Original video in Chinese.
Key Takeaway
- Content products are not information; they are a proof system: truly good content products do not rely on hype, but combine cognition, tools, and hands-on practice so people can directly see how a framework becomes a tool and how it is validated in the real world, thereby building trust.
- The three-layer structure is indispensable: using “AI industry chain investing” as an example — the cognition layer provides the underlying logic framework (video course + text); the tool layer encodes the methodology into a reusable Skill (newtype Equity); the hands-on layer publicly shares the index, position changes, and monthly reports, letting judgments undergo real-world testing.
- This is the prototype of content products in the AI era: the three-layer structure demands a very high level from creators (original frameworks, the ability to encode tools, and the courage to accept public scrutiny), so it is hard to replicate. A good content product should be a system of continuous validation, not just a pile of information.
Content products are not information; they are a proof system.
Today I want to share how I make content products.
A lot of people think that a content product just means making a course and selling it. But I don’t do it that way.
Because selling a course is, at its core, selling information. You have to keep explaining to everyone: why am I worth this price? Why should you trust me? What exactly will you get after buying it?
I think a truly good content product should put cognition, tools, and hands-on practice together to form a proof system. It does not rely on hype; it directly lets you see:
What my framework is, how I turn the framework into a tool, and how I use this thing in the real world.
I’ll use the “AI industry chain investing” product I’ve been making over the past two months as an example.
So-called AI industry chain investing means studying companies related to AI infrastructure in the stock market, such as chips, memory, optical modules, servers, data centers, and similar areas.
I chose to make this product because I’m researching it myself, and I’m putting my own money into it. The AI industry chain is a direction I believe is worth tracking for the long term, so I simply turned my research process, tools, and hands-on records into a product.
This product is by no means just a course. I designed it with three layers.
The first layer is the cognition layer.
The goal of this layer is to help you first understand the underlying logic of the AI industry chain.
For example, many people think investing in the AI industry chain just means buying Nvidia.
But in fact, Nvidia is just the brightest star on stage. There are many other players on the entire stage. Even a company as strong as Nvidia still depends on upstream suppliers, downstream customers, and all kinds of supporting links.
Then taking it a step further, how does money flow among these players? Why does everyone suddenly talk about HBM for a while, and then start hyping optical modules? And so on.
What lies behind this is not randomness, but a set of industry chain transmission logic. That is what the cognition layer is supposed to solve — first, establish an underlying framework in your mind for looking at the AI industry chain.
So I made the video course “The Big Logic of AI Industry Chain Investing.” It costs money outside, and is free inside Knowledge Planet. There is also a text version, available in the paid column on my WeChat public account. Everyone can choose what they need.
The second layer is the tool layer.
Cognition alone is not enough. Because when you’re actually facing the market, you’ll run into all kinds of problems. It might be a piece of news, an earnings report, an industry chain event, or a stock that suddenly shoots up.
At that point, you’ll still ask: how should this be viewed? Where does it sit in the industry chain? Who does it affect? Is it just short-term sentiment, or part of a long-term logic?
So I made another Skill called newtype Equity.
After you install it on the Agent, when discussing AI industry chain-related events and stocks, the Agent will use my methodology to analyze them.
In other words, I’m not teaching you and then walking away. I’m encoding my way of thinking into a tool that can be used repeatedly as much as possible. Later in real-world practice, you don’t have to go back and dig through the course every time; you can use this tool to assist your analysis.
The third layer is the hands-on layer.
This layer is the most important.
I can make this product because I am an investor myself. These methodologies are my own distilled experience. That Skill I just mentioned is also a tool I use in my daily work.
Since I’m continuously doing this, why not make the hands-on process public too?
So I made newtype AI Index.
This index contains seven constituent stocks, selected according to my methodology. The rises and falls of the index and its constituent stocks are all shown on my website newtype.pro. Any position changes are also recorded.
That is, the records are public, the process is transparent, and the results are traceable.
In addition, I previously shared that I built a low-frequency signal system. This system produces a report every month, and I also share the reports on the website.
So, how I actually use what I talk about, and whether my judgments can stand up to scrutiny, you don’t have to just listen to me say it. You can look directly at the records.
You see, if I were just selling a course, then I’d be selling information. But when the cognition layer, tool layer, and hands-on layer are combined, what it really becomes is a proof system.
The cognition layer proves whether you have an original framework.
The tool layer proves whether you can turn thinking into something reusable.
The hands-on layer proves whether you dare to put your own judgment out there and let it be tested by the real world.
Put these three layers together, and what you’re selling is not just information, but trust.
And this kind of approach is hard for others to copy, because the difficulty is too high.
To do the cognition layer, you need an original thinking framework.
To do the tool layer, you need to be able to encode thinking into a tool.
To do the hands-on layer, you need to dare to put your own judgment out in public and let time test it.
Most content creators get stuck at the first step — they don’t have an original framework; they’re just rearranging other people’s knowledge.
The second step filters out another batch of people who don’t understand technology and can’t build tools.
The third step filters out almost everyone else who remains. Because this step requires admitting: I might be wrong. And not just admitting it with words, but being willing to let the mistake be publicly recorded.
So this structure demands a much higher level from the creator than making a course does. That’s where my confidence comes from.
Next, I will continue using this structure in other products. Because my judgment is very clear: this is the prototype of content products in the AI era.
A good content product should not just be a pile of information. It should be a system. It should express your cognition, carry your tools, record your practice, and keep accepting validation.
Back to the sentence from the beginning: content products are not information; they are a proof system.
OK, that’s all for this episode. If you want to understand AI, want to reclaim individual sovereignty, and want to find like-minded people, come join the newtype community. See you next time!