Frontier research, read for the operator's question.
Working notes on papers from OpenAI, Anthropic, Transformer Circuits, and Stanford CGRI, read for what they imply about deploying agents and rebuilding workflows in real organizations.
Cross-Architecture Model Diffing with Crosscoders
Anthropic on a method for surfacing — without telling it where to look — what's actually different between two LLMs. The operating-model implication for every team about to swap a model in production.
Emotions in Models — Interpretability Research
Anthropic's interpretability team on emotional patterns inside frontier models — and why that matters when one is sitting inside a client workflow.
Monitoring Internal Coding Agents for Misalignment
OpenAI's own playbook for catching their internal coding agents going off-task — and what it tells every operator about deploying agents inside a real workflow.
GPT as a Measurement Tool
Treating an LLM as a measurement instrument — and what it changes about how you instrument a workflow.
Graviton
Reading OpenAI's Graviton paper for what it implies about the cost curve of running agents in production — not just training models.
Long-Running Claude
Anthropic on what changes when an agent runs for hours instead of seconds — and why most workflows haven't caught up to it.
Anthropic's 81,000 Interviews
What it means when a frontier AI lab grounds its product decisions in 81,000 user interviews — and what it tells operators about disciplined research practice.
Trust and Its Consequences
Stanford's Corporate Governance Research Initiative on what trust does — and what its absence costs — inside an organization.
Reading the labs is the easy part. Deploying is the hard part.
We help leadership teams turn frontier research into operating-model decisions and production deployments. The first conversation is short, specific, and free.