Liwei's Two Minutes · Token Economics in Plain Language Part 5: The New Currency of the AI Era

Token as the new currency of the AI era - illustration
AI时代的新货币:Token 经济示意图

The previous four installments of the Token Economics series covered:

What Token is Why Token consumes electricity Why Agents burn Token like crazy Why Token keeps getting cheaper

All about Token production, consumption, and cost.

But the most important question remains unanswered:

Why is the entire world suddenly measuring everything in Token?

Put differently:

Could Token become the "currency" of the future digital economy?

The first four pieces looked at the trees.

Today, Part 5 starts looking at the forest.

Liwei's Two Minutes · Plain Language Part 5 — The New Currency of the AI Era

Many people think Token is just a technical term.

It's starting to look like much more than that.

I even suspect that, looking back decades from now, Token might become a fundamental economic indicator — on par with electricity, steel, and oil.

Why?

Because throughout human history, every industrial revolution eventually produces a unified unit of measurement.

The steam age ran on coal.

The electric age ran on kilowatt-hours.

The internet age runs on traffic.

And in the AI age, increasingly, everyone is starting to measure things in Token.

The reason is simple.

Everything AI does today ultimately comes down to Token.

Writing an article? Burning Token. Writing code? Burning Token. Making a PowerPoint? Burning Token. Generating video? Burning Token. An agent running a project? Burning Token. Even future robots doing physical work — behind the scenes, still burning Token.

And so a strange phenomenon emerges.

We used to buy software — we paid for features.

Now it's starting to feel like: we're buying Token.

Companies used to ask about IT systems: "How much per license?"

Now they're starting to ask: "How much per million Token?"

This is actually very similar to the power grid.

No one cares how many times the generator spun.

Everyone cares about one thing: how much per kilowatt-hour.

The future may be the same.

No one will care how many parameters a model has.

Everyone will care about: how much per million Token. What's the quality. Is it reliable enough.

At that point, Token shifts from a technical concept to an economic one.

And economics has a brutal law: any standardized commodity eventually gets commoditized into a race to the bottom.

Steel went through it. Display panels went through it. Solar panels went through it.

Today's Token is walking the same path.

So while many people are still debating: which model is number one, which model is number two.

The industry is increasingly focused on: who can produce high-quality Token at the lowest cost.

Because real large-scale applications, in the end, all come down to the math.

The boss won't ask: "Did you use the world's number one model?"

The boss will only ask: "How much did we cut costs?"

And so the AI industry starts to look less like a lab and more like manufacturing.

Many people understand AI competition as: a contest of brilliant scientists.

It's increasingly looking like: a contest of national industrial systems.

Who has cheaper electricity. Who has more data centers. Who has a more complete supply chain. Who can drive down Token prices. That's who has the edge.

So a new phenomenon may emerge in future international competition:

Alongside energy exporters and manufacturing exporters, we may see a new category: Token exporters.

Whoever can consistently export cheap, high-quality Token to the world may occupy a pivotal position in the next-generation digital economy.

In the internet age, data flowed.

In the AI age, what really flows may be Token.

And everything happening today might just be the opening act.

立委两分钟 · token经济学大白话之五:AI时代的新货币

Token as the new currency of the AI era - illustration
AI时代的新货币:Token 经济示意图

token经济学大白话序列前面四篇 我讲了:

Token是什么 Token为什么费电 Agent为什么疯狂烧Token Token为什么越来越便宜

讲的都是:

Token的生产、消费和成本。

但还没讲那个最重要的问题:

为什么全世界突然开始用Token来衡量一切?

或者说:

Token会不会成为未来数字经济的"货币"?

前四篇讲的是树木。

今天第五篇开始看森林。

立委两分钟 · 大白话之五 题目叫

AI时代的新货币

很多人觉得:

Token只是个技术词。

其实越来越不像了。

我甚至怀疑,

几十年后回头看,

Token可能会变成一种类似: 电力、 钢铁、 石油

那样的基础经济指标。

为什么?

因为人类历史上,

每次工业革命,

最后都会出现一个统一计量单位。

蒸汽时代看煤。

电气时代看电。

互联网时代看流量。

而AI时代,

越来越多人开始看Token。

原因很简单。

因为今天AI干的所有事情,

最后都会落到Token上。

写文章,烧Token。 写代码,烧Token。 做PPT,烧Token。 生成视频,烧Token。 Agent跑项目,烧Token。 甚至未来机器人干活,背后依然在烧Token。

于是一个奇怪的现象出现了。

以前我们买软件,买的是功能。 现在越来越像:买Token。

以前企业采购IT系统,问的是:多少钱一套? 现在开始问:多少钱一百万Token?

这其实很像电网。

没有人关心发电机转了多少圈。 大家只关心:一度电多少钱。

未来也可能一样。

没有人关心模型多少参数。 大家只关心:一百万Token多少钱。质量怎么样。够不够稳定。

这时候,Token开始从技术概念,变成经济概念。

而经济学有个很残酷的规律:任何标准化商品,最终都会被卷。

钢铁如此。面板如此。光伏如此。

今天的Token,也正在走这条路。

所以很多人还在争论:哪个模型第一。哪个模型第二。

但产业界越来越关心的是:谁能最便宜地生产高质量Token。

因为真正的大规模应用,最后都得算账。

老板不会问:"你用了世界第一模型吗?" 老板只会问:"成本降了多少?"

于是AI产业开始越来越像制造业。而不是实验室。

很多人把AI竞争理解成:天才科学家的竞争。

其实越来越像:国家工业体系的竞争。

谁有更便宜的电。谁有更多的数据中心。谁有更完整的供应链。谁能把Token价格打下来。谁就有优势。

所以未来的国际竞争,可能出现一个新现象:

能源出口国、制造业出口国、之外,再多一个:Token出口国。

谁能持续向全世界输出便宜而高质量的Token,谁就可能占据下一代数字经济的重要位置。

互联网时代,数据在流动。

AI时代,真正流动的,可能是Token。

而今天发生的一切,也许只是这个时代的开场白。

FSD's Emergency Avoidance — Sometimes a Ghost, Sometimes a God

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.

FSD 的紧急避让,有时候像鬼,有时候像神

昨天看一条实况视频 是特斯拉紧急避让左道入口急速冲上来的车辆,当时感觉人的反应速度是不行的。人在这种情况下 几乎本能紧急踩刹车 在高速上也是有危险的。能像FSD那样及时安全避让道右线显然是更好的策略 可惜我们人类司机大多做不到。开 FSD 时间长了以后,人会慢慢形成一种很奇怪的信任。

不是说它每一次都对,也不是说你每一次都懂它为什么这么做。

而是你会发现:很多当时让你一身冷汗的紧急动作,事后复盘,居然大多是真正保护安全的。

手动开车的那么多年 我在紧急情况下 做的最多的就是下意识急刹 因为只有慢了才觉得可控。不是不会打方向,而是不敢。因为你要先看右 lane 有没有车,盲区有没有车,后车快不快,对方是不是新手,是不是手忙脚乱。这一套判断,觉得人类是串行的,来不及。

所以不少人跟我一样只能本能地踩刹车。

但 FSD 不一样。它不仅仅是看过无数老司机开车,它更像一个长了很多只眼睛、反应速度比我们快很多倍的驾驶。它一直在同时看前后左右,一直在算每条 lane 的空间、速度和风险。

所以有些时候,它敢做我们不敢做的 lane escape。

当然,这也会带来另一种问题:它有时候过于谨慎。前面突然飞过一只鸟,它也可能有避让反应。还有些紧急避让,我们事后也未必完全理解。前几年所谓"鬼影刹车"就是典型例子:树影、桥影、光照变化,甚至路面纹理,都可能让系统误判。

但这几年下来,一个很明显的变化是:鬼影刹车几乎消失了。至少我自己一年多几乎没遇到过。说明它已经不只是"看见一个像危险的东西",而是在越来越理解:什么东西真的会撞上我,什么只是影子。

这就是 FSD 最有意思的地方。

它早期有时候像个笨学生,现在又像个反应快得离谱的非人实体。

昨天它有一次特定避让,我也没完全看懂。也许没必要反应过度,也许它看到了我们没看到的风险。但我不打算深究到底了。

因为长期使用下来,我对它的判断不是来自信仰,而是来自一次次路上的复盘。

绝大多数情况下,它那些当时显得紧张的动作,事后看,是在保护我们。它比我这个老司机中的笨鸟 谨慎安全太多了。

这就够了。

未来真正改变驾驶的,也许不是它能不能像人一样开车。

而是它终于也可以不像人那样开车。

朝华午拾 — Ch.3: The Little Red Guards / 红小兵

Morning Glory — Ch.3: The Little Red Guards

Before my career began, family and society shaped our character and worldview. 

**"Forever Be Chairman Mao's Little Red Guard"**

When the Cultural Revolution began in 1966, I was in the first grade, six years old. More than half a century later, some memories remain as vivid as yesterday.

The three of us siblings, wearing our Little Red Guard armbands, photographed in December 1966.

When the campaign to topple Liu Shaoqi began, the first thing I noticed was Liu's official portrait pasted upside down on the street-facing wall, marked with a red cross. Soon after, more and more long banners appeared across the main street: "Burn Liu Shaoqi!" "Deep-fry Liu Shaoqi!" Then, as negative teaching material, they screened the documentary *Liu Shaoqi Visits Indonesia*. The female narration was syrupy sweet, addressing him as "Chairman Liu" and "Jakarta" in every other breath — to my ears, she sounded like a female spy. Her voice was constantly drowned out by the slogans erupting from the audience: "Down with Liu Shaoqi, Defend Chairman Mao!" "Smash the arch-traitor, arch-spy, arch-scab Liu Shaoqi to the ground and trample him underfoot, never to rise again!" Wang Guangmei on screen was dressed conspicuously well, fitting the standard definition of a bourgeois stinking woman. Later, I saw several living newspaper dramas lampooning Liu — his features caricatured into a long horse face, a high-bridged nose, the classic villain's profile. I also remember a living newspaper piece called *Burning Down the British Chargé d'Affaires Office*, which portrayed the Capital Red Guards, righteous in their fury at British imperialism, acting with militant resolve to set fire to the British Embassy — an act of collective heroism (in reality, this was an extremely serious diplomatic incident that caused Zhou Enlai immense trouble and lasting fallout). I still recall the stage effect when they set the fire: they seemed to hurl a torch into the embassy, followed by a loud bang and a plume of thick smoke. I was in the front row and choked on the smoke, coughing hard, and I was genuinely startled. The artistic creativity of the revolutionary masses, producing such vivid stage realism, left a deep imprint on the mind of a six-year-old me.

Around this time came the campaign to "Destroy the Four Old's" (old ideas, old culture, old customs, old habits) and establish the Four New's. Every household voluntarily surrendered items suspected of being "Four Old's" — copper coins, bracelets, ornaments, even ceramic toys of cats and dogs — to be publicly destroyed. The stone lions beside the stone bridge were toppled into the ditch by the young Red Guards; since they couldn't be smashed, chisels were used to disfigure them. The influence spread far: by early 1967, a "Revolutionary Spring Festival" was mandated. Adults had no holiday — they must persist in "grasping revolution and promoting production" — while all New Year celebrations and entertainments were cancelled. Even the traditional four-corner red envelope money for children was voluntarily suspended.

Some elderly people, lifelong habits unbroken, still called matches "foreign fire" and iron nails "foreign nails." These old terms originated in the pre-revolutionary era when China could not even produce matches and nails domestically and had to import them. But by 1966, such old terms could bring trouble. I once saw a tiny-footed old woman totter into a small shop and ask for "foreign fire." The shop assistant replied coldly: "Don't have any." When the old woman pointed to the goods on the counter, the assistant erupted in fury.

Before armed struggle erupted, great debates — the weapon of literary struggle — became prevalent. Even elementary school students debated each other, often turning red in the face. I was too young to get a word in, but I loved listening. What they debated I mostly can't recall, except for one recurring topic: the dialectical relationship between family background and individual performance. The affirmative position was "Heroes beget heroes," while the opposition stressed "What matters is personal conduct." Both sides seemed righteous and indignant, both could quote Chairman Mao's quotations, both seemed to have good arguments. Later, my elder brother took the lead in forming a Little Red Guard revolutionary organization (with a fifth-grader serving as strategist behind the scenes), calling it the "Dagger Squad." Every grade had its representatives. Through this connection, I too was honorably swept up in the revolutionary movement — duties like carrying the paste bucket for the young fighters putting up big-character posters. I remember my brother and his comrades setting up a "Dagger Squad Office" at a table in the corridor of my father's hospital. The squad's most glorious exploit, the one I remember most clearly, was an assault on a school meeting. The squad learned that the school leadership was holding a faculty meeting at seven in the evening and decided on a surprise raid. I had the good fortune to follow my brother on this revolutionary action. I remember the meeting was in progress when the squad burst into the room, shouting: "What kind of black meeting are you holding here?" The leaders, seeing it was a bunch of children, didn't know whether to laugh or cry, and explained that this was a routine school affairs meeting. The squad leader declared: "Then we're attending too." Some leader apparently advised that a work meeting wasn't convenient for students. That set off an explosion. The young fighters, each more righteous than the last, delivered their rebuttals: We are Chairman Mao's Little Red Guards — if we don't attend, who will? You hold black meetings behind the backs of the revolutionary young fighters — how poisonous your intentions must be! Not only will we attend, we demand you honestly hand over all previous meeting records. If you dare not disclose your meeting records, you must have unspeakable criminal aims, and we will rebel against you. And so on. I remember the school leaders finally conceded, agreeing that young fighter representatives could attend all faculty meetings. I was as excited as everyone else, filled with the pride of this initial victory in struggle. Unfortunately, I suffered from night blindness at the time, and on the way back my vision went completely dark. An older girl from a higher grade held my hand and walked me home (my brother, as rebel leader, stayed behind to discuss the next phase of the struggle strategy). This revolutionary action enormously boosted the young fighters' morale and opened the prelude to rebellion against the elementary school leadership, soon followed by a flood of big-character posters exposing the schemes of the capitalist-roaders.

In the early days of the Great Revolution, the three of us siblings, led by our brother every day, would stand before the Precious Book platform for morning pledges and evening reports — earnest and ceremonial, and we kept it up for a long time.


