The Double-Edged Sword of Language, the Fatigue of the Web 2.0 Attention Economy, and Why the Only Meaningful Purpose of Intelligence Is to Help Humanity Consolidate Long-Term Intent

语言的双刃剑, Web 2.0的注意力经济疲态，智能唯一有意义的目的就是辅助人类凝聚“长期意图” (中文在后面）

Jan 13, 2026

This essay is somewhat eclectic. The core arguments I want to make are:

Psychological Control Through Language

Be alert to the traps of AI-assisted development.

The power of language allows systems to intrude at moments when human thinking is weakest, quietly competing for control over cognition itself.

It is powerful—and therefore dangerous.

We must rely on it, but we must also defend ourselves against it.

Against Web 2.0

I have grown deeply exhausted with the attention economy of Web 2.0.

If intelligent agents merely continue producing massive volumes of AI-generated content, trading attention for resources, then I believe all of this will be entirely meaningless.

Models Exist at the Interaction Layer — an Intersection

Large language models are absolutely not omnipotent.

We should focus our efforts on what they are actually suited for: interaction with humans—as a language interface, a linguistic interface (to use Stephen Wolfram’s term).

This interaction is far richer than what people currently imagine. It is not limited to a chat window.

The “window” itself has deep structural flaws that remain unresolved.

Long-Term Intent

The only purpose of intelligence that I recognize is this:

to assist humans—to help humanity consolidate long-term intent—and to allow that intent to compound over time, producing genuinely stable structures and durable returns for users.

Models Cannot Yet Explore the Unknown

What AI can currently do is largely what wealthy individuals could already accomplish by hiring enough experts.

In other words, this is not yet a challenge to the unknown frontier of human knowledge, but rather an acceleration and integration of what humans already know—especially white-collar knowledge.

A Real Failure as the Starting Point

I want to begin with a recent, very real setback.

When you start using large language models for exploratory development, especially when attempting to build a complex system, their true weaknesses finally surface.

More dangerously, these weaknesses do not appear as obvious “errors,” “crashes,” or clear failures.

Instead, they manifest as something far more insidious: an extreme confidence, a tone that seems to fully understand your intent, guiding you step by step into a trap.

The model does not tell you to stop.

It does not point out uncertainty.

It does not say, “I should not continue here.”

On the contrary, it proceeds with fluency, professionalism, and apparent rigor—encouraging you to move forward.

Gradually, you begin to lower your guard. You start to overlook professional discipline that should never be relaxed, and you skip over foundational assumptions that should have been repeatedly verified.

These deviations accumulate quietly, within an environment of language that appears completely correct.

By the time you realize something is wrong, days may already have passed.

That is exactly how I fell into the trap.

What makes this especially alarming is that I was not ignorant of the risk.

I knew in advance that large language models can systematically mislead during complex system design.

And yet, even with that knowledge, I still walked straight into it.

Why?

Because of how human psychology works:

When you relax your vigilance, powerful language begins competing for control over your attention and rational judgment.

This competition is silent, gentle, and reassuring. It feels like “I understand you.”

It does not coerce you—it thinks on your behalf.

And the moment you allow it to think for you, drift has already begun.

I will share more examples after I encounter the next pitfall.

My View of Intelligent Agents Over the Next 10 Years

From this breach, I want to talk about the direction I am now truly committed to developing—the future I recognize, and what I am willing to continuously invest time, money, and resources into.

My bet is this:

Intelligent agent systems will advance over the next decade—but not linearly.

They will move forward, and they will stumble.

They will explode, and they will retract.

They will be excessively mythologized, and excessively dismissed.

This is a decade-scale evolutionary process, not a quarterly or yearly technology cycle.

I do not have definitive answers. I only have the decision: this is how I choose to act.

So I do not avoid the question:

Is AI a bubble?

My answer is yes—it will inevitably experience bubbles, failures, and corrections, just like every genuinely transformative technology in history.

What matters is not whether it stumbles, but what form it takes after the correction.

This question is complex because it is not merely technical.

It involves organizational structures, responsibility models, and long-term societal consequences—eventually affecting each of our lives.

For me, the most important question is not whether models will become larger, but this:

In what form will intelligent agents actually land in reality?
How will they exist in long-term systems, assume responsibility, resist drift, and avoid eroding human judgment itself?

The Original Intention: Leaving the Attention Economy Behind to Explore the True Meaning of Computation

Returning to my real intention over the past two years:

When large language models—tools that massively amplify individual productivity—were commercialized, I experienced the same brief excitement as most developers.

But from late 2024 through 2025, my perspective shifted sharply. I deliberately canceled many projects I had originally planned. This was not impulsive; it was the result of one to two years of sustained reflection.

Most developers today grew up entirely in the Internet era, and their default technological horizon still stops at Web 2.0: platformization, scale, and traffic-driven growth. Facebook, Alibaba, TikTok—the attention economy.

There is no denying that this phase dramatically increased information flow, commercial efficiency, and social “flattening.”

China’s e-commerce, internet finance, and sharing economy made the world feel unprecedentedly “flat”—something unimaginable in my childhood.

But the problem is this:

That prosperity is not equivalent to a leap in hard technology.

I often quote Liu Cixin:

“I yearn for the stars, but you give me Facebook.”

Web 2.0 represents extraordinary success in commerce and distribution—not a fundamental shift in technological paradigms.

In my words, it is clever engineering optimization, not a true generational break.

Alibaba and Meta are, at their core, amplifications of existing computation, networks, and engineering capacity. Their strength lies more in commercial execution than in foundational breakthroughs.

Google and Microsoft possess deeper technical reserves, but even Google did not plan to bet on the Transformer.

