Anthropic launches a program for using artificial intelligence (AI) in research—AI for Science—on May 5, offering application programming interface credits to researchers, with an initial emphasis on biology and the life sciences.
Access matters. Many scientific teams cannot afford sustained experimentation with frontier models. But access to a model is only one input to discovery. The harder work is building a trustworthy path from generated idea to scientific claim.
Anthropic's April 15 update adds Research and Google Workspace integration to Claude. The system can search iteratively across the web and an organization's email, calendar, and documents, then return a cited synthesis.
The immediate promise is hours of research in minutes. The larger change is that research assistants can now cross the boundary between public information and institutional memory.
OpenAI's January 30 announcement makes its reasoning models available to scientists at Los Alamos, Lawrence Livermore, and Sandia national laboratories through a partnership using the Venado supercomputer.
The announcement covers an extraordinary range of possibilities: materials, energy, medicine, cybersecurity, mathematics, and nuclear security. Yet the laboratories are interesting not only because their problems are difficult. They are interesting because their knowledge practices are demanding.
By the time a Freedom of Information Act request reaches an analyst, the hardest problem may already be years old.
The relevant records may be scattered across email, shared drives, case systems, collaboration tools, contractor environments, and personal filing habits. The request may cross several components with different search practices. Reviewers may need to reconstruct context that was obvious when the work occurred but disappeared when people moved on.
That is the operating reality behind the 2024 NextGen FOIA Tech Showcase. The Chief Freedom of Information Act (FOIA) Officers Council invited vendors to demonstrate artificial intelligence (AI), electronic discovery, search, case-processing, and redaction tools that might improve federal disclosure work.
The technology can help. It cannot repair missing records, unclear ownership, inconsistent retention, or a workflow no one has measured from end to end. If agencies treat FOIA as a document-processing problem that begins when a request arrives, they will automate the visible end of a much larger knowledge-management failure.
Originally published in 2024; substantially revised in 2026 to deepen the analysis and incorporate additional sources.
NIST’s warning about adversarial manipulation of AI systems should change how organizations define the security boundary. Conventional software security focuses heavily on code, dependencies, infrastructure, identity, and configuration. AI systems add another attack surface: the evidence from which system behavior emerges.
Training corpora, feedback data, retrieval indexes, evaluation sets, model artifacts, prompts, and operational context all influence what an AI system learns or produces. If an adversary can shape those inputs, the system may remain technically available while becoming epistemically compromised.
AI expands the software supply chain into a knowledge supply chain. Security must protect not only what the system executes, but what it is permitted to believe.