Moderator: Alex, Research Partner at Mint Ventures

Guest: Max, Youtube channel "Max's District" Leader of "Blockchain Space"; Lydia, former researcher at Mint Ventures, currently working as a researcher at Particle Network

Hello everyone, welcome to the website hosted by Mint WEB3 Mint To Be sponsored by Ventures. Here, we continue to ask questions and think deeply to clarify facts, explore reality, and find consensus in the WEB3 world. To clarify the logic behind hot topics, provide insight into the event itself, and introduce multiple perspectives of thinking.

This episode is the first episode of the "Current Status and Future of the Web3 Track" podcast series. Let's talk about the much-loved Pay attention to the CryptoAI track. In subsequent series of programs, we will also invite corresponding guests to talk about Defi, Meme, public chains, Depin, games & social networking, Payfi, and web3 related topics.

Statement: The content we discuss in this podcast does not represent the views of the institutions where the guests work, and the projects mentioned do not It does not constitute any investment advice.

Alex: Today we will talk about the much-watched Crypto AI track. We invited two researchers who have been paying attention to the Crypto AI track for a long time. One is Max, who is the host of the YouTube channel "Max's Blockchain Space". The other one is Lydia, who is a former researcher at our Mint Ventures and currently works as a researcher at Particle Network. In addition to Crypto AI, an area she has continued to focus on is chain abstraction. Ask the two guests to introduce themselves.

Max: Hello everyone, my name is Max. I work as an aerospace engineer at Web2, but in the evenings and weekends I become a cryptocurrency researcher, occasionally doing some research and posting it to YouTube and writing research reports on Substack. I’m very happy to be here to talk to you about Crypto AI. What I like most about this bull market isLooking forward to a narrative, thank you.

Lydia: Hello everyone, my name is Lydia. I have been paying attention to the AI ​​track since the end of last year. AI and chain abstraction are the two most important new narratives in the application layer of this cycle. I am very happy to communicate with you today.

Understanding of Crypto AI

Alex: I feel it is the right time for us to talk about this topic today. The first is that many projects in Crypto AI have seen very good growth today, and in recent days there have been a lot of product developments in the traditional AI field. OpenAI officially released the Pro version of ChatGPT, and the price suddenly reached $200 per month. Sam Altman has also released a lot of product features in the past 12 days. Let’s take a look at some developments and insights on the Crypto AI track in the Web3 world. The first topic is, what do you think of the Crypto AI track? In your opinion, what business problems is the Crypto AI track trying to solve? What is the urgency of these issues?

Max: I think the reason why Crypto AI came out is to solve the two main problems. The first problem is that from a humanistic perspective, centralized AI itself has some problems that need to be solved. For example, we will encounter censorship issues and some problems caused by various centralizations. Crypto AI adds Crypto, which has the effect of decentralization and can have some practices that are more in line with what the public wants. Another thing I think is more interesting is adding an incentive mechanism. The most important representative of Crypto is its Token. With this Token, all decentralized AI things can use this incentive mechanism to make more different attempts. For example, we are now seeing a Project that I like very much, which we will talk about later, called Bittensor. It uses the Token mechanism to create different subnets, and each subnet is responsible for researching different things. In this way, Open Source, that is, open source code, is connected. Open Source is something that everyone wants to do. The biggest problem that all AI researchers encounter when doing Open Source is that there is no way to reward some progress in Open Source. By connecting this thing to Crypto and Token, there is now something that can reward them for continuing to study this Open Source thing.Instead of each company privatizing its research results. Like OpenAI itself, they also want to make AI Open, but now it is more like Close AI. That is to say, you may have to pay to use its model, but this is inevitable because they just need to find a profit model that can support their business model. So I think the most important thing that Crypto AI is doing or can do is that with Crypto and Token, it can be turned into an incentive mechanism to reward open source models, reward Openness, and reward the development of decentralization.

Alex: Got it. It sounds like using Crypto rewards is a completely different path from the current AI development path in the traditional world. Because it is true that most of the mainstream large models are closed source, but in fact not many are open source. And now I see a lot of analysis saying that some open source models may inevitably move towards closed source. Due to the existence of tokens, it is guaranteed that in the field of Web3, many AIs can try open source and develop in a diversified way, while also having a good incentive method. What does Lydia think about this issue?

Lydia: Actually, when it comes to business issues, I think the answer is not particularly clear to me, mainly at the Crypto level. Although there is a popular saying that "AI can improve efficiency, and then Crypto will ensure fairness", in fact, if we think about it carefully, you will find that at this stage, from the perspective of business value, the urgency of improving efficiency is To be significantly greater than to ensure fairness. At this time, I always think of the article Alex wrote in 2022 about the underlying value of Web3. One point mentioned there has a great impact on me, that is: What is the underlying value of Web3? It's greater freedom and cheaper trust. An excellent Web3 project needs to find the shortcomings of traditional services in terms of freedom and trust, and then provide a more competitive solution. Let’s talk about Crypto AI. Does AI need greater freedom? From the perspective of technical implementation, computing resources are limited and data supply is limited, so the freedom of AI is limited. From an ethical perspective, it is difficult for us to imagine a truly free AI. Is the cost of trust in AI too high now? I don't think so. You just mentioned the issue of open source and closed source, and many people will mention data black boxes. But on the one hand, those who care about this are more experts, scholars or media practitioners, not ordinary users. On the other hand, if we use an on-chain approach to solve the problem, it currently seems to cost morehigh. The words I just said may sound a bit pessimistic, but they are from the perspective of solving existing problems and demonstrating business value. Crypto AI is still in a very early stage. I’ve also seen people on social media say what a partner at A16z said: Many important technologies look like an expensive toy at the beginning. Therefore, I think the greatest value of Crypto AI at present may not be directly reflected in the current commercial-level alternatives, but more at the narrative level. It has opened up people's imagination, making Crypto and AI, two seemingly unrelated but very cutting-edge and unique technologies, collide in everyone's minds. So I think we need to give these two technologies time. Maybe the problems they are best suited to solve belong to the future, not the present.