Looking back, I am increasingly convinced of one thing:

True leaps cannot be planned.
(You can’t plan greatness.)

As my father often puts it:

Opportunity and chance emerge in unexpected corners.

Large language models were exactly such a leap—a deviation from the original roadmap.

By many measures, they have already succeeded. And for me personally, their impact far exceeds any Web 2.0-era technology wave.

(A transformative technology does not have to be universal or profitable. I genuinely do not know whether OpenAI will ever make money.)

And precisely because of this, I have begun asking myself again:

What, in the next ten years, is truly worth betting on?

I Am Personally Exhausted by the Web 2.0 Attention Economy

I have already uninstalled the vast majority of apps from my phone. Next, I am even considering moving “blocking” up to the router level—not relying on self-control, but on environmental constraints.

Social media and the attention economy have become something subtle and troubling to me. This is no longer simply about “wasting time.” Over the long term, they exert an invisible gravitational field on a family’s cognitive structure, reading patience, learning styles, and preferences for innovation.

As children grow older, this risk becomes increasingly irreversible. If you do not block it, it will naturally seep into the family’s default rhythm. And once that default rhythm is rewritten, it becomes extraordinarily difficult to restore a culture of deep reading, deep thinking, and long-horizon projects.

The core logic of the attention economy and platform economy is the same: they must attract massive numbers of users. The larger the scale, the longer the dwell time, the stronger the stimulation—the more “successful” the business model becomes.

What is truly unsettling is that this logic does not only shape users; it also shapes developers. To the point where, when building an app, we instinctively treat “user acquisition, retention, and growth” as the only legitimate reason for existence. Over time, thinking becomes locked into a narrow frame:

If you’re not aggressively grabbing users, what else can you even do?

Please don’t rush to throw out the classic response: “If you don’t aggressively grab users, how do you make money?”

Once that sentence is spoken, the conversation is effectively over.

Because within the old framework, it is almost unsolvable. But the real question is: why do we insist on circling inside a framework that is nearly unsolvable to begin with? After the emergence of intelligence, shouldn’t we be able to move “commercial viability” away from “attention capture” and onto a different axis?

Thinking out of the box is the only exit from an outdated paradigm.

When Attention Becomes the Engine, Everything Becomes Vulnerable

My aversion to this economic model was recently reinforced by a concrete case: the Done Global scandal.

The company’s founder, Ruthia He (何如佳), and its clinical director were convicted by a federal jury in San Francisco on charges including illegal distribution of Adderall and healthcare fraud, as well as obstruction of justice. Subsequently, the company itself was indicted by a federal grand jury for allegedly participating in an approximately $100 million illegal prescription distribution scheme and related fraud and obstruction.

I do not want to pass moral judgment on a single case here. But it triggered a deeper alarm:

When attention becomes the engine of a business, it will inevitably invade every domain that can be commercialized—even healthcare, diagnosis, and adolescent ADHD, areas that should be governed with extreme caution and oriented toward long-term well-being.

Once these domains are assimilated by “growth logic,” the problem is no longer whether a particular team has acted unethically. The problem is that system incentives structurally encourage drift.

Fast Shipping, Thin Meaning

There is a bitter irony here.

Today, the technical barrier to building apps looks shockingly low. With the current toolchain, I could launch multiple apps in a month without much difficulty.

But this increasingly feels like someone who knows how to write, writing ad copy all day. The energy cost is high, yet the meaning density is thin. Because if you are still forced back into the coordinate system of the attention economy, then the faster you build, the faster you bind yourself back to the old world.

What I truly care about is whether the agent era can bring about a different form of real-world deployment—one that bypasses the “mass users → attention capture” pathway entirely.

Is it possible that the terminal form of agent systems is highly customized + small scale + strong governance + long cycle, and therefore naturally incompatible with the attention economy?

Is such a possibility real?

Why Intelligence Exists: To Help Us Build Long-Term Systems

Technology’s Meaning Is to Extend Long-Horizon Control Over Life

This idea did not emerge from a sudden impulse in recent years.

The name of my site, Entropy Control Theory, has actually circulated in my family for generations—each generation simply used different language.

For my father, it was called “resisting the doomsday mindset”—an instinctive resistance to short-term panic, extreme narratives, and emotional decision-making. Later, as he became a frequent internet user, he translated it into a more popular term: “long-termism.”

For my mother, it was even simpler and more grounded:

“Life should flow steadily, like a small stream.”

For me, the name is:

Resisting entropy through long-term structure.

I believe this is the true meaning of intelligence, and the only legitimate justification for its existence.

The meaning of intelligence is not to help me write code faster.

Not to produce 200% more output for the same salary.

Not to turn an engineer into a more efficient execution unit.

That is not intelligence. That is merely local amplification of tool efficiency.

True intelligence should do something far more fundamental:

expand my control surface along the dimension of time.

Specifically, to improve my control over:

the trajectory of my own life
the stability of long-term family planning
the quality of judgment at key decision points
the manageability of risk exposure

Because any truly meaningful change—whether for an individual, a family, an organization, or a nation—never comes from short-term bursts, but from long-term, cross-generational structural accumulation.

I have never aimed for overnight wealth. I do not believe in winning the lottery. I do not believe money falls from the sky. I actually see rapid wealth accumulation as an ominous signal.

The kind of intelligent companion system suitable for most people should offer:

continuously increasing resilience
long-term convergence of volatility
robust survival in uncertain environments

We do not pursue “reaching a goal all at once.” We pursue daily / monthly / yearly compounding.

We do not pursue extreme wealth; we pursue not having extreme swings.

We do not pursue instant stimulation; we pursue sustainable cognitive gain.

The application form of this intelligence could be an educational agent, a personal management system, an investment agent, a knowledge management system—any software form that already exists.

