分类： AI潮流

Recently, a fellow PhD classmate commented on my two-minute video from yesterday.

He said: "If I had gritted my teeth back then, given up on that humanities PhD, and thrown myself into the C++ wave of the 90s to become a programmer in Silicon Valley — then today, I would probably be writing a different Liwei 2min: My biggest regret is being led astray by C++, never finishing my PhD."

I replied: "yeh u know." Because I know it too well.

Others thought it was a joke. It wasn't. It was two old-timers, looking at each other across thirty-plus years of life, and then laughing at the same time. Because we both knew. He was talking about himself. And the 'what if' he described — that was me.

Years ago, he left our shared advisor and headed south to Silicon Valley. He went through various startups and big tech companies. Today he still works at a major company, responsible for a product used by hundreds of millions. Riding the wind all the way.

I stayed on the other path. Chasing that 'damn' humanities PhD, I missed the early excitement of the 90s, but caught the dot-com bubble at the turn of the century.

Two different life trajectories. Converging at last in Silicon Valley.

What's interesting is — at this age, we've begun to understand and tease each other more. Perhaps this is the most wondrous thing about human nature. What you have slowly becomes taken for granted. What you don't have keeps appreciating in memory.

When young, we thought life was a multiple-choice question. Later we discovered — life is actually a question of what you give up. Every time you choose or are chosen for one answer, you simultaneously give up countless alternatives.

And those abandoned or rejected answers — they'll keep coming back to knock on your door, years later. Telling you: maybe this was the right one after all.

The truth? Nobody knows. Because life's greatest magic trick is this: real life can only be lived once. But parallel universes can be fantasized ten thousand times.

Reality can never beat fantasy. Because fantasy doesn't have to pay the mortgage. Doesn't have to work late. Doesn't have to face the boss. Doesn't have to face middle-age weight gain. Fantasy forever stays frozen on the most beautiful frame.

So many regrets — it's not really that we chose wrong. It's that we discovered: perhaps, somewhere in the unseen, there truly is fate.

Lately I've been thinking about something else. If our generation's regret is being diverted by the era, chosen by the era — then the younger generation's predicament is even more bewildering and challenging: they are being leveled by the era.

When all doors are open, when all knowledge and skills are at your fingertips, when both humanities and sciences face the same shrinking job market, when new graduate hires become fewer and fewer — the new generation faces not career choices and planning. They face no choices, and no way to plan.

This confusion and helplessness, this inability to find one's place or purpose, is becoming the prevailing sentiment across universities. This is not something a platitude about 'embracing AI' can soothe.

For decades, knowledge was a scarce resource. Whoever possessed knowledge held an advantage. A book. A degree. A skill. Any of these could change your fate.

But after AI arrived, knowledge became like tap water. What once required a trip to the library can now be obtained with a single sentence. Designs that once took a decade of experience can now be generated in minutes.

Is knowledge still useful? Of course it is. But possessing knowledge no longer matters — because everyone can possess it. Just like electricity is essential, but owning a power plant no longer matters — because every household has an outlet.

This is a strange era. Knowledge is no longer scarce. Words are no longer scarce. Even intelligence itself is beginning to lose its scarcity.

What remains in the end — is not knowledge. Not degrees. Not titles. But lived experience.

AI knows what heartbreak is. But it has never waited for that call that never came. AI knows what aging is. But it has never watched its parents grow old day by day. AI knows what regret is. But it has never, at sixty, suddenly remembered a life it didn't choose forty years ago.

Knowledge belongs to machines. Experience belongs to humans. Efficiency belongs to machines. Feeling belongs to humans. Perhaps the most precious thing in the future — is not what you know, but what you have truly lived.

So I've come to feel, more and more — life's greatest regret is neither being diverted too early by the era, nor being leveled too late by the era. It is, after having lived a singular, unrepeatable life, still wanting to live on behalf of another self that never existed.

That guy — let him stay in the parallel universe. As for us — let's keep playing this round to the end.

But most crucially, and what worries me more: in this era of breakneck technological change, how do we build social welfare systems that ensure AI's dividends are shared by all? How do we ensure the next generation no longer faces the challenge of countless doors wide open, yet no path to walk through?

