There s a growing pattern of using tokens to generate AI code and documentation slop. Then use even more tokens to understand and review that slop.
Then judge engineers by token usage instead of how empathetic and clear their docs and code actually are
At some point, the system starts optimizing for the wrong thing. Instead of asking Can a human actually work with this? , we continue asking How much did we generate? or How many agents did we spin up today? - are those the success metrics we want?
A clear example of this is what we re seeing in AI-generated UIs for landing pages. Tools like Claude (and others) can produce interfaces quickly, but they often converge into a very recognizable template. Same layout patterns, same spacing, same visual language. It becomes less design and more average of all designs the model has seen.