According to @DeepLearningAI, Andrew Ng argues in The Batch that unlocking AI agents’ value requires controlling your own data and avoiding SaaS data silos and paywalls that block agentic workflows (source: DeepLearning.AI). According to @DeepLearningAI, the update also reports that OpenAI is reorganizing for profit, MiniMax released the open-weights M2 model, Udio teamed with Universal to build an AI music platform, and Google introduced VaultGemma as an open LLM designed not to memorize one-off personal data (source: DeepLearning.AI).

According to Jeff Dean, Google Research released VaultGemma, an open large language model trained from scratch with differential privacy, positioning it as a privacy-preserving foundation for AI applications (source: Jeff Dean on X; Google Research blog). The official blog describes VaultGemma as the world’s most capable differentially private LLM and links a technical report introducing a scaling law for differentially private language models, informing how DP-LM performance scales under privacy constraints (source: Google Research blog; arXiv:2501.18914). For crypto and DeFi trading teams integrating LLMs with sensitive datasets and keys, differentially private training is designed to limit memorization and exposure of individual training examples, reducing data leakage risks in AI-driven workflows (source: Google Research blog; arXiv:2501.18914). No token price or trading volume impacts were disclosed alongside the release; this is a technical update with direct relevance to privacy-preserving AI tools used in Web3 operations (source: Jeff Dean on X; Google Research blog).