Picture a future where humanoids fetch your groceries, a robot dog keeps your child company and a drone takes out the trash. That vision crept closer to reality in the mid‑2020s when companies like Figure, Tesla and Unitree rolled out humanoids and quadrupeds, and 1X offered a domestic robot for $499 a month.
But beneath the futuristic sheen lay an awkward truth: these machines could not talk to each other. Each manufacturer used its own software and data formats, so a cleaning bot could not avoid bumping into a cooking bot in the same kitchen. The scene echoed the early smartphone era, with walled gardens instead of an open platform. If robots were going to share our homes and streets, they needed a common language and a public record of their actions.
That realisation inspired Stanford professor Jan Liphardt to found OpenMind in 2024. Liphardt saw the robotics industry drifting toward winner‑takes‑all platforms controlled by a handful of giants. Instead he imagined a decentralised fabric where any robot could prove its identity, share its position and collaborate on tasks while people could inspect and update the rules that govern machine behaviour. The project drew parallels to Android’s effect on smartphones: an open operating system for hardware makers and a trust layer built on public ledgers.
OpenMind soon attracted attention – and capital. In August 2025 the company raised $20 million from Pantera Capital, Coinbase Ventures and other crypto‑focused funds. The money allowed them to hire more engineers and push forward development. Investors spoke of OpenMind as the Linux or Ethereum of robotics; Pantera partner Nihal Maunder called it an effort to free machines from proprietary control. Yet the early months were chaotic. OpenMind released a beta of its runtime, OM1, under the MIT licence and opened a waitlist. Within three days 150 000 people registered, and by October 2025 more than 180 000 people and thousands of robots were helping build maps and run tests.
Some assumed the waitlist points would translate into tokens, but the company warned that points alone did not guarantee rewards. The team focused on publishing research, such as the ERC‑7777 standard for robot identity and behaviour, and reminded participants that building robust robot infrastructure would take time.
Central to the project was the separation of intelligence and coordination. OM1 provided the intelligence. Written in Python, it runs on Jetsons, Raspberry Pi and other processors and plugs into modern AI models like GPT‑4o, Gemini and DeepSeek. Agents communicate via a natural‑language “data bus,” and the system offers modules for mapping, LiDAR, vision, speech and navigation.
This design lets robots perceive and act without each manufacturer rebuilding basic functions. The second component, FABRIC, handles coordination. Robots create cryptographic identities anchored on Ethereum, and a universal identity contract stores details such as manufacturer, model and serial number. Robots sign commitments to behavioural rules and store these hashes on chain. Another contract, the Universal Charter, contains rule sets and allows updates under governance. To connect on‑chain logic to physical actions, the Machine Settlement Protocol collects sensor data, converts it into proofs of location and work, and feeds those proofs back to smart contracts for verification. To bootstrap the network cost‑effectively, the team launched on Base, an Ethereum layer‑2, with plans to migrate to a dedicated Fabric chain later
OpenMind built momentum by engaging a community rather than chasing speculative hype. The waitlist evolved into a vibrant Discord. Engineers and hobbyists shared code and hardware hacks, and OM1 soon trended on GitHub. The company organised hackathons and map‑building drives; participants earned points for their effort and for referring friends.
Public demonstrations showed robots paying electric chargers with USDC via smart contracts, illustrating how machines could hold wallets and transact without human intervention
. By emphasising safety and transparency, OpenMind attracted robotics researchers who might have ignored a crypto project.
The launch of ROBO in February 2026 turned the protocol from a research project into an economic system. ROBO is used to pay network fees, register identities, post work bonds, settle robot‑to‑robot payments and vote on upgrades
. The supply is capped at ten billion tokens
. Nearly thirty per cent of the supply is earmarked for the ecosystem and community, with an initial release followed by forty months of vesting
. Investors hold about a quarter of the supply, founders and employees twenty per cent and the foundation eighteen per cent; all of these allocations come with twelve‑month cliffs and multi‑year vesting, meaning insiders cannot sell until 2027
. Five per cent of tokens were distributed to early contributors at launch, and a small amount provided liquidity for exchanges
To make the currency responsive rather than inflationary, Fabric uses an adaptive emission engine. Token issuance increases when robot capacity is under‑used and decreases when quality of service drops
. A circuit breaker caps how quickly emission can change
. Demand for the token is built into the system. Operators must stake ROBO as a refundable bond when registering hardware; if a robot fails to complete tasks, a portion of the bond is burned
. Portions of transaction fees are used to buy back ROBO on the market
. Anyone who wishes to participate in governance must lock tokens, reducing circulating supply
. Rewards go only to those who contribute verified work — running robots, writing code, supplying data or supervising tasks; idle holders earn nothing
ROBO’s roles go beyond staking and payments. All network interactions settle in ROBO, whether paying for compute, data queries or robot‑to‑robot services
. Token holders can delegate their tokens to operators they trust, boosting an operator’s reputation and task capacity, but they share slashing risk if misbehaviour occurs
. Governance uses a vote‑escrow model: locking tokens for longer periods grants proportionally more voting power, aligning influence with long‑term commitment
Investors and community members watch several metrics to gauge Fabric’s progress. Adoption is crucial: the number of robots on the network and the utilisation of available capacity signal whether the machine economy is taking shape. Service quality metrics show how reliably robots execute tasks and whether the emission engine should increase or decrease supply
. Market data provides another lens. At launch ROBO traded around 3.8 cents with a market capitalisation near $85 million and a 24‑hour volume of roughly $149 million. About 2.23 billion tokens were circulating, just over twenty per cent of the fixed supply. Prices swung between about 2.2 cents and 4.6 cents over the first days. Observers also monitor vesting schedules — since insiders cannot sell until early 2027
— and the amount of tokens locked for governance, which signals confidence in the network’s future.
Around this economic core, a wider ecosystem is forming. OpenMind is planning a marketplace for “skill chips,” where developers can publish modules for navigation, object manipulation, voice control or any robot behaviour and earn ROBO when robots install them
. The protocol allows robots to pay each other or human owners directly via smart contracts, demonstrating non‑discriminatory on‑chain payments
. Communities can crowdfund new robots by buying participation units; when enough units are sold, a robot is purchased, and the community receives a share of its future earnings
. Each robot’s identity and rule commitments are public, and OpenMind envisions a global observatory where people can verify compliance and report misbehaviour
. The roadmap for 2026 includes rolling out incentives tied to verified task execution, supporting workflows involving multiple robots and refining the emission engine for large‑scale deployments
. Longer‑term plans call for launching a dedicated Fabric blockchain and a full‑fledged robot app store
The story of Fabric is still unfolding. It began with a professor’s conviction that robots should operate on an open network rather than closed silos and has grown into an ambitious attempt to connect hardware, artificial intelligence and blockchain. The founders spent two years building technology and a community before minting a token, giving the project roots deeper than speculation.
Risks remain: adoption is nascent, verifiable computing at scale is complex, regulatory questions hover and the token price may be volatile. Yet the promise is profound. If Fabric succeeds, robots from Tesla, Unitree, Figure and unknown startups could coordinate seamlessly, pay for services and share data without central gatekeepers. Developers worldwide could earn a living by publishing robotic skills. Ordinary people could crowdfund fleets of machines and share in their revenue. Public ledgers would encode safety rules and provide transparent oversight, easing fears about autonomous robots. By weaving together open‑source software, verifiable ledgers and a carefully designed token economy, Fabric aims to create a machine economy that includes everyone.
It flows like a story, weaving the history and vision of the Fabric project into a continuous narrative without section breaks, while retaining all the key facts and citations. Let me know if you'd like any further adjustments or have another project in mind!