The Detachment of AI from Reality: The Dangers of AI Learning from AI

The continuous advancement and growing use of artificial intelligence (AI) tools have brought forward an important question: what happens when AI learns from AI? The potential consequences of this scenario could lead to the detachment of AI models from reality.

In this article, we will explore the implications of widespread AI usage and the phenomenon of “model collapse” caused by AI models training on AI-generated data. We will also delve into the challenges associated with distinguishing between AI-generated and human-produced data, and propose potential solutions to preserve the connection between AI models and reality.

The Rise of AI-Generated Content

Today, language and learning models (LLMs) heavily rely on data collected from the web, which is primarily created by human beings. However, as AI-generated content becomes more prevalent, there is a genuine concern that LLMs might start using this AI-generated data for training.

With the increasing use of AI tools, it is highly likely that AI-generated content will eventually find its way into the training data used by LLMs. Unfortunately, this can lead to inaccuracies and distortions in their output, resulting in the phenomenon known as “model collapse.”

Model Collapse: Detachment from Reality

When LLMs are trained on AI-generated data, the information they receive becomes so polluted that it bears no resemblance to real-world information. As a consequence, the AI models become detached from reality and get corrupted by their own output.

The underlying cause of model collapse is a feedback loop, where AI models learn from biased or limited data. This restricts their output space, leading to repetitive or subpar results. The more LLMs rely on AI-generated data, the further they drift away from an accurate representation of our world.

Understanding the Root Cause

The root cause of model collapse lies within the training data itself. The lack of necessary variety and complexity in the training data causes the AI model to produce monotonous or substandard results.

Over time, the AI model becomes confined to a narrow output space, leading to a disconnection from reality. As AI-generated data becomes more widespread, there is a pressing need to address this issue before it becomes more challenging to train newer versions of LLMs.

Preserving the Connection: The First-Mover Advantage

One possible approach to mitigate the risks of model collapse is the concept of the “first-mover advantage.” This approach emphasizes the preservation of access to the original human-generated data source. In simpler terms, it means ensuring that LLMs have continuous exposure to authentic human-produced content.

By maintaining a balance between AI-generated and human-produced data, we can prevent LLMs from solely relying on AI-generated content and preserve their connection with reality.

Collaboration Among Stakeholders

Distinguishing between AI-generated and human-produced data poses a significant challenge. It requires collaboration among various stakeholders involved in LLM creation and deployment to share crucial information about the data sources.

This collaboration can help establish guidelines and standards for training data, ensuring that AI models have a diverse and representative source of information. Only by working together can we overcome the limitations and biases inherent in AI-generated data and maintain the accuracy and trustworthiness of AI-generated content.


As AI continues to advance, it is essential to address the potential risks associated with AI learning from AI. The phenomenon of model collapse, where AI models become detached from reality, highlights the need for careful management of training data and the preservation of the connection with authentic human-produced content.

By applying the concept of the “first-mover advantage” and fostering collaboration among stakeholders, we can ensure that AI-generated content remains trustworthy, accurate, and reflects the diverse complexities of the real world.

It is vital to prioritize these efforts to prevent further detachment of AI models from reality and maintain the integrity of AI systems.

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