The Department of Defense has taken a necessary, if overdue, step in asking industry and the public to help shape a Trusted AI Defense Industrial Base roadmap. That formal request for comment explicitly positions the Office of Industrial Base Resilience and the Policy, Analysis and Transition office to articulate short, medium and long term actions to help the Defense Industrial Base adopt AI responsibly and at scale. This solicitation is not a rhetorical exercise. It creates the policy scaffolding that will shape how money, authorities and priorities flow across the DIB.

Budgetary posture reveals the scale of the bet the Pentagon is making on AI. In its FY2025 S&T request the Department signaled a concentrated investment posture: roughly $17.2 billion for science and technology activities, with trusted AI and autonomy among the largest single shares of that investment. Public reporting of the Undersecretary for Research and Engineering’s briefing shows roughly $4.9 billion directed toward trusted AI and autonomy within the S&T portfolio. Those line items matter because they determine which research projects mature, which prototypes get fielded, and which firms gain the credibility to compete for follow-on acquisition funds.

The Pentagon’s broader industrial strategy also frames these AI investments. The National Defense Industrial Strategy and its implementation plan emphasize four priorities for the DIB, including workforce readiness, supply chain resilience and acquisition flexibility. AI funding, when aligned with that strategy, can accelerate modernization of suppliers and lower barriers for nontraditional companies. But without deliberate transition funding and acquisition reform, R&D investments risk remaining academic successes rather than deployed capabilities. The implementation documents show intent, but intent without sustained transition pathways will disappoint both taxpayers and warfighters.

Where do those transition pathways appear in practice? One visible channel is small business and early-stage funding targeted at DIB-relevant problems. In 2025 solicitations and SBIR/STTR topics explicitly asked for Trusted AI and supply chain analytics solutions aimed at DIB use cases, signaling concrete mechanisms for channeling federal dollars into commercial innovation that can be adapted for defense purposes. These programs are the practical arteries through which small firms obtain the resources to mature trustworthy AI capabilities and to meet defense security and accreditation requirements.

Security and assurance are not theoretical constraints. Combatting supply chain risk and adversary exploitation of AI requires focused investments in verification, accreditation and secure development pipelines. The services and combatant commands have begun building organizational constructs to coordinate those investments. For example, U.S. Cyber Command and allied elements have formed task forces and centers to integrate AI adoption with cyber and supply chain security, underscoring that investment in capability must be paired with investment in protection and standards. If R&D funding is not matched by funding for security, accreditation and operational integration, the DIB will be poorly postured to field trustworthy systems at tempo.

Three economic realities should guide how the DIB Trusted AI Roadmap parcels funding.

1) Concentration risk. Large, centralized investment buckets for AI research can create winners by default: defense primes and established labs that know how to absorb R&D grants and convert them into programs of record. That outcome may be efficient in the narrow sense of repeatable procurement pipelines, but it risks stifling innovation from small companies that lack clearance, capital, or a preexisting contracting relationship. To mitigate this, the roadmap must earmark funds and streamlined contracting vehicles specifically for nontraditional suppliers and for technology transition.

2) The transition gap. The classic valley of death between prototype and fielding is exacerbated for AI by accreditation, data rights and sustainment burdens. Investment must therefore be two-stage: first to accelerate algorithmic research and systems integration, and second to underwrite operational testbeds, red-teaming, continuous monitoring and certification efforts that permit authorities to operate to be granted more quickly. Without explicit transition money and program offices empowered to shepherd prototypes, S&T spending will generate papers instead of trusted systems.

3) Public goods and shared infrastructure. AI for the DIB is not only about individual algorithms. It is about data plumbing, common testbeds, curated benchmark datasets, and tooling for explainability and pedigree tracking. Those are public goods that individual suppliers will underinvest in if left to market forces. The roadmap should steer a portion of funding toward interoperable, secure infrastructure and shared services that lower the marginal cost of adoption across the DIB. Evidence of this direction exists in programmatic solicitations and in the policy dialogue around data and analytics adoption.

Policy levers that deserve immediate emphasis in the roadmap

  • Create dedicated transition pools. Set aside a defined fraction of trusted AI S&T funding for transition grants that require co-investment from services and include milestones tied to operational testbeds.

  • Expand acquisition and accreditation assistance. Fund “acquisition sherpas” and a centralized accreditation acceleration cell to help small firms obtain necessary cyber and FedRAMP like certifications faster and with less cost burden.

  • Buy shared infrastructure. Underwrite coalition-ready, secure data spaces and common instrumented testbeds that the DIB can use to validate AI models without forcing every firm to duplicate that investment.

  • Invest in workforce and regional ecosystems. Use industrial policy instruments to create local AI and assurance clusters so that the DIB’s modernization does not remain geographically concentrated or captive to a few large vendors.

These levers are not technocratic preferences. They follow logically from the economics of innovation, and from the fiscal evidence that the Department is concentrating meaningful sums into trusted AI and autonomy. If the roadmap merely catalogues technical desiderata without attaching procurement authorities and money to transition pathways, then the political economy of defense acquisition will reassert itself and preserve the status quo.

A closing caution for technocrats and ethicists alike. Funding AI for the DIB reduces certain risks to humans by automating dull, dirty and dangerous tasks. It simultaneously shifts risk into new domains: epistemic uncertainty in models, brittle dependencies on scarce data, and ambiguous lines of accountability when automated systems fail. Economic investment without institutional reform invites moral hazard: contractors and program managers may trade off explainability and maintainability for faster deployment. The roadmap must therefore bind dollars to governance: procurement clauses that require explainability, lifecycle funding for monitoring, and contractual obligations for reproducible validation. That is how policy, money and responsibility can be made commensurate.

The DIB Trusted AI Roadmap is an allocative decision as much as it is a technical one. The choices the Department makes in the near term will determine whether the next decade of defense AI is decentralized, resilient and innovative, or centralized, brittle and constrained by a narrow set of incumbents. The ethical argument for investing in automation is persuasive. The economic case for investing in transition and shared infrastructure is indispensable. If the roadmap follows both logics it will do more than describe a future; it will make that future affordable and accountable.