Waymo Robotaxi Outage in San Francisco Raises Economic and Safety Concerns

Waymo autonomous vehicle stopped at a San Francisco intersection during a citywide power outage, raising concerns about robotaxi crisis readiness.

Waymo robotaxis stalled at intersections during San Francisco’s December power outage, highlighting automation challenges during emergencies.(Representing ai image)

Robotaxis in Crisis: Economic Lessons from Waymo’s San Francisco Outage

- Dr. Sanjaykumar Pawar

Introduction: When Technology Meets Reality
The Rise of Robotaxis and the Economics Behind Them
The San Francisco Power Outage: What Exactly Happened?
Why This Incident Matters Beyond Traffic Jams
Crisis Economics: Infrastructure, Resilience, and Automation
Teleoperation: The Human Backbone of “Driverless” Cars
Regulation and Market Confidence: A Delicate Balance
Lessons from Past Autonomous Vehicle Failures
Economic Implications for Cities and Labor Markets
Global Competition in Autonomous Mobility
Simplifying the Issue: A Real-World Analogy
Data-Driven View: Costs, Scale, and Risk Concentration
Policy Options and the Way Forward
Visual Summary: How a Power Outage Cascades Through an Automated System
Conclusion: A Shot Across the Bow for the Autonomous Economy
Frequently Asked Questions (FAQ)
References & Sources

1. Introduction: When Technology Meets Reality

Autonomous vehicles are often marketed as the future of urban mobility—cleaner, safer, cheaper, and more efficient. But the December 2025 power outage in San Francisco, which caused dozens of Waymo robotaxis to stall at intersections, exposed a critical weakness: what happens when smart systems meet dumb infrastructure failures?

This was not just a tech glitch or a traffic inconvenience. From an economic perspective, it raises deeper questions about resilience, regulation, public trust, and crisis preparedness in an increasingly automated economy.

As an economist, I argue that this incident should be seen as a stress test failure—not only for Waymo, but for the broader robotaxi business model.

2. The Rise of Robotaxis and the Economics Behind Them

Robotaxis aren’t just a flashy tech trend — they represent a deep shift in how cities, companies, and consumers think about transportation. At their core, robotaxis sit at the intersection of technology, urban economics, and platform-driven business models, and that mix is what makes them both exciting and risky.

Below is a clear, humanized breakdown of why robotaxis are rising and what’s really happening beneath the hype.

🚗 Why Robotaxis Are Gaining Momentum

Robotaxis are powered by three major economic forces:

Technological Innovation
- Advances in AI, machine learning, LiDAR, and sensor fusion
- Improved real-time decision-making and mapping
- Falling hardware costs over time
Urban Cost Pressures
- Driver shortages in major cities
- Rising labor and fuel costs
- Growing concerns about congestion, emissions, and safety
Platform Economics
- Once built, software scales cheaply
- One autonomous system can be deployed across thousands of vehicles
- Marginal cost per ride drops dramatically without a human driver

The promise sounds simple — and seductive:

Remove the driver → cut labor costs → scale rapidly → dominate urban transport.

🤖 Waymo vs. Tesla: Two Very Different Bets

Not all robotaxi strategies look the same.

Waymo (Alphabet-backed)
- Slow, methodical rollout
- Heavily geo-fenced cities
- Over 2,500 vehicles operating across U.S. markets
- Billions of dollars in sunk R&D and infrastructure costs
- Focus on safety, redundancy, and regulatory trust
Tesla
- Aggressive rhetoric around full self-driving
- Vision-only approach (no LiDAR)
- Relies on massive consumer data collection
- Promises rapid scale, but with higher uncertainty

Waymo’s approach looks less flashy — but far more cautious.

📉 The Hidden Economic Catch

Here’s where the economics get uncomfortable:

Scaling efficiency ≠ scaling resilience
Autonomous systems perform well in controlled environments
Edge cases (weather, construction, human behavior) are expensive to solve
Each new city requires:
- Local mapping
- Regulatory approval
- Infrastructure adjustments

In other words, robotaxis don’t scale like pure software.

