and CTO of Nicira, which was acquired by VMware–giving him a rare perspective on how breakthrough technologies are governed as they develop and scale.

Martin joins Jai Ramaswamy and Matt Perault to discuss how decades of technology policy can inform addressing harmful uses of AI, defining marginal risk in AI, the importance of open source for long-term competitiveness, and more.

Follow Jai Ramaswamy on X: https://twitter.com/jai_ramaswamy

Follow Matt Perault on X: https://twitter.com/MattPerault

Follow Martin Casado on X: https://twitter.com/martin_casado

Read the a16z AI Policy Brief here: https://a16zpolicy.substack.com/

Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts.

Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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