On October 29, the Army announced a $1.08 billion project to develop its next missile interceptor. The surprising part is who is paying. The contractor has committed $423 million of its own resources, nearly 40 percent of the estimated total. Private capital is sharing the development risk with taxpayers—maximizing the incentive to find flawed assumptions early, which now protects shareholders as well as the public purse.
The missile interceptor shortage is real. In September, the Congressional Budget Office estimated that the United States had probably used half to two-thirds of its inventory of certain interceptors since June 2025, across two conflicts with Iran. Rebuilding those stocks would probably take at least five years, even with higher production rates.
Investors should watch those numbers as closely as generals do. Oil moving through the Strait of Hormuz in early 2025 was equivalent to about a fifth of global petroleum consumption. CBO linked disrupted shipping through Hormuz and the Red Sea to higher energy prices. It projected annual inflation in the personal consumption expenditures index would be half a percentage point above its February forecast in the first quarter of 2027. Secure sea lanes are an economic stabilizer. When they fail, households pay more for fuel, and inflation can push borrowing costs higher.
CBO puts a Patriot interceptor at roughly $4 million. Estimates for Iranian-designed Shahed attack drones range from $20,000 to $50,000. Protecting a ship or a city can justify an expensive interception, but a defender who meets every cheap drone with an expensive missile can win each engagement and still lose the economic contest. One way to ease the shortage is to reduce demand on high-end interceptors, reserving each one for threats only it can stop. That means learning, before assets are committed, which mix of weapons buys the most protection per dollar.
“Digital twins” can help answer this question. A twin is a computer model of a physical system, kept current with real-world data. Connecting twins in a simulated environment lets planners assess how equipment might perform together as conditions deteriorate. A design is far easier to change than a factory layout or fielded hardware.
Consider a hypothetical tanker escort through Hormuz. A credible virtual exercise could vary weather, jamming, and lost communications, then compare combinations of aircraft, sensors, guns, and interceptors. It might show the force running out of interceptors long before other resources are exhausted, a weakness a peacetime demonstration could miss. Planners could investigate that weakness before committing to a force they cannot sustain.
For artificial-intelligence systems, that means more than moving symbols around a map. Radar, infrared cameras, and radio receivers observe different aspects of the same event. Their simulated outputs must describe the same objects at the same moment, with realistic errors, or software may learn patterns that vanish outside the simulation. Physical models can generate examples too dangerous to stage in an exercise. A simulation that records each object’s identity can also label the resulting data, subject to expert checks. Validated models can then serve multiple programs.
The bigger value of digital twins is better procurement decisions. The Government Accountability Office reports that operating and support expenses historically account for about 70 percent of a weapon system’s lifetime cost. If testing suggests a communications upgrade adds more to mission success than added hardware, planners can investigate before placing orders. The comparison must include the cost of keeping either option operationally sustained.
Washington already applies this discipline to Wall Street. The Federal Reserve examines whether large banks could keep lending through a severe hypothetical recession. On September 30, it finalized rules requiring public input on its stress-test scenarios and material model changes. The Pentagon’s biggest purchases deserve a similar test, with transparency included. Unclassified findings should face public scrutiny, while sensitive material can be reserved for cleared reviewers.
These tools can also mislead. A sophisticated simulation can make a weak assumption look authoritative, and synthetic data can reproduce its designers’ blind spots at enormous scale. Independent testing against real equipment must establish where a model can be trusted and how uncertain its predictions are. Live trials remain essential. Any savings estimate must count the cost of building, maintaining, and validating the simulation.
Start with a demonstration tied to a real procurement decision. Set the required performance and compare competing approaches under independently chosen conditions. A sound test may confirm the original purchase. But a million simulated scenarios are worth little if their findings cannot influence what gets bought.
Contracts should require common interfaces and enough rights to reuse models and evaluate rival components, while protecting intellectual property. A smaller firm should be able to contribute a better sensor model without building an entire virtual world.
The United States could pursue collaboration in digital-twin modeling with India, Japan, and Australia—its Quad partners. Shared standards would let partners invest where each is strongest while retaining national control of sensitive models. They could exchange authorized results and test how their forces work together.
Defense contractors ought to welcome hard findings, even when one costs them a contract. When a company has its own capital at risk, early bad news protects shareholders as well as taxpayers. Budgets should reward evidence that arrives while a decision can still change. Otherwise, an expensive simulation can become an elaborate endorsement.
If replenishment takes five years, the cost penalty of a mistaken purchase extends far beyond its price. The Pentagon should reward finding mistakes while they are still cheap to fix.