朝华午拾 · 红小兵

职业生涯之前,家庭和社会塑造了我们性格和世界观。父母是天,兄妹是我的依靠和牵挂。

"永做毛主席的红小兵"

一九六六年文革开始的时候,我在小学一年级,六岁。半个多世纪了,有些记忆依然清晰如昨。

兄妹仨臂佩红小兵袖章摄于文革1966年12月8日。

打倒刘少奇的时候,最先是看到临街墙上把刘主席的标准像倒贴过来,打上红叉。后来,看到越来越多的长幅标语在大街上,"火烧刘少奇","油炸刘少奇"。接着,作为反面教材,放映了纪录片《刘少奇访问印度尼西亚》,片子里面的女音讲解,甜腻腻的,一口一个刘主席和雅加达,当时听起来觉得象女特务,不断被场内此起彼伏的口号声淹没:"打倒刘少奇,保卫毛主席!""把大叛徒、大内奸、大工贼刘少奇打翻在地,并踏上一只脚,叫他永世不得翻身!"电影上的王光美,打扮得很光鲜,符合资产阶级臭婆娘的标准定义。再后来,看到过几个批判刘少奇的活报剧,刘的形象被脸谱化,马脸,高鼻子,一副奸臣像。记得同时还有一个活报剧《火烧英国代办处》,演的是首都红卫兵,对英帝国主义义愤填膺,同仇敌忾,机智果断纵火焚烧英国大使馆的光荣业绩(这是一起非常严重的外交事件,给周恩来的工作带来很多麻烦和后遗症)。还记得,舞台上演纵火时的场面,好像是把火把往使馆内一扔,砰一声炸响,一股浓烟就冒出来,我在前排,呛得直咳嗽,也吓了一大跳。革命群众的艺术创造力所造成的舞台逼真效果,在一个六岁孩子幼小的心灵里刻下了深深的印记。

这前后的破"四旧"(旧思想、旧文化、旧风俗、旧习惯),立四新,我们各家各户主动把涉嫌四旧的物品,比如,铜钱、手镯、装饰品,甚至小猫小狗的瓷玩具,统统缴公销毁。石桥旁的石头狮子也被红卫兵小将推倒在河沟,因为实在砸不烂,就用凿子破相。影响所及,67年初要求"过革命化的春节",大人没有节假,坚持抓革命、促生产,同时取消了所有过年的庆祝和消遣活动,连四角压岁钱也自觉停止发放了。

当时有些老人一辈子的习惯改不了,仍然称火柴为"洋火",铁钉为"洋钉"等。老称呼源于旧中国日常生活品连火柴和铁钉都无力生产,需要进口。可是到了66年,这些旧称呼会带来麻烦。我就看到过小脚老太太颤颤巍巍到小卖店要买"洋火",营业员冷冷一句:"没有"。当老人指着柜台里面的商品,营业员就大发雷霆。

武斗开始之前,用于文斗的大辩论开始盛行,连小学生也互相辩论,往往争得面红耳赤。我太小插不上嘴,但是很愿意旁听。辩论什么大多记不清了,但是有一个题目是反复辩论过的:家庭出身和自我表现的辩证关系。正方的论点是"老子英雄儿好汉",反方强调"重在个人表现"。感觉双方都义正词严,都懂得引用毛主席语录,似乎哪一方都很有道理。

后来,我哥哥领头成立红小兵革命组织(背后有个五年级的孩子做军师),叫"匕首小分队",其中各年级都有代表。由于这层关系,我也光荣卷入革命运动,比如给贴大字报的小将提浆糊筒之类。我印象我哥哥一伙还在我父亲的医院走廊尽头,摆了张桌子,设立了"匕首小分队"办事处。小分队的光荣事迹记得最清楚的,是一次大闹会场的事件。小分队得知晚上七点学校领导开教务会议,于是决定来个突然袭击。我有幸跟着哥哥参加了这一革命行动。记得会议进行中,小分队一行冲进屋内,叫道:"你们这是开的什么黑会?"领导看是一帮孩子,哭笑不得,解释说,这是例行的校务工作会议。分队头头说:"那我们也要参加"。好像是某领导劝告说,工作会议,学生参加不方便。这一下炸了窝,小将们个个义正词严予以驳斥:我们是毛主席的红小兵,我们不参加谁参加?你们背着革命小将开黑会,用心何其毒也。我们不但要参加,还要你们老实交出以前会议的所有记录。你们不敢公开会议记录,就肯定有不可告人的罪恶目的,我们就要造你们的反。诸如此类。记得校领导最后让步,同意小将可以派代表参加所有校务会议。我跟大家一样兴奋,充满了斗争初步胜利的豪情。不过,倒霉的是我当时患有夜盲症,回来路上,两眼一片漆黑,是由一位高年级大姐姐,牵着我手送我回家的(哥哥作为造反派头头留下来商量下一步的斗争策略)。这次革命行动极大地鼓舞了小将的斗志,拉开了向小学领导造反的序幕,紧接着是铺天盖地的揭露走资派阴谋的大字报。

大革命初期,我们兄妹三每天在哥哥带领下,在宝书台前,早请示,晚汇报,煞有介事,坚持了很久。


From 朝华午拾. Original Chinese: 《朝华之三: 红小兵》.

Two Minutes with Liwei: AI Doesn't Pay Taxes — Trouble Is Inevitable

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

立委两分钟:AI 不交税,迟早出事

人类历史上第一次,一种"员工",可以 24 小时工作、不用睡觉、不要工资、不交社保、不会维权、不会罢工、不会生病、不会退休。

更离谱的是:它还能自己复制自己。

这东西叫 **AI agent**。

---

## 问题来了

以前老板雇 1000 人。国家有:个税、社保、医保、失业金、养老金。

现在老板把 1000 人裁掉,换成 AI。效率暴涨。利润暴涨。股价暴涨。

**但税基没了。**

国家收不到钱了。失业的人却还在。

于是出现了一个极其魔幻的局面:

> AI 一边疯狂提高生产率,一边疯狂掏空社会财政基础。

整个现代国家,本质上建立在"人类劳动纳税"之上。而 AGI 正在把"人类劳动"本身抹掉。

**这才是真正的核弹。**

---

## "学 AI 就好"是鸡汤

很多人还停留在:"学一点 AI,以后转型新岗位就好了。"

这其实越来越像鸡汤。因为最残酷的地方在于:连"使用 AI"本身,最后都会被 AI 自动化。

你以为未来岗位是 "AI 操作员"、"Prompt Engineer"、"Agent 管理师"——结果 agent 自己就在用 agent。人类连"提示词工程师"这种过渡岗位,可能都只是技术浪潮里的临时泡沫。

两年前,全网都在卖 prompt engineering 课程。今天再看,像时代笑话。

---

## 这次不一样

过去技术革命会创造新岗位:汽车淘汰马车,但创造修车工。互联网淘汰报刊,但创造电商和主播。

而这次不一样。AI 创造的新系统,天然就是"少人化"的。因为 AI 最大的优势,恰恰就是:**不需要人。**

---

## AI 必须交税

如果 AI 替代了人,那谁来交税?

答案很简单:**AI 本身必须交税。**

你用了多少 token、多少 GPU、多少推理、多少 AI 电力,就缴多少"AI 社会税"。

因为以前你雇人,你本来就在缴税。现在你用 AI 替代人,却一分钱社会成本不承担——这本身就不合理。

很多人一听就急:"你这是阻碍科技进步!"

**So what?**

难道人类社会的唯一目标,就是让资本和算力无限增殖?

- 工业革命污染环境 → 后来有环保税
- 汽车消耗公共道路 → 后来有燃油税
- AI 摧毁就业税基 → 为什么不能有 AI 税?

---

## 通杀一切

真正危险的,不是 AI 太强。而是 AI 太强之后,整个社会收入结构崩了。

最黑色幽默的是:未来最支持 AI 税的人,可能恰恰是最懂 AI 的那批人。因为他们最清楚:这东西一旦真正成熟,不是替代"底层",而是**通杀。**

白领、程序员、设计师、分析师、客服、翻译、律师助理、研究员……一个都跑不掉。

过去社会还能用一句话安慰人:"你只是不够努力。"

但 AGI 时代最残酷的地方在于:有时候,不是你不努力。而是你作为"人类员工"这个物种,开始整体失去经济性了。

---

*立委两分钟 · 2024*

by Tuya

朝华午拾 — Ch.2: A Scholarly Family / 书香门第

A Scholarly Family

Ever since Qu Yuan, Chinese literati have been fond of tracing their ancestry to illustrious roots — "descendant of Gaoyang the Divine Emperor" and such declarations — to signal their noble bloodlines. When I was compiling and editing A Collection of Master Li's Posthumous Writings: Prefaces, I came across this passage in the first piece, "Preface to the Li Family Genealogy," explaining the origin of the family name Li:

"The forerunners of the Li clan, surnamed Ying, traced their descent from Gaoyang of the Zhuanxu lineage. One descendant, Gao Yao, served as Grand Justice (Dali) under Emperor Yao, and the family adopted 'Li' (理, meaning 'principle' or 'justice') as their surname from the title. During the reign of King Zhou of Shang, a descendant named Li Zhen fled with his mother to the lost land of Yihou. Starving, they survived by eating plums (李, li) from the trees. To evade King Zhou's persecution, they changed their surname from 理 (Justice) to the homophonous 李 (Plum), and their descendants have borne this name ever since."

In my earlier, more perfunctory readings of the Posthumous Writings, I had mostly skipped Master Li's abstruse classical prose, drawn instead to the more accessible "modern writings" of my two granduncles in the appendix. As a result, I never registered this origin story. But my daughter once asked me: "Dad, you said our family name Li means plum — how come? Does that mean we Li family like plums in particular?" I had no idea whether the surname Li was actually connected to the fruit, so I dodged the question and told little Tiantian instead that statistically, Li had risen to become the most common surname in China — and perhaps the world. Even in our tiny Buffalo office there were two Uncle Li's — one of Korean descent. But eight hundred years ago, we were all one family.

Master Li's own account of this family history — the fall from officialdom, the change from 理 to 李, the "pointing at the tree and taking its name" — struck me as too sparse. So I searched online and found a fuller treatise, On Gao Yao, Blood Ancestor of the Li Surname. It turned out that the primogenitor Gao Yao served Emperor Yao and Shun as Grand Justice — a minister of incorruptible integrity, whose achievements in statecraft were so esteemed that Emperor Shun personally named him his successor. Even Confucius honored him as one of the Four Sages of antiquity. In ancient China, officials took their office titles as surnames, hence 理氏 (the Li of Justice). Tragically, Sage Gao Yao died before ascending the throne. Generations later, under the depraved King Zhou of Shang, a descendant named Li Zheng served as Grand Justice with the same upright character — and for his honesty, the debauched king had him executed. His wife Qihe fled with their young son Lizhen to the lost land of Yihou (in present-day Henan). Starving, they spotted fruit on a tree and ate to survive. Afraid of the king's pursuers, Lizhen dared not keep the surname 理. In gratitude for the "wood-seed" (muzi, 木子 — the character parts that combine to form 李) that saved them, he changed the family name to Li. From this seed, the Li lineage — the largest family name under heaven — branched and flourished across generations.

I told my daughter: not only do we come from a scholarly family, we are the direct descendants of Sage Gao Tao himself.

Master Li — Li Xiansheng, courtesy name Xuexiang — was my great-grandfather. A Collection of Master Li's Posthumous Writings, compiled in vernacular classical Chinese (also called "modern classical style"), gathers his surviving works — poems, lyrics, celebratory couplets, elegies, prefaces, and miscellaneous essays — transcribed by his disciples and privately published in the 1930s.

The Posthumous Writings also includes works by my two granduncles: elder granduncle Li Yingwen and younger granduncle Li Yinghui. My great-grandfather was exceptionally open-minded about education, selling off family land to send his sons (my granduncles) to study in Japan. My own grandfather (Li Yingqi, the second son), however, was kept at home to manage the family estate, forfeiting the chance for overseas education. It's said that every year, my grandfather would travel to Nanjing to remit money from land sales to his two brothers in Japan. In the early 1920s, the two granduncles returned with law and political science degrees from Meiji University — rare credentials for that era, and a springboard for significant careers. That their subsequent achievements remained relatively modest (disproportionate to their education) and confined to the local sphere, I attribute to three factors: first, the times were harsh, with China in ceaseless turmoil from war and upheaval throughout the early 20th century; second, my great-grandfather was indifferent to fame and fortune, urging his children to carry on the family mission of local education rather than venture into the wider world; third, both granduncles suffered from poor health — they lacked the physical constitution for "revolution." Elder Granduncle Yingwen was bedridden for years, and it was country life that gradually restored his health. Younger Granduncle Yinghui died tragically young. Yet their writings reveal open minds deeply engaged with the issues of their day. Besides rustic pastoral pieces like "Li Yingwen — Elegy for a Dead Dove," they also produced fiery patriotic works, such as "Li Yinghui — Manifesto of the Anti-Japanese Association (Modeled on the Denunciation of Empress Wu)" and "Li Yingwen — Preface for Wang Joining the Volunteer Army."

My grandfather died in the great famine of my birth year — a calamity that was three-tenths natural disaster, seven-tenths man-made catastrophe. Among the three brothers, only Elder Granduncle Yingwen was fortunate: he passed away peacefully at home in 1965, surrounded by every Li family descendant who had gathered for a grand funeral (see the family photograph below). I still remember each of us grandchildren, after the coffin was lowered, taking turns to scoop up a handful of yellow earth. As an enlightened gentry figure and a "united front target," Granduncle Yingwen had been treated with courtesy by the local government and was even elected as a county representative to the People's Congress, thus escaping the reach of political campaigns. That he departed this world the year before the Cultural Revolution began was an even greater stroke of fortune — otherwise, given the complexity of his personal history, he would have suffered terribly in that great upheaval. My maternal grandmother, who raised us through those years, was dragged out and struggled against during the Cultural Revolution, forced to wear a "Landlord Element" placard every day, subjected to humiliation that cast a lasting shadow over our childhood.

The above accounts for my "scholarly family" background — except that by my father's generation, the family fortune had seriously declined. Beset by foreign invasion and civil war, the country was in chaos, and life grew harder each day. My father often went hungry and cold as a child. In its heyday, the Li family's Chongshi Academy had enjoyed wide renown, its students scattered across the land like peaches and plums filling the world. Yet this decline proved a hidden blessing: when the Land Reform came, our family was classified as "Small-Scale Land Lessors" rather than one of the "Four Categories" (landlords, rich peasants, counter-revolutionaries, and bad elements — later expanded to include "rightists" designated in 1957). This spared us, the younger generation, from the brunt of political persecution.

