In my opinion, the problem is not even the cost. The problem is that people are using AI for running recurrent stuff instead of writing code to automate it.

For example. Imagine that you are comparing two documents (let's assume diff doesn't exist). You could ask an AI to compare the differences from you or you could use AI to write a tool to do it. For whatever reason, people are starting to go with the former not realizing that now they basically have to pay to compare documents.

As a developer, I don’t think it’s just that costs are going up. I’m also seeing more people lately talk about “vibe slop”.

On the one hand, organizations are without question using LLM's well beyond what is actually necessary, and as reality kicks in they're forced to scale back accordingly. However at the same time, on intervals counted in months, we're seeing breakthroughs both in hardware and software that dramatically reduce the cost of inference.

Between corporate FOMO and the rapidly decreasing costs of actually running LLM's I'm interested to see at which side of the spectrum these two meet