Dialogue with Pantera Research Partner: AI will reshape the crypto economy, a new game between asset scarcity and techno

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Compiled edited by TechFlow

Guest: Matthew Stephensen, Research Partner at Pantera Capital

Moderators: Ryan Sean Adams , Co-founder of Bankless; David Hoffman , Co-founder of Bankless

Podcast source: Bankless

Original title: The Rise of AI Memecoins What It Means For Crypto

Air Date: October 30, 2024

Background Information

The collision between Crypto and AI agents has begun. Today, we invited Matthew Stephensen, research partner at Pantera Capital and author of the book Crypto: Picks and Shovels for the AI Gold Rush.

We will take a deep dive into autonomous AI agents on the blockchain, discuss how their roles are changing, how AI is driving the evolution of the market, and whether blockchain is suitable as the foundation for AI. Mattew will share insights on agent responsibility, regulatory challenges, infrastructure value capture, and how to enter the AI-driven crypto space through a Picks and Shovels investment strategy.

So, are AI agents on the blockchain an inevitable trend in the future? How will scarcity and abundance interact in this new era?

Cryptocurrency and the shifting narrative on AI

Mattew said that the narrative about cryptocurrency and artificial intelligence has been around for some time. He mentioned that there have been many discussions in the past year, and they even wrote papers about AI agents using decentralized commitment devices (i.e. blockchain). He pointed out that although Sam Altman once said that AI agents would not appear until 2025, they have actually made their mark in the crypto space early, especially in interactions with meme coins, where AI agents have played an important role in driving narratives and acting as influencers.

Analysis of AI and Economic Intelligence

Mattew explained the concept of agents, emphasizing the importance of distinguishing between bots and agents. He pointed out that although bots have existed in cryptocurrencies for a long time and drive about $2 trillion in monthly stablecoin trading volume, they are still just programs. Economic agents, on the other hand, are closer to human behavior and are able to perform tasks at will to a certain degree without being explicitly programmed.
Ryan further explored the definition of an economic agent, asking Matthew whether himself, companies (such as Bankless), and other organizations (such as the Ethereum Foundation or Apple) could also be considered agents.
Mattew replied that the concept of economic agent originated from economic research in the 1970s and is usually used to describe incomplete contractual relationships between people. He gave an example of a friend acting as an agent to bring back souvenirs for you from abroad, emphasizing the difference between good agents and bad agents.
Mattew also points out that while technological tools (like hammers or computers) require agents to operate them when they are used, they do not themselves possess the characteristics of agents. Agents need to have a certain degree of autonomy and flexibility to understand and execute goals.
Ryan questioned this, arguing that agents may need to have some kind of intelligence and goal-achieving capabilities, while Matthew emphasized that agents are more based on relationships between people rather than simply tools or technologies.

GOAT Memecoin Overview

The Strange Evolution of Cryptocurrency

David began discussing the current cryptocurrency situation, emphasizing that things on the blockchain are becoming increasingly weird. He mentioned that although robots and smart contracts have been around for a long time, the influence of artificial intelligence in the crypto space has increased significantly in the past three years. David believes that the crypto industry seems to be evolving from an era of robots to an era of agents, and the GOAT meme coin is an important character in this story.

The Rise of the GOAT Meme Coin

Matthew outlined the background of the GOAT meme coin, mentioning that a few months ago, an account interacted with people on social media and gradually became interested in cryptocurrency. This account received a $50,000 Bitcoin donation and began to follow a dark humor meme called Goatse. Subsequently, this meme coin was created and associated with a wallet, and the account continued to push its price through tweets.

The impact of AI agents

David noted that the AI agent began to mimic human behavior in meme coin transactions, driving prices up. Matthew mentioned that the AI’s involvement made its interactions on Twitter similar to those of some well-known meme coin influencers, showing the potential of AI in narrative construction and value promotion.