They scale like infrastructure-heavy platforms with long payback periods.

🔍 The Real Economic Question

The future of robotaxis isn’t about whether the technology works.

It’s about:

Can companies absorb years of losses?
Will cities fully embrace autonomy?
Can safety scale as fast as deployment?

Robotaxis may transform urban mobility — but the road to profitability is longer, costlier, and far more complex than the hype suggests.

3. The San Francisco Power Outage: What Exactly Happened?

In December 2025, San Francisco experienced a major power outage that exposed an unexpected weakness in the future of autonomous driving. A fire at a PG&E electrical substation cut electricity to nearly one-third of the city, creating chaos on roads already known for complexity. While human drivers adapted quickly, Waymo robotaxis struggled, sparking viral videos and public debate.

Below is a clear, human breakdown of what happened—and why it matters.

What Triggered the San Francisco Power Outage?

A fire at a PG&E substation caused widespread power loss
Large sections of San Francisco went dark, including:
- Traffic lights
- Street signals
- Key intersections

This wasn’t just an inconvenience—it was a real-world stress test for autonomous vehicles.

How Human Drivers Responded

Drivers relied on instinct and local driving norms
Dark traffic signals were treated as four-way stops
Traffic slowed, but it kept moving

In short, humans improvised. It wasn’t perfect, but it worked.

What Happened to Waymo Robotaxis?

Waymo’s self-driving cars reacted very differently:

Many stopped completely at dark intersections
Hazard lights turned on
Vehicles waited for remote human confirmation before proceeding

As more intersections lost power, the number of stopped robotaxis grew. Traffic began backing up fast.

Why Did Waymo Vehicles Stop If They “Knew” the Rules?

Waymo later clarified key points:

Their vehicles are programmed to treat dark traffic signals as four-way stops
However, in uncertain or unusual conditions, cars still:
- Request human oversight
- Wait for confirmation from remote operators

The problem?
➡️ Too many cars asked for help at the same time

The remote support system was overwhelmed.

The Core Issue, Simplified

The robots understood the rule
The system required human confirmation anyway
Humans couldn’t respond fast enough

Result: Cars froze—not because they were confused, but because the backup system failed.

Why This Matters for the Future of Self-Driving Cars

This outage revealed a critical truth:

Autonomous vehicles are not fully autonomous yet
They still depend on human systems behind the scenes
Scaling that support during emergencies is a major challenge

Waymo temporarily halted operations after the incident, signaling how seriously the issue was taken.

The San Francisco power outage wasn’t just a grid failure—it was a reality check.
The robots knew what to do. They just weren’t allowed to do it alone.

As cities push toward automation, this moment highlights one big question:
👉 What happens when both machines and humans hit their limits at the same time?

4. Why This Incident Matters Beyond Traffic Jams

At first glance, this incident may look like just another traffic disruption—annoying, but temporary. Dig a little deeper, though, and it becomes clear that the implications stretch far beyond congested roads and delayed commutes. From an economic, technological, and public-trust perspective, this moment exposes vulnerabilities that cities, investors, and mobility providers can’t afford to ignore.

Below are the three major red flags this incident raises—and why they matter for the future of urban transport.

1. Systemic Risk: When One Failure Shuts Down a City

A single localized outage triggered citywide disruption, revealing how tightly interconnected modern transport systems have become.
In smart cities and autonomous mobility networks, efficiency often comes at the cost of resilience.
When redundancy and fail-safes aren’t robust enough, small technical failures cascade into large economic losses—missed work hours, delayed deliveries, and emergency response slowdowns.

Why it matters:
For policymakers and investors, systemic risk signals fragility. Markets respond poorly to systems that appear efficient on paper but unstable under pressure.

