The Hidden Risk in AI: How Models Can "Forget" Over Time

Artificial Intelligence (AI) is getting better and better at things like writing text, creating art, and even making decisions. But a recent study has uncovered a major problem: AI models can start to "forget" important information if they are trained on data that was generated by earlier versions of themselves. This issue is known as "model collapse."

What’s Going On?

When we train AI, we give it a bunch of data to learn from, kind of like how a student studies textbooks. But imagine if, after the first year, the student only studied notes they made based on the textbooks—and didn’t go back to the original textbooks at all. Over time, the student might miss important details and start making mistakes. That’s exactly what happens with AI models trained on AI-generated data. They start to lose sight of the original, real-world data and become less accurate.

This is a problem for many reasons, especially as AI-generated content becomes more common. If AIs are only learning from their own past outputs, they could lose touch with real-world information, leading to less accurate predictions or incorrect conclusions.

Why Should We Care?

As AI becomes more integrated into our daily lives, from generating news articles to automating tasks at work, this decline in accuracy could affect many industries. For example, if an AI writing assistant starts producing less accurate or lower-quality text over time, it could impact journalism, customer service, or marketing.

But there’s another big concern: copyrighted material. AI models are often trained on publicly available information, which includes a lot of human-made content like articles, images, or music. If the AI becomes less accurate or creative, are we still using this human-made content in a fair way? As the line between human and machine-generated content blurs, we might face challenges in giving proper credit to original creators.

What Can Be Done?

To prevent this, AI developers and companies need to keep feeding their models real-world, diverse data—not just AI-generated stuff. They also need to regularly check that their models are still producing accurate and useful information. As users, we need to be aware of these risks and ask for transparency about how AI systems are being trained.

If you’re interested in reading more about this research, you can find it here .