From Prompt to Token, and Finally to Yourself

I was chatting with some colleagues about AI today, and something in the conversation struck me. So I decided to write this down.

A while ago, one of my colleagues went to Hangzhou for a Google Ads event about AI-generated motion comics. As he told me about what was being discussed there, I suddenly realized something:

AI is no longer just a hot topic within the tech industry.

It has become one of the hottest topics in the world.

To many people, it even feels like the beginning of a fifth industrial revolution.

Looking Back at 2025: Prompt Is All You Need

It was around the end of 2024 when I first started paying serious attention to the term “large language model.”

At the time, DeepSeek had just released V2.5, and most people were still using GPT-4. I had not yet truly encountered the concept of deep reasoning.

Then DeepSeek R1 arrived.

That was the first time I developed an intuitive sense of what “reasoning” in an AI model could look like.

I still remember how absurdly popular DeepSeek became during that period. The website was often inaccessible, and even when you managed to get in, it frequently failed to answer your questions.

But it was also during that period that I saw, for the first time, what it looked like for a large model to “think” through a problem.

Even today, I still remember the shock of watching an AI output its reasoning line by line.

The first thought that crossed my mind was:

Does it have a soul?

Looking back now, that feeling is difficult to describe.

You knew perfectly well that it was ultimately just a model—a system, a technological artifact composed of complex code.

And yet, when its “thought process” appeared in front of you, it was hard not to experience a strange illusion:

as if there really were something on the other side of the screen understanding you and responding to you.

And how did we use AI back then?

It was simple.

We asked questions on a website.

The AI interface of that era was essentially just an input box: you typed in a question, and it gave you an answer.

Later, as multimodal capabilities emerged, a small ”+” button appeared beneath the input box, allowing users to upload files.

More capabilities followed.

But if we go back to that moment, the core form of an AI product was still just a website.

I call that stage AI 1.0.

In the AI 1.0 era, the most important—and almost the only—skill users needed was knowing how to ask questions.

In other words, the entire methodology could almost be summarized in one sentence:

Prompt is all you need.

I still remember reading countless tutorials and articles about how to write better prompts.

There was even a rather “professional” name for it:

Prompt Engineering.

Looking back, the term now feels very much like a product of its time.

But at the time, it really was an important skill.

I suspect one of the first fixed prompting patterns many people learned when they started using large models looked something like this:

You are now an expert in xxx, specializing in xxxx.

And in the beginning, that approach genuinely worked.

Models were nowhere near as capable as they are today, and compute was far more limited.

You often had to define a role, narrow the scope, and provide enough context before the model could reliably produce the result you wanted.

In a sense, prompts were a form of manual parameter tuning.

The more specific your prompt was, the more closely the model’s answer tended to match your expectations.

The more ambiguous you were, the more likely it was to drift away from what you wanted.

So people started studying how to assign roles, provide background information, and break problems into smaller pieces.

For many of us, that was the first time we truly realized that communicating with AI was becoming a new language of its own.

Back then, I mostly used AI for text-related tasks—polishing writing, translating content, things like that.

I did not use it much for programming.

Occasionally, I would paste an entire source file into the chat and ask it to modify something.

But the experience was hardly smooth.

Context would become confused. Copying and pasting code would introduce errors. After going back and forth several times, doing the work myself was often faster.

So at that stage, AI did not yet feel like an overwhelmingly powerful assistant.

What did it feel like?

More like driving a car with an automatic transmission.

It saved you from some basic operations and made certain things easier than before.

But it was still far from being able to genuinely take on complex work for you.

You found it useful, but not astonishing.

You were willing to use it, but you did not depend on it.

Entering 2026: Token Is All You Need

By 2026, I encountered Claude for the first time, along with a completely different way of using AI: local CLI tools.

AI was no longer confined to a web-based chat box.

It could enter the local development environment, directly read project files, understand code structures, and even participate in concrete implementation and modification work.

That was when I truly began learning how to integrate AI into my development workflow.

At first, I was still cautious.

I mostly asked it to write simple functions or handle small, isolated tasks.

I was reluctant to hand over an entire page—or even an entire module—and ask AI to build it in one go.

I was curious about what it could do, but I remained wary of giving it too much control.

Even with such limited usage, however, the improvement in my productivity was already transformative.

Because by this stage, AI was no longer merely a tool that answered questions.

It was becoming an assistant that could participate directly in real development workflows.

It no longer just told you what to do.

It could actually do the work with you.

If the previous stage of AI was like an automatic car—saving you from a few mechanical operations—this was the point when I first began to feel that AI was changing from a tool into a partner.

Gradually, prompts themselves seemed to matter less than they once had.

Prompts were moving into the background. Tokens were moving to center stage.

You could increasingly feel that AI was starting to “understand” you.

You no longer needed to explain every role, every piece of background information, every step, and every requirement in painstaking detail.

