Session Description

A year ago, my argument was that productivity lives in the whole development lifecycle, not in how fast anyone types. AI hasn’t changed that — it made it unforgiving. The instinct is to treat an agent as a fixer, something you point at a problem to make it go away. It isn’t. It’s a multiplier, and a multiplier obeys whatever it’s standing on. Direct it at a shaky team process, and nothing gets repaired; the shakiness simply scales. More defects, longer review queues, green test suites that are quietly wrong — and reworks that grow faster than anyone wants to admit.
Which reframes the whole question. It’s not “how do we add AI?”, it’s “what is it multiplying?” For Scala teams, the honest answer is the language itself. Strongly typed libraries and a compiler that refuses to lie give you a foundation that gets firmer under pressure. This allows to discover failures at runtime – the kind of failures that only surface at runtime on production with other stacks.. The types you already have are complex enough. What’s usually missing is a process built to lean on them.
So that’s what we’ll assemble, one component at a time, like building together a kit. Context Fabric, CI build for Machine Speeds, Tiered Review and testing strategies for the AI era. We will apply the same discipline we’ve been using in large monorepo teams — the Airbnb and Databricks tier — where we’re seeing output climb by roughly half in a matter of months, but in a way that can be split and rearranged to match your context. You won’t leave with a mandate to adopt all of it. You’ll leave with the kit, the cost of each piece, and a clear sense of what to reach for next on your own Scala projects — a way to raise productivity and quality at the same time, instead of trading one for the other.