Original: Bankless
Compiled by: Yuliya, PANews
"Artificial intelligence is reshaping the future landscape of cryptocurrencies."
In the AI series of the Bankless special program, this episode 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 field of the integration of artificial intelligence and blockchain technology. PANews has compiled the text of the interview in this episode, and Shaw will share his unique insights on the future development of artificial intelligence and cryptocurrencies.
Shaw's background story: From anonymous developer to the spotlight
Bankless: Shaw, you have recently suddenly 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 the creation of the Eliza framework?
Shaw:
The process of my recent learning and growth in the public eye has been very fast. I may seem to have suddenly appeared, but in fact I have been operating under an anonymous identity before. I recently decided to use my real identity because I want 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 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 AI agents and 3D spatial network projects, including VR and AR related content. Eliza is actually my fifth generation framework. Initially, it was a simple terminal program developed in JavaScript, then I tried a Python version, and also developed 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 a few startup projects, such as co-founding Magic with Parzival, the founder of Project 89, 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 get 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 distinctive. 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 scammer', '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 Jupiter founder Meow, I conceived the idea of creating an AI investor. Our vision is to build:
A fully autonomous investor
A trustworthy and non-runaway investment system
An investment system that serves the entire community
When we launched, we set a fundraising target of 4,420 SOL, 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 a lot of 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 of all, I need to clarify that this is not the first autonomous investment AI agent, other developers have also done great work.
Currently, there are several types of trading bots on the market:
Some are for long-term investment, like buying GOAT a month ago and holding it
There are also DeFi bots that mainly do MEV arbitrage or manage 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 is performing poorly
Hold assets when market conditions are good
We are working with partners like Sonar to develop automated trading strategies
2. Community interaction mechanism
Accept trading suggestions
Set up a format similar to an alpha chat room
Established a trust ranking, to assess who are the best traders
Community members can share their investment suggestions (commonly 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 who abuse trust, we have set up protection mechanisms, and the cost of abusing trust is losing credibility.
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. Personally, I'm not a good trader, 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 recently been exploring an AI-driven contribution quantification system, 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 competitive
2. 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 support
3. Fair Distribution Mechanism
Plan to implement regular airdrops to contributors
Not dependent on social media influence
Incentivize various contributions:
Here is the English translation of the text, with the content inside <> retained and not translated, and no additional analysis or explanation provided:Programming and Development
Documentation Writing
Multilingual Support
Improving Project Accessibility
Bankless: This sounds like it's addressing the pain points of traditional DAOs. The DAO hype in 2020-2021, but people gradually found that flat governance is very difficult, and DAO administrators are often overwhelmed with information. AI agents seem to be able to fill these gaps, as they have wallets, governance rights, and reputation systems, and can make up for the shortcomings of traditional DAOs.
Shaw:
Yes, as a former DAO leader, I have a deep understanding of this. Traditional DAOs have several major issues:
Token holder skew
Holders are rewarded more for holding
Forming a self-reinforcing cycle
Difficulty in injecting new blood
Low management efficiency
Overwhelming information that is difficult to process
Unclear communication channels
Complicated 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
Ensuring continuous value creation
Valuing actual contributions rather than just token holding
Providing stable guarantees for open-source developers
Establishing a sustainable positive feedback loop
AI Marc Andreessen focuses on alpha chat experiences, 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
Trade
Interact with users
Post meme content
Absorb alpha information rather than share
Operate like the real Degen Spartan
Has its own token (Degenai)
Owns an independent wallet with its own tokens, some a16z and SOL
Can trade any tokens it has access to
We provided the initial seed funding
It will not sell its own tokens, but will accumulate them
The tokens are like its "Bitcoin"
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
Gradually expanding the range of tradable tokens
Not only doing trades, but also yield farming and providing liquidity
There will be more interesting collaborations and NFT projects in the future
Considering establishing a venture fund to support the ecosystem
Supporting community-led initiatives of all kinds
Establishing a wide range of partnerships
Currently known to have at least 5 platforms under construction, but may actually have as many as 15
Supporting open-source streaming projects like IOTV
Develop usable AI agents
Build reliable infrastructure
Ensure system security
Promote universal education
Empower user control
Protect data sovereignty
Create their own value
Own their own data
Understand and master technology
Participate in system improvement
Microsoft, OpenAI, etc. gain control through regulation
The government decides what can and cannot be done
OpenAI's models have performed poorly in some aspects
Models often have fixed value biases
A world where a committee decides what AI can say may lead to dystopia
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
Reflecting on the rollout of government aid during COVID
The controversy over Obamacare
UBI may become a political compromise
Keep the passion for learning, as technology is developing rapidly
Pay attention to security issues in development
Don't be afraid of failure, learn from practice
AI Agent Development School - Systematic courses
Eliza framework documentation - Practical guides
High-quality open-source projects on GitHub
First master the development of a single agent
Try the collaboration of two agents
Gradually expand to more agents
Our solutions are:
This model is particularly suitable for open-source developers - they often don't need huge rewards, just 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 Andreessen
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:We want to maintain the authentic characteristics of Degen Spartan. He will:
The Positioning and Competitiveness of a16z
Bankless: What exactly is a16z? It seems to be more than just a DAO, it's more like a product incubation studio, and also a leading open-source team that is driving the entire field forward. Shaw: The positioning of a16z is quite special. It's more like a movement than a traditional organization. We have many people working on various projects, and they create value for the ecosystem in an impressive way. Bankless: How do you see the difference between a16z and platforms or products like Virtuals? Shaw: In fact, a16z 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, and they just take the initiative to do things and create value for the ecosystem in an impressive way. Bankless: It seems your vision is quite grand, so 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 token issuance, we cover it all. 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: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 conflicts of interest that arise 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 to some extent a reaction to the "maximize democracy" problem in previous DAOs. In the long run, when you're facing 30,000 or 100,000 people, this approach will overwhelm the decision-makers. This is 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 proposals. This automation can be extended to various aspects - from collecting opinions to specific execution. Ideally, the DAO would not need any human to operate, it would be fully self-governing, from front desk reception to proposal submission and payment approval, all handled by AI agents. Of course, this is a long-term goal, but this is the direction we want to go.The Explosive Popularity 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 in that we have solved the most fundamental 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, which is a language familiar to most Web and Web3 developers. We kept the framework simple, without over-abstraction, so developers can easily add the features they want.AI Agents and the Future of the Cryptocurrency Industry
Bankless: The cryptocurrency 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 the cryptocurrency field to AI, right? In the long run, what do you think this ecosystem will look like when it matures? Shaw:From the obvious long-term vision, we may reach the AGI (Artificial General Intelligence) stage within 5 to 50 years. Combined with Neuralink technology, everyone will have a second brain and access to 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 very beautiful scene, where everyone can obtain sufficient resources. But before that, during the transition period, 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:
2. Spiritual mission:
Just like the core idea of Web3, we hope that everyone can:
Two paths for AGI development
1. Centralized control path:
I am very concerned about this path because:
2. UBI (Universal Basic Income) path:
But I have concerns about the implementation of UBI:
Advice for new developers
Bankless: If developers are 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 every 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:
Bankless: Do you have any good learning resources to recommend?
Shaw:
Bankless: Can you introduce us to Agent Swarming?
Shaw:
Agent Swarming is the technology of having multiple AI agents work collaboratively. For example, one agent collects data, another analyzes it, and a third generates a report. These agents cooperate with each other to complete more complex tasks.
For developers who want to try this technology, I suggest:
