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• aa16z2026-10-15Video✓ Read

  What Happens When AI Agents Can Spend Your Money?

  What happens when AI agents start spending your money? VGS' Terry Angelos argues that agentic commerce will need new payment infrastructure that gives AI the ability to transact without sacrificing the protections consumers expect. Terry joins a16z General Partner Alex Rampell to discuss the emerging wallet wars, how agentic tokens could enable secure purchases, and what happens when an AI buys the wrong thing. They also explore why merchants may resist agentic commerce, how AI could change credit card rewards and subscriptions, and who controls the customer relationship when agents make purchasing decisions on our behalf. Timestamps: 00:00 Intro 00:56 What a payment credential actually is 09:48 When agentic commerce changed the game 13:43 The wallet wars 19:42 Stripe as the AOL of agentic commerce 23:51 The new agentic token standard 29:45 The three checks behind every transaction 35:43 Why Amazon blocked Muse 39:47 Over-hyped vs under-hyped 47:09 Where VGS goes next Resources: Follow Terry Angelos on X: https://x.com/terryangelos Follow Alex Rampell on X: https://x.com/arampell Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends! Find a16z on X: https://twitter.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Show on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Listen to the a16z Show on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711 Follow our host: https://x.com/eriktorenberg 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 http://a16z.com/disclosures.

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• aa16z2026-10-12Video✓ Read

  Recursive Self-Improvement and the Future of AI

  a16z General Partner Martin Casado sits down with MakerMaker.AI co-founders Dhaval Adjodah and Owen He to unpack recursive self-improvement, or RSI, and separate the increasingly popular term from what it would actually mean for an AI system to improve itself. Dhaval and Owen distinguish true RSI from simply using AI to automate pieces of AI development. Their goal is a closed loop where the system being improved is also doing the improving, across components including models, harnesses, and eventually more of the AI stack. They explain why closing that loop still doesn’t guarantee runaway “takeoff,” and why measuring real progress requires accounting for both capability gains and the resources required to achieve them. They also share what they’re seeing in their own research, including early signs that self-improving systems can increase performance while improving efficiency. And they dig into the practical challenges of proving those gains are real, from variance and reward hacking to the enormous difficulty of distinguishing genuine improvement from noise. Timestamps: 00:00 - Intro 00:58 - What actually counts as RSI 05:10 - Capability as a numerator and denominator 06:58 - Closed loop vs takeoff 08:32 - Humans as agents in the system 13:39 - Why most RSI papers don't scale 17:41 - Why they chose inference 24:00 - "It's not a belief, it's happening" 25:42 - The Viceroy of Variance 29:39 - The garden hose company in Wisconsin Resources: Follow Dhaval Adjodah on LinkedIn: https://www.linkedin.com/in/rand0m Follow Owen He on LinkedIn:https://www.linkedin.com/in/owen-he-b7b064279/ Follow Martin Casado on X: https://x.com/martin_casado Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends! Find a16z on X: https://twitter.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Show on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Listen to the a16z Show on Apple Pod

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