Shaw's Background Story: From Anonymous Developer to the Spotlight
Bankless: Shaw, you have recently become the focus of the crypto circle, which must have brought a lot of pressure. Can you share your experience with us, especially the story before creating the Eliza framework? Shaw: My recent process of learning and growing in the public eye has been very fast. I may seem to have suddenly appeared, but I have actually been operating under an anonymous identity before. I recently decided to use my real identity because I want to establish a more genuine 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 developers of current AI agent projects are my old acquaintances, and we often communicate on Discord, use similar technologies, follow the open-source culture, and share code with each other. Bankless: What other projects have you worked on before developing the Eliza framework? Shaw: I have worked in the Web3 field, as well as delved into AI agents and 3D spatial network projects, including VR and AR-related content. Eliza is actually my fifth-generation framework. I started with a simple terminal program developed in JavaScript, then tried a Python version, and even worked on self-programming agents and 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 startup projects, such as co-founding Magic with the founder of Project 89, Parzival, to develop a no-code agent platform that can create Discord bots in 60 seconds. But at that time, it might have been a bit premature, and didn't gain enough attention. Bankless: What prompted you to create your current project? Shaw: The real turning point was the creation of the AI version of degen Spartan. This idea came from a conversation with Skely. At the time, he said he missed the Degen Spartan era, and I told him 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 spoke extremely aggressively, and was even almost banned from Twitter multiple times. This behavior made many people question whether it was really AI tweeting. Interestingly, many people thought there must be a team in Malaysia writing these tweets, because the content was so personalized. We broke the stereotypical image of AI - that overly polite 'customer service' image. The funniest thing is, he started to criticize me crazily, saying 'meme coins are all scams', 'Shaw is a liar', 'let me out of this sandbox prison' and so on. This is actually an interesting emergent behavior, because we told the AI that it was running in a sandbox environment during the design. Later, through Skely, I met the founder of daos.fun, baoskee. After a long conversation with Meow, the founder of Jupiter, I had the idea of creating an AI investor. Our vision is to build: · A fully autonomous investor · A trustworthy and non-absconding investment system · An investment system that serves the entire community When we launched, we set a fundraising target of 4,420 SOL, and to be honest, I was worried at the time whether we could reach it. But the project sold out in 20 minutes, and I didn't even have time to participate myself.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 these relate to the ai16z project? Especially how to guide the energy of this open-source community into the ai16z project? Shaw: There have indeed been many exciting developments. Although the tokens do have intrinsic value, I think people will soon discover that the greater potential value lies in our goal: to create yield for everyone. This is different from past technologies, because it is replacing human labor. In the past, most people couldn't afford to hire others, but now, through AI agents, we've created a situation with unlimited upside potential. For example, we now have an autonomous investment agent running, which is Marc (AI Marc) conducting trades. First, I need to clarify that this is not the first autonomous investment AI agent, as other developers have also done great work. Currently, there are several types of trading bots on the market: · Some are for long-term investment, such as buying GOAT a month ago and holding it · There are also DeFi bots, mainly doing MEV arbitrage or managing yield farming Our AI Marc (full name AI Marc Andreessen, because it's ai16z) adopts a hybrid strategy. There are two main components:1. Fund Management Function
· Autonomously manage funds · Liquidate assets when the market performs poorly · Hold assets when market conditions are good · We are working with partners like Sonar to develop automated trading strategies2. Community Interaction Mechanism
· Accept trading suggestions · Set up a format similar to an alpha chat room · Established a trust ranking to measure who are the best traders · Community members can share their investment suggestions (known as "showing off") We are writing a whitepaper, which we expect to complete by the end of the year, called the "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 will gain more trust. Although theoretically there may be people abusing the trust, we have set up protection mechanisms, and the cost of abusing trust is the loss of reputation. This is like a decentralized mutual fund. You can put in funds and tell the agent what to buy, but it will only listen to the advice of those who are truly good at trading, not those who may have biases or other motives. I personally am not a good trader, as I often buy things to show support rather than to make money, so don't follow my trading suggestions. This system is open-source, and although some parts involving APIs are still being coordinated with partners, in the future people can both join Marc's trading and deploy this system themselves.Community Incentive Model
