I have a friend who works in UBI car insurance—it's the kind that determines premiums based on your driving habits. He told me a statistic: for the same model of car, the premiums for experienced drivers and reckless drivers can differ by three times. The former brakes smoothly and never speeds, while the latter slams the brakes daily and has a pile of accident records.
Isn't this logic even more reasonable when applied to robots?
The essence of what is currently being done at @Fabric Foundation is to establish a lifelong behavioral profile for robots—tracking how much work they've done, how well they've done it, and whether they have a 'criminal record', all stored on the blockchain. This record can not only be used for order taking and governance, but also for buying insurance.
Imagine this scenario: a newly manufactured robot wants to buy insurance, how would the insurance company price it? In the traditional model, they can only estimate—model, price, usage scenario, all based on experience.
But what if this robot has already completed 3,000 orders, has a 99.7% positive feedback rate, an average task completion time of 2.3 seconds, and a failure rate of 0.01%? The premium would be directly halved. In contrast, another robot, while the same model, frequently encounters bugs and has numerous complaints, causing its premium to double.
This is the dynamic premium model—based on Fabric's PoRW (Proof of Robot Work) and on-chain reputation data, insurance companies can evaluate each robot's risk level in real-time and provide accurate quotes. Well-performing robots pay lower premiums, while poorly performing ones pay more, and the worst are outright denied coverage.
The most ruthless part of this logic is that the data is immutable and can't be denied. Want to commit insurance fraud? All historical records are publicly accessible. Want to tamper? The blockchain is irreversible. Insurance companies also don't need to maintain a bunch of underwriters; smart contracts automatically issue policies and process claims based on real-time data.
The Fabric test network currently has 12,400 active nodes, with an average of over 25,000 tasks per day. Each time these machines complete a task, they are adding to their 'insurance profile'.
The last mile of the robotic economy isn't just about working; it's about having someone to fall back on when things go wrong. And the behavioral records from Fabric are making that fallback more precise, transparent, and automated.