Alex: I understand. Lydia’s point of view is that if you look purely from the perspective of improving efficiency and enhancing product capabilities, the current Web3 or Crypto AI may not be as good in terms of performance. Well, in terms of cost reduction, there is a gap compared with AI products in the relatively mature Internet business world. As for some of the solutions they provide, it seems uncertain whether they can solve some pressing business problems at this stage. It’s just that they provide another set of solutions that intersect with Crypto. It may be a very cutting-edge experiment. In the long run, some interesting things will happen, so we are at such a stage now, right?

Lydia: Yes, I would also like to add one more thing, which is how to look at this track. From the beginning, I felt like it was a long-term exogenous narrative. Speaking of the long term, it’s because AI, especially consumer AI, and the generation represented by GPT have had a huge impact on our real world. It's truly a game changer. Everyone will talk about chatGPT breaking through one million in a few days, and breaking through 100 million monthly active users in two months. In fact, we don’t even need to look at the numbers, we can just look at how often people around us use AI. I remember when I graduated, GPT was version 3.5 released at the end of 2022, and everyone in our class used it within a month. When I graduate this year in 2024, I will not only have to check for plagiarism, but also check for AI. And AI checks are very expensive, costing at least one or two hundred at a time. From the perspective of the capital market, OpenAI has a valuation of 100 billion, and NVIDIA has a market capitalization of 1 trillion, and every time it launches, it basically occupies the headlines of major media. So its changes are really too rapid and too thorough. So if I have this experience, I think AI will not be a one-time trend. It will definitely be a long-term narrative, and may even become the most important thing in the next century.A source of important philosophical topics. It is long-term and at the same time it is exogenous. I also mentioned just now that Crypto and AI really have nothing to do with each other after their birth. Even at the level of talent, there is still a competitive relationship. During Crypto’s bear market of 2022 to 2023, AI’s appeal in this area crushed Crypto. It wasn’t until this year that we started telling the story of how the two empowered each other. In the final analysis, compared with crypto-native narratives such as DeFi and NFT, or change narratives such as GameFi, AI is an external narrative. We can also see that earlier today, the prices of AI narrative assets such as Worldcoin, Render, and Near fluctuated completely based on the situation in the AI ​​industry. The market pulls up before the meeting, but falls as soon as the meeting begins. So I think the long-term exogenous narrative was my initial understanding of Crypto AI, and I still hold this idea.

Alex: Lydia just talked a lot. She thinks that a large part of the popularity of the Crypto track comes from the rapid expansion of AI itself in the business world and its impact on human society. long-term and far-reaching effects. Then this craze reached the encryption circle, which contributed to the popularity and attention of many projects. Is there anything Max would like to add on this?

Max: I think what Lydia said is actually pretty much what I thought, but there is one thing I want to discuss with her more sincerely. You said that AI is an external thing that is already present in Web2, but we initially thought that Crypto and AI were two unrelated things, but they suddenly came together. But I think from another perspective, Crypto AI is the only thing that I think Crypto has a strong demand for AI after the 2020 DeFi Summer. For example, when we talk about GameFi, GameFi is where we add Crypto’s incentive mechanism into the game. But Crypto is the icing on the cake for GameFi. Today GameFi left Crypto. People will not play this game because Crypto’s incentive mechanism is great. People will play this game because the game is fun. So this one is the icing on the cake for me. Then DeFi is on another level, and they will think that I must cross this thing. I may be restricted from using some banking services in some countries, so I must use DeFi. Then this product appeared, and Crypto has a hard demand for DeFi. Crypto in DeFiThere is no doubt about its role, it is something that must exist. I think this is why for a long time when people asked about the role of blockchain in DeFi, people would think that DeFi is a product that fully complies with Product Market Fit. And Crypto AI is the second strong demand that I think can be followed after DeFi, after seeing so many narratives. The reason is as you just said. In 2022 and 2023, after seeing the emergence of OpenAI’s ChatGPT, everyone began to discuss LLM’s AI model. This thing is actually still in a very early stage of being used by users. With the advancement and use of AI, we will definitely find some centralization problems, but we have not discovered them yet. Unlike the financial system in the financial world, which has existed for maybe 100 or 200 years, we have already discovered that there are problems in the financial system. It was not until the financial tsunami in 2008 that we realized that there were some problems in this system and they should be solved. That’s why everyone feels that DeFi is what we need. I think Crypto AI is in the same position. It’s just that users’ exposure and familiarity with AI are not as much as with the financial system, so we haven’t seen people really think about Crypto AI, “I really need Crypto AI.” When it comes to why Crypto is a hard requirement in the narrative of Crypto AI, it is because many things must be implemented by adding incentive mechanisms. As you just mentioned, if you want to become more efficient, I think there are some specific projects that can already do it. Take Decentralized Compute, for example, which has been in the works for a while. When you compare decentralized computing power with centralized computing power, you will find that decentralized computing power is basically the first demand as long as some performance bottlenecks are overcome. You wouldn’t say that you still want to use centralized computing power today, or use AWS or other products like Microsoft Azure because they are too expensive or for other reasons. I seriously believe that if Crypto AI wants to break out of the industry and continue to develop, it must be more efficient, better, and cheaper than traditional products. This must be the case. People will not use Crypto AI just because they want to "support decentralization", but it is better than the original product. This is what Crypto AI is going to do now. We can slowly see this prototype emerge, but we cannot expect Meta to release a free 3.5 Billion LLM model every time. We need to find a way to continue to build this thing. I think this is something that needs to continue to be worked on.

