Oct 08, 2026

$450K TO TRAIN A FRONTIER AI MODEL?

Thomson Reuters CTO Joel Hron explains why the company built Thomson, a domain-specific frontier model adapted from open-weight checkpoints like Qwen 3.5, using decades of proprietary legal and tax content and expert judgment. The conversation covers the training lifecycle from realignment and continual pre-training to agentic RL, the economics ($450K final run versus roughly $40M total), team structure, CoCounsel orchestration, verification challenges in legal and tax, and the future balance between open-weight and frontier models.

Oct 01, 2026

Better AI Starts With Better Verification

Halluminate, a Y Combinator S25 company, builds RL environments and benchmarks that teach AI models to do non-coding knowledge work, starting with financial services. In this fireside with YC Partner Jon Xu, co-founders Jerry Wu and Wyatt Marshall trace their pivot from evals to training environments, explain why verification quality matters more than task volume, and outline a roadmap toward multi-agent simulated companies. They also address reward hacking, alignment as an industry-wide responsibility, and what they look for in new hires.

Sep 03, 2026

Redefining Chip Architecture with Arm CEO Rene Haas

Arm CEO Rene Haas discusses Arm's evolution from IP licensing to producing physical chips, the transformative role of AI in accelerating chip design and verification, and the critical supply chain bottlenecks impacting data center build-out. He also covers SoftBank's strategic capital deployment, the immense opportunities for Arm in the burgeoning robotics sector, and emphasizes why power-efficient CPUs remain fundamental to all AI workloads, from data centers to edge devices.

Aug 28, 2026

How to Get Your Org to Adopt Coding Agents (Without Shipping Garbage) — Eyal Blum, Figma

Figma's internal AI agent adoption journey faces challenges like reduced developer agency, skepticism from senior engineers, and communication inefficiency. Solutions include investing in verification, using a testing pyramid for agent review, prioritizing detailed planning over prompting, engaging skeptics to build AI safety roadmaps, and implementing attention-aware communication by clearly marking AI-generated content.

Aug 17, 2026

How to Kill the Code Review — Ankit Jain, Aviator

Ankit Jain argues that traditional line-by-line code review is defunct, with AI reviewing code that humans no longer read. He asserts that code review's vital, often overlooked, purpose is 'alignment'—knowledge sharing, mentorship, and architectural feedback—which current AI-driven workflows fail to capture. His proposal involves capturing user-AI interaction prompts as acceptance criteria, generating test plans with an 'AI Slop Registry' (codified recurring review comments), and verifying changes against live previews. The new review surface becomes the 'intent and evidence' rather than the code diff, preserving collaboration and ensuring semantic accuracy.

Aug 03, 2026

Agentic Engineering vs Software Engineering: Beyond Vibe Coding

Anna Gutowska explains the paradigm shift in software engineering towards "agentic engineering," where AI agents execute goals defined by developers. She differentiates this from traditional, AI-assisted, and vibe coding, highlighting the increased importance of human oversight, orchestration, and verification in a world of probabilistic AI systems, and how this redefines the developer's role.