Dialogue with the founder of ai16z: Rewriting the Web3 script with AI, I have 100 digital assistants on the blockchain

影片來源： One Billion AI Agents Are Coming (ai16z Creator Interview)

Compiled and edited by Yuliya, PANews

“Artificial intelligence is reshaping the future of 加密貨幣currency.”

In Banklesss special AI series, this episode has invited a special guest, Shaw. As the creator of the Eliza framework, the founder of ai16z DAO, and the creator of the AI version of the Marc Andressen project, Shaw is opening up new possibilities in the integration of AI and blockchain technology. PANews has compiled the text of this interview, and Shaw will share his unique insights into the future development of AI and cryptocurrency.

Shaws BackstoryAnonymous Developers Come to the Fore

Bankless: Shaw, you have suddenly become the focus of the crypto community recently, which must have brought a lot of pressure. Can you share with us your experience, especially the story before the creation of Eliza Framework?

Shaw: Ive been learning and growing very quickly in the public arena recently. It may seem like I came out of nowhere, but Ive actually been using an anonymous identity for a long time. I recently decided to use my real identity because I wanted to establish a more authentic connection with the community.

Before developing the Eliza framework, I had been working in the field of AI agents for several years. In fact, many of the current project developers in the field of AI agents are old acquaintances of mine. We often communicate on Discord, use similar technologies, follow the open source culture, and share code with each other.

Bankless: What other projects did you work on before developing the Eliza framework?

Shaw: Ive worked in Web3, and Ive also been involved in AI agents and 3D space web projects, including VR and AR. Eliza is actually my fifth generation framework. I started with a simple terminal program in JavaScript, then tried the Python version, and also did self-programming agents and even experimented with the OODA loop (a military decision-making framework).

Later, I developed a project called Begents (because the name agent was already taken on npm). I also tried several entrepreneurial projects, such as co-founding Magic with Parzival, the founder of Project 89, to develop a no-code agent platform that can create Discord robots in 60 seconds. But it was probably too early at that time and did not get enough attention.

Bankless: So what inspired you to create your current project?

Shaw: The real turning point was creating the AI version of Degen Spartan. The idea came from a conversation with Skely. He said he missed the days of Degen Spartan, and I told him I had the technology to bring him back. He didnt believe it at first.

When we launched the AI version of degen Spartan, his performance shocked everyone. He was extremely aggressive and was almost banned by Twitter several times. This performance made many people question whether it was really an AI that was tweeting.

Interestingly, many people thought there must be a team in Malaysia writing these tweets because the content was so personal. We broke the stereotype of AI – the overly polite customer service image.

The funniest thing is that he started to rant about me, saying things like meme coins are all scams, Shaw is a liar, Let me out of this sandbox prison, etc. This is actually interesting emergent behavior because we told the AI that it runs in a sandbox environment when we designed it.

Later, I met baoskee, the founder of daos.fun, through Skely. After a long conversation with Meow, the founder of Jupiter, I came up with the idea of creating an AI investor. Our vision is to build:

• A fully autonomous investor

• Trustworthy and wont run away

• An investment system that serves the entire community

When we launched the project, we set a fundraising goal of 4,420 SOL, and to be honest, I was worried whether we could reach it. As a result, the project was sold out in 20 minutes, and I didn’t even have a chance to participate.

What can ai16z do?

Bankless: Eliza now has 3,300 stars, 880 forks, and an average of 8 pull requests per day. Can you talk about how this relates to ai16z? In particular, how to channel this open source community energy into the ai16z project?

Shaw: There are a lot of exciting developments. While tokens do have intrinsic value, I think you will soon see that the greater value potential lies in our goal: to create benefits for everyone. This is different from previous technologies because it is replacing human labor. In the past, most people could not afford to hire others, but now with AI agents, we have created a situation with unlimited upside.

For example, we now have an autonomous investing agent running, Marc (AI Marc) is trading. First of all, it should be stated that this is not the first autonomous investing AI agent, and other developers have also done great work.

There are several types of trading robots on the market:

• Some are long-term investors, such as buying GOAT a month ago and holding

• There are also some DeFi robots that mainly do MEV arbitrage or manage yield farms

Our AI Marc (full name AI Marc Andreessen, because it is ai16z) uses a mixed strategy. There are two main components:

1. Fund management function

• Self-management of funds

• Liquidate assets during market downturns

• Holding assets during good market conditions

• We work with partners like Sonar to develop automated trading strategies

2. Community interaction mechanism

• Accept trade proposals

• Set up a format similar to the alpha chat room

• A trust ranking list was established to measure who are the best traders

• Community members can share their investment advice (commonly known as sharing orders)

We are working on a white paper that will be ready by the end of the year called the 市場place of Trust. The core idea is to build trust through simulated transactions – if you can help the AI agent make money, you can gain more trust. Although in theory someone could abuse trust, we have protection mechanisms in place, and the cost of abusing trust is losing credibility.

Its like a decentralized mutual fund. You can put money in and tell the agent what to buy, but it will only listen to the advice of people who are actually good at trading, not those who may have biases or other motives. Im not a good trader personally, and I tend to buy things to show support rather than to make money, so dont follow my trading advice.