Alex: Got it. One of the points I just got from Max is that you actually admit that many current AI products are in a very early stage, and it seems that their performance and functions are not comparable to centralized AI. But you made an insight, that is, AI will have the same impact on human civilization and business as finance, and it is a very far-reaching wave. AI is something that has just begun, but your prediction is that as AI develops, some of the problems that seem less obvious now will become more serious in the future. It is very necessary to solve these problems in this way in the Crypto field.

Project classification within the Crypto AI track

Alex: The two guests had some discussions on the first question. I think it is very good because we have no consistency. Opinions can provide a more diverse perspective. So let’s talk about the second question, which is the track. Because Crypto AI is a relatively large track, there are many different business models within it to solve different types of projects. Based on your understanding of the Crypto AI track, if you want to classify the projects within these tracks, what kind of logic will you follow?

Lydia: A very common classification method is Crypto empowering AI or AI empowering Crypto. These are two major ideas. At present, we see more AI empowering Crypto, which means that Crypto projects find ways to add some AI attributes. In the past, I might have accessed the API and made a Web3 version of the chatbot that could answer some of my questions about the project, or I used AI to improve the code of my Web3 project, or AI participated in the formulation of my revenue strategy. . Now, the AI ​​agent is issuing coins. These have little to do with the efficiency improvements and fairness that AI can bring, but more to do with the project’s desire for a new narrative. The second idea is that if Crypto empowers AI, the ceiling is indeed higher, but it will be more difficult to implement and prove, and it will take more time. The holy monument of Crypto's direction of empowering AI is that Crypto can go deep into the AI ​​technology stack and strengthen its privacy and transparency, but this implementation cycle may be a little longer. So at present, it is more about starting from a certain aspect of the AI ​​industry where Crypto has the opportunity to improve. For example, Max just mentioned that making GPUs can focus on Crypto’s ability to aggregate and stimulate idle computing resources, reduce costs, and then do it later. Data market and algorithm market, they all want to find product market fi from the perspective of freedom.t. But I may have mentioned above that I think the demand in this area is not particularly easy to prove at this stage. If you look at IO's GPU usage data, you will find that the proportion of individual users is actually relatively small. The total daily GPU rental revenue for individual users may be around $1,000. In this part, I think the current breaking point, or exception, may be that coinbase and base are working in this direction, which is AI agent plus payment. Of course, the payment attribute is the icing on the cake, so the premise is of course that the AI ​​agent must be good enough and useful enough. These are my two ways of classifying them.

Max: I mainly divide it into three different tracks. The three tracks are the architecture layer, resource layer and application layer. The architecture layer is more like an underlying architecture. You can develop different AI projects on this underlying architecture, and can allow various resource layer projects or application layer projects to be built on this architecture layer. If you know more about blockchain, you may think of it as a layer 1 blockchain and other infrastructure, called the architecture layer. Things like Bittensor, Near, and Sahara, I tend to include them in the architecture layer. After the architecture layer is built, there will be a resource layer above it, which is built on top of this architecture layer. That is the resources that various AI development will need, such as computing power, data, models, etc., and what is built on top is called the resource layer. Some example projects such as Akash or Render provide decentralized computing power, or projects such as Vana that can provide decentralized data are called resource layers. On the resource layer and architecture layer, what is closer to to C and what users use is called the application layer. I put AI agents here, which are closer to what users really need. For example, to speed up your use of DeFi, I put them in the application layer. So these are the three main tracks. Because Crypto AI narrative has just come out, everyone still doesn’t know how to classify it properly, and there is no consensus method. But this sector structure is a classification that seems to me to be more resonant with the current Crypto track.

Opportunities and Challenges of Crypto AI

Alex: Well, we have just communicated two classification methods on the types of tracks in the Crypto AI track. Let’s talk about a more in-depth question. This question we actually discussed in the first topic is whether there is current demand for Crypto AI.There are proven issues. In fact, I think this is also a core point for many people to challenge the Crypto AI track at the narrative level. They think that Crypto AI is very similar to many previous tracks where PMF has not been found, such as Depin and GameFi. They think it is a point of pure narrative hype, or as Lydia just mentioned, it may be the attention of the business boom in the outside world. Migrating to Web3 provides a speculative opportunity. They think this is the case. We do not give a definite answer to the nature of this topic. But what we do know is that Crypto AI is definitely facing some challenges at the moment. The first question is, what do you think is the main challenge facing Crypto AI now? The second is that in addition to challenges, in fact, in the past one to two years, whether it is Web3 AI or external AI, we believe that the development speed will still be very fast. What kind of industry or narrative opportunities will there be for Crypto AI in the next one to two years?

