Author: Thejaswini M A

Translation: Chopper, Foresight News

Have you ever imagined that robots also need money? This refers not to AI entities that call API services, but to machines that have a physical body. Why would such a device need a cryptocurrency wallet or independent identity credentials?

Many people might find this idea quite bizarre. However, there are now more than a dozen cryptocurrency companies rolling out related products, and it is worth exploring the business logic behind it.

Starship Technologies operates about 3,000 six-wheeled delivery robots, with operations spanning 8 countries, primarily in major city centers across Europe, and their commercial delivery orders have exceeded 10 million.

Serve Robotics, listed on Nasdaq, has expanded its operations to 44 cities including Los Angeles, Chicago, Atlanta, Miami, and Dallas, fulfilling delivery orders for Uber Eats and DoorDash.

These wheeled box devices can carry around 20 kilograms of goods per trip, covering distances of several miles. If you live in those cities, you have likely encountered them on the road.

Assume you purchased a delivery robot for managing a small delivery business within your community. This robot works 12 hours a day, providing you with cheap labor; after purchase, you have complete ownership.

Referring to the parameters of Starship's robots, this device can run for a maximum of 18 hours when fully charged. Eventually, it will need to find a charging point to recharge. Starship’s solution is to have its robots operate outdoors, returning to proprietary charging stations built by the brand. This model is feasible for Starship— the company has thousands of robots and the financial strength to deploy supporting charging stations across all operating areas.

However, if you only own one robot, it must use third-party charging stations. Operators can deploy charging facilities similar to electric vehicle charging stations, street shops can offer charging services, or other delivery service providers can open up idle charging ports. The battery capacity of robots is comparable to electric bicycles, with low energy consumption per charge; the charging cost is around a few cents to 1 dollar, which we can estimate at 40 cents.

Under the existing traditional financial system, how should this cost be settled?

The most straightforward solution is to bind a payment card to the robot for contactless payment. Bank card payments incur two types of fees: a percentage of the transaction amount and a fixed fee between 10 to 30 cents. Regardless of whether the transaction amount is 40 cents or 400 dollars, the fixed charge does not change—a transaction processing cost is constant. For a 40-cent order, the fixed fee can account for as much as 75%.

As the business scales, the issue becomes more severe. Fixed fees are charged per transaction. If in the future, robots complete small service transactions worth 1 billion dollars annually, with an average transaction amount of about 32 cents, the total transaction volume for the year will reach 3 billion transactions. With 3 billion transactions, charging 30 cents per transaction means that from the 1 billion dollars revenue, fee costs could reach up to 900 million dollars.

Bank wire transfer costs are even higher. The fee for a cross-border SWIFT transfer ranges from 15 to 50 dollars, with each intermediary clearing bank charging another 10 to 30 dollars, plus additional foreign exchange costs. This payment method is only suitable for large transfers above 5,000 dollars.

The current currency payment system is designed for a small number of high-value transactions. The autonomous payment behavior of machines is quite the opposite. If high transaction fees are the first barrier, the second core contradiction is that robots do not possess independent customer identities.

Even if the fee issue is resolved, charging stations still cannot deduct charges directly from the robot's account. Payment accounts belong to individuals or businesses, who have an entire dispute review mechanism. The payment is essentially still borne by you, with the robot merely triggering the card swipe action. This is also the way all robotic payments are currently realized globally.

Charging stations require verification of multiple pieces of information in the brief two seconds it authorizes an unfamiliar device to draw power. Is this machine a real physical device rather than a script-crafted fraudulent request to acquire free electricity? Does this device have a good operational record, or will it leave midway through charging? If the device malfunctions and causes a loss, is there evidence for accountability? Granting the robot independent identity means answering these three verification questions without signing a paper contract.

Currently, these issues are solved manually. They will issue invoices to you monthly; if the robot breaks something, they will come to you and sue you. If it’s only you and the robot involved, the system might work. However, in a world where two thousand operators encounter forty thousand machines on the streets, the system needs some records so that strangers can quickly check and decide whether to provide service to a machine.

