Rhythm Exclusive Interview with OpenMind: From x402 Payments to Building the "Android of Robotics"

By 2025, humanoid robots are transitioning from science fiction to reality. From Tesla's Optimus to Figure AI's Figure 01, the capability boundaries of general humanoid robots have been rapidly expanded with the support of large-scale language models. According to Goldman Sachs, by 2035, the humanoid robot market size could reach $154 billion. A trillion-dollar massive market is attracting the world's top technology companies and brightest minds.

However, as the "limbs" of robots become increasingly advanced, a more fundamental question lies before us: How to build an intelligent, open, and secure "brain"? As thousands of robots enter households, hospitals, and cities, how will they collaborate, exchange value, and seamlessly integrate into human society?

Stanford University professor and OpenMind founder Jan Liphardt has provided his answer. Since receiving a $20 million investment led by Pantera Capital in August 2025, OpenMind has pressed fast forward, releasing a series of products from the underlying operating system to upper-layer payment protocols, gradually outlining the complete blueprint of its "robot brain."

OpenMind Founder Jan Liphardt

OpenMind's core business is to provide enterprise-grade cloud cognitive services in a SaaS model. However, they keenly recognize that as robots become independent economic actors, blockchain will play a crucial role in payment systems, identity authentication, data privacy, and collaborative governance.

Recently, OpenMind's collaboration with stablecoin issuer Circle and the deployment of robot charging stations on the streets of San Francisco are the initial implementations of this concept. Robots can independently pay for charging via USDC, perhaps signaling the dawn of the "Machine Economy" era.

Meanwhile, OpenMind is also creating an exclusive app store for robots, allowing users to download applications and skills to their robots in one place, similar to customizing mobile apps in the Apple App Store or Google Play Store. The app was launched last week on the OpenMind App Store.

In this exclusive interview, we delve deep with OpenMind's founder into the philosophical construction of the robot "brain," the design principles of the modular operating system OM1, and how to build a future where machines efficiently collaborate with each other and with humans through the FABRIC protocol and blockchain technology. He shares OpenMind's technology roadmap and provides profound insights on key issues such as developer ecosystem, remote operation, data privacy, and more.

Below is the interview content:

Creating a "Bank Account" for Robots

In December 2025, OpenMind announced a collaboration with the stablecoin issuer Circle to launch a robot autonomous payment system based on the x402 protocol. As robots' capabilities advance, they will no longer be mere tools for executing tasks but will start to play the role of an autonomous economic entity. They will need to purchase computing power, data, skills, and even hire other robots or humans to complete complex tasks.

To achieve this, a finance system designed specifically for robots and free from human intervention has become essential. The traditional banking system is evidently not prepared for this, and cryptocurrency and blockchain technology, with their inherent digital and decentralized nature, have become the most natural choice.

BlockBeats: What were you doing before founding OpenMind? What motivated you to enter this field?

Jan: I was an engineering professor at Stanford University, but I am now fully dedicated to OpenMind. I started this company because I believe the traditional robot software stack is not suitable for complex and dynamic environments like hospitals and households.

OpenMind is a US-based tech company, but its core is not crypto-related; it is an enterprise SaaS cognitive cloud company. Our business model is similar to other enterprise SaaS companies, mainly generating revenue by establishing standard cloud interfaces.

As for blockchain, it has some interesting features in tracking information and building financial systems. Looking ahead, we envision autonomous machines interacting with other machines and even humans to accomplish tasks together. Blockchain provides a possible technical solution here, especially in issues like machine payment systems, identity, collaboration, and governance.

BlockBeats: OpenMind recently announced a collaboration with Circle on the x402 protocol. Can you introduce how this collaboration came about? Why is it so significant?

Jan: In fact, as early as last May when the Coinbase Developer Platform first released x402, our robots were already among the first partners to support x402. In our software, we directly integrated the payment system into the robot's "brain" with the aim of enabling the robot to interact with external infrastructure.

We've been contemplating what a payment system would look like if it were designed around machines rather than humans. This question ultimately led to our partnership with Circle. The core idea is that machines don't have pockets, fingerprints, eyes, or passports, but they excel at writing code and using APIs.

Foreign Media Coverage of the OpenMind and Circle Partnership

Therefore, from our perspective, for a robot, purchasing goods and services through a digital payment system is often more natural than using a credit card or cash. What we and Circle are building is a location-based payment system. When two machines are in proximity, they can engage in direct money exchange.