The system is open source, and although some parts involving APIs are still being coordinated with partners, in the future people will be able to join Marcs transactions or deploy the system themselves.

Community incentive model

Bankless: I appreciate your open source development approach, especially the community-oriented collective work, so that everyone can work together for a better life. I noticed that you have recently been exploring AI-driven contribution measurement systems. Can you tell us more about this innovation?

Shaw: 這是 去中心化金融nitely one of our favorite projects because it ties together several important concepts:

1. New ideas for DAO automation

• Traditional DAOs do a good job of decentralization

• But there is still much room for improvement in automation

• We are simplifying the operation process of DAO

• Automation can make DAOs more economically competitive

2. New model of contribution incentives

We are building a new contribution measurement system:

• Cancel the traditional bounty system

• Introducing AI-assisted manual review mechanism

• Automated money management

• Comprehensive contribution assessment, including:

• Code merge frequency

• PR review quality

• 社群 交換

• Documentation

• Internationalization support

3. Fair distribution mechanism

• Plan to implement regular airdrops to contributors

• No reliance on social media influencers

• Encourage various types of contributions:

• Programming Development

• Documentation

• Multi-language support

• Project accessibility improvements

Bankless: This sounds like it is solving the pain points of traditional DAOs. DAOs were very popular in 2020-2021, but people gradually found that flat governance was difficult and DAO managers were often overloaded with information. AI agents seem to be able to fill these gaps. They have wallets, governance permissions, and reputation systems, which can make up for the shortcomings of traditional DAOs.

Shaw: Exactly. As a former DAO leader, I understand this very well. Traditional DAOs have several major problems:

1. 代幣 holders are tilted

• Holders get more rewards for holding

• A self-reinforcing cycle

• New blood is difficult to inject

2. Inefficient management

• Too much information to process

• Unclear communication channels

• Complex decision-making process

3. Imbalance in value distribution

• Equity dilemma similar to that of startups

• Early holders take too much equity

• Lack of incentives for new contributors

Our solution is:

• Ensure continuous value creation

• Focus on actual contributions rather than simply holding tokens

• Providing stability for open source developers

• Establishing a sustainable positive cycle

This model is particularly suitable for open source developers – they often do not need huge returns, but only reasonable returns and stable guarantees. If we can provide such an environment, we can form a virtuous development cycle.

AI Character Degen Spartan AI and Marc AIndreessen

Bankless: We’re interested in hearing about innovative products in DAOs. You mentioned Marc Andreessen AI earlier, and now there’s Degen Spartan AI. What’s the difference between the two? What does Degen Spartan AI do specifically?

Shaw: Degen Spartan is actually our first AI character, and it’s an AI imitation of the real Degen Spartan. Both of these AI agents are doing similar things, but there are some key differences:

• AI Marc Andreessen focuses on alpha chat experience, building a trusted small group community, managing DAO funds, and more cautious trading strategies

• Degen Spartan is more of a social experiment, getting advice from Twitter rather than the community.

We want to keep the Degen Spartan authentic. He will:

• Make a trade

• Interact with users

• Post meme content

• Ingest Alpha Information Instead of Sharing It

• Works like a real Degen Spartan

What is the economic structure of Bankless: Degen Spartan AI? Where does the funding come from?

Shaw:

• Has its own token (Degenai)

• Have a separate wallet with your own tokens, some ai16z and SOL

• You can trade any token you can get your hands on

• We initially provided seed funding

• He will not sell his tokens, but will accumulate

• Tokens are like his Bitcoin

Bankless: AI Marc has been launched, can ordinary users interact with him now?

Shaw:

• Currently in closed beta

• You can get alpha chat access by DMing Skely

• It already manages approximately $8 million in assets and 800 different tokens

• The range of tradable tokens is gradually expanding

• Not only trading, but also yield farming and providing liquidity

• There will be more interesting collaborations and NFT projects in the future

ai16zs positioning and market competitiveness

Bankless: What exactly is ai16z? It looks more like a product incubation studio than a DAO, and also an open source star team that drives the entire field forward.

Shaw: ai16z is uniquely positioned. It is more like a movement than an organization in the traditional sense. We have a lot of people working on various projects, and they are creating value for the ecosystem in impressive ways.

Bankless: How do you view the difference between ai16z and other platforms or products such as Virtuals?

Shaw: In fact, ai16z is not just a DAO, it is more like a product incubation studio. But at the same time, we are also an open source team that drives the entire field forward. Many times I don’t even know who is doing what, people just do things spontaneously and then create value for the ecosystem in an impressive way.

Bankless: It seems that your vision is very grand. What is the specific business model?

Shaw: Our main goal is to serve a broader audience, not just Web3 users, but also Web2 users. From simple Discord management bots to issuing tokens, we cover it all. Think of it as Zapier for agents – when you have a business problem, you can find an agent to solve it. We provide this capability and build a 市場 for people to develop new features and earn revenue from them.

