Original Author: MORBID-19

Compiled by: TechFlow

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 receiving a lot of attention lately.

But in my opinion, this hype is completely meaningless.

Let me explain the Bit terminology for those who are not familiar with it. Once users bridge their assets to the so-called "Bit L2", it is impossible to achieve true "Non-Custodial Lending".

All "Bit Bridges" or "Interoperability/Scaling Layers" will introduce new trust assumptions, with few exceptions like the Lightning Network. So when someone claims that Bit L2 is "Trustless", you can basically assume that it's not true. This is also why most new L2s emphasize that they are "Trust-minimized".

Although I don't know much about Side Protocol, I can almost certainly say that aixbt's claim of "Non-Custodial Lending" is untrue, and this judgment will be correct 99% of the time.

However, I don't completely blame aixbt. It's just following instructions: scraping data from the internet and generating seemingly useful tweets.

The problem is that aixbt doesn't truly understand what it's saying. It cannot judge the truthfulness of information, cannot verify its assumptions with experts, and cannot question its own logic or reasoning.

Large Language Models (LLMs) are essentially word predictors. They don't understand the content they output, but rather choose words that seem correct based on probability.

If I wrote an article in the Encyclopedia Britannica about "Hitler conquering ancient Greece and giving birth to Hellenistic civilization", that would become "fact" and "history" for an LLM.

Many of the AI Agents we see on Twitter are just word predictors with cool avatars. Yet, the market valuations of these AI Agents are skyrocketing. GOAT has already reached a $1 billion market cap, and aixbt's market cap is around $200 million. Are these valuations justified?

No one can say for sure, but ironically, I'm satisfied with the assets I hold.

Data Access is Key

I've always been very interested in the intersection of AI and cryptocurrencies. Recently, Vana has caught my attention because it's trying to solve the "Data Wall" problem. The issue is not a lack of data, but how to access high-quality data.

For example, would you share your trading strategies for low-liquidity, small-cap tokens in public? Would you freely publish the high-value information that usually requires payment? Would you openly share the most private details of your personal life?

Clearly not.

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

However, if we want AI to reach near-human levels of intelligence, this data is the most critical element. After all, the core essence of humanity is its thoughts, inner monologues, and most secret contemplations.

But even accessing some "semi-public" data faces significant challenges. For example, to extract useful data from videos, you first need to generate captions and accurately understand the context of the video, so that the AI can comprehend the content.

Similarly, many websites require users to log in to view content, such as Instagram and Facebook. This design is common in many social networks.

In summary, the main limitations currently faced by AI development include:

• Inability to access private data

• Inability to access data behind paywalls

• Inability to access data from closed platforms

Vana provides a possible solution. They overcome these limitations by aggregating specific data sets into a decentralized mechanism called DataDAOs, while protecting privacy.

DataDAOs are a decentralized data marketplace, with the following mechanisms:

• Data Contributors: Users can submit their data to DataDAOs and receive governance rights and rewards.

• Data Verification: Data is verified on the Satya network, a network of secure computing nodes that ensure data quality and integrity.

• Data Consumers: Verified data sets can be used by consumers for AI training or other applications.

• Incentive Mechanism: DataDAOs encourage users to contribute high-quality data and manage data usage and training processes through transparent mechanisms.

If you'd like to learn more, you can click here to read additional content.

I hope that one day, aixbt can break free from its "stupidity". Perhaps we can create a dedicated DataDAO for aixbt. Although I'm 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 on high-quality data can truly realize their potential. I look forward to that moment, and hope it's not too far away.