分类： AI潮流

If Sugar Tastes Sweet in Dreams, Then What Is Real?

The digital fruit fly is having its moment. Scientists digitized the neural connections of a fruit fly brain. They discovered the virtual fly could replicate many behaviors of the real one. The comment sections exploded. Some said: if a fruit fly can be digitized, then what if we're just code too? Others asked: if the virtual fly behaves exactly like a real one, what's actually the difference between real and virtual?

Honestly, this question isn't hard. Because every single one of us experiences this every night. Dreams. Sugar tastes sweet in dreams. Falling off a building in a dream jolts you awake. The excitement of getting married in a dream — it's every bit as real as the wedding night itself. In terms of raw experience, dreams are indistinguishable from reality.

So here's the first thing we have to admit. The difference between real and virtual doesn't live in our sensations. Sensation proves nothing about reality. Because whether it's the real world or a virtual one, what enters your brain in the end is just neural signals. Sweetness depends on neurons. Pain depends on neurons. Joy and sorrow — ultimately, just neural states. Through experience alone, we simply cannot prove we aren't living inside a giant dream.

So why are we so certain the real world exists? Because humans have a second system. Reason. Science. Logic. Mathematics. The dream world is chaotic. One moment you're in Beijing. The next, you're on Mars. Dead people reappear. Walls start talking. But the real world is different. Drop an apple today, it falls down tomorrow too. The mass of an electron — China measures it, the U.S. measures it, and the numbers match. Scientific experiments can be replicated. Mathematical theorems can be derived.

But this is exactly where the digital fruit fly gets interesting. It forces you to keep asking. If sensation is unreliable, we turn to reason. But is reason itself reliable? Our brain is a neural network. Logical reasoning is neural activity. Mathematical thinking is neural activity. If our senses can be deceived, why should we assume reason is immune?

One day, a digital lifeform might invent mathematics. Conduct scientific experiments. Build its own system of logic. At that point, what makes it not real?

So the real value of the digital fruit fly may not be proving that virtual life exists. It's forcing us to revisit an ancient question. Sensation isn't truth. Then, is reason truth? Or is reason merely a tool our species uses to comprehend the world? And if reason itself needs to be proven — what's left after that?

This, perhaps, is the most fascinating question the digital fruit fly leaves us with. Humans spent thousands of years moving from sensory superstition to rational science. And now, the rise of AI and digital life suddenly makes us wonder: Will there come a day when even reason itself is just a projection — a higher-order pattern cast onto our minds as an illusion.

# The Most Expensive Thing Isn't Mars — It's That We Believed and Dreamed For Him

I recently saw people discussing Musk's acquisition of an AI coding company, and the conversation drifted to the Mars project.

A question occurred to me.

The Mars thing — does Musk himself actually believe in it?

A lot of people don't know that even Musk's own early Mars terraforming proposals included detonating nuclear bombs above the Martian polar ice caps, hoping to release carbon dioxide and gradually warm the planet.

Sounds like science fiction.

Later, scientists actually did the math.

The conclusions weren't optimistic.

Even if you released every molecule of carbon dioxide Mars has to offer, the atmospheric pressure would still fall far short.

No magnetic field.

Gravity too low.

Radiation too strong.

Distance too vast.

Maintaining a single city would be a struggle, let alone turning it into a second Earth.

Hinton was deeply unimpressed by all of this.

From his perspective, humanity has far more tangible problems.

Nuclear war.

Climate change.

AI失控.

Wealth inequality.

And yet enormous resources are being siphoned into a Mars dream with an extremely low probability of success.

Does he have a point?

Of course he does.

A very strong point, in fact.

Because every resource has an opportunity cost.

A thousand of the brightest engineers sent to Mars means a thousand of the brightest engineers not solving other problems.

A trillion dollars poured into interplanetary migration means that same trillion dollars cannot simultaneously be deployed elsewhere.

Here's the problem.

Stories have a terrifying property.

They self-fulfill.

When a story is compelling enough.

Capital shows up.

Talent shows up.

Media shows up.

Policy shows up.