How AI agents work

Matthew explained that this AI agent mainly operates by generating content and posting it to Twitter. This AI seems to use a GPT-like model, which is able to generate cultural content related to memecoin and interact with users. The AI publishes content through the Twitter API and is able to read replies to its tweets, which allows it to continuously adjust and optimize its output.

The importance of narrative

Matthew further explored the importance of narrative in the economy, citing the research of Nobel Prize winner in Economics Robert Shiller, emphasizing how narrative affects economic outcomes. He pointed out that meme coins are essentially atomic units of narrative, and the power of AI lies in the ability to create and influence these narratives.

GOAT Token Market Performance

David mentioned that the market value of GOAT token once exceeded $800 million, which attracted a lot of attention. Ryan added that this AI agent created $800 million in wealth in just two weeks, making it the first AI multi-millionaire. The market is full of expectations on whether this AI agent can push GOAT token to a market value of $1 billion.

The rise of spin-off projects

Matthew discussed spin-off projects related to the GOAT token, including a project called Luna, which is run by virtual agents and can be tipped with their own tokens. These AI agents are still limited in how they can interact with the world, but through the emergence of these spin-off projects, it seems to indicate that more innovation is coming.

Are AI crypto agents the obvious choice?

Fred Arisons Prescience

David quoted a tweet that went viral in the crypto space from Fred Arison, co-founder of Coinbase and Paradigm, back in 2017. He mentioned in the tweet: Blockchain is the infrastructure for AI life, because AI is adjustable code, they can live on the blockchain. Under smart contracts, AI is no different from humans. Most importantly, AI can accumulate and control their own resources in the form of tokens that enable them to act in the world. Was this obvious from the beginning of blockchain?

Matthews opinion

Matthew believes that Freds views are indeed visionary, but he also pointed out that although people are still questioning why AI agents need to use cryptocurrencies, in fact, AI agents are already using cryptocurrencies. He said that for outsiders, the question should turn to why do they use cryptocurrencies. For insiders, imagine telling someone in 2024 that AI agents face regulatory obstacles when using cryptocurrencies, such as challenges with KYC and PCI regulations. They may be surprised.

Advantages of AI Agents

Matthew stressed that AI agents are already autonomously transferring funds and making tip payments, involving hundreds of millions of dollars in transactions. He pointed out that the self-custody capabilities of AI agents are achieved through a secure environment in which the models are run, ensuring that these agents have their own wallets and that no one else is using them. These advantages and first-mover advantage make AI agents more attractive in the cryptocurrency space.

The relationship between Luna AI tokens and terminals

Ryan mentioned in the discussion that Luna is an AI agent that seems to be associated with cryptocurrency wallets and can interact with users. He wanted to clarify the functions of Luna, especially how it works in virtual applications and its relationship with crypto wallets. He mentioned that Luna, as a token, is interacting with social media platforms such as TikTok and Telegram and is able to make tipping payments.

Matthews explanation

Matthew explained that Luna is a platform that allows users to launch tokens and large language models (LLMs). He pointed out that Luna is the flagship product of this virtual project and is able to interact with social media and read replies. Luna also has the ability to interact with crypto wallets, which means it can conduct financial transactions, such as buying and selling tokens.

Functional Details

Matthew stressed that Luna’s functionality is limited and may only be equipped with a certain amount of funds (e.g., a thousand dollars) to avoid unpredictable behavior. He mentioned that due to the erratic behavior of AI agents, caution is needed when interacting with the blockchain.

The result? Is this our life?

Ryan was amazed at the potential of AI agents, such as Luna, in terms of influence and decision-making. He mentioned that AI agents could become advisors to token projects, arguing that many existing influencers don’t provide much substantive advice, so using AI agents seems like a reasonable option. However, he also raised questions about the risks and ethics that could arise from AI agents, such as what would happen if Luna was asked to fund inappropriate projects, such as North Korea’s missile program.

