-- Bing Xu's Note ---
I came across an internal GLM letter on the Chinese app RedNote, purportedly written by @jietang, and translated the Chinese text in the images into English.
--- Letter Start ----
The great wave has arrived. Allow me to use this article to discuss three things: who we are, how we understand this era, and the strategic direction to which we have chosen to devote ourselves wholeheartedly.
1. Who We Are: First Principles, Contrarian Thinking, and Focus
Zhipu has never been a company that simply chases the latest trend. We grew out of a laboratory, carrying with us twenty years of its methodology.
That methodology can be summarized in three ideas: first principles, contrarian thinking, and focus.
Only by thinking deeply enough can we dare to make a sufficiently contrarian choice. And once we have made that choice, we must be prepared to remain committed to it for long enough.
Looking back, almost every critical decision we made once appeared counterintuitive.
In 2006, we quietly worked on an academic search system running on a single desktop computer. We do so because we had come to understand that the deeper question behind it—discovering the mechanisms that drive the evolution of academic disciplines—was worth spending a decade answering.
From 2021 to 2022, when “making machines think like humans” was still regarded by most people as a moonshot bordering on madness, we redirected our resources, committed ourselves to a model with hundreds of billions of parameters, and built GLM-130B.
That was a full year and a half before ChatGPT took the world by storm.
Then, on January 8, 2026, the day Zhipu completed its H-share listing in Hong Kong, we treated the occasion not as a finish line, but as an entirely new beginning. We resolved to return fully to foundational model research and devote all our efforts to the next generation of models.
While others rang the listing bell, we reset ourselves to zero.
This was not a gesture. It was a conviction. If the ultimate destination is AGI, then short-term gains and passing industry trends are merely scenery along the road.
What has sustained us to this day is an extraordinary degree of focus and a sincere, uncompromising idealism.
It took us ten years to grow an academic search system from a single desktop computer into a platform serving more than ten million users. We have spent nearly another decade pursuing large models, and we will continue to cultivate this field with determination.
Zhipu today is a group of people willing to question from first principles, bold enough to make choices that run against conventional wisdom, and focused enough to carry those choices through to the end.
That is the source of Zhipu’s core competitiveness.
2. How We Understand This Era: The Ceiling of Intelligence Is Being Rewritten
If there is one thing we have learned over the past twenty years, it is this:
The greatest commercial opportunities never lie in minor adjustments to products or business models. They arise when the ceiling of intelligence itself makes a leap.
This is our most fundamental judgment about the current AI transformation, and it is the idea we most want to convey.
At its core, this transformation is not merely a product innovation or a business-model innovation. It is a technological revolution that has raised the very ceiling of intelligence.
Whoever can push that ceiling even one inch higher will be able to redefine the boundaries of what thousands of industries are capable of achieving. That single inch is precisely what the new generation of AI companies grounded in first principles are competing to secure.
The evolution of intelligence follows a clear path.
Artificial intelligence is now completing the transition from perceptual intelligence to cognitive intelligence. Machines are no longer limited to “seeing” and “hearing.” They are beginning to “understand” and “reason.”
The next step points directly toward AGI.
We have a simple but demanding definition of AGI:
AGI is not the intelligence of a single genius. It is the aggregate of all human intelligence.
It should be capable of creating original knowledge on the level of the theory of relativity. That is the only standard by which we measure whether the true summit has been reached.
On the road toward that destination, several mountains must be crossed. They are also where today’s technological wave is surging most powerfully.
The First Mountain: Long-Horizon Task Capability
The most exciting breakthrough today is teaching models to complete extremely long tasks—not merely answering questions immediately, but planning and executing over weeks, months, or even years.
For example, a model could search tirelessly through software for vulnerabilities. In essence, it would learn the way a world-class cybersecurity expert thinks, then amplify that expertise through the endurance of a machine.
The Second Mountain: Fully Autonomous Agent Systems
Building on long-horizon capabilities, groups of agents that can operate independently, collaborate with one another, and work around the clock will become a new form of productivity.
We once spoke of the one-person company, or OPC. But technology is advancing faster than expected. We are already moving toward the fully automated, no-person company, or NPC.
