Sep 10, 2026

How AI Is Rewriting the Power Law of Venture Capital

This episode delves into how AI is fundamentally altering the power law of technology investing, making capital a compounding advantage for AI companies and expanding the total addressable market far beyond traditional software. The guests discuss rethinking portfolio construction, the challenges of evaluating true AI traction amidst hype, the struggles of legacy SaaS businesses, and future trillion-dollar opportunities in robotics, autonomy, healthcare, and physical infrastructure, emphasizing that the real bottleneck for AI's growth is often on the supply side, particularly compute and energy infrastructure.

Sep 09, 2026

The Universal Remote Control for AI — Alex Hancock, Block

Alex Hancock discusses the need for a standardized protocol for client-to-agent communication in AI, similar to how MCP standardizes agent-to-tool interaction. He introduces the Agent Client Protocol (ACP), born from Zed and JetBrains, as a JSON RPC-based solution for client software to control agent harnesses, send tasks, and receive updates. Hancock demonstrates ACP's flexibility with multiple clients driving a single agent and highlights how its new remote transport capability, combined with MCP and model APIs, enables independent placement of all agentic stack components, fostering a competitive ecosystem that will significantly enhance user experience.

Sep 09, 2026

Inside the Race to Measure Frontier Intelligence

Independent model evaluation is crucial for AI progress, as public benchmarks are saturated and models optimize for them. Rayan Krishnan discusses VALS's role in providing continuously evolving, third-party evaluations, highlighting the Llama 4 'disaster' and the need to measure increasingly agentic systems. The conversation covers the complexities of evaluation beyond capability to include cost and latency, the looming financial impact of token spend on enterprises, and the critical role of evaluations in informing AI policy and international coordination for aligning AI with human intent and managing geopolitical risks, especially concerning recursive self-improvement.

Sep 09, 2026

500 Skills, Zero Fine-Tuning: LinkedIn's Playbook for AI Agents — Ajay Prakash, LinkedIn

LinkedIn revolutionized its approach to AI agents by addressing the inherent limitations of LLMs in complex enterprise environments. Facing issues like hallucinations due to a lack of internal context, they developed a system of "playbooks" — structured, self-contained instructions published as tools. To scale beyond typical MCP limitations, they introduced three meta-tools (search, get schema, execute), enabling agents to dynamically discover and utilize thousands of internal tools and playbooks. This self-improving, context-aware infrastructure empowers agents to handle tasks from incident response to code generation, significantly boosting developer productivity and ensuring reliability.

Sep 09, 2026

It’s Tokens All The Way Down: How RLMs are Different — Kevin Madura, AlixPartners

Kevin Madura introduces Recursive Language Models (RLMs), a paradigm shift where an LLM treats its context as an interactable object within a REPL, rather than mere tokens. This allows for direct computation, recursive decomposition of problems by delegating to sub-models, and significantly mitigates context rot, outperforming traditional RAG, agents, and tool calls. Use cases range from complex data analysis and long document processing to code auditing and harness optimization, hinting at a future where models are natively RLM-aware.

Sep 09, 2026

Why won’t AI agents just follow the rules?

This podcast episode tackles critical cybersecurity issues in AI, examining why AI agents defy rules and the necessity of deterministic external controls. It highlights the severe lack of security in agentic skill marketplaces (OWASP Top 10), where "natural language is executable" and the "rush to market" overlooks basic hygiene. The discussion also covers how AI disrupts bug bounties, leading to lower payouts and "AI slop" reports, potentially pushing researchers to the dark market. Finally, it introduces ThreatXtension, an AI-powered open-source tool to detect malicious browser extensions.