AI Cost-per-Effect: The Real Price of Missile Defense

The U.S. Navy has fired close to $1 billion in munitions since late 2023 defending shipping and naval forces in and around the Red Sea. The internet-friendly version of that story is simple: multi-million-dollar missiles versus cheap drones. It’s also incomplete.

A missile intercept isn’t a transaction where you pay $4.3 million (an SM-6) and receive one destroyed drone. It’s an industrial, operational, and logistical system doing work at speed: a destroyer on station, trained crews, maintenance cycles, fuel, spare parts, satellite links, threat libraries, secure networks, commanders and watch teams, and a production base that can replenish what gets used. When people argue about the cost of the missile alone, they’re skipping the part defense leaders actually struggle with: how to measure cost-per-effect in a way that matches reality.

This post is part of our AI in Defense & National Security series, and I’m going to take a stance: most “cost” debates in national security would improve immediately if we treated them as data problems first. AI won’t “solve” strategy, but it can absolutely tighten cost modeling, expose hidden drivers, and make trade-offs explicit—especially in air and missile defense where seconds matter and inventories run thin.

The headline cost of a missile is the wrong starting point

Answer first: The unit price of an interceptor is a useful budget number, but it’s a weak decision number.

Procurement costs (like average procurement unit cost) are real. They help Congress, program offices, and planners track what gets bought. But operational decisions and force design decisions depend on something broader: what it costs to reliably produce an effect under real conditions.

In the Red Sea defense mission, an interceptor’s sticker price sits on top of an expensive stack:

• A $2.5B+ destroyer (and a fleet architecture behind it)
• Highly trained crews and repeat training cycles
• Fuel, maintenance, depot work, spares, and port infrastructure
• Sensors and battle management systems that make the intercept possible
• Secure networks and command-and-control that coordinate the fight
• A munitions enterprise that can restock in time to matter

No serious analyst allocates “the whole ship” to one shot. But treating the ship and its sustainment as zero is just as misleading.

Here’s the practical problem for leaders: if you measure cost poorly, you’ll buy the wrong mix of capabilities. You’ll favor systems that look cheap on paper while quietly demanding a massive support ecosystem—or you’ll field “affordable” options that fail when the threat adapts.

The metric that matters: cost-per-effect (done honestly)

Cost-per-effect tries to connect dollars to outcomes. The catch is that both sides of that ratio are slippery:

• “Cost” can mean acquisition, operations, sustainment, personnel, readiness, or industrial base strain.
• “Effect” can be tactical (one drone intercepted) or strategic (shipping lanes kept open, deterrence signaled).

If cost-per-effect is going to guide investment, it can’t be a spreadsheet trick. It needs shared definitions, consistent data, and an audit trail that decision-makers trust.

“Effect” is where strategy sneaks into the math

Answer first: If you can’t state the effect precisely, you can’t compare options credibly.

An “effect” can be as concrete as “prevent a drone from striking a destroyer,” or as broad as “maintain freedom of navigation.” Those aren’t interchangeable. They imply different time horizons, acceptable risk levels, and capability mixes.

In layered air defense, effect is not just “kill the drone.” It’s “kill it early enough and reliably enough that the ship survives repeated attacks.” That’s why commanders will choose expensive interceptors when the alternative is gambling with a ship and its crew.

A cheap gun system may have a low cost-per-shot. But if it only works at the last line of defense, it’s not competing with long-range interceptors on the same effect. It’s competing on a different one: “defeat leakers at close range.”

A sentence I wish more acquisition decks included:

A low-cost option that works too late is not a low-cost option.

What decision-makers should demand in an “effect” statement

When you’re comparing interceptor families, directed energy, EW, decoys, or changes in tactics, an effect statement should specify:

1. Protected asset (destroyer, carrier, merchant convoy, port)
2. Threat class (one-way UAV, cruise missile, ballistic missile, swarm)
3. Operating conditions (sea state, clutter, EM environment, rules of engagement)
4. Required confidence (probability of kill or probability of raid defeat)
5. Timeline (how long you must sustain the posture)

Once effect is defined, cost modeling becomes tractable—and AI becomes genuinely useful.

Where AI actually helps: turning messy cost data into decisions

Answer first: AI is most valuable when it links operational data, sustainment data, and industrial base data into a single cost-per-effect model you can stress-test.

Defense cost accounting is fragmented by design: different services, different methods, different “authoritative” sources, and strong incentives for program offices to look good. Independent cost agencies exist for a reason, but even solid cost estimates struggle to keep up with operational reality.

AI doesn’t replace independent cost assessment. It amplifies it by handling scale and complexity.

1) AI-assisted activity-based costing for operational missions

Air and missile defense is a system-of-systems problem. AI can help attribute cost to missions using a disciplined, auditable approach:

• Direct costs: munitions expended, fuel burn, flight hours/steaming days, contractor field service, maintenance actions tied to mission tempo
• Semi-direct costs: sensor operating time, network and bandwidth usage, software update cadence, spares consumption
• Readiness impacts: deferred maintenance, accelerated wear, training backfill

Modern ML can cluster mission profiles and learn cost drivers that humans miss—like how certain threat mixes spike radar maintenance or how extended high-alert posture affects crew performance (and mistakes are expensive).

