Gearing up to be AI-first is a real engineering shift. With AI-first development you have to lean extra extra hard on integration, end-to-end, and fuzz tests, i.e., "behavioral" tests. Very similar to how reinforcement learning needs to bang around in a simulator before it can figure out what to do, AI-first software projects need to invest heavily in "simulator" tests (NOT unit tests) that the AI can "bang around" on (and have to pass) before you let it merge/release. This is a big shift for engineering teams that are used to thinking mathematically/axiomatically/constructively: the code is correct because it was correct before my change, and I keep it correct each time I make a change. With AI you have dozens of people who can change 100s, 1000s, or more lines of code in a commit (and that's actually the goal) so changes easily violate that base case. The massive increase in testing reliance brings with it massive increases in VM pool infra, build infra, CI, test suite configurability, etc..., etc... So AI-first coding is a huge engineering shift going from carefully controlled code with lighter testing, to generating probabilistically controlled code with huge suites of tests, along multiple dimensions, that can run very fast, concurrently. It's also a big culture shift. It takes time so it makes sense that mature firms running large projects are trying to get it in place before their competitors do.