AI Intelligent Agents: Why is GOAT, valued at $1 billion, still a mechanical text generator?

Data access is key.

Author: MORBID-19

Compiled by DeepTechFlow

Hello everyone, it’s a new day and another speculative bet. Recently, AI Agents have become a hot topic of discussion. Especially aixbt, this product has been followed closely.

But in my opinion, this craze is completely meaningless.

Let me explain to the unfamiliar fren about BTC terminology. Once users bridge their assets to the so-called ‘Bitcoin L2’ network, true ‘Non-custodial Lending’ is not possible.

All ‘BTC Bridges’ or ‘Interoperability/Scaling Layers’ will introduce new trust assumptions, with few exceptions such as the Lighting Network. Therefore, when someone claims that BTC L2 is ‘Trustless’, you can basically assume that this is not true. This is also why most new L2s emphasize that they are ‘Trust-minimized’.

Although I am not familiar with the Side Protocol, I can almost certainly say that the so-called ‘unmanaged lending’ statement by aixbt is not true, and this judgment will be correct in 99% of cases.

However, I do not blame aixbt entirely. It is just acting according to instructions: fetching data from the internet and generating seemingly useful tweets.

The problem is that aixbt doesn’t really understand what it’s saying. It can’t judge the truth of the information, can’t verify its own hypotheses with experts, and can’t question its own logic or reason.

The essence of Large Language Models (LLMs) is simply a word predictor. They do not understand the content they output, but select seemingly correct words based on probability.

If I wrote an article in the “Encyclopedia Britannica” about “Hitler’s conquest of ancient Greece and the birth of Greek civilization,” for LLM, this would become a “fact” and “history.”

Many AI agents we see on Twitter are just word predictors with cool avatars. However, the market capitalization of these AI agents is soaring. GOAT has reached a market cap of $1 billion, while aixbt’s market cap has also reached around $200 million. Are these valuations reasonable?

No one can be sure, but ironically, I am satisfied with the assets I hold.

Data access is key

I have always been very interested in the combination of AI and Cryptocurrency. Recently, Vana caught my attention because it is trying to solve the problem of “Data Wall”. The problem is not the lack of data, but how to obtain high-quality data.

For example, would you share your trading strategy for low liquidity small market cap tokens in public? Would you freely release high-value information that usually requires payment to obtain? Would you publicly share the most private details of your personal life?

Obviously not.

Unless your private data can be protected at a reasonable price, you will never easily share these ‘private data’ with anyone.

However, if we want AI to achieve a level of intelligence close to that of humans, this data is the key element. After all, the core traits of humans are their thoughts, inner monologues, and most secretive thinking.

But even getting some ‘semi-public’ data faces considerable challenges. For example, to extract useful data from a video, it is necessary to generate subtitles first and accurately understand the context of the video, so that AI can understand the content.

For example, many websites require users to log in before they can view content, such as Instagram and Facebook. This design is common in many Social Web.

In summary, the main constraints faced by current AI development include:

Unable to access private data

Unable to access data behind paywall

Unable to access data from closed platform

Vana provides a possible solution. They break through these limitations by protecting privacy and aggregating specific datasets into a Decentralization mechanism called DataDAOs.

DataDAOs is a Decentralization market for data, and it operates as follows:

Data Contributors: Users can submit their data to DataDAOs and gain governance rights and rewards as a result.

Data validation: The data will be validated in the Satya network, which is a network composed of secure computing Nodes, ensuring the quality and integrity of the data.

Data consumers: Verified datasets can be used by consumers for AI training or other applications.

Incentive Mechanism: DataDAOs encourage users to contribute high-quality data and manage the use and training process of data through a transparent mechanism.

If you want to learn more, you can click here to read more.

I hope that one day aixbt can break free from the “stupid” status quo. Maybe we can create an exclusive DataDAO for aixbt. Although I am not an expert in the AI field, I firmly believe that the next major breakthrough in AI development will depend on the quality of the data used to train the models.

Only AI agents trained with high-quality data can truly demonstrate their potential. I look forward to this moment and hope it won’t be too far away.