Even if your expression was incomplete, it could often understand roughly what you meant.

Even if your description contained gaps, it could frequently follow your train of thought and fill in the things you had left unsaid.

And with that, our concerns began to change.

In the early days, everyone cared about one question:

How do I write a better prompt?

Now, more and more people were asking something else:

How many tokens is this going to cost?

Tokens were no longer merely a technical parameter.

They were becoming one of the most concrete and direct units of cost in the AI era.

The longer the context you provide, the more complex the task, and the deeper the conversation becomes, the more tokens are consumed behind the scenes.

As a result, our focus began shifting from:

How do I make AI understand me better?

to:

How do I use AI more efficiently?

If Prompt was what mattered most in the AI 1.0 era, then by 2026, Token had moved to center stage.

At first, many people understood tokens simply as a billing unit.

You say something: tokens are consumed.

AI responds: more tokens are consumed.

Upload a document, attach context, read code, continue a long conversation—all of it consumes tokens.

But the significance of tokens goes far beyond billing.

Tokens fundamentally determine how much an AI can see, how much it can remember, how deeply it can understand a problem, and how much work it can perform in a single session.

In a sense, tokens are to AI what bandwidth was to the Internet, electricity was to the industrial age, and gasoline was to the automobile.

They determine not only cost, but also the boundaries of capability.

Why do some AI systems appear extremely intelligent, yet suddenly seem to forget what you were discussing earlier?

Why can two people use the same model for the same kind of task, yet one person’s workflow feels effortless while the other’s feels constrained at every step?

Quite often, the difference is not simply that one model is smarter than another.

Sometimes, the problem is simply that there are not enough tokens.

So at this stage, the challenge is no longer merely about saying the right thing.

It is about providing the right context.

We learn to filter information, compress descriptions, structure requirements, and control context length.

And we begin to understand that using AI efficiently is no longer just about knowing how to ask questions.

It is about knowing how to allocate tokens.

Prompt determines how you begin. Token determines how far you can go.

And in that sense,

Token is all you need

is becoming an increasingly realistic description of where we are today.

The Future I Imagine: Beyond AI, There Is You

At the beginning, AI was little more than a chat box—a new tool that could answer questions, polish text, and translate content.

Today, it has already become far more than a “tool.”

It is entering writing.

Programming.

Design.

Search.

Customer service.

Operations.

Video editing.

Analysis.

Decision-making.

What AI is replacing is not merely one specific action within one specific profession.

It is replacing large amounts of repetitive, standardized, process-driven work itself.

In the past, many capabilities depended on accumulated experience.

They depended on time.

They depended on someone spending years gradually sharpening a skill.

AI is now rapidly lowering those barriers.

It allows people who cannot write to begin writing.

People who cannot program can begin building products.

People without design training can quickly produce design concepts.

For the first time, many abilities that once required extensive professional training are being opened to ordinary people at unprecedented scale.

And work is far from the only thing AI is changing.

It is entering the way we express ourselves.

The way we learn.

The way we process emotions.

The way we live our everyday lives.

We use it to write emails, build spreadsheets, research information, create plans, modify code, and design strategies.

We also use it to organize our schedules, plan trips, ease anxiety, structure our thoughts, and even, in moments of loneliness, give a place to emotions that previously had nowhere to go.

Little by little, AI is entering the surface layers of our lives.

And step by step, it is moving deeper:

into our processes,

into our decisions,

into our collaboration,

into our habits,

and eventually,

into our dependencies.

That is precisely where we should be most careful.

AI will not necessarily replace you in one dramatic moment.

Instead, every time you think,

“This is so convenient,”

it becomes a little more deeply embedded in your life—

until one day, you realize you can no longer imagine working without it.

So when I look back at the past one or two years, I increasingly believe that what AI is truly changing is not merely one industry, one profession, or one category of tools.

It is changing the entire way human beings process information, organize capabilities, and get work done.

It replaces repetitive labor.

It lowers professional barriers.

It restructures how work is divided.

And it redefines the relationship between humans and tools.

In the past, tools were things we used.

Today, AI is increasingly becoming something that thinks with you, judges with you, and acts with you.

And so we have traveled from:

Prompt is all you need

to:

Token is all you need.

But as I reach the end of this article, I increasingly feel that the most important conclusion of this era may not be:

AI is all you need.

Because AI may determine your productivity.

It may determine how quickly you acquire information.

It may determine how you get your work done.

But it should never determine who you are.

The real question is this:

When AI can do more and more things for you, what remains uniquely yours?

When AI is taken away, do your judgment, your taste, your experience, and your choices still stand on their own?

AI may determine the productivity ceiling of most people in this era.

But more important than whether you know how to use AI is whether, after gaining access to it, you can still preserve the part of yourself that cannot be replaced.

AI determines how fast you can move.

But how far you can go is still determined by you.

Beyond AI, There Is You.