AI Lessons from Global Hotspots for Defense Teams

Ninety-five deaths tied to reported U.S. “narco-boat” strikes is the kind of number that jolts a public debate—and it’s also the kind of operational detail that can fracture trust fast when the story is messy, partial, or late. That trust problem showed up repeatedly in Gen. Dan Caine’s recent public remarks, even when the questions were about Venezuela, Europe, or Gaza.

Caine’s comments matter for a different reason too: they expose what modern military leadership is really optimizing for. Not bravado. Not slogans. Decision quality under pressure, across multiple theaters, with allies watching and the public skeptical.

For this AI in Defense & National Security series, the useful move is to read Caine’s worldview as a requirements document for AI: what leaders need to see, predict, trade off, and explain—at speed—when the “secondary and tertiary considerations” can outweigh the obvious tactical win.

The real job: options plus consequences (and AI is built for that)

Caine’s most revealing line wasn’t about China or Ukraine. It was about how he frames his role: present a range of military options with secondary and tertiary considerations so a President can decide—and then the force delivers.

That’s not just process language. It’s an admission that the hardest part of national security decision-making is rarely “Can we do it?” It’s:

• What happens next (and after that)?
• What does success cost politically, legally, and strategically?
• What second-order escalation pathways does an action open?
• What intelligence gaps could turn a clean plan into a catastrophe?

Where AI fits in the “secondary and tertiary” layer

AI is strongest when it’s used as a consequence-mapping engine—not a magic oracle.

Practical examples defense teams are already pursuing (or should be):

1. Course-of-action (COA) comparison at machine speed

   • Given a target set and constraints, AI-enabled planning tools can generate multiple COAs and highlight friction points: airspace conflicts, ISR gaps, refueling constraints, collateral risk drivers, and timeline sensitivity.

2. Escalation pathway modeling

   • The goal isn’t perfect prediction. It’s to surface “If we do X, the most plausible Y responses are…” along with confidence levels and assumptions.

3. Explainable trade-offs for civilian leaders

   • If leaders can’t explain a strike’s rationale and safeguards to Congress (even in closed session), they shouldn’t be surprised when trust collapses. AI can help assemble decision briefs that clearly separate knowns, unknowns, assumptions, and risk mitigations.

If your AI program can’t support that “options plus consequences” discipline, it’s not helping decision-makers—it’s just adding another dashboard.

Trust is now an operational requirement, not PR

Caine pivoted hard to “loss of confidence in the American military by the American people,” and he described classified briefings to congressional leaders after controversy around strikes. That’s a telling priority shift: strategic legitimacy is part of combat effectiveness.

In late 2025, that’s especially timely. Public trust is strained by:

• information leaks and messaging missteps,
• opaque legal rationales,
• civilian harm concerns,
• and the simple reality that conflicts feel “everywhere” at once.

AI can strengthen trust—or wreck it

AI’s impact on trust cuts both ways.

AI that strengthens trust looks like:

• Auditability: clear logs showing what data was used, what models produced which recommendations, and who approved what.
• Human-in-the-loop discipline: humans own the decision; AI supports, flags, and documents.
• Bias and data provenance controls: especially for target development, identity resolution, and pattern-of-life analysis.
• Rapid after-action clarity: faster, more accurate reconstruction of timelines, sensor feeds, and decisions—so leadership can brief Congress and the public without contradictions.

AI that wrecks trust looks like:

• unreviewable “black box” targeting recommendations,
• overconfident probability outputs treated as certainty,
• models trained on outdated or theater-mismatched data,
• and AI-enabled autonomy that outpaces policy and oversight.

A simple rule I’ve found useful: If you can’t explain the model’s role in one page to a skeptical committee staffer, you’re not ready to operationalize it.

Ukraine’s lesson isn’t just drones—it’s mass, attrition, and adaptation

Caine pointed to Ukraine’s ability to produce “tens of thousands” (and more) drones as an entrepreneurial lesson, then emphasized mass, a high-low mix, and “significantly more attritable things.”

That’s not a niche drone observation. It’s a strategic procurement and planning signal: future fights burn through platforms and munitions faster than our traditional acquisition rhythms can handle.

AI’s role in attritable warfare

Attritable systems only work if you can coordinate them and replace them.

AI becomes the glue in three places:

1. Swarm and team coordination (without pretending it’s fully autonomous)
   • Deconfliction, tasking, dynamic rerouting, and sensor fusion are algorithm-heavy problems.