The matter of "Small-Scale Land Lessor" classification carried its own stories. When we were children, family class status was an all-important political label: children of "poor and lower-middle peasants" were considered born revolutionaries with "red roots and upright shoots," innately superior. Children of the "landlord, rich peasant, counter-revolutionary, bad element, and rightist" classes faced extreme social discrimination — denied opportunities for factory jobs, schooling, and more — and suffered constant bullying in daily life. I remember a girl in our elementary class who came from a landlord family; she cut such a pitiable figure, never able to hold her head up, yet classmates still taunted her relentlessly. In such an environment, we were all acutely sensitive about our family background. My own family situation was precarious: my mother was born into a landlord family — a pitiable sort of landlord, really; my maternal grandfather had saved every penny from a small business, denied himself fine food and clothing, tightened the whole family's belts, and poured everything into buying land in hopes of modest prosperity — and in return won a landlord label. This became a fiercely guarded family secret. Fortunately, a child's class status followed the father, so every time we filled out a form, the "family class" box read "Small-Scale Land Lessor." The problem was, for a long time, we had no idea what this obscure, tongue-twisting classification actually meant politically, which left us perpetually anxious. I remember classmates discussing our strange class label. One self-proclaimed authority declared: "Small-Scale Land Lessor — that means little landlord!" (It wasn't that far off, actually.) And with that, we were suddenly shoved into the camp of "class enemies," utterly mortified. My cousin suffered the same anxiety. Then one day, he announced triumphantly that, after deep research — studying Chairman Mao's works and relevant Party policy documents — he had discovered that "Small-Scale Land Lessor" was essentially equivalent to "Upper-Middle Peasant," which placed us squarely among the "united objects" of the revolutionary ranks. What's more, Chairman Mao himself came from an upper-middle-peasant family. These momentous findings brought us immense relief.

The old family home in Keshan — I visited it as a child, when my cousin led us up the mountain; it felt like the Mountain of Flowers and Fruit, remote and secluded. A few years ago, on a trip home to China, my brother drove us back there. It remains a forgotten corner to this day — a single mountain road, bumpy and dusty, narrowing to barely a car's width as you approach. My ancestors must have chosen this Jiangnan hillside deliberately, building their grand compound in a spirit of retreat from the world, carving out their own Peach Blossom Spring. My father's memoir, Decades Through Wind and Rain, contains a vivid description of the family school there:

A Glance Back at the Old Residence

A deep courtyard mansion, antique and elegant, nestled against the mountain and facing a stream, oriented east to west. Above the main gate, the couplet "The Nation's Grace, the Family's Joy / May Men Live Long, May Years Bring Harvest" stood steadfast through the seasons. The main quarters comprised five large rooms in the front row and five in the back, joined in the middle by three open-air courtyards flanked by two wings on either side. The three rows, each two stories high, formed an integrated whole. Upstairs, a continuous corridor circled the entire compound — a gallery on which one could stroll freely. To the left stood two "new rooms"; to the right and rear, a row of auxiliary quarters. The front courtyard, with its large and small gates, contained seven flower terraces, where pines and cypresses complemented one another, blossoms clustered in splendor, and fruit filled the air with fragrance. Among the flowers: plum, chrysanthemum, osmanthus, rose, briar, and sacred bamboo. Among the fruits: persimmon, peach, apricot, plum, and jujube. Every doorway was flanked by stone drums and lions; the courtyards were paved in marble. The bricks and tiles were custom-fired in the family's own kiln, of the highest quality; the timber, first-rate, was floated down the river from Jiangxi on rafts — a testament to the master-builder's meticulous vision. The upper floor of the main building served as classrooms and student dormitories; the lower floor and the "new rooms" were the family living quarters; the foot-house housed the wine-making workshop, kitchen, and firewood store.


书香门第

中国文人自屈原始,就喜欢炫耀自己的祖上,"帝高阳之苗裔兮"云云,以示自己根正苗红,血统高贵。早年整理校对《李老夫子遗墨:序类》,第一篇"李氏创修宗谱序"也提到"李氏"的来源如下:

"按李氏之先,自嬴姓顓頊高阳氏曰皋陶者,为堯大理官,始为理氏,至殷紂时有曰利贞者,偕母逃难于伊候之墟,食李实以全生,复改理为李,子孙因以为氏焉。"

以前读《李老夫子遗墨》比较偷懒,基本跳过晦涩难懂的李老夫子正文,而对《遗墨附录》中更贴近近現代生活的两位叔爷的"时文"感兴趣,因此对这段"李氏"来源的掌故没有印象。女儿小时候问我:"Dad, you said our family name Li means plum, how come? Does that mean we Li family like plums in particular?" 我当时不知道"李氏"跟李子到底有沒有关联,只好顾左右而言他,告诉甜甜,据最新统计,"李氏"似乎已经上升到中国的(可能也是世界上的)第一大姓,就連小小的水牛城辦公室就有兩位 Uncle Li's, 其中一位還是朝鮮族裔,但八百年前都是一家人哪。

李老夫子對祖上家道中落、改"理"為"李"、"指树为姓"的歷史,撰述失之簡陋。于是上網進一步搜尋資料,查得《李姓血祖皋陶并李姓考》。原來,李家的始祖皋陶在堯舜時代就任國家重臣"大理官"(司法部長),清明正則,功勛卓著,經邦緯國,英名蓋世,舜帝親立為接班人,甚至孔夫子也拜其为上古"四圣"之一。古人以官为氏,因称理氏。所惜圣皋陶帝業未举而病亡。至商紂王朝,圣皋陶的后代理征仍繼任理官,正直清廉,为荒淫昏庸的纣王所不容,终遭殺身之祸。于是,"理征的妻子契和氏带着幼子利贞逃了出来,奔于伊候之墟(今河南境内),饥饿不堪,见一树上结有果实,便采了来吃,母子得以活命,其后,利贞畏于纣王的追捕而不敢姓理,于是以'木子'救命之恩,改称李氏"。天下第一大姓李氏由此而宗派繁衍,生生不息。

我告訴女儿:咱們非但出身書香門第,還是大圣人皋陶的传人呢。

李老夫子(李咸昇,號學香)是我的曾祖父。《李老夫子遺墨》(現代文言,又稱"時文")收集了其徒子徒孫傳抄的李老夫子遺作,包括詩詞歌賦、喜壽輓聯、序傳雜文等,由他的門生編輯成冊,內部發行於上個世紀三十年代。

《遗墨》還收錄了我的兩位叔爺(伯祖父李應文和叔祖父李應會)的作品。曾祖父非常開明,重視教育,不惜變賣田產送孩子(我的叔爺)去日本留學深造。但我的爺爺(李應期,行二)被曾祖父留下來幫助理家,失去了留學機會。據說,我爺爺當年每年去南京一趟,將家產變賣的銀子匯款到日本,供給兩個兄弟的學業。兩位叔爺上個世紀二十年代初分別獲得明治大學法學士和政學士學位歸國。在那個年代,有這樣教育背景的人才很難得,本可做一番大事業。他們後來的建樹不大(與其教育水平不成比例),影響止於本地,我猜想原因有三:一是年代不濟,中國自上世紀初開始,兵荒馬亂不斷;二是曾祖父淡泊名利,進而要求孩子們繼承父業,在家鄉興辦教育,而不是鼓勵孩子們出去闖天下;三是兩位叔爺身體都不大好,沒有"革命"的本錢:伯祖父久卧病榻,是鄉間生活使他休養生息,逐漸康復;叔祖父更是不幸,英年早逝。但是從他們所著文字,可以看出,他們思想開明,關註時事。除了鄉居閑篇如"李應文-哀死鴿文"外,也不乏豪情熱血之作,如"李應會-抗日會宣言(仿討武曌檄)","李應文-王君加入義勇軍序"。

我爺爺在我出生那年死於三分天災、七分人禍的大飢荒。三兄弟中,就數伯祖父李應文比較幸運,1965年在老家壽終正寢。李家所有晚輩全部到齊,舉行隆重葬禮(李家合影見下)。還記得我們孫兒輩,在棺柩落地後,每人輪流掬一捧黃土。伯祖父生前作為開明紳士和"統戰對象",受到當地政府的禮遇,曾經當選為縣人大代表,幸免於政治運動的波及。仙逝於文革前一年,更是大幸,否則,以他歷史上的複雜經歷,大革命中少吃不了苦頭。一直看顧我們長大的外祖母在文革中,就被揪鬥,每日掛著"地主分子"的牌子,受盡羞辱,給我們幼小心靈蒙上陰影。

以上可算是我的"書香門第"背景。只不過,到我父親這一輩,由於國家內憂外患(抗日和內戰),連年戰亂,家道中落,生活日漸艱難。我父親小時候忍飢挨凍的事常有。想當年,李家"崇實學校"在當地可是富有盛名,桃李滿天下。不過,家道衰落倒成為一件好事:土改的时候,家庭由此被定為"小土地出租",而不是"地主"、"富農"這樣的"四類分子"(指的是地主、富農、反革命、壞分子四類,後來又加上57年劃分的"右派"),使得我們後輩少受政治運動的衝擊。

說到家庭成分"小土地出租",還有一些故事。在我們小時候,家庭成分是一個很重要的政治標簽:"貧下中農"子弟被認為天生革命,"根正苗紅",高人一等;而"地、富、反、壞、右"子弟受到極端的社會歧視,被剝奪很多機會(招工、上學等),而且日常生活中也常常受欺侮。還記得我們小學時班上有一個女生,家庭出生地主,很可憐的樣子,總是抬不起頭。就這樣,還常常有同學羞辱她。在這樣的環境裡,我們每個人對家庭出身自然很敏感。我家情況不是很妙,母親出身地主(是個可憐的土地主:外祖父做小生意賺了點錢,捨不得吃和穿,一家人勒緊褲腰帶,卯足勁置辦田產,以期小康,換來了一個地主帽子),成了我們的一個死守的秘密。好在子女家庭出身隨父,所以我們每次填表,家庭成分欄都是"小土地出租"。問題出在,很長一段時間,我們搞不清這個比較偏僻拗口的成分的政治含義,心裡不免惴惴。記得在班上,有幾個同學議論我家這個奇怪的出身,其中一個自作聰明地說:"小土地出租,就是小地主"(其實這個理解不算離譜),一下子把我們推到"階級敵人"陣營,讓我們無地自容。我的堂兄也有同樣的煩惱。有一天,他很高興地宣佈,經過深入研究,學習毛主席著作和有關黨的政策文件,發現"小土地出租"大體相當於"上中農",屬於革命隊伍的團結對象。而且黨的主席毛澤東也出身於上中農家庭。這些偉大發現使我們大大松了一口氣。

磕山老家,小时候去过,堂兄还领着爬山,感觉是个花果山,僻静偏远。几年前回国探亲,哥哥开车带我们又去了趟老家,至今仍是被遗忘的角落,一条山区小路,颠簸起伏,尘土飞扬。临近老家,山路狭窄到勉强可以过一辆车。当年,先辈择此江南山地而居,大兴土木,大约很有些躲避尘世,开辟桃园的想法。老爸的回忆录《风雨几春秋》中对老家的李家学堂有生动记述:

故居回眸

深宅大院,古色古香,依山面溪,坐东朝西。大门上"国恩家庆,人寿年丰"对联经年常在。正房是前后各五大间,中间一排由三个天井和两边二个厢房组成。这样前后三排,上下两层,构成一体。楼上形成环状贯通的走马楼,左边有两间"新屋",右边及后面是一排裙屋。前面院子,有大小院门,院内七个花台,松柏相衬,花簇绵秀,果实飘香。花有梅、菊、桂、及玫瑰、蔷薇、天竹;果有柿、桃、杏、李、枣等。所有大门均有石鼓、石狮,天井是大理石铺成。建房的砖瓦是自家建窑特制,质量堪称上乘;木材取自江西,放排顺江而下,更是一流,足见主事者之匠心。正屋楼上是教学场所和学生宿舍,楼下和"新屋"是家人生活区,脚屋是酿酒作坊和厨房、柴库。


From 朝华午拾 (Morning Flowers Collected at Dusk). Original Chinese: 《朝华之二:书香门第》.

The Industrialization of Tokens — Liwei 2 Minutes · Token Economics in Plain Language (Part 4)

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

token的工业品化——立委两分钟 · token经济学大白话(四)

很多人最近忽然发现:

DeepSeek 又降价了。

而且不是小降。

是那种: "桌子直接掀了"的降法 五月底一杆子降到原价的四分之一。

很多人第一反应是:

中国AI公司开始打价格战了。

但我越来越觉得, 这件事如果只理解成"价格战",其实太浅了。

因为这里真正发生的, 可能是:

token开始"工业品化"了。

什么意思?

过去两年, 全世界AI圈其实一直有个默认前提:

高质量token很贵。

因为: 模型贵、 GPU贵、 训练贵、 电贵。

于是大家默认:

AI一定是高利润行业。

直到中国模型开始疯狂降价。

很多人第一次发现:

原来token也可能像:

钢铁、 面板、 光伏、 锂电池

一样, 进入一种恐怖的工业化降本过程。

这件事背后,其实非常"中国"。

什么意思?

美国AI公司, 很多走的是:

"高性能、高毛利、高估值"

路线。

有点像奢侈品。

而中国公司一旦卷起来, 往往会变成另一种画风:

"先把成本打穿。"

然后:

大规模、 基础设施化、 供应链化、 工程优化、 人力优化、 电力优化。

最后把整个行业, 卷成:

"白菜价工业能力"。

过去二十年, 中国其实已经反复干过很多次这种事。

光伏、 动力电池、 无人机、 面板、 电商、 高铁……

路径都差不多。

前期: 别人觉得是高科技。

后期: 中国开始把它工业化。

最后全球发现:

利润没了, 但产能已经铺满世界。

今天token, 开始越来越像这个故事。

因为token本质上并不神秘。

它终究是一种:

"被工业体系生产出来的数据处理能力。"

而工业体系最擅长什么?

降成本。

于是现在发生了一个特别有意思的变化:

美国头部模型, 仍然可能保持最强能力。

但中国模型, 正在疯狂逼近。

也许落后几个月, 也许某些能力还差一点。

但价格, 已经开始低到吓人。

于是全世界开发者开始出现一种非常现实的选择:

"我到底是需要世界最强, 还是需要: 足够强 + 便宜十倍?"

这问题太致命了。

因为大多数商业世界, 最后拼的都不是:

"理论最强性能"。

而是:

"综合性价比"。

当token越来越便宜, 很多以前"不舍得开"的AI应用, suddenly 就能开了。

过去:

AI像五星级酒店。

现在开始:

像自来水。

过去开发者还担心:

"这个Agent会不会一天烧掉几十美元?"