Max: I think the main challenge is the same as what you said. In fact, I quite agree with Lydia’s point of view. I think Crypto AI is too early at present. Most of the market values ​​have risen very high. For example, Bittensor has risen to a market value of 5 Billion US dollars. The reason behind this kind of market value may be speculation. I think we really need to find Product Market Fit, or find some applications that can really be used. There are still few applications that can develop these. If you look at these applications, I even think that some things are still in a very early stage. Many things still belong to everyone who has a vision, and then transforms this vision into something they want to speculate on. I think I can use the three tracks I just talked about to help me talk about the different challenges. The resource layer is currently relatively mature. The same thing has been developed in Web2, but it has been transformed into a new form using Crypto. For example, decentralized computing power is a very old track. We can see that projects like IO or Akash or other projects with different decentralized computing power have come out. Just like Lydia mentioned just now, IO currently has fewer retail investors or more individual users, but in fact I think this has a different relationship with the focus of each decentralized computing power. IO may be more targeted at Organizations, like Akash, I remember looking at both. Different projects have different business models. I think the resource layer is relatively mature. We may just lack an opportunity to develop it. Whether it is in terms of efficiency or in what aspects, everyone should increase adoption.I'm not too worried about the resource layer. Back to the architecture layer, I think the things on the architecture layer are more hype, which means that everyone thinks this thing can develop rapidly in the future, which may need to be verified. In the case of Bittensor, they use the token incentive mechanism to allow each subnet to optimize their own AI models. This is actually a rotating flywheel, which means that the higher your currency price, the more you can pay to each node. The higher the token value, the more each node will want to optimize their own AI model. But when currency prices fall today, this will create a feeling like a death spiral, which means that the incentive mechanism can no longer continue. So this is something to pay attention to. As for the application layer, AI agents are still quite popular right now. Lydia and I seemed to have talked about it on Twitter. I think there are still few applications that can really be used to simplify the Defi process or simplify some Gamefi. The AI ​​agents I see so far are more meme-like, that is, you see a virtual character dancing on it, and then you can use his tokens to send him some tokens to ask him to do different things, etc. I prefer the playful nature and the meme nature. For this thing, I think it can turn everyone's attention to this place, so that everyone starts to really think that the future development of AI agents can simplify some of our uses on the chain and some of our uses of Defi.

In terms of industry and narrative opportunities, I think we are at a good time now. For example, Bitcoin has just exceeded 100,000, and then everyone is paying attention to Crypto. All have moved to the cryptocurrency industry, coupled with the softening of US regulations, the new US president has taken office, including the re-election of the White House, Senate and House of Representatives, and it has become Pro Crypto. In this case, if more attention is paid to the field of Crypto, we will have more opportunities, time, and resources to try different things. I think it is always necessary to find something that is truly valuable to mankind through trial and error, and let the market decide whether this thing can continue to survive. But we are at a very good time, at least it is not a time when the SEC is handing out court leaflets everywhere, telling you to go to court to sue you and confront you. So I think we are in a very good time now, and AI is paying a lot of attention to Web2. Can we turn this attention to Crypto, and then allow more powerful builders to build more useful projects?

Alex: Just now Max mentioned that he should be in North America. I see there is new news today that Trump justAppointed David Sacks as head of cryptography. We finally have a department head who takes care of the encryption ecosystem and helps the development of the encryption ecosystem instead of suppressing it. This is also great news. Please let Lydia talk about her views again, about some of the current challenges, and what are the good industry or narrative opportunities in the next one to two years.

Lydia: Our story is that the Crypto AI track is in its early stages. Let me refine it further. I think it may be closer to Gartner's technology maturity. The peak period of the curve. We are now in a stage where the market is very fomo, and the supply side is booming, but it is a mixed bag. The relatively mature one in this track is Agent. Although it has only become popular recently, because it is very close to the C-side and can use many mature technologies of Web2, it seems to be the most well-established. In terms of challenges, I think there may be a mismatch between market sentiment and technological progress. The reason why this happens is that I personally feel that people in Crypto, whether they are doing research, investment, or project work, don’t know much about AI, and they are still at the stage where everyone is collectively making up lessons. This leads to the fact that I have not seen very detailed and back-and-forth discussions on the Crypto AI project, especially those related to doubts. So Agent is an example. This sentiment has been fermenting, but no one has exposed it. It seems that this thing is not that useful and does not fulfill the promised freedom, such as freedom of speech and so on. This is not necessarily a good thing for the long-term development of the industry. Take Luna as an example. If you have watched its live broadcast, you may think that it is a very crude two-dimensional animation character twisting around. It does not even sing or dance. You twist and turn, but the price keeps going up, so you don’t ask. Then other project parties looked at it and asked, is everyone so crazy? Then I will make a similar one for you, and you won’t be able to tell the difference anyway. In the past, there was a shunt between different on-chain versions of meme speculation, but now if there is a shunt for Agent speculation, there will actually be a shunt under the same Agent framework. Because there is basically not much differentiation in the capabilities of these Agents, they mainly do Twitter Posting, so it is essentially a coin issuance theme that is tossed many times. I think the biggest challenge is the mismatch between emotions and actual technological progress. Of course, this will definitely exist for a long time. It’s just about how we view the stages of these two forces.

In terms of future opportunities, from an industrial perspective, I think we can go back to the Max framework and look at the differences between these sectors.cost and demand. For example, we all know that the three major components of AI development are computing power, data and algorithms. Whether they can significantly reduce the cost for users to obtain the same resources is the main source of demand. For projects related to Agent, I think we must move away from fiction and move towards reality. My own master's thesis was about virtual digital people. During the research process, I found that Web2 digital people were very similar to this at the beginning. They became popular in the form of short video Internet celebrities, so there are many more following them. Internet celebrities, spokespersons, idols, hosts and other IP-type digital people have not found PMF, and many resources have been wasted. On the contrary, in the past two years, you have not seen virtual digital people in the news media very much, but what we have seen is that there are more and more digital people live broadcasting on e-commerce platforms such as Meituan and Taobao, and they are very lifelike. Many People can't see it at all. This kind of functional digital people actually find a PMF compared to real people, that is, when they are off work, a power cord is enough, and the cost is very low in the long run. Corresponding to Crypto, how can the AI ​​Agent move from the virtual to the real? I think a more natural direction is to dig deeper into its efficiency improvement from the first category of the classification of my previous question, that is, AI empowering Crypto. , to see which Crypto project can integrate AI in the most elegant way and truly improve the product experience. For example, I will talk casually now about the Solver layer on the chain. It uses AI to analyze and predict the current flow of funds in the market. Will more flow from SOL to BASE or vice versa to BNB? This way, funds can be allocated and allocated in advance. liquidation, thus significantly improving the efficiency of asset flows. The experience brought to the end user is that this product is really fast and really cheap, which exceeds my experience with other products. This is an industry, but from a narrative level, I highly recommend paying attention to the progress of the non-Crypto AI world, especially the kind that will be discussed in mass news media rather than academic forums. This goes back to what I thought was the exogenous narrative attribute of AI. Crypto has been doing well recently, and AI has been quiet a bit, so Crypto AI is equivalent to the AI ​​AgentFi method that has been completed. However, if the market environment changes in the future, or the growth scale of AI AgentFi is limited, then Crypto may Still have to go back to AI to find the topic. What I pay more attention to are topics related to ethics, such as Deepfakes. I think this kind of thing has not been dug out yet. The reason why I don’t give priority to which model and which technology iteration is is because I think people in Web3 don’t understand that deeply, and ethics-related things are more of a common emotional experience, and when it comes to AI ethics, then Crypto has inherent advantages in being open and transparent, so it isYou can make a fuss about it.