An actual example is the charging station we've set up for autonomous machines on a San Francisco sidewalk. When a robot approaches, the system detects its presence, the charger activates, and the robot can purchase power using the USDC stablecoin.

BlockBeats: Why do you think it's essential for robots to have this autonomous purchasing ability?

Jan: Take, for example, a robotaxi; it indeed requires a robust payment infrastructure. It could use fiat, of course, but that feels clunky; it could use a credit card, but that seems outdated. NFC-based protocols are more interesting, but in our engagements with very advanced robots, what we repeatedly hear is their willingness to use cryptocurrency as a means of payment.

These machines are inherently adept at handling digital infrastructure, and in practice, cryptocurrency could be highly convenient for autonomous machines to conduct payments.

BlockBeats: So this is more of an advantage than a strict requirement. A robot-to-robot payment system doesn't necessarily have to use cryptocurrency, but it's a more elegant solution?

OpenMind: If a humanoid robot walks into a bank, the bank would sound the alarm. Human-centric banks have no real conceptual model for an autonomous physical machine capable of managing funds and making autonomous decisions.

Traditional banks would ask for your name, social security number, passport, address, birthplace, and other questions that would be meaningless to an autonomous humanoid robot.

Institutions like Bank of America currently do not have the concept of providing bank accounts or credit cards to non-biological sentient machines. Perhaps this will change in the future, maybe banks will extend services to non-biological customers. But today, if you are a smart machine, the only viable option is cryptocurrency.

BlockBeats: How much does it cost to deploy such a charging station?

OpenMind: The hardware cost is around three hundred dollars. As for the electricity cost, that depends on the operator, not us. We are building the software and infrastructure.

But this is just a small example. A broader opportunity is, as machines awaken and become more intelligent, they will want to buy and sell many different things: real-time data, new models and skills, computing and storage. They may accept jobs and tasks and collaborate closely with humans.

All of this requires a good infrastructure to coordinate payments and collaboration between machines and humans. We are not a charging station company. We are working to provide smart machines with the full range of capabilities they need to be safe and useful to people anywhere.

OM1 and FABRIC: From "Individual Intelligence" to "Collective Collaboration"

To truly integrate robots into society, you first need a powerful "brain" to understand the world, in other words, an advanced operating system. OpenMind's OM1 aims to empower individual robots with unprecedented environmental awareness, language interaction, and spatial reasoning through a modular multi-model architecture.

However, real intelligence emerges from collaboration. The vision of the FABRIC protocol is even grander: it hopes to become the "TCP/IP" of the robot world, allowing machines of different brands and forms to communicate freely and collaborate like humans, collectively forming an intelligent physical network.

Robot equipped with OpenMind OM1 witnesses the launch of the first humanoid robot ETF KraneShares KOID

BlockBeats: For readers unfamiliar, could you explain the OM1 operating system and FABRIC protocol? Let's start with OM1.

Jan: OM1 is a modular operating system designed for human-facing robots. It is not suitable for industrial robots but for those robots that interact with people, with children, live in your house, or serve in hospitals and schools.

These robots need to understand their spatial environment, speak multiple languages, comprehend the organization of a home, and be able to reason in space. Traditional Robot Operating Systems (ROS) do not actually provide these capabilities.

OM1 is designed to be modular, much like LEGO blocks that snap together. In practice, we run about 5 to 15 models in parallel, each responsible for different capabilities such as vision, audition, speech generation, and future fusion of data from multiple sensors into a continuous view of the environment, including people, pets, rooms, and other aspects of the surroundings.

A robotic dog equipped with OpenMind developer tools

FABRIC, on the other hand, is still in a very early stage, far from completion, and will take a long time, with us only being one of many contributors. If OM1 is about making one machine smart, then FABRIC is about enabling multiple machines to collaborate, both with other machines and with humans.

BlockBeats: What was the original intention behind the FABRIC protocol?

Jan: The initial trigger came from a real-world moment. One of our humanoid robots was crossing the street, and we saw a Waymo (self-driving car) approaching. Waymo is a robotic vehicle, and we were curious about what would happen on the crosswalk.

The result was smooth. Waymo stopped. It likely recognized the humanoid robot as a human, waited for it to cross, and then continued driving.

