Robotaxi is often framed as a technical moonshot.That framing is wrong.

The technology is not the primary risk.

If Robotaxi fails, it will fail for non-technical, system-level reasons.

Statistical safety is not the same as social acceptance.

A system can be 10× safer than humans and still fail if:

• Incidents are rare but spectacular

• Media amplification is asymmetric

• Human-caused accidents are normalized, machine-caused ones are not

Robotaxi must overcome salience bias, not just engineering benchmarks.

Insurance backing helps—but perception lags data.

Most regulators are not anti-autonomy.They are anti-liability ambiguity.

Robotaxi fails if:

• Responsibility is unclear across software, fleet operator, and manufacturer

• Incident attribution cannot be cleanly resolved

• Legal frameworks lag operational reality

Progress stalls not at approval, but at scalable approval.

The hardest part of Robotaxi is not driving.

It is:

• Fleet maintenance

• Edge-case recovery

• Cleaning, vandalism, misuse

• Geographic scaling without human fallback

Algorithms scale geometrically.Operations scale linearly—and break under friction.

This is where many promising systems historically collapse.

Robotaxi looks extraordinary in slide decks.

It becomes fragile when:

• Utilization is uneven

• Urban density is lower than modeled

• Insurance, maintenance, and downtime are fully accounted for

If margins depend on perfect conditions, the model will not survive contact with reality.

One early, mishandled failure can poison years of progress.

Robotaxi does not get unlimited retries.Trust, once lost, is slow to rebuild.

This makes early-stage discipline more important than speed.

Robotaxi will not fail because autonomy “doesn’t work.”

It will fail if:

• Society cannot agree on liability

• Regulators cannot scale approval

• Operators underestimate real-world friction

• Or trust collapses faster than it can be rebuilt

Technology is necessary—but insufficient.