2. Operational Bottlenecks: Human Oversight Doesn’t Scale Forever

Many advanced traffic and mobility systems still rely on remote human intervention during anomalies.
While this works at limited scale, it becomes a bottleneck as adoption grows across districts or entire cities.
The incident highlights a mismatch between automation promises and operational reality.

Why it matters:
If scaling requires exponentially more human oversight, operational costs rise—and profit margins shrink. That directly impacts return on investment (ROI) for urban transport innovations.

3. Public Trust Shock: Confidence Is Hard to Build—and Easy to Lose

Viral failures spread fast, especially when commuters are affected in real time.
Quiet successes rarely trend, but visible breakdowns reshape public perception instantly.
Once trust erodes, people hesitate—especially in emergencies, where reliability matters most.

Why it matters:
Urban transport is fundamentally a trust-based system. Without confidence, adoption slows. And when adoption slows, so does revenue growth, infrastructure funding, and long-term market viability.

The Bigger Economic Picture

Markets hate uncertainty. This incident doesn’t just disrupt traffic—it introduces doubt. And doubt is expensive.

Slower adoption → lower utilization
Lower utilization → weaker ROI
Weaker ROI → reduced investment in innovation

This wasn’t just a traffic problem. It was a stress test—and the system showed cracks. Fixing them isn’t optional; it’s essential for the future of reliable, scalable, and trusted urban mobility.

5. Crisis Economics: Infrastructure, Resilience, and Automation

In crisis economics, the big question isn’t how efficient a system is on a good day—it’s how well it survives a bad one. Economists define resilience as the ability of a system to absorb shocks and continue functioning. That definition matters more than ever as cities rush toward automation, AI-driven infrastructure, and robotaxis.

At first glance, automation looks like progress. Fewer human errors. Faster decisions. Lower long-term costs. But resilience exposes a deeper, less comfortable truth: systems don’t fail all at once—they fail at their weakest dependency.

Human Systems vs. Automated Systems

Consider the difference between traditional taxis and robotaxis during a crisis.

Traditional taxis:

The driver adapts in real time
Makes eye contact with pedestrians
Waves others through at broken intersections
Reroutes instinctively when roads collapse or signals fail

This is distributed human intelligence. No single point of failure.

Robotaxis:

Depend on electricity
Require constant connectivity
Rely on sensors, maps, AI models
Often need remote human teleoperators

Each layer must work simultaneously for the system to function.

The Problem: Stacked Dependency Risk

This leads to what I call stacked dependency risk.

Stacked dependency risk happens when:

Power grids fail
Networks slow or collapse
AI models face edge cases
Remote operators are overloaded

Individually, these systems are strong. Together, they’re fragile.

In crisis economics, the rule is simple:

System performance is defined by the weakest link, not the strongest one.

A human driver only needs a functioning body and situational awareness. A robotaxi needs everything—all the time.

Infrastructure Resilience Matters More Than Innovation

Automation often assumes stable infrastructure. Crises expose the flaw in that assumption.

Natural disasters, cyberattacks, grid failures, and civil emergencies don’t just disrupt services—they compound dependencies. When one layer fails, it drags the rest down with it.

That doesn’t mean automation is bad. It means resilience must be designed, not assumed.

Lessons for Crisis Economics

To build resilient automated systems, policymakers and economists must ask:

What happens when connectivity drops?
Can systems degrade gracefully instead of failing completely?
Are humans embedded locally—or only remotely?
Do backups reduce dependencies or add more layers?

Efficiency wins headlines. Resilience wins survival.

In crisis economics, automation isn’t just a technical challenge—it’s an infrastructure and governance problem. Until systems can function when parts fail, human adaptability remains the gold standard of resilience.

6. Teleoperation: The Human Backbone of “Driverless” Cars

Despite the futuristic branding, robotaxis are not truly driverless. Behind every smooth autonomous ride is a quiet but critical human safety net: teleoperation. This system ensures that when artificial intelligence hesitates, a human can step in—fast.

What Is Teleoperation in Autonomous Vehicles?

