AI Missile Math: Winning the Magazine-Depth Fight

A modern strike campaign isn’t decided by a single “silver bullet” weapon. It’s decided by arithmetic: how many shots you can take, how many you can afford, how many survive defenses, and how fast you can reload the pipeline. When planners talk about “magazine depth,” they’re really talking about whether your strategy survives contact with rate—rates of fire, production, resupply, attrition, and decision-making.

That’s why the recent discussion around building missiles at scale—with costs far below traditional prime-contractor pricing—hits a nerve. The U.S. can field exquisite weapons, but a long fight against a peer competitor punishes any force that can’t sustain volume. My take: cheaper missiles are necessary, but they’re not sufficient. If you don’t pair mass production with AI-enabled targeting, allocation, and battle-damage assessment, you’ll just burn through inventory faster.

This post is part of our AI in Defense & National Security series, where we focus on practical ways AI changes surveillance, intelligence analysis, autonomous systems, cybersecurity, and mission planning. Here, the center of gravity is the math behind missile employment—and how AI can keep that math from turning against you.

Magazine depth is a strategy problem, not a procurement problem

Magazine depth determines how long you can keep pressure on an adversary without betting everything on a short, perfect war. When missiles are expensive and production is slow, operational plans quietly become “optimistic”: fewer aimpoints, narrower windows, and a heavy reliance on early effects.

The reality of 2025 is that large-scale conflicts feature:

• Distributed targets (mobile launchers, decoys, relocatable radars)
• Layered air and missile defenses (more interceptors, more sensors, more electronic warfare)
• High operational tempo (you don’t get leisurely retargeting cycles)
• Industrial strain (supply chains and propulsion components become bottlenecks)

If missiles cost too much, you ration them. If you ration them, you accept more risk. If you accept more risk, you compensate with more aircraft sorties, more exposure, and sometimes more escalation pressure.

Here’s the part most people miss: cost per missile only matters relative to cost per effect. A $300,000 missile that misses, gets spoofed, or hits a decoy is worse than a $3 million missile that consistently removes the critical node. Volume isn’t the goal—effective volume is.

The “orders of magnitude cheaper” promise—and the hidden constraints

Industry voices pushing lower-cost missiles are responding to a real operational need: build more, faster, and with simpler manufacturing. The promise is attractive because it tackles the visible constraint (inventory) rather than the invisible constraints (targeting certainty, decision speed, and kill-chain resilience).

But even if unit cost drops dramatically, you still face constraints that don’t disappear:

1. Target identification: knowing what’s real vs. decoy
2. Weapon-target pairing: choosing the right weapon for the right target at the right time
3. Deconfliction: avoiding fratricide and wasted salvos
4. Adaptation: reacting to shifting defenses, emissions control, weather, and movement

This is where AI belongs—not as a buzzword, but as mission-planning infrastructure that turns inventory into sustained advantage.

The “missile math” that decides campaigns (and where AI fits)

Missile warfare is an optimization problem under uncertainty. Every strike is a bet placed with incomplete information and a budget of weapons, time, and risk.

At a high level, planners constantly compute a few core quantities:

• Probability of kill (Pk) for a given weapon-target pairing
• Probability of penetration (Pp) through defenses
• Shots required to reach a desired confidence level
• Time-to-effect vs. target mobility and relocation
• Opportunity cost: what you can’t strike if you spend weapons here

Even small errors compound. If you assume a Pk of 0.8 but reality is 0.5, your plan will under-allocate shots and over-promise effects.

Salvo sizing: why “just fire more” fails

The math of salvos is brutal. If each missile has an independent probability p of achieving the effect you need, then the probability of success after n missiles is:

P(success) = 1 - (1 - p)^n

If p = 0.5, you need 5 missiles to reach ~97% confidence. If p = 0.2, you need 14 missiles for that same confidence. That’s the difference between “we can sustain this for weeks” and “we’re out by Wednesday.”

AI improves this in two ways:

• Raises p by improving identification, aimpoint selection, and terminal constraints (right time, right geometry)
• Reduces unnecessary n by predicting which targets are decoys, already degraded, or not worth the shot

A simple, quotable truth:

The cheapest missile is the one you don’t fire because your model proved it wouldn’t matter.

Dynamic retargeting: the kill chain is now a data pipeline

Peer adversaries plan around your decision cycle. They move. They hide. They flood sensors with noise. So the “kill chain” becomes less like a checklist and more like a streaming analytics system.

AI-enabled decision support can:

• Fuse ISR feeds (imagery, radar, SIGINT, open-source indicators)
• Track target behavior patterns (where mobile systems tend to relocate)
• Recommend pre-planned branches (“if radar goes silent, switch to these secondary nodes”)
• Continuously update weapon allocation based on new observations

Done right, this turns targeting from periodic meetings into continuous, auditable updates.