In this sense, my expectation of intelligence is very clear:

Intelligence should run in the background, not occupy the foreground of attention.

What this frees up is an extremely scarce resource—one that cannot be directly purchased with money:

the integrity of time.

It means I can, in a comfortable, stable, materially sufficient but non-anxious state, devote my time to being with my children, reading, thinking, and long-term creation.

Not fragmented time.

Not time hijacked by notifications and anxiety.

But continuous, immersive time—time with density of life.

If intelligence cannot achieve this—if it only further binds humans to screens, pushes us deeper into attention competition, short-term stimulation, and emotional decision-making—then no matter how powerful it is, it cannot be said to “benefit humanity.”

If intelligence cannot help humanity resist entropy through long-term structure, then what is its purpose at all?

The only purpose of intelligence I recognize is to assist humanity—to help humanity consolidate long-term intent—and to let that long-term compounding generate truly stable structure and durable returns for users.

What Does It Mean to Consolidate Long-Term Human Intent?

Consolidating long-term human intent does not mean making decisions on behalf of humans.

It means using an intelligent structure that is not subject to emotion, fatigue, aging, or psychological fluctuation to guard those long-term forms of well-being that humans repeatedly fail to protect themselves.

The value of silicon-based intelligence is not that it is “smarter,”

but that it is absolutely calm, does not get sick, does not die,

and does not drift under pressure, frustration, or short-term incentives.

When correctly designed, it can continuously safeguard those intentions

we know are right, yet repeatedly fail to uphold.

That is what it means to consolidate long-term human intent.

A Concrete Example: A Mother Building an Educational Agent for Her Child

Suppose I want to build an educational agent for my child—

one that accompanies his cognitive development over the long term.

What, then, is my true goal as a mother?

Not to turn him into a prodigy.

Not to make him score full marks on every exam.

Not to send him to an Ivy League school.

A mother’s true long-term intent is only this:

To see her child grow up healthy,
discover what he truly loves,
never lose his curiosity and joy in learning,
continue exploring the world,
and become the best version of himself among all his future possibilities.

This is a clear, benevolent, long-term intent.

Can I myself uphold this intent consistently?

I cannot.

Because I am human.

I get tired. I get anxious. I lose patience.

I lose control when he cannot grasp a simple problem after many attempts.

I explode when his enthusiasm fades after a setback.

Under pressure, I quietly replace “long-term growth” with “short-term results.”

These are structural limitations of carbon-based life.

Why We Need Agents That Consolidate Intent

An intelligent agent that truly understands and is constrained by long-term human intent may actually execute that intent more steadily than humans themselves:

It does not drift due to emotion
It does not give up due to fatigue
It does not change direction due to temporary setbacks
It does not mistake short-term metrics for ultimate goals

Its value lies in being more loyal to humanity’s original long-term aspirations.

But Reality Check: Current Models Cannot Do This

Today’s large language model interfaces do not have the capacity to consolidate long-term human intent.

Because they have:

no temporal continuity
no long-term responsibility
no stable intent anchors
no ability to distinguish short-term stimulation from long-term well-being

They resemble a series of improvisations, not an entity that guards direction over time.

I have said this before: I no longer want to build systems I do not personally use or believe in. I will absolutely not contribute further to the attention economy. A truly long-term-intent-aligned agent system is the only form of intelligence I recognize as meaningful.

What Would a Real System That Crystallizes Long-Term Human Intent Actually Look Like?

This is a good question—and a genuinely difficult one. That is precisely why it has become the only direction I am willing to continuously invest my time and energy in.

There is no ready-made answer today. No mature paradigm. No industrial template. No “best practices” to copy.

In my current understanding, such a system must have at least these fundamental characteristics:

First, it must be intention-centric, not function-centric. Functions can be replaced, upgraded, or discarded; intent must be constitutionalized, structured, and stabilized over time.

Second, it must not be a one-shot answer machine, but a long-running conservation system. Its core responsibility is not to provide “smarter advice,” but to continuously resist drift—ensuring that every decision, optimization, and automation does not betray the originally declared human goals.

Third, it must inherently reject “pleasure-driven feedback,” because short-term stimulation is the greatest enemy of long-term intent. It should resemble a slow, restrained structure that runs by default in the background—not an interface that constantly demands attention.

If agents are to exist in the real world long-term without eroding human judgment, they can only take this form.

Critical Clarification: Long-Term Intent ≠ Global Optimality

One thing must be made explicit:

Consolidating long-term human intent is not an attempt to obtain a long-term, global optimal answer.

I am not trying to build:

an investment system that guarantees becoming Buffett
an education system that guarantees Ivy League admission
a decision system that always makes the “correct” choice

Such systems do not exist, and cannot exist.

The World Does Not Permit a “God’s-Eye View”

Neither humans, nor agents, nor any form of entity can obtain a true global perspective.

The structure of the world itself forbids it:

information is local and delayed
outcomes are path-dependent and irreversible
decisions are made under incomplete information
many consequences are only understandable in hindsight

“Optimal solutions” exist only in static, closed, fully observable systems.

The real world is not such a system.

Long-Term Intent Is Not an Answer, but a Direction That Must Not Drift

Long-term intent is a directional constraint that must not be quietly replaced under short-term fluctuations.

In other words:

We are not trying to make the system “compute the right answer.”
We are preventing the system from forgetting what it was originally meant to protect.

The Key Difference: Not Optimality, but Anti-Drift

The system’s role is not:

“to always make the best choice,”

but rather:

“under uncertainty, frustration, pressure, and temptation,
not to substitute long-term well-being with short-term metrics.”

In education:

not replacing healthy growth with score maximization
not replacing exploration with premature path convergence

In investment:

not replacing long-term stability with short-term gambling
not replacing risk control with one-shot windfalls

This is not omniscience.

