Mixed thoughts on this article, here's my take: There's no need to build a Software Factory. Every company already had a software factory in pre-AI – it is a socio-technical system delivering software (e.g. R&D). The input is the product ideas, the output is those ideas shipped to customers as code running in production. With AI we now have a chance to modernise the existing factory – speed it up. Code generation is solved, all the other bits of the factory are not: cheap&fast CI at scale, tech planning, code review, validation in production, incident diagnosis, etc. Naive way of "modernisation" is to give everyone a Claude Code subscription and call it done. Proper way is to figure out where the bottlenecks are and automate them with high-quality specialised AI agents. To surface the bottlenecks you need to pump in the throughput and see where it breaks – in the same way how you'd find an air leak in a tyre. The analogy with real factories is the story on how they were modernising the cotton mills when the electricity came in: retrofitting a big-ass electric engine (give everyone Claude/Codex) does not work, instead every part of the factory was modernised with smaller electric engines (specialised AI agents) – that's when the mill started to really increase its output. Humans are still there as building agents is REALLY hard, and it is not an end-state and more of a process: e.g. AI labs models are end-of-life every year and good luck just switching a model w/o re-engineering the prompts, evals, a/b tests, etc). But the emphasis really is on the SOCIO-technical system. Building those specialised AI agents is not only hard because of the non-determinism of underlying technology, but because those agents will interact with humans, create incentives, second order effects in a complex system. Every existing software factory is just a projection of a given engineering culture. Things you value as an R&D org, things you've optimised for over the years - they shape whatever the factory you have - with or w/o AI