F-16 to Pakistan: Lessons for AI Defense Partnerships

The F-16 isn’t just a fighter jet in U.S.–Pakistan relations. It’s a recurring test of whether advanced defense technology transfer can “buy” influence, shape partner behavior, and reduce long-term risk.

Sajjan M. Gohel’s recent revisit of the F-16 debate lands on a blunt point: the leverage theory keeps failing. Sustainment packages and upgrades haven’t reliably moderated Pakistan’s military priorities; they’ve often been read as validation. That’s not a niche aviation story—it’s a case study in how Washington handles high-stakes tech partnerships. And it maps uncomfortably well onto the way some leaders want to treat AI in defense and national security: ship the capability now, assume policy leverage later.

If you work in defense innovation, national security risk assessment, or AI-enabled systems, this matters because the U.S. is entering an era where the “F-16 problem” repeats with different hardware: ISR analytics, autonomous targeting support, electronic warfare software, model weights, sensor fusion pipelines, and cyber tooling. The platform changes. The governance challenge doesn’t.

The core problem: tech transfer doesn’t equal control

Giving a partner advanced capability rarely translates into predictable political leverage. The theory is tempting: provide premium systems and sustainment, then use access—spares, upgrades, training—as a dial to influence choices. In practice, partners adapt.

Here’s what tends to happen (and what the F-16 saga illustrates):

• The recipient treats capability as sovereign once fielded, even if the supplier views it as conditional.
• Domestic politics and military incentives outrank diplomatic intent. A military that benefits institutionally from a system will defend it—and the strategy it serves.
• The supplier’s off-ramps are costly. Cutting support can create readiness gaps, push the recipient to alternate suppliers, or create blowback in other cooperation areas.

Gohel’s update underscores a particularly sharp variant of this dynamic: Pakistan has repeatedly navigated U.S. security anxieties—regional stability, counterterror priorities, nuclear risk—to secure continued support, while maintaining core strategic orientations.

A durable rule of tech partnerships: if your entire influence plan depends on “we can always turn off the tap,” you don’t have influence—you have a hope.

For AI systems, the equivalent “tap” is access to model updates, cloud inference, data pipelines, vendor support, and specialized chips. Once those dependencies shift—or once a partner finds substitutes—leverage evaporates.

Why the F-16 case looks even riskier in 2025

The strategic environment has changed, so legacy assumptions about U.S. control are weaker. Gohel points to Pakistan’s deep reliance on Chinese defense equipment and financing, and the reality that U.S.-provided sustainment can indirectly strengthen a military establishment whose ecosystem is increasingly intertwined with China’s.

Three developments make the risk calculus harsher today:

1) Interoperability isn’t just radios anymore—it’s supply chains

In earlier eras, interoperability meant compatible comms, munitions, and doctrine. Now it also means:

• semiconductor and board-level sourcing n- firmware provenance
• MRO (maintenance, repair, overhaul) data flows
• digital mission planning tools and update cadence

That matters because sustainment is not neutral. Sustainment creates technical intimacy: training pipelines, maintenance practices, diagnostic tools, and operational habits. If the broader force is built around Chinese systems, U.S. support can become a stabilizer for a hybrid ecosystem the U.S. doesn’t control.

2) Mixed fleets create mixed security postures

Operating U.S. and Chinese systems side-by-side isn’t automatically a disaster, but it introduces persistent security questions:

• Who touches what hardware?
• Where does mission data go?
• Which contractors sit on which networks?
• How do you prevent “routine maintenance access” from becoming intelligence access?

With AI-enabled capabilities, these questions become sharper because the data is the weapon. Flight logs, targeting workflows, sensor libraries, and operational patterns can be as valuable as the platform itself.

3) “Token partnership” signals can backfire

A system can become symbolic—proof of status, proof of alignment, proof of being indispensable. Gohel argues the F-16 increasingly functions that way: less as a partnership anchor, more as a token that rewards strategic manipulation.

AI partnerships are susceptible to the same signaling trap. A high-visibility AI program, a joint autonomy demo, or a flagship data-sharing initiative can become political currency—while the underlying behavior that worries Washington remains unchanged.

The AI parallel: model access is the new sustainment package

The F-16 debate is a clean analogy for AI in defense partnerships because AI “transfer” is rarely a one-time event. It’s a lifecycle commitment.

For modern military AI—especially intelligence analysis, surveillance, targeting support, and autonomous systems—the enduring value comes from:

• continuous model retraining
• access to updated weights and evaluation benchmarks
• red-teaming and vulnerability patching
• data labeling operations
• cloud or edge compute supply

In other words: sustainment is the product.