🎬 Watch the video version (YouTube)

Thesis: Many people's destinies are determined not by ability, but by the first sorting table their era hands them.

Looking back on my life, I have two deep regrets. By the time large language models arrived to help compensate, the energy and opportunity for frontline battle had already passed. A sigh.

The first regret: Among the Class of '77, many of the brightest were "hijacked" by the window of foreign languages. English was the key to the world — but the ticket was so precious that many spent their entire lives stuck at the ticket gate. I was drafted into the humanities, not because I didn't apply for science. The era made the choice for me.

The second regret: the PhD phase. I had one foot in the door of coding and engineering. OOP and C++ were all the rage, I was hooked. But the thesis and degree pulled me away. I became a self-made manager — VP, Chief, whatever — knowing a little about everything but never again a frontline engineer.

This isn't simple personal regret. It's a sample of an era: when windows of choice are small, a person is shaped not by their interests, but by the shortages of their time.

Today's young people have all the tools. AI, programming, English, expression — everything can be re-learned. The era no longer opens just one door. The only question: with all the doors open, do you dare walk back in?

by Tuya

🎬 Watch the video version (YouTube)

Software Finally Starts Adapting to Me

I've had a very strong feeling lately: I increasingly don't want to learn software anymore.

It's not just laziness — though I am lazy. More fundamentally, the old software logic was: you adapt to me. Where the buttons are, how the menus hide, how the workflows twist — you have to learn it all. If you can't learn, you're stupid; if you can't remember, you're old. Software features multiply, menus grow ever more complex — 90% of which you'll never use in your lifetime — but vendors can't restrain themselves from expanding coverage. This is a kind of "collective menu debt," yet every individual who only needs a fraction of those features must still repay it, must learn to penetrate the complex UI to find their own subset.

But now that AI agents have arrived, this logic can be reversed. A friend who develops agent platforms advocates exactly this, saying conditions are ripe to build software just for yourself.

In fact, I've recently been using Codex to build a tool specifically targeting my own pain points from years of digital life: an automated system that collects anything I'm interested in, auto-classifies, processes, structures, and archives it, ready for retrieval and summarization at any time. I don't need to learn it, because it grew out of my own habits. The ideal state isn't me adapting to generic software — it's custom software adapting to me.

This kind of software has one enormous advantage: it has no market, therefore no competition. It serves just one person. It doesn't need to please investors, chase DAU, pursue growth, or design "user retention." It just needs to make my life smoother, help me lose fewer things, help me think more clearly, and automate the manual workflows I used to do. That's enough.

Which brings me to a regret.

Looking back on my life, my deepest source of inadequacy is that I didn't study science or engineering as an undergraduate — I studied humanities instead. (It really wasn't my fault — I applied for science and engineering, but the first cohort of post-Cultural Revolution college entrants in 1977 barely knew English, so English wasn't a required subject but could be taken as a bonus. I thought the bonus English test would help my application, but the foreign language department, desperate for English-capable students, forcibly pulled me in. No negotiation.) But your first degree is, in some sense, your underlying operating system. If your foundation isn't solid enough, you can patch it later, upgrade it, install plugins — but that gap in fundamentals will always be there. This has been my Achilles' heel for decades.

Fortunately, large model agents have arrived. My requirement for myself is now simple: since I didn't study enough before, let the tools fill the gap. Let coding agents become my private science-and-engineering assistant and personal secretary. They don't replace my judgment, but they compensate for my weaknesses. I don't need a market-facing software matrix. I just need an increasingly handy, increasingly understanding toolbox.

Efficiency first, fit first. If it can help me retain what's in my mind and bridge what I didn't learn before, that's enough. This "personal dynamic knowledge base" agent is no simple project, but it's nearly operational. Looking at it now, building your own wheels for your own use isn't actually that hard.

🎬 Watch the video version

After writing this Token series for so long, I want to tackle one final, unavoidable question.

What exactly is the relationship between Token and intelligence?

Many readers wrote in after the earlier installments:

If AI training runs on Tokens, inference runs on Tokens, and Agents are voraciously consuming Tokens — doesn't that mean Token equals intelligence?

The answer is both yes and no.

Let's start with no. Because intelligence is clearly more than just Token.

