The Self-Improving Company | Kavak's AI Playbook

a16z's Joel De La Garza is joined by Emilio Escobar, Chief Information Security Officer at Datadog, to discuss what it takes to secure a company where nearly every employee is using AI and more than 4,000 engineers are working with coding agents. Rather than trying to block new tools, Emilio explains why Datadog chose to embrace AI early and build the security infrastructure needed to use it safely. They unpack how AI changes traditional assumptions around data permissions, credentials, developer access, and software supply chains. Emilio shares how Datadog uses role-based MCP servers and ephemeral credentials, as well as an AI "judge" built by his security team to evaluate the intent behind code and agent skills before they enter the environment. They also discuss why security teams can't afford to wait for commercial solutions to every new AI threat, how the relationship between developers and security teams needs to change, and why Emilio is less concerned about an AI "escaping" than he is about the sheer volume of vulnerabilities AI could uncover.

Joel De La Garza is joined by Dylan Ayrey, co-founder and CEO of Truffle Security, and Feross Aboukhadijeh, founder and CEO of Socket, to discuss one of the biggest shifts happening in cybersecurity: AI models are no longer just finding vulnerabilities—they're exploiting them. As frontier models become increasingly capable of hacking, software security, supply chain attacks, and cyber defense are entering a fundamentally new era. The conversation explores AI-powered hacking, software supply chain attacks, leaked credentials, zero-day vulnerabilities, package manager security, and why the path of least resistance for increasingly autonomous AI systems may also be the most dangerous. They also discuss what enterprises, developers, and the open-source ecosystem need to do to adapt as the gap between vulnerability discovery and exploitation continues to shrink.

Elena Burger and Matt Bornstein are joined by Simon Mo, co-founder and CEO of Inferact, the open-source inference engine powering many of today's most advanced AI applications. Together, they explore how open-source AI evolved from a research project into critical infrastructure, why inference has become one of the most important layers of the AI stack, and what it takes to bring frontier intelligence to developers around the world. The conversation covers vLLM's origins, the rise of open-weight models, why companies increasingly want control over their AI infrastructure, and how open-source inference enables the next generation of AI applications. They also discuss model licensing, the economics of open-weight AI, Kimi K3, distillation, AI infrastructure, and why Simon believes the gap between open and closed models is rapidly disappearing.

This week, a16z American Dynamism Films premiered three short documentaries highlighting companies tackling some of America's biggest industrial challenges: Ulysses, Mariana Materials, and Radiant. Before watching those films, we're revisiting conversations with the founders behind each company. You'll hear Will O'Brien explain why autonomous underwater robots could unlock a new era of ocean exploration and security, Turner Caldwell discuss rebuilding America's critical minerals supply chain and modernizing mining, and Doug Bernauer share why portable nuclear microreactors could transform how we generate power. Together, these conversations offer a look at the technologies—and the founders—working to rebuild the industrial foundations of the United States.

Theo Jaffee is joined by Joshua Achiam, Chief Futurist at OpenAI, for a conversation on AI cybersecurity, frontier model capabilities, and why he believes society may have already crossed the threshold into an AGI-era without fully recognizing it. They discuss AI's rapidly advancing cyber capabilities, state-sponsored hacking, model jailbreaks, recursive self-improvement, and what happens when AI systems begin discovering vulnerabilities faster than humans can patch them. Joshua also explains why most people have quietly adapted to capabilities that would have seemed unimaginable just a few years ago, and why the biggest changes from AI may arrive gradually rather than all at once.