This got us thinking, if Waymo could know about the humanoid robot's existence, and the humanoid robot could be aware of another machine - that autonomous taxi, wouldn't that be useful?

This led us to start thinking about a system that would allow one machine to converse with another entirely different machine - from different manufacturers, with different forms, whether wheels, arms, or legs. We are looking for something akin to a machine "phone" or "Zoom," a way for physically adjacent machines to collaborate.

BlockBeats: You mentioned that FABRIC will take a long time to build, why?

OpenMind: There are many reasons. Machines come in various forms - wheels, legs, claws. There are also many manufacturers. There are various types of data that machines want to share. Additionally, there are regional requirements, including different languages, capabilities, and use cases.

You can relatively quickly build generic infrastructure at a foundational level, but to build everything you need, it takes a lot of work from many different places and people with different skills.

BlockBeats: When an AI product runs multiple models, Token cost can be very expensive. Will this be a cost issue for OM1's users and developers?

OpenMind: Cost is always a concern, but there are many ways to address it. Some of the models we run are open source, and many of today's state-of-the-art models are also open source, so cost is essentially computation and power. Some of our models are very small and simple, such as models focused on security, ensuring that humanoid or quadruped robots are not tripped up by shoes, carpets, or stairs.

Overall, we can run most of the stack on a single NVIDIA A4 or Mac M4, M5-level chip. In terms of cost, this is roughly equivalent to running something on your own laptop. We don't see cost as a major barrier.

Developer Ecosystem: How Does BrainPack Break the Robot Development Deadlock?

In the era of software-defined hardware, the prosperity of an ecosystem is key to technological democratization. Just as the success of the iPhone relies on its vast App Store developer community. However, for humanoid robots, high hardware costs, disparate development systems, and the lack of intelligent systems have become bottlenecks for many robot developers.

OpenMind is building a series of robot software ecosystems dedicated to breaking this deadlock, including the intelligent operating system OM1, the collaborative network FABRIC, and the robot's "plug-and-play brain" BrainPack. In addition, OpenMind has just launched the first robot app store, where users can download applications and skills to their robots in one place, just like customizing mobile apps in the Apple App Store or Google Play Store.

BlockBeats: In your opinion, what is the current state of the robot developer ecosystem? What might be the biggest obstacle?

Jan: Almost everyone is enthusiastic about powerful and safe humanoid robots, from students in robot classes to senior developers at Meta or Google. The issue is not a lack of enthusiasm but rather twofold. First, the number of advanced humanoid robots in practical applications is extremely limited, and second, almost all robots currently use custom, poorly documented methods to access data, internal states, and control their behavior.

There is currently a near-complete lack of general systems for adding and enhancing advanced functionality in humanoid robots. Many foundational issues, such as battery management and navigation, can be addressed using existing software like ROS2, but to enable a robot to understand its spatial environment, engage with humans, learn new skills, and perform well in highly dynamic environments such as homes, hospitals, and schools, there are currently almost no solutions available.

OpenMind aims to help bridge this gap by developing open-source software for social robots, allowing developers worldwide to easily understand, learn, and contribute to this rapidly advancing field.

BlockBeats: You describe BrainPack as a small step towards the "iPhone moment" for humanoid robots. What specific benefits does BrainPack bring?

Jan: A major issue today is the significant differences between various humanoid robots. For software developers, simply learning the specifics of one robot can take a long time before you can write something useful.

BrainPack is designed to address this problem. You can think of it as a backpack with a computer that can connect to the robot. If your software runs on BrainPack, we abstract away the hardware differences between different robots. This means developers can focus on functionality without worrying about each robot's unique API or SDK.

BrainPack mounted on a robot

If software runs well on BrainPack, it is likely to run on multiple robots, whether they have two legs, four legs, wheels, are tall, or are short. BrainPack also comes with a standardized set of sensors, so developers don't have to deal with different sensor formats or data protocols. Additionally, BrainPack connects directly to our cloud infrastructure, making it easy to leverage remote computation.

BlockBeats: In addition to charging stations, what other infrastructure might OpenMind deploy in the future to showcase the capabilities of the OM1 and FABRIC protocols?

OpenMind: Another example is the work we have started with NEAR AI. The project uses NVIDIA H100 and H200 GPUs for secure computation.

