The post misses a few things:

1. The ai bubble is currently being subsidized to an unimaginable degree. If you were to actually pay true cost for your token usage, you wouldn't be saving that much over an engineer's salary. Probably even worse once AI companies start to extract a real profit. 95% of companies diving into agentic labor will be in for a rude awakening when they balance next year's budget.
2. The cost to keep ai useful in its current form has a high floor. Unless you keep up with expensive training, your models will drift. You can only scale your model intelligence with more hardware (roughly). In two years, Claude opus 4.8 will still be bloating context to learn about the latest cloud platforms and libraries. A human engineer will get those passively at no cost to the company.
3. As the complexity of the task grows the complexity of the ai babysitter must match it. Even if Ai stays cost effective, companies can now save money by spinning up bespoke in-house software to cut out vendors (think observability platforms, task tracking, product design, marketing systems, etc...). No matter how many adversarial reviews and sub agents you spin up, an Ai can't grasp the full context of your company and it's shifting priorities. The software engineer role transitions to a pseudo-sysadmin + product architect.

C-suites don't want know about software and don't care about non functional requirements (security, availability, audit ability, etc...). They just want to wave a magic wand and have a product appear, which is what Ai provides the illusion of. That's why all current Ai software is garbage, but the smarter companies will catch on