AI Arms Race in Defense: What Leaders Must Do Now

AI competition in defense isn’t a far-off forecast—it’s already shaping budgets, force design, and battlefield outcomes. The clearest proof is the way drone warfare has matured in real time: what started as small-unit improvisation is steadily turning into industrialized autonomy, with software updates arriving faster than traditional acquisition cycles.

Defense One Radio’s 200th episode featured Paul Scharre—CNAS executive vice president, former Army Ranger, and author of Army of None and Four Battlegrounds—on the “global AI arms race.” The value in Scharre’s lens is that it bridges strategy and engineering reality. He doesn’t treat “AI” like magic dust. He treats it like a stack of capabilities—data, compute, models, integration, training, and doctrine—where weakness in any layer becomes operational risk.

This post is part of our AI in Defense & National Security series, where we focus on practical pathways: surveillance and ISR, intelligence analysis, autonomous systems, cybersecurity, and mission planning. The bottom line: the race isn’t about who demos the flashiest model. It’s about who can deploy reliable AI-enabled systems at scale—under jamming, deception, and time pressure.

The global AI arms race is really a deployment race

The core point most leaders miss: the decisive advantage comes from operationalizing AI, not inventing it. Nations can access similar research papers, similar open-source tooling, and increasingly similar baseline models. The separation happens in deployment: how quickly AI moves from lab to field, how safely it’s used, and how well it’s adapted to messy real-world conditions.

In defense terms, that “deployment race” shows up as:

• Time-to-field: Can you push models, updates, and autonomy behaviors in weeks—not years?
• Resilience: Does the system degrade gracefully when GPS drops, comms are jammed, or the adversary spoofs inputs?
• Scale: Can you support thousands of endpoints (drones, sensors, vehicles, radios) with training, updates, monitoring, and audit trails?
• Governance: Can you prove—internally and to oversight bodies—why an AI-enabled system behaves the way it does?

Here’s what works in practice: treat AI as a continuous capability (like cyber defense), not a one-time procurement. If you’re building an “AI program” that expects to be “done” after an acquisition milestone, you’re building something your adversary will outpace.

Myth: “We just need more autonomous weapons”

Autonomy matters, but the real multiplier is human-machine teaming at speed. Autonomy that can’t be trusted, tested, or controlled becomes a liability. Autonomy that’s integrated into a kill chain (or a defensive chain) with clear rules and reliable fail-safes becomes decisive.

A stance I’ll defend: the winners will be the organizations that can say “no” to fragile autonomy and “yes” to robust, bounded autonomy—the kind that’s built around verification, monitoring, and mission-specific constraints.

Drone warfare is the proving ground—and it’s teaching hard lessons

AI reshaping drone warfare isn’t just about “smarter drones.” It’s about the economics and tempo of conflict. Ukraine has shown how drones can become consumable, rapidly iterated systems. That dynamic pressures every military to rethink what “dominance” looks like when low-cost platforms can find, fix, and sometimes finish targets.

AI accelerates this in three concrete ways:

1. Perception at the edge: Computer vision for detection, tracking, and classification reduces reliance on constant human attention.
2. Navigation under denial: Autonomy helps drones operate when GPS is degraded or comms are disrupted.
3. Mass coordination: Algorithms can support swarming behaviors, deconfliction, and distributed sensing.

But it also introduces predictable failure modes that adversaries will weaponize:

• Deception attacks (decoys, spoofed signatures, adversarial patterns)
• Model brittleness (performance collapses outside training conditions)
• Overconfidence (operators trusting a system beyond its validated envelope)

If you’re leading an AI effort in ISR or unmanned systems, the non-negotiable requirement is test realism. Synthetic data helps. So do sims. But if your evaluation doesn’t include camouflage, smoke, weather, sensor noise, electronic warfare, and weird edge cases, you’re validating a demo—not a capability.

Practical takeaway: build an “EW-first” autonomy checklist

Before an AI-enabled drone capability goes operational, insist on documented answers to questions like:

• What happens when GPS drops for 15 minutes?
• What happens when video is degraded, frames are lost, or latency spikes?
• How does the model behave when presented with decoys that match the target signature?
• What’s the fallback mode if confidence drops below threshold?
• Can the system produce forensic logs to reconstruct decisions after an incident?

That checklist becomes a procurement filter: you stop buying autonomy that only works in perfect conditions.

Competing with China (and others) means securing the full AI stack

Great models don’t matter if your data pipelines are clogged, your networks can’t support edge updates, or your security posture can’t withstand compromise. In Scharre’s framing of a global competition, the most actionable shift for defense leaders is to treat AI as strategic infrastructure.