That creates two uncomfortable truths:

1. If the U.S. can’t or won’t keep supporting the system, it shouldn’t promise strategic outcomes based on it.
2. If the U.S. does keep supporting the system, it may end up enabling outcomes it dislikes.

The F-16 case demonstrates how easy it is to slide into “support inertia”—continuing upgrades because stopping feels riskier or more disruptive than continuing. AI programs can fall into the same groove: extending pilot projects, renewing contracts, and widening access because reversing course is politically and operationally hard.

A better framework: decide what you’re buying—capability, access, or behavior

The U.S. should treat partner tech support as a purchase with explicit deliverables, not as a relationship token. For AI-enabled defense cooperation, that means being specific about what success looks like and what failure triggers.

Here’s a practical way to structure the decision.

1) Define the objective in one sentence

If the objective is “counterterror flight operations,” say that. If it’s “regional air defense stability,” say that. If it’s “intelligence cooperation against transnational threats,” say that.

Ambiguous objectives lead to ambiguous programs—and ambiguous programs tend to become permanent.

2) Separate “capability outcomes” from “behavior outcomes”

Most technology transfers can deliver capability outcomes (more sorties, better ISR, improved readiness). They’re far worse at delivering behavior outcomes (restraint in escalation, reduced proxy activity, political reform).

So write the policy as two columns:

• Capability outcomes we expect (measurable, operational)
• Behavior outcomes we want (diplomatic, strategic)

Then be honest about which one the program can actually influence.

3) Create verifiable conditions that don’t rely on trust

Trust is good. Verification is better.

For AI and digital systems, conditions can include:

• audited network segmentation for mission systems
• restrictions on data replication and cross-domain movement
• third-party security assessments and logging requirements
• controlled update channels and cryptographic signing
• limits on where models can run (approved compute environments)

These aren’t perfect. But they’re real controls, unlike vague hopes that “access equals leverage.”

4) Bake in an exit ramp that won’t cause strategic whiplash

A credible exit ramp is operationally pre-planned. If withdrawing support will create immediate instability, you don’t have a plan—you have a hostage situation.

A mature off-ramp includes:

• phased reduction triggers
• contingency baselines (what minimal support remains and why)
• partner transition planning (and what the U.S. will not backfill)
• internal messaging discipline so reversals don’t look like panic

What defense leaders can do now: a risk checklist for AI partnerships

If you’re evaluating AI-enabled cooperation with a partner state, run this checklist before the press release. I’ve found these questions surface the real risks faster than 50-page concept notes.

1. Dependency mapping: What does the partner depend on us for—chips, cloud, updates, training, labels, MRO?
2. Substitution risk: If we cut support, can they replace it with another supplier within 6–18 months?
3. Data exposure: What operational data will the system generate, store, and transmit? Who can access it physically and digitally?
4. Co-mingling: Will U.S.-enabled AI systems share networks, facilities, or contractors with Chinese-origin systems?
5. Incentives: Which domestic institutions benefit most from the capability, and what strategy do they use it to pursue?
6. Misuse pathways: What’s the most likely “unwanted use” that still looks plausible and deniable?
7. Measurement: What metrics will tell us in 90 days and 1 year whether the partnership is meeting objectives?

If you can’t answer these crisply, the program is likely being justified by relationship logic rather than risk logic.

The stance: stop treating high-end tech as a loyalty test

The F-16 story isn’t about whether Pakistan “deserves” the jet. It’s about whether Washington keeps mistaking hardware support for strategic alignment.

That mistake is even costlier with AI because the enabling layer—data, compute, and updates—spreads across missions. AI also scales quickly: a model trained for ISR triage can be adapted for border surveillance, internal security, or targeting support with minimal friction if governance is weak.

For the U.S., the smarter posture is disciplined selectivity:

• Provide cooperation where objectives are narrow, verifiable, and aligned.
• Avoid open-ended sustainment where the recipient’s core incentives run in the opposite direction.
• Treat AI transfer as a continuous security relationship, not a shipment.

Where this goes next for AI in defense and national security

This F-16 case belongs in any serious “AI in Defense & National Security” playbook because it exposes a pattern: policy often assumes technology can compensate for a misaligned partnership. It can’t.

If your organization is building or buying military AI—autonomous systems, intelligence analysis platforms, or decision-support tools—your real differentiator won’t be the demo. It’ll be whether you can prove governance: risk assessment, monitoring, auditability, and off-ramps that actually work when politics get messy.

If you’re rethinking an AI partnership, procurement approach, or export control posture, it’s worth stress-testing your assumptions now—before your “sustainment package” becomes the only policy tool you have left.