2. The market’s tunnel vision around generative AI tools (more code! more art!) creates an ecosystem-wide effect whereby emphasis and usage of tools for other things is boxed out. People have certainly built a ton of great AI tools for design, documentation, and dev tools — things that suggest bug fixes/refactors as you write, summarize dense onboarding docs, and create test data that looks like production data, for example — and it doesn’t mean companies will use them
3. Focusing on fixing the simple yet time-consuming problem of “more code! where is my more code?!!?” will balloon the responsibilities of these other areas, whose demands can grow exponentially (because complexity can, especially in software products)
4. Companies have thus left themselves with a perfect storm: (a) buckets of new code, (b) smaller “leaner” teams after layoffs (so less density of institutional knowledge), and (c) a drought of investment in tools that actually help with the most difficult parts of releasing good software products
5. They will find (if they haven’t already) that they quickly need to hire people back, this time with AI-related skills that thanks to AI’s newness basically no one has
