Kristofer Palmvik.

I’ve now seen dozens of companies struggling to put AI to work writing code, and each one has moved through five clear tiers of automation. That felt familiar, and I realized that the federal government had been there first – but for cars. In 2013, the NHTSA created the five levels of driving automation1. This was helpful, because while the highest level at the time was only level 22, it let everyone have a common language for both where things were, and where things were going.

Remote work doesn’t kill belonging. Indifferent leadership does. The office never created belonging on its own, it just made it easier to do the things that create belonging: casual conversations, shared meals, visible recognition, the spontaneous moments of human contact that accumulate into trust. If you move to remote or hybrid and don’t deliberately recreate those conditions, you haven’t gained flexibility. You’ve just moved the loneliness into people’s homes.

Although we started our MVP exploration with large generative prompts, that’s obviously not a sustainable solution in this type of context, which demands very high standards of accuracy and clear reasoning grounded in data. The solution instead became an orchestrated multi-agent pipeline with 23 specialized agents collaborating in a strictly linear chain, with built-in QA loops, access to AI tools (so-called “tools”), and rigorous data integrity and formatting rules.

The valuable part of an open source contribution was never the code. It was the understanding. Someone found a problem, understood it well enough to describe it, maybe understood the codebase well enough to propose a fix. The code was a vehicle for that understanding. Now the code is free and the understanding is what's scarce. A detailed bug report with reproduction steps and a clear description of expected versus actual behavior is worth more than ten agent-generated PRs. It always was. The gap has just gotten enormous.

The productivity gains from these models are real, and so is the friction and understanding that come from engaging with the work on a tangible and frequent basis. Despite the countless failed attempts at trying to democratize coding while not understanding coding, we're faced with the reality that you cannot understand code without engaging with it. And it's become clear that if you don't keep engaging and writing it, you can lose touch with that understanding, which will in turn make you a less capable orchestrator in the first place, rendering this phase of AI coding a strange and needlessly stressful interlude.