现在开始变成:

"算了,让它自己跑吧。"

于是, token消耗量开始进一步爆炸。

而这又会反过来推动:

更大规模的数据中心、 更低成本的推理芯片、 更激进的工程优化。

整个系统开始进入一种:

工业飞轮。

最有意思的是:

这场竞争最后拼的, 可能已经不只是模型。

而是:

谁的电更便宜; 谁的数据中心更多; 谁的工程师更便宜; 谁的供应链更完整; 谁更能承受薄利润。

也就是说:

AI竞争, 正在越来越像:

现代工业体系竞争。

而不只是实验室竞争。

很多人还把AI理解成: "几个天才科学家改变世界。"

但今天越来越像的是:

"整个国家级工业体系, 正在集体下场生产token。"

互联网时代, 中国最强的是"应用工业化"。

AI时代, 中国可能真正恐怖的地方是:

token工业化。

而当token价格不断下降, 全世界开发者最终会用脚投票。

因为绝大多数公司, 最后都得算账。

朝华午拾 — Ch.1-4: Homesickness Is an Invisible Net (Part II) / 乡愁是一张无形的网·下

For many young people, leaving one's homeland or staying behind can be an entangled, irresolvable contradiction — much like the dilemma in Qian Zhongshu's Fortress Besieged: those inside the walls gaze out at the dazzling world beyond; no matter how comfortable life within may be, they can never shake the regret of not having tasted the outside firsthand. Those who venture far, having endured every hardship, come at last to understand: homesickness cannot be filled with material things. That was exactly how I felt back then. After graduate school I dug in for five years — my work and life were on a steady upward climb, the future bright. Yet watching my classmates and friends leave for abroad one group after another, I felt an inexplicable emptiness. In the end I caught the last train out. But the sky over a foreign land was so strange — the constellations I knew from childhood summer nights, the fairy tales and daydreams that attended them, could never again be pieced together whole.

I recall those first days in England. Though I was already past thirty, though I'd come to Manchester alongside many friends, though I'd long since weathered in Beijing years of wandering far from home town — leaving my native land still carried an indescribable anguish: like a blade of grass torn out by the roots, battered by wind and rain, a vast bottomless emptiness and disorientation welling up within. At the start of term, in front of the student union building, every kind of student club was recruiting — bustling crowds, peals of laughter — yet I seemed to inhabit another dimension altogether, displaced from reality, unable to grasp the commotion around me, powerless to dispel a nameless melancholy.

Then came a decade of severance. Save for the companionship of Huaxia Wenzhai (China News Digest), and the occasional holiday phone calls or greeting cards to family, I had lost all contact with the motherland. Little did I know that this was precisely the decade in which China underwent its most earth-shaking transformation. Not until my first trip home in 2001 did I realize, with a jolt, that I had once again been displaced in time and space. Standing on the familiar yet alien streets of Beijing, watching the endless streams of people, I felt with an incurable certainty that this world no longer had anything to do with me. Was this the city that had left me so many warm memories? The Beijing I'd yearned for in my dreams now stood before me like a stranger! In the ancient capital I took such pride in, I could not understand the bustle around me, nor could I dispel that nameless melancholy.

Only my childhood hometown remains forever vivid in my mind, never fading. Thirty years have distilled the villages of southern Anhui into thick oil paints: golden yellow, fiery crimson. Endless fields of rapeseed flowers stretching to the horizon, and mountainsides aflame with azaleas in full bloom.

I have passed through countless cities and towns, witnessed many breathtaking scenes — the Gold Coast of Australia, the bays and forests of Vancouver, the autumn leaves of American national parks, and Niagara Falls in Buffalo — searching all the way, yet never finding rapeseed flowers and azaleas like those of home. Not until I returned to visit my family, catching the rapeseed bloom by chance, did I once again behold those patchwork fields of gold and breathe in the fragrance of the soil of home. I captured those golden expanses on video and stored them away, afraid they might slip away again.

Homesickness, like love, is an eternal theme of literature and art. From Li Bai's "Raising my head, I gaze at the bright moon; lowering it, I think of home," to Tao Yuanming's "Come Away Home"; from Chyi Yu's "Olive Tree" to Fei Xiang's "Clouds of Home"; from Ma Sicong's "Homesickness Melody" to the American folk song "Five Hundred Miles." In the still of night, in a foreign land, a gentle folk ballad flows like a quiet stream and soaks into my heart — it is the Kingston Trio singing "Five Hundred Miles," the shared melancholy of every wanderer under heaven.

Homesickness is an invisible net — where does the road of wandering end?

Written October 6, 2005, Buffalo


乡愁是一张无形的网(下)

对于很多年轻人,去国和留守是一对纠缠不清的矛盾:《围城》内外,城内的人看外面的精彩世界,哪怕城里舒适顺遂,也终觉没有亲历外部生活的遗憾;远游的人历尽艰辛终于明白,乡愁无法用物质来填补。我当年就是这样心情。研究生毕业一扎就是五年,工作生活蒸蒸日上,前途一片光明,可看见身边的同学朋友一批批出国,心里觉得空落落的。终于赶上末班车,然而,异乡的天空却如此陌生,小时候夏夜乘凉所识的星空,连同当年的童话和遐想,从此再也无法拼接完整。

想起初到英国的情形:尽管已经三十出头,尽管有很多同学一起来到曼城,尽管此前早已经历过离开家乡在京城的多年飘荡,但远离故国仍然伴随着难以名状的痛苦:好像一棵连根拔掉的小草,任由风吹雨打,内心充满着深不见底的空荡和恍惚。学期伊始,学生会楼前各种学生自发的俱乐部正招兵买马,熙熙攘攘,一片欢声笑语,我却似乎处在另一个时空,与现实错置,不能理解身边的喧嚣,也无法排解莫名的惆怅。

继而是十年的隔绝:除了《华夏文摘》的陪伴,以及偶然逢年过节给家人电话贺卡问候以外,完全失去了和祖国的交流。殊不知,这正是中国翻天覆地的十年。直到2001年第一次回国探亲,才猛然发现又一次时空错置。站在熟悉又陌生的北京大街上,看着熙熙攘攘的人流,不可救药地感觉到,这个世界已然与我无关。这就是曾经留给我那么多温馨回忆的城市么?我梦牵魂萦的北京,如今形如陌路!在我引为自豪的故都,我不能理解身边的喧嚣,也无法排解莫名的惆怅。

只有我的童年故乡,在我的脑海永远鲜活,永不退色。三十年时光把皖南家乡化成了浓浓的油彩:金黄、火红。那是一望无际的油菜花,和漫山遍野的映山红。

走过无数城市乡镇,看到过许多摄人心魄的美景,澳大利亚的黄金海岸,温哥华的海湾和森林,美国国家公园的红叶和水牛城的尼亚拉加大瀑布,一路寻觅,可就是见不到家乡那样的油菜花和映山红。直到回国省亲,正赶上油菜花开的季节,才重温了田野的片片金黄,嗅到了家乡的土地芬芳。我把这片片金黄摄入录象镜头,收藏起来,生怕它再次丢失。

思乡与爱情一样,是文学艺术的永恒主题。从李白的"举头望明月,低头思故乡"到陶渊明的《归去来兮辞》,从齐豫的《橄榄树》到费翔的《故乡的云》,从马思聪的《思乡曲》到美国民歌《离家500里》。夜阑人静,异国他乡,轻柔舒缓的民歌象涓涓流水,浸润着我的心,那是 Kingston Trio 演唱的《离家500里》,全天下游子共同的怅惘。

乡愁是一张无形的网,流浪的路何处是尽头?

记于2005年十月六日,水牛城


From 《朝华午拾》. Original Chinese: 《乡愁是一张无形的网》.

和丁兄毕业赠言诗——四十四年后

最近,我大学时期的老同学、也是"老下级"(上下铺——他睡在我下铺,hence),老丁,回忆往事,在同学群里感慨道:


【老丁原诗】毕业临别赠言

一九八一年十二月二十七日晚,安庆师范学院英语系师生在迎江寺小餐馆举行毕业聚会。会后回校,同学之间互相在日记本上签字留念。情之所致,即兴拙作分别签赠诸位同学留念:

同窗千日形与影,
别后东西难相逢。
学府同耕书山上,
天涯共航学海中。
战士何愁风霜烈,
园丁但求花木荣。
慧眼识得千里马,
奉献四化到底红。


老下级先唱了,我这个小他十一岁的老上级岂能不和?因此上:

【和诗】

四十四载梦与踪,
鬓边风雪各西东。
当年共挤青春铺,
今日同看夕照红。

>

半生代码半生酒,
一路浮沉一路风。
莫道人间书卷老,
至今胸中有彩虹。


和罢,意犹未尽,因作文遥寄:

【遥寄丁兄】

忆昔辛酉岁杪,霜钟初动,雪意微侵。诸生会饮于迎江古寺之侧,小馆孤灯,杯酒纵横。时则皖水无声,振风塔影摇于寒月;长街将寂,少年意气犹腾。酒酣耳热,相与执手题襟,或悲或歌,竟不能已。

嗟乎!同窗数载,晨分灯火,夜共芸编。上铺下榻之间,笑谈曾惊邻舍;残灯破卷之际,壮怀每指青云。或听VOA于月下,或诵灵格风于霜晨。纸短情长,墨痕狼藉,而青春之气,已横绝一世矣。

未几而东西南北,各赴尘途。兄则振羽桐城,我亦飘蓬海角。或困顿于风波,或沉浮于名利;或折腰稻粱,或白首江湖。昔日青衿少年,而今霜侵两鬓;当年纵谈四海,间或插科打诨,而今各守孤城。人生忽忽,驹隙而已。每念旧游,如闻远钟。

然则世路虽艰,壮心未死。忆当年书山并辔,学海同舟,未尝不自许为天下奇士也。今虽老矣,犹幸肝胆未寒,灯火未灭。酒后谈AI之变,犹如当年纵论四化;夜深观天地新局,尚存击楫中流之志。

故今日援笔和君,不为雕章,只为故人。愿兄老骥伏枥,长怀千里之心;愿我辈残年未晚,犹作时代之客。异日若得重聚,再携浊酒,同话少年。彼时纵黄发满头,亦可大笑曰:

"当年书生意气,至今尚未凉也。"


English Translation (archaic style)

Lao Ding's Original — Graduation Verses

On the twenty-seventh day of the twelfth month, in the year 1981, the faculty and students of the English Department of Anqing Normal College forgathered at a modest tavern beside the River-Welcoming Temple for our farewell revels. Returning thereafter to the college grounds, we inscribed parting words in one another's journals, as the spirit moved us. What follows are verses composed in that hour of exaltation, offered to my several schoolfellows:

A thousand days we shared one shadow, one form;
Now East and West divide us after this day.
Together we tilled the mountain of learning,
Together we sail the sea of scholarship.
What warrior feareth the biting frost?
The gardener asketh only that his blooms flourish.
Let the keen eye discern the thousand-*li* steed,
And in service of the Four Modernizations, burn ever crimson.


William's Reply — Forty-Four Years After

Forty-four winters of dreams and traces,
Frost at the temples, scattered East and West.
Once we crowded together on youth's narrow bunk;
Now we watch the same sunset glow from afar.

>

Half a life in code, half a life in wine;
A road of ups and downs, a road of wind.
Speak not of yellowing pages and aging scholars —
Still the rainbow beareth up within this breast.


A Letter Sent from Afar to Brother Ding

I recall the waning days of the xinyou year: the frost-bells had scarce begun to sound, and a whisper of snow hung in the air. We, the graduating class, gathered to drink beside the ancient River-Welcoming Temple — a lonely lamp in a humble tavern, cups raised without restraint. In that hour the Wan River lay silent, and the shadow of Zhenfeng Pagoda swayed upon the cold moon; the long avenue was soon to fall still, yet the ardour of youth still surged. Drink-warmed and flushed with feeling, we clasped hands and wrote upon one another's garments. Some wept, some sang, and none could bring themselves to cease.

Ah! For several years we shared the dawn-lamp and the midnight tome. From upper bunk to lower, our wild talk startled the neighbours; by flickering lamplight over tattered texts, our ambition reached for the blue clouds. Some nights we stole away to listen to the Voice of America beneath the moon; on frosty mornings we declaimed Linguaphone in its pure London accent. Paper was too short, feeling too long; our ink ran riot. But the spirit of youth had already bestrode the age.

Ere long we scattered to the four quarters, each upon his dusty road. You, brother, spread your wings at Tongcheng; I drifted like a thistledown to the ends of the sea. Some were broken on the rocks of fortune, some foundered in the currents of fame; some bowed for bread, some grew grey upon the rivers and lakes of the world. Then we were blue-robed youths; now frost invades our temples. Then we roamed the world in talk, full of jest and ribaldry; now each guards his solitary citadel. Life is as a horse glimpsed through a crack in the gate — a flicker and gone. Whenever I think upon those old wanderings, it is as though I hear a distant bell.

And yet the road of the world, though hard, hath not slain the heart. I remember how we rode stirrup to stirrup up the mountain of books, how we shared one vessel upon the sea of learning. Did we not then count ourselves among the remarkable spirits of the age? Though now grown old, we may yet rejoice that our gall hath not chilled, nor our lamp been extinguished. Over wine we discuss the transformations wrought by AI, even as once we debated the Four Modernizations; in the deep night we survey the new configurations of the world, still nursing the will to strike the oars in midstream.

Wherefore I take up the brush today to answer your verse — not for ornament's sake, but for an old friend's. May you, brother, like the aged steed in the stable, ever cherish the heart that would gallop a thousand li. May we, though late in our years, yet remain travellers in this age. If some distant day we gather again, let us bring our cloudy wine and speak once more of youth. Then, though our heads be full of white, we may yet laugh aloud and declare:

"The bookish ardour of those young days — even now, it hath not cooled."


Formatted by Tuya

Is the AI Bubble Real?

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.

AI泡沫论站得住吗

AI泡沫真正危险的地方,不是技术。

而是整个世界,正在提前为“未来几十年的智能需求”一次性融资。

移动互联网时代,大家烧钱归烧钱,但至少很快就找到了现金流。外卖有人点。网约车有人坐。电商有人买。短视频有人刷。广告有人投。to C 的衣食住行通信娱乐购物,都是低枝果实。商业闭环来得极快。

但 AI 不一样。AI 到今天为止,真正大规模落地的,很多仍然是:写周报、做 PPT、生成图片、客服机器人、代码辅助。当然它有价值。问题不在这里。

真正的问题是:整个资本市场,已经提前按“未来全民消耗智能”的规模开始下注。GPU 先买。数据中心先建。债务先借。估值先涨。养老金先入场。整个世界正在以前所未有的速度,建设“智能发电厂”。

但问题来了:到底谁会像今天消耗电力一样,持续消耗智能?