AI-enabled missile operations: practical use cases that matter in 2025

AI adds value where humans face too many variables, too fast, with too much at stake. Below are the applications I’ve seen teams gravitate toward because they’re measurable and operationally relevant.

1) Predictive analytics for inventory and campaign endurance

If you can’t forecast expenditure and replenishment with discipline, you’ll either over-shoot early or hesitate too long.

AI-driven forecasting models can estimate:

• Expected missile consumption by phase and target set
• Sensitivity to changes in defense density (more interceptors = higher shots per kill)
• Effects of production disruptions and component shortages

This isn’t glamorous, but it’s decisive. The ability to predict “days of fire” under different plans changes what commanders can credibly commit to.

2) Weapon-target pairing that respects real constraints

A common failure mode in planning is treating all missiles as interchangeable. They aren’t. Range, seeker type, payload, flight profile, and datalink resilience all matter.

A decision-support tool can recommend pairings that account for:

• The target’s signature and likely countermeasures
• Current EW environment and GPS denial
• Collateral constraints and rules of engagement
• Logistics state (what’s available on which platform, where)

If you’re trying to scale missile production with lower-cost systems, this becomes even more important: you’ll field a more diverse mix, and diversity demands smarter allocation.

3) Battle-damage assessment (BDA) that reduces wasted follow-up strikes

BDA delays are a silent magazine-depth killer. If you can’t quickly confirm effect, you either:

• Re-strike “just in case” (wasting missiles), or
• Wait too long (letting the target recover)

AI-assisted BDA can score confidence using multi-source indicators—imagery change detection, communications shifts, radar emissions, and observed movement patterns. The win is speed plus restraint: re-strike only when confidence is low and the target matters.

4) Cybersecurity and threat modeling for the missile enterprise

As missiles get cheaper and more software-defined, the attack surface expands: datalinks, mission planning systems, manufacturing test equipment, and suppliers.

AI-driven cybersecurity can help by:

• Detecting anomalous behavior in mission data uploads
• Flagging suspicious supplier telemetry and quality drift
• Modeling how an adversary might poison training data or sensor feeds

A blunt stance: cheap missiles that can be spoofed, tampered with, or mis-targeted are an invitation to strategic embarrassment. Security has to scale with production.

Building cheaper missiles is only half the fix

Industrial scale changes the operational question from “Can we afford to fire?” to “Can we decide well enough to fire?” If you produce missiles at far lower cost, you gain options—but only if your planning and targeting keep pace.

Here’s what that looks like in practice.

The doctrine shift: from “exquisite strikes” to “managed volume”

Managed volume means you plan for repeated cycles of:

1. Sense
2. Decide
3. Strike
4. Assess
5. Adapt

AI supports steps 2 and 4—decision and assessment—so volume doesn’t become waste. The goal isn’t maximal firing; it’s maximal pressure per missile.

The interoperability shift: models must be shareable and auditable

Coalition operations are the norm, not the exception. AI tools that can’t share assumptions, confidence levels, and inputs across partners create friction.

Strong programs build in:

• Transparent confidence scoring
• Data lineage (where did this assessment come from?)
• Human override paths that are fast, not ceremonial

If an AI model recommends a salvo size increase, a commander needs to know why in plain language, quickly.

The measurement shift: track “cost per effect,” not “cost per unit”

Procurement loves unit price. Operators live with outcome.

A practical metric set I like:

• Cost per validated effect (confirmed degradation or destruction)
• Missiles expended per critical node removed
• Median time from detection to strike
• Re-attack rate (how often you had to hit the same target again)

These metrics tie AI performance directly to magazine depth.

Field checklist: what to ask before buying “AI for missile operations”

If you’re evaluating AI-driven mission planning or targeting analytics, the fastest way to cut through demos is to ask operational questions. Here are the ones that separate serious systems from slideware:

1. What decision is the model responsible for—and what’s the human responsible for?
2. What data feeds it in real time, and what happens when those feeds degrade?
3. How does it express uncertainty (confidence, error bars, alternative hypotheses)?
4. Can it simulate adversary adaptation (decoys, relocation, emissions control)?
5. How do you validate it without waiting for wartime truth data?
6. How does it integrate with existing mission planning systems and classification boundaries?

If the vendor can’t answer these crisply, you’re not buying a warfighting capability—you’re buying a dashboard.

Where this goes next for AI in defense and national security

The push to produce missiles at scale is a signal that the U.S. is taking sustained conflict math more seriously. That’s healthy. But inventory without intelligent employment is just a different kind of fragility.

For leaders working the AI in Defense & National Security problem set, the opportunity is clear: connect industrial scale to operational optimization. Use AI to raise probability of effect, shorten decision loops, and reduce waste—so “more missiles” becomes “more credible deterrence.”

If you’re planning for 2026 budgets and beyond, here’s the question that should sit on top of your roadmap: when missile costs drop and volumes rise, do your targeting and assessment systems get smarter—or do they just get louder?