Top AI Researchers Roundtable & BBQ Party in Atherton

Event Overview Since launching the series in May 2026, we’ve successfully hosted four exclusive, closed-door Top AI Researchers Roundtables, fostering meaningful connections and in-depth conversations among researchers and leaders from the world’s foremost AI labs and research institutions. Participants have included: Ed Chi — VP of Research at Google DeepMind Xuedong Huang — CTO of Zoom 12 researchers from OpenAI 5 researchers from Anthropic 21 researchers from Google DeepMind 5 researchers from xAI 6 researchers from NVIDIA 2 Stanford professors We’re excited to continue this tradition with our 5th Top AI Researchers Roundtable, taking place on October 18, from 3:00 PM to 5:00 PM, at a private residence in Atherton. This invitation-only gathering will once again bring together leading researchers from OpenAI, Google DeepMind, Anthropic, xAI, and other frontier AI labs and research institutions for an intimate, closed-door discussion exploring some of the most important questions shaping the future of artificial intelligence. This will be a fully closed-door, off-the-record conversation focused on the future of AI systems—from frontier research breakthroughs and emerging technical challenges to the next wave of opportunities that could shape the industry over the coming years. Following the roundtable, guests will continue the discussion over a private BBQ reception with fellow researchers, founders, investors, and industry leaders. Core Discussion Topics As AI becomes more capable, it is transforming not only how we build technology, but also how we secure it. From AI-powered cyberattacks and autonomous agents to recursive self-improvement and AI-driven safety research, the next generation of AI raises fundamental questions about security, trust, and control. This roundtable brings together leading AI researchers to explore five critical questions shaping the future of AI security and safety. Rather than formal presentations, the discussion will focus on open questions, technical challenges, differing perspectives, and the breakthroughs needed to build more trustworthy AI systems. 1. Hackers vs. Defenders: Who Wins in the Age of AI? AI is making both hackers and defenders more powerful. Who ultimately gains the advantage? AI can discover software vulnerabilities, automate cyberattacks, and dramatically lower the cost of targeting organizations. At the same time, it can help defenders identify threats, repair vulnerabilities, and protect systems at unprecedented scale. Will AI fundamentally shift the balance toward attackers or defenders? Key Discussion Questions Will defense win naturally as AI improves, or only if we make deliberate choices today? Which choices matter most? What evidence would tell us which side is winning right now? Large enterprises can afford sophisticated AI defenses. What happens to small businesses, hospitals, and everyday users? 2. Recursive Self-Improvement: How Would We Know If AI Started Improving Itself? What are the earliest warning signs of an AI system becoming more capable on its own — and are we prepared to detect them? Recursive self-improvement (RSI) describes a process in which AI systems help improve the technology behind future AI systems, potentially accelerating the pace of progress. Early signs of unexpected autonomous improvement could emerge in code changes, computing resource usage, or network activity. As AI becomes increasingly involved in developing and maintaining its own infrastructure, distinguishing authorized optimization from unintended autonomous behavior becomes a critical challenge. Key Discussion Questions What minimum monitoring and alert systems should every frontier AI lab have in place today? How can we distinguish authorized AI-driven optimization from the first signs of unintended self-improvement? Is preventing an AI from copying itself outside a controlled environment fundamentally different from preventing human insiders from stealing model assets? 3. Can We Trust Software Written by AI? AI is increasingly writing the software used to build the next generation of AI. Who checks the checker? AI-generated code is becoming an integral part of software development, including the systems used to train, deploy, and secure AI models. But what happens when AI writes the code, reviews the code, and helps maintain the infrastructure on which that code runs? Hidden vulnerabilities, unintended errors, or deliberately introduced backdoors could spread across interconnected systems before they are discovered. Key Discussion Questions Can we independently verify AI-generated code without relying entirely on another AI system? How concerned should we be about AI-generated backdoors today, compared with two years from now? What would it take to build a trustworthy, end-to-end AI software development pipeline? 4. When AI Agents Become the Target: Can Prompt Injection Be Solved? We are giving AI agents access to our emails, files, and financial systems. Can attackers hijack them using nothing more than carefully crafted instructions? AI agents are evolving from conversational assistants into systems capable of browsing websites, managing information, writing software, and taking real-world actions. This introduces a new security challenge: prompt injection. Attackers can embed malicious instructions in documents, websites, emails, or other content that AI agents process, potentially causing them to act against their users' intentions. Unlike traditional software vulnerabilities, prompt injection exploits the difficulty of distinguishing trusted instructions from untrusted content. Key Discussion Questions Is prompt injection fundamentally solvable, or must we assume that AI agents will sometimes be manipulated? What does secure agent architecture look like: strict permissions, sandboxing, independent monitoring, or something entirely different? Would you trust today's AI agents with access to your personal bank account? What would need to