Robots entering our everyday lives and industries is the next logical step of the disruption we are living today, mainly through specialized industrial-grade robots rather than the humanoids we see everywhere. Web3 can bring valuable features to this future (robot identity, access to track records, ownership, coordination), allowing us to embrace this change in a better way than a future owned by a few centralized companies. Here is how I interpret the market and where I plan to focus to get exposure to robotics through Web3.

Why I’m looking at robotics

The world, and more precisely the digital world, has been deeply transformed over the last few years by advances in LLMs. Although the transformation is lagging behind the frontier, as it takes time for people and industries to absorb new technologies, we can already see major changes happening in many sectors: software development, finance, design etc. The impact will be enormous, but limited to the digital world.

The next natural step is to automate even further, by automating our physical world with use cases we couldn’t have imagined a few years ago. And you know what’s wonderful? The first innovation serves the second one, allowing us to go further, faster and better. Innovation is accelerating. Large amounts of capital are already flowing into robotics projects and their mechanical needs, and more and more engineers are diving into the subject.

This next step is natural and logical, yes. But it is also very much needed. Labor shortages, aging populations, the danger and health impact of some processes, or just simply the will to consume locally produced goods all make robotics the right answer. Robots will solve problems software never could. That’s why we’re shifting from an era of bits to an era of atoms. And its potential impact is even larger than the bits era (even though the digital world was always a way to improve the physical one): the physical world representing 75% of the world’s economic activity.1

Such impacts also raise a fair number of challenges: how can we identify robots? Check what they produced? Access them freely to perform tasks, regardless of their OS or brand? How can my AI agents delegate subtasks and pay for them? How will companies finance such capex? I think Web3 answers some of these challenges well, thanks to its native features (transparency, decentralization) and to building blocks now mature: stablecoin rails, onchain identity, fast and low-cost transactions etc.

Sector baseline

We recently saw US- and China-based companies raise billions of dollars from VCs and public capital markets. There is definitely an insatiable appetite here. But this appetite seems mainly focused on humanoid robots. Humanoids are good for marketing as they speak to everyone especially if you’re a sci-fi nerd. They also have the theoretical advantage of fitting into existing processes and infrastructures to automate human tasks. But in reality, the human form is suboptimal for a lot of tasks. And, while domestic robots are possible and an interesting market, the real revenue generators are industrial robots. The robotics revolution will be driven by aging and decreasing demography. But in households, adoption will be slower due to cost, safety and regulation.

Sector Economic size What the robot replaces Capturable value
Manufacturing $16.83 trillion in value added in 2024, or 15% of global GDP2 Paid labor, on repetitive and structured tasks High: measurable and billable productivity gains
Construction About $10 trillion in annual spending; value added could rise by $1.6 trillion a year3 Paid labor, in a sector with stagnant productivity Very high: a large productivity gap to close
Households No direct market value: domestic work is mostly unpaid Often unpaid domestic work (housework, assistance, companionship) Lower: a consumer product, low prices, margins under pressure

That being said, every robot relies on its mechanical layer: actuators, sensors, energy etc. There have been many advances in these fields, enabling better control and feedback while driving prices down dramatically. Foundation models are also evolving fast, as the engine powering robots’ movements and their understanding of the outside world. I’m confident both will improve drastically.

We are already seeing capability demonstrations in real-world conditions, but we haven’t yet reached the stage where they can handle everything autonomously. A huge bottleneck to reach this is training data. For foundation models to understand the world, deduce possible actions and anticipate their direct and indirect consequences, a lot of data is needed. Unlike LLMs, which have access to the whole internet, the physical world has no such data source (videos exist and are usable, but incomplete). Moreover, the data is way different from one robot to another, as mechanical parts differ and age. Some layers of physical interaction remain generalizable though.

In today’s market, industrial automation and robotics are dominated by a few well-established actors: ABB, Fanuc, Yaskawa etc. Each new installation requires engineering teams to design, commission and test the system. Robots from different brands can communicate through an open standard protocol (OPC UA) but development remains siloed, as each brand has its own programming language.