Teleoperation refers to remote human oversight and intervention in autonomous vehicle fleets. Instead of a driver behind the wheel, humans operate from centralized hubs, monitoring vehicles and resolving complex situations.

In practice, robotaxis rely heavily on:

Remote fleet response agents who oversee multiple vehicles
Human confirmation during edge cases, such as unclear road rules
Centralized monitoring hubs that coordinate decisions in real time

A simple way to understand it?
👉 It’s like a call center for cars.

Why Robotaxis Still Need Humans

Autonomous systems excel at predictable scenarios, but real-world driving is messy. Humans still outperform AI when it comes to judgment, context, and ambiguity.

Consider these real-world challenges:

Temporary construction zones
Police officers giving hand signals
Broken or obscured traffic lights
Unexpected pedestrian behavior

Now scale that up.

The Hidden Scaling Problem

Imagine this scenario:

7,000 traffic signals suddenly go dark
Hundreds of robotaxis request human assistance at once
Only a limited number of trained teleoperators are available

This is where things break down.

From an economics standpoint, this is a classic queuing problem:

Demand for human intervention spikes instantly
Human operator capacity grows slowly and expensively
Vehicles wait in line—sometimes blocking traffic or freezing in place

Unlike cloud servers, humans can’t autoscale.

Why This Matters for the Future of Autonomy

Teleoperation is often marketed as a temporary bridge to full autonomy. In reality, it’s becoming a permanent pillar of the system.

Key implications include:

Higher operational costs than advertised
Human bottlenecks during city-wide disruptions
Safety risks when response queues grow too long
Regulatory concerns around response time and accountability

The promise of fully autonomous vehicles assumes edge cases will shrink. But cities are dynamic, chaotic systems—and edge cases are the norm, not the exception.

Robotaxis may drive themselves most of the time, but humans are still doing the hard thinking. Teleoperation isn’t a backup—it’s the backbone.

Until AI can reason, prioritize, and adapt like a human under pressure, driverless cars will always have people behind the scenes, quietly keeping traffic—and trust—moving.

7. Regulation and Market Confidence: A Delicate Balance

In the race to scale autonomous vehicles and other frontier technologies, regulation is often painted as the villain—the thing that slows innovation and scares off builders. But that framing misses a bigger, more practical truth: market confidence depends on trust, and trust depends on clear, credible rules.

Leading safety and autonomy experts like Philip Koopman and Missy Cummings have consistently argued that stronger regulatory guardrails aren’t about stopping innovation—they’re about making it sustainable. Their focus areas are especially telling because they target failure points that markets care deeply about.

Where Experts Say Regulation Matters Most

Koopman and Cummings emphasize stricter oversight in three critical areas:

Backup remote operations
When autonomous systems fail, human intervention must be reliable, fast, and auditable. Vague standards here create massive liability risk.
Large fleet thresholds
Scaling from 50 vehicles to 5,000 isn’t linear—it multiplies risk. Regulation should change as fleets grow, just like it does in aviation.
Emergency preparedness audits
Companies should prove, not promise, that they can handle edge cases, public safety incidents, and system-wide failures.

These aren’t academic concerns. They directly affect public safety, legal exposure, and—most importantly—investor confidence.

Regulation Is Not Anti-Innovation (Economically Speaking)

From an economic perspective, smart regulation often accelerates adoption rather than slowing it. Here’s how:

It reduces tail-risk events
Low-probability, high-impact failures are what destroy companies overnight. Regulation helps surface and mitigate those risks early.
It stabilizes investor expectations
Clear rules allow capital markets to price risk accurately. Uncertainty is far more damaging than compliance costs.
It builds long-term consumer trust
Public confidence is a prerequisite for mass adoption, especially in safety-critical technologies like autonomous vehicles.

In other words, regulation doesn’t kill innovation—chaos does.