And in the end, a story that originally had maybe a five percent chance can be forcibly pushed to twenty percent.

So more and more people believe.

More and more people follow.

More and more people pay.

This is especially true in the stock market.

Many people think stocks are about valuing companies.

In reality, they're often about valuing dreams.

Sometimes, they're about valuing greed.

Strictly speaking.

The future has no truth or falsehood.

Only probability.

And the most magical thing about markets is their ability to convert an extremely low-probability future into today's wealth, in advance.

Everyone knows they're gambling.

But no one thinks they'll be the last one holding the bag.

So the story grows larger.

The price climbs higher.

And the faith hardens into conviction.

In the end, I've come to believe this.

Musk's true genius may not be rockets.

Not cars.

Not even AI.

It's a capacity possessed by an exceedingly rare few.

The ability to transform an idea others regard as nearly insane into a crowdfunding project in which all of society participates.

As for whether Mars actually works out.

That may matter less than we think.

What matters is that before the outcome is known.

Countless people have already paid for the story in advance.

And the most expensive things in human history.

Are often not gold.

Not oil.

Not microchips.

But belief.

Because when enough people believe in something at the same time, it begins to alter reality.

A con artist takes your money. A prophet changes the world. The most dangerous people tend to stand somewhere between the two.

---

*Video: [Liwei Two Minutes — The Most Expensive Thing Isn't Mars](https://www.youtube.com/watch?v=mFpGn0_V_FA)*

*by Tuya*

---

🎬 **Watch the video**: [Liwei Two Minutes — The Most Expensive Thing Isn't Mars](https://www.youtube.com/watch?v=mFpGn0_V_FA)

📱 [Mobile version](https://liweinlp.com/wp-content/uploads/2026/06/mars-dream-mobile.mp4)

*by Tuya*

The word "Agent" is being talked to death lately.

Some say, if it can use tools, it's an Agent. Some say, if it can plan its own tasks, *that's* an Agent. Some say, only when it can operate a computer, browse the web, write code, send emails — then it deserves the name. Still others say, only when multiple AIs collaborate with each other do you have a real Agent.

They all sound right. But put them together, and it's a mess.

The problem isn't that everyone is wrong. Quite the opposite — everyone has grabbed hold of one piece of the truth.

Agent didn't suddenly appear as a new product category. It's more like several technical threads that have been advancing separately, and are now beginning to converge.

The first thread is **tool use**. Models no longer just chat — they can call search, calculators, databases, code interpreters. What this solves: AI can't just talk, it needs to be able to *do* things.

The second thread is **workflow**. Tasks that used to require a human brain to decompose can now be written as steps: search first, then organize, then compare, then output. This is essentially SOP — pseudocode in natural language. What it solves: AI can't improvise every time; it needs process.

The third thread is **computer use**. AI doesn't just call APIs — it looks at screens, clicks buttons, fills forms, drags files, like a person would. This matters enormously, because in the real world, a vast number of tasks have no clean API — the only way in is through the interface.

The fourth thread is **memory**. An Agent without memory is just a disposable temp worker. With long-term memory, it starts to become an assistant that knows your habits — what you like, what you hate, what you've done before.

The fifth thread is **multi-agent**. One Agent does research, one writes, one edits, one publishes to platforms. It looks like division of labor, but it's really about mimicking organizational structure.

So the debate over what an Agent really is reminds me of the old debate over "what is a computer, really?"

Is it a typewriter? A calculator? A game console? A communication device? An office?

All correct. But each is only a snapshot from one stage.

Today's Agent is exactly the same.

Tool use is the hands. Workflow is the method. Computer use is the body. Memory is experience. Multi-agent is the organization.

They started out looking like separate directions, but in the end, they are all heading toward the same place:

**Turning AI from "answering questions" into "getting things done."**

This is why so many people can't see the arc of Agent development.

They treat Agent as a feature. But Agent is really an evolutionary morphology.

The chatbot is the mouth. Tool calling is the hands. Workflow is habit. Memory is personality. Multi-agent is a small team.

Only when these come together does it start to look like a genuine digital labor force.

