Posts
Part 1: The Backlog Economy | Part 2: The PE Market Map | Part 3: The Testimony | Part 4: The AI Mirage (You Are Here) | Part 5: The Policy Toolkit
The AI Mirage: How Automation Became the New Excuse for Scarcity
Why the Same Firms That Created the Shortage Are Now Selling the "Solution"
The Setup: How We Got Here
We've established the pattern across fire trucks, veterinary care, dental practices, housing, and healthcare:
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2. STRIP CAPACITY (close facilities, reduce staff)
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3. CREATE SCARCITY (backlogs, waitlists)
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4. EXTRACT VALUE (price increases, dividends)
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5. REPEAT
But there's a problem with this model: eventually, people notice.
When fire departments wait four years for trucks, senators hold hearings. When vet bills hit $8,000, people complain. When hospitals are understaffed, nurses strike. The scarcity becomes politically visible.
Enter the AI narrative. The same private equity firms and corporate consolidators that created the capacity shortages are now investing billions in "AI solutions" that promise to solve them—without actually adding capacity.
This is the 2026 pivot: automation as justification for permanent scarcity.
The Fire Truck Example: "Smart Dispatch" as a Substitute for Trucks
The Pitch
Translation: You don't need to buy more trucks. You just need our software.
The Reality
2. Optimization Has Limits: You can’t algorithmically dispatch a truck that’s in the repair shop. You can’t optimize around equipment that breaks down mid-response. AI can’t conjure trucks that don’t exist.
3. The Risk Shifts: “Optimized” dispatch means running closer to theoretical capacity limits. There’s no slack, no redundancy. When something goes wrong (multiple simultaneous calls, equipment failure, traffic), the system collapses catastrophically.
4. The Incentive Misalignment: The company selling the AI software profits whether response times improve or not (they’re paid upfront or via subscription). They have zero liability if the “optimized” system fails during a crisis.
Who's Selling This?
Companies marketing AI dispatch systems to fire departments include:
- ESO Solutions (owned by Warburg Pincus, private equity)
- ImageTrend (private, PE-backed)
- Motorola Solutions (public, but increasingly PE-influenced)
And here's the kicker: Some of these companies have investment ties to the same firms that own fire truck manufacturers. They profit on both ends—selling the trucks late and expensive, then selling the software that "optimizes" around the shortage they created.
The Healthcare Example: AI Triage as a Nurse Substitute
The Pitch
Translation: You don't need to hire more nurses. You just need our AI.
The Reality
2. AI Can’t Provide Care: Triage algorithms can flag high-risk patients. They can’t bathe patients, administer medication, catch subtle changes in condition, or provide emotional support. These tasks still require human nurses—who are now stretched even thinner.
3. The Outcomes Degrade: Studies show that nurse-to-patient ratios above 1:4 correlate with increased mortality, higher infection rates, and worse patient satisfaction. AI doesn’t change this—it just provides a tech narrative to justify continuing the unsafe ratios.
4. The Liability Shield: When something goes wrong (missed diagnosis, delayed intervention), hospitals can blame “algorithmic error” rather than staffing decisions. The AI becomes a liability shield for cost-cutting.
COMPANIES:
• Epic Systems AI modules (clinical decision support)
• Olive AI (administrative automation) - SHUT DOWN 2023 after burning $850M
• Enlitic, Arterys, others (diagnostic AI)
TOTAL PE INVESTMENT IN HEALTHCARE AI: $12+ billion (2020-2025)
OUTCOME DATA:
• Nurse-to-patient ratios: WORSE (1:4 → 1:6 average in PE-owned hospitals)
• Hospital-acquired infection rates: UP 8% (2018-2024)
• Patient satisfaction scores: DOWN (PE-owned hospitals rank lower)
• Mortality rates: MIXED (some improvement in specific conditions, overall trends flat or negative)
THE GAP:
PE firms invested $12B in AI to “solve” staffing shortages.
Hiring the needed nurses would have cost ~$8B (one-time + ongoing salaries).
They chose the option that creates recurring software revenue instead of solving the problem.
Who's Selling This?
Major players in healthcare AI being marketed as nurse substitutes:
- Epic Systems (Sepsis prediction, deterioration index tools)
- Google Health / DeepMind (patient deterioration algorithms)
- GE Healthcare (Command Center AI for "capacity optimization")
And who's buying? Private equity-owned hospital chains: HCA Healthcare, Tenet, Community Health Systems. The same firms that cut nursing staff are now spending millions on AI tools marketed as making those cuts sustainable.
The Veterinary Example: AI Diagnostics to "Increase Throughput"
The Pitch
Translation: Your vets can handle more appointments without hiring more vets. More revenue, same costs.