The GM Cruise Shutdown: A Case Study in Lost Trust

The GM Cruise shutdown following a 2023 accident is a textbook example of what happens when trust collapses. Once regulators, the public, and investors questioned Cruise’s safety culture and transparency, confidence evaporated fast. Capital pulled back. Expansion halted. Momentum vanished.

This wasn’t just a technical failure—it was a governance failure.

The real challenge isn’t choosing between innovation and regulation. It’s designing smart, adaptive regulation that protects the public while giving markets the confidence to invest for the long term.

Because in emerging tech, trust is the ultimate growth engine—and regulation is one of the few tools that can protect it.

8. Lessons from Past Autonomous Vehicle Failures

History matters in economic analysis—especially in emerging technologies like autonomous vehicles (AVs). While self-driving tech is often framed as a purely engineering challenge, the past decade shows a different reality. The biggest setbacks didn’t come from sensors or algorithms alone, but from governance failures, safety missteps, and weak crisis response. Below are key lessons from high-profile autonomous vehicle failures that investors, policymakers, and tech leaders can’t afford to ignore.

1. GM Cruise (2023): One Incident Can Collapse an Entire Operation

A single high-profile accident triggered intense regulatory scrutiny.
California regulators revoked Cruise’s permit almost immediately.
Operations were halted nationwide, leading to layoffs and reputational damage.

Lesson:
In autonomous driving, trust is fragile. Years of technical progress can be undone by one poorly handled incident. Regulatory relationships and transparent safety protocols matter as much as miles driven.

2. Uber ATG: When Economics Don’t Add Up

Uber invested billions into its Autonomous Technology Group (ATG).
Persistent losses, safety concerns, and unclear timelines eroded confidence.
The unit was eventually sold off, marking a strategic retreat.

Lesson:
Autonomy is not just a tech race—it’s an economic endurance test. Without a clear path to profitability and risk control, even well-funded projects can collapse under their own weight.

3. Waymo: Success Brings New Risks

Waymo is widely viewed as the most cautious and technically advanced AV player.
It achieved limited commercial success through controlled rollouts.
However, scaling up introduces new fragilities: public exposure, regulatory complexity, and operational costs.

Lesson:
Even “successful” autonomous vehicle programs face scale-related vulnerabilities. What works in pilot zones doesn’t automatically translate to mass deployment.

4. The Real Bottleneck: Governance, Not Driving

Across these cases, a clear pattern emerges:

The vehicles often worked well enough.
Failures came from decision-making, oversight, and crisis management.
Companies underestimated how fast public sentiment and regulators could turn.

Key Insight:
Early-stage autonomy struggles less with driving itself and more with safety governance, ethical responsibility, and institutional readiness.

5. Why This Matters for Economic Analysis

Autonomous vehicles reshape labor markets, logistics, and urban planning.
But adoption curves depend on public trust and regulatory stability, not hype.
Ignoring historical failures leads to inflated valuations and policy mistakes.

The history of autonomous vehicle failures teaches a simple but powerful lesson:
Technology doesn’t fail in isolation—systems do.

For autonomy to succeed at scale, companies must prioritize governance, safety culture, and crisis response as core economic assets, not afterthoughts.

9. Economic Implications for Cities and Labor Markets

The rise of robotaxis is often framed as a breakthrough for urban efficiency and sustainability. Cities across the globe are embracing autonomous vehicles with the expectation that they will reshape transportation systems, reduce costs, and modernize labor markets. While the potential benefits are real, the economic implications are far more complex—especially when systems fail.

Why Cities Are Betting on Robotaxis

Urban governments and planners are drawn to robotaxis for several high-impact reasons:

Fewer road accidents: Autonomous systems don’t get tired, distracted, or impaired, which theoretically reduces human error.
Lower emissions: Many robotaxis are electric, supporting climate goals and cleaner air initiatives.
Reduced congestion: Optimized routing and shared rides promise smoother traffic flow and better use of road space.

From an economic standpoint, these outcomes could lower healthcare costs, reduce infrastructure strain, and improve productivity across cities.