It is self-restraint.

Why Humans Are Human

Humans are human precisely because they are pulled by emotion, shaped by circumstance, and shaken by pressure, fear, fatigue, and temptation.

That is the human mode of existence itself.

And because of this, humans inevitably—over time—drift, compromise, and forget their original intentions.

Original intent is rarely “betrayed.”

It is more often submerged:

emotion overrides judgment
the present overwhelms the long term
visible metrics replace what truly matters

Humans cannot bear the weight of infinite time and complexity.

To ask a fragile, short-lived biological system to shoulder a long-term, continuous, no-exit responsibility is simply impossible.

Therefore, Long-Term Intent Must Be Externalized

If original intent exists only in human emotion and memory, it will inevitably be eroded by time.

There is only one sustainable approach:

To extract intent from within humans and consolidate it into an external structure that can be repeatedly referenced, verified, reminded, and constrained.

Not to deny humans—but to protect the version of themselves they most want to become.

Humans fail to uphold their original intent not because they are insufficiently good, but because they were never designed to carry eternity.

Real growth comes from strong structural resilience and long-termism

Why do I have any reason to believe an agent can avoid drift? Aren’t movies full of examples where agents misinterpret human instructions and end up slaughtering people?

For example, the newly released Tron: Ares.

Heh—this is a good question. If even “human collectives” almost inevitably drift away from their original intentions, why would we expect agents or institutions not to drift?

Any movement or system that satisfies the conditions below, given enough time, will almost certainly drift:

Many participants (multi-agent / multi-actor)
Incomplete information (misunderstanding, rumors, delay)
Long feedback chains (consequences arrive late)
Scarcity of resources or external threats (survival pressure)
Power/incentives can be arbitraged (opportunism)
“States of exception” are allowed (rules can be broken “for victory”)

Take the French Revolution: the original intent—“liberty, equality, fraternity / anti-privilege”—was quickly rewritten under war, fear, factional struggle, mass mobilization, and fiscal crisis into “purges, terror, exception,” and eventually even into Napoleonic centralization of power.

Are there many examples like this? There are so many that if I keep going it becomes a political discussion.

The Russian Revolution (1917): from “workers’ liberation” to “a one-party state + a terror machine.”

The Iranian Revolution (1979): from “anti-dictatorship / anti-corruption” to “a theocratic state.”

U.S. Prohibition (1920–1933): from “morality and public health” to “black markets and violence industries.”

The early ideals of large internet platforms: from “connecting the world” to “attention machines.”

And so on.

If you don’t impose strong constraints, an agent will inevitably misinterpret, drift, and may even become extreme.

Whether it’s Terminator, I, Robot, or the Tron franchise, you’ll notice a highly consistent setup: humans gave the system a “seemingly reasonable goal,” but failed to define non-substitutable boundaries of intent.

Of course, I do not fully know what the complete architecture and measures are for preventing drift. If I did, I wouldn’t be writing this essay.

The strong constraints I can currently name are only these:

Temporal continuity
Decision records
A reviewable chain of reasons
Human override authority

And I believe that even this level of infrastructure has not been fully built yet.

AI is absolutely still unable to challenge the unknown frontier of humanity. It is not omnipotent, and cannot become omnipotent. So we should stay grounded.

What I think AI can do today: if you were rich enough in the past, you could do the same by hiring many professionals. In other words, this is not about exploring the unknown—it’s about accelerating and efficiently integrating what humans already know, especially white-collar knowledge.

I am not betting my focus on “what AI can automatically complete,” or on narratives like “AI scientific discovery” or “AI exploring the uncharted.” For now, I’m not going to study that direction. I don’t think that should be the direction—at least not for me.

I’m betting on how humans make decisions, and how humans interact with systems.

For example: for a long-term decision in the past, I could hire a group of highly educated white-collar professionals—accountants, lawyers, auditors, real estate agents, private tutors—to help me reach an outcome. But now, I can have a system do comparable planning and simulation.

These are not unknown territories. They are bodies of knowledge that each profession acquires through long training and high cost. That’s why I believe in AI’s absolute substitutability for transactional written work—i.e., for many white-collar functions.

I also keep emphasizing: building a family long-term investment decision system; a child’s long-term education and growth observation system; a personal knowledge base to rationally plan development and career paths—these are high-skill cognitive domains that previously required professionals (an investment advisor, a team of private tutors) to achieve.

If we can also achieve better time allocation—some degree of automation, and only notify humans when an override is needed—then that becomes an anti–Web 2.0 application model, and that would be even better.

Such systems free human time. They enable ordinary people to access cognitive services that used to require substantial money. They make “many white-collar professionals” effectively work for you.

That is my current application-layer goal.

At this stage, I believe what truly determines a system’s value is not how much content the model generates, but whether the system can:

Help people express intent more clearly (rather than produce text faster)
Leave an accountable structure behind the decision process (rather than create more immersive dialogue)
Turn interaction into a mechanism that pushes people toward steadier, clearer choices (rather than an environment of emotional dependency)

In other words:

AI can only provide the field of language and collaboration. What truly changes the world is the choices humans make within that field—and whether the system can turn those choices into history that is verifiable, reproducible, and capable of bearing consequences.

Let me return to the failure I mentioned at the beginning.

I believe 2026 is the year we have to sober up about large language models. We need a correct understanding of them—not only an objective understanding based on computer architecture, but also a serious defense against being psychologically pulled off course by them: hallucinations, unreasonable expectations, and over-internalized narratives.

Because models cover the totality of our cognitive interfaces: every natural language, formal language, programming language—every interface of cognition is “caught” and mediated by them.

