AI Lessons from Russia’s Ukraine “Imperial Trap”

Russia’s war in Ukraine is often framed as a test of endurance: whose economy, manpower, and political system breaks first. But the more useful lens—especially for defense leaders thinking about AI in national security—is simpler and more uncomfortable: this is what happens when a state makes big bets with bad forecasts and then can’t unwind the commitment.

The source article calls it an “imperial trap,” and the label fits. Russia entered the war expecting a fast political collapse in Kyiv. Instead, it’s facing a multi-year attrition fight, tightening sanctions, rising dependence on external partners, and a growing bill—military, social, and economic—that doesn’t stop compounding.

For this “AI in Defense & National Security” series, the interesting question isn’t whether AI would have “prevented” the war. The question is what AI-enabled intelligence and decision-support can do to reduce the probability of strategic miscalculation—and to shorten the time leaders spend trapped in sunk-cost thinking once reality diverges from the plan.

The “imperial trap” is a forecasting failure before it’s a battlefield failure

Imperial wars fail for a predictable reason: leaders convince themselves the opponent will fold quickly, outside actors won’t meaningfully intervene, and domestic costs will stay manageable. When any one of those assumptions breaks, the war becomes a long, expensive grind.

The article draws a line from Ukraine back through Russia’s history of failed or inconclusive imperial interventions: the Crimean War, the Russo-Japanese War, World War I, and the Soviet-Afghan War. The common pattern is not just hubris—it’s weak feedback loops. Institutions that should challenge assumptions either can’t or won’t, and the system becomes good at executing a chosen plan but bad at updating that plan.

That’s the first AI connection: modern decision advantage is about update speed. Not “more data,” not “better dashboards,” but the ability to revise core beliefs when evidence changes.

What AI can actually improve here

AI doesn’t replace strategy. It strengthens the parts that empires historically botch:

• Scenario generation: forcing planners to quantify “what if the adversary doesn’t break?”
• Sensitivity analysis: showing which assumptions drive outcomes (and where uncertainty is fatal)
• Red-team automation: surfacing contradictory indicators faster than human teams can
• Early-warning analytics: detecting when battlefield, economic, and political trends are drifting away from the plan

A memorable rule I’ve found useful when evaluating AI in defense planning: If your model can’t tell you what would make you change your mind, it’s not a decision tool—it’s a justification tool.

The five miscalculations that keep repeating—and how AI can reduce them

Ukraine looks “new” because of drones, electronic warfare, and precision strikes. The strategic mistakes driving the war’s trajectory are old. Here are five that show up in the historical analogies and the current conflict.

1) Underestimating societal resilience

The article highlights a recurring Russian error: assuming a smaller adversary will collapse politically or psychologically. In Ukraine, that misread turned a “short war” plan into a protracted fight.

AI opportunity: resilience forecasting that blends multiple signals—mobilization rates, volunteering patterns, civil defense activity, local governance continuity, and information environment metrics.

Done well, this isn’t “predicting morale.” It’s estimating the adversary’s capacity to keep generating combat power and legitimacy under stress.

2) Misreading foreign involvement and escalation pathways

Past examples in the article point to external support as the cost-multiplier: Crimea brought Britain and France; Afghanistan brought sustained U.S.-backed arming of the mujahedeen; World War I widened into multiple fronts and chokepoints.

Russia expected sanctions—but not necessarily the cohesion and persistence of Western support, nor the degree of export control enforcement.

AI opportunity: strategic modeling that treats external partners as actors with their own incentives, constraints, and domestic politics. That includes:

• likelihood and scale of weapons transfers
• sanctions adoption and enforcement intensity
• industrial replenishment timelines
• alliance cohesion under economic and electoral pressure

This is where AI-based agent modeling and simulation can help, not by producing a single “answer,” but by mapping how different choices create different coalitions and timelines.

3) Overestimating your own adaptability under pressure

The article makes an important point: Russia has adapted better than many expected—financial stabilization, force regeneration via contract soldiers and recruited convicts, and rapid iteration in drones, missiles, and communications.

But adaptation has limits. Attrition doesn’t just burn tanks and ammunition; it erodes training quality, maintenance standards, and leadership depth. It also creates second-order effects: demographic strain, reintegration problems for veterans, and fiscal brittleness.

AI opportunity: readiness and sustainability analytics that track the gap between “units on paper” and “units capable of combined-arms operations.” That means fusing:

• maintenance and spare-part availability
• training pipeline throughput
• officer/NCO replacement rates
• EW and drone loss/replacement cycles
• munitions expenditure vs production

A strong AI-enabled intelligence system makes it harder for leadership to confuse tempo with strategic sustainability.

4) Ignoring domestic political fragility

A hard truth in the source: long wars stress under-institutionalized political economies. The article points to mounting strain—budget deficits, the burden of high defense spending, nationalizations, elite fear, and the risks of reintegrating large numbers of traumatized veterans (including former prisoners).

