Introduction: An Unsettling Resonance
Have you ever caught yourself in that moment—reaching for ChatGPT to handle a task, then suddenly realizing your brain seems to be “rusting”?
If you’ve felt this way, you’re not alone. According to a 2025 study published by MIT Media Lab, subjects who used ChatGPT over extended periods showed significant declines in neural activity, linguistic performance, and behavioral outcomes. Researchers call this phenomenon “Cognitive Debt”—when we continuously rely on AI to complete thinking tasks, our brains quietly accumulate a “debt.”
But here’s the even more alarming half of this story: While we outsource our mental heavy lifting to AI, the AI itself is undergoing a degradation process called “Model Collapse.”
This isn’t science fiction. It’s a dual crisis unfolding right now. For a deeper dive into the risks AI may pose, check out our comprehensive analysis of AI dangers.
What Is Model Collapse? AI Is “Eating Itself”
The Campfire Is Dying
Imagine a campfire. The flames are warm and bright, but if we stop adding fresh wood and instead keep throwing the burnt embers back into the fire, the flames will eventually die out.
This is precisely the situation AI models face today.
“Model Collapse”—also known academically as “Model Autophagy Disorder” (MAD) or more sarcastically as “Habsburg AI”—occurs when AI models stop learning from fresh, human-created data and instead begin learning from content generated by themselves or other AIs. When this happens, performance degrades.
According to research published in Nature, when generative AI is indiscriminately trained on both real content and AI-generated content, it leads to a collapse in the model’s ability to generate diverse, high-quality outputs.
Two Stages of Degradation
Researchers Shumailov et al. identified two critical stages of model collapse:
Early Model Collapse: The model begins losing information from the tails of the distribution—primarily affecting minority data. This stage is particularly insidious because overall performance may appear to improve while the model actually loses its ability to handle edge cases.
Late Model Collapse: The model loses a significant portion of its performance, begins confusing concepts, and loses most of its variance. At this stage, model outputs become increasingly simplified and homogenized, eventually becoming nearly useless.
It’s like repeatedly photocopying the same document—each copy loses a bit of clarity until it becomes completely unrecognizable. IBM’s technical experts provide a detailed explanation of this phenomenon.
Cognitive Debt: Your Brain Is Paying the Price
MIT’s Alarming Findings
In 2025, MIT Media Lab researcher Nataliya Kosmyna led a groundbreaking study. The research team divided 54 subjects into three groups: one using ChatGPT, one using search engines, and one relying entirely on their own brains to write essays.
Researchers used electroencephalography (EEG) to monitor brain activity across 32 regions. According to TIME magazine’s coverage, the results were striking:
- The ChatGPT group had the lowest brain engagement and “consistently underperformed at neural, linguistic, and behavioral levels”
- Over time, ChatGPT users became increasingly lazy, progressing from asking structural questions early on to simply copy-pasting entire essays by the study’s end
- The brain-only group not only reported higher satisfaction but also exhibited stronger brain connectivity
- When the ChatGPT group was asked to write without AI assistance, their performance lagged significantly, producing content rated as “biased and superficial”
These findings align with trends we discuss in our MIT AI Report analysis. Researchers termed this phenomenon “cognitive debt”—when you rely on AI long-term, your brain accumulates a “debt” that reduces long-term learning performance during independent thinking.
The Atrophy of Critical Thinking
Critical thinking is like a muscle: use it and it grows stronger; neglect it and it atrophies.
AI bypasses the crucial “exercise” process of wrestling with problems and thinking deeply by providing instant answers. When we start using ChatGPT to look up how to reheat chicken nuggets, we’re forgetting how to independently solve even the simplest problems.
Even more concerning, research in Frontiers in Psychology found that users with lower self-esteem are more likely to develop problematic AI dependency. This dependency creates a vicious cycle: users rely on AI because they lack confidence in their own expression, and this reliance further erodes their confidence.
Understanding the differences between ChatGPT models can help you choose and use these tools more wisely.
The Vicious Cycle: Humans and AI Are “Getting Dumber Together”
Here’s the most disturbing part: Human cognitive decline and AI model collapse are not two separate events—they’re a mutually reinforcing vicious cycle.
