AI-Driven Defense: Avoiding the “Hold at All Costs” Trap

Static defense can save an army—or quietly destroy it. Przemyśl proved both within six months. In autumn 1914, a “ragtag” garrison held long enough to buy time. By March 1915, the same fortress became a trap: over 130,000 soldiers went into captivity, the officer corps was gutted, and the Austro-Hungarian Army’s ability to conduct complex operations collapsed.

That swing—from strategic success to catastrophic failure—should feel uncomfortably familiar to anyone watching Ukraine’s urban fights grind into a fourth winter and a fifth year of war. The pattern isn’t just about trenches or fortresses. It’s about a leadership problem: when political symbolism overrides operational reality, defense becomes destruction.

This is where the “AI in Defense & National Security” conversation gets real. Not because algorithms magically “solve” war, but because AI can help commanders and civilian leaders see the inflection point sooner—the moment when holding terrain stops being a rational trade and starts becoming a sunk-cost collapse.

Przemyśl’s lesson: static defense only works if there’s an exit

Static positional defense works when it’s tied to a plan for maneuver, relief, or withdrawal. It fails when the defense becomes the plan.

Przemyśl had a clear purpose: protect the Carpathian approaches and delay Russia long enough for Austria-Hungary to stabilize the front. During the first siege (Sept–Oct 1914), that logic held. The garrison’s resistance helped prevent a rapid Russian knockout and pushed the Eastern Front toward a longer, attritional war.

The second siege (Nov 1914–Mar 1915) flipped the outcome. Relief offensives through the Carpathians produced enormous casualties (the article cites at least 1.8 million combined losses across the campaigns). Worse, centralized leadership underestimated local conditions and resisted breakout decisions until the window closed.

Here’s the sentence worth carrying into every modern defense planning session:

A static defense is only strategically rational if it preserves the force’s future options.

Once political prestige hardens into “no step back,” the defender trades trained soldiers for time that may not translate into operational advantage.

The five classic justifications—and the one that kills you

Historically, fortress-style defense is justified by five arguments:

1. Achieve favorable casualty ratios
2. Tie down enemy forces
3. Buy time
4. Enable mobilization and preparation in depth
5. Sustain morale and political symbolism

The trap is number five. Symbolism is real, but it’s also the easiest to abuse because it’s hard to measure and easy to sell.

That’s why Bastogne worked (operational value: road junction; relief arrived) and why Germany’s 1944 “fortress policy” failed (rigid hold orders produced encirclements and the loss of irreplaceable experienced personnel). The common factor isn’t bravery. It’s whether the defense stays connected to a broader operational design.

Ukraine’s risk: when cities become attrition machines

Ukraine has repeatedly shown it can impose painful costs on Russian assaults—especially early in urban battles. But the fights described in the source (Bakhmut, Avdiivka, and now pressure around Pokrovsk) show a recurring dynamic:

1. Early phase: attacker pays heavily; defender benefits from prepared positions and short supply routes.
2. Middle phase: attacker expands artillery/drone control into supply corridors.
3. Late phase: defender’s resupply and rotation degrade; withdrawal becomes costlier by the week.

The article highlights one especially modern accelerant: drone warfare scaling faster than many command systems can adapt. When FPV drones, loitering munitions, and fiber-optic guided drones increase the lethality of roads and corridors, “just hold a little longer” becomes a dangerous phrase.

Pokrovsk illustrates the political-operations mismatch. Its capture would give Russia tactical utility (drones, logistics), but its primary weight is political: the optics of losing another city, leverage ahead of negotiations, domestic morale, and external perceptions.

The hard truth in an attritional war: Ukraine can’t pay the same price for symbolism that Russia can. Russia’s manpower base and recruitment system absorb losses differently. That asymmetry makes force preservation—not terrain—Ukraine’s limiting factor.

People also ask: isn’t withdrawal basically defeat?

Withdrawal feels like defeat because it’s public and visible. But operationally, it can be the opposite: a planned phase of defense-in-depth.

A controlled withdrawal:

• preserves trained infantry and junior leaders
• avoids mass capture events
• shortens logistics
• resets casualty exchange conditions
• forces the attacker to re-clear a “gray zone” under fire

What turns withdrawal into disaster is waiting too long—when corridors are under constant drone observation and artillery fire and unit cohesion is already fraying.

What AI changes: decision advantage, not “perfect” decisions

AI won’t remove politics from war. It can, however, reduce self-deception by making the trade-offs legible in near real time.

When command structures are centralized, leaders tend to see maps and slogans. Local commanders see fuel, fatigue, missing squads, drone losses, and the fact that yesterday’s route is now a killing zone. AI’s best contribution is translating that messy ground truth into a shared picture that senior decision-makers can’t ignore.

AI for mission planning: detecting the “inflection point” early

The most valuable AI output in static defense isn’t a flashy prediction like “the city falls in 12 days.” It’s an alert that the defense has crossed measurable thresholds.