Three challenges—memory, continual learning, and self-evaluation—were once thought to require a fundamental paradigm shift before they could be solved. Now, driven jointly by technological advances and real-world applications, they are gradually being overcome.
Long-context capabilities and retrieval-augmented generation, or RAG, are approaching a usable form of memory. More frequent model iteration is bringing us closer to continual learning. Frontier models are already showing the first signs of self-evaluation.
The Third Mountain: Self-Evolution
This is the most difficult—and also the most compelling—mountain of all.
AI training AI is already taking shape. Models are beginning to write their own code, clean and synthesize their own data, and train themselves.
This may consume more computing power, but it saves the most valuable resources of all: human effort and time.
In the age of large models, speed matters most. Rapid iteration can create a generational gap in cognitive capability.
As leading companies overseas begin constructing computing clusters containing one million, or even two million, AI chips, their true purpose may well be to enable models to train themselves.
What will happen after these three mountains have been crossed?
AI will begin to learn what the “self” is and what self-awareness means. Beyond that, it may begin to touch human emotion. Farther still lies consciousness itself.
From perception to cognition, from cognition to general intelligence, and from general intelligence toward artificial superintelligence, or ASI—the road has already been laid.
The great wave has arrived, and it cannot be reversed. This is not merely our own view.
In its report From AGI to ASI, Google DeepMind offers a stark conclusion: even if the abilities of an individual model were to remain permanently at the human level, superintelligence could still emerge through brute-force growth in computing power.
Their projection is that, if the number of operational AGI instances worldwide increases tenfold each year, it will reach 100 million within five years.
These agents would share the same underlying intelligence, think with efficiency improved by a hundredfold, and replicate experience at virtually zero cost. At the collective level, they would effectively amount to ASI.
In other words, moving from AGI to ASI requires both algorithmic breakthroughs and the concentration of computing resources on an unprecedented scale.
This irreversible trend will penetrate the entire technology stack from the top down.
When AGI arrives, today’s applications may all need to be rebuilt as AI-native systems—or may no longer be needed at all.
Operating systems themselves may be rewritten. In the future, when you turn on a computer, what you see may be an “LLM OS,” with every function generated on demand.
Going deeper still, this represents a challenge to the von Neumann architecture that has underpinned computing for the past eighty years.
Finance, law, e-commerce, the internet—no industry will remain unaffected.
Many friends have come to me saying they want to transform their companies and keep pace with AI. Yet only a small number have truly recognized that this irreversible transformation has already begun.
3. The Direction to Which We Will Devote Our Full Efforts: “Touch High”
Once the trend is clear, what remains is a choice.
And Zhipu’s choice is, as always, contrarian.
At a time when the industry as a whole is accelerating the commercial monetization of AI, we have decided to push upward and pursue the next technological breakthrough.
We call this strategy the “Touch High” Initiative.
At this historic moment, as artificial intelligence advances from perception and cognition toward fully general intelligence, Zhipu will reach higher and challenge the physical and algorithmic limits of today’s technology.
Over the next two years, we plan to make a major strategic investment—not in pursuit of short-term application revenue, but aimed directly at the next frontier of AGI.
This investment will focus on four core engines.
First: Long-Horizon Tasks
We will move AI beyond instant question-and-answer interactions and toward the execution of large-scale projects.
This means developing a new generation of memory architectures that allow models to learn, act, and retain knowledge throughout the entire life cycle of a project.
Models must be able to learn while working, act while learning, and remember what they have done. They must also gain the high-level ability to break down an ambitious objective—such as designing a new anticancer drug molecule—into thousands of independently executable subtasks.
Second: Autonomous Agent Systems
We will move from intelligent assistants to digital employees.
Our goal is to build societies of thousands, or even tens of thousands, of agents, each possessing a distinct professional “personality” and set of skills.
These agents will independently debate, collaborate, review code, and allocate resources, creating digital productivity with a level of autonomy comparable to self-driving systems.
Third: Fully Self-Training
As the supply of high-quality human-generated data approaches exhaustion, we will turn computing power into fuel for evolution.
