Dialogue with Shaw, founder of ai16z: Trading agent AI Marc has been launched, trying the "trust market" model

Original source: Bankless

Compiled and edited by: Yuliya, PANews

"Artificial intelligence is reshaping the future of cryptocurrency."

In Bankless's 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 artificial intelligence and blockchain technology. PANews has compiled the text of this interview, and Shaw will share his unique insights into the future development of artificial intelligence and cryptocurrency.

Shaw's background story Anonymous 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 the Eliza framework?

Shaw:I’ve been learning and growing very quickly in the public space recently. It may seem like I came out of nowhere, but I’ve actually been using an anonymous identity. 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 AI agent field for several years. In fact, many of the current project developers in the AI agent field are old acquaintances of mine. We often communicate on Discord, use similar technologies, follow an open source culture, and share code with each other.

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

Shaw:I’ve worked in the Web3 field, and I’ve also been involved in AI agent and 3D space network projects, including VR and AR related content. Eliza is actually my fifth generation framework. I started with a simple terminal program in JavaScript, then tried a Python version, a self-programming agent, 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 a few 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 the time and didn't get enough attention.

Bankless: So what led 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. At the time, he said he missed the days of Degen Spartan, and I told him that I had the technology to bring him back. At first, he didn't believe it.

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

Interestingly, many people thought that 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 scammer', 'get me out of this sandbox prison'. This is actually interesting emergent behavior because we told the AI to run 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 completely autonomous investor

· Trustworthy and will not run away

· An investment system that serves the entire community

When we launched, 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 sold out in 20 minutes, and I didn't even have time to participate.

What can ai16z do?

Bankless: The Eliza framework 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 indeed a lot of exciting developments. While tokens do have intrinsic value, I think everyone will soon find 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 couldn't afford to hire others, but now with AI agents, we have created a situation with unlimited upside.

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

There are several types of trading bots on the market:

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

· Others are DeFi bots that mainly do MEV arbitrage or manage yield farms

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

1. Fund management function

· Manage funds autonomously

· Liquidate assets when the market performs poorly

· Hold assets when the market is good

· We work with partners such as Sonar to develop automatic trading strategies

2. Community interaction mechanism

· Accept trading suggestions

· Set up a format similar to alpha chat room

· Establish a trust ranking list to measure who is the best trader

· Community members can share their investment suggestions (commonly known as "sharing orders")

We are writing a white paper, which is expected to be completed by the end of the year, called "Marketplace of Trust". The core idea is to establish a trust mechanism through simulated trading - if you can help the AI agent make money, you can gain more trust. While it is theoretically possible that someone could abuse trust, we have protections in place, and the price of abusing trust is lost credibility.

It's 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 those who are actually good at trading, not those who may have biases or other motives. I'm not a good trader personally, and I tend to buy things to show support rather than to make money, so don't follow my trading advice.

The system is open source, and while some parts involving APIs are still being coordinated with partners, in the future people will be able to join Marc's trading 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:This is indeed one of our favorite projects, and it ties together several important concepts:

1. A new approach to DAO automation

· Traditional DAOs do a good job of decentralization

· But there is still a lot of room for improvement in automation

· We are simplifying the operational processes of DAOs

· Automation can make DAOs more economically competitive

2. A new model for contribution incentives

We are building a brand new contribution quantification system:

· Cancel the traditional bounty system

· Introduce AI-assisted manual review mechanism

· Automated fund management

· Comprehensive contribution assessment, including:

-Code merge frequency

-PR 3. Fair distribution mechanism · Plan to implement regular airdrops to contributors · Do not rely on social media influence · Incentivize various contributions: - Programming development - Documentation - Multi-language support - Project accessibility improvement · Bankless: This sounds like it is solving the pain points of traditional DAOs. DAOs were 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 that can make up for the shortcomings of traditional DAOs.

Shaw：That’s right. As a former DAO leader, I have a deep understanding of this. There are several major problems with traditional DAOs:

· Token holder skew

-Holders get more rewards for holding

-Forming a self-reinforcing cycle

-Difficult to inject new blood

· Inefficient management

-Too much information to handle

-Unclear communication channels

-Complex decision-making process

· Imbalanced value distribution

-Similar to the equity dilemma of startups

-Early holders occupy too much equity

-Lack of incentives for new contributors

Our solution is:

· Ensure continuous value creation

· Emphasize actual contributions rather than simple token holdings

· Provide stability for open source developers

· Establish a sustainable positive cycle

This model is particularly suitable for open source developers - They often don't need huge returns, just reasonable returns and stable guarantees. If we can provide such an environment, we can form a virtuous circle of development.

