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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Latest writing

January 9, 2024
in AI Strategy, Defense Technology, Government Technology, Innovation Management
7 min read

From Endorsement to Evidence: Implementing the Responsible Military AI Declaration

When the United States began organizing the first multinational meeting of states endorsing the Political Declaration on Responsible Military Use of Artificial Intelligence and Autonomy, the central challenge was already clear: agreement on principles is easier than evidence of implementation.

That remains the decisive issue. A declaration can create a community and a common vocabulary. It cannot, by itself, show that military organizations have changed how they design systems, authorize use, train personnel, investigate incidents, or accept risk. The next stage must make responsible practice observable without forcing states into a single legal system, technical architecture, or doctrine.

The right objective is not uniformity. It is comparable accountability.

January 8, 2024
in AI Strategy, Defense Technology, Innovation Management
3 min read

How does the Leopard 1A5 stack up against Russian armor?

Title: “Unveiling the Leopard 1A5: A comparative View Against Russian Armor”

January 8, 2024
in AI Strategy, Defense Technology, Innovation Management
6 min read

GIDE and the Organizational Work of AI Adoption

It is tempting to describe the Global Information Dominance Experiments (GIDE) as a technical program: connect data, apply analytics and artificial intelligence, and accelerate decisions across the joint force. That description is accurate but insufficient. The more difficult problem GIDE confronts is organizational.

Defense institutions are not short of promising prototypes. They are constrained by the distance between a technical possibility and an operational capability: fragmented authorities, incompatible incentives, acquisition boundaries, security processes, data ownership disputes, uneven skills, and a rational reluctance to alter mission workflows on the strength of a demonstration.

Recurring experiments matter because they can make those dependencies visible and negotiable. Properly structured, GIDE is not only testing technology. It is rehearsing a different way for the Department to build, govern, and absorb digital capability.

January 8, 2024
in AI Strategy, Defense Technology, Innovation Management
6 min read

Autonomous Resupply Is a Logistics-System Redesign

When the U.S. Army and Defense Innovation Unit selected three vendors to develop autonomous navigation kits for Palletized Load System trucks, the obvious story was the vehicle: a heavy tactical truck capable of moving supplies with fewer people exposed to dangerous routes.

The more important story is the logistics system around it.

Autonomy does not remove humans from contested sustainment. It changes where they work, what information they need, how risk is distributed, and which failures become possible. An autonomous resupply vehicle creates value only when mission planning, loading, dispatch, route management, remote supervision, maintenance, cybersecurity, recovery, and receiving operations can treat it as a dependable participant in the distribution network.

January 7, 2024
in AI Strategy, Defense Technology, Innovation Management
6 min read

GIDE’s Real Value Is Learning at Operational Speed

The most important result of the Global Information Dominance Experiments (GIDE) is not a particular dashboard, algorithm, or data pipeline. It is the creation of a repeatable mechanism through which the Department of Defense can learn what an AI-enabled decision system must become while operators are using it.

That distinction matters. A technology demonstration asks whether a component can work under prepared conditions. An operational learning system asks a harder set of questions: Does the capability improve a consequential mission decision? Can it function across organizations and security boundaries? Do users understand when to trust it? Can the Department identify what failed, change the system, and test the change again before the operational context moves on?

December 19, 2023
in AI Governance, Standards, AI Assurance
3 min read

AI safety needs better questions before better rules

The National Institute of Standards and Technology (NIST) has issued a request for information (RFI) on the safe, secure, and trustworthy development and use of artificial intelligence. Responses will inform future guidance on evaluation, red teaming, risk management, and related measurement challenges.

The request arrives after a year of rapid capability releases and equally rapid calls for guardrails. Before guidance becomes more specific, the field needs to ask more precise questions about systems, evidence, and use.

December 14, 2023
in Cybersecurity, Defense Technology, Innovation Management
3 min read

A cyber challenge can build an ecosystem, not just a winner

The Defense Advanced Research Projects Agency (DARPA) has opened registration for the Artificial Intelligence Cyber Challenge (AIxCC), published an exemplar challenge and scoring approach, and added prize funding. Competitors will work toward systems that can find and repair vulnerabilities in widely used software at scale.

Prizes attract teams. The lasting value of a challenge can be the common infrastructure and professional community built around the competition.

December 6, 2023
in AI Strategy, AI Assurance, Enterprise Architecture
3 min read

Gemini makes evaluation a portfolio capability

Google has introduced Gemini 1.0, a family of multimodal artificial intelligence models in three sizes: Ultra, Pro, and Nano. The models are designed to work across text, images, audio, video, and code, and to run in environments ranging from data centers to mobile devices.

The release adds another capable model family to a fast-changing field. For organizations, the strategic response is not to crown a universal winner. It is to become good at evaluating fit repeatedly.