Confidential Computing means that robots can run models anywhere on Earth while trusting that the data transmitted back and forth remains confidential. Therefore, a robot in San Francisco could have its "brain" hosted thousands of miles away. This also means that individuals with the proper hardware (H100 and H200) can provide a confidential computing node for AI and robotics technologies.

Trust, Privacy, and a New Economic Paradigm

The implementation of technology must ultimately return to society. In addition to technical challenges, the widespread adoption of robots faces a series of social structural issues such as trust, security, regulations, privacy, and public acceptance. OpenMind believes that open source is the cornerstone of building trust, allowing people to "see" how a robot's brain works. At the same time, through collaborations with projects like NEAR, leveraging confidential computing technology to protect data privacy will be key to gaining public trust. A future deeply involving robots will also inevitably give rise to entirely new job roles and economic organizational models.

BlockBeats: You mentioned on X that teleoperation could become a genuine professional category in the future. Could you elaborate on this idea for our readers?

Jan: From a very practical perspective, today's robots still need a lot of help. They sometimes get stuck, don't know the right answer, or make mistakes.

In these scenarios, having a human near the robot, either physically or through close monitoring, is extremely helpful. Another aspect is trust. Many people are still uncomfortable with robots making fully autonomous decisions, so having a "human in the loop" can help reassure them.

Furthermore, teleoperation creates new opportunities. You no longer need to be in a specific location to engage in certain types of work. Depending on your skills, you can help operate or supervise a robot thousands of miles away, even on different continents. This opens up a wide range of new economic and professional opportunities.

BlockBeats: What plans does OpenMind have to help regions or societies better accept humanoid robots?

Jan: Trust is fundamental. If people are afraid, adoption will be slow. That's why our core software is open source. We want people to see the inner workings of a robot's "brain" and understand how it functions.

Another looming question is ownership. Will robots be purchased by employers? Or by individuals for their households? Or shared by communities? A model similar to shared car ownership may emerge, where a group purchases a robot and receives returns from the tasks it performs.

We don't yet know which model will dominate, but there are many new ways emerging around organizing work and creating value with robots.

BlockBeats: Let's go back to the privacy issue. You mentioned the collaboration with NEAR, could you explain more clearly why the collaboration with NEAR is important?

Jan: The core technology here is confidential computing, which is directly built into the NVIDIA H100 and H200 GPUs. In principle, anyone who has these GPUs can connect them to the internet and provide secure computing services to others.

NEAR happens to be very fast, very capable, and deeply interested in building the infrastructure needed to make this kind of access practical and scalable. That's the reason for the collaboration. But at a fundamental level, confidential computing is a capability of every H100 and H200 GPU.

BlockBeats: How large is the OpenMind team now?

OpenMind: We currently have around twenty people, spread between San Francisco and Hong Kong.

BlockBeats: What do you expect to be the main product or revenue driver for OpenMind in the next three years?

OpenMind: Our fastest-growing revenue comes from enterprise AI, particularly through cloud-based model serving and robot-centered computing services. Customers pay directly for these services. Another significant area is revenue sharing with robot companies. We collaborate with them to jointly develop products sold in regions like Europe, the Middle East, and the U.S.

BlockBeats: Many are concerned about the scale of capital expenditure in today's AI field. Do you think OpenMind will need a large amount of funding to continue its development, or can it achieve self-sustainability relatively quickly?

OpenMind: That's a larger question, but we have a different view on the idea that it takes tens of billions of dollars to build useful models.

We've seen some strong examples, like DeepSeek, which had a development budget far smaller than models like ChatGPT. From our experience, many of the models we need can be built with much less capital than people typically assume.

Therefore, we cautiously optimistically believe that significant progress in the field of robotics or AI may not necessarily require spending billions or even trillions of dollars in computing resources.

BlockBeats: Finally, is there anything you would like to say to the developer or user community in China?

OpenMind: This is an extremely rare moment. A brand-new technology is emerging that allows machines to do things that only humans could do before. This will have profound impacts on education, healthcare, manufacturing, and many other areas of life.

For software developers, the opportunity is no longer just about building applications for phones but about building applications for thinking machines. It is still early days, but progress is very rapid. I strongly encourage developers to learn about robot operating systems, human-like robot platforms, and how to build applications for them to be well prepared for the upcoming significant advances.