The underwriting gap — where the insurance models haven't arrived yet

The insurance landscape for Physical AI becomes clearer when you map it on two axes: coverage availability and model maturity.

Automotive sits in the top-right: high coverage, mature models. Car insurance is mandatory in most jurisdictions. Decades of actuarial data. Telematics-based pricing using real-time driving behavior. Progressive wrote over $6 billion in net premiums in a single month in 2025. The models are deep, the data is rich, the products are everywhere.

Traditional machinery sits in the top-left: coverage exists but models are outdated. Commercial General Liability (CGL) policies have covered manufacturing equipment for decades. There's actuarial data from thousands of incidents. But the models were built for deterministic machines — PLCs, relays, hydraulic systems. They don't account for AI-based failure modes: neural network misclassification, adversarial inputs, emergent fleet behaviors.

Cyber insurance sits in the bottom-right: coverage is low relative to the risk surface, but models are maturing rapidly. Global cyber premiums reached an estimated $16.5 billion in 2025 and are projected to exceed $42 billion by 2030. Carriers are investing heavily in AI-driven underwriting and real-time threat assessment. It's a young market, but it's growing faster than almost any other insurance category.

Physical AI sits in the bottom-left: low coverage, immature models. No top-10 global insurer has launched a standalone robot safety insurance product. No actuarial data exists for AI-driven failure modes in physical systems. No fleet-level coordination models exist for underwriting purposes. Commercial policies available today are either endorsements on existing machinery coverage or experimental riders that don't address the new failure categories.

That bottom-left quadrant is where the gap lives. And it's not a small gap. EY noted in 2025 that humanoid robots will fundamentally reshape commercial insurance. Accenture found that 71% of insurance executives envision deploying autonomous mobile robots within 5-10 years. QED Investors argued that a robot incapable of being insured cannot exist outside a controlled environment.

The industry sees the market. It cannot yet price it. Pricing requires data — failure rates, diagnostic coverage ratios, incident causation patterns, mean time to dangerous failure — that the Physical AI industry hasn't generated yet because the safety infrastructure to produce that data doesn't exist at scale.

The underwriting gap is not just an insurance problem. It's a deployment bottleneck. Until robots can be insured at reasonable premiums, operators face unquantified liability. Until operators face quantified liability, safety investment looks like a cost rather than a prerequisite. Breaking this cycle requires the safety data layer that neither the insurance industry nor the robotics industry has built yet.