Opportunity Window: Following the Trend or Shaping It?

With AI being so hot right now, everyone says it’s the general trend, and you really want to jump in and do something. But you always worry: are you truly following a trend, or just blindly following the crowd? This is a soul-searching question for anyone throughout history who has wanted to make a big move.

There’s a saying attributed to ZENG Guofan: “Do not engage in matters that have been profitable for a long time; do not go to places where everyone is competing.” If something is already this hot, with people talking about it in every street corner, isn’t participating at this point just joining the commotion? Shouldn’t we look for our own “Blue Ocean”? But it depends on how you look at it.

Imagine it’s 2018, when China’s real estate market was still quite hot. A high school student says they want to major in architecture in college. If you made a calm judgment then and told them the construction industry was already too hot, that it would eventually become a “sunset industry,” and advised them against it—you’d be helping them. But think about this: the Computer Science major has been popular for thirty years and has always been one of the most difficult majors to get into. If twenty years ago, you told a very bright high school student that Computer Science was too hot and advised them against it—wouldn’t you be ruining their future?

The same question applies today. With the rise of AI programming, many recent Computer Science graduates are finding it hard to get jobs. Does that mean the major has no future? With so many people taking civil service exams today, would you dare tell them “don’t go where everyone is competing”?

Whether or not to enter a field has little to do with how “hot” it looks. Zeng Guofan’s maxim belongs to selection bias.

In this session, let’s talk about how to judge the timing of entry. You can’t accurately predict the future, but you can have a more advanced mindset than just “anti-involution.”

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Trends Are Real #

First, we must have a fundamental belief, a prior: trends exist.

If you dare to come out and do things, you must believe in the existence of trends.

One mistake I see smart people make is over-believing in market efficiency. You might think that if a problem can be solved, someone must have solved it already; if an industry is truly profitable, countless talent and capital would have flooded in, and competition would have quickly driven profits down to zero… If you believe that all valuable information is instantly absorbed, every local advantage is immediately leveled, and every mismatch is quickly corrected, then you’re basically saying it doesn’t matter what you do.

Reality is not like that.

Financial markets are perhaps the closest thing to “efficient markets” because trading is so convenient. We can imagine that if a company has fundamentally good news, the market, upon learning this news, will raise its valuation. With so many traders watching, good news should be “priced in” within a day, so that the stock price seen by ordinary investors roughly reflects the company’s value. Right?

If the stock market were like this, today’s price rise would just be today’s thing; if other news comes out tomorrow, it would take another path. If financial markets were efficient, stock prices should exhibit some kind of “random walk”—right?

Then you need to read a classic paper published in 2012 by Tobias J. Moskowitz and others from the University of Chicago, titled “Time Series Momentum” [1]. They analyzed 58 highly liquid contracts across stock indices, currencies, commodities, and bond futures. They found that returns over the past 1 to 12 months have some positive predictive power for future returns, before partially reversing over longer cycles.

In plain language: if something has been moving in one direction, it often continues to move that way for a while.

In other words, if the stock market has been performing well over the past six months, it’s highly likely to keep rising next month.

This isn’t to say one should “chase the rise and kill the fall,” because this study looks at long-term statistical patterns… but this paper proves that market trends exist.

Behavioral finance has long had an explanation for this [2]: the market under-reacts to news in the short term, and only gradually catches on, thus forming a trend; once it reaches a certain point and more people follow, the market over-reacts.

If this is true for financial markets, it’s even truer for other markets. Information is not instantly recognized by the world, and prices don’t reflect the truth all at once—people’s understanding of things is gradual.

The real question isn’t “is it hot,” but rather “what stage of hotness has it reached.”

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What Is the Window of Opportunity? #

This leads to the core mental tool we want to discuss: the “Window of Opportunity” theory proposed by strategic management scholar Fernando F. Suarez and others in 2015 [3].

This theory states that if you want to enter a field where there are other players and various existing or potential products, how do you know if you’re too early, too late, or just at the right time?

The answer is one sentence: Your window of opportunity for entry opens when a “Dominant Category” emerges and closes when a “Dominant Design” appears.

The “Category” is the name people give to this new thing. For example, once upon a time, people had many names for phones that could access the internet: PDA, palmtop computer, multimedia phone… This meant a dominant category hadn’t formed yet. It was only when everyone unified under the term “Smartphone” that the dominant category emerged: investors and customers no longer asked “what is this thing,” but instead asked “who is leading in this track”; the recruitment market saw related positions, and media coverage shifted from novelty reporting to industry analysis.

If you enter before a dominant category is formed, you must spend a huge cost to educate the market. Customers, capital, media, and regulators don’t understand what you’re doing. You’re basically clearing the land for those who come after you—isn’t that a thankless task?