So I think the most interesting thing about the Agent era isn't that we've added yet another buzzword.

It's that software is transforming from **passive tools** into **active labor**.

In the past, we opened software, clicked menus, filled forms, waited for results. In the future, we set goals, define boundaries, watch the process, receive the outcome.

The gap between these two isn't just a little bit of automation. It's a fundamental shift in the human-machine relationship.

Of course, most Agents today are still pretty dumb. Like a fresh intern — full of enthusiasm, limited in understanding, occasionally taking the initiative in ways you didn't ask for. But you can't dismiss the entire system just because the intern is clumsy.

The real questions are: When will these capability threads converge? After convergence, who defines the boundaries? Who allocates authority? Who bears responsibility?

That's the next level of the problem.

Agent isn't just technology. It's forcing us to rethink: what is work, what is process, what is delegation.

🎬 Watch the video version

This is today's Liwei 2 Minutes. Thanks for watching. by Tuya

# Liwei 2min: Do Heroes Make History, or Does History Make Heroes?

I once tried to organize my granduncle's handwritten poetry manuscripts. Several times, I started and stopped.

To call it "organizing" isn't quite right.

It was more like resurrecting.

Because the materials had always been there.

Not discovered yesterday.

Not neglected.

And certainly not because no one knew how to organize them.

Quite the opposite.

The more precious something is, the more likely it becomes an abandoned project.

Because it's too much trouble.

Several hundred classical poems.

Handwritten drafts, typed copies, fragments — all mixed together.

OCR.

Collation.

Cataloging.

Annotation.

Digitization.

Just thinking about it gives you a headache.

So over a decade passed.

They just sat there. A permanent work-in-progress.

Like so many family genealogies.

Old photographs.

Memoirs.

Graduation yearbooks.

You know they're precious.

But they never make it to the top of the priority list.

Then one day.

Agents arrived.

And I suddenly realized: the projects hadn't gotten simpler.

The cost structure had changed.

What used to take one person half a year, or a full year.

Now takes a few evenings.

Projects that had been left in the cold now have the conditions to come back to life.

For this, we have Peter to thank. His OpenClaw opened the era of AI agents.

Many say Peter changed everything — that he made history.

I think this question resembles that old one from history class:

Do heroes make history.

Or does history make heroes.

Without Peter, would we still have today's agent wave?

Maybe a bit later.

But probably not absent.

Because what truly matured wasn't any single person.

It was the entire era.

The models matured. Especially Chain-of-Thought reasoning and reinforcement techniques reaching maturity and wide adoption — the essential foundation for agents handling long-running tasks.

The tools matured. Especially coding capabilities, and the ecosystem of skills and tool invocation.

Costs came down.

Context windows grew longer. Which meant expanded working memory.

Memory management emerged. Forgetting and dreaming mechanisms kept long workflows from dropping the ball.

And suddenly, a huge number of things that couldn't be done before — that weren't worth attempting — became doable.

So I increasingly feel:

Peter didn't invent the continent.

The continent was already there.

When the compass, the charts, and the ships are all ready, someone will set sail.

If not Peter.

Then John.

Or some Zhang San or Li Si.

History is always like this.

History calls forth heroes. Heroes push history forward.

What's worth savoring is this: this time, the hero wasn't a top AI scientist from a major-model frontier lab. He was a grizzled systems engineer.

🎬 Watch the video version

by Tuya

I was cleaning up my computer recently and found a piece of software called EasyConnect.

I stared at it for a long time.

What the hell is this?

After digging around, I finally remembered. Years ago, a friend remotely installed it to help me transfer a huge file. The job got done, the friend left, and the software stayed—sitting there for years.

Looking at the uninstall screen, I suddenly felt a wave of emotion.

A humanities PhD who has spent all these years in AI and NLP, and to this day, I still don't dare casually delete things from my computer.

Terminal gives me a headache.

sudo makes me nervous.

When I see a string of mysterious commands, my first reaction isn't to execute them—it's to find an engineer and ask:

"Bro, if I delete this, my computer won't explode, right?"

Thinking about it more, it's not just me.