In practical terms, the “AI stack” in defense includes:

• Data readiness (labeled datasets, sensor metadata, data rights, lineage)
• Compute strategy (cloud + on-prem + tactical edge, capacity planning)
• MLOps and monitoring (versioning, drift detection, evaluation, rollback)
• Cybersecurity for AI (model integrity, supply chain security, red teaming)
• Integration (C2 systems, mission systems, identity, authorization)
• Workforce and doctrine (operators, maintainers, test teams, commanders)

If you’re missing any one of these, you’re not “behind on AI.” You’re behind on operational AI.

The uncomfortable truth: procurement speed is now a strategic variable

Traditional acquisition timelines were designed for hardware-centric platforms with long service lives. AI changes the pacing function. When software upgrades can change mission performance monthly, your contracting and certification model becomes part of deterrence.

That’s why 2026 budget conversations (and the end-of-year reprogramming pushes we always see in December) matter: money will follow organizations that can show measurable readiness improvements quarter by quarter.

Responsible autonomy isn’t a PR topic—it’s a battlefield requirement

Autonomous and AI-enabled systems raise real ethical and legal concerns. But for defense teams, the most immediate problem is simpler: uncontrolled autonomy increases the chance of strategic failure.

“Responsible AI” becomes operational when it’s translated into engineering controls and command decisions, such as:

• Clearly defined human roles (who authorizes what, at what stage)
• Bounded target sets (where autonomy is permitted, and where it’s prohibited)
• Rules of engagement mapped to system behaviors
• Assurance evidence (tests, audits, logs, red-team results)

Here’s a snippet-worthy way to put it:

The goal isn’t “AI that replaces humans.” The goal is “AI that earns trust under stress.”

If your organization can’t explain how an AI-enabled targeting aid was validated, or what it does when uncertain, you’ll lose time to internal friction—then lose capability to external competition.

People also ask: “Can we regulate an AI arms race?”

Some limits and norms are possible, but they won’t substitute for preparedness. Verification is hard, dual-use is everywhere, and the diffusion of models is accelerating. The most realistic approach is risk reduction plus readiness:

• Reduce escalation and accident risk with stronger human control, logging, and incident review.
• Increase deterrence with resilient AI-enabled sensing, decision support, and defensive autonomy.

A readiness playbook for defense and national security leaders

The fastest way to create advantage is to stop treating AI like a science project and start managing it like force readiness.

1) Define “AI readiness” with measurable metrics

Pick metrics that are operational, not promotional. Examples that work:

• Time from model update to field deployment
• Percent of mission data with usable labels and lineage
• Mean time to detect performance drift
• Success rates in jammed/spoofed test conditions
• Operator workload reduction (measured, not assumed)

2) Build an AI test-and-evaluation pipeline that looks like reality

If you only test in calm conditions, your adversary will “test” you in chaos.

Minimum standard for credible T&E:

• Contested EW scenarios
• Deception and decoy campaigns
• Rare-event stress testing
• Continuous regression tests after every model update

3) Operationalize MLOps across classification levels

AI that can’t cross the boundaries between unclassified experimentation and classified operations becomes a permanent pilot. The right goal is controlled mobility: repeatable processes for moving models, features, and evaluation evidence to the environment where missions actually happen.

4) Treat AI cybersecurity as mission assurance

AI systems expand the attack surface. A serious program includes:

• Model provenance and signing
• Dataset integrity checks
• Supply chain controls for dependencies
• Red teaming focused on adversarial ML threats

5) Train commanders and operators on “how AI fails”

Most training focuses on “how to use the tool.” You also need training on:

• When to distrust outputs
• What drift looks like
• How deception presents itself
• What the fallback plan is

This is where adoption accelerates: people accept AI decision support when they understand its edges.

Where this is headed in 2026: autonomy at scale, or fragility at scale

The next phase of the AI arms race won’t be decided by one platform or one model release. It will be decided by which organizations can field scalable autonomy—and keep it safe, secure, and effective as adversaries adapt.

If you’re responsible for mission planning, ISR, autonomous systems, or cyber defense, the mandate is clear: build an AI capability that survives contact with the enemy’s countermeasures. That means prioritizing resilience, assurance, and deployment speed over flashy demos.

If you want a practical next step, start with a readiness audit: map your AI stack (data → compute → MLOps → security → integration → training) and identify the single biggest bottleneck slowing fielding. Fix that bottleneck before you fund the next prototype.

The open question for defense leaders going into 2026 is blunt: when autonomy becomes common and cheap, will your advantage come from having AI, or from having AI you can trust—at scale, under fire?