The emergence of a “Dominant Design” means the industry has basically settled on how this thing should be made. There is a consensus on “what a good product should look like,” with architectures, interfaces, standards, channels, and procurement rules all fixed, even the positions of the top players are set.

By then, it’s too late to enter. You missed the rule-making phase and can now only find small gaps in other people’s rules. It’s almost impossible to provide a groundbreaking innovation; you can only fight price wars and engage in “involution” to earn a meager living.

If you come too early, you’re helping others feed the pigs; if you come too late, you only get the soup. Only by entering during the “Window of Opportunity” between the emergence of the dominant category and the dominant design can you catch the meat.

That is the period of rule construction and, more importantly, the dividend period of creation.

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Case Studies #

Let’s look at three classic cases.

First, the Smartphone and Apple.

Apple was neither the first to make a phone nor the first to make a mobile computing device. Before the iPhone, there were PDA phones, business phones, camera phones, music phones, BlackBerry, Nokia, and Microsoft’s mobile system. Nokia started from communication, BlackBerry from business email, and Microsoft from the desktop—each major player used its own advantages to define the phone.

But at that time, no one clearly knew what a phone was actually for. It was only when mobile internet became common that the dominant category of “Smartphone” was formed.

Following this, and precisely at a time when there was no consensus on “what a smartphone should actually look like,” Apple launched the iPhone—instantly defining a set of dominant designs: multi-touch large screens, mobile operating systems, app stores, and developer ecosystems. Once the dominant design is formed, it’s very difficult for others to enter and compete.

The Android ecosystem exists because Apple was too expensive and not free enough, leaving some space. Later competitors could only fight price wars.

Second, Electric Vehicles (EVs) and Xiaomi.

You might ask, when Xiaomi started making cars, the dominant category of EVs already existed, and the dominant design seemed settled: Tesla was stable, and NIO, XPeng, and Li Auto all had a strong presence. Wasn’t Xiaomi just blindly following the crowd?

Xiaomi gambled that the dominant design of EVs was not yet completely locked: it deepened the concept of “Software-Defined Vehicle,” created a “Human-Car-Home Ecosystem,” and claimed the car’s cabin is the “Third Space.” Xiaomi has an advantage in smart ecosystems and software.

Third, the MP3 Player and Microsoft.

You might not know that Microsoft launched an MP3 player in 2006 called Zune, directly competing with Apple’s iPod.

Actually, its hardware design was decent, and some features were quite innovative, like supporting wireless sharing and subscription models. But it ultimately failed miserably.

The key was that the iPod already existed, and the dominant design of the MP3 market was settled. Without truly sounding the horn of differentiation, that wasn’t your window of opportunity. Plus, with the rise of smartphones later, the entire product category of MP3 players ceased to exist—even fighting a price war was no longer an option.

To enter gloriously, you must leverage the dominant category and define the dominant design.

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The D/N/S Three-Curve Trend Clock #

Using the “Window of Opportunity” analysis, AI is currently at a moment where the dominant category of “Agent Applications” exists, but the dominant design of “how individuals should specifically use them” has not yet been settled.

The Window of Opportunity theory seems particularly useful for major players. But what if I’m just an ordinary person who wants to open a small shop in Harbin—how can I know if I should enter? We need a more general tool.

Let’s synthesize previous wisdom: Rogers’ “Diffusion of Innovations” theory [4], the Bass Diffusion Model [5], Shiller’s “Narrative Economics” [6], Organizational Ecology [7], and the Industry Life Cycle theory [8]…

I asked AI to synthesize all these theories into a simplified mental model called the “D/N/S Three-Curve Trend Clock”:

D/N/S stands for Demand, Narrative, and Supply. To know the best time to enter, we need to see where these three curves are positioned.

The D curve is “Real Demand.” It’s not just people being interested, but people actually spending money. People are truly migrating budgets from old solutions and embedding the new thing into their workflows, even if the product is crappy and they have to endure it… This is the most fundamental driver.

The N curve is “Narrative.” Narrative is the society’s recognition of your thing; narrative determines the legitimacy of this business. Without a proper name, one cannot speak with authority; only with a narrative can there be capital, valuation, budgets, and positions.

The S curve is “Supply”—how many people are competing with you. This curve represents the level of competition crowding and resource locking. Early competitors help you prove demand and educate the market, while late competitors snatch your profits.