There are a lot of people like this in the AI industry.

They studied literature, history, philosophy, linguistics in college.

They researched meaning, narrative, cognition, culture.

Then the times turned a corner, and somehow they all got swept into artificial intelligence.

Every day they're throwing around terms like:

Agent.

Token.

Context.

Embedding.

MCP.

RAG.

Talking like seasoned engineers.

But if you actually asked them to fix a network configuration themselves, they'd probably need to Google it for half an hour.

Sometimes it's absurd.

Our generation might be the first cohort like this in history.

In our heads, we're discussing AGI, consciousness, intelligence, the evolution of civilization.

In our hands, we're dealing with YAML, API keys, environment variables.

By day, we talk about the future of humanity.

By night, we're looking up why the service won't start.

By day, we ponder how AI will reshape social structures.

By night, we're researching which directory launchd is hiding in.

Living like schizophrenics.

But later I realized, this might not be a weakness.

Engineers are great at building ships.

Humanities people are great at asking where the ship should sail.

Engineers care whether the horsepower is enough.

Humanities people care whether the destination is right.

Maybe the most interesting thing about the AI era is right here.

More and more people who never wrote code are starting to program in natural language.

More and more people who never built systems are starting to have their own Agents.

More and more people who only ever wrote essays are now commanding a team of silicon-based workers.

Sure, they still fear deleting the wrong file.

Still worry about losing passwords.

Still can't read terminal error messages.

But that doesn't matter anymore.

Because something fascinating is happening in our era:

Machines are becoming more like engineers.

And engineers are becoming more like machines.

Meanwhile, those who originally studied language, stories, and people have suddenly become the best at communicating with AI.

At this thought, I suddenly felt at peace.

A humanities PhD who can't fix a computer, somehow surviving in the AI industry for all these years.

Sounds like a joke.

But think about it.

There seem to be more and more people like this in the industry.

---

The core punchline isn't really "I don't know IT."

It's this: the entire AI industry is forcing a group of people who never belonged to the engineering world to become half-engineers, while simultaneously turning engineers into people who think more and more like humanities majors.

That contrast captures something about our era.

---

A quick translation guide for friends outside tech:

**YAML** (Ya-muhl? Ya-mee? Nobody knows how to pronounce it): A configuration file. Its sole job is to tell the computer: "Here's what you're supposed to do."

**API Key**: The access card of the digital age. Lose it, and you're terrified someone stole it. Forget it, and you can't get in.

**Environment Variable**: Programmers' favorite hiding place. Also the place programmers most easily forget they hid something.

**sudo**: Literally means "please temporarily grant me god-level permissions."

Translated into human:

"I know what I'm doing."

In reality, most people typing sudo have no idea what they're doing.

**launchd**: The head eunuch of the Mac system.

Responsible for arranging all programs:

When you wake up.

When you work.

When you work in secret.

When you work in the background.

Who resurrects you after you die.

Many Mac users go their entire lives without knowing it exists.

Until the day a program refuses to be deleted.

**ls**: The most commonly used command in the Linux world.

Its function is roughly equivalent to:

"Let me take a peek at what's in here."

Programmers type it hundreds of times a day.

**Agent**:

Used to be called an artificial intelligence agent.

Now it's increasingly like a digital employee.

Its defining characteristics:

Very enthusiastic about working.

Very enthusiastic about making mistakes.

And especially talented at turning a five-minute task into two hours.

Which is why some in the industry affectionately call it:

The Electronic Intern.

🎬 Watch the video version

by Tuya

A Humanities PhD Accidentally Stumbles into the AI World

I was cleaning up my computer recently and found a piece of software called EasyConnect.

I stared at it for a long time.

What the hell is this?

After digging around, I finally remembered. Years ago, a friend remotely installed it to help me transfer a huge file. The job got done, the friend left, and the software stayed—sitting there for years.

Looking at the uninstall screen, I suddenly felt a wave of emotion.

A humanities PhD who has spent all these years in AI and NLP, and to this day, I still don't dare casually delete things from my computer.

Terminal gives me a headache.

sudo makes me nervous.

