Ben Congdon

TL;DR: “Private by obscurity” has been dissolved. Internal tools often have layering boundaries that are enforced only by convention. It’s natural to assume a “high trust environment”, where privileged actions are discouraged by obscurity and goodwill instead of hard technical boundaries. Coding agents have dissolved this obscurity, and as a result internal platform engineering now really demands a security mindset.1 During a recent codebase audit, a coworker and I discovered an unfortunate set…

The rise of coding agents has made it easy for a single engineer to spend thousands of dollars a day in LLM tokens. This is a new class of expense, and it will change the future cost structure of software engineering. We are between stable equilibria today in SWE: the old one, of needing humans to drive any code change, and a yet-to-be-established new one, where AI agents write most code. Taking as a premise that AI agents will write a large fraction of code in the new equilibrium, we will need…

Coding agents and reasoning models let individuals consume many more LLM tokens than they could a year ago. It’s now easy for a single engineer to spend thousands of dollars in daily token usage. This is being actively encouraged through the recent memetic spread of “Tokenmaxxing” – the idea that if you consume more tokens, you’re more “AI native” and therefore producing more valuable output. Tokenmaxxing is not The Way. Plainly, it’s a textbook instance of Goodharting. Token leaderboards come…

In late January, I published a post1 (archive) on the Databricks engineering blog about “SAFE”, the feature flagging and experimentation platform I’ve been working on for the past few years. SAFE is what I’ve been spending most of my time on during my time at Databricks, and it’s been rewarding to see the project grow from an initial prototype to a mature internal platform. I’ve been the tech lead for SAFE for a while now, and the project has scaled significantly in headcount, scope, and usag...