Scott, Dillon, and Matt dig into AI routers — the layer that decides which model actually answers your prompt — and why Stripe reportedly paid ~$9B for OpenRouter while Ramp built its own. The conversation covers what routers really do today (less 'smart judge,' more traffic control), why orgs want a single gateway for spend and model policy, and whether providers will quietly downgrade you to a cheaper model. Plus: burnout recovery, smart chess boards, flaky end-to-end tests, and a reindeer hot dog in Bergen.

Scott, Dillon, and Matt dig into AI agent skills — what they are, when they earn their place, and when a plain prompt does the same job. Scott's team routes every unit and integration test through a mandatory skill, Dillon dips in and out of them, and Matt loads almost none and lets the model figure it out. Plus anti-slop cleanups, the ponytail skill, handoff docs, and why ASD-STE100 is the only PR-description prompt Matt needs.

Scott, Dillon, and Matt dig into 'tokenomics' — the economics of AI models and whether the ever-climbing cost of running them is actually buying anyone more value. They swap notes on the rug-pull theory of AI pricing, tokenizer changes that quietly tax you 30%, and how their companies are clamping down (HubSpot defaulting to Sonnet and zeroing out the Fable budget). Plus a standup of weddings, World Cup corruption, a 16-0 soccer loss, and a first-ever Fourth of July party.