Unverified50% confidenceOpinionExact time
AI companies have already scraped content from across the web on a massive scale during the training phase, and even if access barriers are set up in the future, the knowledge already absorbed by the models cannot be retroactively compensated.
1
Sources
50%
Confidence
Long-term
Relevance
9/26/2026
First Seen
Sources
Related Entities
Related Claims
VerifiedAI-generated content flooding the internet in large volumes will become training data for the next generation of models, forming a self-reinforcing feedback loop that causes the tails of the distribution to gradually disappear.77% similarUnverifiedAI倾向于过度工程化，这是因为训练数据大量来自GitHub开源项目、技术博客和Stack Overflow，这些内容天然偏向展示最佳实践和设计模式76% similarVerifiedA 2023 study by Oxford University and other institutions showed that training AI models on data containing large amounts of AI-generated content causes them to lose the ability to capture the tails of the data distribution, leading to homogenization of outputs and quality degradation, known as model autophagy.75% similarUnverifiedAI code generation tools bypass steps such as debugging and manually tracing data flows, at the cost of stripping away the cognitive training that accompanies those steps.75% similarUnverifiedAs the proportion of AI-generated content on the internet rises, models that crawl web data for pre-training in the future will find it increasingly difficult to avoid content pollution sources.75% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/951876API
curl https://kongchang.com/api/v1/knowledge/claims/951876MCP
get_claim(id=951876)