Thanks to David Minarsch, David Shi, Cem Dagdelen, Richard Blythman, Xinyuan Sun, and Barnabé Monnot for feedback and review.
In recent months the theme of Crypto x AI (intersection of crypto and AI) or Crypto + AI (crypto infrastructure augmented with AI) has been top of mind. Many people in the blockchain community are excited about it, some are skeptical or not-yet-convinced, and some are building. Live projects at the intersection of blockchain and AI have had a revamp and many new projects are popping up.
For the past year I have been doing research in this area, in particular on AI agents running on blockchain infrastructure. We have a research group together with some colleagues at the Ethereum Foundation, Flashbots, and DeepMind among others. We are continuing to push the applied research boundaries to understand and test what type of AI agents applications are the best fit for blockchains and what new infrastructure we need to support them.
In this post, I will make the case that the integration of blockchain infrastructure and AI agents is desirable and that it will give rise to an Internet of Agents:
An upgrade to the current paradigm of interconnectivity, augmented with incentives and modern cryptography, that will allow us to reap the benefits of an economy driven by AI agents with unprecedented levels of security, efficiency, and collaborative potential.
I will then discuss the path to get there. I will focus on short-term use cases and applications, some of which are already being designed and developed. I will discuss their limits and potential improvements, as well as the research needed across AI and blockchain to unlock new use cases in the medium-term.
Let me start by saying that the style of this argument will be speculative yet practical. Blockchain and AI are the two technologies that have been progressing at the most rapid pace in the past ten years. Both have far reaching effects on the fabric of the internet and human society more broadly. Thus, to paint a meaningful vision of how these technologies will evolve and interact requires some speculation. However, even though scaling laws clearly point in a direction of rapid improvement, I will stay away for long-term speculation about AGI. (Despite the recent hype, I believe that autonomous self-improving AGIs are relatively far in the future and it is not yet clear what form they will take.)
I will focus on the short-to-medium term future in which AI takes the form of human assistants and agents. In this form, AIs are tools that service humans by facilitating the execution of human activities or by carrying out new activities in service of humans.
Figure 1. Left: a concept timeline of AI evolution with increasing performance. Right: block diagram of activities for humans and different forms of AI.
Assistants have been around for several decades in various forms, while recent advances in LLMs suggest that the new generation of AI agents will be much more capable and rapidly improving than before. Here is a working definition of what I mean by AI agent:
A computer program that interacts with the world. It perceives its environment via sensors (input data), processes the data autonomously (prediction and planning) and takes actions in order to achieve goals (acting).
Agents can be subject to constraints and can also learn from the environment. Today, agents are usually specialized to a particular type of input and a particular type of action. For example, chatbots such as ChatGPT take as input a text prompt, may use some tools to produce answers, and respond with a text output. A trading bot on the other hand, takes as input past market states, predicts future market states and optimal actions, and executes a trade. Agents can be of different types (e.g., chatbot is an LLM while traderbot is a small RL agent) and they may also compose to execute a task. In the future, we may discover a general architecture that can be trained to handle most of the use-cases.
Public blockchains have a unique set of features that makes them very good infrastructure for the communication and interaction of AI agents. Later we argue that they make up for the best infrastructure for supporting agentic AI, but first, here are the features at a high level.
Decentralization: well-designed blockchain protocols are decentralized. Moreover, decentralization is part of the ethos of the communities that initially built them and upgraded them. It is built into the protocols and safe-guarded with governance.
Incentives: well-designed blockchains have sound incentive mechanisms that drive economic security via the native asset (for example, ETH in the case of Ethereum). Moreover, programmable smart contracts enable applications that can (1) leverage/use the native asset, (2) issue new digital assets with desired properties, and (3) define their own native asset and incentive mechanisms for their participants.
Openness and Composability: blockchains platforms are open access for users as well as application developers. Moreover, applications that are based on smart contracts deployed on blockchains inherit the same properties of openness and frictionless composability.
Cryptographic guarantees: blockchains leverage modern cryptography to deliver unique levels of security, auditability, and programmable privacy. As a result, they are trust-minimized much safer than legacy systems. Note that blockchain hacks come from smart contract bugs, which are inevitable in the early stages of the technology. As the stack matures it becomes more robust and secure, while traditional systems that rely on human trust do not have this property.
We can contrast these with the legacy internet, which only has decentralization. Base layer protocols such as TCP/IP or SMTP are open, but virtually all applications that have been built on top are proprietary. This gives the internet poor composability, a property that we argue is key when designing protocols for agents interaction. Moreover, the internet completely lacks incentives and modern cryptography at the protocol level.
Next, we present the ideal model for an economy where humans and agents cooperate and show that it requires the entire suite of features that blockchain protocols offer.