Brothers, in the past two years, whenever the conversation turns to embodied intelligence, robotic automation, or the implementation of intelligent agents, everyone's attention is almost always drawn by the same narrative. Whose robot is more human-like, whose movements are smoother, who can move boxes, twist screws, and fold clothes on stage, who represents the future. However, I increasingly feel that this excitement can easily lead people astray, because what truly determines the industry landscape has never been a few showcase videos, but rather who defines the underlying collaboration methods, who sets the thresholds for hardware and software, and who decides the distribution of data and profits.
For embodied intelligence to enter the real world, it cannot avoid three issues. The first is hardware fragmentation. Different manufacturers, different forms, different sensors, different motors, different communication protocols, and different safety standards lead to the same capability being unable to be reused across platforms. The second is software closure. Many so-called ecosystems actually treat developers as outsourcing, viewing interfaces as charity. Open one day and retract the next; the deeper you go, the harder it becomes to extricate yourself. The third is data and profit centralization. Robots running in your factory have their data flowing back to someone else's cloud, tasks being scheduled by others, and settlements being extracted by others, leaving you with only rented efficiency.
This model may seem to progress quickly in the short term but will ultimately lock the industry in the long term. Small and medium hardware manufacturers that produce cheaper and more durable joints or sensors cannot integrate into the mainstream system. Developers who write smarter planning or control modules can only distribute according to platform rules, with revenue sharing ratios and listing qualifications not determined by themselves. Enterprise users fare worse; deploying robots was originally for stability and control, but now critical capabilities depend on external platforms. Once interfaces change, authorizations tighten, or prices rise, all processes must restart. Embodied intelligence, which is touted as the next generation of productivity, risks becoming the next generation of rental business.
It is precisely because of this that I prefer to spend my time on those critical infrastructures that are not attention-grabbing but essential. The path of Fabric Foundation is a typical choice against the wind. It does not invest all resources in a flashy piece of hardware, nor does it rely on closed-source large models to create mystique. Instead, it focuses on something more challenging and long-term: creating an open collaborative base for embodied intelligence, allowing robots, developers, hardware manufacturers, and scene providers to collaborate under the same set of rules without being choked by a giant's proprietary standards.
When many people hear this narrative for the first time, they instinctively feel it's just empty talk; open source, decentralization, and collaborative economy all sound grand. But once you truly break it down, you will find its logic is quite robust and pragmatic. What is most lacking in the implementation of embodied intelligence is not vision, but reusable engineering capabilities and sustainable incentive structures. Open source does not equal charity; its value lies in compressing repetitive labor to a minimum, lowering the threshold for ecological participation, and bringing value distribution closer to actual contributions.
First, let's talk about the most intuitive point: hardware decoupling. One of the biggest problems in the robotics industry today is that every manufacturer wants to create its own closed stack. The hardware interface is theirs, the driving protocol is theirs, the motion control stack is theirs, and even the task orchestration tools are theirs. The result is that switching to a different robot is akin to switching to a different world. Developers' time is wasted on adaptation and transportation, hardware manufacturers are stuck on isolated islands of incompatibility, and scene providers are forced to compromise among a few suppliers.
Fabric Foundation emphasizes using a unified abstraction layer to hide hardware differences as much as possible at the bottom, allowing the upper-level skills and tasks to be transferable across platforms. Its core significance does not lie in how stunning a specific function is, but in its attempt to create an extensible base for embodied intelligence, similar to the early days of mobile internet. You do not need to rewrite control logic for each motor model, nor do you need to rewrite a perception pipeline for each depth camera, much less learn a new configuration method for each manufacturer's toolchain. For developers, this means the same capability can run on more devices, significantly improving the input-output curve. For hardware manufacturers, as long as they connect their hardware to the underlying specifications, they have the chance to access a larger market instead of relying on sales teams to negotiate integration one by one. For scene providers, robot supply becomes more interchangeable, bargaining power stronger, and safety enhanced.
However, just having technical abstraction is not enough; for the ecosystem of embodied intelligence to operate, collaboration and distribution must be solved. Many so-called platforms superficially provide tools and markets, but in reality, they hold critical resources in their own hands. You contribute code, and the platform takes the traffic. You contribute data, and the platform takes the training profits. You contribute devices, and the platform takes the scheduling rights. In the end, the more you participate, the more dependent you become, and the more dependence makes you vulnerable. This is not a sustainable industrial structure.
Fabric Foundation emphasizes a mechanism design that links contribution, credit, and profit. The most discussed aspect here is ROBO's positioning in the ecosystem. Many people tend to treat tokens as price tags, where fluctuations dictate everything. But if you only focus on price, you will miss the most critical aspect: tokens here are more like fuel and constraint tools for the collaborative system. It places participation rights, resource calls, task settlements, and credit accumulation in the same ledger, using economic means to ensure rule execution.
I prefer to understand this process as the connection of three links. The first link is the resource access link. For any device to enter the collaborative network, it is not just about connecting to the internet; it needs to be recognized, constrained, and measured. Hardware manufacturers and node providers need to invest costs, and the system needs a unit that can measure and constrain; at this point, ROBO plays the role of ticket and guarantee. You cannot rely on verbal promises to ensure service quality, nor can you rely on centralized reviews to cover the long tail of global devices; staking and punishment mechanisms are more universal means.
