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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January 11, 2024
in AI Strategy, Government Technology, Innovation Management
6 min read

The NIST AI RMF Is an Operating Model, Not a Checklist

The Federal Artificial Intelligence Risk Management Act of 2024 proposed requiring federal agencies to use the National Institute of Standards and Technology’s AI Risk Management Framework. The idea was sensible and bipartisan: federal AI should be governed through a common, credible risk-management structure rather than a patchwork of improvised agency practices.

The danger is equally familiar. Institutions can “adopt” a framework by mapping its language into policy, completing templates, and producing inventories while leaving the decisions that determine AI risk largely unchanged.

The NIST AI Risk Management Framework is most valuable when treated as an operating model: a way to connect mission context, evidence, authority, delivery, monitoring, and accountability across the lifecycle of a system.

January 11, 2024
in AI Strategy, Government Technology, Innovation Management
6 min read

Federal AI Procurement Is a Risk-Management Control Plane

The Federal Artificial Intelligence Risk Management Act of 2024 proposed more than agency adoption of the NIST AI Risk Management Framework. It also contemplated acquisition support and contract language that would carry risk-management expectations into federal purchasing.

That may be the proposal’s most operationally important feature.

Federal agencies build some AI systems themselves, but they increasingly acquire models, cloud services, data, integrations, and AI-enabled products from vendors. If governance obligations stop at the agency boundary, agencies can remain publicly accountable for systems whose material evidence and operational control sit inside private contracts.

Procurement is therefore not an administrative step after AI policy. It is the control plane through which public accountability becomes enforceable across the supply chain.

January 11, 2024
in AI Strategy, Government Technology, Innovation Management
7 min read

Civil-Rights Risk Lives in the Decision System, Not Only the Model

The Department of Justice’s January 2024 interagency convening on artificial intelligence and civil rights reflected an important federal position: existing civil-rights and consumer-protection laws continue to apply when decisions are mediated by algorithms.

That proposition is necessary. Its technical consequence deserves equal emphasis.

Discrimination rarely resides in a single model parameter or fairness metric. It can enter through the choice of problem, the collection of data, a proxy variable, the design of an interface, unequal access to a digital service, the discretion granted to staff, or the absence of a practical appeal. An AI system can satisfy a narrow statistical test and still participate in an unlawful or inequitable decision process.

Civil-rights analysis must therefore treat the whole decision system as the unit of accountability.

January 11, 2024
in AI Strategy, Defense Technology, Government Technology, Innovation Management
2 min read

Early 2024: Senate Commerce Chair Cantwell to Introduce Comprehensive AI Legislation

A New Stance on AI: Insights into Senate Commerce Chair Cantwell’s Upcoming Legislation

The path to a comprehensive artificial intelligence (AI) legislation is set to take a significant turn, thanks to initiatives by Senate Commerce Committee Chair, Maria Cantwell. According to sources cited by FedScoop, Cantwell, a Democrat from Washington, is expected to introduce an array of bipartisan bills tackling various facets of AI.

January 11, 2024
in Defense AI, MLOps, AI Assurance, Software Acquisition
5 min read

Project Linchpin and the Architecture of Operational AI

Originally published in 2024; substantially revised in 2026 to deepen the analysis and incorporate additional sources.

The Army’s Project Linchpin is significant for a reason that extends beyond any individual model or sensor use case. It treats artificial intelligence as a continuously operated capability system: data is prepared, models are trained and evaluated, software is integrated, deployments are observed, and operational feedback informs the next release.

That sounds familiar to anyone who has built a mature software or machine-learning platform. Inside a defense acquisition environment, however, it represents a substantial change in what the government is actually buying and governing.

The unit of acquisition is no longer only the algorithm. It is the trusted pipeline through which algorithms become—and remain—operational capabilities.

January 11, 2024
in AI Strategy, Defense Technology, Government Technology, Innovation Management
2 min read

Reevaluating Strategies: Navy’s 4th Fleet Highlights the Need for Command Center Upgrades for Unmanned Operations

Unmanned systems command has certainly been a focal point for the Navy’s 4th Fleet, but as these technologies advance, the challenge of managing them effectively grows. Rear Adm. Jim Aiken recently shared insights into the progress and pitfalls experienced in combatting this issue.

January 11, 2024
in Government Technology, Defense Technology, Innovation Management
6 min read

A Data Standard Is an Institutional Contract

The Department of Defense’s release of Assistance Data Standard Version 1.0 sounded like a specialized acquisition-system update. It was more consequential than that.

Contracts, grants, cooperative agreements, research transactions, and prototype agreements encode how the Department allocates resources, structures incentives, shares risk, and moves technology toward mission use. When those instruments are represented inconsistently across systems, the Department loses the ability to see its own portfolio, compare outcomes, trace modifications, and learn which acquisition mechanisms work.

A data standard is therefore not merely a technical schema. It is an institutional contract about meaning.

January 11, 2024
in AI Strategy, Government Technology, Innovation Management
6 min read

The Best Government AI Service May Prevent the Contact

Artificial intelligence can translate documents, summarize case histories, route requests, help employees search policy, and provide conversational assistance outside business hours. Those are useful capabilities. They can also make a broken service easier to enter without making it easier to complete.

For public services, the objective should not be a more sophisticated chatbot or a lower call-handle time. It should be a reduction in the effort people must spend obtaining an accurate, lawful, and timely outcome.

The highest-value AI intervention may be the one that prevents the person from needing to contact the agency at all.