The Hidden Costs of System Failures

However, when robotaxis malfunction or behave unpredictably, cities absorb a set of external costs that are rarely discussed upfront:

Traffic delays: Stalled or confused vehicles can block intersections and disrupt public transport.
Emergency response interference: Police, fire, and ambulance services may face delays navigating around immobilized autonomous vehicles.
Public frustration: Repeated failures erode trust, leading to resistance from residents and political pressure on city officials.

These costs don’t appear on company balance sheets—but they impact urban economies through lost time, strained services, and reputational damage.

Labor Market Shifts: Replacement or Reconfiguration?

Robotaxis were initially marketed as a direct replacement for human drivers. In practice, the labor impact looks different:

Remote operators monitor vehicles and intervene during edge cases.
Engineers and technicians maintain software, sensors, and hardware.
Crisis response teams handle accidents, system breakdowns, and public safety concerns.

Rather than eliminating jobs entirely, robotaxis restructure employment toward more technical and centralized roles.

Concentrated Risk in Fewer Hands

This shift comes with a trade-off. While new jobs are created, they are concentrated among smaller, highly specialized teams. A single software flaw or operational mistake can affect thousands of vehicles at once, amplifying economic and safety risks.

The Bigger Economic Picture

Robotaxis have the potential to transform cities and labor markets for the better—but only if policymakers account for both the benefits and the external costs. Sustainable adoption will depend on regulation, accountability, and realistic expectations about how technology reshapes work, not just replaces it.

10. Global Competition in Autonomous Mobility

The race for autonomous mobility is heating up, and Waymo is far from the only player. Around the world, companies are innovating aggressively, each taking a unique approach to self-driving technology. Understanding the competitive landscape is key to spotting which players may lead the future of transportation.

Key Global Players

Tesla: Tesla is known for its rapid deployment of autonomous features, relying heavily on real-world data from its fleet. The company claims its vehicles operate with minimal remote oversight, allowing for fast iteration. This strategy prioritizes speed over perfect automation, making Tesla a unique contender in the AV space.
Zoox (Amazon): Zoox is building self-driving vehicles from the ground up, designed specifically for autonomous operation rather than retrofitted from human-driven cars. Their purpose-built approach allows for a focus on safety, passenger experience, and operational efficiency in ride-hailing contexts.
China: In China, the government is heavily backing autonomous vehicle pilots, creating an environment where AV technology can scale quickly. State support enables large-scale testing and deployment, especially in urban centers, giving Chinese companies a potential advantage in data accumulation and regulatory alignment.

Why Crisis Resilience Matters

While daily efficiency is crucial, global economic trends suggest that the first autonomous system to prove crisis resilience—the ability to operate reliably during unexpected events or infrastructure challenges—will set the industry standard.

Emerging markets highlight this challenge. Cities in developing regions often have weaker or inconsistent road infrastructure, irregular traffic patterns, and unpredictable weather conditions. A self-driving system that can only handle well-marked roads in San Francisco may fail in these environments. Companies that design AVs capable of navigating unpredictable conditions will have a clear edge in global expansion.

The Road Ahead

The global autonomous mobility market is evolving rapidly. Success will not just be measured by miles driven without human intervention but by the system’s adaptability across diverse economic and geographic conditions. Companies that combine technological innovation with operational resilience are likely to dominate the next decade of transportation.

The competition is intense: Tesla’s fast rollout, Zoox’s purpose-built precision, and China’s state-backed scale all represent different strategies. However, the ultimate winner will be the one whose autonomous systems prove robust, safe, and reliable in every environment—normal conditions and crisis alike.

11. Simplifying the Issue: A Real-World Analogy

Imagine walking into a fully automated supermarket. There are no cashiers, just sleek aisles, shelves stocked with products, and an AI-powered checkout system that scans and bills your items instantly. Everything seems seamless, efficient, and futuristic. If you need help, a central remote support team is just a click away.