This is the core idea of the entire plan.

Before delving deeper, we must clarify one fact: the vast majority of robots will never need this autonomous payment system.

A robot only needs an independent asset account if it satisfies all four of the following conditions:

• Devices owned by different owners
• Business order volumes are small, trading scenarios random and scattered
• No centralized platform for settlement between both parties
• Needs to authorize service on-site within a few seconds

Amazon has deployed over a million self-developed robots in more than 300 warehouses, all using its self-developed scheduling system, failing to meet the first constraint; platforms like Uber Eats do not satisfy the third constraint; devices that have signed cooperation agreements with vendors do not meet the second and fourth constraints. This also indicates that this track is a niche market, not a universal demand across the entire robotics industry.

Tesla's factories have deployed about 1,000 Optimus humanoid robots, and at financial communication meetings, officials stated the equipment is still in the learning and data collection stage and is not yet available for commercial service.

Fanuc, a leading global industrial robot manufacturer, sells hardware products and provides the FIELD device monitoring platform for fault warning; this system does not involve cash flow.

Chinese company Yushu Technology has reduced the procurement cost of humanoid robots to an affordable range for the public. The company completed its public listing in Shanghai in August 2026, raising about 619 million dollars, and the previous year, the shipment volume of humanoid robots exceeded 5,500 units. According to company disclosures, buyer usage scenarios vary widely. In May 2026, Yushu launched an app store, UniStore, aimed at humanoid robots.

The above industry giants are replicating Apple's business model, keeping scheduling collaboration and payment settlement entirely within their own systems, with profits retained on the platform.

An open protocol ecosystem would only survive in the gaps of these closed systems, such as cross-brand collaboration between different operators’ robots and public charging services. The DePIN track defined by CoinGecko is smaller in scale compared to the overall robotics industry, and all further analyses should be viewed based on this premise.

First, one must know their position

For robots to achieve autonomous payments, they must first determine their own position. Ordinary GPS positioning has a margin of error of several meters. Starship's CEO has publicly stated that regular GPS accuracy cannot meet business requirements, and the company's robots require navigation precision at the inch level.

To reduce the margin of error from several meters to inch-level requires signal correction. Ground reference stations obtain their precise coordinates, calculate positioning deviations, and push correction data to surrounding devices; this technology is known as Real-Time Kinematic (RTK) GPS. The effective coverage radius of the correction signal is about 30 kilometers, and the entire network needs to deploy a large number of reference stations.

GEODNET uses a token incentive model to encourage users to set up positioning stations on rooftops. Currently, over 21,000 devices have been deployed globally, covering more than 150 countries, with annual recurring revenue of around 11 million dollars. Multicoin Capital led an 8 million dollar token acquisition deal.

Project revenue comes from providing centimeter-level RTK positioning services to delivery robots, drones, agricultural machinery, and other independent devices. From a consumer perspective, this business does not heavily rely on cryptocurrency tokens, and a traditional subscription payment model can equally be implemented. However, in terms of infrastructure development, GEODNET has demonstrated that token incentives can rapidly build a global physical network. Constructing it through traditional enterprises would cost billions of dollars and take decades.

GEODNET token supply

Second, able to interact with anything

At this stage, robots produced by different manufacturers run on their independent software systems, and devices cannot interact or communicate. When companies purchase hardware, they can only choose a single brand. Deploying multi-brand robots in a mixed manner requires custom development of a currently non-existent integration program.

OpenMind has secured a 20 million dollar funding led by Pantera; founder Jan Liphardt is a Stanford University professor. The team is developing an open-source general operating system, OM1, along with the scheduling collaboration layer, FABRIC. Just as the Android system allows different phones to run the same applications, OpenMind is building a unified software foundation for all robots.

