This is my working notebook on AI engineering, strategy, knowledge infrastructure, organizational transformation, and the human systems that determine whether innovation becomes real capability.
The notes range from emerging research questions to practical operating models. Some will become papers, tools, talks, or products. Others are here because thinking improves when it is made visible.
Evidence before theaterSystems over slogansUseful, accountable AI
When a technology changes quickly, a fixed policy can be obsolete while everyone is still complying with it.
Google DeepMind's February update to its Frontier Safety Framework addresses that problem by linking stronger safeguards to capability thresholds in areas that could create severe harm. The details will continue to evolve. The organizational principle should endure: controls should respond to what a system can do, not only to the name or generation printed on it.
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.
Software has spent decades waiting for people to click the buttons. OpenAI's January 23 preview of Operator presents an artificial intelligence agent that can see web pages and act through a browser on a user's behalf.
The interface is new. The organizational question is old: What is this actor allowed to do?
Robots and autonomous vehicles need experience. The hard question is where that experience should come from when real-world data is expensive, dangerous, rare, or incomplete.
NVIDIA's January 6 announcement of Cosmos world foundation models offers one answer: generate and manipulate simulated physical environments at scale. The company presents the platform as infrastructure for training and evaluating physical artificial intelligence (AI) systems, including robotics and autonomous vehicles.
The first consequential defense artificial intelligence story of 2025 does not arrive as a new model or a weapons demonstration. It arrives as an invitation to test.
The Department of Defense's (DoD) Chief Digital and Artificial Intelligence Office (CDAO) begins January with a crowdsourced assurance pilot in military medicine. The setting matters. A medical system can perform impressively on average and still fail a clinician or patient at exactly the wrong moment. Its quality cannot be separated from the people, workflow, uncertainty, and consequences around it.
Revised and substantially expanded July 17, 2026, with the subsequent change in federal AI policy reflected explicitly.
Executive Order 14110 was remarkable in scope. Issued in October 2023, it assigned artificial-intelligence actions across a wide portion of the federal government: safety and security, privacy, civil rights, consumer protection, workforce, innovation, competition, international leadership, federal use, and technical standards. Many assignments carried deadlines measured in days or months.
By early 2024, progress was naturally reported as a sequence of completed actions. Agencies had issued requests for information, convened experts, begun standards work, created hiring pathways, launched pilots, and prepared guidance. The original version of this post praised that momentum but did little to explain what “progress” should mean.
The later policy record creates a useful natural experiment. In January 2025, a new administration revoked Executive Order 14110 and established a different federal AI policy. OMB subsequently replaced core agency-use and acquisition guidance. Yet many technical problems, statutory obligations, agency missions, and organizational constraints remained.
This reveals the correct unit of progress. It is not the number of executive-order tasks marked complete. It is the amount of durable state capacity created: people, evidence, standards, architectures, acquisition mechanisms, data, evaluation systems, and decision processes that remain useful when the policy language changes.
A label on a political advertisement can tell us that artificial intelligence helped make it. It cannot tell us whether the message is true, who authorized the representation, how materially the content was altered, or whether millions of people saw it before the label appeared.
That is the problem the Artificial Intelligence (AI) Transparency in Elections Act tried to address in 2024. Senate Bill 3875 would have directed the Federal Election Commission (FEC) to require disclosures when covered political communications contained content “substantially generated” by AI. The bipartisan proposal recognized a real gap: voters could encounter a synthetic voice, image, or video without knowing that part of the apparent evidence had never occurred.
The bill advanced out of committee and reached the Senate calendar, but it did not become law before the 118th Congress ended. The later record makes the underlying design question more useful, not less. What would an effective disclosure regime need to accomplish—and what should no one expect a label to solve?