Aug 26, 2026

The mathematics of AI uncertainty

Zoubin Ghahramani, a leading researcher at Google DeepMind and professor at Cambridge, argues that incorporating uncertainty is a missing piece for ever-improving AI. He discusses the critical difference between correctness and confidence in AI, tracing the historical evolution of probabilistic models from early neural networks to modern Bayesian approaches. Ghahramani highlights how current large language models often 'fake' uncertainty and explores successful implementations in areas like weather forecasting and AlphaFold, ultimately advocating for architectural innovations over pure scale to build more robust, trustworthy, and human-aligned intelligent systems that understand their own limitations.

Aug 22, 2026

Stealing Reasoning Traces from Proprietary LLM APIs — Ilia Shumailov & Alexander Panfilov

A paper by Ilia Shumailov and Alexander Panfilov exposes a critical vulnerability in proprietary LLM APIs: encrypted reasoning traces, returned for conversation state management, can be extracted and replayed. This enables universal jailbreaking, privacy leaks of sensitive user data, and poisoning of AI agent traces. The study highlights significant implications for AI safety, model monitorability due to opaque internal reasoning, and even subtle forms of "distillation" where smaller models mimic frontier ones. The discussion covers architectural and system-level defenses, advocating for rigorous scientific inquiry in AI safety research.

Aug 21, 2026

Agentic SDLC at Uber — Uday Kiran Medisetty & Adam Huda, Uber

Uber has transformed its software development with an agentic AI-powered factory, leading to a dramatic increase in engineer productivity. The presentation details six key infrastructure components: a unified model gateway with strict PII and safety guardrails, an MCP gateway for streamlined agent tool access and token optimization, agentified dev pods for rapid execution, a managed skills marketplace, a comprehensive context graph, and the Cortana AI assistant. Adam Huda then demonstrates an end-to-end feature development workflow, highlighting a critical shift to inner-loop validation (stopping short of CI) and automated, managed maintenance loops. The ultimate takeaway is that the bottleneck has moved from technical execution to strategic decision-making: "should we build it?" rather than "can we build it?"

Aug 14, 2026

IBM’s cloud collab, Meta’s Muse Glimmer & OpenAI’s upcoming Astra model

This episode explores IBM's massive AI infrastructure partnership with Together AI and NVIDIA, Meta's open-source Muse Glimmer model enabling powerful on-device AI, and OpenAI's delayed Astra model due to critical cybersecurity capabilities. Discussions cover the economics of industrial-scale AI, the implications of local vs. cloud AI, and the profound security challenges and opportunities presented by both open and closed frontier models.

Aug 06, 2026

Chasing Trillion-Dollar Companies, Founder Ambition, Token Budgets, & Regulatory Capture

Elad and Sarah discuss the rapid rise of AI's multi-trillion-dollar companies, debating whether this growth is sustainable or an anomaly. They explore how founder ambition is shaped by fear of AI labs, optimal strategies for startup exits, and the psychological impact of impending AGI on researchers. Key bottlenecks like compute power, the emergence of an oligopoly, and the growing threat of regulatory capture—exemplified by California's tax policies—are also analyzed, highlighting the critical societal trade-off between safety and technological progress.

Jul 31, 2026

AI Security Costs Rise: Cost of a Data Breach Report & Claude Opus 5

The podcast explores key AI developments, beginning with IBM's 2026 Cost of a Data Breach Report, highlighting AI's increasing role in both cyberattacks and defense, and the economic asymmetry it creates. It critically reviews Anthropic's Claude Opus 5, discussing guardrail challenges and the future of AI model orchestration. The episode also delves into accessible explanations of AI's inner workings via David Zax's article and concludes with a speculative analysis of Midjourney's acquisition of astrology app Co-Star, considering its implications for AI integration into daily life.