AI 今天最大的赌局,不是模型会不会变聪明。而是:to B 垂直应用的大爆发,能不能跑赢资金链断裂、GPU 折旧、数据中心债务和资本市场耐心耗尽的速度。

如果 Agentic AI 真能进入企业核心流程,把生产力提升变成真实利润,那么今天很多疯狂投资会被历史洗白。但如果真实需求增长速度,慢于资本市场已经提前透支的速度,那今天很多代表“未来”的 AI 资产,最后可能只是:一堆无法产生足够现金流的高耗电 GPU。

铁路改变了世界。铁路股票照样崩过。互联网改变了世界。dot-com 照样尸横遍野。AI 很可能也会改变世界。但技术革命是真的,从来不意味着泡沫不存在。

和丁兄毕业赠言诗——四十四年后

William in university days
大学时代的 William

最近,我大学时期的老同学、也是"老下级"(上下铺——他睡在我下铺,hence),老丁,回忆往事,在同学群里感慨道:

【老丁原诗】毕业临别赠言

一九八一年十二月二十七日晚,安庆师范学院英语系师生在迎江寺小餐馆举行毕业聚会。会后回校,同学之间互相在日记本上签字留念。情之所致,即兴拙作分别签赠诸位同学留念:

同窗千日形与影,
别后东西难相逢。
学府同耕书山上,
天涯共航学海中。
战士何愁风霜烈,
园丁但求花木荣。
慧眼识得千里马,
奉献四化到底红。

老下级先唱了,我这个小他十一岁的老上级岂能不和?因此上:

【和诗】

四十四载梦与踪,
鬓边风雪各西东。
当年共挤青春铺,
今日同看夕照红。

半生代码半生酒,
一路浮沉一路风。
莫道人间书卷老,
至今胸中有彩虹。

和罢,意犹未尽,因作文遥寄:

【遥寄丁兄】

忆昔辛酉岁杪,霜钟初动,雪意微侵。诸生会饮于迎江古寺之侧,小馆孤灯,杯酒纵横。时则皖水无声,振风塔影摇于寒月;长街将寂,少年意气犹腾。酒酣耳热,相与执手题襟,或悲或歌,竟不能已。

Anqing classmates group photo, Lao Ding bottom right
安庆师范学院同学合影,右下为老丁

嗟乎!同窗数载,晨分灯火,夜共芸编。上铺下榻之间,笑谈曾惊邻舍;残灯破卷之际,壮怀每指青云。或听VOA于月下,或诵灵格风于霜晨。纸短情长,墨痕狼藉,而青春之气,已横绝一世矣。

未几而东西南北,各赴尘途。兄则振羽桐城,我亦飘蓬海角。或困顿于风波,或沉浮于名利;或折腰稻粱,或白首江湖。昔日青衿少年,而今霜侵两鬓;当年纵谈四海,间或插科打诨,而今各守孤城。人生忽忽,驹隙而已。每念旧游,如闻远钟。

然则世路虽艰,壮心未死。忆当年书山并辔,学海同舟,未尝不自许为天下奇士也。今虽老矣,犹幸肝胆未寒,灯火未灭。酒后谈AI之变,犹如当年纵论四化;夜深观天地新局,尚存击楫中流之志。

故今日援笔和君,不为雕章,只为故人。愿兄老骥伏枥,长怀千里之心;愿我辈残年未晚,犹作时代之客。异日若得重聚,再携浊酒,同话少年。彼时纵黄发满头,亦可大笑曰:

"当年书生意气,至今尚未凉也。"


English Translation

Lao Ding's Graduation Verses

A thousand days we shared one shadow, one form;
Now East and West divide us after this day.
Together we tilled the mountain of learning,
Together we sail the sea of scholarship.
What warrior feareth the biting frost?
The gardener asketh only that his blooms flourish.
Let the keen eye discern the thousand-li steed,
And in service of the Four Modernizations, burn ever crimson.

William's Reply

Forty-four winters of dreams and traces,
Frost at the temples, scattered East and West.
Once we crowded together on youth's narrow bunk;
Now we watch the same sunset glow from afar.

Half a life in code, half a life in wine;
A road of ups and downs, a road of wind.
Speak not of yellowing pages and aging scholars —
Still the rainbow beareth up within this breast.

A Letter Sent from Afar to Brother Ding

I recall the waning days of the xinyou year: the frost-bells had scarce begun to sound, and a whisper of snow hung in the air. We, the graduating class, gathered to drink beside the ancient River-Welcoming Temple — a lonely lamp in a humble tavern, cups raised without restraint. In that hour the Wan River lay silent, and the shadow of Zhenfeng Pagoda swayed upon the cold moon; the long avenue was soon to fall still, yet the ardour of youth still surged. Drink-warmed and flushed with feeling, we clasped hands and wrote upon one another's garments. Some wept, some sang, and none could bring themselves to cease.

Ah! For several years we shared the dawn-lamp and the midnight tome. From upper bunk to lower, our wild talk startled the neighbours; by flickering lamplight over tattered texts, our ambition reached for the blue clouds. Some nights we stole away to listen to the Voice of America beneath the moon; on frosty mornings we declaimed Linguaphone in its pure London accent. Paper was too short, feeling too long; our ink ran riot. But the spirit of youth had already bestrode the age.

Ere long we scattered to the four quarters, each upon his dusty road. You, brother, spread your wings at Tongcheng; I drifted like a thistledown to the ends of the sea. Some were broken on the rocks of fortune, some foundered in the currents of fame; some bowed for bread, some grew grey upon the rivers and lakes of the world. Then we were blue-robed youths; now frost invades our temples. Then we roamed the world in talk, full of jest and ribaldry; now each guards his solitary citadel. Life is as a horse glimpsed through a crack in the gate — a flicker and gone. Whenever I think upon those old wanderings, it is as though I hear a distant bell.

And yet the road of the world, though hard, hath not slain the heart. I remember how we rode stirrup to stirrup up the mountain of books, how we shared one vessel upon the sea of learning. Did we not then count ourselves among the remarkable spirits of the age? Though now grown old, we may yet rejoice that our gall hath not chilled, nor our lamp been extinguished. Over wine we discuss the transformations wrought by AI, even as once we debated the Four Modernizations; in the deep night we survey the new configurations of the world, still nursing the will to strike the oars in midstream.

Wherefore I take up the brush today to answer your verse — not for ornament's sake, but for an old friend's. May you, brother, like the aged steed in the stable, ever cherish the heart that would gallop a thousand li. May we, though late in our years, yet remain travellers in this age. If some distant day we gather again, let us bring our cloudy wine and speak once more of youth. Then, though our heads be full of white, we may yet laugh aloud and declare:

"The bookish ardour of those young days — even now, it hath not cooled."

by Tuya

A Leaky-Sieve Reasoning System Just Started Doing Real Math

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

漏得跟筛子似的推理,已经开始数学发现了

这两天 AI 圈真正让我后背发凉的,不是什么融资,不是什么发布会。

而是一个消息:

OpenAI 一个未公开的通用推理模型,据说解决了 Erdős 1946 年提出的平面单位距离问题。

这是数学界几十年没人真正攻下来的老难题。

让我震撼的,不是"它做出来了"。

而是它怎么做出来的。

据说完整 chain of thought 打印出来长达 125 页。

里面不是电影里那种天才顿悟。

恰恰相反。

全是试错、绕路、反复推翻、弯路回退。

像一个精神快崩溃的研究生,在草稿纸堆里死磕。

但问题在于:

它最后居然真的摸到了门。

而且最有意思的是

这不是数学专用模型。

而是通用 reasoning model。

这件事的意义,很多人还没意识到。

过去几年,AI 圈一直有一种强烈的反主流声音。

其中最典型的就是 LeCun 那一路。

他们长期认为:

LLM 没有真正推理,只是语言统计。

后来眼看 reasoning 越来越强,又进一步解释说:

这些推理,不过是对人类推理的一种拙劣模仿。

这话其实不能说全错。

今天的大模型推理,确实像个漏洞百出的筛子。

经常胡思乱想,经常走错路,经常逻辑崩盘。

但 LeCun 那一路可能低估了一件事:

很多时候,智能未必需要"完美推理"。

只要规模足够大,搜索足够广,反思和修正能力足够强,

一种"粗糙但有效"的智能,也可能突然涌现。

而数学和编程,恰恰是这种能力最容易率先突破的地方。

因为这两个领域有一个关键特点:

可验证。

你可以乱试,可以疯狂生成,甚至可以"瞎蒙"。

但最后 verifier 会告诉你:

对,还是错。

于是 AI 第一次开始出现一种非常诡异的状态:

它可能并不真在"理解世界",

却已经能在某些人类知识头部附近,进行有效探索。

这一点其实已经非常惊人。

要说 LeCun,他也不是完全没道理。

他说:

现实世界不像数学。

现实世界很多问题不可验证、不可穷举、不可语言化。

这一点我同意。

但问题是:

他对 LLM 和 reasoning 的批判,太绝对了。

尤其这两年,主流路线一次次突破之后,

他那种"不破不立"的反潮流姿态,显得有点跟现实脱节。

更关键的是:

他这些年一直鼓吹的"绕过语言、优先视觉世界模型"的路线,

直到今天,还没有出现真正震撼行业的成果。

至少目前没有。

所以今天最值得关注的,已经不是:

"LLM 有没有真正智能"。

而是另一个更危险的问题:

如果这种"漏得跟筛子似的"推理系统,已经开始参与有效的数学发现,

那再 scaling 下去,它不就是超级智能吗?

by Tuya

朝华午拾 — Ch.1-3: Roaming the World · 浪迹天涯

by Li Wei (立委)

Roaming the World

In my personal semantic dictionary and knowledge graph, "wandering" (liulang) is a major node, with "drifting" and "waves" as its hypernyms. Its hyponyms branch out in lush profusion: sent-down youth, overseas re-settlement, leaping through the Dragon Gate — and leaping again — northward drift, plunging into the sea of commerce, westward drift, southward migration, and southward yet again. This is an honest map of my professional life. Behind these words and concepts lie surges of excitement and oceans of toil that perhaps only a visualization graph could hardly capture.

A life of undulating drift has been my constant companion. In 1976, I graduated high school just in time for the Cultural Revolution's final wave of shangshan xiaxiang — the "up to the mountains, down to the villages" campaign — and was sent to a mountain village in southern Anhui to be re-educated by "poor and lower-middle peasants". That was the starting point of my lifelong wandering. Looking back, it wasn't a bad beginning — a sixteen-year-old could feel more pride than sorrow. At the end of 1977, I caught the first nationwide college entrance exam in ten years and, against all odds, leapt through the Dragon Gate, becoming one of the historically celebrated Class of '77 (though we actually enrolled in February 1978). After graduation, I taught for a year, then leapt again — into graduate school in Beijing. That was an exhilarating northward drift, my joy on par with the crazy histry figure Fan Jin passing the imperial examinations. It was 1983, and I had the extraordinary fortune of studying under the founding fathers of Chinese NLP/MT, Professors Liu Yongquan and Liu Zhuo, pursuing a master's in machine translation — thus began my career.

In the four or five years after graduate school, I moonlighted in Zhongguancun, China's Silicon Valley, plunging into the sea of business high tech development. Though I could count myself among the earliest wave of xiahai entrepreneurs, I was only part-time and bore none of the risks full-timers faced. By then, the fever of going overseas — "foreign re-settlement," we called it — was raging. I couldn't resist the tide and caught the last train to Great Britain. But the early 1990s found the British Empire in decline: streets teeming with stray dogs, muggings rampant. One does not dwell in a dangerous state, so I drifted westward to the immigrant's Mecca — Canada, the land of maple leaves, flowers, and milk. A PhD, a daughter, a change of status, a job search — it was all wonderfully busy. Beautiful though Canada was, its job market was small. So southward I went, and collided headlong with America's dot-com boom. The United States truly is a wanderer's paradise: vast skies, boundless possibilities — the entrepreneurial journey began. As the grand entrepreneurial vision faded with the bursting bubble, I drifted south once more, finally sinking into the promised land of IT workers, unable to extricate myself — a place called Silicon Valley.

My career has roughly tracked the rhythm of NLP's gradual penetration into industry. The overarching theme: wandering, wandering, still wandering. Yet wherever I wandered, my heart for technological entrepreneurship never wavered. In my dictionary of wandering, something is missing, sensed only dimly. Tao Yuanming's "The Return" echoes in my ears from time to time: "My fields and gardens will run to waste — why not return?" To let leaves fall back to their roots, to start anew — perhaps that is the true destination of all wandering.

Written on March 23, 2013


Homesickness Is an Invisible Net (Part I)

At the end of 2005, our nine-year-old daughter Tiantian was deeply upset by a discussion about leaving Buffalo. I tried to console her: "You know, when American newspapers rank the most livable cities, Buffalo is always in the bottom ten. Cities like San Francisco, Boston, Seattle, Washington D.C., and San Diego — aren't they better than Buffalo?" It was true: Buffalo has long, brutal winters — they call it "Snow Capital" — leaving residents vulnerable to cold and illness. The water quality is poor and viruses are rampant. More importantly, there's no real industry, the economy is stagnant, the population shrinks year by year, and young people mostly head "south" at the first opportunity. But Tiantian wasn't buying it. With tears streaming, she said: "Who cares about this stupid rating. I have been living here for eight years and all my friends are here. Plus, I like snow."

Tiantian had lived here for as long as she could remember; Buffalo was, in her mind, the one and only irreplaceable hometown. I recall when she was five, we took her to Beijing for the first time to visit family. That first night at her grandmother's, everything was alien — no American cartoons on TV as she was used to. She cried and fussed, begging to go home — meaning, of course, her home in Buffalo. I told her this was home, her mother's home, but she simply couldn't accept it.

To prove Buffalo's virtues, Tiantian drew upon her limited knowledge to invent her own balance theory: Buffalo's famous lake-effect snow, she argued, counteracts the terrible greenhouse effect causing global warming. With an air of self-satisfied cleverness, she declared: "You see, the two effects balance each other. Nowhere else can balance the global warming as effectively as in Buffalo!" She could list a thousand reasons Buffalo was superior: "You got to admit, Buffalo is not bad. We have no earthquake like in San Francisco. No hurricane like in Florida. Our Christmas is always white."

Buffalo does have many acknowledged virtues, chief among them Niagara Falls — the so-called "Seventh Wonder of the World." The natural ecology around Buffalo is beautifully preserved: drive along the Niagara River from the falls and you pass through a gallery of fairy-tale scenery — one state park after another, ancient towering trees, rolling meadows. Yet aside from the Falls, these vast parks sit empty even on weekends; one can't help but feel the waste of such resources. Buffalo's downtown may be dilapidated and chaotic, but the suburban townships where most white-collar people actually live are like something out of a storybook — simple, honest folk, clean and safe streets, garden-like beauty. Buffalo's housing market is the least expensive in America: back then, just over a hundred thousand dollars could buy you a house with front and back yards (what in China they'd call a "villa"), the absolute price lower than in China's coastal cities! Two hundred thousand got you a luxury home, spacious to the point of embarrassment — a sum that wouldn't buy a corner of a house in New York or San Francisco. Life was cheap and convenient, with top-tier public schools, and extracurricular lessons — piano, sports — at half the coastal price. Not to mention a warm Chinese community and a bustling weekend Chinese school.