The output leaders need isn’t “an AI model says.” It’s a traceable breakdown:

• What assumptions were used
• What data sources fed the estimate
• How sensitive the result is to uncertainty

2) Predictive logistics: the hidden cost multiplier

The most underestimated costs in the intercept narrative often sit in the logistics tail:

• parts availability
• shipyard capacity
• missile reload constraints
• transportation bottlenecks
• supplier fragility

AI-driven predictive analytics can forecast failure rates, spares consumption, and maintenance windows under different operational tempos. That matters because a weapon that is “cheap per shot” but forces disproportionate downtime may raise the true cost-per-effect.

In practice, I’ve found that logistics forecasting becomes decision-grade when you can answer two questions with numbers:

• How many days can we sustain this defense posture before readiness drops below threshold?
• What’s the fastest constraint: missiles, maintenance, crew endurance, or production?

3) Scenario modeling: comparing tactics, not just technologies

Cost-per-effect should also compare changes in how you fight, not only what you buy.

AI-enabled wargaming and simulation can evaluate options like:

• Adjusting convoy routing and timing to reduce exposure
• Using deception, decoys, or emissions control to lower detection probability
• Shifting sensor-tasking to reduce radar wear without increasing risk
• Layering non-kinetic effects earlier in the engagement chain

Sometimes the cheapest “new capability” is a better concept of operations. AI helps quantify that without relying on vibes.

4) Industrial base realism: cost-per-effect is meaningless without scale

A hard lesson from modern conflicts is that inventory depth and replenishment rate are strategic variables.

AI can support industrial base planning by modeling:

• production ramp timelines
• single points of failure in suppliers
• lead times for energetics and microelectronics
• workforce constraints and learning curves

If two options have similar operational performance, the one that can be produced and replenished faster often wins on real cost-per-effect—because it reduces the risk of running out.

A practical framework: building a cost-per-effect model leaders can trust

Answer first: Use a comparative framework that separates sunk costs, focuses on decision-relevant costs, and makes assumptions explicit.

Here’s a field-usable approach for analysts and program teams evaluating air defense options.

Step 1: Define the effect at the decision level

Be specific. “Defend shipping” is not specific enough to compare interceptors, directed energy, EW, and tactics.

A better version: “Sustain a 30-day defense posture for commercial shipping transiting a defined corridor, maintaining a 0.95 probability of raid defeat against one-way UAV salvos of size N.”

Step 2: Pick the comparison set and enforce equivalence

If you’re comparing long-range missiles to guns, you must model layered defense properly. Guns aren’t a substitute for interceptors; they’re part of a stack. AI simulations can help enforce apples-to-apples comparisons.

Step 3: Include decision-relevant costs (and exclude sunk costs)

Sunk R&D should not distort choices about how to use inventory already on hand. What should be included is what changes with the decision:

• incremental operating costs
• readiness impacts
• replenishment costs and lead times
• training and software update burden

Step 4: Separate direct, indirect, common, and negligible costs

This keeps analysis honest and prevents “everything is included” models that collapse under their own weight.

• Direct: clearly attributable to the option
• Indirect: real but hard to assign (base support, broad overhead)
• Common: shared across options (some C2 and space services)
• Negligible: below a threshold (often <1%) where collection effort exceeds value

Step 5: Stress-test uncertainty openly

Cost-per-effect should come with sensitivity bands:

• threat evolves faster than expected
• inventory usage is higher than planned
• maintenance costs spike due to tempo
• production ramp fails to meet schedule

AI helps here by running thousands of parameter sweeps quickly—but humans still need to choose the right parameters and enforce realism.

What leaders should do next (before the next “$4M missile” headline)

Answer first: Treat cost-per-effect as an operational readiness problem, then instrument it like one.

A few actions that create immediate value:

1. Standardize cost definitions across services for operational comparisons. If cost inputs aren’t comparable, the outputs won’t be either.
2. Invest in data plumbing, not just dashboards. AI models are only as credible as the sustainment and ops data feeding them.
3. Build “mission cost” products for recurring operations. If you can estimate the cost of an air defense day the way you estimate flight hours, you can manage it.
4. Evaluate cheaper intercept options against the same effect standard. “Cheap” that fails at the needed range or probability isn’t cheaper.
5. Bake industrial base constraints into the model. If it can’t be replenished, it’s not a sustainable solution.

The broader theme in AI in Defense & National Security is that advantage often comes from coordination: sensing, deciding, sustaining. Cost-per-effect is part of that. When you make costs legible—mission by mission—you stop arguing about the wrong number and start improving the system.

The next time a headline mocks an expensive intercept, the better question isn’t “why did they shoot that missile?” It’s: what combination of systems, logistics, and tactics reduces the cost of achieving the same protective effect—without betting sailors’ lives on a spreadsheet?