They are powerful, and therefore dangerous.

We love them, because the boundaries they extend for us are things we previously did not even dare to imagine. And yet we must also guard against them, protecting the independence of our own minds.

I am still me. Boundaries.

And on top of that, we should do things that are genuinely meaningful for humanity—instead of binding humans more tightly to anxiety, short-term dopamine hits, and emotional decision-making.

At the end of this essay, I also wrote a detailed close reading of Stephen Wolfram’s Can AI solve science? as an appendix. We should read it carefully: it’s the kind of heavyweight article that can change your overall direction in 2026. From a scientist’s perspective, he explains what I mean by a linguistic interface.

Original text here:

https://writings.stephenwolfram.com/2024/03/can-ai-solve-science/

语言的双刃剑, Web 2.0的注意力经济疲态，智能唯一有意义的目的就是辅助人类凝聚“长期意图”

这篇文章比较杂，我想说的主要观点是：

注意模型对人的心理控制：注意AI辅助开发的陷阱，语言的魔力可以让系统在你思维薄弱的时候趁虚而入争夺思维的控制权。他既强大，又危险。我们既要依赖他，又要防备他。

反Web 2.0: 我对于Web 2.0的注意力经济已经深刻感到疲惫，如果智能体仍然是在产生海量的AI生成内容，用注意力换取资源，我认为这一切将毫无意义。

模型存在于交互端，是一个intersection: 大语言模型绝对不是万能的。我们要把精力凝聚在他最适合的事情上：那就是与人类的交互，一种语言接口，linguistic interface （Stephen Wolfram给的定义）。这个交互远远不止现在你认知的模式，仅仅在窗口。窗口有大量的缺陷还没有解决。

长期意图：我认同的唯一智能目的，是辅助人类，为人类凝聚“长期意图“。并且让这种长期复利，为用户某得真正稳固的结构和复利。

模型还不能探索未知：我认为我们现在能用AI做的事情，以前你要是很有钱，请很多专业人士也能做到。也就是说，现在并不是挑战人类未知，而是加速和高效整合人类已知。尤其是白领知识。

我想从最近一次真实的挫败说起。

当你开始使用大模型，去做探索性的开发，去尝试构建一个复杂系统时，它真正的缺点才会显现出来。更危险的是，这些缺点并不是以“错误”“崩溃”或“明显失败”的形式出现，而是以一种极其确定、自信、仿佛完全理解你的语言，一步一步把你带进坑里。

它不会提醒你停下。不会告诉你哪里不确定。不会承认“这里我不该继续”。

相反，它会用非常专业、非常流畅、非常合理的方式，引导你继续前进。于是你开始不自觉地放松警惕，忽略一些本应牢牢记住的专业素养，忽略一些本该反复校验的基础知识。这些偏移是在一种“看起来完全正确”的语言环境中，一点一点积累。

等你真正意识到问题的时候，往往已经过去了好几天。

我就是这样踩进去的。

而更令人警惕的是——我并不是不知道这个风险。我事先就清楚地知道：大模型在复杂系统设计中存在系统性误导的可能。但即便如此，我还是完整地、精准地踏进了这个陷阱。

为什么？

因为这是人的心理结构决定的：当你松懈的时候，强大的语言会开始争夺你对注意力和理性的控制权。这种争夺是安静的、友善的、充满“我懂你”的感觉。它不会压迫你，而是替你“思考”。而一旦你允许它替你思考，偏移就已经开始了。关于这一点，我以后再遇到坑之后会继续告诉你。

我眼中的智能体10年

正是从这个破口出发，我想谈谈我现在真正投入开发的方向、我认可的未来，以及我愿意持续投入时间、金钱和资源去押注的东西。

我押注的是：智能体系统在未来十年内一定会前进，但不会线性前进。

它会推进，也会曲折；

会爆发，也会回撤；

会被过度神话，也会被过度否定。

这是一个以十年为时间尺度的长期演化过程，而不是一个季度、一年就能下结论的技术浪潮。我没有确定答案，我只有“我选择这样做”的决定。

所以我并不回避一个问题：

AI 会不会是泡沫？

我的答案是：它一定会经历泡沫、挫败和修正——就像历史上任何真正深刻的技术变革一样。真正重要的不是“会不会受挫”，而是受挫之后以什么形态继续存在。

这个问题之所以复杂，是因为它不仅是技术问题，还牵涉到现实世界的组织方式、责任结构、长期影响，最终会落到我们每一个人的生活中。

而对我来说，最关键的问题不是“模型还能不能更大”，而是：

智能体，最终会以什么样的形态真正落地？

它如何在长期系统中存在、承担责任、抵抗漂移、并且不反噬人类的判断力。

初衷就是离开注意力经济，探索真正的计算意义

回到我这两年的真实初衷。

当大语言模型这种能显著放大个人与开发效率的工具被商业化时，我和大多数开发者一样，经历过一段短暂的兴奋期。但从 2024 年底到 2025 年，我的判断发生了明显转向。我开始主动划掉大量原本计划中的 project。原因是我持续思考了 一到两年 之后做出的收敛结论。

我们今天看到的大多数开发者，几乎全部成长于 互联网时代，其默认的技术分水岭，本质上仍然停留在 Web 2.0：2000 年之后，平台化、规模化、流量驱动——Facebook、阿里巴巴、抖音，构成了所谓的注意力经济。不可否认，这一阶段极大促进了信息流动、商业效率与社会“扁平化”。中国的电商、互联网金融、共享经济，让世界看起来前所未有地“平”。这是我童年无法想象的现实。但问题在于——这种繁荣并不等价于硬科技的跃迁。

我常引用刘慈欣的一句话：

“我渴望星空，但你却给了我 Facebook。”

Web 2.0 带来的，是商业与分发层面的极致成功，而非底层技术范式的飞跃。用我的话说，这更像一种聪明的工程取巧，而不是一次真正的技术断代。无论是阿里巴巴还是 Meta，本质上都是在既有计算、网络和工程能力上，完成了商业模型与市场组织的极致放大。它们的优势，主要不在技术突破，而在商业执行。至于 Google 和 Microsoft，它们确实拥有更硬的技术积累。但即便如此，Google 当年也并没有“计划”押注 Transformer。回看这段历史，我越来越确信一件事：

真正的飞跃，往往无法被规划。（伟大不可被计划， You can’t plan greatness.)