Just as a person's thoughts are not equal to the words they speak. A scientist's great discovery is not equal to the few dozen pages of the published paper. Einstein's theory of relativity is not equal to the tens of thousands of words in that paper. Words are merely carriers of thought. Similarly, Token is merely a carrier of intelligence — whether in the form of text, voice, or video.

But if we conclude from this that Token is unimportant, that would also be wrong.

Because we can never see thought itself. We can only see the traces thought leaves behind. A sentence, an article, a piece of code, a design draft, a video. The same is true of large models. We can't see the billions of calculations inside the neural network. We can't see the weight matrices. We can't see Attention. The only thing we can perceive is Token.

We cannot directly trade intelligence, but we can trade Token. We cannot directly measure intelligence, but we can measure Token.

At this point, a more fitting analogy suddenly occurred to me.

Money.

Dollars, yuan, gold — none of them equals wealth. True wealth exists in land, factories, goods, services, technology, and labor. But why can't modern society function without money? Because money provides a unified form of value expression — what Marx called a commodity equivalent — that can be measured, circulated, traded, and accumulated.

Today's Token is playing a similar role. It is not intelligence itself, but it increasingly resembles the currency of intelligence.

Over the past few years, the entire AI industry has essentially been revolving around Token. During training, people discuss how many trillions of Tokens were used. During deployment, they discuss how many Tokens per second can be processed. When purchasing APIs, they discuss how much input Tokens cost and how much output Tokens cost. When Agents run, they discuss how many Tokens were consumed for input and output. Even competition between nations is increasingly manifesting as: who can produce high-quality Tokens more cheaply.

Thus Token has gradually evolved from a technical term into an economic concept.

Of course, there is one point that is particularly easy to confuse. The same Token plays entirely different roles during training versus inference.

During training, Token is more like ore. Massive amounts of data are shredded, compressed, refined. Countless Tokens are smelted into model weights during training. The process resembles steelmaking, oil refining, turning ore into steel.

Inference is completely different. The model is already trained. What users purchase is not the training process but the output results. At this stage, Token is more like electricity, like money — more like an intelligence product delivered to the user. You ask AI to write articles, write code, make presentations. What you receive is Token. Even video, images, and voice will ultimately be priced through Token.

So from the user's perspective, intelligence almost always appears wrapped in the cloak of Token. This is why many people get the feeling that Token equals intelligence. It's actually as natural as associating money with wealth — because money and wealth have always been two sides of the same coin. Token and intelligence are increasingly becoming two sides of the same coin.

But history also tells us: don't mistake money for wealth itself. Similarly, don't mistake Token for intelligence itself.

To summarize. What is Token? It is the standard unit of measurement after data is industrialized and fragmented. Why tokenize? Because only by breaking things down can we count them; only by counting can we train. Why is AI consuming ever more electricity? Because the entire industry is producing and consuming Token at massive scale. Why are Agents exploding? Because machines have begun producing, exchanging, and consuming Token themselves. Why is Token getting cheaper? Because industrialization is underway. Why are nations competing over Token? Because Token is becoming a new means of production.

Ultimately, Token is to intelligence what money is to wealth. It is not wealth itself, but it is wealth's most important form of expression. It is not intelligence itself, but it is the way intelligence is produced, circulated, traded, and perceived. The internet era flowed with information. The AI era flows with Token. And what flows behind Token may well be the thing humanity has begun to produce industrially for the first time: intelligence.

Yesterday I watched a real-time dashcam video of a Tesla making an emergency swerve to avoid a car that suddenly shot up from the left lane entrance ramp. My immediate thought: human reaction speed simply can't handle that.

In that situation, most of us instinctively slam the brakes — which on a highway is itself dangerous. Being able to safely dodge to the right lane like FSD did is clearly the better strategy. Unfortunately, most human drivers just can't pull it off.

After driving with FSD for a long time, you develop a very strange kind of trust.

Not that it's always right. Not that you always understand why it did what it did.

But you realize: many of those heart-stopping emergency maneuvers that made you break out in a cold sweat — when you replay them later, most of them genuinely protected your safety.