The Reality
2. Diagnostic Accuracy Falls: AI tools are trained on large datasets, but rare conditions and breed-specific issues often fall outside training data. The AI confidently suggests common diagnoses, missing edge cases. Vets under time pressure trust the AI and miss things.
3. The Owner Experience Degrades: Shorter appointments mean less explanation, less education, less emotional support. Owners feel rushed. Trust erodes. But corporate metrics show “improved efficiency.”
4. Burnout Accelerates: Vets report that AI tools add cognitive load (reviewing AI suggestions, overriding incorrect recommendations) rather than reducing it. Combined with increased patient volume, burnout rates spike.
Who's Selling This?
- VetAI (diagnostic imaging analysis)
- Antech Diagnostics (AI-enhanced lab results, owned by Mars Inc.—the same company that owns VCA)
- IDEXX Laboratories (AI tools for bloodwork interpretation)
Notice the pattern: Mars Inc. owns VCA (800+ vet clinics) and Antech Diagnostics (the AI tool provider). They profit on both ends—reducing vet staffing costs at the clinics and selling AI subscriptions to justify the reduced staffing.
The Unifying Theory: AI as Permanent Scarcity Infrastructure
Here's what's actually happening across all these sectors:
1. FINANCIALIZE essential service
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1. CREATE SCARCITY (consolidate, strip capacity)
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1. WAIT for political pressure to build
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1. SELL AI “SOLUTION” that optimizes around scarcity
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1. MAKE SCARCITY PERMANENT (because now there’s a tech narrative)
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1. EXTRACT VALUE on two levels:
a) Original service (inflated prices due to scarcity)
b) AI subscription (recurring revenue to “manage” scarcity)
Why This Is Brilliant (From an Extraction Perspective)
1. Narrative Control: "We're not understaffed—we're using cutting-edge AI!" It reframes cost-cutting as innovation.
2. Recurring Revenue: Unlike one-time capital investments (building a truck factory, hiring nurses), AI tools are subscription-based. Predictable, recurring revenue that compounds over time.
3. Liability Diffusion: When something goes wrong, blame the algorithm. "The AI didn't flag the deteriorating patient." Not: "We cut nurse staffing to dangerous levels."
4. Political Inoculation: Harder to criticize a company for "innovating with AI" than for "price gouging." The tech veneer provides political cover.
5. Lock-In: Once a fire department adopts AI dispatch or a hospital integrates clinical AI, switching costs are high. They're locked into the ecosystem—making it easier to raise subscription prices over time.
Why This Is Insidious (From a Public Welfare Perspective)
1. Optimization ≠ Resilience: AI systems optimize for efficiency, which means eliminating slack and redundancy. When crisis hits (pandemic, natural disaster, multiple simultaneous emergencies), optimized systems collapse. There's no reserve capacity.
2. Masking Structural Problems: AI can marginally improve a degraded system, but it can't fix the underlying capacity shortage. It's a band-aid on a gunshot wound—and it prevents political pressure to actually solve the problem (build more trucks, hire more nurses, open more clinics).
3. Empowering Extraction: By making scarcity "manageable," AI allows private equity firms to extract value longer. Without AI, cities would eventually revolt and demand more trucks. With AI, they accept fewer trucks "optimized by algorithms."
4. Irreversibility: Once AI infrastructure is in place and workforce is reduced, it's extremely hard to reverse. Rebuilding manufacturing capacity or hiring back nurses takes years. The scarcity becomes structural.
The Sectors Where This Is Happening Now (2026)
FIRE/EMS: AI dispatch, predictive maintenance, route optimization
→ Sold as substitute for buying more trucks
HOSPITALS: AI triage, sepsis prediction, capacity management
→ Sold as substitute for hiring more nurses
VETERINARY: AI diagnostics, treatment recommendations
→ Sold as way to increase patient volume per vet
DENTAL: AI cavity detection, treatment planning
→ Sold as way to increase “production” per dentist
LOGISTICS/TRUCKING: Route optimization, autonomous trucks (future)
→ Sold as substitute for hiring drivers or expanding fleet
EDUCATION: AI tutoring, automated grading, “personalized learning”
→ Sold as substitute for hiring more teachers
ELDER CARE: AI monitoring, fall detection, medication reminders
→ Sold as substitute for hiring more caregivers
The Tell: Follow the Investment Patterns
Here's how you know this is intentional, not coincidental:
Pattern 1: Same Investors, Both Sides
Private equity firms that own capacity-constrained essential services are also investing in AI companies that "optimize" around that constraint:
- KKR owns Heartland Dental (2,800 practices). Also invested in Tend (AI-powered dental chain) and healthcare AI companies.
- Blackstone owns hospital real estate via REITs. Also invested in healthcare AI startups via Blackstone Innovations Investments.
