Author: Jan Liphardt

Compiled by: Deep Tide TechFlow

The original author is Jan Liphardt, an associate professor in the Department of Bioengineering at Stanford University, who received his Ph.D. from the University of Cambridge.

He is also the founder of Openmind. Openmind focuses on developing multi-agent open-source software aimed at making robots smarter and ensuring that humans can inspect and understand the decision-making processes of robots. He is also one of the main authors of the ERC-7777 standard, a protocol jointly developed by Openmind and Nethermind.io, which aims to regulate the interaction methods within a society of human-robot collaboration.

Main Text

Autonomous intelligent robots were once seen as an unattainable sci-fi concept, but now, large language models (LLMs) and Generative AI have given machines the ability to plan, learn, and think. Moreover, these software programs that can win math competitions and write novels can also control physical robots, allowing a digital character to seamlessly switch between the digital and physical worlds. Thus, in the future, robots walking in your community or working alongside you will be able to express consistent views and behaviors in X/Twitter, prediction markets, and real life.

However, we face an important challenge: how to integrate these intelligent machines into human society, from schools, hospitals, and factories to homes and daily life? Most existing systems are designed for humans, defaulting to require fingerprints, parental consent, and birth dates, conditions that clearly do not apply to intelligent machines. Moreover, there is still significant controversy over how to regulate these machines—should we ban their development, suspend their research, or limit them to generating human-understandable emotions (as proposed by the EU)? It gets more complex if a large language model with 200 billion parameters runs on a computer in low Earth orbit, controlling a trading robot or a physical robot in an SEC office—what laws should its behavior follow?

We urgently need a global system that can support financial transactions, allowing humans and intelligent machines to jointly vote on rules, while possessing immutability, openness, and strong resilience. Fortunately, over the past 16 years, thousands of developers and innovators have built such a system—a parallel framework for decentralized governance and finance. From the beginning, the goal of blockchain has been to support 'non-geographical communities exploring new economic models' by constructing a system 'that can interact with any user' (Satoshi, February 13, 2009). Today, this vision has become clearer—unlike other human-centered technologies, financial, and regulatory systems, blockchain and smart contracts can equally support both humans and intelligent machines. Thus, decentralized cryptographic networks provide crucial infrastructure for this emerging field, whose benefits will be fully realized in healthcare, education, and defense.

Of course, there are still many obstacles to overcome in this process. Achieving seamless integration of human-robot collaboration and inter-machine collaboration is crucial, particularly in high-risk areas such as transportation, manufacturing, and logistics. Smart contracts can help autonomous machines discover each other, communicate securely, and form teams to complete complex tasks. Low-latency data exchange (such as communication between robot taxis) may occur off-chain, for example, through virtual private networks, but prior steps, such as discovering robots or humans that can take you to the airport, are well-suited to be accomplished through decentralized markets and mechanisms. Scaling solutions like Optimism will be key to supporting these transactions and traffic.

Moreover, fragmented regulations around the world are a major barrier to innovation. Although regions like Ontario are at the forefront of autonomous robotics, most areas lag far behind. Decentralized governance provides urgently needed standardization in this field by establishing programmable rule sets based on blockchain. Establishing global standards regarding safety, ethics, and operations is crucial to ensure that autonomous intelligent robots can be deployed on a large scale across countries without compromising safety and compliance.

Decentralized Autonomous Organizations (DAOs) are accelerating the research and development of robots and AI. Traditional funding channels are inefficient and relatively closed, limiting the rapid development of the industry. Token-based models (such as the DeSci DAO platform) break through these bottlenecks while providing incentives for ordinary investors to participate. Additionally, some emerging AI business models introduce micropayments and profit-sharing with data or model providers, all of which can be realized through smart contracts.

The combination of these advantages will drive the rapid development of autonomous intelligent robots and bring about many anticipated practical applications.

A New Paradigm for Robots and Intelligent Machines

Many people may worry that the widespread adoption of intelligent machines will create competition with humans, viewing cognition as a zero-sum game. However, the reality is that there is still a severe shortage of well-educated talent in various fields such as education and healthcare.

A study by UNESCO highlights the severe global shortage of teachers, stating that 'by 2030, an additional 44 million primary and secondary school teachers will be needed globally'—this does not even include teaching assistants who provide one-on-one tutoring for students falling behind. In this context, autonomous intelligent robots can bring significant advantages to the education sector, alleviating the teacher shortage crisis. Imagine a child learning complex concepts through a robot by their side, with the robot patiently guiding them step by step to master new skills—not only deepening their understanding of the subject but also enhancing their social skills. We have grown accustomed to humans teaching robots, but this one-way relationship is gradually shifting.

At the same time, the World Health Organization (WHO) warns that the healthcare industry is facing a 'human resources crisis.' Currently, the healthcare systems of 100 countries worldwide are short of about 7.2 million professionals, and with the aging problem intensifying, this gap is expected to grow to 12.9 million by 2035. This shortage is particularly severe in nursing, primary care, and related health fields. This crisis not only affects the quality of care that patients receive but also threatens the work efficiency of healthcare practitioners. In this context, autonomous intelligent robots can play an important role in various ways, such as monitoring chronic disease patients, assisting in surgical procedures, and providing companionship services for the elderly. They can also automatically monitor medicine and equipment stock, replenishing them in a timely manner when needed. Additionally, robots can significantly improve efficiency and consistency in tasks such as transporting medical waste, cleaning treatment rooms, and assisting in complex surgeries. In a healthcare industry that urgently needs to improve productivity, robots are undoubtedly an important asset.

In the defense sector, applications of autonomous systems have already shown initial results, particularly in drone swarms and maritime combat assets. The potential of robots to perform high-risk tasks or tasks that humans cannot complete (such as disaster rescue or hazardous operations) is just beginning to be tapped.

From Prototype to Practical Application

These concepts may seem distant, like a sci-fi plot from the 22nd century, but in reality, Ethereum has long been used to store the decision-making and action rules of AI and robots. According to Coinbase, AI agents have already begun to trade with cryptocurrencies among themselves.

The openness and auditability of decentralized cryptographic networks provide a secure platform for robot developers to share data, models, and technological breakthroughs. This mechanism significantly accelerates the transition of autonomous robots from prototypes to practical applications, allowing them to be deployed more quickly in critical areas such as hospitals and schools. Imagine walking down the street with a humanoid robot, and a passerby might stop to ask you, 'Aren't you afraid?' You could confidently respond, 'No, I'm not afraid, because the behavior rules of this machine are public and immutable.' Then, you could even provide them with a link to the Ethereum contract address that stores these rules.

Decentralized ledgers can also serve as coordination hubs, enabling heterogeneous systems composed of different types of robots to find each other and collaborate without centralized intermediaries. This mechanism is conceptually similar to traditional defense C3 technologies (Command, Communication, and Control), but its infrastructure is decentralized and transparent. Immutable records ensure that every interaction and action can be tracked, establishing a trustworthy foundation for collaboration.

In interactions between robots, smart contracts can simplify task assignment and resource sharing, enabling efficient coordination. In human-robot interactions, decentralized systems that focus on privacy protection can securely manage sensitive data, such as biometric information or medical records, thereby enhancing user trust in data security while clarifying responsibility.

This new world may raise some questions—what does all this mean for us?—but in fact, every reader of this article has been working towards this realization for nearly 20 years by building infrastructures capable of handling governance, collaboration, communication, and coordination between humans and intelligent machines.

Note: The views expressed in this article are solely those of the author and do not necessarily reflect the position of CoinDesk, Inc. or its owners and affiliates.