Forty years ago, on October 22, 1978, Deng Xiaoping visited Japan for the first time. Traveling from Tokyo to Kyoto, a distance of 370 kilometers, he took the world’s first high-speed train—the Japanese “Shinkansen” Hikari. The Japanese accompanying party asked him what he felt, and he said, “I just feel like there is an urge to run, so we are quite suited to ride such a train now.”
AI also has an urge to run.
In the past two years, NVIDIA’s revenue skyrocketed from $60 billion to $216 billion, and its stock price multiplied tenfold. An investment wave around AI has swept across the globe—optical modules, data centers, cooling, robots, AI applications, one wave after another. Every day there are new stories of rising prices, and there are always people regretting not jumping in earlier.
But while there is an urge to run, before we run, we must first see the road clearly.
AI is the longest track that our generation can encounter. The internet took ten years from 1995 to the IPO of Google, and another eight years to the IPO of Facebook. It experienced the burst of the 2000 bubble in between, with the Nasdaq dropping 78%. AI is likely to follow a similar path—it might still be in a position akin to 1998 or 1999, and the biggest opportunities may only arise after the future bubble bursts, possibly hidden in some corner that no one pays attention to today.
Currently, model capabilities are advancing rapidly, capital is flooding in crazily, and valuations have been pushed to unsettling heights. In this environment, there are two types of people:
One type rushes in to buy now—betting that they timed it right. They might make money, but it’s more likely that they bought halfway up the mountain and then get thrown out by a correction.
The other type waits until the crash happens—but the problem is, when it really crashes, will you dare to buy? Do you know what to buy? If you know nothing about this industry, you will only panic more in the face of fear.
I choose the third option: not rushing to buy stocks now, but to build a “knowledge warehouse.”
Because no matter how AI develops, when the real opportunities arise, we don’t want to miss them—so we must first become experts who understand the entire industry inside and out. The so-called “killer-like intuition” merely comes from the cognition of having “mountains and rivers in one’s chest.”
Starting today, I will begin doing something both clumsy and slow—I will study the AI industry research from a global perspective, bit by bit, clarifying the entire AI industry chain from beginning to end. Who is making money, where does the money come from, where does it flow to, who is irreplaceable, and who is eating what others have left behind.
When the market offers us an opportunity—whether it’s a crash, a correction, or some overlooked corner—I will be able to make a judgment in seconds: “Is this price worth buying in?”
Additionally, my approach will have two differentiating factors:
One is that my investment foundation is solid; I have rich experience and an extremely fast evolution in investing. Over the past three years, my returns have been clear to my long-time fans, and few have reached my level. Of course, the most crucial thing is not the return rate, as that may involve luck; the most important is my universally recognized speed of evolution—I believe the AI era will be even more so, where it's not about who is better, but who evolves faster.
We need not elaborate on the past; starting from now, we shall “wait and see” for the future.
The second is that I focus on one thing: how does this thing make money? My rapid evolution over the past few years largely stems from my focus: I only pay attention to the wealth opportunities behind the phenomena. Most of the articles seen now teach you to use new skills or new GitHub, continually pursuing viral hits and new things. While these things are indeed important, from an investor’s perspective, I care more about the underlying wealth opportunities.
When the iPhone 4 was released, did you, like others, marvel at the phone's design and performance, or did you study the investment opportunities behind it?
This article is the first in a series of research pieces, primarily aiming to do one thing: light up the map. If researching the entire AI industry chain is likened to playing a large open-world game—the first step is not to rush to fight the Boss, but to first light up the map: which major regions, which key nodes, what are the main quests, and what are the side quests. Once the map is clear, regardless of what situation arises later, I can make judgments in seconds.
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The full text can be viewed on the official account, which has much better formatting; Twitter is still more suitable for posting short content.
https://mp.weixin.qq.com/s/exJXyOySRjoSf42jHyEu4A
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