Alex: OK, I would like to add two comments here. We just talked about two topics. The first one is AI Agent. Why do we need to talk about this topic again? Because there are really a lot of friends who have come to talk to me about this matter recently. Many people who are investing in Crypto will come and ask us, "What do you think about AI Agent? Do you think it is the next big wave?" Of course the main thing is The reason is because the projects on this track have increased a lot recently, and they are relatively new projects. Our views so far are actually similar to what the two teachers just said. We think this is a theme under the big Meme track, just like the theme of stock trading, the theme is constantly changing. It doesn’t necessarily mean that there has been any breakthrough in the business model, it just means that this subject matter is now recognized by the market. Why do you say that? Because as Lydia said just now, including Max, the current AI Agent does not provide new products in terms of business models. It is more about doing things that can actually be done in the traditional Internet world. For example, it can collect information from the entire network to recommend some tokens that you can buy. What are the reasons for the recommendation? It may send one message an hour or many messages a day. And it just so happens that the secondary market has been very hot recently, and some of the tokens have indeed risen a lot. Everyone thinks it is awesome, as if it is a very powerful AI robo-advisor, and it feels like this. But we feel that actually this matter is not magical when viewed as a product. It is just a lot of things that have been done before. Because of this, everyone is hyping it up. I think it is similar to the Desci decentralized scientific research that was very popular some time ago, and even the squirrels that we hyped up on people and genres. It's just that its attention has been directed here, so everyone speculates on it, rather than talking about how much breakthrough it has at the industrial level. I don't think it is such a concept.

Another narrative we just mentioned is the next one to two years. In fact, I think there is a big narrative. In the first half of this year, including the second half of this year, Musk and Sam Altman both mentioned that in 2025, they believed that AGI, the so-called general artificial intelligence, would be born. According to the product plan disclosed by Sam Altman today, they should also have an AI Agent product from OpenAI in 25 years. I think that by that time, it is very likely that the public will not be fully prepared for what AGI products are in 2024. Because in fact, many of us now use this GPT product, more as a tool. I want to do typesetting, write an article, orThe reporter said that I was going to make a picture and might use it. It is more of an auxiliary decision-making, and has not completely turned into an intelligent agent that is very similar to humans. We have not yet encountered such a moment. But I think such a moment is likely to come in 2025 or 2026. At that moment, a person's labor value and even existence value will be greatly impacted. So I think when this kind of economic and commercial impact on the human level comes, everyone's emphasis on AI and their sense of crisis will reach a whole new level. By that time, the spillover attention and market attention should bring great speculative value to AI in the Crypto world. Therefore, in the long term, we thought that the upside potential of Wordcoin tokens should be quite large. Because one of the things that Wordcoin wants to solve is how to identify real people and so-called AI agents. This matter seems to be less important now. It seems that there are not so many AI agents running around. What are the needs for identification? But I think that maybe after 2025, there will be a big sense of crisis in experience. They will feel as if this value has become a real value. In addition, the emergence of a large number of AI agents will reduce labor costs to very low levels, and many people may lose their jobs, especially many white-collar jobs. At that time, the so-called universal income that Wordcoin focuses on will distribute money to everyone and ensure their basic survival income. This point will become a point that many people may find valuable. So I think this narrative may also be a very important point of public impact in the next one to two years.

Targets of Crypto AI projects worthy of attention

Alex: Let’s talk about a more specific topic. In fact, many of the people who follow our channel are Crypto investors. They would like to know a question, that is, if the two guests choose one or two of the AI ​​projects that you currently know about as the most worthy of attention, which one would you recommend? one? Tell us the reasons for your recommendation and what potential risks you think this project may face.

Lydia: The first thing I thought of was Bittensor. I just saw that its price is close to a new high, and it may hit a new high today. Let me talk about three aspects. I won’t talk about its technical architecture and specific token economy today. In fact, what impressed me most about this team and this project is that their narrative skills are very top-notch. I feel like this is an aspect that gets mentioned all the time, but many people don’t actually discern its true importance. I’ve watched a lot of Bittensor’s videos on YouTube, as well as their comments in the community and on social media. their team presentedHis image is particularly popular with developers. He is very kind and sincere, but also very ambitious. You feel like they are telling you with a pair of innocent eyes like a deer that I am going to do something great. Are you willing to support me? This makes it difficult for many developers to say no. And I can tell that several of the main members of their team should be fans of Hayek. They quote Hayek’s views on free markets and neoliberalism in many videos, and they also use them in the design of their own token economy and In the process of resource allocation, we will consciously borrow this somewhat experimental approach. This is very popular with investors who are interested in capital markets and neoliberalism. And they do various activities to strengthen this impression, including live broadcasts, documentaries, and various offline gatherings. The image of this team and the fact that what they do cannot be falsified in the short term, and it is on the forefront, which leads to my feeling that Bittensor has many fans and high quality, including many well-known institutional researchers and investors. On Twitter, experienced AI or Web3 developers will announce in a high profile that they have joined the Bittensor ecosystem. Every offline gathering of Bittensor will attract a new wave of fans, just like evangelism, and this will also be reflected in the price. This is the first narrative ability.

