The Risks of AI Learning from AI: Model Collapse and Data Degradation

When artificial intelligence is trained on data generated by other AI systems, it faces unique risks such as model degradation, loss of diversity, and the emergence of fundamental training limits. Just a few years ago, most AI models learned from data produced by humans-texts, images, and user behavior gathered from the internet, books, articles, forums, and social networks. This rich, varied landscape enabled models to extract meaningful statistical patterns. However, as generative AI technologies proliferate, the balance is shifting: an increasing share of content online is now created not by people, but by algorithms.

How the Closed Loop of AI Training Emerges

At first, it may seem unproblematic for AI to learn from AI-generated text, images, or code. After all, if a model can produce plausible content, why not use it for training? Synthetic data appears logical, cost-effective, and scalable, which is why it's increasingly used to train neural networks. But a crucial risk emerges: once AI starts learning from data created by other AI systems, it no longer directly reflects reality. Instead, a closed loop develops, where models increasingly rely on statistical approximations of previous generations rather than original sources, leading to a gradual shift in distributions, loss of diversity, and accumulation of errors.

The real danger lies not in the use of synthetic data itself, but in the scale and lack of control as AI-generated content saturates the digital ecosystem. As distinguishing between human- and AI-generated data becomes more difficult, new neural networks are trained more often on "reflections" of earlier models rather than on fresh, real-world information. To understand why this is risky, it's important to explore how the AI training loop forms and how it leads to model degradation over time.

The Formation of the AI Training Loop

The closed loop starts subtly. Generative models produce text, images, and code that appear in public sources-websites, blogs, documentation, educational materials, and even machine learning datasets. These outputs look plausible and formally correct, so they easily become part of new training samples. Over time, models learn from a mix of human and synthetic data, with algorithms focusing only on statistical structure, not origin. If AI-generated text fits expected patterns, it is treated as valid-even with simplifications or errors.

As synthetic content grows, each new model generation increasingly "chews over" the outputs of its predecessors. This is when accumulation effects begin: rare errors and formulaic language become the norm. Crucially, AI cannot consciously correct this process-it doesn't distinguish between original knowledge and copies, or realize it's training on its own derivatives. The system closes in on itself, losing touch with the original diversity of the real world.

This mechanism underpins what researchers call "model collapse": gradual degradation as models repeatedly train on synthetic data. But first, let's clarify what synthetic data is and why it's used.

What Are Synthetic Data and Why Use Them?

Synthetic data are artificially created rather than directly collected from the real world. In AI, this includes text, images, audio, video, or structured datasets generated by algorithms. While they may mimic real distributions, they aren't direct reflections of human experience.

Synthetic data arose out of necessity. In many domains, real data are hard to obtain or restricted by legal and ethical boundaries-such as medical records, financial information, or user behavior data, which require anonymization or cannot be used at scale. In these cases, synthetic data provide a convenient alternative: they can be generated in any volume, their structure can be controlled, classes can be balanced, and rare scenarios modeled. In computer vision, robotics, and control system testing, this approach often makes practical sense.

Problems arise when synthetic data become the main source of training. Generative models reproduce average patterns, smoothing out outliers and reducing diversity. The more data created this way, the more original distribution is distorted. Moreover, synthetic data inherit the limitations and errors of the models that created them; any biases, oversimplifications, or knowledge gaps are inevitably transferred and amplified with repeated training.

Synthetic data are not inherently harmful-but they become problematic when forming a closed ecosystem where AI increasingly learns from its own output rather than real-world interactions.

Model Collapse: How and Why Models Degrade

Model collapse refers to the progressive decline in neural network quality when training on data generated by other models. This degradation isn't caused by a single mistake or poor architecture, but by the cumulative effect of statistical distortion.

The core issue is loss of data diversity. Generative models tend to reproduce the most probable patterns, handling "average" cases well but neglecting rare or atypical examples. When such data are used for training, rare cases disappear from the distribution, pushing the model toward formulaic responses. The next stage is distribution shift: the model learns from an approximation of reality created by another model, and each new generation intensifies this shift. Errors that were once random become systematic, now "baked in" to the training set.

Especially dangerous is the "averaging" of knowledge. AI doesn't distinguish between important and secondary information-it simply optimizes for probability. As a result, complex ideas are oversimplified, explanations become homogeneous, and depth is lost. The model may appear confident and coherent, but its grasp on reality weakens.

Degradation often goes unnoticed at first. Metrics may even improve as the model reproduces expected patterns. The real issues emerge later-lower accuracy on new data, recurring errors, and reduced ability to handle atypical queries. Model collapse is not a bug in a specific model, but a systemic effect where AI loses contact with the real world.

Why Data Quality Matters More Than Dataset Size

For years, machine learning followed the logic that "more data means better models." This held true when datasets grew with real human content. But when much of the data becomes synthetic, quantity is no longer an advantage. Large, low-quality datasets amplify noise-scaling up errors and distortions rather than eliminating them. The model starts treating these patterns as normal simply because they recur.