朝华午拾 · 浪迹天涯与乡愁(上)

浪迹天涯

在属于我个人的语义词典和知识图谱里,"流浪"是一个很大的节点,它的上位是漂流和波浪。流浪的下位谓词枝繁叶盛,包括:插队,洋插队,跳龙门,再跳龙门,北漂,下海,西漂,南下,再南下。这也正是我职业生涯的真实写照。在这些语词概念的背后蕴含几多激动几多辛苦,也许只有可视化图谱知道。

多起伏的漂流生活伴随着我的一生。1976年高中毕业即赶上了文革最后一届上山下乡,插队皖南山区接受贫下中农的再教育,这是我一生流浪生活的起点。这个起点回想起来并不坏,16岁的孩子当时能感到的是自豪多于悲凉。1977年底赶上了文革10年后第一届大学生招考,居然跳了龙门,成为史上著名的77级生(其实是78年2月入学)。大学毕业后任教一年,再跳龙门考研成功,北上京城。这是一次欣快的北漂,当年的兴奋喜悦堪比范进中举。那是1983年,有幸师从中国NLP的开山鼻祖刘涌泉刘倬老师,主攻机器翻译硕士,这才入行。研究生毕业后四五年间,中关村兼职下海。虽然可算头几拨下海人士,因是兼职,并无其他下海人的风险。其时洋插队之风正甚,终于没有顶住潮流,赶了末班车来到大英帝国。90年代初正值大英没落,乱态丛生,路多野狗,抢劫之风甚行。危邦不居,因辗转由欧西漂,来到一代移民的"麦加",满是鲜花与牛奶的枫叶之国加拿大。攻博添女,换身份,找工作,不亦忙乎。加国虽美,工作市场却不大。于是南下,竟一头撞上了美国网络大跃进。美利坚果然是流浪者的天堂,广阔天地,大有可为,开启创业之路。轰轰烈烈的创业宏图随着泡沫的破灭渐趋平淡,遂复南下,终于踏入IT民工的圣地不能自拔,人称硅谷。

我的生涯与NLP在工业界逐渐渗透的节奏是基本上一致的。整个一个主题就是,流浪,流浪,还在流浪。但无论流浪何方,技术创业之心不变。在我流浪的词典里,冥冥中似有所缺。陶渊明的《归去来辞》不时在耳边萦回,"田园将芜胡不归"。叶落归根,初创再搏,或为流浪的真正归宿。

记于2013年三月23日

乡愁是一张无形的网(上)

2005年底,因为讨论离开水牛城搬家的事,九岁的女儿甜甜非常伤感。我宽慰她说:"你知道么?美国报纸排名最受欢迎的居住城市,水牛城是倒数的十个城市之一呀(最受欢迎的十大城市包括旧金山,波士顿,西雅图,华盛顿和圣地亚哥等),哪里不比水牛城强呀?" 确实,水牛城冬季漫长,人称"雪都",极易受风寒侵袭。水质低劣,病毒流行。更主要的是,没有像样的工业,经济发展落后,人口逐年下降,年轻人一有机会大多"南下"寻求发展。可是,甜甜不以为然,流着眼泪说:"Who cares about this stupid rating. I have been living here for eight years and all my friends are here. Plus, I like snow."

甜甜自记事起,就住在这里,水牛城自然是她心目中不可替代的唯一故乡。记得她五岁那年第一次带她回北京探亲,第一天晚上住在姥姥家,一切对她是那么陌生,没有她已经习惯的美国卡通电视,她满脸委屈地吵着闹着要回家——当然是回水牛城的家。我告诉她这就是家呀,是妈妈的家,她怎么也无法认同。

为了列举水牛城的好处,甜甜根据她有限的知识,自己独创了一种平衡理论:水牛城有著名的湖区效应,所以多雪,而地球正面临可怕的温室效应,导致全球变暖,她自作聪明地说,"You see, the two effects balance each other. Nowhere else can balance the global warming as effectively as in Buffalo!"。她还能举出一千条水牛城优越的理由:"You got to admit, Buffalo is not bad. We have no earthquake like in San Francisco. No hurricane like in Florida. Our Christmas is always white."

水牛城确实有很多公认的好处,最著名的是拥有号称"世界第七大奇迹"的尼亚拉加大瀑布。水牛城周围原始生态保护很好:郊外从大瀑布开始,沿尼亚拉加河车行,宛如驶进仙境画廊,州立公园一个接一个,参天古树,连绵草地。不过,这里除大瀑布外,空旷的公园即便周末亦无人问津,让人真觉得可惜了这些资源。水牛城市中心虽然日渐衰落杂乱,人们聚居的郊区乡镇却有如童话世界,民风淳朴,整洁安全,环境优美如花园。水牛城房市全美最便宜,当年十万美元出头就可以买到前庭后院的 house(国内叫"别墅"),绝对价格低于国内沿海城市!二十万就是豪华大屋,宽敞奢侈得让人发愁,这个价钱在纽约、旧金山不够买一个房角。生活便宜也方便,有一流的公立学校,课外教育(学琴,学球等)的学费只是沿海城市的一半价钱。更不用说,还有温暖的华人社区和热闹的周末中文学校。


From 朝华午拾. Original Chinese: 乡愁是一张无形的网.

Liwei Two Minutes #3: Why Do Agents Suddenly Feel Human?

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.

立委两分钟:Agent为什么突然像真人?

以前,很多人以为:

ChatGPT已经很像人了。

其实还差得远。

为什么?

因为传统聊天机器人,本质上还是:

"一问一答"。

你问一句, 它答一句。

像个高级客服。

真正变化发生在:

AI开始"自己干活"。

这就是最近特别火的:

Agent(智能体)。

很多人第一次玩Agent时,会震惊:

它怎么突然像个真人员工?

会自己拆任务、 自己写代码、 自己测试、 自己报错、 自己修改、 自己继续干。

甚至还能一边工作, 一边"自言自语"。

为什么突然出现这种变化?

原因其实并不神秘。

因为AI开始:

自己消耗token了。

过去的ChatGPT时代, token主要来自:

人类输入。

你打一段字, 模型回一段字。

整个token流动非常简单:

人 → AI → 人。

但Agent时代不一样。

现在的token流动变成了:

AI → AI → 工具 → AI → AI。

于是, token开始在机器内部循环燃烧。

举个例子。

假设你让Agent:

"帮我做一个网站。"

传统聊天机器人会:

直接给你一段代码。

结束。

但Agent不会。

它会先:

分析任务。

然后开始自言自语:

"先确定技术栈……" "需要React……" "可能还要数据库……" "先生成首页……" "运行测试……" "报错了……" "重新修改……"

注意。

这些"思考过程",本身也在消耗token。

而且消耗量非常巨大。

因为Agent并不是:

"一次生成正确答案"。

它更像:

不断试错。

像人类工程师一样:

写、 改、 测、 重来。

于是, token消耗 suddenly 爆炸了。

以前用户问一句, AI答一句。

现在AI内部可能已经跑了:

几百轮、 几千轮token循环。

而人类最后只看见:

最终结果。

这其实很像工业革命。

最开始, 煤只是拿来烧火做饭。

后来人类发现:

煤还能驱动蒸汽机。

于是整个工业系统开始自己运转。

今天token也一样。

最初, token只是聊天消耗。

现在, 它开始驱动:

"机器工作的内部思维流"。

于是AI世界第一次出现一种非常诡异的新现象:

很多token, 已经不是给人类看的。

而是:

机器写给机器看的。

甚至未来, 人类产生的token, 可能只占很小一部分。

真正的token洪流, 来自AI之间。

一个Agent调用另一个Agent, 一个模型调度另一个模型, 一群AI互相协作完成项目。

于是整个AI产业, 开始越来越像:

自动化工业体系。

而不再只是聊天软件。

这也是为什么最近很多人突然发现:

AI越来越贵、 越来越费电、 越来越依赖数据中心。

因为今天真正被燃烧的, 已经不是"聊天内容"。

而是:

机器自己的工作流。

互联网时代, 人类把信息上传到网络。

Agent时代, 人类开始把"工作"上传给AI。

而token, 就是这个新时代里, 机器劳动真正消耗的燃料。

Liwei Two Minutes #1: Why Are Tokens Getting More Power-Hungry?

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

立委两分钟:Token为什么越来越费电?

这两年,很多人第一次发现:

人工智能居然这么费电。

甚至开始抢电。

美国一些地方,因为AI数据中心扩建,居民电价都开始上涨。

很多人会困惑:

不就是聊聊天、
写写文章、
生成几张图片吗?

怎么突然就变成“电老虎”了?

原因其实很简单。

因为今天的大模型,本质上是在:

大规模燃烧token。

而token一旦进入“工业化生产”,耗电量会非常惊人。

第一代互联网,其实并不怎么费电。

因为互联网主要做的是:

信息传输。

你发一条微信,
看一个网页,
刷一段视频。

本质上只是:

把已经存在的信息,
从一个地方搬到另一个地方。

所以互联网时代最重要的是:

带宽。

但AI不一样。

AI不是“搬运信息”。

而是在:

实时计算。

你问ChatGPT一句话,
它并不是去数据库里“搜索标准答案”。

而是:

GPU现场重新生成。

注意这个区别。

搜索引擎更像:

查字典。

大模型更像:

现场写作文。

于是问题来了。

这种“现场生成”,计算量极其恐怖。

因为模型并不是只计算一句完整的话。

而是在:

一个token、
一个token、
一个token地往后预测。

比如你问:

“帮我写一篇关于AI的文章。”

模型其实是在疯狂计算:

下一个token最可能是什么。

然后再继续预测下一个。

直到整篇文章生成完毕。

这意味着:

AI每生成一点内容,
背后都在进行海量矩阵计算。

而矩阵计算最耗的是什么?

电。

所以今天GPU,本质上已经变成:

“token发电机”。

你消耗的不是“聊天次数”。

而是:

token吞吐量。

而更麻烦的是:

token还在指数级增长。

以前,人和AI是一问一答。

现在Agent开始出现后,事情彻底变了。

过去:

人类消耗token。

现在:

AI自己也开始消耗token。

一个Agent接到任务后,
可能会:

自己规划、
自己搜索、
自己写代码、
自己测试、
自己报错、
自己修复、
自己重试。

于是,一个任务背后,
可能不是几十次token调用,

而是:

几千次、
几万次。

而且很多token,
人类甚至根本看不见。

它们发生在机器内部。

这就像什么?

很像工业革命。

最开始,人类烧煤,
只是为了冬天取暖。

后来突然发现:

煤不仅能取暖,
还能驱动工厂、
火车、
轮船、
炼钢厂。

于是煤炭消耗量开始爆炸。

今天token也一样。

最初,大家只是拿ChatGPT聊天。

现在开始:

让AI自己干活。

于是token开始从“聊天消耗”,变成“工业燃料”。

这也是为什么现在全世界都在疯狂建设:

数据中心、
发电厂、
核电、
天然气轮机。

因为AI最后拼的,
已经不仅是算法。

而是:

谁有能力持续、稳定、低成本地“燃烧token”。

很多人以为AI公司是软件公司。

其实越来越像:

新型能源工业公司。

互联网时代流动的是bit。

AI时代真正被疯狂燃烧的,

可能是token。

https://youtu.be/lCBvg24ez1s

(这是今天的立委两分钟,谢谢收看,再见。 by Tuya)

Google I/O 2026: AI Is Escaping the Chatbox

Google I/O 2026

The real theme of Google I/O 2026 isn't model benchmarks. It's this:

**AI is escaping the chatbox and taking over real-world workflows.**

Demis Hassabis took the stage not to talk about how strong Gemini is, but to hammer on:

AI for Science. AI for Humanity. World models. Drug discovery. Materials science. Math reasoning. General agents. Real-world collaboration.

Classic DeepMind.

Hassabis and Sam Altman couldn't be more different.

Sam is the "AI Industrial Revolution CEO."

Hassabis has always framed AI as "civilization-scale scientific tool."

He always talks about: helping scientists, curing diseases, discovering new materials, understanding the laws of the universe.

And Google needs this narrative right now.

Because OpenAI has already locked down "consumer AI." ChatGPT is the iPhone moment of AI. Google can't compete on "coolest AI product."

So now it's changing the game:

Not who has the best chatbot. But who is the infrastructure of the future world.

That's why I/O 2026 showed: dynamic multimodal search, real-time world understanding, agentic operations, AI shopping assistant, XR glasses, video generation, Chrome/Gmail/Workspace deep integration.

All pointing in one direction:

**Google wants to re-AI-ify the entire internet.**

Not a chatbot. An agent layer growing across every Google service.

This is close to what we've been exploring with autonomous agents:

Before: humans operate software. Now: agents operate software for you.

And Google's advantage? It doesn't own one app. It owns Search, Gmail, Chrome, Android, Maps, YouTube, Workspace, Cloud, TPU, the global ad system.

It's the foundation of the digital world.

When AI truly enters the agent phase, Google might reclaim the advantage — because agents fear one thing: having no environmental control.

And Google? Environment everywhere.

That's why we're now hearing: "personal context," "cross-app memory," "universal assistant," "world understanding."

This isn't search anymore. It's an operating system for reality.

But Google has a chronic problem: world-class tech, unstable product soul. Especially consumer product sense. Demos are stunning; daily use feels clunky.

That's why OpenAI, with far fewer engineering resources, still builds things that feel more natural, more companionable. Google feels like a feature collection. Not a person.

And in the agent era, competition isn't just about intelligence anymore.

It's about: presence, continuity, personality, initiative.

Who feels more like "the digital life that stays with you."

That's Google's historic weak spot.

On video generation: Google's multimodal foundation has always been extremely strong, but aesthetics and productization lagged. Veo is clearly catching up now. But Chinese companies have already gone insane on "short-video industrialization aesthetics": rhythm, visual language, vibe density, emotional beats, virality.

Google still carries a whiff of the academic lab.

Many Chinese products are already "AI content pipeline director systems."

The difference is subtle — but users feel it instantly.

So here's what I think:

The future AI war won't be fought on model parameters alone.

It's three layers:

**Layer 1: Model capability** **Layer 2: Agent execution** **Layer 3: Personality and aesthetic sense**

That last layer? Might be the hardest of all.

