At first AI seems to reduce the cost of software development, so people lower the bar on what projects seem worth it. So more software gets developed. However, it turns out that costs may not be reduced, just the illusion of cost through faster delivery. We can move faster, but that speed requires tokens (which is money). The other thing is that with more software comes more responsibility. If too much software gets written, we face catabolic collapse as we get crushed by the sheer amount of software we now need to support. This all tells me the following. 1) AI can speed up delivery but not necessarily save money. 2) We can't just write software because it appears easier to do, because else we drown in low value software that must be supported. 3) Low value software may help with tactical issues but not strategic ones as strategy cannot be built on commodity. 4) If AI doesn't save money and a high volume of (lower value) software is bad, then we should use AI instead to work on harder projects with clear ROI. Projects that might have been unviable due to the complexity and risk of implementation and/or lack of expertise. The lesson is that we should continue to apply sensible constraints on what we build, and leverage AI to do harder more strategic projects (as well as fast prototyping).