AI Token Investment Guide: 6 Projects Worth Following

Artificial intelligence has become a hot topic, but do you know how to buy the dip on AI concepts in the crypto market? Today, I will take a look at the real strategies of 6 mainstream AI Tokens, helping you avoid detours.

Three Major Tracks, How to Classify AI Tokens

AI Tokens can be roughly divided into three categories: Data Sharing, Computational Optimization, and Application Layer. Each category has a different investment logic.

Data-sharing type: feeding AI with data

Ocean Protocol (OCEAN) has a simple core logic - if AI doesn't have good data, it won't run fast. OCEAN builds a decentralized data exchange that allows data providers to securely sell datasets, and AI models can access them directly. This not only solves data privacy issues but also makes data more valuable.

Similar to this is Numerai (NMR), which uses a black technology: allowing data scientists to upload AI prediction models to participate in competitions, with the winners able to earn NMR Token. It is equivalent to bringing Wall Street's quantitative strategies on-chain.

Optimization Type: Making AI Run Faster and Cheaper

The selling point of Fetch.ai (FET) is autonomous agents – it allows AI not only to learn but also to make decisions on its own. This is particularly valuable in supply chain management and DeFi automation. The Token FET serves as the “fuel” for this network; the more frequent the transactions, the more valuable FET becomes.

DeepBrain Chain (DBC) is taking a different path: reducing AI computing costs. Training a large model is extremely expensive, and DBC uses distributed computing to spread this cost while protecting data privacy. This is a savior for small and medium AI teams.

Application Layer Type: Directly embedding AI into smart contracts

SingularityNET (AGI) aims to be an App Store for AI services—developers publish AI algorithms, and users pay with AGI tokens to access them. It is simply a cloud service for AI.

Cortex (CTXC) is more aggressive: it directly moves AI models into blockchain smart contracts, supporting complex calculations such as image recognition and NLP. However, this is also its risk point—on-chain computation is expensive, and the usable scenarios are limited.

What should investors look at

Level One: Can Technology Really Be Implemented?

AI + blockchain sounds great, but many projects are just hype. Focus on: what the team has done, which chains the project is deployed on, and whether there are actual users using it. Don’t be fooled by the white paper.

Level Two: Policy Risk

AI regulation is tightening globally, and data privacy regulations are also being upgraded. Project compliance directly affects future survival space. This aspect is the easiest to be overlooked and the easiest to fall into pitfalls.

Level 3: Ecological Maturity

Are there any large enterprises or platforms that have integrated? Is there actual trading volume and usage data? Popularity and actual user growth are two different things.

Honestly

AI Token is indeed a big cake, but this market is still very new. Regulatory uncertainty and rapid technological iteration pose questions about whether projects can survive.

If you want to enter the market, it is recommended: first research the technology and the team, then calculate the investment return. Don't just look at the price increase; consider whether this project solves real problems or is a false proposition. At the same time, prepare yourself psychologically - these types of projects are highly volatile, and there may be opportunities for quick wealth, but you could also lose everything.

The earlier you enter a project, the greater the risk, but if you catch the right trend, the returns can also be substantial. The choice is yours.