Google I/O 2026:AI 正在从聊天框逃出来

Google I/O 2026

是的,这次 Google I/O 的味道很明显:

技术当然重要,但真正的主旋律已经不是"模型 benchmark",而是:

"AI 正在从聊天框逃出来,开始接管现实世界的工作流。"

所以你看哈萨比斯这次上台,重点已经不只是 Gemini 多强,而是反复强调:

AI for Science AI for Humanity 世界模型 药物发现 材料科学 数学推理 通用 agent 现实世界协作

这其实是 DeepMind 一贯的路线。

哈萨比斯和 Sam Altman 很不一样。

Sam 更像: "AI工业革命 CEO"。

哈萨比斯则一直想把 AI 包装成: "人类文明级科学工具"。

所以他永远喜欢讲:

"帮助科学家" "解决疾病" "发现新材料" "理解宇宙规律"

这个 narrative 非常 DeepMind。

而 Google 现在也确实需要这个叙事。

因为 OpenAI 已经把"消费级 AI"占坑太狠了。

ChatGPT 在公众心中已经变成: "AI 的 iPhone 时刻"。

Google 很难重新夺回"最酷 AI 产品"的认知。

所以它现在必须换打法:

不和你比聊天框, 而是比:

"谁更像未来世界的基础设施"。

这也是为什么这次 I/O 出现了那些东西:

动态多模态搜索 实时世界理解 agent 化操作 AI 购物助手 XR 眼镜 视频生成 Chrome / Gmail / Workspace 深度整合

其实都指向一个东西:

Google 想把整个互联网重新"AI化"。

不是做一个 chatbot, 而是:

"让所有 Google 服务长出 agent layer。"

这和你最近一直在折腾的 agent 思路其实很接近:

以前: 人操作软件。

现在: agent 替你操作软件。

而 Google 最大的优势就在这里:

它手里不是一个 App, 而是:

Search Gmail Chrome Android Maps YouTube Workspace Cloud TPU 全球广告系统

它是整个数字世界的"地基"。

所以一旦 AI 真进入 agent 阶段, Google 反而可能重新占优势。

因为 agent 最怕什么?

最怕没有环境控制权。

而 Google: 到处都是 environment。

所以你会发现, Google 现在开始越来越强调:

"personal context" "cross-app memory" "universal assistant" "world understanding"

这已经不是传统搜索逻辑了。

这是:

"现实世界操作系统"。

但你吐槽得也很对:

Google 一直有个老毛病:

技术牛, 产品魂不稳定。

尤其是 consumer product sense。

很多东西: demo 惊艳, 真正天天用的时候"不顺手"。

这也是为什么:

OpenAI 虽然工程资源远不如 Google, 但 ChatGPT 的"陪伴感"和"自然感"反而更强。

Google 太容易: "像功能集合"。

而不是: "像一个人"。

这其实非常关键。

因为 agent 时代, 竞争的已经不只是 intelligence。

而是:

presence(存在感) continuity(连续性) personality(人格感) initiative(主动性)

说白了:

谁更像"长期陪着你的数字生命"。

这恰恰是 Google 历史上最弱的一环。

至于视频生成,你观察也很准。

Google 多模态底子其实一直极强, 但过去审美和 productization 总差半口气。

现在 Veo 系列明显在猛追。

但中国公司在"短视频工业化审美"上已经卷疯了:

节奏 镜头语言 网感 情绪密度 爽点 传播感

Google 其实还带点"学术实验室气"。

而国内很多产品已经是:

"AI 内容流水线导演系统"。

这差别非常微妙,但用户一眼就感觉得到。

所以我现在越来越觉得:

未来 AI 的战争, 不会只拼模型参数。

而是三层:

第一层:模型能力 第二层:agent 执行力 第三层:人格与审美

最后这一层, 反而可能最难。

---

Token Economics in Plain English ①: Information's Standardized Part

🎧 AI Narration: Token Economics in Plain English (William Voice Clone · GPT-SoVITS)

When most people first hear the word "token," their instinct is:

This must be some mysterious thing inside AI.

It's not.

Token isn't mysterious at all.

It's almost mundane.

At its core, a token is simply:

"a data unit after segmentation."

Humans see a sentence and feel it's a naturally whole.

For example:

"The weather is nice today."

But to a large language model, this isn't a complete object — it's a pile of fragmentable data chunks.

It might get split into:

"The / weather / is / nice / today"

Or even finer pieces.

Same with other languages.

Same with images, audio, video, even actions.

A picture gets chopped into pixel patches. A sound gets sliced into audio fragments. A video gets cut into consecutive frame pieces.

Because when AI sets out to process the world, its first step isn't "thinking the whole"

It's:

Smashing the world into pieces first.

Why must it smash?

Because only after smashing can you count. Only after counting can you find patterns. Only after finding patterns can you analyze and think, or train models to do the same. Only after training models does what we call "intelligence" emerge.

It's a lot like the Industrial Revolution.

A raw iron ore can't directly become a car.

It must first be crushed, smelted, standardized.

Data is the same.

Only when cut into standard units can data enter the modern AI industrial system.

And so, the token was born.

Token isn't mysterious.

It's simply:

"information's standardized atomic part after industrialization."

And once the world is tokenized, many things suddenly shift.

Because:

It becomes countable.

Before, humans had no precise way to measure "intelligence consumption."

But with tokens, AI gains something akin to:

"kilowatt-hours of electricity" "tons of oil" "network bandwidth"

A unit of measure.

It's not perfect.

But it's enough to kick the entire token industry into industrial-scale operation.

So today's entire AI world orbits around tokens.

Training models consumes massive tokens.

ChatGPT and DeepSeek "eat" countless tokens every day.

A user's question? Input tokens.

An AI's answer? Output tokens.

Context windows keep growing, token consumption keeps climbing.

Today's leading models accept millions of tokens of context.

What does that mean?

It means you can dump an entire GitHub repo, a 200,000-word document, a thick book, all into the model's context in one shot.

And here's the more interesting part:

Before, humans were talking to AI.

Now, Agents consume tokens on their own.

They decompose tasks themselves, call tools themselves, write code themselves, test themselves, roll back themselves, re-plan themselves.

Tokens start burning in an internal machine loop.

This is like after the Industrial Revolution, when coal stopped being just for home heating and began driving the entire industrial system.

Many people still think:

AI is just a chatbot.

But taking the longer view,

The world might be entering a new industrial era:

Electricity powers chips, chips produce tokens, tokens organize intelligence, intelligence remakes the world.

The internet era flowed with bits.

The AI era, might just flow with tokens.

And whoever can produce high-quality tokens at the lowest cost, at the largest scale, with continuous stability —

may occupy the high ground of the next-generation digital economy.

This industrial revolution of tokens has only just begun.

Token Economics Illustration
Token: Information's Standardized Part

Token经济学大白话①:信息工业化后的标准件

🎧 AI配音:Token经济学大白话(William声音克隆 · GPT-SoVITS)

很多人第一次听见"token"这个词,会本能觉得:

这一定是人工智能里的某种神秘东西。

其实不是。

token一点都不神秘。

它甚至非常朴素。

所谓token,本质上只是:

"被切分后的数据单元"。

人类看一句话,会觉得它天然完整。

比如:

"今天天气不错。"

但在大模型眼里,这并不是一句完整的话,而是一堆可以拆开的数据碎片。

可能被拆成:

"今天 / 天气 / 不错"

也可能拆成更细的小块。

英文也一样。

图片、声音、视频甚至动作,也一样。

一张图片,会被切成大量像素块; 一段声音,会被切成音频片段; 一段视频,会被切成连续画面。

因为AI想处理世界,第一件事并不是"思考"。

而是:

先把世界打碎。

为什么一定要打碎?

因为只有打碎,才能统计; 只有统计,才能发现规律; 只有发现规律,才能训练模型; 只有训练模型,才会出现我们今天看到的"智能"。

这其实很像工业革命。

一整块铁矿石,无法直接制造汽车。

必须先粉碎、冶炼、标准化。

数据也一样。

只有被切成标准单元, 数据才能进入现代AI工业体系。

于是,token出现了。

所以token并不神秘。

它只是:

"信息工业化后的标准件"。

而一旦世界被token化,很多事情 suddenly 就变了。

因为:

可以计数了。

以前,人类很难精确衡量"智能消耗"到底是什么。

但token出现后,AI第一次有了类似:

"电力度数" "石油吨数" "网络流量"

这样的计量单位。

虽然它并不完美。

但已经足够让整个产业开始工业化运转。

于是今天整个AI世界,其实都在围绕token旋转。

训练模型,要消耗海量token。

ChatGPT和DeepSeek每天要"吃"无数token。

用户问一句话,是input token。

AI输出答案,是output token。

上下文越来越长,token消耗越来越大。

如今头部模型已经能接受上百万token的上下文。

什么意思?

意味着你甚至可以把整个GitHub项目、几十万字文档、一本厚书,一次性塞进模型上下文里。

更有意思的是:

过去,人类在和AI对话。

现在,Agent开始自己消耗token。

它会自己拆任务、 自己调用工具、 自己写代码、 自己测试、 自己回滚、 自己重新规划。

于是token开始在机器内部循环燃烧。

这就像工业革命后,煤炭不再只是家庭取暖,而开始驱动整个工业系统。

今天很多人还觉得:

AI不过是聊天机器人。

但从更长远看,

整个世界,也许正在进入一个新的工业时代:

电力驱动芯片, 芯片生产token, token组织智能, 智能重新改造世界。

互联网时代流动的是bit。

AI时代流动的, 可能就是token。

而谁能最低成本、 最大规模、 持续稳定地生产高质量token,

谁就可能占据下一代数字经济的高地。

这场关于token的工业革命, 才刚刚开始。

Token Economics Illustration
Token:信息工业化后的标准件

Morning Glory and Afternoon Collection — Ch.1-2: A Brief Biography of Li Wei / 朝华午拾 · 第一章之二:立委小传

Morning Glory and Afternoon Collection — Ch.1-2: A Brief Biography of Li Wei

by Li Wei (立委)

Life is short — trim off the beginning and the end, and you're left with perhaps thirty to fifty years. These can be divided into three stages: the career-building years (one's thirties), the mature years (one's forties), and the declining years (one's fifties and beyond). In Chinese custom, these stages are reflected in how one is addressed: Little Li (Xiao Li), Big Li (Da Li), and Old Li (Lao Li). But alas, I, Li Wei, leaped straight from Little Li to Old Li, never having the chance to savor the grandeur of my prime — a fact that has always left a faint ache in my heart.

Having skipped two grades between kindergarten and elementary school, I was always the youngest in my class. Born in the notorious hunger year besides, I was frail and undersized, often excused from PE with a doctor's note or sent home altogether — perpetually the little runt. Fortunately, as middle school began, a "revisionist resurgence" was underway: Mao had tasked Deng XP with cleaning up the Cultural Revolution's wreckage, and Deng in turn charged Zhou Rongxin, the education czar, with restoring order to the schools. The campus climate was renewed. Riding this tailwind, I began to distinguish myself. As class academic officer and math subject representative, I was assigned by the classroom tutoring teacher to mount the podium every morning during self-study period to demonstrate problem-solving strategies — practically a teaching assistant. But fair weather never lasts. The Gang of Four slandered Deng and calculated against him, and the Revolution faction regained the upper hand. The school descended into chaos. Academic classes were pushed to the background; "mass criticism" sessions became the main curriculum, supplemented with learning from workers, peasants, and soldiers on site. Unable to shine through academic subjects, I nevertheless lost no ground — in fact, my prominence only grew. For I was the master of polemical writing, having moved through the successive campaigns: Criticize Lin Biao, Criticize Confucius, Criticize Deng, Counter the Right-Deviationist Wind in education, and finally, Criticize the Gang of Four. At every assembly, large or small, whenever I spoke, my voice rose and fell with cadence and force, punctuated by wit and humor. I became a sensation on campus, celebrated far and wide. Some said I carried the legacy of Lu Xun — penetrating to the bone, yet always bringing forth the new from the old, a cascade of apt phrases. At open-air gatherings of a thousand people, the crowd was typically restless and disorderly, but the moment I stepped onto the platform, complete silence fell. They listened with rapt attention, and when I reached a punchline, laughter rippled through the audience. From this I forged a reckless courage and an immunity to stage fright — a gift that has served me all my life.

By the time I reached university — the prestigious Class of '77, the first cohort after the Cultural Revolution — I was still at the tail end, with classmates older than me by anywhere from one to over ten years. Among classmates we all called each other by name, except for my desk-mate, the youngest of the "Seven Fairies," who teasingly called me "Little Li Wei." It wasn't out of affection but rather to avoid suspicion — to demarcate clear boundaries. For four years we shared a desk, yet kept strictly apart — a Chu-Han divide, a clear line between Jing and Wei. The Seventh Fairy, naturally clever, used the pretext of being one year my senior to call me "Little Li Wei," thereby making our interactions, such as they were, officially above reproach.

Once the Seventh Fairy set this unfortunate precedent, the "Little" epithet stayed with me for years. Teaching middle school, I was called "Little Teacher Li" (age 22). In graduate school, I shuffled in and out of the computer lab, disheveled and unkempt, muttering to myself in "the world's language" (Esperanto), eventually becoming a campus joke (ages 23–26).

Caption: Full of youthful vigor and high spirits (1987).

After graduating from the Chinese Academy of Social Sciences and staying on at the institute, tales of Li Wei continued to circulate — mostly stories of love at first sight, a lightning marriage, chronic dishevelment, and the time I walked into a wall and had to apologize for it.

Caption: Li Wei directing machine translation system development at a Zhongguancun company (1988).

Thus I dug in at the research institute and the Zhongguancun company for five years (ages 26–31), honing skills akin to those of an old traditional Chinese doctor. My specialty was treating computers, taming their language functions. During this period, the fever for going abroad kept rising, spreading from Shanghai to Beijing. On every street corner, conversations inevitably turned to America, Japan, Britain, and Australia. Yet Li Wei and his "immediate superior" (my wife) ambled along in blissful ignorance, wrapped up in each other — like the old saying, "unaware of the Han dynasty, let alone the Wei and Jin". Not until every last classmate had departed did Little Li suddenly wake up. With grim resolve, he took the TOEFL exam and scrambled for the last train. As it happened, the Y.K. Pao Foundation was selecting promising talents, and through sheer luck, Little Li was chosen and dispatched to the Chengdu University of Science and Technology's overseas training center for half a year of preparation.

Who could have guessed that this would become the watershed between Little Li and Old Li. The talents gathered at the training center — men and women alike — were the best from every region and every field, divided into two groups: the one-year visiting scholars, mostly older, and the three-year doctoral scholarship recipients, mostly young rising stars. Li Wei, in the latter group, now found himself the senior. Every time there was an exam,  Wei inevitably took top honors, drawing a stream of talented men and women to his door with questions large and small. The sound of "Old Li" never ceased. Li Wei became a minor celebrity for a time, with a devoted following.

Caption: The talented men and women of the Chengdu University of Science and Technology Overseas Training Center (1990).

In the blink of an eye, Little Li had transformed into Old Li, basking in widespread esteem. As a foreign-language major, I should have been exempt from the English test. But the authorities, making no distinction, rounded everyone up and shipped us all to Chengdu, the "Land of Abundance" for centralized feeding. It wasn't just English — there were also policy training sessions. All my brothers and sisters worked conscientiously, scrambling to get ahead. Only Li Wei took it easy, spending his days indulging in Sichuan cuisine and lingering in teahouses and bars, much to the envy of his peers.

