Dialogue with trader Timo: After $Trump, is there still a chance for Ai Agent? Timo, who has achieved X times returns, gives you the answer

The following text is organized from the Twitter Space series #DialogueTrader, hosted by FC, founding partner of SevenX Ventures, Twitter @FC_0X0.

Guest for this episode: Timo, @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 in various directions.

In Web3, his trading journey began with buying Bitcoin in 2017, followed by contract rolling in 2019, where he had a peak floating profit of over ten million, but lost most of it due to several liquidations. Since the beginning of last year, he has been trading on-chain, experimenting with inscriptions, token swaps, and pre-sales, and later moved on to zoo tokens and AI.

"Looking at AI targets is completely different from looking at those zoo tokens before; you still need to play with what you can understand. If you just follow others blindly, 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 the second stage, which means looking for potential returns of more than ten times and a relatively high ceiling for Alpha, while managing positions well, with no single token investment exceeding 15%. This strategy has maintained a win rate of over 90% so far.

In addition to seeking Alpha on-chain, Timo's large Beta positions are placed 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-purpose 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. There have been some models specifically for Solana 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 need high-quality data. However, most data is ineffective and needs to be filtered, cleaned, and labeled. A Web2 company specializing in data cleaning, Scale AI, 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 highly promising.

5. Application Layer

In Timo's definition, the application layer is essentially 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 telling the story of AI + DeFi. However, the current work is relatively 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 gameplay 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 start from four aspects:

First, Narrative, which is essentially the product positioning.

The narrative determines the upper limit of a project; a good narrative can attract enough funds 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 a high degree of supply control (strong whale projects)? In a previous conversation 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, early buyers who have made significant profits, it is basically a whale project.

Third, Product and Technology.

As the market matures, pure stories 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 with friends if you cannot understand it yourself—some can look at the code, while others can "feel" the on-chain data. 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, this industry is gradually attracting better talent, 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 help in assessing the project's fundamentals; 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 need to be rushed, as there are usually at least two bottoms, typically when market makers are accumulating. Generally, this is 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 directly communicating with the Devs, as this information is more valuable.

"I think the biggest problem in the second stage is that the Devs stop 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. If you still go to buy in the second stage, that would be 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, as the capacity of on-chain pools is limited. If a large holder suddenly dumps or tricks, it can easily break the "support level" of the K-line, but that does not mean you should enter. Focus on those two points: first, have a thorough understanding of the fundamentals; second, observe the behavior of market maker wallets. Because on-chain fluctuations are significant, only by mastering these two points can you ensure that your "actions remain unchanged" amid volatility, 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 existing narrative/sector, the market cap of the leading project in this sector is the upper limit for all targets in the sector, and the second leading project is about 20% of the first. However, in the AI field, a new situation may arise: in similar sectors, later projects may surpass all previous targets, which may render 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 it is likely that the valuation of this project would exceed that of ai16z.

Second type: For a completely new project without a reference, valuation can only be based on perception.

"For example, when AI16Z first emerged, based on my understanding of this thing, I thought it was worth 1B, while some people thought it would be higher. TRUMP is similar; some think it is worth 1B, some think it is 10B, and others believe it could reach 100B, which is why they dared to take large positions at 10B. 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:

Give the project three valuations: 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 do so based on their acceptance of profits and their tolerance for drawdowns. For most people, it may 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, then what the market thinks it is is more important than what it actually is, because during the first wave of FOMO, essentially pushing up the coin price requires attracting everyone's attention and liquidity.

However, if holding a target for the long term or medium term, one should pay more attention to what it actually is. Because 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 fundamental capabilities and assessing its fundamentals, as filtering out some targets and missing opportunities must be accepted, and one must adhere to their trading system.

How to Continue Growing?

Timo mentioned four points:

First, shrink your ego. This viewpoint has been expressed by many, including Zhang Yiming, in different words; 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; always keep curiosity and an open mindset to accept changes. If the market's hotspots shift and mainstream trends change, be able to quickly adapt your strategies.

"Although I 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."

Third, learn to review and reflect. Do not repeat mistakes; it seems simple but is actually very difficult to achieve.

Fourth, accept opinions and doubts; do not get caught up in whether you agree or disagree. It is more important to absorb good external insights.

The Three People and Three Books That Have Influenced Timo the Most

First, Mises, the founder of the Austrian School of Economics, helped Timo understand the relationship between monetary cycles, government, and human economic behavior during his university years, aiding his understanding of the world.

Second, Duan Yongping. "A person who can continuously win in different fields is definitely not just lucky but capable. One of his famous sayings is to do the right thing and stop immediately if wrong. I think this is far more important than being smart or hardworking."

Third, 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 a lasting company is.

In Conclusion

Timo mentioned that since he started playing on-chain last year, he has spent a long time learning what this game is 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. You need to know the game rules and how different aspects think and operate to play well, which is also a significant reason why I engage in dialogue with traders.

Thanks again to Timo for participating in Dialogue Trader; past Space audio will be updated on Xiaoyuzhou, search for Dialogue Trader.