According to DeepLearningAI, websites are increasingly deploying advanced methods such as decoys, anti-crawling blockers, and paywalls to limit AI crawlers from accessing their data (source: DeepLearningAI, The Batch). This shift marks a significant change in the AI industry, as open web data becomes less accessible for training large language models and generative AI systems. Businesses relying on web-scraped data now face new operational risks and may need to seek alternative data acquisition strategies. The trend signals a growing 'shadow war' between content owners and AI developers, reshaping the landscape for AI training datasets and pushing companies to invest in proprietary data or licensing agreements to maintain competitive advantages.

According to DeepLearning.AI, researchers have introduced a new method to estimate exactly how many bits of information a language model memorizes from its training data. The team conducted rigorous experiments using hundreds of GPT-2–style models trained on both synthetic datasets and subsets of FineWeb. By comparing the negative log likelihood of trained models to that of stronger baseline models, the researchers were able to measure model memorization with greater accuracy. This advancement offers AI industry professionals practical tools to assess and mitigate data leakage and overfitting risks, supporting safer deployment in enterprise environments (source: DeepLearning.AI, August 28, 2025).