Over all my years of manual driving, my default in emergencies was always the reflexive hard brake. Because only by slowing down did I feel any sense of control. It wasn't that I didn't know how to steer — I was afraid to. Because you have to check: is the right lane clear? Is there a car in my blind spot? How fast is the car behind me? Is the other driver a novice? Are they panicking? This entire judgment chain is serial — the human brain simply can't process it fast enough.

So most people, like me, instinctively hit the brakes.

But FSD is different. It's not just that it has watched countless expert drivers — it's more like a driver with many sets of eyes and reaction speeds many times faster than ours. It's constantly watching all four directions, constantly computing the space, speed, and risk in every lane.

That's why sometimes, it dares to execute lane escapes that we wouldn't dare attempt.

Of course, this brings another problem: sometimes it's overly cautious. A bird suddenly flies past in front — it might trigger an avoidance reaction. And some emergency dodges, even in hindsight, we may not fully understand. The infamous "phantom braking" from a few years ago is the classic example: tree shadows, bridge shadows, lighting changes, even road texture could trigger false alarms.

But here's what's remarkable: phantom braking has almost disappeared in recent years. I've barely encountered it myself in over a year. This tells us it's no longer just "seeing something that looks like danger" — it's increasingly understanding: what will actually hit me, and what is merely a shadow.

This is the most fascinating thing about FSD.

In its early days, it sometimes acted like a clumsy student. Now it behaves like an inhumanly fast-reacting entity.

Yesterday it executed one particular avoidance maneuver that I didn't fully understand either. Maybe it overreacted. Maybe it saw a risk we didn't. But I'm not going to dig deeper into it.

Because after long-term use, my trust in it doesn't come from faith — it comes from replaying every drive, time after time.

The vast majority of the time, those tense maneuvers that felt excessive in the moment — looking back, they were protecting us. It is far more cautious and safe than this old-timer-among-clumsy-drivers.

And that's enough.

What will truly transform driving in the future may not be whether it can drive like a human.

It's that it finally can drive unlike a human.

For the first time in human history, there is a kind of "employee" that can work 24 hours a day — no sleep, no salary, no social security, no rights claims, no strikes, no sickness, no retirement.

And the truly absurd part: it can replicate itself.

This thing is called an **AI agent**.

---

## Here's the problem

In the old world, when a boss hired 1,000 people, the state collected: income tax, social security, health insurance, unemployment insurance, pension contributions.

Now the boss fires all 1,000 and replaces them with AI. Efficiency skyrockets. Profits skyrocket. Stock prices skyrocket.

**But the tax base evaporates.**

The state can no longer collect revenue. The unemployed are still there.

And so we arrive at a surreal paradox:

> AI is simultaneously driving unprecedented productivity growth and hollowing out the fiscal foundation of society.

The entire modern state is built on the premise that human labor pays taxes. AGI is erasing "human labor" itself.

**This is the real nuclear bomb.**

---

## "Just learn AI" is wishful thinking

Many people still comfort themselves: "Just pick up some AI skills, transition to a new role, and you'll be fine."

This is increasingly delusional. Because the cruelest part is: even the act of "using AI" will eventually be automated by AI.

You think the jobs of the future are "AI Operator," "Prompt Engineer," "Agent Manager" — but agents are already using agents. Even "prompt engineer," that transitional role, may turn out to be nothing more than a temporary bubble in a technological wave.

Two years ago, the entire internet was selling prompt engineering courses. Today, that looks like a punchline.

---

## This time is different

Past technological revolutions created new jobs. The automobile killed the horse carriage but created the auto mechanic. The internet killed print newspapers but created e-commerce and live-streamers.

This time is different. The new systems AI creates are inherently *de-peopled*. Because AI's single greatest advantage is precisely this: **it doesn't need people.**

---

## AI must pay taxes

If AI replaces people, who pays the taxes?

The answer is simple: **AI itself must pay taxes.**

For every token you consume, every GPU you run, every inference you perform, every kilowatt of AI electricity — you pay a corresponding "AI social tax."

Because when you used to hire a person, you were already paying those taxes. Now you replace the person with AI and bear zero social cost — that is fundamentally unfair.

Many will shout: "You're stifling technological progress!"

**So what?**

Is the sole purpose of human society to allow capital and compute to multiply without limit?