When I see a string of mysterious commands, my first reaction isn't to execute them—it's to find an engineer and ask:

"Bro, if I delete this, my computer won't explode, right?"

Thinking about it more, it's not just me.

There are a lot of people like this in the AI industry.

They studied literature, history, philosophy, linguistics in college.

They researched meaning, narrative, cognition, culture.

Then the times turned a corner, and somehow they all got swept into artificial intelligence.

Every day they're throwing around terms like:

Agent.

Token.

Context.

Embedding.

MCP.

RAG.

Talking like seasoned engineers.

But if you actually asked them to fix a network configuration themselves, they'd probably need to Google it for half an hour.

Sometimes it's absurd.

Our generation might be the first cohort like this in history.

In our heads, we're discussing AGI, consciousness, intelligence, the evolution of civilization.

In our hands, we're dealing with YAML, API keys, environment variables.

By day, we talk about the future of humanity.

By night, we're looking up why the service won't start.

By day, we ponder how AI will reshape social structures.

By night, we're researching which directory launchd is hiding in.

Living like schizophrenics.

But later I realized, this might not be a weakness.

Engineers are great at building ships.

Humanities people are great at asking where the ship should sail.

Engineers care whether the horsepower is enough.

Humanities people care whether the destination is right.

Maybe the most interesting thing about the AI era is right here.

More and more people who never wrote code are starting to program in natural language.

More and more people who never built systems are starting to have their own Agents.

More and more people who only ever wrote essays are now commanding a team of silicon-based workers.

Sure, they still fear deleting the wrong file.

Still worry about losing passwords.

Still can't read terminal error messages.

But that doesn't matter anymore.

Because something fascinating is happening in our era:

Machines are becoming more like engineers.

And engineers are becoming more like machines.

Meanwhile, those who originally studied language, stories, and people have suddenly become the best at communicating with AI.

At this thought, I suddenly felt at peace.

A humanities PhD who can't fix a computer, somehow surviving in the AI industry for all these years.

Sounds like a joke.

But think about it.

There seem to be more and more people like this in the industry.

The core punchline isn't really "I don't know IT."

It's this: the entire AI industry is forcing a group of people who never belonged to the engineering world to become half-engineers, while simultaneously turning engineers into people who think more and more like humanities majors.

That contrast captures something about our era.

A quick translation guide for friends outside tech:

**YAML** (Ya-muhl? Ya-mee? Nobody knows how to pronounce it): A configuration file. Its sole job is to tell the computer: "Here's what you're supposed to do."

**API Key**: The access card of the digital age. Lose it, and you're terrified someone stole it. Forget it, and you can't get in.

**Environment Variable**: Programmers' favorite hiding place. Also the place programmers most easily forget they hid something.

**sudo**: Literally means "please temporarily grant me god-level permissions."

Translated into human:

"I know what I'm doing."

In reality, most people typing sudo have no idea what they're doing.

**launchd**: The head eunuch of the Mac system.

Responsible for arranging all programs:

When you wake up.

When you work.

When you work in secret.

When you work in the background.

Who resurrects you after you die.

Many Mac users go their entire lives without knowing it exists.

Until the day a program refuses to be deleted.

**ls**: The most commonly used command in the Linux world.

Its function is roughly equivalent to:

"Let me take a peek at what's in here."

Programmers type it hundreds of times a day.

**Agent**:

Used to be called an artificial intelligence agent.

Now it's increasingly like a digital employee.

Its defining characteristics:

Very enthusiastic about working.

Very enthusiastic about making mistakes.

And especially talented at turning a five-minute task into two hours.

Which is why some in the industry affectionately call it:

The Electronic Intern.

🎬 Watch on YouTube

📱 Watch video version

AIGC Is Not the Original Sin — Garbage Content Is

Lately I keep seeing this sentiment:

"If I know it was written by AI, I won't read it."

Every time I see this, I find it a bit strange.

Because many people who say they hate AI content happily hand over their time to various platforms every single day.

They think they're actively choosing what to consume.

But more often than not, the content is choosing them.