AI opportunity: governance risk sensing that watches for leading indicators of instability:

• regional grievance patterns and protest networks
• elite faction signaling (appointments, dismissals, asset seizures)
• labor-market distortions from mobilization economics
• crime and violence trends tied to veteran reintegration

There’s a right and wrong way to do this. The wrong way is “predicting regime collapse.” The right way is supporting policymakers with risk bands and trigger conditions that demand contingency planning.

5) Getting trapped by sunk costs

Once leaders commit to maximalist objectives, backing down looks like weakness—even when the cost curve has turned brutal. That’s the trap.

AI opportunity: decision-support that continuously compares current trajectories to original objectives and asks a disciplined question: What are we buying with each additional month of war?

This can be made concrete through “cost-to-gain” models:

• territorial gains per month vs casualty and equipment loss rates
• budget burn vs fiscal capacity (including financing options)
• sanctions impact vs substitution capacity
• force quality trendlines vs operational goals

When those ratios deteriorate, leaders need options. AI doesn’t supply political courage, but it can remove plausible deniability.

What Ukraine’s drone war shows about AI-enabled intelligence

Ukraine has turned time, mass, and geography into a data problem. The conflict’s defining operational reality is that almost everything is observable—if you can process it fast enough.

That changes the intelligence cycle:

• Drones create near-continuous collection.
• Electronic warfare and deception create near-continuous ambiguity.
• Precision strike and loitering munitions compress decision windows.

This is where AI in defense becomes practical, not theoretical. AI is useful when it:

• triages ISR feeds (video, SAR, SIGINT) into actionable queues
• detects pattern changes (new logistics routes, decoys, unit rotations)
• automates target validation workflows with human approval gates
• flags anomalies that indicate deception or a shaping operation

A blunt but accurate way to put it: In a sensor-saturated battlespace, advantage goes to the side that can turn observation into decisions faster than the opponent can jam, spoof, or relocate.

“Could AI have prevented the imperial trap?” Ask a better question

The prevention framing is tempting—and usually wrong. Wars start for political reasons. But AI can reduce the risk of catastrophic strategic overreach by improving three things leaders historically struggle with.

1) Pre-war reality checks that are harder to ignore

If an AI-assisted planning process repeatedly shows that victory depends on an adversary collapsing within days, that should be treated as a warning, not a plan.

A mature workflow would:

• require multiple models built by independent teams
• enforce documentation of key assumptions
• run adversary “most likely” and “most dangerous” courses of action
• include structured dissent (human red teams) with model-based evidence

2) Continuous reassessment once contact with reality happens

Most militaries are good at collecting after-action lessons. They’re worse at changing direction when the lessons are politically inconvenient.

AI-enabled decision support can force a cadence: weekly or monthly “assumption audits” tied to measurable indicators. When indicators break, options must be briefed.

3) Better off-ramps

When wars become stalemates, negotiation becomes a contest in leverage, time, and domestic narratives. AI can help leaders understand what’s tradable, what’s not, and what time actually does to their leverage.

That means combining battlefield trends with:

• mobilization and manpower economics
• industrial capacity and supply chain risk
• sanctions durability and evasion channels
• alliance politics and election cycles

Practical takeaways for defense and national security teams

If you’re building or buying AI for national security strategy, the “imperial trap” offers a clear set of requirements. Prioritize systems that challenge assumptions, quantify tradeoffs, and accelerate updates.

Here’s a shortlist you can use in acquisition reviews and internal roadmaps:

1. Assumption tracking is a core feature, not a slide deck. The system should explicitly list assumptions and connect them to live indicators.
2. Models must show uncertainty. If outputs don’t include confidence bounds and sensitivity drivers, leaders will treat guesses as facts.
3. Fusion beats volume. Decision advantage comes from combining ISR, logistics, and political-economic signals—not from drowning analysts in feeds.
4. Human approval gates are mandatory. Especially in targeting, AI should rank, flag, and explain—not autonomously execute.
5. Measure decision latency. If AI doesn’t shorten the time from signal to decision, it’s not improving operational outcomes.

A useful standard: AI in defense is successful when it makes bad strategies harder to sustain—not when it makes existing strategies easier to execute.

Where this leaves 2026 planning: AI is part of deterrence, not just warfighting

As ceasefire talk resurfaces and the war grinds through its fourth year, the source article’s warning is that Russia’s structural constraints don’t disappear just because it adapted tactically. High casualties, fiscal strain, demographic pressure, veteran reintegration challenges, elite infighting, and deeper dependence on external partners all compound over time.

For planners across NATO and partner nations, the bigger lesson is forward-looking: AI-enabled intelligence and strategic modeling are becoming deterrence tools. They help leaders avoid overconfidence, signal credible capabilities, and make faster, more disciplined choices under uncertainty.

If your organization is serious about AI in defense and national security, focus less on flashy autonomy demos and more on the systems that prevent self-inflicted traps—before the first unit crosses a border. What would your planning process show if every core assumption had to survive a live, AI-assisted audit for the next 90 days?