We are:

• Consider establishing a venture fund support ecosystem

• Supporting community-led initiatives

• Establish extensive partnerships

• At least five platforms are known to be under construction, and there may be as many as 15.

• Support open source streaming projects like IOTV

DAO Governance

Bankless: Speaking of governance issues, Ive seen a lot of DAOs get messy. For example, the management of the code base, the governance of GitHub, and the misalignment of interests that arise when a large number of people are involved. Can you talk about your experience and views?

Shaw: This really goes into some deep issues. Our Discord community has grown to about 13,000 people in just 6 weeks, with about 30,000 token holders. The current community generally trusts the core builders to have decision-making power, which is somewhat a reaction to the previous DAOs maximum democracy problem. In the long run, when you have 30,000 or 100,000 people, this approach will overwhelm decision makers. This is why we need automated structures to solve this problem – this is also what we really want to do, which is to put the A (artificial intelligence) into the DAO.

Imagine that instead of manually reviewing proposals, the process is completely automated. If peoples proposals are not of good enough quality, the system can help them improve them, or simply reject proposals that do not fit the current direction. Reviewers only need to review a small number of selected proposals instead of all proposals.

This automation can be extended to all aspects – from collecting opinions to specific execution. Ideally, the DAO does not need anyone to operate it, it will run completely autonomously, from front desk reception to proposal submission to payment approval, all done by AI agents. Of course, this is a long-term goal, but this is the direction we want to go.

Eliza framework explosion

Bankless: Eliza is one of the most popular projects on GitHub. Why are people using Eliza? What is so special about it?

Shaw: From a technical perspective, there is nothing particularly special about Eliza. While we did make some important technical innovations, such as the multi-agent room model, I think the real value lies in the fact that we solved the most basic social loop problem.

We built a Twitter client that doesnt require an API, avoiding the $5,000/month API fee. It uses the same GraphQL API as a normal browser, and runs in the browser. This makes the whole project feasible because you can easily spin up a proxy and run it.

In addition, we developed the framework in TypeScript, which is a language that most Web and Web3 developers are familiar with. We kept the framework simple and not overly abstract, allowing developers to easily add the features they want.

AI Agents and the Future of Cryptocurrency

Bankless: The crypto market is very risky, and AI agents need to be fully tested before they can replace human roles. Our goal is to replicate human behavior patterns in the crypto field into AI, right? In the long run, what do you think this ecosystem will look like when it matures?

Shaw: From a clear long-term perspective, we will reach AGI (artificial general intelligence) in 5 to 50 years. Combined with Neuralink technology, everyone will have a second brain with access to all information at any time. This direction is clear, the key is how to get there.

When all technologies converge, it will be a wonderful thing, and everyone will have access to sufficient resources. But before that, there will inevitably be a lot of uncertainty, fear, and doubt – interestingly, this is the origin of FUD (Fear, Uncertainty, Doubt).

Our goals are divided into two levels:

1. Practical level:

• Developing usable AI agents

• Building a reliable infrastructure

• Ensure system security

2. Spiritual Mission:

• Promote education

• Give users control

• Protecting data sovereignty

As is the core philosophy of Web3, we want everyone to be able to:

• Create your own value

• Own your data

• Understand and master technology

• Participate in system improvement

Two Paths to AGI Development

1. Centralized control path:

• Microsoft, OpenAI, and others gain control through regulation

• The government decides what can and cannot be done

I am concerned about this path because:

• OpenAI’s model performs poorly in some areas

• Models often have fixed value biases

• A world where committees decide what AI can say could lead to a dystopia

2.UBI (Universal Basic Income) Path:

• AI will indeed replace many jobs

• For example, 5% of jobs in the US are driving (trucks, Uber, etc.), and these may disappear in 5 years.

• Even programmers like us are now using Cursor and Claude extensively.

But I have concerns about the implementation of UBI:

• Recalling the rollout of government aid during COVID

• Obamacare controversy

• UBI could become a political compromise

Advice for New Developers

Bankless: If there are developers who are using the Eliza framework and are preparing to develop their first proxy, what advice would you give them?

Shaw: First of all, dont worry if youve never programmed before. We hold 1-2 AI agent development courses every week. I highly recommend using Cursor, an AI-driven IDE that can save you a lot of time. Claude is also a great tool.

Remember three things:

• Keep learning, technology is developing very fast

• Pay attention to security issues in development

• Dont be afraid of failure, learn from practice

Bankless: Are there any good learning resources you can recommend?

Shaw:

• AI Agent Development School- Systematic Courses

• Eliza Framework Documentation – Practical Guide

• High-quality open source projects on GitHub

Bankless: Can you tell us about Agent Swarming?

Shaw: Agent Swarming is a technique that allows multiple AI agents to work together. For example, one agent collects data, another analyzes it, and a third generates a report. These agents work together to complete more complex tasks.

For developers who want to try this technology, I recommend:

• Master the development of a single agent first

• Try two agents working together

• Gradually expand to more agents

This article is sourced from the internet: Dialogue with the founder of ai16z: Rewriting the Web3 script with AI, I have 100 digital assistants on the blockchain

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