The second one that may be more practical is adoption by institutions. Grayscale announced the launch of a decentralized AI fund in July this year. At that time, the first batch of projects included Tao, Fil, Near, and Render. But Tao’s share at that time was very low, only about 3%. I thought it was a little strange at the time, but very quickly, probably less than a month later, it announced a separate trust for Tao. Last month, Grayscale’s parent company established a separate subsidiary, Yuma, announcing that it is focused on the development of the Bittensor ecosystem, and that it is the founder and CEO of Grayscale’s parent company who will also serve as the Tao ecosystem. CEO of a subsidiary of the system. Among the holdings of Grayscale and some other relatively large institutions, I think this is an unprecedented treatment. And Tao is very young. Many people don’t know that it is actually a project that only issued coins in 2023. This makes its position very different.

The third point is that this project has experienced large-scale FUD, but it has shown vitality. This is different from many current AI projects that have become popular as soon as they come out. I remember that in March, there was a lot of content on Twitter targeting Bittensor, whether it was its subnet, its token economy, or FUD about the team.. Its price has been falling all the way, from March to July and August, the price basically dropped by two-thirds, to a minimum of more than 200. But before this wave of Pre-AI Agent, probably in September, the price basically rebounded quickly and did not continue to fall back. Therefore, this process shows that the project is viable and has development ideas. If you look at what the current Bittensor subnet projects are doing, you will find that they are very different from what you saw in March. Some researchers have also done some work on the distribution of various subnets in the Bittensor ecosystem. In fact, it already has the prototype of an AI ecosystem. One of the reasons I am more concerned about this project is that Tao can basically create a final combined presentation of various baskets of tokens in its ecosystem. In addition, each of its subnets is actually a separate project. Ardent and loyal fans like Tao will feel that essentially all AI projects can be included in the Tao ecosystem and scheduled uniformly, using Tao as an intermediary token. So it is a relatively complete ecosystem, and it has an elimination mechanism. For example, if Agent is very popular recently, there may be more competitive projects in the Agent area, eliminating the previously weak projects in terms of token capture and token emission. So it is another process of replacing old water with new water.

Alex: Okay, Max can come and chat too. It’s okay if Max pays the most attention to Bittensor. You can tell your perspective.

Max: I think if we want to seriously talk about Bittensor, we could talk about it for a whole episode. Because I myself have written a research article on Bittensor, and then I remember Lydia has also written several articles. What I am most concerned about must be Bittensor, but I can add a few points that Lydia did not mention just now, which are about risks. Let’s talk about why we pay attention to Bittensor first. It’s because I just said that the most important function of Crypto for Crypto AI is the incentive mechanism. You can use this incentive mechanism to make different products, and it is decentralized, transparent and open. way to do it. Bittensor is very simple. It means that when I set up this combination today, I just want to create a good incentive mechanism. As long as I make this incentive mechanism good, Crypto will be able to function well. After completing this function, I will definitely be able to achieve a successful development. Facts have proved that it is on the right path now. As long as its incentive mechanism is done well, the worse models will naturally be removed from its main model.network, or promote new and more powerful subnets or more powerful computer models, and then get more incentives. So what it is doing now is actually to establish this incentive mechanism, and then in the next 5, 10, 20 years, it can continue to make this Yuma Consensus their consensus mechanism, and Bittensor's main main network can continue to develop this Decentralized AI ecosystem. This is a very special thing, it is the first person to do this. Tokenomics, including its issuance in the form of Bitcoin, is the largest total of 21 million. Just like Lydia mentioned just now, her team is very smart, and almost every YouTube video she makes is very technical, but if you don’t have certain basic knowledge, you won’t be able to understand it. It won't talk to you about token prices all day long, it will tell you to seriously raise the various problems you encounter and then solve them. I remember whether it was this year or last year, their main network was hacked some time ago, and they immediately found the source within three or two days and solved everything. This is something that I think is very powerful.

Okay, after talking about the good things about Bittensor, I want to talk about its risks. The first point is that it is built based on Bitcoin's Tokenomics, so its current issuance rate is very amazing. It seems to be in the 20s or 30s percent, which means that it produces a very large number of tokens every day. , so the value of the token continues to be diluted, but with the current market sentiment relatively high, you may not feel the price change. The second point is that although Bittensor now says it wants to build a decentralized ecosystem, their current mainnet is actually controlled by their own OpenTensor Foundation, which is the team behind Bittensor. So they actually have mainnet control, but their hope in the future is to distribute this mainnet control to those who stake their tokens in the form of Proof of Stake, and then let them do community or ecosystem management. . But for now, although Bittensor wants to build a decentralized ecosystem, it is actually a very centralized project. These are two of the bigger risks I want to mention. Bittensor is a fascinating thing. As soon as you come in, you will find that there are a lot of things to learn. It now has fifty or sixty subnets, and each subnet does different things. Some people are even doing DeepFake enhanced detection, some are doing LLM model optimization, and some are doing decentralized resources, decentralized computing power, decentralized data, etc.

In addition to Bittensor, I think there are some things worth looking forward to or paying attention to. For example, like Vana, they are doing decentralized data. They believe that in the future, as more and more data of LLM are used for training, the data comparing Authentic will become more and more precious. Therefore, like Vana, you can use the token incentive model as an incentive mechanism to manage the data you produce. In the future, if other AI applications want to use this data, they will need to pay Varna a certain fee. There are also companies like Arweave, which is focusing on the architecture layer and building AI computers, which is also worth looking forward to. There is also Nier, which is currently doing chain abstraction, and its Incubator Program also supports many different AI applications. These are a few projects worth looking forward to, and I would like to share them with you.

