Google's CEO has admitted defeat.

I saw a news item yesterday. I think it will inspire readers who are involved in product development, so I'd like to share my thoughts with you.

In the latest podcast interview of The New York Times, Google CEO Sundar Pichai admitted that Gemini lags behind in the direction of Coding Agent.

What he meant was that in the past, we didn't have a product entry point that could directly reach developers like Claude Code, nor did we have the high - frequency usage scenarios that Anthropic obtained through Cursor.

So, in terms of real - world usage data, we were at a disadvantage.

Do you see the key logic in this?

Google didn't lose in terms of model capabilities but in terms of products.

It has the most powerful computing power, the largest research team, and the most papers. However, in the Coding Agent product area, it has been left behind by two smaller companies.

The reason is simple. The other companies got their products into the hands of users first and obtained real - world usage data and feedback.

This incident really struck a chord with me because it confirms a judgment I've been making: Technology is not the barrier; scenarios are.

Whoever finds the real - world scenarios of users first and embeds the product into them will get the ticket to start the flywheel.

Anthropic developed Claude Code, allowing developers to write, modify, and debug code using AI directly in the command line.

Cursor integrated AI into the code editor, making it a tool that developers use every time they turn on their computers.

I believe that they don't have stronger technology than Google. Instead, they have applied technology to a real, high - frequency usage scenario that developers encounter every day and embedded AI capabilities into the product.

The more people use it, the more data is generated. The faster the model iterates, the better the experience, and the more people will use it.

Do you notice? This is a classic product growth flywheel.

Google has the technology but lacks the scenarios, so the flywheel can't start. This is the fundamental difference between product thinking and technology thinking.

People with technology thinking will think: My model is stronger, my parameters are more, and my scores are higher, so I should win.

People with product thinking will think: Who are the users? Where are the scenarios? What are the needs? What incremental value can be created?

When developing a product, the core always lies in three things: Who are the users, what are the needs, and where are the scenarios.

No matter how high the model parameters are, if users can't find a daily - use scenario, it can't outperform products with lower parameters that are fed data in real - world scenarios every day.

This principle holds true for the entire AI industry today.

In the past year, AI products have emerged in an endless stream. There are large models, small models, open - source and closed - source ones, general and vertical ones. New products are released every few days, and it's hard to keep up with the news.

But have you noticed a phenomenon?

Most AI products are downloaded and tried once, and then never opened again.

Why?

It's not that the products are not useful, but that they don't solve a specific and high - frequency enough need. In other words, they haven't found a scenario where users will come back repeatedly.

Even OpenClaw, which was very popular recently, is rarely used after installation by many people. It's not that the product is bad, but that many people don't have a usage scenario for it.

The AI products that truly stay are those embedded in your daily work processes. It's not because they are cool or have many functions, but because you can't do without them in certain daily tasks.

I've mentioned in the AI Individual Entrepreneur Training Camp that in the AI era, what really matters is not how many tools you use, but whether you can embed AI into your work processes.

The same logic applies to individuals as it does to product development.

So, the question is, who is most likely to utilize AI to the fullest?

In my observation, it's those who have clear business scenarios, fixed work processes, their own methodologies, and the ability to make judgments and decisions.

As you know, I've been working on the AI Individual Entrepreneur Program since last year. In fact, this is a model that helps ordinary people build an AI team.

Combine methods with AI to empower various work processes.

You are the entire business chain. From customer acquisition to content creation, delivery, and repurchase, every step is in your hands.

You know your own needs and scenarios best. You can identify a problem today and use AI to solve it.

For example, if you are in content creation, AI can help you with topic analysis, writing first drafts, modifying titles, and formulating distribution strategies.

It's not about letting AI do everything on its own. Instead, it's about integrating a proven content creation method and letting AI execute efficiently. We are still the decision - makers.

For example, if you are in consulting, AI can help you collect industry information, draft proposals, and organize customer profiles.

Similarly, a proven consulting methodology is the core here. This is the model I'm using as a product consultant now.

I used to work alone, but now I work with an AI "employee".

Therefore, in the future, there will be two types of people using AI. One type simply uses AI tools and is led by them. The other type integrates methods with AI and becomes the master of AI.

Each scenario is specific. The more specific it is, the greater the value AI can bring.

This is the real structural advantage of AI individual entrepreneurs. It's not that they have stronger technical capabilities than large companies, but that they are closer to the scenarios, make decisions faster, and iterate faster. The key is that they have a proven method.

Let's get back to the Google incident.

There was another thing he said in the interview that impressed me deeply: "The changes that occur in 30 to 60 days now might have taken 5 years in the past."

This means that the window of opportunity is shrinking rapidly.

If you don't integrate AI into your work scenarios today and get it running in a specific process, you'll be left behind by those who have already started tomorrow.

This gap is not due to a difference in talent but a difference in efficiency and time.

Even Google's CEO is worried. What he's worried about is not the lack of technology but the fact that the product hasn't taken off and the data flywheel hasn't started.

The same goes for ordinary people. Stop hoarding tools and bookmarking tutorials.

Find your most high - frequency work scenario, integrate AI into it, and let it work for you every day, give you feedback, and help you iterate.

Once you start, the flywheel will naturally turn.

This article is from the WeChat official account “Tang Ren” (ID: RyanTang007), written by Tang Ren and published by 36Kr with authorization.