AI-Proofing National Security Strategy Under Scrutiny

Rep. Don Bacon’s recent critique of the new US National Security Strategy landed because it wasn’t vague. He called it a throwback to “1930s foreign policy,” argued that a one-year $150B-plus budget bump won’t fix structural readiness issues, and said Defense Secretary Pete Hegseth’s approach has weakened alliances.

Whether you agree with Bacon or not, his comments spotlight a hard truth inside defense planning: national security strategy is still built more like a narrative than a testable model. It’s often optimized for internal consensus and political timing, not for measurable assumptions, scenario stress-testing, and transparent trade-offs.

This post is part of our AI in Defense & National Security series, and I’m going to take a stance: if a strategy can’t be audited—by Congress, by allies, and by the public record—it isn’t a strategy, it’s a press release. AI won’t replace leadership, but it can force strategic clarity by turning assertions into variables, and variables into outcomes you can argue with.

Why this National Security Strategy debate matters (beyond politics)

The debate matters because Congress is signaling a credibility gap: voters say they support NATO and Europe, while parts of the strategy rhetoric appear to pull the other way. When strategy and electorate don’t line up, execution suffers—funding, basing, posture, munitions production, and allied coordination all get slower and more brittle.

Bacon’s “1930s” label is essentially an accusation of strategic nostalgia: a belief that the US can reduce alliance commitments without paying a price in deterrence. That’s not just a philosophical dispute. It’s an empirical question about:

• how adversaries respond to signals of reduced unity
• how quickly partners hedge (politically and militarily)
• what it does to forward logistics, access, and surge capacity
• how it changes risk tolerance in gray-zone conflict

Here’s the practical problem: traditional strategy documents don’t “show their work.” They assert priorities but rarely publish the assumptions, sensitivity analyses, or alternative options considered. That’s exactly where AI-enabled strategic analysis can help—if it’s implemented with discipline.

What AI can actually improve in national security strategy development

AI improves strategy when it makes assumptions explicit and stress-tests them at scale. The goal isn’t to “automate policy.” The goal is to reduce unforced errors—especially the kind that show up later as readiness gaps, alliance surprises, or budget whiplash.

1) Turning slogans into measurable assumptions

A strategy typically includes statements like “focus on the Western Hemisphere,” “prioritize the Indo-Pacific,” or “reduce burdens overseas.” AI can’t judge values, but it can translate claims into testable inputs.

A simple example: if the strategy implies fewer standing forces in Europe, the model should force clarity on:

• warning time assumptions (days/weeks)
• lift and sealift availability
• munitions stockpile drawdown rates
• host-nation support and access constraints
• expected adversary mobilization timelines

Once you write those down, you can do something rare in strategy work: argue about numbers instead of vibes.

2) Scenario planning that doesn’t collapse under complexity

Most strategy shops run a small number of table-top exercises because they’re expensive and time-consuming. AI-enabled simulation and probabilistic modeling can run thousands of scenario variations—changing one factor at a time to see what truly drives risk.

That matters directly to Bacon’s critique. If the strategy claims alliance posture is less central, AI can help answer:

• How often do “Europe-lite” postures increase simultaneous conflict risk?
• What’s the second-order effect on Indo-Pacific deterrence when Europe becomes uncertain?
• Which capabilities substitute for forward presence—and which don’t?

Good modeling won’t “predict war.” But it will surface where the strategy is fragile.

3) More transparent trade-offs for Congress and allies

Congressional scrutiny is not a nuisance; it’s a control mechanism. The problem is that lawmakers are frequently asked to vote on strategies that don’t expose the underlying trade space.

AI-enabled decision support can produce auditable trade-off dashboards:

• If you cut X force structure, what happens to Y operational plans?
• If you add $150B for one year, which readiness metrics move—and which don’t?
• If alliance commitments shift, what’s the modeled change in basing access and partner procurement?

If you want better oversight, you need artifacts that can be inspected. Black-box AI doesn’t help here. Explainable models, documented inputs, and version-controlled assumptions do.

The budget plus-up problem: why one-year money doesn’t buy one-year readiness

A one-year $150B-plus increase sounds massive, but readiness is constrained by time, production capacity, and training pipelines. You can obligate funds quickly; you can’t instantly grow skilled labor, expand energetic materials production, or produce certified microelectronics at scale.

This is where AI is unusually practical: it can map strategy to industrial reality.

What AI-driven readiness modeling looks like

Instead of asking, “How should we spend the plus-up?” ask, “What’s the fastest path to measurable deterrence improvement under real constraints?”

