Golden Dome and the AI Arms Race It Could Trigger

The fastest way to make an adversary build something scarier isn’t a new missile. It’s the promise of a shield.

That’s the uncomfortable lesson sitting underneath the 2025 “Golden Dome” debate. When Washington signals it wants a strategic missile defense system ambitious enough to thin out an intercontinental ballistic missile (ICBM) salvo, rivals don’t shrug and walk away. They go hunting for the paths around it—undersea, inside the atmosphere, and eventually in space itself.

For leaders and practitioners working at the intersection of AI in defense & national security, this matters for a simple reason: the next action-reaction loop won’t be driven by metallurgy and rocket motors alone. It’ll be driven by software. Autonomy, AI-enabled sensing, AI-assisted targeting, and deception at machine speed are exactly the tools you’d build if you wanted to sidestep a space-heavy missile shield.

Why ambitious missile defense often backfires

A strategic missile shield creates incentives for the other side to make your defense irrelevant rather than trying to beat it head-on. That incentive is old. What’s new is how quickly AI can accelerate the adaptation cycle.

The historical pattern is straightforward:

• One side invests in defenses that threaten to reduce the credibility of the other side’s deterrent.
• The other side responds by increasing offensive capability, changing delivery paths, adding penetration aids, or developing entirely different systems.
• Costs spiral because defense is typically more expensive per “shot” than offense—especially when an attacker can add decoys and complexity cheaply.

During the Cold War, this logic helped shape arms-control guardrails that implicitly accepted a hard truth: mutual vulnerability is stabilizing because it makes outcomes predictable in crisis. Trying to escape that vulnerability tends to push adversaries into less predictable systems.

Here’s the part that’s easy to miss in 2025: AI makes “less predictable systems” easier to design, deploy, and operate. Autonomy reduces the operational friction that used to limit exotic concepts.

The “defense tax” problem (and why it matters for budgets)

If interceptors are costly and the attacker can add missiles, decoys, or new delivery modes more cheaply, your defense becomes a budget magnet. You can win tests and still lose the cost-exchange ratio.

This is one reason large, space-based interceptor constellations create strategic risk beyond the technical debate. Even partial success can force rivals to invest in:

• Mass (more launchers, more missiles)
• Penetration aids (decoys, chaff, jammers)
• Bypass routes (cruise missiles, hypersonics, undersea systems)
• Preemption logic (pressure to act early before defenses are fully online)

AI doesn’t change the economics of rockets, but it does improve the attacker’s ability to plan routes, coordinate salvos, and exploit gaps in sensing.

The real problem: Golden Dome shifts the offense into domains that favor autonomy

If your crown-jewel defense is optimized for objects that travel through space on predictable trajectories, attackers will prioritize things that don’t. That’s not cynicism; it’s engineering.

The source article highlights the destabilizing pull toward systems designed to evade U.S. defenses—such as Russia’s nuclear-powered cruise missile concept (Burevestnik) and its autonomous nuclear torpedo concept (Poseidon). Regardless of how much of the public messaging is theater, the strategic signal is clear: “You’re building a shield; we’re building ways around your radar and your intercept geometry.”

From an AI in national security perspective, notice what these bypass routes have in common:

• They stress sensing and classification more than raw interceptor speed.
• They reward autonomous navigation and long-duration mission management.
• They create more opportunities for deception, ambiguity, and surprise.

Cruise missiles and AI-enabled route planning

Cruise missiles are attractive because they stay in the atmosphere and can fly complex profiles. Add AI-enabled mission planning and you get:

• Terrain/sea-skimming optimization
• Adaptive rerouting around known sensors
• Coordinated timing with other attack elements

The operational concept becomes less about a single exquisite missile and more about software-driven pathfinding plus coordination.

Hypersonics and the “tracking gap” problem

Hypersonic glide vehicles and maneuvering systems are hard because they compress timelines and complicate prediction. AI shows up in two places:

1. Defender side: sensor fusion, track continuity, and intercept planning under uncertainty
2. Attacker side: maneuver policies, terminal deception, and target selection

Even if space-based interceptors improved boost-phase options for classic ballistic trajectories, adversaries will keep investing in in-atmosphere maneuver where interception is harder and attribution is slower.

Undersea autonomous systems and decision-time compression

Autonomous undersea platforms are strategically alarming because they combine stealth, persistence, and ambiguous intent.

If a system can loiter for long periods and approach from unexpected vectors, the defender’s problem shifts toward:

• persistent maritime domain awareness
• anomaly detection at scale
• rules of engagement that won’t accidentally escalate

Those are AI problems as much as they are sonar problems.

AI makes the action-reaction loop faster—and harder to control

Arms races are dangerous partly because they’re slow. AI flips that: adaptation becomes rapid, iterative, and sometimes opaque even to the operators.