Data quality is defined not just by correctness, but by representativeness. The real world is heterogeneous, contradictory, and full of exceptions-these make a model resilient to unexpected situations. Synthetic data, especially from generative models, smooth distributions and remove inconvenient examples. Another key factor is data provenance: when a model learns from data created by another model, it learns an interpretation, not facts. Even if plausible, this adds a layer of abstraction that distances the system from reality.

The result is a paradox: the dataset grows, metrics improve, yet the model's real ability to understand and generalize declines. That's why modern systems increasingly emphasize data curation, source diversity, and quality over sheer volume.

How AI Starts Reproducing Its Own Errors

When AI is trained on other models' outputs, errors stop being random and become persistent patterns perceived as correct. In traditional training, errors are distributed randomly and new data help correct them. In closed-loop training, synthetic data already contain a filtered version of reality, with recurring simplifications, inaccuracies, and biases. Re-training doesn't correct these-they become entrenched.

This creates a feedback effect: the model generates content with certain distortions, which then enter datasets, and new models learn and reinforce those same distortions but with greater confidence. What was once a rare error becomes a typical response.

Especially problematic is the lack of self-critique: AI cannot "know" it's making a mistake if the error aligns statistically with training data. The system can appear confident, logical, and consistent, while actually losing accuracy and depth. Over time, such models struggle with novel or atypical tasks, excelling at familiar templates but failing with rare, complex, or contradictory requests. This is model degradation in practice-not as clear failures but as a gradual loss of flexibility.

Where This Problem Is Already Emerging

The closed-loop effect of AI learning from AI-generated content is already visible in several fields. One of the most striking is online text content-articles, instructions, product descriptions, and answers to questions are increasingly AI-written. New models trained on web data inevitably absorb this content, resulting in text that is more formulaic, predictable, and semantically poorer, even if grammatically correct.

Image generation faces a similar trend: models reproduce a recognizable "AI style," with smoothed details, repetitive compositions, and similar faces and poses. New systems trained on such images increasingly fail to capture rare features or unconventional scenes.

In search and recommendation algorithms, the effect appears as a reinforcement of templates: AI answers reference AI-generated texts, and recommendations loop around existing popular content, shrinking diversity and closing the informational environment.

Even in programming, early signs are visible: AI-generated code is more often used in tutorials and repositories, and new models inherit not just good solutions but hidden anti-patterns that propagate widely. The common thread in all these cases is the disappearance of the "original source"-as human input diminishes, AI learns from its reflections, and the closed loop becomes a real developmental boundary.

Why This Is a Limit of the Current Neural Network Training Approach

Training AI on AI-generated data highlights a structural limit of today's machine learning paradigm. Most neural networks are built on the same principle-extracting statistical patterns from vast datasets. This works as long as the data reflect the full diversity of the real world. When the data source closes in on itself, the model no longer "learns reality," but only refines its own approximations. At this point, scaling-adding parameters, layers, or computing power-fails to solve the problem, as the input becomes less and less informative.

Another limitation is the lack of truth-checking mechanisms. Modern models optimize for probability, not for correspondence to external reality. If the dataset is full of synthetic content, the model cannot tell where it errs because, statistically, the answers seem acceptable. This vulnerability leads to degradation not just of quality, but of meaning. Models become slick, confident, and formally correct, but gradually lose their ability to handle novel tasks, new domains, or real-world contradictions. Such AI can serve as an interface or assistant, but its developmental potential is capped.

For this reason, many researchers speak not of a "data crisis," but a paradigm crisis. Without a steady flow of primary, diverse, and verifiable data, neural network progress within the current training framework becomes less effective.

Possible Ways Forward

Recognizing the problem of closed-loop AI training does not mean hitting a dead end-it signals the need to rethink data practices, training methods, and system architectures. Solutions exist, but none are simple or universal.

• Control data provenance: Separating human and synthetic content, labeling sources, and filtering training samples can reduce closed-loop effects. This requires infrastructure and standards, but is crucial for data quality.
• Hybrid datasets: Use synthetic data as a supplement, not a replacement for real data. This is especially effective for rare scenarios, provided the training base remains grounded in reality.
• Active collection of primary data: While costly and slow, gathering real-world data reconnects models with reality and will become an increasingly valuable strategic resource over time.
• Changing the learning paradigm: Future models may combine statistical learning with external validation, simulations, environmental feedback, and human-in-the-loop decision-making, reducing the risk of becoming stuck in their own outputs.

Conclusion

Training AI on AI-generated data is not a one-off problem or a temporary side effect of generative model growth. It is a fundamental limitation of the current approach to AI development. As AI-generated content fills the digital landscape, the risks of degradation, homogenization, and detachment from reality become increasingly acute.

It's not about "bad AI" or the failures of particular models, but about systemic dynamics-statistical learning without source control begins to work against itself, and scaling is no longer a solution. The future of AI will depend not only on architectures and computational resources, but on the data ecosystem these models are trained on. The ability to preserve diversity, originality, and connection to reality will become the key factor for sustainable AI development.