The following text is organized from a series of Twitter Spaces #DialogueTraders, hosted by FC, founding partner of SevenX Ventures, Twitter @FC_0X0.

This episode's guest: Timo, AI investor, Twitter @timotimo007

Want Alpha? Play with What You Understand

In Web2, Timo is a VC specializing in the primary market AI sector, having invested in many domestic AI companies across different directions.

In Web3, his trading journey began in 2017 with buying Bitcoin, and in 2019 he engaged in contract rolling, achieving a peak floating profit of over ten million, but lost most of it through several liquidations. Since early last year, he has been trading on-chain, experimenting with inscriptions, token swaps, and pre-sales, and then moved on to zoo tokens and AI.

"Looking at AI targets is completely different from looking at those zoo tokens; you still need to play with what you can understand. If you just blindly follow others, you will only become someone else's liquidity in this market."

Timo's strategy for trading AI targets on-chain can be summarized as focusing on playing the second stage, which means looking for potential returns of over ten times and high upper limits for Alpha, while managing positions carefully, with no single token investment exceeding 15%. So far, this strategy has a win rate of over 90%.

In addition to seeking Alpha on-chain, Timo's large Beta positions are held on exchanges, including BTC, ETH, SOL, DOGE, PEPE, and AAVE.

What to Buy: Which AI Targets Are Worth Attention?

Overall, Web3 AI is actually following Web2 AI, from the application of technology to narratives and talent levels, so the trends in Web3 AI can basically refer to the development of Web2 AI.

Specifically, looking at the industry chain, there are problems that need to be solved at every stage of Web3 AI, and where there are problems, there are new opportunities.

1. Protocol Layer

When the intelligence level of the Agent itself reaches a certain state, Agents should be able to interact intelligently across ecosystems/platforms, and this interaction requires underlying protocols. Currently, no one is working on this aspect.

2. Model Layer

Currently, many models are general large models that understand a bit of everything but not too well. Therefore, if you want the model to perform better and more professionally in a specific field, proprietary data is needed for training the model. Previously, some models specifically for Solana appeared in the market, and more proprietary models are likely to emerge, presenting many opportunities. Additionally, the inference cost of models is also an area that can be improved.

3. Data Layer

Just as people need to find high-quality learning materials, models also require high-quality data. However, most data is ineffective and needs to be filtered, cleaned, and labeled. Scale AI, a Web2 company specializing in data cleaning, is already valued at over $1 billion.

4. Tool Layer

If we compare models to the human brain, they need tools to complete tasks. Currently, many projects aiming to create agents face two issues regarding tools: first, whether they have the capability to intervene; second, whether the tools are willing to let them intervene. Therefore, Timo believes that if an open platform can be created where everyone can access various tools, it would be very promising.

5. Application Layer

In Timo's definition, the application layer consists of various AI Agents. In Web3, we need to focus on applications that can attract the attention of Web3 users and encourage them to use them frequently. This is also why many projects are discussing the AI + DeFi narrative. However, the current implementations are quite basic, limited to fundamental and technical analysis of tokens, and cannot automatically execute desired trading strategies, so there are many opportunities here, but high-quality on-chain data is needed to train the models.

How to Buy: How to Operate in the Second Stage?

The second stage play can be summarized in two steps: first, analyze the fundamentals; second, analyze the market. Fundamental analysis determines whether a target can be bought, while market analysis determines where to buy.

Fundamental analysis can be approached from four aspects:

First, Narrative, which is essentially the product positioning.

The narrative determines a project's upper limit; a good narrative can attract enough capital in the market to create FOMO. Timo suggests focusing on three types of narratives: 1) those that users are likely to use frequently; 2) those that can cut liquidity; 3) infrastructure-related.

Second, Control of Supply, which refers to whether there is a strong whale.

Why play projects with high control of supply (strong whale projects)? In previous discussions with Mai, he explained in detail that strong whale projects have relatively longer lifecycles and greater imaginative space, making it a win-win game for both whales and retail investors. Judging whether it is a strong whale project is also simple; check GMGN for wallet holdings. If the wallets in the front row are mostly small fish that bought early and made significant profits, it is generally a whale project.

Third, Product and Technology.

As the market matures, pure narratives are becoming harder to buy into; attention must also be paid to products and technology. For analysis in these two areas, Timo suggests forming a team if you cannot understand it yourself, with some people looking at the code and others responsible for "feeling" the on-chain activity. It is extremely difficult for one person to complete all the analysis.

Fourth, Team Background.

Timo believes that if the DEV of Open AI can score 100 points, the average level of AI DEV in Web3 might only be around 30 points. However, the industry is gradually attracting more talented individuals, so it is essential to look for "regular troops" and thoroughly verify their resumes and research achievements.

In fact, directly communicating with the DEV team to obtain first-hand information can greatly assist in judging a project's fundamentals, and many DEVs are willing to engage in such communication.

Regarding the entry position, based on Timo's experience, many projects in the second stage do not require urgency because there are usually at least two bottoms, typically when market makers are accumulating, which is generally a reasonable time to build positions. The operations of market makers can be monitored and tracked through their wallets.

How to Avoid Mistakes in the Second Stage?

Pay attention to two aspects.

1. Research must be thorough enough.

There is plenty of time to analyze projects in the second stage, so do not be lazy. Additionally, Timo suggests collecting first-hand information, such as direct conversations with developers, as this information is more valuable.