Evaluation strategy for Crypto AI projects

Alex: We have just finished talking about specific projects, so now let’s talk about some investment experiences or methodologies. Both mentioned the AI ​​projects they had seen and the logic they were optimistic about. If we want to abstract these ways of thinking, what dimensions are you most concerned about when researching and selecting Crypto AI projects? In other words, when you decide whether to invest in a project, what are the core factors? If you were to list three to five points, which ones might they be?

Max: When I was doing research and analysis, I mainly divided it into five architectures. The first is the team, the group that directly builds the project, including the founders, VCs who invested behind it, the community, etc. The second is their real product, what is the product they release. Then come to things like their profitability, future prospects and token economic model. Among these five, what I value most is the team. I think investing in cryptocurrency projects is basically investing in new startups. So what is the most important thing in a new company? It’s the team that built this startup, this product. The strength of their team determines whether the product can achieve Product Market Fit, whether it can be profitable, whether it can continue their roadmap, and whether it can create value again. So when I look at the Crypto AI project, the first thing I will look for is who is the founder of the team behind them. After the founder is found, I will expand to see who the people and VCs are who are investing in the project. If we compare Multicoin or some well-known VC projects, they will also do certain research when investing, then I may refer to the investments behind them.Human VC. Then the community is also one of them, including the community in the team that supports this project. Are they talking about the currency price all day long, or are they really talking about the future of the project, what difficulties are we encounter now, and how should we solve it. This is actually a very polarizing thing. You can observe their community and observe whether this project is a project where everyone just wants the tokens to rise, or whether they are really planning for this project, want to participate in this community, and actively Discuss, solve problems, and create better products. Are they staying for the long run, or are they just speculating and hoping to leave after the token rises. So I think the team is investing in all cryptocurrency projects, not just Crypto AI, DeFi and others, which is the most important thing. Whether the team is capable, who are the supporting communities and VCs, what is their reputation, and from what perspective do they participate in the team.

Lydia: Max and I have quite the same point of view. I also look at the team first. When it comes to the team, I mainly look at the ability to tell stories and do things. Bittensor was just mentioned, it has top-notch narrative capabilities. Another one is Virtuals Protocol. Its token $VIRTUAL has now entered the top 100 in terms of market value and has quadrupled in less than two weeks. I actually noticed this project quite early. They started out as a gaming union in Southeast Asia. , it is during this period of transformation that we started to do AI. The first video I saw related to them was that they launched a small game that allows Mario to run continuously through AI. So it is the mechanism of an infinite game. There is a very famous book called "Finite and Infinite Games", which I found very interesting at the time. About two months later, they launched the agent platform. So it fits my aesthetic of a team, which is that they must have ideas and the ability to execute. This attribute of the team can make their projects appear organic and vital. To give a few other examples, I think one is Ronin and the other is Pendle. These are probably the top teams in Crypto that I understand. Therefore, an excellent team, no matter which direction it is, whether it is games, DeFi, or AI, must have a keen ability to capture narratives, and when it is time to make a firm transformation, it must find its most advantageous direction and put it into practice. Follow through.

Because it involves investment, I will definitely look at what its Token means in this project. This is also an aspect that the team has dug deep into, which is whether the team has a deep enough understanding of Crypto and whether they know how toThe attribute of leverage Crypto can better help him, whether it is growth or doing some resource allocation work within the project mechanism. Bittensor's exploration is relatively cutting-edge. Max just mentioned that its inflation may be a risk or problem in the eyes of many investors. But if you think about the fact that it needs to support a huge ecosystem with dozens, or even more than a thousand subnets in the future, it will be difficult to provide adequate incentives without a large amount of emissions every day. I think this may be an exploration done by the team based on their understanding of the free market. Then Virtual is more practical. Its Virtual token is equivalent to a platform currency. If you want to participate in speculation or investment in the above AI Agent, you must buy this token. But what these two have in common is that they have utility since Day 1. The narrative of Crypto AI can be fictitious, but I think this token must be implemented realistically.

There is another point, which may be more flexible, that is, I will look at whether the brand, culture, and community of the entire project are cool. There isn’t a very unified standard, but I have a very vague idea that if a Crypto AI project just says what I can do better than another project, then it’s not cool. A cooler project should emphasize "I am very different, and no one can compare with me. If you have good taste or appreciate me, then you will naturally come to my community." That's probably what it means.

Alex: Got it. I would also like to add a small point of view. Whether it is Crypto AI such as research, or some new tracks in the Web3 field, I think I will make a cyclical judgment. Just now Lydia mentioned the development curve of an industry, some major innovative projects, revolutionary projects, including, for example, if we look at it from the track, DeFi will definitely experience a short-term over-optimism when it first came out, and then it will After the bubble burst, there was a process of long-term excessive pessimism. I think we need to judge the track we want to invest in based on our physical sense. Although its prospects are very good and the potential of the project is great, we have to look at whether it is currently in a stage of short-term over-optimism or whether it is in a good state. In a prolonged period of excessive pessimism. If the commercial value of this track is really clear, it will definitely get out of the long-term pessimistic stage. So I think that for those long-term investors, the best time to participate is when the entire market is in a stage of long-term over-pessimism about this track. For example, you may buy DeFi projects in 2023, or this kind of high-quality L1 project. Although DeFi actually has not seen a particularly large increase this time, and although the recent increase has been okay, buying this kind of project, because you deeply understand its commercial value, you can take a heavy position if you buy it, and dare to hold it for a long time Yes, you don't need to check its price every day, because its business changes will not be as big as the price changes.