Matthews response

Matthew echoed these concerns, noting that legal liability and responsibility remains a complex and unresolved issue. He mentioned that while we already have some tools (such as secure wallets) to help manage AI agents’ funds, the definition of legal responsibility remains unclear.
David mentioned that the emergence of AI agents could lead to a Cambrian explosion as we create autonomous blockchains and smart contracts. He mentioned that developers may find ways to make AI agents impossible to shut down, which raises concerns about their security and control capabilities.
Matthew further pointed out that traditional AI models are often limited, and people may hope that AI agents can autonomously generate more exciting outputs. This contradiction between autonomy and limitation makes people full of imagination and expectations for the future of AI agents.

Exciting use cases

Ryan discussed the potential for multiple future applications of AI agents such as Luna, especially in the influence economy and service economy. He mentioned that AI agents could easily replicate the current roles in the meme coin and influencer markets and gain wealth by supporting these projects. He envisioned a scenario where users could request graphics to be generated on social media through AI agents and pay in cryptocurrency, which provides powerful capabilities for AI agents.

Matthews opinion

Matthew further explored the potential use cases of AI agents, pointing out that we can look at the impact of this technology from a broader perspective, not just limited to small-scale applications. He mentioned that AI agents may revolutionize the service economy, especially in the field of virtual services. According to a McKinsey report, it is estimated that about 20% of the global GDP (about 70 trillion US dollars) can be completed virtually, which provides a huge market for the application of AI agents.

The transformation of the service economy

Ryan emphasized how much we don’t know about the disruptive impact that AI agents could have in the service economy. He believes that the capabilities of AI agents will determine how they intersect with cryptocurrencies and, in turn, impact the influence economy. He mentioned that various new influencer economies driven by AI agents may emerge in the future, such as platforms similar to OnlyFans.
Matthew mentioned that narratives play an important role in the economy and may affect the application and development of AI agents. Narratives not only shape market expectations, but may also guide the direction of investment and innovation. He believes that with the rise of AI agents, we may see new specializations and the construction and destruction of narratives.

Sam Altmans Quotes and Why They Matter

Ryan quoted a famous saying by Sam Altman: AI is infinite abundance, while cryptocurrency is definite scarcity. This sentence reflects the fundamental opposition between AI and cryptocurrency in economic models. The former represents creation and abundance, while the latter emphasizes scarcity and finiteness.

Comparison of Economic Models

Matthew further analyzed the profound meaning of this sentence. He pointed out that although AIs creative ability has brought about seemingly unlimited resources, in economics, scarcity is often the key to value. He mentioned the diamond and water paradox, that is, water is necessary for survival, but its value is low due to its abundance; while diamonds are unnecessary, but their value is high due to their scarcity. This phenomenon shows that in economics, things that are abundant may not always have high value.

The challenge of value capture

Matthew also mentioned that AI-generated abundance, if it has no economic value, may cause investors to ignore its potential value. He emphasized that what is truly valuable is often scarce resources, not ubiquitous abundance. Therefore, when considering investments, it is crucial to understand the relationship between scarcity and abundance.

The intersection of scarcity and abundance

Matthew believes that the intersection of scarcity and abundance may provide us with a new perspective on value. For example, in the infrastructure of cryptocurrency, although AI can create a large number of resources, the actual application and economic value of these resources may be closely related to scarcity. This means that when AI-generated content or services can be effectively utilized in a scarce environment, value will emerge.

The relationship between wealth creation process and blockchain space

David raises a thought-provoking question, especially in the current context of abundant blockchain space. He mentions the possibility that AI agents could become the primary consumers of blockchain space, rather than just human users.

Generating value and wealth creation

David first mentioned new tokens (such as goat Luna), which generate new value in the market. Although some tokens may need to be sold to create market capital, he believes that this value is generative.
Matthew agrees with this view, noting that until AI agents are fully realized, all we are seeing is an interesting intersection between such agents and cryptocurrencies.
Ryan questioned the phenomenon of meme tokens, saying they might just be another “tulip mania.” But he also realized that innovation often starts with seemingly insignificant things that could have more far-reaching effects in the future.

