What happens when AI models start learning from each other instead of just from us? It’s not sci-fi. In fact, it’s happening right now.
As AI-generated content floods the internet, whether it is text, code, or even images, newer models are training on older models’ outputs. That means generative models are not only absorbing patterns, but also quirks, shortcuts, and biases from their forerunners. And the results of it are even more weird, subtle, but yet important!
In 2019, most of the AI models that were avaliable were trained on raw internet data which was all written by humans. But how about now, in 2025? That same internet is filled with essays, code snippets, stack overflow answers, and art— yet it is all generated by AI.
So now, when new models are being developed; these models now train on these new contents, they are all:
– Reinforcing patterns from earlier models
– Losing originality and nuance
– Picking up errors or biases that were never human to begin with
Training the AI models is just like training a student with another student’s notes- without ever referencing the textbook. However what AI models learn from preexsisting data is not always correct. For example, let’s say you use Copilot to write your code, and there is one bug that you are not aware of. If that piece of code ever ends up public, and future models train on it, they may start:
– Repeating non-optimal patterns
– Copying formatting habits or variable names
– Misunderstanding complex logic because it’s just mimicking past output
So in some ways, models are passing on habits, good and bad—just like humans.
However this doesn’t just happen to code. Instead, language models and art generators are also caught in this loop. In fact, AI-generated essays are often picked up and reposted online, and those reposts can easily cause for many new problems to occur in the training datasets—treating machine-made content as if it were original human work. The same thing is happening in the visual world: AI-generated images are being scraped into future art datasets. Overall, patterns get recycled. Originality fades. Today, we are entering a world of AI echo chambers, where everything starts to look and sound… the same.
But don’t worry, there’s good news too—researchers are aware of this issue and actively working on various solutions to cause this problem from occuring. One idea that they have is to start watermarking AI content so that it can be later identified and filtered out during training. Another approach can involve carefully curating datasets to reduce the amount of AI-generated content that gets fed back into the system. Today, more and more human oversight is being used to ensure better diversity, depth, and nuance. On top of that, researchers are also exploring meta-learning techniques to help models detect and correct feedback drift.
As we build the next generation of intelligent systems, we have to ask ourselves: Are we really feeding these models enough originality? Are they actually reflecting human knowledge, or are they just echoing past outputs? And can we break the loop before things become way too predictable?
These questions aren’t just philosophical—they’re practical. And how we answer them will shape the future of how AI thinks, codes, creates, and communicates. And that makes it one of the most important challenges in computer science today.
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