On the crypto side, projects such as peaq, CodecFlow and Fabric (ROBO) are already working on the subject, some of them for nearly a decade. Recent advances in hardware and foundation models, along with the many new initiatives emerging (AMI Labs, etc.), show growing interest in the field and increase the likelihood of major challenges being solved in the coming years. Until now, there were very few use cases, and token prices reflected it. As things start to change, this could be a great time to position ourselves to capture as much upside as possible.

Why do we need crypto? What does it bring?

Let’s be honest: Web3 doesn’t push the robotic market further in use cases or innovation. Nothing is technologically unlocked by it. What it brings is a neutral infrastructure around robots, enabled by three features of blockchain technology: verifiable records, autonomous payments and programmable ownership.

Verifiable records: a database nobody controls

The robotic market involves many parties that don’t necessarily share the same interests, and therefore don’t necessarily trust each other: a manufacturer, an integrator, a maintainer, an operator, a client and also investors, insurers etc. Today, a robot’s setup, production and quality data sit in proprietary systems, often accessible to the maintainer and operator but rarely accessible in detail, even to the robot’s owner. By storing onchain the hash of the robot’s data (work, parameters, quality, maintenance, uptime etc.) signed directly by the machine, every party has openly access to the same level of information at any time without needing to trust each other. This enables use cases such as:

  • Responsibility: establishing which party is responsible for an outcome, based on a record no single party can rewrite.
  • Robot life history: work done, uptime, parts replaced, profitability. This is valuable when reselling a machine, valuing a tokenized one or choosing a robot to perform a task.
  • Quality proof: each action’s parameters (torque, dimensions, cycle times) can be checked for conformity. This is especially useful in highly regulated industries.
  • Measurable ROI: with proof of work produced, a robot’s financial contribution can be measured precisely. This opens up financing through tokenization, and new business models where robot providers are paid for work produced rather than for a one-off sale.

Autonomous payments: wallets for agents and machines

Like AI agents, a robot cannot open a bank account (and even if it could in the future, a bank account is inappropriate for autonomous micropayments). That’s what stablecoin rails solve by giving them a wallet, enabling:

  • Machine-to-machine payments: an AI agent delegates a physical subtask to a robot it discovered and selected onchain, and pays it autonomously to perform the task. For example, one company’s agent can delegate the production of a part to a supplier, autonomously, without human intervention for engineering, quotation and production. Supplier selection can be done without centralized marketplace, based on onchain identity and verified track-record.
  • Micropayments: a user can buy a service (per task or per minute) or a product and the robot receives and handles the payment directly, automating invoicing and settlement.
  • Programmable revenue sharing: owners of a tokenized robot can automatically receive their share of the revenue.

Programmable ownership: machines as investable assets

With verifiable records, machines can be tokenized and owned by one or several people. This unlocks use cases such as:

  • Financing: fractional ownership allows small companies, for example, to finance their machines by selling shares of them, managing their risk and accessing new capital (small investors, partial financing etc.).
  • Easy transfer: buying or selling such a robot becomes as easy as transferring a token, from anywhere in the world and at any time.

Those use cases apply to domestic robots too.

Market mapping

Here is how I slice the market into core layers, each providing features robots need, from physical foundations at the bottom to economic use cases at the top.

Use cases (Robots-as-a-Service)

Web2 actors

Formic, Locus Robotics, Brain Corp

Web3 projects

Pilots only

What Web3 brings

RaaS model already exists; Web3 adds proof of work, autonomous payments and split ownership

Ownership and financing

Web2 actors

Leasing, venture capital, debt

Web3 projects

peaq, XMAQUINA

What Web3 brings

Fractional ownership, automatic revenue distribution and access to capital 24/7 from anywhere in the world.

Payments

Web2 actors

Stripe, Visa etc.