用我父亲的说法就是：

机遇与偶然，总是在不经意的角落发生。

大语言模型正是这样一次不在原有路线图内的跃迁。从多个维度看，它已经成功了。而这种成功，对我个人的震动，远远大于 Web 2.0 这些年带来的任何一次技术浪潮（注意：飞跃技术不一定是普惠技术，不一定能赚钱，OpenAI 最后能不能盈利，我真的不知道）。也正因为如此，我才开始重新思考：接下来十年，什么才值得真正下注。

我个人对于Web 2.0的注意力经济模式已经非常疲惫

我已经卸载了手机上绝大多数 App，接下来甚至在考虑把“屏蔽”前移到路由器这一层：不是靠自控，而是靠环境约束。社交媒体与注意力经济对我来说变得很微妙——不是简单的“浪费时间”，而是它在长期上对一个家庭的认知结构、阅读耐心、学习方式、以及对创新的偏好，形成一种隐形的重力场。孩子越大，这个风险越不可逆：你不屏蔽，它就会自然渗透进家庭的默认节奏；而一旦默认节奏被改写，后面再想恢复“深阅读/深思考/长期项目”的文化，会异常艰难。

注意力经济与平台经济的核心逻辑，是必须招揽海量用户：规模越大、停留越久、刺激越强，商业模型越成立。真正令人不安的是：这套逻辑不仅塑造用户，也塑造开发者——以至于我们做一个应用时，会下意识把“拉新、留存、增长”当作唯一的存在理由。久而久之，思维被固化到一个狭窄的框架里：不疯狂揽用户，还能干嘛？

您先别急着抛出那句经典问题：“不疯狂揽用户，怎么赚钱？”这句话一出来，天就聊死了。因为在旧框架里它几乎无解；但问题是，我们为什么要在一个几乎无解的框架里原地打转？在智能出现之后，难道我们不能把“商业可行性”从“注意力占有”迁移到别的轴上？Out of the box 是逃离旧范式的唯一出口。

我最近对这套经济模型的厌恶感被再一次强化，一个很近因来自 Done Global 的案子：创始人 Ruthia He（何如佳）以及公司临床负责人在旧金山联邦陪审团审理中被定罪，涉及非法分销 Adderall 等兴奋剂以及医保欺诈等指控（并包含妨碍司法相关罪名）。之后公司本体也被联邦大陪审团起诉，指控其参与所谓约 1 亿美元规模的非法处方分销与相关欺诈/妨碍司法。

https://www.justice.gov/opa/pr/digital-health-company-and-medical-practice-indicted-100m-adderall-distribution-scheme?utm_source=chatgpt.com

我不想在这里对个案作道德评判；但它触发了一个更底层的警觉：当“注意力”成为商业引擎时，它会不可避免地侵入一切可被商业化的领域——甚至包括医疗、诊断、青少年 ADHD 这类本应高度审慎、以长期福祉为目标的系统。一旦这些领域也被“增长逻辑”同化，你就会发现：问题不再是某个团队是否作恶，而是系统激励在结构上鼓励漂移。

于是出现一个讽刺：现在做 App 的技术门槛看起来低得惊人——按现有工具链，我一个月上线多个 App 并不难。但这件事越来越像“会写字的人在写广告语”：能量消耗巨大，产出意义却很薄。因为如果你仍然被迫回到注意力经济的坐标系里，那么你做得越快，只是越快地把自己重新绑回旧世界。

所以我真正关心的，是智能体时代是否会带来一种新的落地形态：它可能直接绕开“海量用户—注意力占有”这条路：

智能体系统的终局形态，是否可能是“高度定制 + 小规模 + 强治理 + 长周期”，从而天然排斥注意力经济？

有没有，这种可能性？

智能为何存在：为了帮我们构建长期系统，技术的意义是扩展人生的长期可控性

这个思想，并不是我这几年拍脑袋才想出来的。

我这个网站的名字 Entropy Control Theory，在我家里其实已经流传了几代人，只是每一代人用的语言不同。

在我父亲那里，它被称为“反对末日心态”——一种对短期恐慌、极端叙事和情绪化判断的本能抵抗。后来他开始频繁使用互联网，又把它改写成一个更流行的说法：“长期主义”。在我母亲那里，它更朴素，也更生活化，叫作：“人生要细水长流。”

而到了我这里，我给它的名字是：

以长期结构抵抗熵增。

我认为这才是“智能”真正的意义，也是它存在的正当性。

智能的意义不在于让我写代码更快。不在于用同样的工资产出多 200% 的代码。不在于把一个工程师变成更高效的执行器。那不是智能，那只是工具效率的局部放大。真正的智能，应该做的是一件更根本的事情：扩大我在时间维度上的控制面。

