When “Hold at All Costs” Turns Fatal—And How AI Helps

A fortress can save an army—or quietly destroy it.

In 1914, the Austro‑Hungarian fortress city of Przemyśl did what static defenses are supposed to do: it bought time. Months later, the same fortress became a trap that helped wipe out the empire’s experienced leadership and accelerated a broader military collapse. That whiplash—success in the first siege, catastrophe in the second—has an uncomfortable relevance to Ukraine’s war as it enters its fifth year and as battles over cities become as political as they are operational.

This matters for anyone working in AI in defense and national security because the core failure wasn’t courage or engineering. It was decision-making under uncertainty: leaders overvalued symbolism, underestimated logistics and attrition, and centralized authority until frontline reality couldn’t change the plan. Modern AI won’t “solve” that problem by itself. But it can force clarity about when a static defense is still serving strategy—and when it’s sliding into a sunk-cost disaster.

Static defense works when it buys time—not when it buys headlines

Static positional defense is strategically rational only when it creates a payoff larger than the ground being held. In Przemyśl’s first siege (September–October 1914), the fortress tied down Russian forces long enough to prevent a rapid collapse on the Eastern Front. The garrison’s job wasn’t to be heroic forever. It was to shape time and tempo.

The second siege (November 1914–March 1915) flipped that logic. Relief attempts through the Carpathians bled the broader force, while centralized leaders refused to authorize timely breakout or withdrawal. When Przemyśl finally fell, more than 130,000 soldiers went into captivity. Worse, the empire’s professional officer and NCO backbone was effectively gutted, leaving a force that struggled to execute complex operations without heavy reliance on allies.

Here’s the clean principle that transfers directly to modern war:

A static defense is only “defense” if it preserves future options. When it removes options, it becomes destruction in slow motion.

The five classic justifications—and the one that breaks first

Historically, fortress-style defense is justified by a familiar set of claims:

• Favorable casualty ratios
• Fixing enemy forces (preventing redeployment elsewhere)
• Buying time for maneuver, mobilization, or preparation in depth
• Enabling better defensive preparation
• Morale and political symbolism

The problem is that in long wars, these criteria decay at different rates. Morale and symbolism often grow more salient as costs rise, while casualty ratios and logistics often deteriorate first.

That’s how “hold because it matters” turns into “hold because we’ve already paid so much.”

Ukraine’s dilemma: tactical logic fades, political logic intensifies

A pattern highlighted in recent Ukrainian battles is brutally consistent: early defenders can impose costs, but once the attacker extends fire control over supply corridors—especially with drones—the defense can turn into an attritional trap.

Urban fights like Bakhmut and Avdiivka illustrate that shift. The defense can start as a rational exchange—forcing attackers into costly assaults—then become untenable as the attacker’s standoff fires and drone reconnaissance tighten the noose.

Pokrovsk sits inside that same moral and operational geometry. Its value isn’t purely tactical; it carries propaganda weight and negotiation leverage. As political stakes rise, permission to withdraw becomes harder to obtain, and centralized approval chains can trap units in pockets where resupply, rotation, and cohesion degrade.

This is where the campaign theme—data-driven defense planning—stops being theoretical. In a drone-saturated battlespace, the “moment to leave” is often visible first in logistics telemetry, drone loss curves, casualty exchange rates, and route viability—signals that humans can miss or explain away when the politics get loud.

Why drones change the withdrawal math

The Ukraine war has made one point unavoidable: withdrawal under observation is withdrawal under attack. If your routes are watched by persistent ISR, every kilometer of retreat can become a kill corridor.

That creates a dangerous incentive: commanders delay withdrawal because it looks risky now, even if waiting makes it catastrophically riskier later.

AI’s highest value here isn’t a magic retreat button. It’s building a decision system that continuously answers:

• How vulnerable are the last supply corridors, today vs. next week?
• Are drone attrition rates rising faster than replacement?
• Are casualty ratios trending toward parity (or worse)?
• Are units losing cohesion (attachments/detachments, ad hoc taskings)?

When those indicators are tracked like a cockpit—not debated like a vibe—leaders can justify hard calls earlier.

“No step back” is often a command-and-control problem, not a bravery problem

Centralized control can be useful when communications are reliable and the situation is stable. In fluid attritional war, over-centralization becomes a failure mode.

Przemyśl’s second siege shows what happens when distant headquarters believes conditions are better than they are. A similar risk appears when frontline formations need higher approval for tactical withdrawals. By the time permission arrives, the window may have closed.