- The Industrial Revolution polluted the environment → we got environmental taxes.
- Cars consume public roads → we got fuel taxes.
- AI destroys the employment tax base → why can't we have an AI tax?

---

## It takes everyone

The real danger is not that AI is too powerful. It's that once AI becomes powerful enough, the entire social revenue structure collapses.

And here's the darkest irony: the people most likely to support an AI tax in the future may be exactly those who understand AI best. Because they know most clearly: once this thing truly matures, it doesn't just replace the "bottom rung."

**It sweeps the board.**

White-collar workers, programmers, designers, analysts, customer service, translators, paralegals, researchers — no one escapes.

In the past, society could comfort people with one line: "You just didn't work hard enough."

But the cruelest truth of the AGI era is this: sometimes, it's not that you didn't work hard. It's that you, as a member of the species "human employee," are beginning to lose economic viability altogether.

---

*Two Minutes with Liwei · 2024*

by Tuya

Recently, many people suddenly noticed something:

DeepSeek cut prices again.

And not by a little.

The kind of cut where you just flip the table over. By the end of May, prices dropped to a quarter of what they were.

Many people's first reaction: Chinese AI companies are starting a price war.

But I increasingly feel that understanding this only as a "price war" is way too shallow.

Because what's really happening here might be this: tokens are becoming industrialized.

What does that mean?

For the past two years, the global AI world has operated under a quiet assumption: high-quality tokens are expensive.

Because: models are expensive, GPUs are expensive, training is expensive, electricity is expensive.

So everyone defaulted to the idea that AI must be a high-margin industry.

Until Chinese models started slashing prices like crazy.

And for the first time, many people discovered: tokens might actually be like steel, display panels, solar panels, lithium batteries — entering a terrifying process of industrial cost reduction.

Behind this story is something deeply Chinese.

What do I mean?

American AI companies often follow a path of "high performance, high margins, high valuation." A bit like luxury goods.

But once Chinese companies start competing, things tend to look different: "First, crush the cost."

Then: massive scale, infrastructure-ization, supply-chain-ization, engineering optimization, labor optimization, power optimization. Eventually grinding the entire industry into "cabbage-price industrial capability."

Over the past twenty years, China has done this repeatedly. Solar power, EV batteries, drones, display panels, e-commerce, high-speed rail... The pattern is roughly the same.

Early stage: others think it's high tech. Later stage: China industrializes it. End result: profits vanish, but production capacity blankets the world.

Today, tokens are starting to look more and more like this story.

Because tokens are not fundamentally mysterious. They are, in the end, "data processing capability produced by an industrial system." And what is an industrial system best at? Reducing costs.

So now an especially interesting dynamic has emerged: American frontier models may still maintain the strongest capability. But Chinese models are closing in fast — maybe a few months behind, maybe still a bit weaker in certain areas. But the price is already shockingly low.

So developers around the world are facing a very pragmatic choice: "Do I need the world's strongest, or do I need strong enough + ten times cheaper?"

This question is deadly.

Because in most of the business world, what ultimately matters is not "theoretical peak performance" but "overall cost-effectiveness."

As tokens get cheaper and cheaper, many AI applications that were previously "too expensive to run" suddenly become viable.

In the past, AI was like a five-star hotel. Now it's starting to look like tap water.

Developers used to worry: "Is this agent going to burn dozens of dollars a day?" Now the attitude is shifting to: "Whatever, let it run."

And so token consumption begins to explode further. Which in turn drives even larger data centers, cheaper inference chips, more aggressive engineering optimization. The whole system enters a kind of industrial flywheel.

The most interesting part is: what this competition ultimately comes down to may no longer be just the model.

It's about: who has cheaper electricity; who has more data centers; who has cheaper engineers; who has a more complete supply chain; who can better tolerate thin margins.

In other words: AI competition is increasingly looking like modern industrial system competition, not just lab competition.

Many people still think of AI as "a few brilliant scientists changing the world." But what it increasingly resembles is "an entire national industrial system collectively entering the field to produce tokens."

In the internet era, China's greatest strength was "application industrialization." In the AI era, what might be truly terrifying about China is: token industrialization.