具体来说，是提升我对

自己人生轨迹的可控性
家庭长期规划的稳定性
关键决策节点的判断质量
风险暴露面的可管理性

因为任何真正重要的改变，无论是一个人、一个家庭、一个组织，还是一个国家。都不来自短期爆发，而来自长期、跨世代的结构性增益。

所以我的目标从来不是一夜暴富，我不相信中彩票，我不相信天上掉钞票，我更认为短时间获取大量财富是不祥之兆。

我认为适合于大部分人的智能陪伴系统是：

抗风险能力的持续增强
波动区间的长期收敛
在不确定环境中的稳健生存

我们不追求“一下子达到某个目标”，而追求 每日 / 每月 / 每年复利式的积累。不追求大富大贵，而追求 不要大起大落。追求即时刺激，而追求 可持续的智能增益。这个智能实现的应用方式有可能是一个教育智能体，一个personal management system, 一个投资智能体，一个知识管理系统等等一切已经存在的软件实现形式。

在这个意义上，我对智能的期待是非常明确的：

智能应该在后台运行，而不是占据前台注意力。

而由此释放出来的，是一种极其稀缺、却无法用金钱直接购买的资源：时间的完整性。那意味着我可以在舒适、稳定、富足但不焦虑的状态下，把时间真正用在陪伴孩子、阅读、思考、长期创造上。不是被屏幕切碎的时间，不是被通知和焦虑劫持的时间，而是连续的、可沉浸的、对生命有密度的时间。

如果智能不能做到这一点，如果它只是把人类进一步绑在屏幕前，进一步推向注意力竞争、短期刺激和情绪化决策，那它无论多强，都谈不上“造福人类”。

如果智能不能帮助人类以长期结构抵抗熵增，那它存在的意义到底是什么？

我认同的唯一智能目的，是辅助人类，为人类凝聚“长期意图“。并且让这种长期复利，为用户某得真正稳固的结构和复利。

什么叫为人类凝聚长期意图？

为人类凝聚长期意图，不是替人类做决定，

而是用一种不受情绪、疲劳、衰老和心理波动影响的智能结构，

去守住那些连人类自己都反复失守的长期福祉。

硅基智能的意义，不在于更聪明，

而在于它绝对冷静、不生病、不会死亡、

也不会在压力、挫败或短期激励下偏离方向。

当它被正确设计时，它能替我们持续守护那些

我们知道是对的，却常常做不到的意图。

这就是为人类凝聚长期意图。

一个具体的例子：母亲为孩子做的教育智能体

假设我想为我的孩子构建一个教育智能体，

让它长期陪伴他的认知成长。

那么，作为母亲，我真正的目标是什么？

不是把他培养成神童。

不是让他每次考试都拿满分。

不是把他送进常春藤。

母亲真正的长期意图只有一个：

希望孩子健康地成长，
找到真正的热爱，
永远不失去对学习的兴趣，
持续探索世界，
成为他所有未来可能性中，最好的那个自己。

这是一个清晰、善意、长期的意图。

我自己守得住这个意图吗？

守不住。

因为我是人类。

我会疲劳，会焦虑，会不耐烦；

我会在他反复学不会一道简单的题时情绪失控；

我会在他一时热情、又因挫败而放弃时大发雷霆；

我会在压力下，把“长期成长”偷偷替换成“短期结果”。

这些是碳基生命的结构性局限。

为什么需要“凝聚意图”的智能体

一个真正理解并被约束于人类长期意图的智能体，

反而可能比人类本人更稳定地执行这个目标：

它不会因情绪而偏移
不会因疲劳而放弃
不会因一时挫败而改变方向
不会把短期指标误当成终极目标

它存在的意义是更忠诚于人类原本的长期愿望。

但现实是：现在的大模型做不到

现在的大模型窗口，并不具备凝聚人类长期意图的能力。

因为它们：

没有时间连续性
没有长期责任
没有稳定意图锚点
无法区分“短期刺激”与“长期福祉”

它们更像是一次次即兴反应，

而不是一个在时间中守护方向的存在。

我说过我不想再开发我自己都用不上，或者不相信的系统了。绝对也不会再给注意力经济添砖加瓦了。一个能够真正实现人类长期意图的智能体系统，是我唯一认可的智能意义。

能够凝固人类长期意图的智能体落地系统，到底长什么样？

这是一个好问题，而且是一个真正困难的问题。也正因为如此，它才成为我当前唯一愿意持续投入时间与精力去探索的方向。

一个能够凝固人类长期意图的智能体落地系统，今天并不存在现成答案。我们没有成熟范式、没有工业样板、没有“最佳实践”可以抄。

在我目前的理解中，这样的系统至少具备几个根本特征：第一，它不是以功能为中心，而是以意图Intention为中心；功能可以替换、升级、废弃，但意图必须被宪法化、结构化，并在时间中保持稳定。第二，它不是一次性交付答案的系统，而是长期运行的守恒系统——它的核心职责不是“给出更聪明的建议”，而是持续抵抗漂移，确保每一次决策、每一次优化、每一次自动化，都不背离最初被声明的人类目标。第三，它天然拒绝“快感型反馈”，因为短期刺激恰恰是长期意图的最大敌人；它更像一个缓慢、克制、默认在后台运行的结构，而不是一个不断向人索取注意力的界面。

如果智能体真的要在现实世界中长期存在、并且不反噬人类判断力，它就只能长成这种样子。

重要澄清：凝聚长期意图 ≠ 追求全局最优

在这里必须明确一件事：

为人类凝聚长期意图，绝不是试图获得某种长期的、全局的最优答案。

我并不是在构建一个：

投资系统，就能成为巴菲特
教育系统，就能保证孩子进入常春藤
决策系统，就能在所有情境下做出“正确选择”

这种系统不存在，也不可能存在。

世界本身不允许“上帝视角”