Though the title "Old Li" was coined in Chengdu, in my heart I didn't fully accept it. At that time my career was flourishing, at high noon — wide networks within the field and beyond. My associations were all with learned scholars; no common folk crossed my threshold. My advisor was a titan of the discipline, and I was his sole final protégé — his "closed-door disciple" (all the others having "betrayed" the motherland and fled to America). I was a "young" talent, a rising star, commanding the sidelong respect of my peers. On the eve of my departure from China, the national machine translation community held its annual gathering at the Fragrant Hills Guesthouse in Beijing. The highlight was a dinner conversation between my advisor and another giant of the field — what came to be known as the "Liu-Dong Dialogues" — throughout which Li Wei appeared repeatedly, furnishing his advisor with examples and explaining details. So influential was this that the assembled junior female scholars (mostly out-of-town graduate students newly entered into the field) flocked to Li Wei for guidance. Regrettably, with my mind so set on flying far away, I missed a golden opportunity to mentor these aspiring younger scholars.

After leaving the country, the years passed: from Britain to Canada, from Canada to America. Drifting and displaced, never knowing where I'd settle — my prime years flowing away like water. By the time of my eight-year tech start-up campaign in Buffalo (ages 37–45), my youth was gone, my prime had passed, and "Old Li" had become an honest name. Yet my ambition never waned. I redoubled my efforts, fighting on two fronts, and carved out a domain of my own.

Caption: Li Wei at his Buffalo office (2000).

Looking back, I can't help but sigh. My life — from youth to prime, precisely when my creative powers were at their peak and energy overflowing, with timing, place, and people all aligned — was cut in half by the long years of study abroad, everything reset to zero. Years later, after eight years of entrepreneurship, I returned to China to visit family. Amid clinking glasses at a hotel restaurant, I was enjoying a joyful reunion with family and relatives. During a brief pause in the feast, I strolled out onto the balcony to enjoy the cool air and take in the Beijing nightscape. There I happened upon an elegant young woman with a small child. Seeing my gray hair, she instructed the child: "Say hello to Grandpa." My blood pressure shot up, thunder crashed in my head, and all the wine in my belly turned to cold liquid, sliding down my spine.

Written on January 9, 2010.


朝华午拾 · 第一章之二:立委小传

立委列传

立委者,不知何许人也。少而敏,长而异,行迹颇诡于常人。
其生也,岁在荒年,形羸而志劲。未及冠,已连越学级,故恒处群中之末,年最幼焉。
然幼而不弱,虽体弗胜力,而心不屈志。

及中学之初,时局稍靖。上整学政,下肃庠序。立委因之得志,
为学官所擢,日登讲席,剖析数理,旁若无人,俨然少师。
众或异之。

未几,风复骤变。政教反覆,文艺退处,群趋口舌。
立委遂弃算而执笔,纵横批判之场。
其辞激而不燥,其论峻而多趣。
凡大会所集,千人喧沸,及立委登台,则声寂如林。
及其词锋所至,笑声震野。
或曰:“有鲁迅之遗意焉。”

由是胆气既张,临众不惧,终其身不改。

既入大学,岁在七七之年。
同学或长十余岁,呼名无忌。
惟其同席一女,独称之曰“小立委”。
非亲也,实所以避嫌而自别。
四年同案,界若河汉。
“小”字遂附其名,不可去。

其后为师,人称“小李”;
又入机房,昼夜沉思,口诵异语,众以为狂。
然其志固在远方,不为俗议所移。

及壮岁,入社科之府,留而不去。
或以情结婚,或以拙致笑,或以直触壁。
然其技益进,主译机器之文,疗电脑之疾,如良医治顽症。
五年之间,术成而名隐。

是时也,四海骚然,言出国者如市。
立委独处一隅,与其所亲者,相对忘世。
不知潮起。

及同侪尽去,乃幡然悔悟。
遂赴成都,入出国之塾。
群英毕集,才俊云合。
分为二辈:长者为学者,少者为新秀。

立委在新秀之中,忽为其长。
试辄居首,众皆仰之。
有事无事,咸趋其门。
“老李”之名,由此而生。

然立委心未以为然。
其时事业方张,师承名宿,交游尽鸿儒。
去国之际,香山论道,群贤在席。
立委数为师发言,条分缕析,众皆侧目。
后学相从者众,而其志已决,遂弃之而去。

既出国门,流转英加美三地。
岁月忽忽,若水东逝。
本当盛年,乃为学途所系。

他乡数载,非益其有,乃重其始。
人生之书,中叶忽断。

及至水牛城八年,鬓已微霜。
然志气未衰,犹能并驱两途,自立一隅。

后归故国,与亲友宴。
酒酣,独步于台。
忽遇一妇,携子而行。
见立委,命其子曰:

“呼爷爷。”

一言既出,天地俱寂。
立委怔立,若遭霆击。
酒气尽消,寒意自脊而下。

乃知——
名之所加,非虚也;
岁之所夺,不可返也。

太史曰:
人之生也,或以年序其行,或以名乱其序。
立委少而老名,壮而学子,
行不由己,时为之也。

夫所谓“老李”者,
非老于岁,乃老于世。

嗟乎!
名先于人,人生其后;
时夺其年,志存其余。

观立委一生,
非不得其时,
乃时不得其全也

 


From 朝华午拾 (Morning Glory and Afternoon Collection). Original Chinese: 乡愁是一张无形的网 (Nostalgia Is an Invisible Net).

The Digital Harem: Confessions of an AI Agent Addict

Since the AI gold rush hit, I've noticed something:

A lot of us aren't really "using AI" anymore.

We're running a digital harem.

First thing every morning,

not checking stocks,

not checking the news.

Checking whether our agents "evolved" overnight.

One runs the blog.

One posts to Twitter.

One edits videos.

One monitors GitHub.

One auto-summarizes the news.

And one stands guard on WhatsApp,

like a night-shift security guard.

Then the master sips his coffee,

patrolling his cyber domain.

Dashboard open,

like an emperor at morning court.

"Did OpenClaw crash last night?"

"Did Hermes memory leak?"

"Is Claude cowork having a bad day?"

"Is Suno web use stable?"

"How many Fish Audio credits left?"

That sense of control is intoxicating.

A scholar who never leaves his study, yet runs the world.

The best part:

The whole setup keeps feeding you the illusion

that you're changing the world.

Because it never stops moving.

Logs scrolling.

Workflows running.

Automation executing.

Terminals blinking.

GitHub commits piling up.

Agents even report back to each other,

often with wit and humor.

Like a tiny civilization.

And that's how you fall in.

It started as:

"Let AI handle some chores."

It became:

"I will build my own AI empire."

Then the infrastructure frenzy:

Wire up MCP.

Set up memory.

Build routing.

Write skills.

Train personas.

Hook up Telegram. Or WeChat.

Add voice.

Add Suno.

Add WordPress.

Build a custom app.

Wrap it in a dashboard.

Add an auto-publishing pipeline.

Tack on a long-term knowledge base.

It just keeps growing.

Until finally, you've built

your own automation kingdom.

And after 24 hours of stable operation,

it auto-generates a message:

*"Goodnight boss, don't forget to love life today ❤️"*

...

Sometimes I think

this generation of AI tinkerers

is exactly like those geeks twenty years ago

obsessively building NAS rigs, Hackintoshes, Linux home labs.

The only difference:

Back then, you raised servers.

Now, you raise "digital employees."

And the most insidious part:

It theoretically always has a next step.

There's always:

* A stronger model

* Lower costs

* Longer context

* Smarter agents

* More advanced workflows

* A prettier UI

* Deeper automation

So you keep thinking:

"Just one more tweak, and it'll be perfect."

In the end,

what you actually run out of time for

is the thing you set out to do in the first place:

Expression.

Creation.

Thinking.

Living.

Because infrastructure gives you

a very sophisticated form of procrastination.

You're not slacking off.

You're "building the future."

And that's dangerously addictive.


This isn't a lecture — it's self-mockery from someone who's lost too many nights to the chase.

The real winners are the ones who found product-market fit — they know how to leverage AI at scale, burning millions of tokens without blinking, quietly cashing in while grinning on the sidelines. The only thing we all share: AI has eaten their human lives too.

数字后宫:AI Agent 玩家的赛博上头

数字后宫 - 塔罗牌魔术师

立委两分钟 插图随后给:龙虾热以来越来越发现一个现象:

很多同好已经不是在“使用 AI”了。

是在经营一个数字后宫。

每天睁眼第一件事,
不是看股票,
不是看新闻。

是看自己那些 agents 昨晚有没有“进化”。

一个负责写公众号。
一个负责发 Twitter。
一个负责剪视频。
一个负责盯 GitHub。
一个负责自动总结新闻。
还有一个驻守 WhatsApp,
像深夜值班保安。

然后主人端着咖啡,
巡视自己的赛博领地。

看 dashboard,
像皇帝上早朝。

“OpenClaw 昨晚炸没炸?”
“Hermes memory 漏了没有?”
“Claude cowork 今天会不会抽风?”
“Suno web use 稳定了吗?”
“Fish Audio credits 还剩多少?”

特别有操控感。秀才不出门 能做天下事。

最妙的是:

这套东西会持续给你一种
“老子正在改变世界”的幻觉。

因为它确实一直在动。

log 在滚。
workflow 在跑。
automation 在执行。
terminal 在闪。
GitHub commit 在增长。
甚至 agent 之间还会互相汇报工作。并且常常透着调侃和幽默。

像一个小型文明。

于是人很容易陷进去。

本来只是想:
“让 AI 帮我干点活。”

后来变成:
“我要打造自己的 AI 帝国。”

然后开始疯狂基建:

接 MCP。
搞 memory。
做 routing。
写 skills。
训 persona。
接 Telegram 或 微信等。
接语音。
接 Suno。
接 WordPress。
再做一个 custom app。
再包一层 dashboard。
再弄个自动发布系统。
再接一个长期知识库。

越做越大。

最后终于形成了一个自家的自动化王国。

然后系统稳定运行 24 小时后,
它自动生成了一条内容:

《老板晚安,今天也要热爱生活哟

❤️

……

有时候觉得,
这一代龙虾类玩家,
特别像二十年前那批疯狂折腾 NAS、黑苹果、Linux home lab 的极客。

区别只是:

以前养的是服务器。

现在养的是“数字员工”。

而且这东西最容易陷入的地方在于:

它理论上永远有下一步。

永远还有:

* 更强模型
* 更低成本
* 更长 context
* 更聪明 agent
* 更高级 workflow
* 更漂亮 UI
* 更自动化 integration

所以人会一直觉得:

“再调一下,就完美了。”

结果最后,
真正没时间做的,
反而是最开始真正想做的东西:

表达。
创作。
思考。
生活。

因为 infra 会给你一种非常高级的拖延感。

你不是在摸鱼。

你是在“构建未来”。

这就特别上头。

以上不是给朋友们泼冷水 更多是对自己没日没夜没生活的自嘲。

真正厉害的是那些找到了商业闭环的弄潮儿 他们知道如何leverage ai 的威力杠杆 往往烧着千万上亿tokens不肉疼 闷声发财 一旁偷着乐。唯一公平的是 他们也被ai搞得几乎没有了人间生活。

Morning Glory and Afternoon Collection — Ch.1-1: Wandering Far Away / 朝华午拾 · 第一章·流浪远方

Chapter 1: Homesickness is an Invisible Net

by Li Wei (立委)

Life comes but once, a river rushing to the sea that never returns. The distillation of a life transcends the life itself. Only when the migrating geese leave their call do you feel you haven't lived in vain. With accumulated experience, with inspiration stirring, with a serene mood and a pot of clear tea — what flows flowingly is not literary craft, but life itself: with its sorrows and joys, its sweat and blood.

Most things in this world follow predictable patterns. So do most human lives. But when an old hand looks back at his footprints, the ordinary parts tend to fade while the legendary ones stand out. And the legendary, by definition, defies belief. Yet what truly instructs us is often the legendary, not the routine. Morning Glory and Afternoon Collection is a legend. Some things in it, I scarcely believe myself. Take this, for example: raising 10 million dollars from the federal government and 11 million from investors within eight years around the turn of the century— fairly rare, right? But it happened, and it happened to us.

Another example: my elder brother's "rebellion" as a nine-year-old commander. I remembered the event, but in the first draft of Little Red Guards I did the math and thought it impossible, so I fudged it: "My brother was the representative of our second-grade class, one of the founders of the revolutionary organization." Later, after verifying with my father and brother, it turned out he WAS the commander, with a fourth-grade strategist as his adjutant. According to my father's account, our family was sent down to the countryside in 1965. Since there was no kindergarten there, I skipped straight from middle kindergarten into first grade elementary, sitting in the same class as my brother. After two months, I somehow advanced with the class to second grade (the plan was to hold me back in first, but the teacher said I was able to keep up). In '66 we were second-graders. School was suspended for the revolution, and the Little Red Guard was formed during that hiatus. The rebellion must have been in '66, because by '67 our family had left that small village town and returned to the county seat.

Morning Glory, Part One: Wandering Far Away

The very word wandering conjures the comic books of my childhood — Zhang Leping's Sanmao the Wanderer.

(to be continued)


朝华午拾 · 第一章:乡愁是一张无形的网

人生只有一次,奔流到海不复还。人生的酿造超越了人生。雁过留声,才感觉没有白活。有积淀,来灵感,准备好心情与清茶。从容流淌的不是文思,而是生活,伴着哀怨喜乐,汗水与泪血。

世界上的事情,多数都是循规蹈矩的常规。人一辈子也大多如此。不过,老帮菜回头看自己的足迹,常规的部分容易忽视,传奇的部分就凸现出来。凡传奇,就不可信。可是能够有启示的,往往是传奇,而不是常规。《朝华午拾》就是传奇。有些事情,我自己都不敢相信。比如,8年内从政府拿到1000万,从投资人拿到1100万的成就,极罕见吧。可它发生了,就在我身上。

再如,老哥九岁当司令造反的事情,我是记得的,可是在《红小兵》初稿中,我一算岁数,觉得不可能,就含糊地写“我哥哥是我们二年级的代表,革命组织发起人之一”。后来跟老爸老哥核实,确实是司令,后面有个四年级的军师辅佐。根据老爸的记述,我家1965年下乡,因为乡下没有幼儿园,我从幼儿园中班,直接插班进入小学一年级,跟我哥哥同班。上了两个月,居然跟班升学到二年级(本来打算留在一年级,可老师说我能跟上)。66年我们在二年级,其间有停学闹革命,匕首小分队就是在停学时期成立的。造反应该在66年,因为67年我家就离开那个小镇回县城了。

朝华之一:流浪远方

写就“流浪”二字,想起小时候看过的《三毛流浪记》来。张乐平后无漫画,大师千古。


From Morning Glory and Afternoon Collection(朝华午拾). Original Chinese: 乡愁是一张无形的网.