Here’s How the Cycle Works:
- Humans increasingly rely on AI for daily thinking and creative tasks—from writing work reports to Mother’s Day cards, even personal journals
- Human output quality declines—because brains no longer undergo deep thinking processes, the content we create becomes more simplified and lacks personal character
- This low-quality content trains the next generation of AI—the internet is flooded with AI-generated or AI-assisted content, which becomes the primary source of AI model training data
- AI models begin to degrade—model collapse occurs, and AI outputs become more homogenized and simplified
- Humans receive lower-quality AI outputs—this further reinforces our habits of shallow thinking
- The cycle repeats
This is a “collective dumbing down” cycle in which humans and AI participate together, mutually accelerating each other’s decline. We’re feeding an AI that’s simplifying the world with our own simplified thoughts, and this AI produces even more simplified content that influences our thinking.
With the development of next-generation models like GPT-5 and Claude Opus 4.5, whether this problem will be solved remains unknown.
Why Aren’t Tech Giants Solving This Problem?
Frustratingly, the technical solutions to model collapse are actually known: expanding model parameters, using more fresh human data, implementing data provenance mechanisms, and more. So why isn’t this problem being actively addressed?
Three Core Business Reasons
1. Cost Considerations
AI-generated data is essentially free, while obtaining high-quality, real-world human data is extremely expensive. For companies, maintaining the campfire with free “embers” is far more economical than spending big money to find “fresh wood.” This is also why effectively managing GPU resources is so important for enterprises.
2. Strategic Advantage
In fierce market competition, high-quality datasets are secret weapons. According to research from NYU’s Center for Data Science, AI models themselves aren’t a defensible technical “moat”—companies maintain competitive advantage primarily by closely guarding their unique human datasets. No company wants to easily share their solutions or data.
3. The “Good Enough” Mentality
As long as current services are “good enough” for most enterprise customers and can handle daily tasks, there’s insufficient motivation to invest heavily in solving what seems like “tomorrow’s problem.”
An industry observer’s sharp comment reveals this attitude:
“I’ve interviewed countless companies at trade shows using off-the-shelf large language models. When I ask them how they guarantee service quality amid model degradation, they just shrug and say: ‘We can’t.'”
The Way Forward: Rebuilding Healthy Human-AI Collaboration
Facing this dual crisis, we shouldn’t completely abandon AI—instead, we need to use it more consciously and cautiously.
Advice for Individuals
1. Establish “AI-Free” Thinking Time
Reserve time each day for thinking and creating without AI assistance. This could be morning journaling, afternoon brainstorming, or evening reading. Give your brain the opportunity to “exercise.”
2. Think First, Then Verify
Before using AI, try forming your own preliminary ideas or answers. Then use AI to supplement, verify, or refine. This keeps your thinking muscles active while still enjoying AI’s efficiency.
3. Preserve Your Unique Voice
When you constantly use AI to make your writing “sound better,” you may start feeling insecure about what you actually want to say. Remember: your original, imperfect thoughts are what make you unique.
Advice for Enterprises
1. Invest in High-Quality Data
As researchers like those at Gretel emphasize, thoughtfully curated synthetic data generation—rather than indiscriminate use—can prevent model collapse. This requires deep understanding of knowledge gaps to ensure data quality and diversity.
2. Establish Data Provenance Mechanisms
The Data Provenance Initiative, composed of researchers from MIT and other universities, has audited over 4,000 datasets. Enterprises should actively participate in such collaborations to ensure training data sources and quality. Understanding MLOps best practices can help enterprises build more robust AI management processes.
3. Choose Responsible AI Partners
When selecting AI training platforms or services, understanding providers’ commitments to data quality and model maintenance is crucial. This concerns not only current performance but long-term sustainability. Learn how our AI Stack enterprise solutions help enterprises build sustainable AI infrastructure.
Conclusion: Who Will Add Fuel to the Fire of Intelligence?
We find ourselves in a strange paradox: we’re outsourcing more and more mental activities to a powerful tool, yet that tool is “degrading” precisely because of our over-reliance on it.
This isn’t just a technical problem—it’s a philosophical question about humanity’s future. As we increasingly merge our minds with AI, who bears responsibility for ensuring that the true flame of intelligence—whether human or machine—never goes out?
The answer may lie in this: We need to become smarter AI users, not more dependent AI consumers.
In this era of human-machine symbiosis, maintaining the capacity for independent thought isn’t just being responsible to ourselves—it’s being responsible to the AI systems we rely on. Because ultimately, AI quality depends on the quality of content we feed it, and our thinking quality depends on how we choose to use these tools.
The flame of intelligence needs fresh fuel. The question is: are you willing to be the one who adds it?
