Large language models

Sep 08, 2026

Inside OpenAI’s Breakthroughs in Mathematical Reasoning

OpenAI mathematicians discuss how AI, particularly models like Astra and GPT-5, are rapidly advancing mathematical capabilities. They delve into recent breakthroughs in sphere packing and the discovery of a non-sofic group, emphasizing that AI's reasoning traces remarkably resemble expert human thought, including strategic decision-making and backtracking, rather than mere brute-force. The discussion also explores the changing role of human mathematicians, AI's emergent 'taste' in problem-solving, and the potential for a new era of accelerated mathematical discovery and understanding.

Sep 01, 2026

Can AI Learn Mathematical Intuition?

Daniel Litt, a mathematician, discusses AI's impact on his field, highlighting its strengths in computation and applying known techniques, while noting its weaknesses in intuition and theory-building. He emphasizes that the goal of math is understanding, not just proofs, and warns that current academic incentives might be distorted by AI-generated papers. Litt raises broader concerns about preserving human intellectual development as AI advances.

Aug 27, 2026

How Anthropic Builds: Lessons from Labs — Mike Krieger, Anthropic

Mike Krieger, a former CPO at Anthropic, details his transition to an IC role to build directly with AI, advocating for "unreasonable" asks like porting entire codebases. He shares lessons from Instagram on scaling and highlights Anthropic's internal use of Claude as a proactive teammate, flexible lab structure, and critical insights on AI product design, vertical applications, and mental health in the fast-paced AI industry.

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 25, 2026

⏭️ Forward Deployed: Voice AI on what works in 2026

This podcast episode delves into the pragmatic realities of building and deploying voice agents in enterprise. Featuring leaders from Decagon, Vapi, Retell, Daily, and Smallest AI, the discussion uncovers why the current state-of-the-art relies on cascaded pipelines (STT -> LLM -> TTS) instead of direct voice-to-voice models. Key challenges explored include managing latency versus intelligence, ensuring system reliability through fallback models, the complexities of turn-taking, and architectural strategies to overcome LLM context limitations and optimize costs. The experts also touch upon the differences between inbound and outbound use cases, multilingual considerations, and the potential future of hybrid voice agent architectures.

Aug 20, 2026

AI Agents vs Business Rules: Which Should Make Decisions?

This video contrasts deterministic business rules with probabilistic AI agents for enterprise decision-making. It argues that instead of one superseding the other, a hybrid approach combining rules-first processing with AI agent escalation for complex, unstructured, or judgment-heavy cases, augmented by deterministic guardrails and human oversight, leads to optimal automation.