In missile defense debates, people often focus on physics (burn times, orbits, intercept windows). Physics matters, but the next decade is also a contest over:

• Perception: what the system “believes” is happening
• Decision latency: how quickly it recommends or takes action
• Deception resilience: how well it handles spoofing, decoys, and adversarial inputs

The “autonomy escalator” risk

The more your architecture depends on ultra-fast detection and engagement, the more you’ll be tempted to automate decisions.

That creates a predictable escalation pressure:

1. Human-in-the-loop is too slow for some engagement windows.
2. Human-on-the-loop becomes the compromise.
3. In a crisis, safeguards get relaxed “temporarily.”
4. Temporary becomes normal.

This is how you end up with machine-speed interactions in strategic contexts—exactly where misunderstanding and false positives are most catastrophic.

AI deception becomes a strategic weapon, not a tactical trick

Expect adversaries to invest heavily in AI-enabled deception because it’s cheaper than building more interceptors. Examples include:

• Decoy generation that looks “real” across multiple sensors
• Electronic warfare tuned by reinforcement learning
• Cyber operations against command-and-control and sensor tasking

A practical one-liner that holds up in procurement meetings:

If your system can be fooled, your adversary will mass-produce the lie.

A smarter approach: build stability, not just interceptors

The safest missile defense posture is one that improves real-world protection against likely attacks without convincing rivals their deterrent is collapsing. That sounds like threading a needle—because it is.

The RSS article argues that the space-based interceptor component of Golden Dome carries the highest strategic-stability risk and may incentivize more destabilizing offensive programs. I agree with the direction, and I’d make it more operational for 2026 planning cycles.

Prioritize the AI-enabled sensing and command layer

If you want resilience without lighting the arms-race fuse, spend on the parts that:

• increase detection and attribution
• strengthen communications under attack
• improve integrated air and missile defense across regions

In practice, that often means:

• multi-layer sensor fusion (space + airborne + terrestrial)
• robust data labeling and red-teaming against adversarial inputs
• hardened, redundant communications paths

AI belongs here—but with disciplined engineering and governance.

Make autonomy safer through policy, testing, and architecture

Autonomy in strategic defense shouldn’t be treated as a feature. It should be treated as a controlled hazard.

Actionable steps I’ve found teams can actually execute:

1. Define “no-go” automation zones (decisions that always require human authorization)
2. Instrument everything (audit logs, model telemetry, human override timing)
3. Run deception-first testing (assume spoofing is constant, not rare)
4. Design for graceful degradation (fail “dumb,” not “fast”)

If your AI-enabled command-and-control can’t explain what it’s seeing and why it’s recommending an action, it doesn’t belong in strategic defense.

Use the space-debris and stability angle as diplomatic leverage

There’s a practical diplomatic argument that doesn’t require trust-falls: space sustainability is shared infrastructure. Any architecture that increases the likelihood of debris events raises costs for everyone—commercial and military.

A credible U.S. posture is:

• maintain norms against debris-generating behavior
• separate “sensing and communications” from “interception” in space where possible
• propose verifiable constraints on the most destabilizing categories (space-based interceptors and certain exotic strategic delivery systems)

This isn’t naive idealism. It’s bargaining with leverage: restraint for restraint, targeted at the systems that compress decision time and increase surprise.

Practical questions defense leaders should be asking now

The Golden Dome debate becomes more productive when you translate it into procurement and operational questions. Here are the ones that cut through buzzwords.

“What does this system incentivize the adversary to build?”

If the answer is “more autonomous undersea systems” or “more stealthy cruise missiles,” your plan must include serious investment in countermeasures there—or you’re paying to move the threat, not reduce it.

“What’s the cost per defended outcome, not cost per interceptor?”

Measure cost against realistic attack adaptations:

• salvos with decoys
• mixed-mode attacks (ballistic + cruise + drones)
• degraded communications

If your economics collapse under plausible adaptation, you’re building a brittle symbol.

“Where does AI reduce risk, and where does it increase it?”

• AI reduces risk when it improves sensing, classification, and operator decision support under uncertainty.
• AI increases risk when it automates escalation-sensitive choices or creates opaque failure modes.

Write those into requirements, not after-action reports.

Where this fits in the AI in Defense & National Security series

This series is about how AI changes surveillance, intelligence analysis, autonomous systems, cybersecurity, and mission planning. Golden Dome is a clean case study because it shows how defensive ambition can accelerate offensive autonomy.

If Washington frames strategic defense as “we’ll build an impenetrable shield,” rivals will respond with systems that are harder to see, harder to predict, and easier to hand off to machines. If Washington frames it as “we’ll improve detection, resilience, and limited defense while building guardrails,” it has a shot at reducing risk without triggering the worst incentives.

The question worth sitting with as budgets and architectures solidify for 2026: Are we paying for protection—or paying to motivate the next generation of AI-enabled superweapons?