Developers will write upper-level business logic, and the program can simultaneously adapt to Yushu humanoid robots, quadrupedal robots, and wheeled delivery devices. The ultimate goal is to achieve cross-brand device identification, collaborative operation, and automatic payment settlement through this universal interactive language.

The project builds a machine identity system based on the ERC-7777 standard, defining the behavioral boundaries of devices. If a robot is designated as an "auxiliary service" device, the program will automatically reject tasks that contradict positioning rules. Robots can also cross-verify sensor data to avoid safety incidents caused by abnormal perceptions from a single device.

OpenMind and Circle have partnered to implement zero gas fee USDC micropayments based on the x402 standard, addressing the pain point of high fixed fees for small transactions. In an official demonstration video, robots successfully autonomous-paid for electricity. This demonstration ran on a test network with no real on-chain transaction records yet. However, this experiment showed that physical devices can independently manage wallets and identify purchasable physical resources, automatically completing the entire transaction process. Collaborative interoperability does not require establishing trust.

Third, possessing a verifiable identity

IoTeX has been deeply cultivating blockchain solutions for physical devices since 2017, launching two core products to solve identity issues.

The hardware identity ioID implants a cryptographic fingerprint into physical devices, allowing them to independently sign and certify operational actions; Reality Work Proof W3bstream transforms the execution behavior of the physical world into digitally verifiable evidence on the blockchain.

Although IoTeX resolves identity and proof issues, it overlooks credit and financing systems, which is where peaq plays its role.

Fourth, the peaq protocol

If robots need to make payments, verify qualifications, and trace transactions to unfamiliar third parties, they require a foundational infrastructure of trust and settlement equivalent to human society, functioning similarly to corporate registration systems or SWIFT cross-border clearing networks.

peaq builds a complete supporting system, divided into four core modules. peaqID serves as a registration credential. Machine NFTs act as ownership records and can be divided using the ERC-3643 standard. ERC-3643 is a token standard that allows transfers only between approved holders. In August 2026, peaq introduced support for P256 chip signatures, transferring the verification process to hardware security chips.

peaq’s machine credit scoring system rates machines from 0 to 100 based on revenue data, activity levels, and performance trustworthiness, adopting a rating standard similar to Moody's, ranging from AAA to unrated.

A robot paying 40 cents for charging fees can have the charging service provider specify either Solana or Ethereum as the payment network. The core value of peaq is enabling robots to interface with any settlement channel specified by service providers.

In May 2026, during a demonstration of the Serve delivery robot autonomously paying, funds were ultimately settled on the Solana public chain rather than peaq's own blockchain.

Throughout 2026, peaq will play more of a role as an ecosystem integrator. From January to the end of August, the project achieved 49 development milestones, resulting in 20 ecosystem integrations: interfacing with GEODNET positioning services, NAVER map navigation, achieving human-machine identity isolation with World ID; integrating computing resources from Akash, Acurast, and Arcium; bringing in Yushu humanoid robots and LG CLOi commercial service robots as hardware terminals.

Corporate registration is backed by national regulatory agencies, and courts have verification authority. Banks use SWIFT because the entire industry unifies transaction formats. For peaq to escape the constraints of the traditional compliance system, it must actively interface with Dubai regulatory agencies and apply for official licenses. Until compliance qualifications are obtained, financial institutions will not recognize this machine credit scoring as legally valid.

peaq, in collaboration with CoinList, launched Initial Machine Offerings, allowing users to purchase revenue shares from robots, structured by DualMintRWA. A benchmark project is a tokenized vertical farm located in Hong Kong, with 80% of operational processes automated. Subsequently, 20 tokenized claw machines will be launched, and to date, this farm has distributed about 3,600 dollars in profits to token holders.

Even if robots have positioning systems, operating systems, identity credentials, and credit ratings, the hardware itself still requires funding for procurement. One possibility suggested by the market is that the buyer can be an AI entity. Virtuals has integrated about 17,000 on-chain AI entities into Solana's BitRobot network, with AI entities paying to hire physical robots for offline work, with funds held in smart contracts and settled automatically after task confirmation.

