Crypto AI Depth Discussion: From Industry Status to Investment Opportunities

Host: Alex, Investment Researcher

Guests: Max, YouTube channel host; Lydia, researcher at Particle Network

Understanding Crypto AI

Alex: Today, let's talk about the hot topic of Crypto AI. First, I would like to ask the two guests to share their views on the Crypto AI sector. In your opinion, what business problems is Crypto AI trying to solve? How urgent are these problems?

Max: I think Crypto AI primarily addresses two issues:

1. Decentralization: Address issues such as censorship in centralized AI.

2. Incentive Mechanism: Incentivize open-source models and decentralized development through tokens.

For example, Bittensor incentivizes different subnetworks to research AI through tokens, solving the problem of difficulty in obtaining rewards in open source. Crypto allows AI to develop more openly and in a decentralized manner.

Lydia: From a commercial value perspective, I don't think the urgency of Crypto AI is apparent at the moment.

1. AI needs efficiency improvements rather than decentralization.

2. Ordinary users do not care about the open source and data black box issues of AI.

3. Using blockchain to solve the trust issue of AI currently has a higher cost.

Crypto AI opens up people's imagination more on a narrative level, allowing the collision of the two cutting-edge technologies, Crypto and AI. It may address future issues rather than current ones.

Alex: To summarize, Max believes that Crypto can make AI more decentralized and open, while Lydia thinks that the current commercial value of Crypto AI is still unclear and is more of a cutting-edge experiment. Both viewpoints are insightful.

Classification of Projects in the Crypto AI Track

Alex: Crypto AI is a larger track with many different business model projects inside. How will you categorize these projects?

Lydia: A common classification method is:

1. Crypto empowers AI: improving certain aspects of AI from the Crypto perspective, such as privacy, transparency, etc.

2. AI Empowered Crypto: Crypto projects incorporate AI attributes, such as AI chatbots, AI participation in strategy formulation, etc.

Currently, there are more of the second category, but the first category has greater potential, although the difficulty of implementation is higher.

Max: I mainly divide it into three levels:

1. Architecture Layer: Underlying infrastructure, such as Bittensor, Near, etc.

2. Resource Layer: Provides the resources required for AI, such as Akash( computing power), Vana( data), etc.

3. Application Layer: User-facing AI applications, such as various AI agents.

This classification is quite similar to the layering in the blockchain industry.

Opportunities and Challenges of Crypto AI

Alex: What are the main challenges currently facing Crypto AI? What industry or narrative opportunities might arise in the next 1-2 years?

Max: The main challenge is:

1. The project valuation is too high, more speculation.

2. There are relatively few practical applications, many of which are still in the vision stage.

3. The incentive mechanism may create a death spiral.

In terms of opportunities, as the cryptocurrency market warms up and the regulatory environment improves, more resources will be invested in the Crypto AI field to attempt innovation.

Lydia: I think the biggest challenge is the mismatch between market sentiment and technological progress:

1. Crypto practitioners have insufficient understanding of AI, making it difficult to engage in in-depth discussions.

2. Lack of questioning and scrutiny of the project.

3. Many projects are seriously homogenized.

Future opportunities lie in:

1. The long-term and far-reaching impact of AI will continue to be unleashed.

2. It is important to pay attention to the progress of non-Crypto AI, especially topics related to ethics.

Alex: Let me add that AGI may emerge in 2025, and by then, the impact of AI on humanity will be even greater, which may bring new speculative value to Crypto AI.

Notable Crypto AI Projects

Alex: Which 1-2 Crypto AI projects are you currently most optimistic about? Why?

Lydia: I am most optimistic about Bittensor, the reasons are:

1. The team has strong narrative ability and is appealing to developers.

2. Gaining favor from institutions like Grayscale.

3. Experienced large-scale FUD tests.

4. The ecosystem is relatively complete, with an elimination mechanism.

Max: I am also most concerned about Bittensor, here are a few additional points:

1. A well-designed incentive mechanism can sustain the development of the ecosystem.

2. The team is professional and actively solves problems.

Risk aspect:

1. The token issuance is large, which creates inflationary pressure.

2. It is still relatively centralized.

In addition, Vana( decentralized data), Arweave( AI computing), etc. are also worth paying attention to.

Evaluation Strategies for Crypto AI Projects

Alex: When researching and selecting Crypto AI projects, what core factors do you focus on the most?

Max: I mainly focus on five aspects: the team, the product, profitability, future outlook, and the token economic model. Among them, the most important is the team, which includes:

1. Founder Background

2. Strength of Investors

3. Community Atmosphere

Because an excellent team determines whether a project can truly realize its vision.

Lydia: I also value the team the most, mainly looking at:

1. Narrative Ability

2. Execution

3. Understanding of Crypto Depth

In addition, I will pay attention to:

1. The actual function of the Token

2. Is the project's brand culture unique?

Alex: I will combine the cycle analysis; currently, Crypto AI may be in a short-term optimistic phase, and it might be a better long-term investment opportunity during the future bear market.

Sharing Common AI Tools

Alex: What AI tools do you use on a daily basis? What insights do you have from using them?

Lydia:

1. GPT: Practice English, psychological counseling

2. Perplexity: Search for information

3. Doubao: Summary Video

Max:

1. ChatGPT: Summarize articles, generate images

2. Researching the generation of research reports using AI

Alex:

1. GPT: Writing articles, learning new concepts

2. Perplexity: Alternative to traditional search engines

AI has great potential in the field of education and may disrupt traditional classroom models in the future.