The Slow Decay of Intelligence

Every major leap in artificial intelligence has depended on people, even when the narrative suggests otherwise. From the datasets for machine learning that trained early models to the feedback that tuned modern LLMs, human insight is the foundation of progress.

Through data augmentation and human input, AI trains by parsing text, sound, and imagery. By flooding datasets with AI-generated imitations of human data, models corrupt what they later use to train. Researchers call this phenomenon model collapse, a slow decay in the accuracy and diversity of AI systems when they begin to learn from their own synthetic outputs. When AI models are trained on their own outputs, they lose the noise and variation that make human data rich. Over time, they converge on sameness. In that sameness, the capacity to reason, adapt, and imagine begins to fade.

What makes this tricky is that this isn’t necessarily an error in itself, but rather an issue with data augmentation. Each repetition of model-generated material removes one degree of separation from human truth. Over time, what remains is a hollow version of human knowledge, optimized for performance metrics but empty of context.

The Mechanics of Collapse

A study from Fudan University examined what happens when generative models train on each other’s data. The result was, regrettably, both predictable and alarming. When one model remains static while the other learns from it, the data quality and diversity of output declines slowly. When both learn from each other’s outputs, this collapse only accelerates. [1]

This acceleration has a cause. Each model amplifies what the other already prefers, and each pass removes signals that fall outside the pattern. Without a human in the loop to correct deviation, these signals disappear entirely. Things that the AI may output rarely become invisible, the common output becomes dominant, and soon the system forgets what rarity looked like at all. The researchers described it as a Matthew Effect of data, where popular patterns gain stability while minority information fades. The more a model consumes synthetic data, the more it rewards the same repetition. The cycle eventually ends in model uniformity, a condition where the same prompt yields the same answer across every system.

Interested in learning more? Subscribe to the Sapien Newsletter for more on building ethical, verifiable AI infrastructure.

The Missing Variable

Researchers found that reintroducing the human in the loop reversed this collapse.  When they reintroduced a small amount of human-created content into the training loop, they were able to track output diversity stabilizing.The models began to recover.

AI systems need new material, information untouched by previous models, grounded in the world that exists outside the feedback loop. human data carries entropy, the randomness that machines cannot fake. Every real sentence contains variance in tone, perspective, and error. Every image carries imperfection, its own unique lighting. These are points of data that cannot be generated synthetically, and inherently depend on the minute differences between humans. [1]

However, this data is not easy to source. The web that once offered a near-limitless supply of human data is now saturated with synthetic content. Large models scrape one another, and every synthetic post poisons the well a little more. To keep artificial intelligence alive, someone must build a new source of truth. That source cannot come from centralized institutions alone. It must come from people.

People as the Source of Stability

To prevent model collapse, AI companies must shift their approach to data governance. That shift begins with curating verifiable datasets for machine learning grounded in human contribution. The foundation of every model is judgment, and maintaining human in the loop systems preserves that judgment. Machines can repeat, interpolate, and approximate, but they cannot yet know why something matters.

This convergence has social and economic consequences. It creates cultural flattening, where every creative output begins to resemble the same “average”. It builds feedback systems that reinforce bias. Worse yet, it undermines the reliability of AI outputs used in medicine, law, and science. The most sophisticated systems in history are now training on datasets polluted by their own output. The result is a machine that cannot tell the difference between what is real and what is a copy of a copy and training or fine tuning LLMs on such material only copies the error.

Without mechanisms to verify data quality and human input, AI development risks becoming an industry that manufactures entropy while losing information. The information that powers every model must be traceable to people, or the model will drift into fiction.

A Future That Learns From Its Source

Sapien was built to fix this by keeping the human in the loop through decentralized participation. Sapien is a decentralized data foundry where people can contribute verified knowledge to train AI systems.The system is built around a simple but powerful principle called Proof of Quality. It aligns economic and behavioral incentives with accuracy and truth. This system replaces the linear supply chain of traditional data labeling with a feedback loop of quality, incentive, and transparency. It aligns every participant, from enterprise to individual, around a single goal: creating data that sustains intelligence.

By transforming contribution into a verifiable, incentivized system of trust, Sapien reconnects AI to the world it was built to model and ensures that the intelligence shaping the future remains informed by human judgment, not detached from it.

Read Next:

What happens when we run out of human data? - Exploring the Limits of Internet-Sourced Training Data

How our token guaranteed Proof of Quality - Sapien Tokenomics

Why AI can't say "I don't know" - How Bad Training Pushes AI Models to Guess

FAQ:

What is model collapse?
Model collapse occurs when AI systems train on their own synthetic data, causing gradual degradation in diversity, reasoning, and accuracy. The model becomes self-referential, recycling its own patterns instead of learning from reality.

Why does model collapse happen?
AI systems learn patterns from human-generated data. When that data becomes repetitive or synthetic, models lose contextual accuracy and begin reinforcing their own errors.

How can human input prevent model collapse?
Human data reintroduces natural variation, imperfection, and context that machines cannot simulate. It acts as a corrective force against bias, grounding models in real-world signals.

Is model collapse a technical issue or a governance issue?
Both. Technically, collapse stems from feedback loops. Structurally, it stems from data governance failures where synthetic content is mixed with real data without verification. If left unchecked, AI systems will lose their grounding in reality, producing outputs that appear coherent but are factually hollow.

How does Sapien’s protocol address this?
Sapien enforces Proof of Quality, a decentralized mechanism where contributors validate each other’s work and build verifiable onchain reputations. It ensures that every dataset traces back to accountable human input.

How can I start with Sapien?
Schedule a consultation to audit your LLM data training set.

Sources: [1] Convergence Dynamics and Stabilization Strategies of Co-Evolving Generative Models, Weiguo Gao and Ming Li, 2025 - https://arxiv.org/abs/2503.08117

‍