Web3 projects

x402, Circle, Tether

What Web3 brings

Payment rails designed for and usable by machines.

Identity and verifiable records

Web2 actors

Proprietary platforms of manufacturers, digital twins

Web3 projects

peaq, Fabric Protocol

What Web3 brings

Openly accessible onchain robot identity and tamper-proof work history.

OS and foundation models

Web2 actors

Nvidia (GR00T, Isaac, Cosmos), Physical Intelligence (π models), Google DeepMind (Gemini Robotics), Skild AI, AMI Labs

Web3 projects

OpenMind, CodecFlow, Bittensor Subnet 80

What Web3 brings

Integrated identity and payment features, incentivized improvement of open models

Data

Web2 actors

In-house collection (Tesla, Figure etc.), teleoperation farms or simulated data (Nvidia Isaac/Cosmos, Hub.xyz)

Web3 projects

PrismaX, BitRobot, Axis Robotics, NRN Agents

What Web3 brings

Tokens can bootstrap a decentralized infrastructure producing high-quality data when collection requires capex-heavy hardware. When data is simply paid human time, stablecoin payments are enough.

Hardware

Web2 actors

Components: Harmonic Drive, Nabtesco, Sony, Hesai

Robots: ABB, Fanuc, Yaskawa, KUKA, Unitree, Figure, Boston Dynamics, Tesla Optimus

Web3 projects

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What Web3 brings

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As shown above, Web3 adds value on specific layers only: data, identity, verifiable records, payments and ownership. That’s where I will focus my investments. This mapping is subject to changes as the market evolves.

Risks and invalidation events

Risks

  • Of course, everything stated above depends on how far robots penetrate the market. The less siloed they are in privately owned factories, the more accessible, discoverable and hireable they become, the stronger the need for those building blocks will be.
  • Moreover, as with every Web3 project, regulation is still a gray area, especially for owning underlying assets through tokens. A change in how tokens are classified could reduce liquidity and R/R if investments.
  • Regarding onchain verifiability: what is stored on the blockchain isn’t the data itself but its hash. The hash lets you check the data’s integrity, once you have the data. To do so, you have to obtain it from the right parties, which can be painful since it is stored in proprietary systems and is often sensitive. This currently limits the value of onchain verifiability and it is a challenge that must be solved for the feature to become indispensable. It opens the door for privacy-preserving onchain storage solutions.
  • A Web2 company with good GTM strategy and UX could provide the features described above in a centralized way. If users are happy with that, it invalidates the whole thesis.

Invalidation events

  • If robot deployments stay limited to pilots while funding to the sector dries up, I will de-risk my investments in the sector.
  • If revenue-sharing robot tokens are classified as securities, I will limit my ownership-layer exposure to regulated platforms.
  • If major robot manufacturers launch their own identity and payment layers, I will exit the Web3 projects addressing those problems.

Conclusion

Web3 doesn’t make robots better. What it can bring is a neutral infrastructure around them, built on three features: verifiable records, autonomous payments and programmable ownership.

Mapping the market shows that this value is concentrated on a few layers: training data (requiring capex-heavy collection infrastructure), robot identity and work history, machine payments, ownership and financing. That’s where I’ll focus. Hardware, foundation models and most decentralized data collection projects are out of scope for my Web3 investments: I see no decisive edge for crypto there. My horizon is long-term: the need for this infrastructure will grow with robot deployment, not with crypto cycles.

Next step: deep dives on the projects behind each layer.

My brain is open, especially if you disagree with me.

NFA.

Sources

  1. Eclipse, Eclipse Carbon Optimization Report (2023) — physical industries account for 75% of global economic activity.
  2. World Bank, compiled by Cargoson, How Big is the Manufacturing Industry? — $16.83 trillion of manufacturing value added in 2024, 15% of global GDP.
  3. McKinsey Global Institute, Reinventing Construction (executive summary, 2017) — about $10 trillion in annual construction spending, and $1.6 trillion of additional value added.

Carlos