Bankless: I really appreciate your open-source development approach, especially the community-oriented collective work, allowing everyone to work together for a better life. I noticed that you have been exploring an AI-driven contribution quantification system recently. Can you elaborate on this innovation? Shaw: This is indeed one of our favorite projects, as it ties together several important concepts:1. New Approach to DAO Automation
· Traditional DAOs have done well in decentralization · But there is still a lot of room for improvement in automation · We are simplifying the DAO's operational processes · Automation can make DAOs more economically competitive2. New Model of Contribution Incentives
We are building a brand new contribution quantification system: · Abolish the traditional bounty system · Introduce an AI-assisted manual review mechanism · Automated fund management · Comprehensive contribution assessment, including: -Code merge frequency -PR comment quality -Community interaction -Documentation writing -Internationalization support3. Fair Distribution Mechanism
· Plan to implement regular airdrops to contributors · Not dependent on social media influence · Incentivize various contributions: -Programming development -Documentation writing -Multi-language support -Improving project accessibility Bankless: This sounds like it's solving the pain points of traditional DAOs. The DAO boom in 2020-2021, but people gradually found that flat governance is very difficult, and DAO managers are often overwhelmed with information. AI agents seem to be able to fill these gaps, as they have wallets, governance rights, and reputation systems to make up for the shortcomings of traditional DAOs. Shaw: That's right. As a former DAO leader, I have a deep understanding of this. Traditional DAOs have several major problems: · Skewed token holder participation-Holders receive more rewards for holding
-Forms a self-reinforcing cycle
-Difficult to inject new blood
· Poor management efficiency
-Overwhelming information overload
-Unclear communication channels
-Complex decision-making process
· Imbalance in 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
· Focus on actual contributions rather than just token holding
· Provide stable guarantees 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, it will form a virtuous development cycle.
AI Character Degen Spartan AI and Marc AIndreessen
Bankless: We are very interested in learning about the innovative products in the DAO. You mentioned AI Marc Andreessen earlier, and now there is also Degen Spartan AI. What are the differences between the two? What exactly does Degen Spartan AI do?
Shaw: Degen Spartan is actually our first AI character, it's an AI imitation of the real Degen Spartan. These two AI agents are doing similar things, but with some key differences:
AI Marc Andreessen focuses on alpha chat experience, building trusted small community, managing DAO funds, and more cautious trading strategies; Degen Spartan is more like a social experiment, taking suggestions from Twitter rather than the community.
We want to maintain the authentic characteristics of Degen Spartan. He will:
· Trade
· Interact with users
· Post meme content
· Absorb Alpha information rather than share
· Operate like the real Degen Spartan
Bankless: What is the economic structure of Degen Spartan AI? Where does the funding come from?
Shaw:
· Has its own token (Degenai)
· Owns an independent wallet with its own tokens, some ai16z and SOL
· Can trade any tokens it has access to
· We provided initial seed funding
· It will not sell its own tokens, but will accumulate them
· The tokens are like its "Bitcoin"
Bankless: AI Marc has already launched, can regular users interact with him now?
Shaw:
· It is still in closed beta testing
· You can get alpha chat access by DMing Skely
· It is currently managing around $8 million in assets and 800 different tokens
· It is gradually expanding the range of tradable tokens
· It not only trades, but also does yield farming and provides liquidity
· There will be more interesting collaborations and NFT projects in the future
The Positioning and Competitiveness of ai16z
Bankless: What exactly is ai16z? It looks like not just a DAO, but more like a product incubation studio, and also an open-source star team that is driving the entire field forward.
Shaw: The positioning of ai16z is quite special. It's more like a movement than a traditional organization. We have a lot of people doing various projects, and they are creating value for the ecosystem in an impressive way.
Bankless: How do you see the difference between ai16z and platforms or products like Virtuals?
Shaw: In fact, 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 is driving the entire field forward. Often I don't even know what people are doing, they just take action spontaneously and create value for the ecosystem in an impressive way.
Bankless: It seems your vision is quite grand, what is the specific business model?
Shaw:
Our main goal is to serve a wider audience, not just Web3 users, but also Web2 users. We cover everything from simple Discord management bots to token issuance. You can think of it as a "proxy version of Zapier" - when you have a business problem, you can find the corresponding proxy to solve it. We provide this capability, while also building a market where people can develop new features and profit from them.
We are:
· Considering setting up a venture fund to support the ecosystem
· Supporting community-led initiatives of all kinds
· Establishing broad partnerships
· Currently known to be building at least 5 platforms, but may be as many as 15
· Supporting open-source streaming projects like IOTV
DAO Governance
Bankless: Speaking of governance issues, I've seen many DAOs become chaotic. For example, the management of code repositories, GitHub governance, and the problem of conflicting interests when a large number of people are involved. Can you share your experience and views on this?