The second link is the capability distribution link. How to enable the skills modules, task scripts, and control strategies written by developers to be reused by more people and continuously iterated. Pure open source can certainly promote dissemination, but sustained maintenance requires returns; otherwise, open-source projects can easily stagnate after the heat fades. Through on-chain settlement and profit distribution, skill providers can earn profits when called, creating incentives for maintenance and upgrades. More importantly, this kind of profit is not a gift from the platform but a default rule at the protocol level, reducing human uncertainties.
The third link is the credit accumulation link. What is most scarce in embodied intelligence is not demonstration effects, but stable execution. Whether a robot can complete a task on time and maintain safety and consistency in complex environments requires long-term records and evaluations. Centralized platforms can certainly score, but scoring rules can change at any time, and data can be hidden or modified. Only by sedimenting task execution records, performance metrics, faults, and dispute resolutions into a history that cannot be easily tampered with can credit truly be transferable. The value of transferable credit lies in the fact that whether you change hardware suppliers, scene partners, or even countries and regions, your historical contributions can still be recognized, allowing you to take on higher-value tasks, access lower-cost resource calls, and facilitating specialization.
Only when these three links are connected can embodied intelligence move from project-based to network-based. Today, many robotics companies take on projects like engineering contracts, relying on customization and piling up labor, and scatter after delivery. Network-based means you can break down complex tasks into composable modules, hand over modules to different nodes for execution, and market-match the capabilities and credits of nodes. Thus, scalability no longer relies solely on the production capacity of a single company, but on the collaborative efficiency of the entire network.
Of course, no matter how well it is said, we must face practical resistance. The path of an open base has three hurdles that must be overcome.
The first hurdle is cross-hardware consistency. Hardware decoupling does not mean that physical world differences disappear. The backlash and stiffness of different joints, the noise characteristics of different sensors, and the stable boundaries of different body structures will cause the same task to perform differently on different devices. To absorb these differences at the bottom level requires a lot of engineering work and enough real scene data for calibration. This process will not happen overnight, but the direction is correct because the earlier a unified abstraction is established, the more subsequent iterations can reuse the results.
The second hurdle is ecosystem cold start. No matter how good the skill modules are, if there are not enough devices and scenes to call upon them, it is hard to generate profits. Even with plenty of devices, without high-quality skills to support, it is difficult to retain users. Even with many scenes, without a stable scheduling and settlement mechanism, it is hard to operate in the long term. Cold starts require strategy and patience. A feasible path is to first delve into a few high-value scenes, such as warehouse inspections, security patrols, facility maintenance, park delivery, and industrial quality inspection. These scenes require high stability and measurability, making them more suitable for building positive cycles with credit and settlement mechanisms. Once established in these scenes, it will naturally attract more participants to expand into long-tail applications.
The third hurdle is safety and governance. Open networks fear two things the most: first, witch attacks and profit manipulation; second, malicious tasks and unclear accountability. Embodied intelligence differs from pure software; it affects the property and personal safety in the real world, so governance must be prioritized. Clear task permission boundaries, secure device sandboxes, risk grading, and insurance-like mechanisms are needed, as well as rapid punishment and isolation of node violations. This requires both technical solutions and community governance with standardized processes. If done well, it will become a competitive barrier. If done poorly, any incident could severely undermine trust.
Many people ask why so many complex problems need to be resolved within an open system; isn't it easier for giants to remain closed? My understanding is that the complexity of embodied intelligence dictates that it cannot be monopolized by a few companies for the long term. It requires massive amounts of specialized industry knowledge, diverse hardware forms, and constantly changing scene adaptations. Relying on a few companies to handle everything can produce a few star products in the short term, but in the long term, it will encounter boundaries, costs will soar, innovation will slow, and the ecosystem will become closed. Open collaboration is not just an ideal but a more realistic choice to tackle complex systems.
If you view embodied intelligence as the next generation of productivity, then the spread of productivity must rely on standardization, modularity, interchangeability, and collaboration. The value of Fabric Foundation lies in the fact that it is more like paving a road for the industry rather than building a toll booth on the road. It attempts to pull critical capabilities out of the corporate black box, allowing more people to participate and contribute, and enabling contributors to receive more predictable returns. Within this framework, ROBO acts more like a coordinator, establishing executable rules for the flow of resources and profits, allowing trust between participants to not rely entirely on centralized endorsements.
I don't want to call it a savior. No project can solve all the industry's problems by itself. It requires time for validation and greater real-world participation. It may take wrong turns, encounter competition and regulatory pressure, and seem slow at certain stages. However, if you are willing to shift your focus slightly from short-term excitement, you will find that what the industry truly lacks is a team and community willing to establish a foundation, collaborate on rules, and embed value distribution mechanisms into agreements. The endgame of embodied intelligence is not the performance of a few companies, but the collaboration of countless participants.
So I prefer to conclude this long article with a more straightforward judgment. The truly strong robot ecosystem of the future will be one that allows hardware manufacturers to have a path to follow, developers to earn money, scene providers to have choices, device nodes to have sources of profit, and data and credit to be reliably recorded. As long as a system can continuously advance in this direction, it is worth serious research and participation. As for the short-term noise, controversies, and fluctuations, they will eventually pass, leaving only reusable technologies, executable collaborative processes, and foundational structures that can support growth.
If you are already engaged in robot development, hardware production, or scene implementation, or if you just want to find a longer-term direction, you might consider shifting your focus from a single product to the underlying changes in collaborative methods. Because once the method of collaboration changes, the distribution of industry benefits will be rewritten, and the sources of innovation will also be rewritten. Many people wait until the landscape is determined before entering, often ending up as followers. The real opportunities are often hidden in the early stages of infrastructure, in those less flashy but crucial areas.
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