Now, imagine a citywide power cut. Suddenly:

Doors won’t open – you can’t even enter the store.
Payments fail – your digital wallet and AI checkout are useless.
Customers trapped – staff aren’t there to assist physically.

This scenario mirrors the recent Waymo outage, and it highlights a crucial lesson: automation works brilliantly until something fundamental breaks.

Why Automation Can Fail

Dependency on Core Systems
Automated systems rely heavily on underlying infrastructure. Just like the supermarket depends on electricity, autonomous vehicles depend on cloud servers, GPS, and AI algorithms. A glitch anywhere can halt operations.
Limited Human Intervention
Automation reduces human error but also limits human flexibility. In the supermarket analogy, no cashier can open the door manually. Similarly, when Waymo’s system went down, remote teams couldn’t immediately intervene in real-time traffic scenarios.
Ripple Effects
One failure often cascades. In the store, power outage stops checkouts and locks doors. In self-driving cars, a single software outage can disrupt routing, safety protocols, and customer trust.
Importance of Redundancy
Humans naturally create backup solutions. Stores might have manual tills or emergency doors. For self-driving technology, robust backup systems, offline safety checks, and contingency plans are essential to prevent complete shutdowns.
Learning from Real-World Analogies
Comparing tech failures to everyday experiences helps non-experts understand complexity. The fully automated supermarket shows that while technology promises efficiency, relying entirely on automation without safeguards is risky.

The Waymo outage is more than just a tech hiccup—it’s a reminder of the limits of automation. Just like a supermarket shut down during a blackout, self-driving cars shine in smooth conditions but can stumble when core systems fail. The future of automation isn’t just about innovation—it’s about preparing for the unexpected.

12. Data-Driven View: Costs, Scale, and Risk Concentration

The rise of robotaxis promises a revolution in urban mobility, but a closer look at the numbers shows a complex balance between cost, scale, and operational risk. While companies rarely reveal full financial details, industry estimates provide a useful lens.

High Per-Vehicle Costs

Each robotaxi is a significant investment, costing $100,000 or more when factoring in advanced sensors, LIDAR systems, and onboard computing hardware.
These upfront costs mean scaling a fleet isn’t just about buying more cars—it’s about managing capital-intensive assets that must operate reliably in complex urban environments.

Limited Remote Oversight

Remote operators, who provide critical human support during unexpected events, can only supervise 10–20 vehicles at most.
This ratio highlights a bottleneck: while autonomous systems handle routine driving, any incident—such as a sensor glitch or unpredictable pedestrian behavior—requires human intervention.

Non-Linear Risk Demand

Crises don’t scale linearly. A single accident or technical issue can trigger spikes in demand for human oversight, far beyond what average daily monitoring predicts.
This creates a concentration of risk: the more vehicles in operation, the higher the chance that multiple crises occur simultaneously, stressing limited human resources.

Economic Implications

As fleets grow, risk grows faster than revenue unless companies invest heavily in scaling oversight.
Exponential scaling of human support is extremely costly, often outpacing the revenue generated per additional robotaxi.
In practical terms, this means companies face a trade-off: expand aggressively and absorb higher operational risk, or grow cautiously with constrained fleet sizes to maintain safety standards.

Strategic Takeaways

Investing in advanced AI and predictive monitoring can reduce—but not eliminate—the need for human intervention.
Understanding the interplay between cost, fleet size, and risk concentration is essential for sustainable robotaxi operations.
Companies that optimize oversight efficiency can achieve growth while keeping risk manageable, but miscalculating this balance could be financially and operationally dangerous.

Robotaxi fleets are not just about autonomous vehicles—they are a complex system where cost, scale, and risk are tightly intertwined. Proper planning, investment, and real-time human oversight remain critical to turning the robotaxi vision into a safe, profitable reality.