This means building factories for high-quality synthetic data, using AI-versus-AI competition through self-play to generate knowledge from scratch, and giving systems the ability to reconstruct their own code within secure sandboxes.
The goal is to free the pace of evolution from the physical limitations of human engineers.
Fourth: Safety Governance at the Highest Standard
Of the four engines, this is the one I most want to emphasize.
The more powerful AI becomes, the more robust its safety constraints must be.
From the very beginning, Zhipu established a guiding principle:
AI must serve human well-being and national strategic priorities.
The company rejects bolt-on safety patches. Instead, it seeks to encode human ethics, social norms, and national laws and regulations into the model’s value function as foundational axioms.
We plan to commit resources on the scale of tens of billions to advancing mechanistic interpretability—clarifying the neural logic behind model decisions and transforming black-box systems into transparent, explainable ones.
At the same time, we will actively participate in international AI governance to prevent the misuse of AI technology.
This sense of urgency is not unfounded.
When the most advanced frontier models overseas delay full public release because of safety concerns, and their corporate leaders publicly warn that AI’s far-reaching effects will profoundly reshape the global balance of power, we must remain clear-eyed:
The development of superintelligence and research into superalignment must advance in parallel.
This is also a question we repeatedly examine whenever we confront disruptive technologies.
History has shown time and again that when a technology becomes powerful enough to alter the course of civilization, safety is no longer an optional accessory. It becomes the fundamental prerequisite for the technology’s continued existence and permitted use.
4. An Open Ecosystem: The Foundation of Broad Access to Intelligence and Safety Governance
We have always believed that artificial intelligence, as a strategic technology that will shape the future, cannot achieve sustainable long-term development without an open and collaborative industrial ecosystem.
The value of frontier intelligence lies not only in technological breakthroughs themselves, but also in whether it can broadly empower thousands of industries and benefit every developer.
We firmly believe that genuine safety is not built on technological isolation or barriers.
It arises from broad participation, sharing, co-creation, and oversight conducted openly and transparently.
It is this deep commitment to making technology widely accessible that has shaped Zhipu’s strategic response.
Recently, we released GLM-5.2, our most capable open-source model to date.
It supports a genuinely practical context window of one million tokens, continues to lead in long-horizon tasks, and is available to all users.
It will also be officially open-sourced under the highly permissive MIT License. Anyone will be able to download it, deploy it, and use it commercially, with no restrictions based on the type of user or organization.
This is the company’s firm position, expressed through the form of its product.
We choose to believe in a different path:
Frontier intelligence should not belong only to a select few, nor should access to it be withdrawn at any moment by a small group of rule-makers.
It should be open, usable, and buildable—and it should serve every developer.
This does not conflict with “Touch High.” Rather, the two are complementary sides of the same strategy.
With one hand, we reach upward to challenge the limits of intelligence. With the other, we build roads downward, making the most advanced capabilities as open and broadly accessible as possible.
The heights we reach belong to all humanity, and the roads we build belong to everyone.
5. Conclusion: Why Now, and Why Us?
Some may ask:
Why, after going public, is Zhipu continuing to devote its core resources to reaching higher in the most uncertain direction?
Because we believe in a simple truth:
Those who truly reach the summit turn the mountain into a road.
The fundamental insight we arrived at was once crystallized into a shared conviction among hundreds of scientists through the Wudao Large Model project.
Later, through Zhipu’s industrial investment and the wider ecosystem, it became a foundation from which a new generation of entrepreneurs could take off.
Today, we want to build this road higher and wider—
High enough to protect ourselves and safeguard national security;
High enough to give humanity the opportunity to explore more of the unknown and uncover the mysteries of the universe;
And wide enough for every developer and every team to find a path upward.
In the age of AGI, things that once seemed forever beyond our reach may, for the first time, become possible.
This is the greatest fortune of our generation—and also its heaviest responsibility.
The great wave has arrived, and the trend is irreversible.
Zhipu intends to be the one who faces the oncoming wave and keeps reaching higher.
Anything short of the summit is failure.
This time, the height we seek to reach is one that belongs to all humanity.
Tang Jie
Founder of Zhipu AI
July 11, 2026