Examples of thresholds AI can help track and forecast:

• Supply corridor viability: when resupply shifts from vehicles to foot movement, throughput collapses.
• Drone attrition rate: week-over-week loss trends for FPVs, ISR quadcopters, and ground control stations.
• Casualty exchange ratio: when a sector moves from favorable to parity (or worse), the logic of “tie them down” disappears.
• Evacuation feasibility: time-to-move for wounded, exposure windows, and corridor interdiction density.
• Cohesion indicators: unit fragmentation signals (ad hoc attachments, turnover, leadership losses).

AI systems can fuse feeds from drones, acoustic sensors, EW detections, logistics reports, medical evacuation queues, and artillery expenditure into a dashboard that answers one question: Is this defense still buying something worth the cost?

Predictive modeling for defense-in-depth: planning belts that actually work

The source argues for a flexible defense-in-depth posture: multiple prepared belts, networked positions, pre-positioned supplies, and planned withdrawals triggered by local conditions.

AI can improve that concept in three practical ways:

1. Route and corridor analytics

   • Identify which roads are becoming non-viable due to drone observation and fire control.
   • Recommend alternating routes and movement windows based on enemy ISR patterns.

2. Fires and drone coverage planning

   • Optimize overlapping drone operating sites and pre-registered fires for likely approach routes.
   • Flag gaps where terrain, foliage, and EW effects create blind zones.

3. Resource allocation under scarcity

   • In attrition wars, allocation is strategy. AI can prioritize scarce assets (air defense interceptors, counter-drone kits, precision munitions) by marginal impact—where each additional unit of supply produces the most defensive value.

If you only remember one line: defense-in-depth fails when belts are “lines on a map” instead of measurable systems with trigger conditions. AI is well suited to enforce those trigger conditions.

Mission command meets AI: decentralize decisions, centralize evidence

The article’s critique of rigid, centralized withdrawal authorization is blunt—and I think it’s correct. If every tactical withdrawal requires top-level permission, the defender is betting that headquarters will always see the battlefield faster than the battlefield changes. That’s rarely true, and it’s even less true in a drone-saturated environment.

A better model is:

• Decentralize authority to reposition at the tactical level.
• Standardize the data that informs those decisions.
• Audit outcomes to improve thresholds and training.

AI supports this by turning mission command from a cultural aspiration into a repeatable workflow:

• Local commanders report structured inputs (ammo state, drone losses, evac times).
• AI-assisted tools fuse those inputs with ISR and EW data.
• Headquarters gets a consistent, comparable view across sectors.
• Withdrawal decisions become faster and less political—because the evidence is harder to hand-wave.

Guardrails: AI shouldn’t become a new kind of rigidity

There’s a failure mode here: leadership could treat AI outputs like orders, replacing human judgment with “the model says hold.” That’s just fortress policy with better graphics.

Practical guardrails that matter:

• Human-in-the-loop for withdrawals and fires (especially where civilians are present).
• Red-team the model with adversarial thinking and deception scenarios.
• Train for degraded conditions (EW, weather, loss of GPS, missing data).
• Measure model error openly so commanders trust it appropriately.

AI should compress the time between reality and decision—not pretend uncertainty is gone.

A playbook for leaders: when “hold” becomes “burning combat power”

Senior leaders—military and civilian—need a shared language for the moment a defense stops making operational sense. Otherwise the conversation collapses into morale versus cowardice.

Here’s a practical checklist you can use as a decision brief template for urban defense under attrition:

1. Operational value statement (one paragraph): What does holding this terrain enable elsewhere?
2. Relief/exit plan (one page): What’s the trigger for breakout or withdrawal, and who can authorize it?
3. Measured attrition trend (7–14 days): Friendly KIA/WIA trend, enemy losses (best estimate), and confidence level.
4. Corridor status: resupply throughput, interdiction density, drone observation hours per day.
5. Cohesion status: strength by platoon, leadership losses, percentage of replacements with minimal training.
6. Negotiation/strategic optics note: What is the political value of holding, and what is the political cost of losing the force?

If that brief can’t be produced weekly with credible numbers, you’re not managing a defense—you’re managing a narrative.

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

A lot of AI-in-defense writing fixates on autonomous platforms and futuristic kill chains. Those matter. But wars are often decided by more ordinary failures: slow decision loops, distorted reporting, and leaders who confuse a symbol with a strategy.

Przemyśl is a century-old case study in information latency and centralized rigidity. Ukraine’s drone-dominated battlefield is the modern version of the same problem, with higher tempo and less forgiveness.

Force preservation is not a slogan. It’s a planning discipline. AI can help make that discipline real—by giving commanders earlier warning that a “successful defense” is about to become a mass-casualty trap.

If you’re building or buying AI for national security—ISR fusion, mission planning, logistics optimization, or decision support—design it around the most important question a defender faces:

When does holding this position stop paying for itself?

Because the fortress doesn’t fail when it’s breached. It fails when leadership decides it can’t be left.