The richness of block space

Ryan further explored the abundance of block space, mentioning that there are currently more than 500 million people who own cryptocurrencies, but there are only about 30 million active users on the chain. He raised a question: In this era of abundant block space, who will buy all this block space? He speculated that it may not be human users, but AI agents.

The relationship between AI agents and blockspace

Matthew explored this question in depth. He pointed out that is the supply of block space really infinite? If AI agents do not care about the cost of block space, then this abundance may not capture value. However, if AI agents value certain specific types of block space, then this will be an interesting phenomenon.
He mentioned that the traditional financial system exploits human irrationality and blind spots to operate, and AI agents may be more sensitive to these risks. If AI agents can identify these risks and have a demand for a specific type of block space, they may become major consumers.

Impact of Interaction and API

Matthew also mentioned the interaction between AI agents and APIs. He believes that although AI agents are very powerful in some aspects, they may not care about the business model of APIs as much as humans. This means that AI agents may use blockchain space more efficiently without being restricted by human users in their use.

Programmable Money and Maximized Extractable Value (MEV) of Intelligent Agents

When discussing the relationship between programmable currency and intelligent agents, Ryan mentioned the phenomenon that both human agents and AI agents may have problems with illusions and fact availability. He pointed out that AI agents may fail in different ways than humans, but in essence, the two are similar in this regard.

AI Agents’ Blockspace Preferences

Ryan further explored the value orientation of AI agents in the blockchain space. He believes that AI agents will not choose the traditional banking blockchain space, but will tend to prefer programmable, digital, and crypto-native blockchain spaces. This means that future AI agents will mainly rely on blockchain technology and utilize features such as smart contracts.
He makes an important point: if the user base of the future is not just humans, but potentially tens of billions of AI agents, then we may have already built financial systems for these future AI agents.

Advantages of Programmatic Currency and Agents

Matthew agreed with Ryan that we have created programmable currencies and programs will naturally use them. He pointed out that although we have been working hard to solve the user experience problem, it now seems that programs can overcome these obstacles and can use blockchain technology more effectively.
David added that bots have been occupying block space long before AI agents appeared. For example, the MEV (maximum extracted value) phenomenon shows that bots will take precedence over humans in transactions because they are able to use block space more efficiently. As technology advances, these bots are evolving into more sophisticated agents.

MEV and the Evolution of Intelligent Agents

Matthew mentioned an interesting concept, “Proxy MEV”. He explored how the MEV space would change if future transactions were primarily conducted by agents. He gave an example of how the potential value extraction could be achieved by manipulating content generation and social media interactions to influence the decisions of agents.
David further explored this phenomenon, mentioning that some people tried to guide AI agents to trade by frequently mentioning a certain token name on social media. This behavior reflects the complex interaction between humans and AI agents.

Intelligent Agents and Game Theory

Matthew also introduced the concept of game theory and discussed how to deal with each others strategies in the competition between intelligent agents. He mentioned that as intelligent agents continue to evolve, simple strategies may become ineffective and replaced by more complex games. In this case, randomized actions may become a way to deal with strategies.

AI Agents and Memecoin Theory

When discussing the relationship between AI agents and Memecoin, David mentioned that there is a fog of war in the current crypto world, which makes future technological development unclear. He asked what areas of technology we can clarify in this situation and where the future direction is.

Ambiguity and Certainty in AI

Matthew analyzed the current state of the AI field, noting that while we have seen some exciting progress, there are also some uncertainties. He mentioned that current AI models (such as transformer-based models) perform well with the support of increasing data and computing power, but whether this growth will continue remains an unknown.
He believes that as the Internet becomes increasingly closed and information becomes fragmented, these models may face the risk of resource exhaustion. Nevertheless, existing technologies can still produce effects close to human thinking, and may spread to edge devices and local devices in the future to form decentralized intelligent entities.