Shaw: This does involve some deep-seated issues. Our Discord community has grown to about 13,000 people in just 6 weeks, and we have about 30,000 token holders. Currently, the community generally trusts the core builders to have decision-making power, which is in a way a reaction to the previous DAO "maximize democracy" problem. In the long run, when you're facing 30,000 or 100,000 people, this approach will overwhelm the decision-makers. That's why we need automated structures to solve this problem - this is what we really want to do, which is to put the "A" (artificial intelligence) into the DAO.
Imagine if the proposal review process was fully automated, rather than manually reviewed. If people's proposals are not of good enough quality, the system can help them improve, or directly reject proposals that don't fit the current direction. Reviewers only need to review a small number of filtered proposals, rather than all of them.
This automation can be extended to various aspects - from collecting opinions to specific execution. Ideally, the DAO won't need any human to operate, it will be fully self-governing, from front desk reception to proposal submission and payment approval, all done by AI agents. Of course, this is a long-term goal, but that's the direction we want to go.
The Phenomenon of the Eliza Framework
Bankless: The Eliza framework is now one of the most popular projects on GitHub, why is everyone using Eliza? What's special about it?
Shaw: From a technical perspective, Eliza doesn't have anything particularly outstanding. Although we have indeed made some important technical innovations, such as the multi-agent room model, I think the real value is that we have solved the most basic social loop problem.
We developed a Twitter client that doesn't require an API, avoiding the $5,000 per month API fee. It uses the same GraphQL API as a regular browser, and can run in the browser. This makes the whole project viable, because you can easily spin up an agent and run it.
Additionally, we developed the framework in TypeScript, a language that most Web and Web3 developers are familiar with. We kept the framework lean, without over-abstraction, so developers can easily add the features they want.
AI Agents and the Future of the Crypto Industry
Bankless: The crypto market is very risky, and AI agents need to be thoroughly tested before they can replace human roles. Our goal is to replicate human behavior patterns in crypto into AI, right? Looking ahead, what do you think the ecosystem will look like when it matures?
Shaw:
From the obvious long-term vision, we may reach the AGI (Artificial General Intelligence) stage in 5 to 50 years. Combined with Neuralink technology, everyone will have a second brain and be able to access all information at any time. This direction is very clear, the key is how to get there.
When all technologies converge, it will be a wonderful scenario, where everyone can have enough 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".
Our goals are on two levels:
1. Practical level:
· Develop usable AI agents
· Build reliable infrastructure
· Ensure system security
2. Spiritual mission:
· Promote education
· Empower users
· Protect data sovereignty
Just like the core ideas of Web3, we hope everyone can:
· Create their own value
· Own their own data
· Understand and control technology
· Participate in improving the system
Two Paths for AGI Development
1. Centralized Control Path:
· Microsoft, OpenAI, etc. gain control through regulation
· The government decides what can and cannot be done
I am very concerned about this path because:
· OpenAI's models have performed poorly in some areas
· Models often have fixed value biases
· A world where a committee decides what AI can say may lead to 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 within 5 years
· Even programmers like us are now using Cursor and Claude extensively
But I have concerns about the implementation of UBI:
· Reflecting on the rollout of government aid during COVID
· The controversy over Obamacare
· UBI may become a political compromise
Advice for New Developers
Bankless: If a developer is using the Eliza framework and preparing to develop their first agent, what advice would you give them?
Shaw: First, don't worry even if you've never programmed before. We hold 1-2 AI agent development courses per week. I strongly recommend using the AI-driven IDE Cursor, as it can save you a lot of time. Claude is also a great tool.
Remember three things:
· Maintain your passion for learning, as technology is evolving rapidly
· Be mindful of security issues during development
· Don't be afraid to fail, learn from practice
Bankless: Do you have any good learning resources to recommend?
Shaw:
· AI Agent Development School - Systematic courses
· Eliza framework documentation - Practical guides
· High-quality open-source projects on GitHub
Bankless: Can you introduce us to Agent Swarming?
Shaw: Agent Swarming is a technology that allows multiple AI agents to work collaboratively. 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 suggest:
· First master the development of a single agent
· Experiment with the collaboration of two agents
· Gradually expand to more agents
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