"I think the biggest problem in the second stage is that the DEV stops working. You think they will continue, but they don't, or they never intended to, but you never contacted them and have no idea about the project's situation, yet you still go to buy in the second stage—that's just foolish."

2. Entry position and position management.

"Always remember not to go all in. As Munger said, if I knew where I would die, I would never go there."

Moreover, building a position requires patience; do not rush to buy enough of what you want. At the same time, avoid so-called technical analysis because the capacity of on-chain pools is limited. If a large holder suddenly dumps or manipulates, it can easily break the "support level" of the K-line, but that does not mean you should enter the market at that moment. Focus on those two points: first, have a thorough understanding of the fundamentals; second, observe the behavior of market maker wallets. Because on-chain volatility is significant, only by mastering these two points can you ensure "actions remain unchanged" amidst fluctuations, thus achieving a high win rate.

Based on Timo's personal experience, most of the targets he buys initially show a floating loss, for example, buying 1% and experiencing a 30% floating loss, but he conducts thorough analysis, so he can accept it. "Because I know it will definitely rebound, so I might not care too much."

How to Value AI Targets?

The reason for asking this question is that valuation determines how to take profits. Timo believes there is no absolute answer or standard for valuation, and he attempted to categorize it:

First type: For a new target in an existing sector, use comparative valuation.

Basically, for an already existing narrative/sector, the market cap of the leading project in that sector is the upper limit for all targets in that sector, and the second leading project is about 20% of the first. However, in the AI field, a new situation may arise where projects that come later in the same sector surpass all previous targets, making the principle of "the second leading project is 20% of the first" no longer applicable.

For example, Timo believes that the AI targets currently in the market only have some leading advantages and lack a competitive moat. If the core DEV of Open AI were to create a framework project, it would need to be reassessed, as the valuation of that project would likely exceed that of ai16z.

Second type: For a completely new project with no reference, valuation can only be based on perception.

"For instance, when AI16Z first emerged, based on my understanding of this thing, I thought it was worth 1 billion; some people thought it was worth more. The same goes for TRUMP; some people think it’s worth 1 billion, some think 10 billion, and others believe it could reach 100 billion, which is why they dared to take large positions at 10 billion. This can only be subjective.

For fast-moving projects, the most comparable factor is sentiment, judging where the spread chain has reached through group chats and social media content."

Returning to how to take profits, Timo's method is:

Assign three valuations to a project: the first stage valuation, the second stage valuation, and the final valuation. If the first stage reaches the target position, sell 30%; if it reaches the second target position, sell another 30%.

"Of course, this is a changing matter and cannot become a fixed standard. In terms of taking profits, everyone should base it on their acceptance of profits and their tolerance for drawdowns. For most people, it might be better to sell early than to face a drawdown."

Is the Fundamental Itself Important, or Is What the Market Thinks It Is More Important?

If it is a super short-term target, what the market thinks it is is more important than what it actually is, because driving up the price during the first wave of FOMO essentially requires attracting attention and liquidity.

However, if you are holding a target for the long term or medium term, you should be more concerned about what it actually is. The iteration of this market is very fast; if you are not capable, you will quickly be disproven.

In the second stage, it is still necessary to spend more time focusing on a project's foundational capabilities and judging its fundamentals, as filtering out some targets and missing opportunities must be accepted, adhering to your trading system.

How to Continue Growing?

Timo mentioned four points:

First, reduce your ego. This viewpoint has been expressed by many, including Zhang Yiming, in different ways; essentially, it is about letting go of your emotions and external noise to see the essence of things clearly.

Second, maintain curiosity and an open mindset. In the Crypto market, what is played at each moment is different, so always keep a curious and open mindset to accept changes. If the market's hotspots shift and mainstream trends change, be able to quickly adapt your strategies.

"Although I still firmly believe that the main trend will be AI in the future, if it is something else, I will also quickly change my approach and strategy."

The third point is to learn to review and reflect. Don't make the same mistakes again; it seems simple but is actually very difficult to achieve.

The fourth point is to accept opinions and doubts. Don't get caught up in whether you agree or disagree; it's more important to absorb good external insights.

The Three People and Three Books That Influenced Timo the Most

The first is Mises, the founder of the Austrian School of Economics, who helped Timo understand the relationship between monetary cycles, government, and human economic behavior during his university years, allowing him to better comprehend the world.

The second is Duan Yongping. "A person who can continuously win in different fields is definitely not relying on luck but on ability. One of his famous sayings is to do the right thing and stop immediately if you are wrong. I believe this is far more important than being smart; being diligent matters more."

The third is Taleb, the author of "Fooled by Randomness." "Many events are actually random; essentially, no one assigns the so-called causal relationships to them. I think the core is to respect objective laws, respect probabilities, and also respect black swans."

The three recommended books are: "Fooled by Randomness," "The Crowd," and "Built to Last," with the third book greatly helping Timo, who was investing in Web2 at the time, to understand what constitutes a lasting company.

In Conclusion

Timo mentioned that since he started playing on-chain last year, he has spent a long time learning what this game is all about, from dev to narrative to market making, etc. Understanding the market rules before participating is very important.

I completely agree.

Previously, when I was playing games at a VC friend's house, he also told me something similar: first determine the purpose of the game designer, then start playing the game. You need to know the game rules and how different aspects think and operate in order to play well. This is actually one of the important reasons why I engage in dialogue with traders.

Thanks again to Timo for participating in the dialogue with traders. Previous Space audio will be updated on Xiaoyuzhou; search for "Dialogue Traders."

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