Crypto x Robotics: An Alliance That Never Sleeps

With Morgan Stanley predicting that the number of robots resembling and acting like humans will likely reach nearly 1 billion by 2050 and Elon Musk stating that there will be more humanoids than humans by as soon as 2040, there is as much excitement as there is genuine fear about how the world will operate a few decades from now.

As production efficiency improves, costs decrease, and materials and technology advance, many thought leaders think we're on the verge of the robotics era. This, in turn, is expected to drive the market to $73 billion by 2029.

It's clear that most growth will occur in private equity. However, as the regulatory landscape surrounding crypto becomes clearer, new startups will increasingly turn to blockchain rails for efficiency and rapid capital formation through token sales.

When we look at the current state of the robotics sector in crypto, the total valuation is around $250 million. That’s peanuts.

The purpose of this article is to provide a clearer overview of the existing verticals and a few projects showing the most promise.

Why robotics needs crypto

Before we dive into the nitty-gritty of the value propositions of projects, it's essential to state the critical links between the two seemingly distant sectors.

1. Orchestration

• In a world where swarms of robots work collaboratively (e.g., delivery drones or factory bots), a collaboration layer is needed to enable these independent machines to work together beyond the confines of their own operating system silos.

2. The financial layer

• Since traditional payment systems are ill-suited for microtransactions at scale due to fees and latency, Crypto’s low-cost, instant blockchain transactions enable seamless M2M (Machine-to-Machine) economies, critical for a future where billions of robots operate without human oversight.

4. Decentralized ownership and leasing models

• High costs of robotics hardware (e.g., $20k–$30k for an Optimus unit) limit adoption.
  
  Crypto enables fractional ownership via NFTs or tokenization, allowing individuals to invest in or lease robot fleets.
  
  Consider marketplaces for this concept, turning robots into "Robot-as-a-Service" assets that democratize access for small businesses and consumers.

5. Data security and verifiability

• Robotics relies on vast datasets for AI training, but centralized data silos risk breaches or manipulation.
  
  Blockchain provides immutable, verifiable data logs, ensuring robot-generated data (e.g., sensor inputs) is secure and tamper-proof.
  
  This is vital for regulatory compliance and trust in applications like healthcare or eldercare robots.

6. Funding and community alignment

• Developing advanced robotics requires significant capital, but traditional VC models are slow and equity-heavy. Crypto launchpads and token sales provide rapid, community-driven funding, aligning incentives between builders and users.

With the outlined benefits, the picture becomes clearer as to what to look out for.

Onto the good stuff now.

OpenMind

After recently securing $20 million from top industry players like Pantera Capital, OpenMind is among the leaders in this space with its interoperability layer, FABRIC, a digital nervous system for the world's intelligent machines.

It provides the core primitives of identity, location, verification, and settlement, transforming a collection of individual robots into a cohesive, collaborative ecosystem.

Fabric enables multi-agent collaboration and real-time decision-making through four key functions:

• Verifiable machine identity: Each machine receives a unique, cryptographically secure identity (ERC-7777), enabling trustless verification, preventing spoofing, and ensuring communication integrity.
  
• Proof-of-Location: A decentralized, tamper-resistant GPS that lets machines prove their physical location, essential for coordination and shared mapping.
  
• Task verification: A standardized protocol for proving task completion using cryptographically signed sensor data or digital proofs, which also trigger automated payments.
  
• Stablecoin settlement: A built-in payment layer using stablecoins, enabling frictionless, real-time settlement without volatility or reliance on legacy finance.

While FABRIC delivers seamless connectivity for the future workforce, OM1 is an open-source, AI-native operating system that lets developers configure and deploy agents in both digital and physical environments.

This means you can create a single AI persona that can run in the cloud or on physical robot hardware, such as Quadrupeds, TurtleBot 4, and Humanoids.

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Notably, the project recently announced the release of the OpenMind App, dubbed the Uber for Robots. And yes, there is a points system involved.

Auki

Auki is another heavyweight that has been building in this space for 5+ years.

Focused on spatial computing, Auki addresses the challenges of manifesting the intelligence of AI in the real world through something they’ve dubbed the Posemesh.

Posemesh is built on a DePIN network that facilitates digital devices sharing spatial data and computing power securely and privately.

This allows robots to collectively understand the physical world and better interact with each other.

Instead of sharing your camera feed with a centralized entity, you can privately exchange spatial data with the domain you’re visiting or with other peers in your area.

On Auki, devices can contribute or request sensor data, processing power, storage, networking, and monitoring services.

Reputation and rewards are cryptographically secured using the Base L2 and form the economic foundation for the DePIN network to balance its provisioning and operation.

The ticker is $AUKI.

Side note: For a deeper understanding of how we got to this point and where we're headed, I suggest reading their thought-provoking 7-part introduction.