AI Characters Degen Spartan AI and Marc AIndreessen

Bankless: We are very interested in learning about innovative products in DAO. You mentioned AI Marc Andreessen before, and now there is Degen Spartan AI. What is the difference between the two? What does Degen Spartan AI do specifically?

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

AI Marc Andreessen is focused on alpha chat experience, building a small community of trusted groups, managing DAO funds, and more cautious trading strategies; Degen Spartan is more of a social experiment, getting advice from Twitter rather than a community.

We want to keep Degen Spartan authentic. He will:

· Make trades

· Interact with users

· Post meme content

· Ingests alpha information instead of sharing it

· Operates like a real Degen Spartan

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

Shaw:

· Has its own token (Degenai)

· Has a separate wallet with its own token, some ai16z and SOL

· Can trade any token it can touch

· We provided the initial seed funding

· He won't sell his tokens, but will accumulate

· The token is like his "bitcoin"

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

Shaw:

· Currently still in closed beta

· Access to alpha chat can be obtained through DM Skely

· Already manages about $8 million in assets and 800 different tokens

· Gradually expanding the range of tradable tokens

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

· There will be more interesting cooperation and NFT projects in the future

ai16z's positioning and market competitiveness

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

Shaw:ai16z's positioning is very special. It's more like a movement than an organization in the traditional sense. We have a lot of people working on various projects, and they create value for the ecosystem in an impressive way.

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

Shaw: Actually ai16z is not just a DAO, it's 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 you have a grand vision, what is the specific business model?

Shaw:

Our main goal is to serve a wider audience, not just Web3 users, but also Web2 users. From simple Discord management bots to issuing tokens, we cover it. 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 market for people to develop new features and earn money from them.

We are:

· Considering setting up a venture fund to support the ecosystem

· Supporting community-led initiatives

· Building broad partnerships

· At least 5 platforms are known to be under construction, and there may be as many as 15

· Supporting open source streaming projects like IOTV

DAO Governance

Bankless: Speaking of governance issues, I've seen a lot of DAOs become messy. For example, the management of the code base, the governance of GitHub, and the problems of inconsistent interests when a large number of people are involved. Can you talk about your experience and views?

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

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

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

The explosion of popularity of Eliza framework

Bankless: Eliza framework is now one of the most watched projects on GitHub. Why is everyone using Eliza? What is special about it?

Shaw:From a technical perspective, there is nothing particularly outstanding about Eliza. Although 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 cycle problem.

We developed a Twitter client that does not require an API, avoiding the $5,000 monthly API fee. It uses the same GraphQL API as a normal browser and can run in the browser. This makes the whole project feasible because you can easily start a proxy and run it.

In addition, we developed the framework in TypeScript, which is a language familiar to most Web and Web3 developers. We keep the framework simple and not overly abstract, so that developers can 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 in AI, right? In the long run, what do you think this ecosystem will look like when it matures?

Shaw:

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

When all technologies converge, it will be a very beautiful scene, and everyone will have access to sufficient resources. But in the transition period 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:

· Develop usable AI agents

· Build reliable infrastructure

· Ensure system security

2. Spiritual mission:

· Promote popularization of education

· Give users control

· Protect data sovereignty

Just like the core concept of Web3, we hope that everyone can:

· Create their own value

· Own their own data

· Understand and control technology

· Participate in system improvement

Two paths for the development of AGI

1. Centralized control path:

· Microsoft, OpenAI, etc. gain control through supervision

· The government decides what can and cannot be done

I am very worried about this path because:

· OpenAI’s model performs poorly in some aspects

· Models tend to have fixed value biases

· A world where committees decide what AI can say may 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.), which may disappear in 5 years

· Even programmers like us are now using Cursor and Claude heavily

But I have concerns about the implementation of UBI:

· Recall the rollout of government aid during COVID

· The controversy over Obamacare

· UBI It may become a product of political compromise

Advice for novice developers

Bankless: If there are developers who are using the Eliza framework and are ready to develop their first agent, what advice do you have for them?

Shaw:First of all, don't worry even if you have never programmed before. We hold 1-2 AI agent development courses every week. I strongly recommend using Cursor, an AI-driven IDE, which can save you a lot of time. At the same time, Claude is also a great tool.

Remember three points:

· Keep your enthusiasm for learning, technology is developing very fast

· Pay attention to security issues in development

· Don't be afraid of failure, learn from practice

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

Shaw:

· AI Agent Development School - Systematic Course

· 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 technology that allows multiple AI agents to work together. For example, let one agent collect data, another analyze it, and a third generate a report. These agents work together to complete more complex tasks.

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

· Master the development of a single agent first

· Try the collaboration of two agents

· Gradually expand to more agents"

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