AI can help integrate:

• supplier capacity limits (especially second- and third-tier)
• lead times for critical components
• test and certification bottlenecks
• depot and maintenance throughput
• training seat availability and instructor ratios

A useful output isn’t a glossy chart. It’s a constraint-aware plan like:

1. Fund munitions and spares that hit the flightline and ship readiness inside 6–18 months.
2. Invest in multi-year procurement where it expands production capacity (not just unit count).
3. Prioritize data infrastructure that reduces maintenance downtime and improves supply forecasting.

If you’re trying to reconcile big strategic promises with a limited time horizon, this is the difference between optics and outcomes.

Alliance risk is modelable—and it should be modeled

Bacon’s strongest argument is that strategy can’t ignore voter and ally expectations without consequences. Alliances aren’t sentimental; they’re infrastructure—bases, access, intelligence sharing, interoperability, and political legitimacy.

AI can improve alliance management in strategy work in three concrete ways.

1) Sentiment-to-risk translation (done responsibly)

Open-source indicators—parliamentary votes, defense budget signals, procurement shifts, diplomatic language changes—can be tracked to identify when partners are hedging.

This doesn’t mean spying on allies or automating diplomacy. It means flagging when the alliance “system” is under strain so policymakers can respond early.

2) Interoperability as a first-class metric

Strategy documents often treat interoperability as a nice-to-have. It should be a metric. AI can analyze:

• comms compatibility and data link coverage
• training and exercise patterns
• shared logistics and stockpiles
• common operating picture maturity

If the strategy deprioritizes Europe or NATO-like constructs, leaders should be forced to answer: what’s the plan for maintaining interoperability anyway? If there isn’t one, deterrence erodes quietly.

3) Predicting second-order effects of posture changes

Forward posture isn’t just about the local theater. It affects global signaling.

AI-enabled scenario planning can estimate the probability of:

• opportunistic coercion in other regions
• increased gray-zone activity when unity looks shaky
• higher demand for ISR, cyber, and space resilience to compensate

If you’re going to bet against alliances as a center of gravity, you should have quantified risk bounds—not rhetorical confidence.

A practical blueprint: 3 ways AI can improve strategic defense policymaking

If you want AI in national security strategy to help (and not create new problems), focus on these three moves first.

1) Build a “strategy ledger” of assumptions

Create a living repository that lists every major assumption, the data supporting it, who owns it, and how it’s measured over time.

• Assumption example: “Allies will maintain X posture and access agreements.”
• Measure: exercise participation, procurement alignment, basing access renewals, force contributions.
• Review cadence: quarterly.

This is boring work. It’s also what makes strategy real.

2) Use AI for red-teaming, not decision replacement

AI is excellent at generating alternative hypotheses and stress-testing the logic chain:

• “If the US reduces commitment signals, what are the plausible adversary interpretations?”
• “Which early warning indicators would show the plan is failing?”
• “What’s the minimum viable posture to keep deterrence stable?”

Human leaders still decide. AI makes it harder to ignore inconvenient branches of the decision tree.

3) Demand explainability and audit trails

If Congress can’t understand how a recommendation was produced, it won’t trust it. If allies can’t understand it, they won’t align with it. If operators can’t understand it, they won’t use it.

Minimum standard for any AI-enabled strategic analysis:

• documented inputs and data provenance
• sensitivity analysis (what changes the outcome most?)
• uncertainty ranges (confidence bands, not false precision)
• version control for models and assumptions

When someone says “AI recommends,” your next question should be: “Based on what, and how sensitive is that?”

People also ask: can AI predict whether a strategy will fail?

AI can’t predict strategic failure with certainty, but it can identify failure modes early and quantify which assumptions are most fragile.

Think of it like engineering. You don’t predict the exact day a bridge will fail. You model stress, load, materials, and weak points, then monitor indicators. Strategy should work the same way.

AI is most valuable when it helps answer:

• What has to be true for this strategy to succeed?
• What indicators will tell us it’s failing?
• What are the lowest-cost adjustments that reduce risk fastest?

That’s not politics. That’s competence.

Where this leaves defense leaders heading into 2026

Bacon’s critique is a signal that the National Security Strategy conversation is drifting toward fundamentals: alliances, posture, resources, and credibility. Those debates are healthy. What’s unhealthy is pretending that a strategy is “strong” because it reads confidently.

For the AI in Defense & National Security community, the opportunity is straightforward: make strategy measurable without making it mechanical. Use AI to surface trade-offs, quantify risk, and document assumptions—then let elected leaders make the value judgments openly.

If your organization is building, buying, or governing AI for national security strategy, the next step is simple: start with one high-stakes decision (force posture, munitions, cyber resilience, or alliance interoperability), build an auditable model, and force the strategy to answer to the numbers.

What would change in Washington if every major strategic claim had to come with its assumption ledger—and a dashboard showing exactly how it breaks under stress?