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 obtained his PhD 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 co-developed by OpenMind and Nethermind.io, aimed at regulating interactions in a society where humans and robots collaborate.

Main Text

Autonomous intelligent robots were once seen as an unattainable science fiction concept, but today, large language models (LLMs) and generative AI have equipped machines with the ability to plan, learn, and think. Not only that, but 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. Therefore, the robots walking in your community in the future, or the robots working alongside you, will be able to exhibit 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, parents, and birthdates, which are clearly not applicable to intelligent machines. Moreover, there is still significant controversy over how to regulate these machines—should we prohibit their development, pause their research, or limit their ability to generate human-understandable emotions (as proposed by the EU)? More complex is the question of which region's laws should govern the behavior of a large language model with 200 billion parameters running on a computer in low Earth orbit that controls a trading robot or a physical robot in an SEC office.

We urgently need a global system that can support financial transactions, allowing humans and intelligent machines to jointly vote on rules, while possessing immutability, transparency, 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-geographic communities exploring new economic models' by building 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 indiscriminately support both humans and intelligent machines. Thus, decentralized cryptographic networks provide critical infrastructure for this emerging field, whose benefits will be fully realized in areas such as healthcare, education, and defense.

Of course, there are still many obstacles to overcome in this process. Achieving seamless connectivity between human-robot collaboration and inter-robot collaboration is crucial, especially in high-risk fields 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 robotic taxis) may occur off-chain, such as via virtual private networks, but prior steps, such as discovering robots or humans that can take you to the airport, are very 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 across the globe are a major obstacle to innovation. While regions like Ontario are leading the way in autonomous robotics, most areas are still far behind. Decentralized governance provides much-needed standardization for this field by establishing a blockchain-based set of programmable rules. Establishing global standards regarding safety, ethics, and operations is crucial to ensuring that autonomous intelligent robots can be deployed on a large scale across countries without compromising safety and compliance.

Decentralized Autonomous Organizations (DAOs) are accelerating research and development in robotics and AI. Traditional funding channels are inefficient and relatively closed, limiting rapid industry development. Token-based models (like DeSci DAO platforms) break through these bottlenecks while providing ordinary investors with incentives to participate. Additionally, some emerging AI business models introduce micro-payments and revenue-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 many anticipated practical applications.

A New Paradigm for Robots and Intelligent Machines

Many may worry that the proliferation of intelligent machines will compete 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 many fields, including education and healthcare.

A UNESCO study points out that the global teacher shortage is severe, stating that 'by 2030, the world needs an additional 44 million primary and secondary school teachers'—this does not even include teaching assistants who provide one-on-one tutoring to help struggling students catch up. In this context, autonomous intelligent robots can bring significant advantages to the education sector, alleviating the shortage of teachers. Imagine a child learning complex concepts through a robot nearby, 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 been accustomed to humans teaching robots, but this one-way relationship is gradually shifting.

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

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

From Prototype to Real Application

These ideas may sound distant, like a science fiction scenario 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 started using cryptocurrencies to trade 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 prototype to real application, enabling them to be deployed faster in critical areas such as hospitals and schools. Imagine when you walk down the street with a humanoid robot, passersby might stop and ask you, 'Aren't you afraid?' You can confidently reply, 'No, I'm not afraid because the behavior rules of this machine are public and immutable.' You could even provide them with a link pointing to the Ethereum contract address that stores these rules.

The decentralized ledger can also serve as a coordination center, enabling heterogeneous systems composed of different types of robots to find each other and collaborate without a centralized intermediary. This mechanism is conceptually similar to traditional defense C3 (Command, Control, and Communication) technologies, 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 allocation and resource sharing, enabling efficient coordination. In human-machine interactions, privacy-focused decentralized systems can securely manage sensitive data, such as biometric information or medical records, thereby enhancing user trust in data security while clarifying accountability.

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 the infrastructure that can handle 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 positions of CoinDesk, Inc. or its owners and affiliates.