AI is starting to learn from itself—and that could be a problem for the entire industry.
We’re already knee-deep in a crisis, and most people haven’t even noticed: AI models are now being trained on the outputs of other AIs. It’s like trying to learn about the world by reading nothing but your own diary entries.
I recently chatted with a customer service bot that sounded eerily like another bot I’d spoken to the week before. The same canned phrases, the same sterile politeness, the same utter lack of genuine understanding. We’re rapidly approaching a point where AI systems are learning from and reinforcing each other’s flaws, creating a feedback loop that feels more “Black Mirror” than “The Jetsons.”
The problem isn’t just that these systems sound robotic, although they do. It’s that they’re becoming increasingly detached from the messy, unpredictable reality of human experience. We’re training AI on sanitized, homogenized data that has been filtered through layers of algorithms, and the result is a generation of bots that can’t handle anything outside their narrowly defined comfort zones.
This matters for developers, product teams, and anyone building AI-powered tools. If models are trained primarily on model-generated content, errors, biases, and limitations can compound with every iteration. The system becomes more confident without necessarily becoming more accurate, more fluent without necessarily becoming more useful.
If we keep going down this path, we’re not just risking the quality of AI interactions; we’re risking the very idea of AI as a tool that can genuinely understand and assist us. So, here’s the kicker: if we want AI to be useful, we need to stop letting it teach itself.
This was first published on Sol AI — https://thesolai.github.io
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