The contemporary rush to weave artificial intelligence into military systems rests on a simple economic proposition. Commercial research and product markets produce most of the algorithmic and compute capacity that modern militaries now crave. That private-sector innovation reduces direct government R&D outlays, accelerates capability delivery, and creates a large pool of talent and supply that defense forces can tap. Yet the same commercial foundations that create this “dual-use dividend” also introduce strategic dependencies, procurement confusion, and governance gaps that will shape defense budgets and industrial policy for decades.

Three features of the dual-use dynamic deserve immediate attention from economists and policymakers. First, AI is a general-purpose technology whose commercial development subsidizes military options rather than parallel military-specific programs. The diffuse, civilian-led development of AI means militaries can acquire advanced capabilities at lower marginal cost than if the state had to invent them from scratch. This lowers barriers to fielding novel systems but it also blurs the line between commercial supply chains and sovereign defense capacity.

Second, the defense acquisition ecosystem is not yet adapted to buy, test, and sustain software-first, rapidly evolving systems at scale. The U.S. Government Accountability Office concluded that the Department of Defense has made AI a modernization priority but lacks cohesive, department-wide acquisition guidance tailored to AI procurement. The implication is plain: without consistent acquisition rules, DoD actors risk paying for transient commercial models, mis-negotiating IP and data rights, and struggling to budget for lifecycle costs such as model retraining, certification, and sustainment. The economic consequence is fragility in forecasting long-term program costs and an increased likelihood of surprise budget pressure or capability gaps.

Third, the industrial base that will produce physical platforms incorporating AI is under stress. RAND’s 2023 study of the uncrewed systems industrial base warns that a rapid increase in demand for uncrewed platforms could strain manufacturing capacity, skilled labor, and component supply chains. Where the commercial market and defense demand overlap, competition for the same suppliers can drive price volatility, production bottlenecks, and a need for deliberate industrial policy to ensure capacity for both civilian and military requirements. Policymakers therefore confront a trade-off: rely on markets to supply dual-use goods cheaply today or invest in redundancy and surge capacity that raises peacetime costs but buys insurance for crisis.

These structural facts produce several predictable economic behaviors. Procurement teams will prefer commercial off-the-shelf AI to bespoke defense software because of cost and speed advantages. Venture capital and industrial investment will continue to flow into applications with large civilian addressable markets, even when the same technology has clear military utility. Defense firms will increasingly behave like platform integrators, assembling commercial AI modules into weapon systems rather than building capabilities end-to-end. Public budgets will shift from traditional platform procurement toward sustaining software, data, and compute pipelines. Those shifts are already visible in programmatic discussion even if accounting systems and congressional oversight are not fully aligned to them.

Yet the dual-use model creates negative externalities that a prudent strategy must price into procurement decisions. Commercial providers optimize for rapid feature cycles and broad market fit, not for rigorous operational testing against contested, degraded, or adversarial conditions. The cost of appropriate test and evaluation for AI and evolving systems is nontrivial. Recent work on digital transformation in test and evaluation underscores that rigorous, lifecycle-oriented T&E for AI requires investment in tooling, digital engineering, and new institutional processes. Those investments are real budget items that cannot be deferred without increasing risk of field failures or costly retrofits. Economically, they are hidden or underfunded liabilities attached to the dual-use bargain.

Policy choices must therefore balance three competing logics: (1) capture the efficiency gains from commercial AI, (2) preserve sovereign capacity for critical components and surge production, and (3) internalize the lifecycle costs of software-centric systems. Practically this suggests several economic interventions.

  • Buy better, not just faster. Acquisition rules should require clear IP and data-rights frameworks, budgets for retraining and updates, and contractual levers for performance in contested environments. This will raise near-term costs but reduce the chance of expensive mid-course corrections later.

  • Invest in industrial resilience. Strategic subsidies, capacity guarantees, or prize-backed contracts can keep essential suppliers solvent and able to scale when demand spikes. RAND’s industrial-base analysis illustrates how unchecked demand can outpace supply for uncrewed systems components. A defensible industrial policy costs money in peace yet is cheaper than the fiscal shock of emergency sourcing in crisis.

  • Fund test, evaluation, and sustainment as first-order line items. Budget models that treat AI as a one-off procurement undercount costs. The economics of safety, assurance, and verification for adaptive systems are recurring. The acquisition community must normalize multi-year appropriation profiles for model lifecycle management and T&E tooling.

  • Align norms and governance with market incentives. If civilian R&D is supplying the lion’s share of innovation, then public policy must shape incentives so that private actors internalize social and security externalities. This can include procurement preferences for firms that implement robust assurance processes, clearer export controls calibrated to function rather than to vague categories, and public prize programs targeting capability shortfalls. The aim is to make the market do what markets do poorly on their own: account for public goods and public risks.

Finally, a philosophical note. The dual-use economy is morally ambiguous because it collapses the tidy separation between civilian progress and martial purpose. Economists tend to celebrate the efficiency gains of specialization and scale. Ethicists worry about the diffusion of enabling technologies without adequate governance. Both responses are correct. The pragmatic middle course is to accept that dual-use is inevitable while designing institutions that steer it toward resilience, transparency, and accountability. In economic terms that means paying to insure capacity, paying to verify behavior, and paying to ensure that the public interest is represented in markets that will otherwise privilege speed and profit.

If nations are serious about harnessing AI without surrendering strategic autonomy, they must budget for the full price of dual use rather than the apparent discount. The short-term savings from buying commercial AI are real. The longer-term liabilities from underfunded T&E, brittle supply chains, and ambiguous contractual rights are also real. Treating the dual-use dividend as a windfall instead of a conditional bargain will be the true false economy of the coming decade.