How to Prepare

For master's program admissions, the importance of different components varies by school and program, but generally, the Statement of Purpose (SOP) and Letters of Recommendation (LORs) play the most significant roles. Work experience, GPA, and test scores are less important and are substitutable.

Application Materials Importance Hierarchy​

There’s a kind of problem that doesn’t feel like a problem—until it’s too late. It doesn’t come with alarms or crashes. It’s quiet. Subtle. It doesn’t hit your servers—it hits your assumptions. That’s the kind of problem Google is facing right now.

People still use Google. The brand is strong. The product mostly works. But something is shifting, and you can feel it if you watch closely. Users are beginning to treat Google not as a destination, but as a backup. Their first stop is now somewhere else—often, something like ChatGPT.

This shift isn’t just about tools. It’s about defaults. And when defaults change, whole industries fall.

How Disruption Actually Feels​

One of the most misunderstood ideas in tech is Clayton Christensen’s Innovator’s Dilemma. People summarize it as: “Big companies get disrupted because they’re slow.” But that’s not quite right. The real trap is subtler: great companies get disrupted because they’re smart. They do the right things—serve their customers, protect their margins, avoid risky bets—and by doing so, they optimize themselves into irrelevance.

That’s the trap Google is in.

Google’s business was built on the long tail. Billions of weird, specific queries:
“how to fix error code 0x80070005”,
“organic chemistry final study guide”,
“best dog shampoo for allergies.”

These were gold. They brought ad impressions, affiliate clicks, SEO empires. But they also taught users something: that the web is a collection of 500-word blog posts, most of which are fluff.

Now, LLMs are compressing all of that into one neat reply box.

Why the Web Is Getting Quiet​

This isn’t hypothetical. Sites like StackOverflow, The Verge, and Quora are already seeing it: a drop in search-driven traffic that seems to correlate with the rise of AI assistants. You can feel it in the ambient silence of the internet—fewer blog posts, fewer tutorial threads, fewer of the answers that used to live five clicks deep.

The irony is that LLMs are trained on that very content. But users don’t care. They’re not asking where the answer came from—they just want it faster, cleaner, and with fewer popups.

And suddenly, Google is the middleman. The one being skipped.

The Wrong Kind of Hard​

Google isn’t blind. They’ve shipped Bard (now Gemini), launched AI search previews, and are investing billions into their own LLMs. But the dilemma isn’t technical—it’s economic.

AI search is expensive. Compute-intensive, low-margin, hard to monetize. Worse, many of the queries LLMs are good at—long-tail informational ones—are the exact queries that brought in little ad revenue to begin with.

So Google is stuck between two models:

1. The old one, which is lucrative but increasingly irrelevant.
2. The new one, which users like, but which might never pay off.

This is what disruption feels like. You see the new thing coming. You even build it yourself. But you can’t make it make sense inside the machine you’ve already built.

What Actually Gets Disrupted​

People think disruption is about technology. It’s not. It’s about value chains.

The entire value chain of search—crawl → index → rank → ads → click—is being short-circuited by LLMs. Not because they’re better at everything. But because they’re good enough at the one thing users actually want: answers.

And that means it’s not just Google at risk. Any product that depends on content discoverability—test prep, recipe blogs, niche tutorials—is in danger too. Their business models assumed that Google would always be the front door. What happens when it’s not?

Where This Is Going​

There’s a temptation to think the dust will settle, that this is a hype cycle, and things will return to normal. But that’s unlikely. LLMs aren’t replacing the web. They’re reshaping it.

We’re not watching the end of the internet. We’re watching a redistribution.

• High-quality, trusted communities (like Reddit and Wikipedia) are still doing fine. Why? Because LLMs aren’t just competing with links—they’re trained on them.
• Specialized tools—compliance bots, internal code copilots, niche search engines—are quietly thriving.
• The next breakout companies in AI probably won’t look like OpenAI. They’ll look like infrastructure. Quiet, sticky, boring—but essential.

What Google Can’t Afford to Forget​

The most dangerous thing about the Innovator’s Dilemma is that it doesn’t feel like a dilemma. It feels like strategy. It feels like protecting your business. And that’s exactly why companies like Google are vulnerable.

If Google wants to avoid becoming Yahoo, it needs to forget what made it successful—and start building like a startup again. Not just with AI features bolted on, but with a product that assumes the old search model is dead.

The scary part? It might be.

Definition of Liberal Arts: Liberal Arts is intended to provide chiefly general knowledge and to develop general intellectual capacities (such as reason and judgment) as opposed to professional or vocational skills.

Growing up in two different countries, China and Canada, I encountered a common trend: faculties were often divided into the Faculty of Arts and Faculty of Science. However, my experience working as an engineer at Presence, a pioneering AR tech startup, has taught me that what is often underestimated in the tech industry is the value of liberal arts education.

In some extreme cases, engineers believe that hard skills like coding are the only skills that matter, while liberal arts education is dismissed as irrelevant or impractical. However, I argue that this is a flawed perspective. In fact, liberal arts education can be just as valuable as hard skills for engineers working in the tech industry.