And as token prices keep falling, developers around the world will ultimately vote with their feet. Because the vast majority of companies, in the end, have to do the math.

by Tuya

The truly dangerous thing about the AI bubble isn't the technology.

It's that the entire world is front-loading financing for "decades of future intelligence demand" — all at once.

During the mobile internet era, people burned cash, sure. But they found revenue fast. Food delivery had customers. Ride-hailing had riders. E-commerce had buyers. Short videos had viewers. Ads had advertisers. Consumer-facing sectors — food, clothing, housing, transport, communication, entertainment, shopping — were all low-hanging fruit. The business loop closed quickly.

But AI is different. To this day, most of what's genuinely deployed at scale is still: writing weekly reports, making slides, generating images, customer service bots, coding assistants. Valuable? Yes. That's not the problem.

The real problem is this: the capital markets are already betting at the scale of "everyone consuming intelligence all the time." GPUs bought first. Data centers built first. Debt taken on first. Valuations pumped first. Pension funds entered first. The world is building "intelligence power plants" at unprecedented speed.

But here's the question: who, exactly, is going to consume intelligence the way we consume electricity today — continuously, at scale?

The biggest gamble in AI right now isn't whether models will get smarter. It's whether the explosion of B2B vertical applications can outpace the depletion of funding, GPU depreciation, data center debt, and the capital market's patience.

If Agentic AI genuinely penetrates core enterprise workflows — turning productivity gains into real profits — then a lot of today's crazed investments will be vindicated by history. But if the growth of real demand moves slower than the pace the capital markets have already priced in, then a lot of what today represents "the future" of AI may end up as: piles of power-hungry GPUs generating insufficient cash flow.

The railroad changed the world. Railroad stocks still crashed. The internet changed the world. Dot-coms still littered the battlefield. AI will probably change the world too. But a technological revolution being real has never meant a bubble doesn't exist.

What really sent a chill down my spine in the AI world these past few days wasn't a funding round. It wasn't a product launch.

It was this:

OpenAI's unreported general-purpose reasoning model reportedly solved the Erdős planar unit distance problem — first posed in 1946.

A decades-old problem that mathematicians couldn't crack.

What shook me wasn't that "it solved it."

It was how it solved it.

The full chain of thought reportedly printed out to 125 pages.

Not the kind of genius epiphany you see in movies.

Quite the opposite.

All trial and error. Dead ends. Backtracking. Repeated reversals and detours.

Like a mentally shattered grad student grinding away in a mountain of scratch paper.

But here's the thing:

It actually found the door in the end.

And what's most interesting:

This wasn't a math-specific model.

It was a general-purpose reasoning model.

Most people haven't grasped what this means yet.

Over the past few years, there's been a loud contrarian voice in the AI world.

The most prominent being the LeCun camp.

Their long-held position:

LLMs don't do real reasoning. Just statistical language modeling.

Then, as reasoning capabilities visibly improved, they doubled down:

These so-called reasoning outputs are merely crude imitations of human reasoning.

They're not entirely wrong.

Today's large model reasoning does resemble a sieve riddled with holes.

Wild speculation. Frequent wrong turns. Logic that collapses mid-stream.

But the LeCun camp may have underestimated one thing:

Intelligence doesn't always require "perfect reasoning."

With enough scale, sufficiently broad search, and strong self-reflection and course-correction,

a kind of "rough but effective" intelligence can suddenly emerge.

And mathematics and programming happen to be where this breaks through first.

Because they share one crucial property:

Verifiability.

You can flail. Generate wildly. Even "guess blindly."

But in the end, the verifier tells you:

Right.

Or wrong.

And so for the first time, AI enters a deeply unsettling state:

It may not actually "understand the world,"

yet it can already conduct effective exploration near the frontier of human knowledge.

That alone is staggering.

Now, LeCun isn't entirely without a point.

He says:

The real world isn't like mathematics.

In the real world, many problems are unverifiable, inexhaustible, and impossible to formalize in language.

I agree with that.

But the problem is:

His critique of LLMs and reasoning is far too absolute.

Especially now, after mainstream approaches have broken through time and again,

his "tear it all down to build anew" contrarian stance feels increasingly out of touch.