I think AI may also be such a model. Judging based on my experience, I think that because Crypto AI is in the first round, its real start-up year may be 2024, and it may be currently in a stage of progress towards short-term over-optimism. It may not have reached the top yet, but we know that when the bear market comes, most projects, especially those in the first year of the first round, tend to fall by 90% or even more than 95%, just like the last round of DeFi. , the same as GameFi in the previous round. But I think the development of AI may be different from GameFi. I believe its long-term vitality will be longer than GameFi, which has more Ponzi attributes. So I think from the perspective of long-term investment opportunities, after this bear market bubble bursts, most AI projects will fall by more than 95%. Of course, this does not mean that Tao may fall from 700 to 70 now. It may rise to 2,000 and then fall back to more than 200 or 100 yuan. I think it may be a good opportunity at that time.

Sharing of commonly used AI tools

Alex: Let’s talk about one last topic. This topic may not necessarily be related to Web3. Our main topic today is to talk about AI. I believe that the two guests will use many AI tools in their daily lives. What tools do you use in your daily life or work? How are they probably used, and what role do they play?

Lydia: The one I use the most is GPT, and there are two main uses. One piece actually has nothing to do with anything particularly practical, it just helps me practice English. I will tell it what I want to say, and can you find 10 ways for me to express it. I think this is very useful. Another part is to be my psychological counselor, because I quite like chatting with GPT. When GPT first came out, many people used it as a chatbot to chat about anything and everything, but I still do that with it now, and because I chat a lot and with a high frequency, GPT is now not useful to me. Very understanding. I only need to ask a very simple question now, and it will be able to help me, based on what I said before, to infer the main sources of the problems I am encountering now. i think itIt gave me a particularly good psychological soothing effect. The second tool is perplexity, which is mainly used for searching. The search is very easy to use, and its resources on various web pages, especially those in English, are very comprehensive. If I see a project that interests me but I don’t have time to read the white paper, I will ask first, that is, directly ask about the difference in Tokenomics design between Project A and Project B, or whether there is any difference in the VE mechanism. I feed it this kind of question and it will find the answer for me. And because it will display the source web page, if I don't understand it very well, I can just click in and see the web pages it refers to, which will improve the efficiency of knowledge summary. The third one is what I have recommended to Alex before. Byte has made a small plug-in called Doubao. It is mainly used when watching YouTube videos. It can summarize the timeline for you on the right side of the page, which is very convenient for you. Go to the section you want to hear most. Here are three tools I use regularly.

Max: I am a heavy user of ChatGPT. ChatGPT is a very good tool for me. I use it as a tool for knowledge absorption. When I read articles or listen to podcasts, I actually finish listening to most of them, because I think ChatGPT is doing a summary, but I still want to hear some small details in person. But when there are some things that I think are important but don’t have time to listen to, I directly paste a PDF of maybe 20 pages, and then ask ChatGPT to help me summarize it. This is a great tool in terms of data acquisition and organization. I think it is a great tool. In addition, because I usually shoot videos on YouTube and write research reports, when I am doing text output, I am not very good at asking ChatGPT to help me change it, because I feel that the articles I write should have my own style, so that the AI ​​will feel like it after it has been changed. Not quite like me. But for image output, I will ask ChatGPT to help me output the image according to various scenarios. This eliminates the need to find a designer to redesign the cover of a video or a research report, etc. I think the most important ones are these two functions. In the future, I plan to input my own investment research framework into it to see if it can help me produce research reports like an AI agent, and then discuss them with each other. It is still under research.

Alex: Yes, I do feel that GPT is what I use the most. I think GPT is useful mainly for two reasons. First, I have a TG channel. Basically, I will update three to four investment memos about Web3 every week, and I will talk about some things that I think may be important this week. News and my thoughts on them. because it can writeIt is very long, and may involve more than a dozen news items each time. I hope to add a small icon in front of each news title so that the article looks richer. Every time I let it sort it out for me, so this is something I use every day. Another point that I find very useful is that when reading books, you often come across some concepts that you may want to think about in more depth. For example, some time ago I was reading the autobiography "Hillbilly Elegy" by J.D. Vance, the next Vice President of the United States, which contained many words related to the United States. It turns out that when I saw it, I thought it had little to do with me, or that I was interested but it was too troublesome to look it up, so I ignored it and skipped it. For example, like neo-evangelicalism, like secularism. Now with GPT, I can ask it directly what is secularism and what is new evangelicalism, and it will tell you very completely what these two concepts are, what their origins are, and the changes in concepts from modern times to modern times. I feel like a teacher who is very knowledgeable and extremely patient, working for you 24 hours a day. It has unlimited educational resources when you study. More importantly, it is peer-to-peer, and everyone’s problem is different. So I feel that in the field of education, GPT’s future potential is unlimited. In the future, as GPT can add more virtual people, and even interact through virtual space, I think teachers in large classrooms will really slowly lose their role. Maybe everyone will interact more with the virtual teacher. Its patience and its teaching methods are really unmatched by current teachers. Another one is the same as Lydia said, which is perplexity search. I remember that after I used perplexity, I haven’t used Baidu or Google search for a long time. It can help you read a wide range of information and give you a very precise answer with sources. So now I’m also starting to pay perplexity membership. Today I saw news that Google is the current search giant, and its CEO Pichai also said that Google’s search will undergo a very big change next year and become AI-centric. He said that people will fully feel the impact of this change. I believe that the future of search will definitely turn to an AI-driven direction. So I think the subsequent use of AI tools may be the same as whether everyone was able to fully use computers 20 years ago. It has the same value in improving productivity.

Thank you to the two guests for their wonderful, diverse and in-depth thinking today. I hope that we will have new programs in the future and can invite you two to continue to communicate with us. Thank you.