Limitation of Engineering Education​

Throughout my academic and professional experience in the technology industry, I have come to recognize three major issues that are rarely discussed.

1. Fixed Reward Mechanism​

In academia, technical interviews, and in the industry, the standards for evaluating and rewarding engineers are often fixed. Engineers tend to obsess over code cleanliness, optimization of memory and computation usage, and test coverage. While these standards may contribute to the development of better engineers, they may result in less creative problem-solvers overall. In fact, some experts in the field, like Dan Abramov, have highlighted how an obsession with clean code can be problematic. Although there is value in these standards, they prioritize certain skills over others, and consequently, limit engineers' capacity to be well-rounded creators.

Examples of these reward mechanisms include getting an A in a course because your exam answers were elegant, or landing a job offer because you wrote a perfect algorithm that solved a Hackerrank problem faster than anyone else. Additionally, building a better API product than Stripe does not necessarily mean that people will abandon Stripe and use your product.

Types of Ego​

❌ 1. Insecure Ego (Status-Seeking, Comparison-Driven)​

"I went to the best college. I worked for the best company. I am from the best country."

• Behavioral Pattern: Bragging, Entitlement, Comparison
• Failures in the early life is good to fix this ego. If it’s really severe, go do things that are hard and that humble you—like reading or tackling tough problems—to wear down that ego.

❌ 2. Defensive Ego (Shame-Avoidant, Fear-Based)​

"I don't accept any negative feedback. I will do anything to avoid being criticized."

• Behavioral Pattern: Denial, Hyper-sensitivity, Deflection
• This kind of ego is extremely dangerous and can limit a person’s growth. If you find yourself reacting this way, you need to pause, open up, and learn to accept your flaws. This kind of ego is rooted in the absolute refusal to face difficult truths.

✅ 3. Aspirational Ego (Growth-Oriented, Self-Expressive)​

"I want to be the best version of myself, and I will do whatever it takes to build an outstanding product."

• Behavioral Pattern: Growth-oriented, Self-expressive
• This ego is the most positive and healthy ego. It is the ego that is most likely to lead to success. Do make sure your passion and energy are channeled toward doing the right things—like writing, creating, or building something meaningful.

References​

• Paul Graham's Keep Your Identity Small
• Build your organizational identity
• Understanding the connection between the identity and the ego

For the past year, I have met a lot of entrepreneurs and engineers. I found a common pattern: these knowledgable people not only read more than me but are very smart in choosing the right information to read and digest.

In terms of intelligence, everyone is similar. What matters more is whether a person wants to learn/read and from which source the person learns (the information input). Just like what people always say in the gym -- 'You Are What You Eat', this applies to information and learning.

Information Hygiene 👩🏻‍⚕️​

The first thing to practice is to identify the quality of information. For the modern era, a lot of online media sites and content creators tend to tune their content to maximize the impression rate or view count (if you want to learn how to write news to catch eyeballs 👁 here is a link) a better term to describe the quality of information is Information Hygiene

The following chart is my understanding of information distribution:

Definition of Goals​

• Subjective vs Objective
• Goals can be gamed by blurring its measure of success:
  • ❌Wrong: I made a good product because I think people will like it
  • ✔️Correct:
    • Physical Fact: Humans landed on the moon
    • Economic Fact: People like to pay millions of dollars for a software license per year

First Principal thinking​

• Get to the fundamentals than learning the buzz words
  • ❌Wrong: Learning definitions of Cloud, SaaS, PaaS, Metaverse...
  • ✔️Correct: Understanding FSM "finite state machine", Turning Completeness, Gödel's incomplete theorem... (This is why getting a degree in Business is so less valuable than getting a degree in Engineering. If I give you a book that has the same business strategy as Airbnb, you probably won't build another Airbnb; Whereas, if I give you a snippet of code, whether its 0 or 1, you can justify it in hours)

Why China?​

At the end of 2021, I decided to relocate back to China due to both family and personal reasons. Extended WFH was a huge one: I was losing a ton of passion and efficiency. After evaluating a few opportunities, I decided to join Airbnb, and work out of their Beijing office!

I was talking to my friend who does meditation on a routine the other day. I have always wondered why someone enjoys spending the time to meditate. His theory is that the idea of meditation is to focus on living experience because not everyone is experiencing living. He gave me a few examples:

• When you are enjoying an extremely fancy dinner that costs you 300$, but you know that you are having a technical interview tomorrow morning, you have to go back and sleep early. Are you enjoying the dinner, and tasting every single spice in your food?
• When you are drinking an amazing espresso shot, and someone is knocking on the door which draws your attention away from the espresso. Are you tasting the coffee?
• You can stay in the library for 12 hours and chatting with friends on Facebook. Does that count as studying if your library experience is not studying?

Why​

• Your question begins with “Why”, Problem description

Requirements​

• functional requirements - hard requirements:
  • must achieve
• non-functional requirements:
  • soft, but user experience oriented like latency, search speed, autocomplete…