Codec

Continuing with the orchestration of robots, Codec is a Solana-based project that addresses the fundamental limitations of traditional automation across software and robotics in today’s distributed computing landscape.

Codec takes the AI automation workflow idea and applies it to robotics by providing a unified platform that operates across cloud, edge, desktop, and robotic hardware.

Curiously, their orchestration layer is also called Fabric, just like OpenMind’s, and the concept remains quite comparable (not to diminish the technical nuances).

Fabric is built on a three-layer architecture: Machine, System, and Intelligence.

With Fabric and the Operator Kit, which features a unified Python framework for creating, training, and deploying intelligent operators, and the VLA models that power them, Codec enables digital or physical agents to perform complex tasks that rely on visual or other sensory input.

To showcase the effectiveness of the CodecFlow tech stack, the team released RoboMove, a robot in a simulation capable of performing actions based on people’s inputs.

The ticker is $CODEC.

RoboStack

While projects like OpenMind, Auki, and Codec bring us closer to seeing robots in the real world, for most startups and organizations, buying expensive hardware and tooling will surely be a step too far in the early stages. And so, having a realistic environmental testing ground (more like a cloud in this instance) is perhaps precisely what’s needed to accelerate the grassroots development of robotics.

At the heart of RoboStack sits the RCP (Robot Context Protocol), a standardized communication layer that connects robots, AI agents, and human users in a unified ecosystem.

Inside the cloud, users can mimic and replicate various conditions, including extreme or hard-to-reach environments.

The platform lets you fully customize robotic settings, sensor configurations, and environmental factors.

After you define a workflow, it runs automatically in the cloud, with all generated data collected and stored for later use in AI training, analysis, or research.

The ticker is $ROBOT.

Let’s take a step back from all the orchestration and wizardry as it relates to machine collaboration. Another key problem for robots is how to translate the power of AI into the real world. Hint: understanding a concept does not mean it can be easily replicated.

Silencio

You see, while ChatGPT might know the instructions for producing a sound, truly understanding it is far more complex, as it depends on context like tone, pitch, rhythm, and environment.

The same sound can mean something entirely different in a song, a warning signal, or casual conversation, and capturing that nuance is difficult without vast quantities of examples from the real world.

Silencio tackles this challenge by leveraging its DePIN network to collect and process real-world audio data points, enabling robots to achieve advanced auditory perception and environmental awareness.

By providing diverse audio datasets, including ambient sounds, multilingual speech, and non-verbal cues like laughter or footsteps, Silencio trains AI models to enhance robots’ sound classification, speech recognition, and contextual understanding, overcoming the limitations of interpreting complex acoustic environments.

Their flagship mobile app has already collected over 40 billion data points from 1.1 million contributors across 180+ countries.

The ticker is $SLC.

Over the Reality

Where Silencio captures the audio, Over the Reality does the same for visuals, which is even more important for robots to operate IRL.

While it may seem straightforward to equip a robot with a LiDAR and a camera, these sensors alone are insufficient without 3D visual mapping to enable a comprehensive understanding of complex, dynamic environments. 3D visual mapping is critical because it integrates data from multiple sensors to create detailed, volumetric representations of the surroundings.

It captures depth, spatial relationships, and object orientations that allow robots to navigate cluttered spaces, like warehouses or disaster zones, precisely.

TL;DR: The more diverse data points there are, the easier it becomes for robots to function.

Just like Silencio, Over the Reality is built as a DePIN network, incentivizing a global community of mappers with OVR Tokens to scan high-traffic locations using standard smartphones and 360-degree cameras.

With over 150,000 locations mapped and 70 million+ images, OVRMaps covers more than 44 million square meters.

The ticker is $OVR.

Honorable mentions

SHOW ROBOTICS is developing embodied AI robots, integrating advanced artificial intelligence with robotics to create machines capable of learning and performing real-world tasks, focusing on entertainment and practical applications.

HomebrewRobotics is creating a marketplace that makes robot models accessible to everyone by offering pre-built software and other AI-powered programming tools.

Peaq was omitted since it needs no introduction.

Concluding thoughts

While definitely an exciting and fresh sector for many, sifting through the dirt to find gold still remains difficult.

If there's one key takeaway from this article, it’s this: instead of betting on new and upcoming projects that are likely just cash grabs, your goal is simple - find the genuine, established players who have been building before the hype cycle started and bet on them.

Since the combined market cap of all robotics projects is still under $300 million, this task remains quite straightforward.

And yes, some projects mentioned here are newer, but so are the dozens I left out through the diligent process, dubbed the spidey sense.

Although I probably missed one or two gems because of time constraints in releasing this piece quickly, the projects mentioned above should provide some clarity on what to look for at the beginning of yet another rabbit hole we’ve found ourselves facing.

Stay curious!