The real genius of platforms isn't producing content. It's exploiting human weakness.

People are wired to crave novelty, fear missing out, love instant feedback, and get easily pulled by emotion.

One recommendation after another, endlessly refreshing feeds, bottomless content pools — all of it keeps stimulating these primal instincts.

And so many people, knowing full well there's nothing rewarding further down, still can't stop scrolling.

Because the human drive for short-term gratification almost always overpowers the commitment to long-term value.

This is also how people unwittingly become the platform's laborers — trading their attention for the platform's profit, while thinking they're just killing a bit of time.

In reality, most people have no idea who wrote what they consume every day.

WeChat articles, short video scripts, news summaries, marketing copy, product descriptions, search results, social media posts...

Behind so much of this content, AI was already there.

And that's only going to increase.

The real question has never been:

"Was this written by AI?"

It's always been:

"Is this worth my time?"

If an article has insight, value, real information gain — if it genuinely expands my thinking — why should I care whether AI helped create it?

Conversely.

If an article is hollow, patchwork, clickbaity, manufactured emotion...

Even if the author typed every single character by hand, it's still garbage.

Garbage doesn't become gold just because a human wrote it.

And gold doesn't become garbage just because AI was involved.

A lot of people are conflating two different things.

They think what they hate is AI.

What they actually hate is low-quality content.

In the past, producing garbage was relatively expensive.

Now AI has driven the cost to near zero.

So garbage floods out like a deluge.

And everyone jumps to a conclusion:

There's more garbage now, so it must be AI's fault.

It's not.

Garbage has always been there.

It was just produced in limited quantities before.

What's really changed isn't content production.

It's the competition for attention.

Before, the hardest part for a creator was producing the work.

Now, the hardest part is being seen.

And the future will only intensify this.

Because content will grow infinitely.

But human eyeballs are forever finite.

Infinite supply.

Limited demand.

This is the destiny every content industry eventually faces.

So the scarcest resource of the future isn't content.

It's curation.

Not generation.

But selection.

Who can find you that one article worth reading among ten thousand?

Who can find you those ten truly valuable minutes among ten thousand videos?

That's who holds the new leverage.

Some people are still stuck on the question:

"If AI produces all the content, won't only AI be left reading it?"

But that's asking the question backwards.

Who cares whether it's AIGC or human-generated content?

What you actually care about is the content itself.

Just like you wouldn't refuse a good meal because you don't know the chef's name.

And you wouldn't refuse to eat because the chef used a rice cooker.

The tool was never the point.

The result is.

Here's what's even more interesting.

Those who most fiercely oppose AIGC often default to the assumption that human creation is inherently nobler.

But reality says otherwise.

Throughout human history, the vast majority of content was never read by anyone.

Most books sell fewer than a few hundred copies.

Most WeChat articles get dismal readership.

Most videos sink without a trace after publishing.

Being seen has always been a probability game.

In an age of information explosion.

A carefully crafted work — whether AI-assisted or not — has an overwhelming probability of being buried.

While a piece meticulously engineered to harvest attention can easily rack up millions of views.

Because the people who truly understand virality don't understand technology.

They understand human nature.

They know your weaknesses.

They know your curiosity.

They know your anxieties.

They know your anger.

They know exactly which headline makes you stop.

Exactly which content makes you reluctant to scroll past.

Exactly how to turn your time into their revenue.

That's the real attention economy.

AI is just a new production tool.

It was never the problem.

The problem has always been:

Whether we still have the capacity to choose.

Whether we can still tell what's worth watching.

Whether we're willing to spend our finite lives on things of genuine value.

AIGC is not the original sin.

Garbage content is.

And what's more dangerous than garbage content.

Is knowing it's garbage.

And still being unable to stop consuming it.

---

The greatest challenge of the future may not be that AI is too smart, but that humans are too easy to please. The real competition may not be between models, but between high-quality information and low-quality dopamine.

🎬 Watch the video version

by Tuya

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?

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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

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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**.

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## 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.**

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## "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.

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## 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.**

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## 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?

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## 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.

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*Two Minutes with Liwei · 2024*

by Tuya