More critically:

The "bypass language, prioritize visual world models" approach he's championed for years

has yet to produce any truly industry-shaking result.

At least not so far.

So the real question today is no longer:

"Do LLMs have genuine intelligence?"

It's a more dangerous one:

If a reasoning system "as leaky as a sieve" is already contributing to real mathematical discoveries,

then with more scaling...

Isn't that superintelligence?

by Tuya

Liwei Two Minutes: Token Economics in Plain Language #3 — Why Do Agents Suddenly Feel Human?

People used to think ChatGPT was already very human-like. It's not. Not even close.

Why? Because traditional chatbots are fundamentally "one question, one answer." You ask one thing, it replies once. Like a high-end customer service rep.

The real change happened when AI started "working on its own." That's the hottest thing right now: Agents.

The first time you play with an Agent, it's shocking. It suddenly acts like a real employee.

It breaks down tasks on its own, writes code, runs tests, reports errors, fixes bugs, keeps going. It even "talks to itself" while working.

Why this sudden change? The reason isn't mysterious. Because AI started burning its own tokens.

In the ChatGPT era, tokens mainly came from human input. You type some words, the model replies. The token flow was simple: Human → AI → Human.

Agent era is different. Now the token flow is: AI → AI → Tool → AI → AI. So tokens are burning inside the machine in loops.

Here's an example. Say you tell an Agent: "Build me a website."

A traditional chatbot would just give you a block of code. Done. But an Agent won't.

It will first analyze the task. Then start talking to itself: "Let's decide on the tech stack..." "Need React..." "Probably need a database..." "Generate the homepage first..." "Run the tests..." "Got an error..." "Fix and retry..."

Notice: this "thinking process" itself consumes tokens. And it consumes a massive amount.

Because the Agent isn't "generating the correct answer once." It's more like trial and error. Just like a human engineer: write, revise, test, redo.

So token consumption suddenly exploded. Before: user asks one question, AI answers once. Now: the AI might have run hundreds or thousands of token cycles internally. And humans only see the final result.

This is a lot like the Industrial Revolution. At first, coal was just for cooking. Then people discovered coal could power steam engines. And the entire industrial system started running itself.

Today's tokens are the same. Initially, tokens were just for chatting. Now they're driving the "internal thinking flow of machine work."

So a very strange new phenomenon has appeared in the AI world: Many tokens are no longer for humans to see. They're machine-to-machine communication.

In the future, human-generated tokens might only be a tiny fraction. The real token flood will come from AI-to-AI interactions. One Agent calling another Agent, one model orchestrating another model, a swarm of AIs collaborating on projects.

So the entire AI industry is starting to look more like an automated industrial system. No longer just chat software.

This is also why so many people have suddenly realized: AI is getting more expensive, more power-hungry, more dependent on data centers.

Because what's really being burned today isn't "chat content." It's the machines' own workflows.

In the internet era, humans uploaded information to the network. In the Agent era, humans are uploading "work" to AI. And tokens are the fuel that machine labor truly consumes in this new era.

In the past two years, many people realized for the first time: AI is this power-hungry. It's even starting to compete for electricity.

Isn't it just chatting, writing articles, generating some images? How did it suddenly become an energy monster?

Because today's large models are, at their core, burning tokens at massive scale. Once tokens enter industrial production, the power consumption will be staggering.

The internet is about information transmission. AI is about real-time computation. A search engine is like looking up a dictionary. A large model is like writing an essay from scratch.

The model predicts one token at a time. Behind every bit of generated content is a sea of matrix computation.

Today, GPUs have essentially become token generators. What you consume isn't chat sessions — it's token throughput.

After agents emerged, AI itself started consuming tokens. Thousands, tens of thousands of times — invisible to humans.

It's like the Industrial Revolution. Coal went from heating homes to driving factories, trains, and ships. Tokens went from chatting to industrial fuel.

The whole world is frantically building data centers, power plants, nuclear reactors. AI competition is no longer about algorithms — it's about who can burn tokens continuously, stably, and cheaply.

AI companies increasingly look like subsidiaries of a new energy-industrial complex. The internet flows with bits. AI burns tokens.

That's today's 立委两分钟. Thanks for watching. Goodbye. by Tuya