Together, these three curves divide an opportunity into five stages:

1. Undercurrent Period: Demand is budding, but there’s no narrative and no supply. This is full of risk. You might think you’ve invented something that solves a pain point, but it might just be a niche hobby.
2. Wind Rising from the Grass: Demand starts to rise, the narrative just emerges, and supply is very low. Experts can start quietly accumulating capabilities and relationships here, because the window is right in front of them.
3. Best Entry Window: Demand is verified, the narrative is legitimized, supply is not yet crowded, and the dominant design is not yet locked. If you don’t act now, when will you?
4. At the Zenith: Both demand and narrative are strong, and supply is rising rapidly. The industry is very lively, with many people and much money. It’s a bit late to enter now, but if you have super-strong capabilities, strong channels, and strong differentiation, you can still get in.
5. Past Mid-Day: Demand growth slows down, the narrative has been blown into a myth, supply is in excess, and the dominant design is locked. Entering now means fighting price wars.

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Applications: Script Kill, Computer Science, and Civil Service #

According to this framework, if you want to open a “Script Kill” shop in Harbin, you need to consider: Has tourism brought more demand to Harbin? Is the Script Kill gameplay already recognized by the public? How many Script Kill shops are there in Harbin now? Do you have the ability to differentiate yourself?

Should you still major in Computer Science today? The demand is still there (digitalization across all industries). The narrative has changed: it used to be just about writing code; now you must be an engineer who collaborates with AI. Regarding supply, it’s indeed quite crowded because of AI involvement. Overall, I think Computer Science is still in the fourth stage, and with the narrative change, a new window might even open—it hasn’t reached “Past Mid-Day” yet.

What about taking civil service exams? Demand has long stabilized and is now shrinking. As local governments’ financial capacities decline, civil service positions will decrease. Narrative-wise, it’s no longer just “seeking a job within the system” but has become a social “risk-aversion myth.” Supply is severely overloaded (competition ratio of 98:1 in 2026). Overall, spending time preparing for civil service exams now is like buying a house at a high price in 2020.

We can also use this framework to analyze a more interesting story: with so many people revolting at the end of the Qin Dynasty, why was LIU Bang the ultimate winner?

CHEN Sheng and WU Guang created the “Dominant Category” of rebellion. Their slogan, “Are kings and nobles born to their rank?” proved to everyone that revolting against the Qin was a legitimate narrative. But they entered too early; investors and users hadn’t figured out how this business should be run, so their main role was market education… they ultimately became tragic pioneers.

XIANG Yu organized effective supply, but he provided an outdated design (the enfeoffment system), which lacked appeal to a public longing for stability. But by then, the anti-Qin narrative was deeply rooted.

Liu Bang not only entered exactly during the “Window of Opportunity” but also provided two new designs—one was a unified order, and the other was the “Three Chapters of Law” to dissolve the public resentment caused by the high pressure of the Qin system. This organized a more powerful supply, and he won in one move.

After Liu Bang, people like HAN Xin and PENG Yue also wanted to revolt, but Liu Bang’s design was already dominant, and the window of opportunity had closed.

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Conclusion #

With the Window of Opportunity theory and the D/N/S framework, you don’t have to talk idly about “times making heroes.”

Whether a business is hot or whether many people are pursuing it is not the essence. The essence lies in the interaction between demand, narrative, and supply, and whether there is a dominant category and dominant design.

We must respect the hard constraint of the time window. If the thing you want to do doesn’t have much real demand yet and even the dominant category isn’t established, you’d better do something else first.

【Ode】

Undercurrents move while voices stay low, The narrative forms as the first winds blow. The category is clear, the gate opens wide, Before the design locks, ride with the tide. Early birds perish in the wasteland’s maw, Latecomers follow the market’s cold law. To turn the tide’s roar into a new way, Is how heroes shape the dawn of the day.

Notes

[1] Moskowitz, Tobias J., Yao Hua Ooi, and Lasse H. Pedersen. “Time Series Momentum.” Journal of Financial Economics 104, no. 2 (2012): 228–250.

[2] Barberis, Nicholas, Andrei Shleifer, and Robert Vishny. “A Model of Investor Sentiment.” Journal of Financial Economics 49, no. 3 (1998): 307–343.

[3] Suarez, Fernando F., Stine Grodal, and Aleksios Gotsopoulos. “Perfect Timing? Dominant Category, Dominant Design, and the Window of Opportunity for Firm Entry.” Strategic Management Journal 36, no. 3 (2015): 437–448.

[4] Rogers, Everett M. Diffusion of Innovations. 5th ed. New York: Free Press, 2003.

[5] Bass, Frank M. “A New Product Growth for Model Consumer Durables.” Management Science 15, no. 5 (1969): 215–227.

[6] Shiller, Robert J. Narrative Economics: How Stories Go Viral and Drive Major Economic Events. Princeton: Princeton University Press, 2019.

[7] Hannan, Michael T., and Glenn R. Carroll. Dynamics of Organizational Populations: Density, Legitimation, and Competition. New York: Oxford University Press, 1992.

[8] Utterback, James M., and William J. Abernathy. “A Dynamic Model of Process and Product Innovation.” Omega 3, no. 6 (1975): 639–656.