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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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AI Safety Needs Public Measurement Infrastructure

The early debate over funding the U.S. Artificial Intelligence Safety Institute could be read as an ordinary appropriations story: lawmakers sought initial resources so NIST could recruit specialists, convene a consortium, and begin work on AI evaluation and safety standards.

The more consequential question is what kind of institution the country expects to build.

An AI Safety Institute should not be a policy office that comments on company evaluations, nor a consortium whose consensus is defined by its largest members. It should function as public measurement infrastructure: an institution capable of developing independent methods, testing important claims, making results reproducible, and creating a shared technical basis for decisions that markets cannot supply on their own.

The Path to Effective CJADC2: True Interoperability over AI

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

CJADC2 is often described as a technical effort to connect sensors, data, networks, and decision-makers across domains. That description is accurate but incomplete. It encourages a familiar mistake: treating decision advantage as a product of moving more data into more algorithms at greater speed.

The harder problem is not whether an artificial-intelligence model can identify a pattern. It is whether a coalition can turn that pattern into coordinated action when its participants operate under different authorities, classifications, policies, vocabularies, systems, and risk tolerances.

The decisive architecture of CJADC2 is therefore not the AI layer. It is the interoperability of the human and organizational system around it.

The Missing Middle Between an AI Experiment and a Mission Capability

The Department of Defense has become increasingly proficient at demonstrating advanced technology. The harder achievement is converting a promising result into a capability on which operators can depend.

The Global Information Dominance Experiments (GIDE) illuminate both sides of that problem. They provide recurring opportunities to integrate data, software, analytics, and artificial intelligence across services, combatant commands, the Joint Staff, and international partners. Yet the strategic value of GIDE will not be determined by what works during an experiment. It will be determined by what survives after the experiment’s temporary concentration of people, access, infrastructure, and senior attention dissolves.

The missing middle is transition: the set of technical, organizational, contractual, financial, security, and operational mechanisms that turn evidence into durable capability.

Human–AI Teaming Requires a Science of the Team

The phrase human–machine teaming is used so frequently that it can obscure how little it explains. Placing an AI system in a workflow with a human does not create a team. Nor does assigning the human final authority guarantee meaningful control. A team exists only when participants have interdependent roles, exchange information, adapt to one another, and coordinate their actions toward a shared outcome.

That is why DARPA’s work on quantitative models of human–AI teams is more important than the original 2024 solicitation cycle that prompted this essay. The enduring research problem is not simply whether an AI model performs well or whether a person approves its output. It is whether the combined system behaves competently, legibly, and safely under realistic conditions—including conditions neither participant encountered during development.

GEARS and the Architecture of Retrofit Autonomy

The Army’s Ground Expeditionary Autonomous Retrofit System (GEARS) is strategically interesting for a reason more durable than the selection of three vendors. It treats autonomy as a capability that can be integrated into an existing fleet rather than as a property of a newly designed vehicle.

That choice shifts the center of gravity from platform replacement to architecture. If autonomy can be added through modular navigation kits, stable interfaces, government-controlled data, and repeatable test evidence, the Army can modernize more vehicles, preserve competition, and improve software without waiting for the lifecycle of the underlying truck.

If those conditions are absent, “retrofit” merely moves vendor lock-in from the vehicle to the autonomy stack.

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.

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