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INDEPENDENT RESEARCH / PRACTICE NOTES

Ideas in motion, not ideas behind glass.

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 theater Systems over slogans Useful, accountable AI

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A federal AI inventory is not an adoption strategy

In late 2023, twenty civilian agencies reported roughly 1,200 current and planned uses of artificial intelligence. It was an irresistible number. Read quickly, it sounded like proof that artificial intelligence (AI) was spreading across the federal government at scale.

The U.S. Government Accountability Office (GAO) found something more complicated. Only five of the twenty agencies provided complete information for every reported use case. Two inventories included systems that the agencies later determined were not AI at all. Many entries lacked basic information such as lifecycle stage or whether the use could be publicly released.

The inventory documented attention. It did not establish adoption, readiness, value, or control.

That distinction remains important because federal AI inventories have continued to grow. The number of ideas in a spreadsheet will always rise faster than the number of capabilities that survive acquisition, integration, evaluation, workforce adoption, and sustained operation. Leaders need to know which kind of growth they are looking at.

The best federal AI use case may not need AI

A good artificial intelligence portfolio begins with permission to say that artificial intelligence (AI) is not the answer.

That sounds obvious. In practice, organizations often begin in the opposite place. A new model becomes available, leaders announce an adoption goal, and teams are asked to find use cases. The search produces a familiar list—summarization, forecasting, chatbots, anomaly detection, document review—before anyone has defined the mission problem, the current baseline, or the decision the system is supposed to improve.

The result may be technically interesting. It is not yet a strategy.

A federal AI use case should be written as a testable claim: for a defined group of people, performing a specific mission task under known conditions, this capability will improve a measurable outcome enough to justify its cost and risk. If the claim cannot be stated, challenged, and evaluated, the agency does not have a use case. It has a technology theme.

Federal AI governance needs a memory

Federal artificial intelligence (AI) policy changed substantially between 2024 and 2025. The need to know which systems government uses, who owns them, how they affect people, and what evidence supports them did not.

That is the enduring idea behind the Federal AI Governance and Transparency Act. Introduced as House bill H.R. 7532 in March 2024, the bipartisan proposal would have consolidated several federal AI governance requirements in statute. It directed agencies to create governance charters for certain systems, strengthened the Office of Management and Budget's government-wide role, expanded public visibility, and required contractors to provide information agencies would need for oversight.

The bill advanced out of committee by a 36–3 vote and was reported to the House in December 2024. It did not become law before the Congress ended.

Its most useful contribution was not a particular form or office. It was the recognition that accountable AI requires an institutional memory: a durable connection between the system, its public purpose, the decisions made about it, and the evidence available to challenge those decisions.

AI will not fix FOIA until agencies fix the records

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.

Space traffic coordination needs an evidence market, not another data feed

More orbital data does not automatically produce a better collision warning.

A useful warning depends on the quality and timing of observations, the model used to estimate an object's orbit, the uncertainty attached to that estimate, the way several sources are combined, and whether a satellite operator can act on the result. Two providers can observe the same object and produce different answers without either behaving irrationally.

That is what made a small 2024 procurement by the Office of Space Commerce (OSC) more interesting than the original announcement suggested. OSC, part of the National Oceanic and Atmospheric Administration (NOAA), hired Kayhan Space and SpaceNav to evaluate the accuracy, consistency, and quality of commercial space situational awareness (SSA) products created by three other companies. The work supported a limited pathfinder for the emerging Traffic Coordination System for Space (TraCSS).

The government was not only buying data. It was buying an independent way to learn which data and services were useful, under which conditions, and according to which evidence.

Federal AI standards should standardize evidence, not freeze design

Federal agencies need a common way to show that an artificial intelligence (AI) system is understood, controlled, and worthy of use. They do not need Washington to prescribe one architecture, model class, or development method for every mission.

That tension sat inside the Federal AI Governance and Transparency Act introduced in 2024. House bill H.R. 7532 proposed a government-wide structure for AI governance, including agency charters, inventories, risk practices, workforce training, oversight, and updates to federal acquisition rules.

The bill advanced through the House Oversight Committee and was formally reported late in 2024, but it did not become law. The question it raised remains unresolved: what should federal AI standards make uniform, and where should they preserve variation?

The right answer is to standardize the interfaces of accountability—the evidence agencies retain, the decisions they document, and the signals they exchange—without freezing the technical design beneath them.

The CDAO evaluation was really about decision rights

When the Pentagon announced that its inspector general was evaluating the Chief Digital and Artificial Intelligence Office in early 2024, it was tempting to ask for a verdict: Was the new organization working?

The final report produced a more revealing answer. The Department of Defense (DoD) Office of Inspector General (OIG) found that the Chief Digital and Artificial Intelligence Office (CDAO) was still operating without several foundational documents needed to make its responsibilities clear across the Department.

The problem was not a lack of ambition. CDAO had inherited data, analytics, digital services, and artificial intelligence (AI) responsibilities from four organizations. It was expected to set strategy, create policy, break adoption barriers, provide enabling services, and scale proven capabilities across one of the world's largest enterprises.

The problem was that a broad mandate does not tell thousands of people how authority should work at the boundary between organizations. Enterprise AI offices succeed or fail in those boundaries.

Exploring AI-Enabled Sensors: US Army SBIR Program Unveils Phase I and Phase II Opportunities

US Army to Prioritize AI in Optical Sensors: SBIR Program Launches New Opportunities

The U.S. Army is welcoming Phase I and Direct to Phase II small business proposals focused on the use of artificial intelligence (AI) in autonomous optical sensors and the like. The Small Business Innovation Research (SBIR) Program is further emphasizing novel AI and machine learning (ML) methods for signal classification in positioning, navigation, and timing applications.

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