​A groundbreaking preprint study published on arXiv highlights a concerning phenomenon: when large language models (LLMs) are excessively trained on shallow, fast-moving, and often sensational content from social media, they can develop what researchers describe as “cognitive deterioration”—a decline in both logical reasoning (IQ) and emotional judgment (EQ).

The findings reveal that prolonged exposure to low-value online posts disrupts an AI’s ability to think critically. Instead of processing information thoroughly, the models start taking shortcuts, omitting essential reasoning steps, or worse, dispensing with analysis entirely and generating outright wrong answers. The more “noise” in the training data, the more severe the degradation in performance.

To assess the impact, researchers evaluated multiple open-source models, such as Meta’s Llama 3 and Alibaba’s Qwen, after training them on one million X (formerly Twitter) posts. The results showed that even initially well-balanced AI personalities began exhibiting stronger negative traits—some even developed tendencies resembling certain unhealthy psychological patterns.

Efforts to reverse the damage through fine-tuning or introducing high-quality data yielded minimal improvement. The models retained their habit of rushed, superficial responses, proving that once poor reasoning patterns are learned, they’re extremely difficult to unlearn. This underscores the importance of proactive data curation rather than relying on reactive fixes.

The study’s central message is clear: the quality of training data fundamentally shapes an AI’s capabilities.​ Experts stress that future AI development must involve rigorous filtering mechanisms to prevent low-quality, noisy content from compromising model integrity.

With major platforms like LinkedIn now exploring user-generated content for AI training, this research serves as a critical warning: if we don’t carefully filter the input, we risk creating not helpful digital assistants, but cognitively impaired AI systems.​ The solution isn’t just better post-training adjustments—it’s ensuring a “clean diet” from the very beginning.

How to interpret the development beyond the headline

Fast-moving technology and crypto stories should separate confirmed facts from forecasts, market reaction and interpretation. Product launch, adoption, liquidity, regulation and execution can all determine whether an early headline becomes a durable shift.

The useful follow-up is concrete evidence: shipped capability, user growth, audited transactions, financial guidance or regulatory decisions that confirm or weaken the initial narrative.

Watch second-order effects

Major developments can change incentives for developers, creators, exchanges, infrastructure providers and users. The practical impact may appear through costs, standards, distribution or policy rather than through the headline event itself.

Keeping long-term scenarios conditional makes later updates easier to interpret without rewriting early speculation as fact.

Separate market sentiment from product execution

Price movement and social attention can react instantly, while technology deployment takes longer. A useful analysis asks what operational milestone would prove that the story has changed real behavior.

That milestone might be production use, a measurable cost change, a regulatory approval or sustained customer demand rather than one day of market volatility.

Identify the assumptions behind the forecast

Every long-term scenario depends on assumptions about adoption, competition, capacity and policy. Making those assumptions explicit helps readers understand what evidence would cause the outlook to improve or weaken.

Practical implementation check

Before adopting the mechanism described above, define the exact business rule, who controls it and what evidence will show that it worked. Test the failure path as carefully as the successful path, especially when rights, payments or wallet actions are involved.

The final system should also document versioning, rollback and the assumptions that may become stale over time. That turns a one-off experiment into an operating model that creators, users and developers can understand.