Over the past few years, I’ve found myself thinking less about whether robots and AI will change the world and more about who will control that change. The conversation has shifted. It’s no longer about “if” automation becomes dominant. It’s about how it unfolds and who benefits when it does. That’s the context in which Fabric starts to make sense.
Fabric positions itself as a global, open network for building and governing general-purpose robots. At first glance, that might sound abstract. But the idea is actually simple. Instead of robotics being controlled by a handful of large corporations with closed systems, Fabric proposes a shared infrastructure where data, computing power, and oversight are coordinated through public ledgers. In practical terms, that means contributors can participate in developing robotic intelligence and be rewarded for it. It reframes robotics from a corporate race into a networked ecosystem.
The urgency behind this idea becomes clearer when you look at how quickly automation is progressing. Autonomous vehicles already demonstrate measurable safety improvements compared to human drivers. AI systems assist doctors in diagnostics, optimize logistics networks, and manage large-scale industrial processes. For families, businesses, and governments, the attraction is obvious: better performance, lower cost, and greater safety. Over time, robots may become the preferred option not because of ideology, but because they simply perform better.
However, this creates a serious social and economic tension. For decades, many professions have provided accessible entry points into stable income. Taxi driving, warehouse work, call center support, and numerous service jobs have supported families across generations. If these roles are increasingly automated, the wealth generated by that productivity shift could concentrate in the hands of a few technology owners. History shows that rapid technological shifts without inclusive economic models tend to widen inequality.
This is where Fabric’s model becomes important. By coordinating robotics development through a decentralized framework, it attempts to distribute participation and ownership. Contributors are not just users; they become stakeholders. Data, training inputs, operational improvements, and governance decisions can all be structured through transparent systems. Instead of value flowing vertically into one organization, it can circulate across a broader network.
Another dimension that makes robotics fundamentally different from traditional labor is skill transfer. Human expertise requires time, training, and experience. Becoming a skilled professional often takes years of education and deliberate practice. Machines, by contrast, can replicate learned capabilities instantly. Once one robotic system masters a task, that improvement can theoretically propagate across an entire network in seconds. This dramatically accelerates progress but also amplifies the stakes. The speed of skill diffusion means that power can accumulate quickly if control is centralized.
Industries worldwide would feel this shift. A surgical robot trained with the best techniques could share those refinements globally. A logistics robot optimized in one region could immediately enhance efficiency elsewhere. Call center automation could adapt across languages and cultures almost instantly. The benefits are enormous, but so is the need for governance that prevents monopolization.
Fabric’s broader vision is not anti-automation. It accepts that robotics and AI will expand. The focus instead is on structuring the growth in a way that balances efficiency with fairness. Public ledgers provide transparency. Open participation creates accountability. Incentive structures encourage responsible contribution rather than extractive control.
There are, of course, challenges. Coordinating global contributors requires robust technical standards and strong security practices. Governance models must balance openness with reliability. Incentive systems must reward meaningful contributions rather than superficial participation. None of this is simple. But ignoring the governance layer while automation accelerates would be far riskier.
When I step back and look at the bigger picture, Fabric feels less like a robotics startup and more like an attempt to redesign the ownership model of automation itself. If robots become the backbone of transportation, healthcare, manufacturing, and services, then the system managing them cannot be an afterthought.
The future of robotics is not only about hardware performance or software intelligence. It is about economic design, participation rights, and long-term societal stability. Automation can either concentrate opportunity or distribute it. The difference will depend on the infrastructure that supports it.
Fabric’s premise is straightforward but powerful: as machines become more capable, the networks that govern them should remain open, shared, and accountable. Whether it succeeds or not will depend on execution and adoption. But the question it raises is unavoidable.
If robots are going to build the future, who should own that future?
