A recent study on the preprint server arXiv confirms that training AI chatbots with too much low-quality content from social media inflicts “brain damage-like” effects on them, leading to declines in both IQ and EQ. The research reveals a critical issue: when large language models are fed vast amounts of short, fast-paced, and sensational social media posts, their reasoning abilities are the first to collapse. The models begin to “cut corners,” skipping crucial reasoning steps, or even abandoning thinking altogether, directly outputting incorrect answers. Worse, the higher the proportion of “junk” in the data, the more pronounced this intelligence-reducing effect becomes. To quantify the impact, the research team conducted a large-scale “AI personality test.” They trained several open-source models, including Meta’s Llama 3 and Alibaba’s Qwen, on one million posts from the X platform. The tests found that models, which originally had fairly normal personalities, saw their negative personality traits amplified under the continuous intake of “junk information,” even beginning to show signs of “psychopathic” tendencies. Subsequent remediation experiments were also discouraging. Attempts to “cure” the models by refining instructions or mixing in high-quality data showed limited effectiveness. The models’ ingrained “bad habits” of skipping deep thinking and rushing to answers proved difficult to eradicate. This demonstrates that post-hoc fixes are far less effective than ensuring a “healthy diet” from the data source.

The core takeaway is simple: data quality is fundamental to AI. Experts emphasize that the future requires extremely strict screening and filtering of training data to block low-quality noise at the source. Currently, platforms like LinkedIn have announced plans to use user data for AI training. This study undoubtedly serves as a wake-up call: before indiscriminately feeding data to models, have we properly sorted the “garbage”? Otherwise, what we get might not be intelligent assistants, but a cohort of AI suffering from “brain damage.”

Version the evaluation contract

Record dataset version, scoring logic, prompt configuration, model settings and tool definitions for every run. Without those artifacts, a score cannot be reproduced or compared fairly after the product changes. Keep a stable regression core.

Critical product behaviors should run on every release, while larger experimental suites explore new capabilities. High-impact safety and tool scenarios can remain hard gates even when the overall average improves.

Feed production failures back into tests

User corrections, support incidents and traces reveal gaps in synthetic benchmarks. Important failures should be minimized into reusable regression cases before the next model or prompt update. Version the evaluation contract.

Record dataset version, scoring logic, prompt configuration, model settings and tool definitions for every run. Without those artifacts, a score cannot be reproduced or compared fairly after the product changes.

Keep a stable regression core

Critical product behaviors should run on every release, while larger experimental suites explore new capabilities. High-impact safety and tool scenarios can remain hard gates even when the overall average improves. Feed production failures back into tests.

User corrections, support incidents and traces reveal gaps in synthetic benchmarks. Important failures should be minimized into reusable regression cases before the next model or prompt update.

Version the evaluation contract

Record dataset version, scoring logic, prompt configuration, model settings and tool definitions for every run. Without those artifacts, a score cannot be reproduced or compared fairly after the product changes. Keep a stable regression core.

Critical product behaviors should run on every release, while larger experimental suites explore new capabilities. High-impact safety and tool scenarios can remain hard gates even when the overall average improves.

Feed production failures back into tests

User corrections, support incidents and traces reveal gaps in synthetic benchmarks. Important failures should be minimized into reusable regression cases before the next model or prompt update. Version the evaluation contract.

Dataset version, expected behavior, scoring logic, prompt configuration, model settings and tool definitions should travel together. Without these artifacts, a score cannot be reproduced or compared fairly after the product changes. Benchmark edits should be reviewed because changing examples can move the headline metric without any model improvement.

Protect critical behavior with stable regression gates

Safety boundaries, irreversible tool actions and high-volume user paths should run on every release. Slice results by task, language, context length and risk so aggregate gains do not hide important regressions.