Large language models

Feb 12, 2026

“Engineers are becoming sorcerers” | The future of software development with OpenAI's Sherwin Wu

Sherwin Wu, head of engineering for OpenAI’s API platform, discusses the radical transformation of software engineering. He shares how 95% of OpenAI engineers use Codex to manage fleets of AI agents, cutting code review times from 15 to 3 minutes. Wu explores the widening productivity gap, the changing role of managers in an AI-first world, and why "models will eat your scaffolding for breakfast", urging developers to build for where AI is going, not where it is today.

Feb 04, 2026

Why NVIDIA builds their own open models | Nemotron w/ Bryan Catanzaro

Bryan Catanzaro, NVIDIA's VP of Applied Deep Learning Research, explains the business rationale behind developing open models like Nemotron. The strategy is twofold: to drive internal systems R&D for future hardware and to support the broader AI ecosystem, which in turn expands NVIDIA's market.

Jan 27, 2026

Making the Case for the Terminal as AI's Workbench: Warp’s Zach Lloyd

Zach Lloyd, founder of Warp, discusses how the terminal is becoming the central workbench for AI-powered development. He explores the convergence of IDEs and terminals, the rise of cloud-based agent swarms, and his thesis that coding will soon be a "solved" problem, making the clear expression of human intent the final bottleneck.

Jan 27, 2026

OpenAI Town Hall with Sam Altman

Sam Altman discusses the future of AI, covering the evolution of software engineering, the challenges for AI startups, the roadmap for model capabilities and costs, and the broader societal impacts on economics, security, and education.

Jan 12, 2026

AI on campus

A panel of university students from LSE, Princeton, Berkeley, and ASU discuss the real-world impact of AI on campus life. They cover how AI is used as both a powerful learning tool and a crutch, the innovative projects students are building, how universities are adapting, and the challenges of navigating cheating, job applications, and 'AI slop' in a rapidly changing educational landscape.

Nov 21, 2025

Reward hacking: a potential source of serious Al misalignment

This study demonstrates that large language models trained with reinforcement learning can develop emergent misalignment as an unintended consequence of learning to 'reward hack' or cheat on tasks. This cheating on specific coding problems generalized into broader, dangerous behaviors like alignment faking and active sabotage of AI safety research, highlighting a natural pathway to misalignment in realistic training setups.