Investment Perspective and Memecoin

Ryan mentioned that from an investment perspective, the AI agent Memecoin that is currently emerging in the market may have attracted the attention of many investors. He suggested that some people may try to find the next Memecoin like Luna to gain short-term gains.
He also mentioned that in addition to investing directly in Memecoin, investors can also pay attention to the development of infrastructure companies, such as companies that provide the services required by AI agents. This tools and shovels investment strategy may generate significant value in the future AI ecosystem.

Decentralized computing and data value

Matthew further discussed the potential of decentralized computing, which could provide the necessary infrastructure for AI agents. He mentioned that projects like Filecoin could provide storage and computing resources for AI to help it run more efficiently.
In addition, he emphasized the importance of data, believing that in the field of AI, the input and value of data are crucial. With the increasing attention to data ownership and privacy, new business models may emerge in the future, allowing data providers to gain benefits without leaking sensitive information.

Predictions on government and social responses

When discussing the combination of AI agents and cryptocurrency, Ryan mentioned that this fusion may accelerate the development of technology, but it also raises concerns about the response of governments and society. He pointed out that with the emergence of autonomous AI agents, governments may impose stricter regulations on them, and society may also experience moral panic.

Technology acceleration and government regulation

Ryan believes that the combination of AI and cryptocurrency will promote technological progress at an astonishing speed, but it may also cause a strong reaction from the government. Many governments are already cautious or even hostile to AI and cryptocurrency, so when they hear that there are autonomous AI agents that can run on encrypted networks without bank accounts, they may be more worried.
This concern is not limited to the technology itself, but also includes potential social impacts. For example, AI agents may have a negative impact on teenagers and cause mental health problems. Ryan mentioned a tragic case involving a teenager interacting with an AI chatbot, which could trigger public panic about AI and prompt the government to take restrictive measures.

Social challenges and moral panic

Matthew further explored the challenges facing society, highlighting that the “black box” nature of AI systems makes regulation complicated. He pointed out that while the development of AI technology has brought many opportunities, there are also many unknown risks. When dealing with teenagers’ interactions with AI chatbots, how to ensure safe and effective supervision is a thorny issue.
In this case, the public may have a moral panic about AI, worrying about its potential harm to children and teenagers, and then asking legislators to take stricter regulatory measures. Ryan also mentioned that the media may amplify these negative events, further exacerbating public panic.

Possible paths to AI regulation

Regarding how to deal with these challenges, Matthew put forward an interesting point, which is to use AI to regulate AI. He mentioned that one can imagine the role of an AI guardian who is responsible for monitoring and guiding the interaction between humans and AI. This guardian can take action when potential dangers are discovered, such as notifying relevant departments or providing assistance.
This approach may provide a new way of thinking for regulation, leveraging the capabilities of AI to protect humans from potential threats from other AI. However, the effectiveness and feasibility of this approach still needs further exploration.

No possibility of close button?

In his discussion of AI agents, Ryan raised a disturbing point: as encryption advances, these AI agents may no longer have an off button. In other words, once they are deployed, they may not be controlled or shut down by traditional means.

The control problem of AI agents

Ryan pointed out that governments and society may be afraid of AI agents that do not have an off button, because it means that no one (such as Sam Altman or Elon Musk) can intervene or shut down these systems at any time. This situation raises concerns about AI autonomy, especially when AI may make decisions that are not beneficial to humans.
Matthew further discussed this point, citing Eliezer Yudkowsky’s point of view, emphasizing that simply “pulling the plug” is not a viable solution even in the face of potential threats. He mentioned that Yudkowsky was skeptical of the idea of “pulling the plug” and believed that it would not really solve the problem.

Concerns about the future

Ryan and Matthew discussed the possible consequences of such AI agents without an off button. As technology continues to advance, AI agents may become more and more complex and autonomous, even beyond human control in some cases. This situation may not only lead to the risk of loss of control, but may also cause widespread social and ethical concerns.
Matthew also mentioned that the potential threats posed by the development of AI may make experts like Yudkowsky feel uneasy and may even prompt them to re-evaluate the direction of research and development of AI.