1. Targeting cycle compression

   • The bottleneck in high-volume operations is often not weapons—it’s analysis, prioritization, and authorization. AI can speed triage and reduce analyst overload, especially for ISR streams.

2. Supply chain and sustainment intelligence

   • Attrition warfare is logistics warfare. AI that forecasts consumption rates, predicts shortages, and flags single points of failure is combat power.

The airpower point matters for AI too

Caine’s comment about Ukraine also underscored something easy to miss: the importance of putting air power over a battlefield—and what it means when you can’t.

AI-enabled air and missile defense, electronic warfare decision aids, and contested ISR management are now core to achieving (or denying) that air advantage. In other words: AI isn’t just about drones. It’s about seeing and deciding in a battlespace where seeing is hard.

China, the Indo-Pacific, and “multiple simultaneous dilemmas”

Caine described a Joint Force goal that shows up in a lot of operational thinking: create “multiple simultaneous dilemmas” so adversaries are cautious.

This is where AI in national security stops being a buzzword and becomes a math problem: you need to coordinate posture, signaling, readiness, cyber defense, and ISR across regions—while keeping forces available for the unexpected.

What “multiple dilemmas” demands from AI

To generate dilemmas credibly, you need:

• Cross-theater situational awareness: a unified, role-based common operating picture that fuses intel, operations, cyber, logistics, and diplomatic constraints.
• Decision advantage under uncertainty: tools that quantify uncertainty instead of hiding it.
• Fast anomaly detection: early warning across maritime, space, cyber, and information domains.
• Wargaming at scale: not one exquisite tabletop exercise, but thousands of simulation runs to explore what breaks.

This is also where AI governance becomes a strategic issue. If your AI systems can’t share data across classification boundaries (safely), can’t interoperate with allies, or can’t be updated quickly, your “dilemmas” become predictable—and predictable is manageable.

“Buy behind the curve” is a cultural problem—and AI can expose it

Caine’s procurement critique landed because it’s true: the U.S. system often buys after technology has matured elsewhere. He talked about changing DoD culture, changing company culture, writing better contracts, and sharing risk.

If you want a practical AI takeaway, it’s this: AI programs fail more from contracting and adoption friction than from model accuracy.

A better way to buy and field AI for defense

Here’s what tends to work when the goal is operational impact (not a demo):

1. Contract for outcomes, not artifacts

   • Instead of “deliver a model,” specify measurable outcomes: reduced analyst queue time, improved track continuity, faster COA generation, lower false positives—paired with test protocols.

2. Plan for model refresh from day one

   • The battlefield changes. So does the data. Your contract should include retraining cadence, drift monitoring, and red-team testing.

3. Build for classification reality

   • If the tool only runs in an unclassified sandbox, it won’t touch the hard problems. Fielding plans must include secure environments and accreditation pathways.

4. Make human workflows the product

   • The UI and the approval chain matter as much as the algorithm. AI that doesn’t match how staff officers and analysts actually work will get bypassed.

5. Treat trust and oversight as features

   • Audit logs, explainability notes, and policy controls aren’t “extras.” They’re what keeps programs alive when scrutiny hits.

“People also ask”: Will AI replace commanders?

No—and it shouldn’t. AI replaces bottlenecks, not accountability. The winning pattern is AI that accelerates sensemaking, surfaces risks, and documents assumptions, while commanders retain decision authority.

What defense leaders should do next (a short field checklist)

If you’re leading AI adoption in a defense or national security organization, use Caine’s themes—trust, mass, dilemmas, and buying ahead of the curve—as a practical checklist.

• Can our AI outputs be briefed to Congress without embarrassment? If not, fix auditability and explainability.
• Are we optimizing for “mass” and attrition, or still building only exquisite tools? If it can’t scale, it won’t matter.
• Do we have drift monitoring and model refresh baked into operations? If not, performance will decay silently.
• Can allies plug in (policy and tech)? If not, coalition operations will fracture under pressure.
• Are we building consequence-mapping tools, not just detection tools? If not, we’re stuck at the “can we?” layer.

Caine’s public posture is cautious, but the signal is loud: the U.S. is preparing for a world where crises overlap, legitimacy is fragile, and industrial speed matters. AI can help—especially for intelligence analysis, mission planning, autonomous systems oversight, and cybersecurity—but only if it’s built to support decisions that have to survive second- and third-order consequences.

So here’s the question worth sitting with as 2026 planning cycles kick off: If a major crisis hits next quarter, will your AI stack make leaders more confident—or will it create another thing they can’t fully explain?