Author: Liu Ye Jinghong

introduction

Recently, I've mostly focused my energy on the AI field, and my output of Web3-related content has decreased accordingly. However, after more than a year of reflection, I've accumulated many new insights and experiences about this industry, which are worth sharing.

Early readers may recall that my writing career began with investment research and analysis of projects and sectors. But I don't know when it started, I rarely wrote these kinds of articles anymore. Behind this lies both an expansion of my personal perspective—allowing me to glimpse the higher-level, more fundamental operating logic of the Web3 world; and a series of changes in my personal resources and wealth concepts.

During this period, friends kept asking me, "How's project X going?" "Is that sector still worth investing in?" I was often speechless, because in the current environment, these questions are difficult to answer definitively.

After a period of reflection and sorting out, I would like to systematically discuss why I have gradually gone from being enthusiastic about investment research and analysis of specific projects to giving up.

Core Concept 1: The Reversal of Information Barriers – When AI Becomes a Tool for Creating Fog

Undeniably, a core profit model in the Web3 industry stems from information asymmetry. For investment research, whoever can identify a project's potential value earlier and make strategic moves in advance will reap substantial returns. However, it is precisely for this reason that I ultimately abandoned this path.

Looking back at 2018 and 2019, I was still doing project rating. Thanks to my computer science background, many blockchain concepts that seemed obscure to outsiders were quite familiar to me. This allowed me to relatively easily distinguish which projects were empty rhetoric and which projects truly had technological merit.

However, by 2025 (note: referring to the current and near-future industry environment), this methodology had become almost obsolete. It wasn't that blockchain technology itself had developed beyond my comprehension, but rather that project teams had become incredibly adept at using the latest AI models to "package" themselves. Projects that were easily spotted as fraudulent in the past can now, with the support of AI, create seamless narratives, technical white papers, and even convincing GitHub codebases.

I might as well be frank: Over the past two years, I've helped some exchanges and project teams write numerous promotional materials that appear "technically professional" to outsiders, but their true authors are actually AI. Even seemingly active project interaction data and on-chain transaction records are often generated in bulk using scripts written by AI.

This means that in the era of widespread AI adoption, the cost of traditional investment research is increasing exponentially. To verify the authenticity of a project, you need to invest far more effort and time than ever before. Public information channels are severely polluted by AI-generated "noise," as if we are watching a "magical clash" between AIs, while truly valid information is obscured by layers of noise. I personally have also tried using AI to analyze Web3 projects, but with little progress, feeling trapped in a vicious cycle of mutually verifying AI-generated content.

Core Concept Two: Decoupling of Value – The Superficial Harmony Between Project Quality and Token Price

To many who haven't yet delved deeply into Web3 investment research, this appears to be a high-return path. Indeed, in the first two cycles, I earned considerable profits through investment research. But that was a relatively "simple" era for the industry—good projects truly did appreciate in value.

Today, Web3 has developed into a highly mature and well-defined industry chain. From project preparation, fundraising, issuance, and promotion to market capitalization management, every step is managed by professional institutions or incubators. Even many KOLs you see have the support of stock exchanges.

As an independent researcher who is "outside" the field, the possibility of conducting research and profiting solely from publicly available information is extremely slim.

A deeper problem lies in the fact that in most Web3 projects, the technical team and the operational team are separate. In other words, while there may indeed be a group of tech geeks dedicated to building exceptional technology, the price movement of the token is not determined by them. During the project's fundraising phase, the market-making power for the token is often transferred to a professional operational team.

Therefore, when a project announces significant positive news, such as a technological breakthrough, it may actually be an excellent opportunity for the trading team to distribute their holdings. This explains the common phenomenon: why does a technological breakthrough sometimes lead to a price crash?

Ultimately, the industry has evolved into what it is today: the quality of a project itself and its token price performance are two completely different things. This is precisely the reason why I find myself unable to answer friends' questions like, "Is the project good? Is the token worth buying?"

Core Concept 3: The Disappearance of Fundamentals – An Era Where Traffic and Sentiment reign supreme

This is perhaps the most disheartening point: in today's meme-saturated world, project quality itself has become irrelevant. Project teams don't care, and most participants don't care either. Traffic and sentimentality have become the sole indicators of a project's success.

I'm also keeping an eye on some projects, such as the highly anticipated Monad ecosystem airdrop, but its overall popularity and community engagement may be far less than a certain suddenly popular meme project.

This precisely reveals a harsh reality of Web3 today: "I'm here on Web3 to make money; my goal is to profit, not to build a high-quality project." When the entire market consensus is based on this, in-depth research into the fundamentals of a project becomes insignificant, even somewhat "out of place."

On the other hand, as I've come into contact with higher-level industries, I've gradually come to understand that the quality of the project itself isn't a key issue when many project teams negotiate with investors or management firms. As long as they choose a seemingly appealing and trending sector, and use AI to weave a compelling narrative, the rest is all about interpersonal relationships and the allocation of resources. As for the project's development progress, that's merely a timeline they use to decide when to distribute resources.

Conclusion: The True Value of Investment Research

My purpose in writing this article is not to completely deny the value of "investment research." On the contrary, investment research itself plays an immeasurable role in broadening one's horizons, deepening one's understanding, and building a knowledge system. It has at least helped me grow from a clueless "newbie" into a participant who can avoid most pitfalls.

However, if your only goal is to make a short-term profit, then I believe that in this day and age, the path to making money solely through investment research based on publicly available information has become extremely narrow.

Nowadays, publicly available investment research content has largely evolved into a "traffic-driving tool." For example, I once spent a month managing an investment research account, and my articles easily garnered tens of thousands of views. However, the end result of this path is often driving traffic to third-party paid communities, which then guide you to buy certain tokens through various means. Ultimately, the profit still comes from "selling tokens." Because I believe this model is unethical and I failed to profit from it, I later abandoned it.

My years of investment research experience have given me a deeper understanding than ever before of Warren Buffett's famous quote:

" Never invest in a business you cannot understand. "

"Never invest in a company you don't understand."

In the past, I thought "understanding" meant understanding the technology and the business model. But now I realize that in Web3, "understanding" also includes understanding the underlying capital structure, the power struggles, and human nature. And these are precisely the things that publicly available information can never tell you.