13. Policy Options and the Way Forward

As cities increasingly integrate autonomous fleets and advanced mobility solutions, aligning innovation with resilience has become more than just a technical challenge—it’s a public safety priority. Policymakers have a crucial role to play in shaping frameworks that encourage growth while safeguarding communities. Here are some practical strategies worth considering:

Tiered permits based on fleet size: Not all autonomous operations are created equal. A small pilot program carries far less risk than a citywide deployment of hundreds of vehicles. Introducing tiered permits allows regulators to scale oversight proportionally, ensuring that larger operators meet higher safety and contingency standards. This approach incentivizes responsible expansion while keeping emerging technology from overwhelming municipal resources.
Mandatory crisis simulation testing: Technology may perform flawlessly under normal conditions, but real resilience is tested during crises. By requiring operators to conduct scenario-based simulations—ranging from extreme weather events to system failures—policymakers can ensure fleets are prepared for rare but high-impact events. These drills help identify hidden vulnerabilities, improve response protocols, and build public trust in autonomous systems.
Redundant remote operation centers: Centralized control systems are critical, but they also represent single points of failure. Mandating redundant remote operation centers ensures that if one hub goes offline—due to cyberattacks, natural disasters, or technical glitches—backup centers can maintain fleet operations. Redundancy not only strengthens resilience but also reduces the risk of service disruptions in high-demand urban areas.
Real-time coordination with city emergency systems: Autonomous fleets must seamlessly integrate with existing emergency infrastructure. Real-time communication with fire, police, and medical services allows fleets to respond to unexpected events, clear pathways for emergency vehicles, or provide situational data that supports first responders. Policy that encourages—or requires—this integration enhances overall city safety and positions autonomous systems as reliable partners in crisis management.

For companies, the takeaway is simple but powerful: build for the worst day, not the average day. Planning for peak stress scenarios, rare system failures, and emergency situations isn’t just a regulatory checkbox—it’s a competitive advantage. Operators who embed resilience into their core operations can reduce downtime, protect passengers and assets, and earn the public’s trust in a rapidly evolving transportation landscape.

By combining thoughtful policy design with forward-looking operational practices, cities and companies can create a future where innovation thrives alongside safety and reliability.

14. Visual Summary: How a Power Outage Cascades Through an Automated System

Power Outage Cascade Visualization

Power outage

Traffic lights fail

Robotaxis request help

Remote systems overload

Congestion multiplies

Systems economics in action: a single outage ripples through the entire network.

Power outage → traffic lights fail → robotaxis request help → remote systems overload → congestion multiplies

This is systems economics in action.

15. Conclusion: A Shot Across the Bow for the Autonomous Economy

The San Francisco outage was, as Koopman put it, “a shot across the bow.”

Not a catastrophe—but a warning.

Robotaxis may well be the future. But futures are not built on optimism alone. They require:

Redundancy
Regulation
Respect for real-world chaos

From an economic standpoint, crisis readiness is not a cost—it’s an investment in credibility.

Waymo’s challenge now is not technological.
It is institutional.

16. Frequently Asked Questions (FAQ)

Q1. Does this mean robotaxis are unsafe?
Not necessarily. It means they are less prepared for large-scale emergencies than advertised.

Q2. Why didn’t human remote operators solve the issue faster?
Because demand for assistance spiked suddenly, exceeding operational capacity.

Q3. Will regulation slow innovation?
In the short term, maybe. In the long term, it often accelerates adoption by building trust.

Q4. Are human-driven cars better in crises?
Humans adapt intuitively—but are also error-prone. The goal is hybrid resilience, not replacement.

Q5. What should cities do next?
Demand transparency, stress testing, and coordination before approving large fleet expansions.

17. References & Sources

Reuters Analysis: Waymo’s San Francisco outage raises doubts over robotaxi readiness during crises
Statements from Waymo (December 2025)
Expert commentary: Philip Koopman (Carnegie Mellon University), Missy Cummings (George Mason University)
California DMV & CPUC regulatory discussions
Academic literature on resilience economics and automation