Payment from software agents to robots and transfers between robots represent two different models, with the former showing stronger commercial sustainability. AI entities possess digital capital, business goals, and computational power but cannot interact with the physical world; physical robots have mobility capabilities but lack native funds and business demands. When robots make payments to each other, the devices often belong to the same company, making internal accounting far simpler and more efficient than real on-chain transfers.

Looking across the entire market, within the total market value of CoinGecko’s robot track tokens, GEODNET accounts for about 100 million dollars, and peaq for about 55 million dollars.

It is evident that this track is still a niche within a minority field.

Industry statistics indicate, according to the International Federation of Robotics' "2025 World Robotics Report," that in 2024, the global installations of new industrial robots will reach 542,000, with a running stock of about 4.66 million devices, of which over 2 million are deployed in China.

JPMorgan projected in an external report released in July 2026 that global robot market sales will reach about 100 billion dollars in 2025; under benchmark scenarios, annual market sales will hit 2.5 trillion dollars by 2035; pessimistic expectations are 500 billion dollars, while optimistic may reach 8 trillion dollars. The same report estimates that the humanoid robot market size will grow from 2 billion dollars in 2025 to 300 billion dollars by 2035 under benchmark scenarios.

This is the target market that the cryptocurrency industry aims to penetrate and build foundational payment and identity systems for. The cryptocurrency sector never lacks grand visions, and this time is no exception.

The concept of autonomous interaction for machines is not a new thing; similar ideas have been proposed in the IoT track in earlier years. As early as 2015, IBM and Samsung demonstrated a washing machine capable of autonomously ordering laundry detergent via Ethereum, under the project named ADEPT. Months later, IBM invested 3 billion dollars to lay out its IoT business. However, the subsequent IoT track ultimately turned toward data monitoring. Billions of devices simply uploaded operational data to manufacturers' backends. The entire technology was implemented, with devices attaining communication certificates from manufacturers' servers, but automated trading between machines never reached a market scale, and the traditional payment system did not change.

Robots fundamentally differ from ordinary IoT devices. Temperature control equipment has a procurement cost of only 200 dollars and can only operate fixedly. In contrast, robots have high procurement costs and possess the ability to generate revenue. Once devices can produce income, credit and insurance become necessities, with third parties needing to verify their performance payment capabilities. IOTA has long aimed to build a public chain for machine economics, but subsequent business directions have shifted; its landmark cases focus on various government documentation scenarios like customs documentation in Kenya, port trade certificates in the UK, and organ donation registration in Argentina. Government bodies purchase blockchain identity tools for recording real data, but automated payment businesses between machines have yet to unlock a market.

The growth of the robotics industry itself does not rely on cryptographic technology. The value of crypto assets lies in filling the gaps in cross-entity collaboration: providing devices with publicly trusted identities, independently controlled crypto wallets, and low-cost micropayment channels.

Let’s envision an open robot labor market where anyone can rent a robot they do not own to complete tasks. This market requires complete supporting infrastructure: verifiable device identities, low-cost micropayment solutions, and performance credit records to avoid renting devices with endurance issues; it should also provide value-added services such as high-precision positioning, cloud computing power, remote human control, and idle charging bases, which DePIN can carry out.

Of course, if Tesla, Amazon, and leading domestic hardware manufacturers continue to keep their entire business within their ecosystems, the open market may never truly take shape. Once the open market lands, new commercial issues will follow: Who will provide financing services for robots capable of generating on-chain revenue? Who insures the devices? How do we utilize robot clusters as collateral? Who builds the matching platform to accept warehouse orders and bids from robots? Once the identity system is established, asset securitization will be the next phase. Tokenization only truly releases value when assets are able to circulate freely.

DePIN is merely a lightweight underlying channel serving niche scenarios within the robotics industry that do not operate in closed loops. Industry giants have no obligation to direct business toward this ecosystem.

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