AI agents function reliably only with comprehensive observability that reveals causal relationships in complex systems—not through language models alone.
AI outputs are economically valuable only when humans assess their correctness and relevance to the business context, rather than adopting them blindly.
Anthropic is systematically working to optimize Claude for standard chemical tasks such as NMR spectral analysis to relieve chemists of time-consuming work with various molecular representations.
Microsoft warns CTOs of seven new attack patterns on AI agents: from natural language injections through goal hijacking to visual attacks on computer-use agents.
LLMs can be forced to leak data through targeted prompt attacks, but they disclose training data only with low probability in everyday usage scenarios.
Corporate AI spending has spiraled out of control; OpenAI promises more efficient models, while the Jevons Paradox could drive renewed demand growth over the long term.