Mission command isn’t just a cultural preference; it’s a survivability feature. Modern AI can support mission command by giving higher headquarters confidence that subordinate decisions are anchored to shared metrics rather than gut feel.

What AI-enabled mission planning should actually do

If you’re building or procuring AI for mission planning and operational decision support, aim it at the real choke points:

1. Common operational picture integrity

   • Fuse ISR, EW reports, drone feeds, logistics status, and casualty reporting.
   • Flag contradictions (e.g., “route open” claims vs. observed interdiction).

2. Attrition forecasting that’s honest about uncertainty

   • Not “predictions,” but ranges with confidence bands.
   • Trend-based alerts: “casualty exchange has worsened for 3 consecutive reporting periods.”

3. Route survivability scoring

   • Combine enemy drone density, artillery responsiveness, EW conditions, weather/fog, and time-of-day risk.
   • Output: “withdrawal corridor A likely collapses within X hours/days.”

4. Decision triggers and playbooks

   • Predefine measurable thresholds for withdrawal, reinforcement, or re-posturing.
   • Keep humans in charge, but remove ambiguity about what “too late” looks like.

The point is to make the system argue with you when you’re drifting into sunk costs.

Defense-in-depth needs data discipline, not just more trenches

A flexible defense-in-depth posture—multiple prepared belts, networked positions, pre-registered fires—has become the practical alternative to rigid “hold forever” lines. The concept is straightforward: attrit, displace, repeat—while preserving trained personnel.

But defense-in-depth fails when it becomes a slogan rather than an engineered system. The modern version requires tight integration of:

• Prepositioned supplies (and visibility into consumption rates)
• Pre-surveyed drone launch/recovery sites
• Overlapping drone coverage of likely avenues of approach
• Rapid re-tasking of fires and EW assets
• Controlled withdrawals that units rehearse, not improvise

AI supports this by making the belts measurable and comparable. If Belt 1 is burning drones at 2× the replacement rate, or if resupply has shifted from vehicle to foot traffic, that isn’t “bad luck.” It’s a quantitative warning that the belt’s utility is expiring.

Practical “tripwires” worth operationalizing

Here are tripwires I’d want formalized in any prolonged defense where drones dominate the close fight:

• Supply corridor degradation: primary resupply shifts from vehicle to dismounted movement as a norm, not an exception.
• Drone coverage gap: overlapping ISR/strike coverage drops below a defined threshold on key approach routes.
• Attrition ratio trend: favorable casualty exchange deteriorates to parity for multiple cycles.
• EW effectiveness decline: enemy FPV success rate rises despite unchanged friendly countermeasures.
• Cohesion indicators: excessive cross-attachments, missing junior leaders, rising desertion/medical non-battle losses.

These aren’t perfect. They are better than “hold until it feels impossible.”

People also ask: can AI really prevent fortress-style disasters?

Can AI tell commanders when to withdraw?

AI can’t decide to withdraw, and it shouldn’t. What it can do is surface the leading indicators that humans tend to rationalize away—logistics collapse, route interdiction, and negative attrition trends—early enough to preserve options.

Isn’t this just optimizing retreat?

No. It’s optimizing force preservation and future combat power. In attritional wars, the decisive asset is often trained personnel and cohesive units, not a particular city block.

What about politics and morale?

Politics doesn’t disappear because the data is clear. But decision support can translate “morale cost” into “operational cost” by showing what continued defense will likely consume—experienced infantry, drones, artillery tubes—relative to the military value of the terrain.

The lesson Przemyśl teaches AI-era militaries

Przemyśl’s first siege worked because it stayed aligned with operational purpose: delay, tie down, survive. The second siege failed because symbolism and centralized control crowded out frontline truth until the garrison’s only remaining options were annihilation or captivity.

Ukraine’s challenge in late 2025 isn’t a lack of bravery. It’s managing a war where drones make withdrawals deadlier, politics makes withdrawals harder, and manpower scarcity makes every experienced soldier more valuable than another ruined intersection.

For defense and national security teams building AI for intelligence analysis, risk assessment, and mission planning, the bar is simple: make it harder for institutions to lie to themselves. If AI is going to earn its place in operational headquarters, it should clarify when a static defense is still buying time—and when it’s buying a catastrophe.

If you’re evaluating AI decision support for defense-in-depth planning, ISR fusion, or withdrawal corridor survivability analysis, build toward systems that deliver three things commanders can act on: shared metrics, early warning, and credible options. The rest is will.

The uncomfortable question worth asking going into 2026: when the next “symbolic city” becomes the center of gravity in headlines, will decision-makers have the data—and the delegated authority—to leave before the fortress becomes a grave?