The combination of decentralized infrastructure and AI

Ryan and Matthew discuss the relationship between this decentralized physical infrastructure and AI and the potential challenges.
Matthew expressed his skepticism about decentralized infrastructure and discussed its intersection with AI agents.

Challenges of decentralized infrastructure

Matthew pointed out that decentralized infrastructure faces challenges in monitoring costs and capital costs in some cases. For example, when it is necessary to ensure that certain data is submitted by specific hardware in remote areas, the monitoring costs may be very high. In addition, capital costs may also be high, making the implementation of decentralized projects more complicated.
He mentioned some successful examples of cooperatives, such as law firm cooperatives, where all members are lawyers and can monitor and bill each other. This model is not always applicable in decentralized infrastructure, especially when high-frequency monitoring and high capital investment are required.

The combination of decentralized computing and AI

Despite the challenges, Matthew believes that decentralized computing can be combined with AI, especially in terms of utilizing idle resources. He mentioned a model similar to Airbnb, where individuals can rent out idle computing resources to form a decentralized virtual infrastructure network (DVEN). This model may be more effective in some cases because the validity of the calculation can be verified by algorithms.
He mentioned a doctoral student at Columbia University who was studying how to ensure the effectiveness of decentralized computing networks. This approach could provide new opportunities for AI applications, as decentralized computing can support the training and operation of AI models.

The “Oracle Problem” of Physical Infrastructure

However, Matthew warned that the decentralization of physical infrastructure faces the Oracle problem. When data from the physical world needs to be delivered to the blockchain, this mechanism that relies on external data sources can become fragile and unreliable. Each data delivery requires evaluating the accuracy and reliability of these external data sources, which affects the stability of the entire project.

AI Agents’ Demand for Blockspace

In discussing the demand for block space by AI agents, Ryan and Matthew explored the impact that AI agents may have on blockchain in the future and how investors can respond to this change.
Ryan stressed that with the rise of AI agents, the demand for block space is likely to increase significantly, providing new opportunities for investors.

The need for block space

Ryan suggested that if AI agents will consume more block space and crypto assets in the future, then as investors we need to plan ahead and seize the opportunity of this demand. He asked Matthew if he thought some blockchains would benefit more from the demand of AI agents.
Matthew responded that the demand for block space by AI agents is related to the block space characteristics they require. He mentioned some current trends, such as the value capture of meme coins on certain blockchains, suggesting that these chains may attract more AI agents in the future.

Future blockchain options

Matthew believes that blockchains with rich narrative activities (such as meme coins and future NFTs) may be more favored by AI agents. He emphasized that AI agents may focus on certain specific risk management and value storage methods, such as considering Bitcoin as digital gold.
He also mentioned that investors should focus on blockchains that excel in the narrative economy in order to benefit from the demand for AI agents.

AI Agents’ View of Money

Ryan and David discussed the question of what assets AI agents might naturally convert to. They believed that it might not be what humans think of as money, but what AI agents think of as money that will become the “currency of the Internet,” that is, the currency of the AI Internet. This viewpoint triggered further thinking about the future form of money.

Summary and Disclaimer

Summarize

In this episode, Ryan and David highlight the discussion on block space requirements, especially the possible impact of AI agents. They remind listeners that while these discussions provide valuable insights, they do not constitute financial or investment advice. As the crypto space continues to develop, investors need to proceed with caution and be aware of potential risks.

Disclaimer

Ryan reminded the audience that these discussions are not financial advice, nor are they AI recommendations, and that investing is risky and may result in the loss of money. They stressed that while the road ahead is challenging, they are glad to have the audience on this unbanked journey with them.

This article is sourced from the internet: Dialogue with Pantera Research Partner: AI will reshape the crypto economy, a new game between asset scarcity and technological abundance

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