- Warburg Pincus owns TeamHealth (hospital staffing). Also owns ESO Solutions (fire/EMS dispatch AI).
Pattern 2: Acquisition of AI Startups by Consolidators
Corporate consolidators are buying AI companies that help justify reduced capacity:
- Mars Inc. (owns VCA, 800+ vet clinics) also owns Antech Diagnostics (vet AI tools)
- HCA Healthcare (largest for-profit hospital chain) acquired AI health tech companies and integrated clinical AI across all facilities
- CVS Health / Aetna acquired Signify Health (AI-powered home health), integrating it with MinuteClinic to reduce need for full physician staffing
Pattern 3: PE-Backed "AI-First" Companies Built on Extraction Model
New companies launched with AI as the core product—but the business model is identical to old PE playbook:
- Forward Health (AI-powered primary care): Subscription model, minimal physician time, heavy AI reliance. Reduces doctor access while charging premium prices.
- Ro / Hims (telehealth): AI-assisted diagnosis, minimal physician interaction. Optimizes for transaction volume, not care quality.
- Wheels Up / NetJets (private aviation): AI-optimized fractional ownership and route planning. Reduces actual fleet size while selling more "access."
The Gaslighting Language: How AI Pitches Obscure Extraction
Pay attention to the language in AI marketing to essential service providers:
“Optimize resource allocation”
= Do more with less (cut capacity, maintain revenue)
“Increase provider productivity”
= Make workers handle more volume in same time
“Data-driven decision making”
= Algorithmic justification for cost-cutting
“Reduce waste and inefficiency”
= Eliminate slack, redundancy, and resilience
“Unlock hidden capacity”
= Squeeze more output from existing (degraded) infrastructure
“Future-proof your operations”
= Lock into our subscription model
“AI-augmented workforce”
= Fewer humans, same or worse outcomes
The Counterargument: "But AI Can Actually Help!"
Fair point. AI isn't inherently bad. There are legitimate use cases where automation improves outcomes WITHOUT reducing capacity:
Good AI: Augmentation With Adequate Capacity
- Radiology AI that flags potential issues for radiologists to review (when there are enough radiologists)
- Predictive maintenance for fire trucks (when there are enough backup trucks to cover repairs)
- Clinical decision support for rare conditions (when nurse staffing is adequate for base care)
The difference: AI as a tool for humans with sufficient capacity vs. AI as a substitute for humans to justify insufficient capacity.
The Test: Is Capacity Being Added or Reduced?
Here's the simple test for whether AI deployment is legitimate or extractive:
Capacity maintained or increased + AI added = Better outcomes
EXTRACTIVE AI:
Capacity reduced + AI added = Same or worse outcomes + higher profits
If a company is cutting staff, closing facilities, or reducing fleet size while simultaneously deploying AI "to optimize," that's extraction, not innovation.
What This Means for 2026-2030
The AI pivot is just beginning. Here's what to expect:
Near-Term (2026-2027)
- More AI pilots in fire/EMS, hospitals, vet clinics marketed as "efficiency solutions"
- Cities and hospitals adopt AI to cope with equipment/staffing shortages
- Initial results show marginal improvements (measured against degraded baseline)
- Tech media celebrates "AI solving the healthcare/public safety crisis"
Medium-Term (2027-2029)
- AI subscriptions become embedded in budgets; switching costs make them permanent
- Pressure to add actual capacity (trucks, nurses, vets) decreases because "AI is handling it"
- Outcomes plateau or decline as systems reach optimization limits
- High-profile failures (AI-dispatched fire truck delayed, AI-triaged patient deteriorates) trigger investigations
Long-Term (2030+)
- Capacity shortages become structural and permanent—too expensive to rebuild
- Two-tier system emerges: Wealthy access human-delivered services, everyone else gets AI-"optimized" services
- Regulatory backlash: Calls to ban AI in life-safety sectors OR mandate minimum capacity levels regardless of AI deployment
The Warning Signs: How to Spot AI Gaslighting
If you're a municipal official, hospital administrator, or concerned citizen, here's how to identify when AI is being sold as cover for extraction:
🚩 Red Flag 1: AI deployment coincides with staff reductions, facility closures, or fleet downsizing
🚩 Red Flag 2: AI vendor is owned by (or invested in by) the same PE firm that owns your service provider
🚩 Red Flag 3: Success metrics focus on "efficiency" and "cost reduction" rather than outcome improvements
🚩 Red Flag 4: AI deployment is framed as permanent solution to temporary "supply chain issues"
🚩 Red Flag 5: Subscription pricing that escalates over time; high switching costs; vendor lock-in
🚩 Red Flag 6: Promises that sound too good to be true ("20-30% improvement with no new hires")
🚩 Red Flag 7: Lack of transparency about algorithm logic, training data, or failure rates
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