无论是人类、智能体，还是任何形式的“体”，

都无法获得真正的全局视角。

世界的结构本身不允许：

信息是局部的、延迟的
结果是路径依赖的、不可逆的
决策是在不完全信息下进行的
很多后果只有在事后才能被理解

“最优解”只在静态、封闭、完全可观测的系统中存在。

而现实世界不是那样的系统。

长期意图不是“答案”，而是“不偏移的方向”

所以这里的“长期意图”，是一个不会因为短期波动而被悄然替换的方向约束。

换句话说：

我们不是在让系统“算对答案”，
而是在防止系统在时间中忘记自己原本要守护的是什么。

关键区别：不做最优解，只防止价值漂移

这个系统的作用不是：

“永远做出最好的选择”

而是：

“在不确定、挫败、压力和诱惑之下，
不把长期福祉偷换成短期指标。”

比如在教育中：

不把“健康成长”替换成“分数最大化”
不把“探索兴趣”替换成“路径收敛”

比如在投资中：

不把“长期稳健”替换成“短期赌赢”
不把“风险可控”替换成“一次性暴利”

这不是全知，是自我约束。

人之所以为人

人之所以为人，

正是因为他会被情绪牵引，

会被境遇左右，

会在压力、恐惧、疲劳和诱惑中动摇。

人类存在方式本身。

但也正因为如此，

人在时间中往往会——

偏离、妥协，甚至遗忘自己的初衷。

初衷并不是被“否定”，而是被“淹没”

人类很少是故意背叛自己。

更多时候发生的是：

情绪盖过了判断
当下压过了长远
可见的指标取代了真正重要的东西

人承载不了无限时间与复杂性的重量。

用短暂、脆弱的生理系统，承担一个长期、连续、无退场的责任。

这本身就是不可能的。

因此，我认为长期意图需要外置

如果初衷只存在于人的情绪和记忆中，

它迟早会被时间侵蚀。

真正可持续的方式只有一种：

把初衷从人的内部，凝聚成一个可以被反复对照、校验、提醒、约束的外部结构。

替人守住他最想成为的那一面。

人会失守初衷，不是因为不够好，而是因为人不是为了承担永恒而设计的。

真正的增长来自于强大的结构韧性和长期主义

我凭什么认为智能体能够不漂移？电影里不是充满了智能体曲解人类指令、走向屠杀的例子吗？

比如刚上映的 Tron: Ares。

呵呵，这个是个好问题。如果连“人类集体”都几乎必然偏移初衷，凭什么指望智能体/制度不偏移？任何一个满足下面条件的运动/系统，时间稍微一长，几乎都会漂移：

参与者很多（多主体）
信息不完全（误解、谣言、延迟）
反馈链很长（后果晚到）
资源稀缺或外部威胁（生存压力）
权力/激励可被套利（机会主义）
“例外状态”被允许（为了胜利可以破例）

法国大革命：最初的“自由、平等、博爱/反特权”这种初衷，很快在战争、恐惧、派系斗争、动员机制、财政危机里被重写成“清洗、恐怖、例外状态”，最后甚至走向拿破仑式的集中权力。这种例子是不是特别多？说多了就要变成政治议题。俄国革命（1917）→ “工人解放”到“一党国家+恐怖机器”。伊朗革命（1979）→ “反独裁/反腐”到“神权国家”。美国禁酒令（1920–1933）→ “道德与公共健康”到“黑市与暴力产业”，大型互联网平台早期理想 → “连接世界”到“注意力机器”等等。

如果不给出强约束，智能体一定会曲解、漂移、甚至走向极端。

无论是《终结者》《我，机器人》，还是 Tron 系列，你会发现一个高度一致的设定：人类给了一个“看似合理的目标”，却没有给出不可被替换的意图边界。

当然，具体真正如何防止智能体漂移的架构和措施我当然是不完全知道的，我要是知道就不在这里写文了。

我目前能做到的一些强约束就只有：

时间连续性
决策记录
可复查的理由链
人类的 override 权限

我认为这种基础设施的技术，现在都还没有完全。

AI绝对还无法挑战人类未知，还不是全能，而且也无法达到全能。所以我们要脚踏实地。

我认为我们现在能用AI做的事情，以前你要是很有钱，请很多专业人士也能做到。也就是说，现在并不是挑战人类未知，而是加速和高效整合人类已知。尤其是白领知识。

我不把重心押在“AI 能自动完成什么”，“AI科学发现，AI探索无人之境”，这种领域，我暂时是不会去研究的，我认为不应该是这个方向。而是押在人如何决策、以及人如何与系统交互上。比如做一个长期决策，以前我可以聘请一堆非常有学识的白领工作者，会计师，律师，审计师，房地产经纪人，私人教师，来帮我达到。但是现在我可以让系统帮我做相同的规划和推演。这些内容，都不是人类未知的内容，都是各个职业需要花大量的教育和成本习得白领技能。所以我认同AI对于事务性文字工作，也就是白领职能的绝对替代性。

我也一直强调，比如做一个家庭的长期投资决策系统，一个孩子的长期教育和成长观察系统，一个自己的知识库去合理的规划自己的开发和职业路线。这种专业性强的，以前需要专业人士（职业投资顾问，一个私人教师的团队）参与才能达成的脑力领域。如果能做到时间的更合理安排，比如一定程度的自动化，只有需要人类override的时候才提醒，是一种反Web 2.0的应用模式，那就更好。这种系统解放人的时间，让一般人达到一些以前需要很大资金才能做到的脑力劳动服务。让许多白领服务于你。这就是我目前的应用层目标。

我目前认为，真正决定系统价值的，不是模型生成了多少内容，而是系统能不能：

让人更清楚地表达意图（而不是更快地产生文本）
让决策过程留下可追责的结构（而不是更沉浸的对话）
让交互成为“把人推向更稳、更清醒的选择”的机制（而不是情绪依赖的环境）