According to Sawyer Merritt on Twitter, Elon Musk stated that approximately 10 billion miles of training data is required to reach safe unsupervised self-driving for Tesla vehicles. Currently, Tesla has accumulated around 7.18 billion miles of real-world driving data, which forms the foundation for its AI-driven autonomous vehicle system. This substantial data requirement highlights both the complexity of real-world environments and the AI industry's ongoing push for large-scale data collection to improve self-driving safety. Businesses in the AI automotive sector can interpret this as an indicator that achieving reliable unsupervised driving is closely tied to massive data acquisition and advanced neural network training, opening opportunities for companies specializing in data annotation, sensor technologies, and AI safety validation (Source: Sawyer Merritt on Twitter, quoting Elon Musk: https://x.com/elonmusk/status/2009161554785128729).

According to Sawyer Merritt, Tesla has initiated a new email marketing campaign promoting its Full Self-Driving (Supervised) feature, positioning it as a holiday gift. The email emphasizes that, with FSD (Supervised) enabled, Tesla vehicles can operate from start to finish with minimal human intervention. Tesla highlights that its AI-powered FSD system has accumulated over 6.7 billion miles of driving experience, which contributes to improved road safety and a reduced likelihood of collisions. This campaign underscores Tesla's ongoing investment in AI-driven autonomous vehicle technology and its strategy to leverage extensive real-world data for continuous improvement and market differentiation (Source: Sawyer Merritt on Twitter).

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).