“AI Slop” - When Optimization Metrics Replace Human Readability
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.”
Maybe we should be measuring success differently:
How readable is the documentation without context?
How long does it take a new engineer/PM to understand and contribute?
How often do humans need to rewrite or simplify what the AI produced?
Does the output reduce or increase cognitive load?
Instead of another “50 agents built before breakfast” post, it’s time to also ask: what is the human cost of maintaining all of it? what should we actually optimize for? Token efficiency, or human understanding?
Replies
@tal_elor Human greed overrides everything else. It has done so, from the beginning of time.
Now, greed coupled with laziness, and lack of attention today will never let us optimize for human understanding.
The problem is that we are geared towards finding shortcuts and not utlizing our minds. Instead we find it easy outsourcing it to AI which has already made us several points dumber in the IQ scale.
There are lot of things we 'ought' to do, but are we really doing those things?
Athena
@daddygator123 I'm less worried about AI making people dumber, and more worried about organizations measuring the wrong things.
People optimize for incentives. If we reward quantity, we'll get quantity. If we reward clarity and maintainability, we'll get more of that too. Don't you think?
@tal_elor I had this debate with a CEO friend who was relentlessly pushing his team to maximize token usage. His reasoning was interesting: before you can optimize quality, you need to overcome inertia. Most teams are still underusing AI because they default to familiar workflows.
Token usage is a crude metric, but it can work as a temporary forcing function to build the habit of experimentation. The end goal is not more tokens. It is discovering which workflows produce real leverage, then measuring quality, speed, and maintainability.