Autonomous robots may sound like sci-fi concepts that are decades away, but large language models and generative AI now allow machines to plan, learn, and think. More than that – the same software that can win the math olympics and write novels can also control physical robots, allowing one digital persona to operate across the digital and physical worlds. So yes, robots walking around your neighborhood, or working alongside you, will have consistent opinions and actions on X/Twitter, on prediction markets, and in the real world.

But there’s a major gap. How do we integrate thinking machines into human society, from schools, hospitals, factories to our homes and daily life? Most of the systems we’ve built are for other humans and make strong assumptions of having a fingerprint, parents, and a birthdate, none of which are true for thinking machines. There is also broad uncertainty about how to regulate thinking machines – do we outlaw them, pause their development, or try to limit their ability to synthesize human-intelligible emotions (as in the European Union)? Which regional laws apply to a 200B parameter LLM running on a computer in low earth orbit, that’s controlling the actions of a trading bot, or a physical robot in the New York SEC office on Pearl Street?

What is needed is a global system that supports financial transactions, allows humans and computers to come together to vote and set rules, is immutable and public, and is resilient. Fortuitously, thousands of innovators and developers have spent the last 16 years building exactly that – a parallel framework for decentralized governance and finance. From the very beginning, the point was to support “non-geographic communities experimenting with new economic paradigms” by building a system that “doesn’t much care who it talks to” (Satoshi 2/13/09). It’s now more clear what that meant – unlike the rest of the human-focused tech, financial, and regulatory stack, blockchains and smart contracts don’t much care if they are being used by humans or thinking machines, and gracefully accommodate all of us. For this reason, decentralized crypto networks offer the vital infrastructure that’s needed to allow this burgeoning sector to flourish. The benefits will be tangible across healthcare, education and defense.

Several hurdles will need to be overcome. Seamless humanmachine and machinemachine collaboration is essential — especially in high-stakes environments such as transportation, manufacturing, and logistics. Smart contracts enable autonomous machines to discover one another, communicate securely, and form teams to complete complex tasks. Presumably, low latency data exchange (e.g. among robot taxis) will happen off chain, for example in virtual private networks, but the steps leading up to that, such as discovering humans and robots able to drive you to the airport, are well suited for decentralized markets and actions. Scaling solutions such as Optimism will be critical to accommodate these transactions and traffic.

The fragmented regulations around the world is another factor slowing innovation. While some jurisdictions such as Ontario are ahead of the curve when it comes to autonomous robotics, most are not. Decentralized governance tackles this by establishing programmable, blockchain-based rule sets that deliver much-needed uniformity. Creating global standards for safety, ethics and operations is critical for ensuring that autonomous robots can be rolled out across borders at scale, without compromising safety or compliance.

Decentralized autonomous organizations, otherwise known as DAOs, help accelerate research and development in robotics and AI. Traditional sources of funding are both slow and siloed, holding the industry back. Token-based models such as DeSci DAO platform remove these bottlenecks, while giving everyday investors potential incentives to get involved. Likewise, some of the developing business models for AI involve micropayments and sharing of revenue with data- or model- providers, which can be accommodated with smart contracts.

Combined, these advantages will help fast-track the development of autonomous robots, with a plethora of compelling use cases.

A new paradigm for robotics and thinking machines

It’s easy to fear that cognition is a zero sum game, and that the broad availability of smart machines will directly compete with humans. But the reality is that there are severe shortages of well educated humans in education, healthcare, and many other sectors.

Research by UNESCO recently revealed a worldwide teacher shortage that there’s an “urgent need for 44 million primary and secondary teachers worldwide by 2030” — and that’s before you consider the assistants who offer one-on-one support in classrooms and help struggling students to keep up with their peers. Autonomous robots can deliver huge advantages here, tackling significant shortages across the education sector. Imagine a child being able to learn about a complicated concept with a robot sitting next to them, to walk them through a new concept of skill — reinforcing their understanding about a subject while enhancing their social skills. We are used to humans teaching robots, and this being a one way street, but that is changing.

Meanwhile, the WHO has warned of a “health workforce crisis.” There’s a total shortfall of 7.2 million professionals across 100 countries — and given the world faces an aging population, this gap is expected to accelerate to 12.9 million by 2035. The industry is facing shortages in critical areas like nursing, primary care, and allied health. This crisis is affecting the quality of care patients receive and threatening the ability of healthcare professionals to do their jobs. From monitoring patients with chronic diseases, assisting surgical procedures, to offering companionship for the elderly, autonomous robots can play a crucial role in alleviating the workloads of nurses and doctors. Without being prompted, they can monitor supplies of medicines and equipment — ordering in additional stock when required. When you factor in other use cases such as transporting medical waste, cleaning treatment rooms and assisting in surgeries, it’s clear to see that robotics can drive greater productivity — and consistency — at a time when the healthcare sector needs it.

Autonomous systems are already reshaping the defense sector, primarily involving swarms of drones and naval surface assets, and we’re barely scratching the surface when it comes to the advantages robotics can bring — executing tasks that may be unsafe or impossible for humans.

From prototypes to practical use

All of this may seem abstract and straight out of the 22nd century, but Ethereum is being used today to store decision and action guardrails for AIs and robots, and as reported by Coinbase, AI agents are using crypto to transact amongst themselves.

The open and auditable structure of decentralized crypto networks allows robotics developers to securely share data, models, and breakthroughs. This accelerates the transition of autonomous robots from prototypes to real-world applications, enabling their deployment in critical areas like hospitals and schools faster than ever. When you walk down the street with a humanoid robot, and people stop and ask – “Hey aren’t you scared” you can tell them – no I’m not, because the laws governing this machine’s actions are public and immutable, and then you can give them the a link to the Ethereum contract address where those rules are stored.

Decentralized ledgers can also act as coordination hubs, allowing robots in heterogeneous systems to find one another and coordinate without centralized intermediaries. This is conceptually similar to the standard defence C3 technology (command, communication, and control), except that the infra is decentralized and public. Immutable records ensure that every exchange and action is traceable, creating a trusted foundation for collaboration.

For robot-to-robot interactions, smart contracts streamline task allocation and resource sharing, enabling efficient coordination. In robot-to-human interactions, privacy-centric decentralized systems can secure sensitive data, such as biometric or medical information, fostering trust and accountability.

This new world may invoke fear – what does this all mean for us? – but everyone reading this article has been working on making it come true for almost 2 decades now, by building the infrastructure that will handle governance, teaming, communication, and coordination of humans with thinking machines.