Subvolts take Regulate AI by focusing on misuse, not development, applying existing laws and targeting specific risks. Jan 20, 2026 - Developers
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This is a solid interview with a venture capitalist who has deep experience in technology platform shifts and AI infrastructure.
How Should AI Be Regulated? Use vs. Development
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From Subvolts
Summary and analysis by Subvolts editorial, AI-assisted.
This AI + a16z podcast episode features Martin Casado discussing AI regulation, focusing on how AI is used rather than its development. They explore learning from past tech shifts, understanding AI's marginal risks, and the importance of open source for US competitiveness. The conversation emphasizes applying existing legal frameworks to AI-driven wrongdoing.
This episode discusses how to make rules for artificial intelligence. Instead of banning AI development, it suggests focusing on how people use AI to break laws. This approach ensures existing rules still apply when AI is involved in wrongdoing.
This episode offers insights into practical AI regulation by drawing parallels with previous technology platform shifts. It's for policymakers, legal professionals, and tech leaders grappling with AI governance. Casado's experience provides a grounded perspective on balancing innovation with risk, relevant as frontier labs like OpenAI, Google Gemini, and Anthropic navigate increasing regulatory scrutiny.
- Hosta16z
- GuestMartin CasadoGeneral Partner, a16z
- Episode Title: How Should AI Be Regulated? Use vs. Development
- Show: AI + a16z
- Length: 47 minutes
- Guest: Martin Casado
- Core Regulatory Principle: AI should not excuse wrongdoing; existing laws apply.
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.
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- marginal risk
- Martin Casado
- the specific, additional risk introduced by using AI in a particular situation.
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Questions people ask
- What is the core principle for regulating AI?
- The core principle is that AI should not excuse wrongdoing. Existing criminal, civil rights, consumer protection, and antitrust frameworks should still apply when AI is used to break the law.
- What approach should new AI laws take?
- New laws should be evidence-based, clearly defining marginal risks and targeting harms directly, rather than broadly restricting AI development.
Page generated Oct 19, 2026. Summaries are Subvolts' own; the audio, artwork and notes belong to a16z.