“The Wave Has Arrived”: Zhipu Co-Founder Tang Jie’s Letter to Staff

After a lockup expiry sent the stock down more than 19%, Zhipu’s Tang Jie published a full-staff letter announcing a full return to foundation-model research and a two-year “Touch High” plan. The message is very clear: strategic investment, no chasing of short-term application revenue, resources concentrated on the underlying capabilities needed for AGI.

The framing is deliberately philosophical. Tang defines AGI not as one genius’s intelligence but as the sum of all humanity’s wisdom, and insists on continuing what the company calls its “counter-intuitive” path — the same instinct that put GLM-130B into the open before ChatGPT existed. The GLM-5.2 release is the product expression of that stance: a top-3 model on the Artificial Analysis leaderboard, shipped with a million-token context and MIT weights anyone can download, deploy, and commercialize.

Tang Jie identifies four technical peaks that must be crossed on the road to AGI.

On AI safety, Tang Jie believes capability and containment must scale together. He rejects the industry norm of treating safety as a post-hoc compliance layer, insisting instead that human ethics, social norms, and national law be embedded as foundational axioms in the model’s value function from the outset. Zhipu has committed resources in the tens of billions to “mechanical interpretability” — research aimed at converting opaque model decisions into transparent, auditable logic — and Tang cites the fact that leading overseas frontier labs have withheld full public release of their most capable models due to risk concerns as a signal, not a curiosity. His conclusion is that when a technology reaches the level of force capable of altering the course of civilization, safety is no longer an ancillary feature; it becomes the prerequisite for the technology’s permitted existence.

On Open Source, Tang frames it not as a commercial strategy but as a structural commitment to inclusive intelligence. His position is that genuine AI safety cannot be built on technological closure and barriers — it requires broad co-construction, co-sharing, and oversight conducted in the open. That conviction produced the GLM-5.2 release under the most permissive MIT license, with no restrictions based on entity type, supporting a one-million-token context and available to any developer for download, deployment, and commercialisation. For Tang, “Touch High” — pushing the frontier upward — and open access are not in tension: one hand reaches for the summit, the other paves the road down so that the heights reached belong to all of humanity, not to a handful of gatekeepers.

The Wave Has Arrived — To every Zhipu employee and every partner who cares about the future of artificial intelligence

Allow me to use this article to address three things: who we are, how we see this era, and the strategic directions in which we have decided to invest all our strength.

(I) Who We Are: “Essence, Counter-Intuition, Focus”

Zhipu has never been a company that chases trends. It grew from a laboratory, carrying twenty years of methodology from that laboratory. This methodology can be summarized in three words: essence, counter-intuition, focus — think deeply enough, and you dare to bet against the grain; choose contrarily enough, and you must hold your ground long enough.

Looking back, almost every key decision we made once appeared “counter-intuitive.” In 2006, we sat in obscurity with an academic search system running on a single desktop computer, because we had reasoned through that behind it lay the question of “excavating the mechanisms of disciplinary evolution” — a matter worth answering over ten years. Between 2021 and 2022, when “making machines think like humans” was regarded by most as a moonshot-level fantasy, we reallocated resources, bet on hundred-billion-parameter scale, and produced GLM-130B — a full year and a half before ChatGPT set the world alight. And on the day Zhipu listed on the Hong Kong Stock Exchange on 8 January 2026, we treated it as a brand-new starting point, resolutely returning wholesale to foundational model research and driving full force toward the next generation of models.

Others rang the bell; we reset to zero. This is not a posture — it is a conviction. If the destination is AGI, then short-term interests or industry trends are merely scenery along the road to the endgame.

(II) How We See This Era: The Upper Bound of Intelligence Is Being Rewritten

If there is one thing the past twenty years have taught us, it is this: genuine commercial opportunity never resides in minor tweaks to products or business models; it resides in the leap of intelligence’s upper bound. This is our most fundamental judgment about the current AI transformation, and the insight we most wish to convey.

The evolution of the intelligence upper bound follows a clear trajectory. Artificial intelligence is completing the transition from perceptual intelligence to cognitive intelligence — machines no longer merely “see” and “hear”; they are beginning to “understand” and “reason.” The next step points directly toward AGI.

We hold a plain yet exacting definition of AGI: AGI is not the wisdom of any single genius, but the sum of all human wisdom. It should be capable of creating original knowledge on the order of the Theory of Relativity — that is the only standard by which we measure whether the summit has truly been reached. On the road to that destination stand several peaks that must be crossed; they are also where today’s technological waves are most turbulent: Peak One: Long Horizon Task; Peak Two: Autonomous Agent System; Peak Three: Self-Evolving.

(III) The Direction in Which We Invest All Our Strength: “Touch High”

Once the trend is clear, what remains is choice. And Zhipu’s choice is, as always, “counter-intuitive” — at a moment when the industry is broadly accelerating commercial monetization, we have decided to break upward. We have named this strategy the “Touch High Plan.” Over the next two years, we plan to invest strategically — not pursuing short-term application monetization, but aiming directly at AGI’s next high ground. This investment will be concentrated on four core engines: Long Horizon Task, Autonomous Agent System, Fully Self Training, and Extreme Safety Governance.

(IV) Open Ecosystem: The Underlying Logic of Inclusive Intelligence and Safety Governance

We have always believed that artificial intelligence, as a strategic technology leading the future, cannot develop over the long term without an open and collaborative industrial ecosystem. We are convinced that genuine safety is not built on technological closure and barriers, but on broad co-construction, co-sharing, and oversight conducted in the open.

(V) Conclusion: Why Now, Why Us

Some will ask: why, after listing, does Zhipu continue to pour its core resources into the most uncertain direction of “touching high”? Because we believe a simple truth: those who truly reach the summit turn the mountain into a road. The wave has come; the trend is irreversible. Zhipu will be the one who meets the crest of the wave and reaches upward.

Among China’s large-model founders, Zhipu’s leaders have been unusually explicit — and unusually consistent — in tying the company to AGI since 2019. The vocabulary, the roadmap, and the refusal to pivot all predate the July 2026 letter by years. Read together, the public statements explain why “Touch High” was less a reversal than a return.

“We don’t build China’s ChatGPT”

Zhang Peng set the position early and has not moved off it. In a 2023 interview he explained why Zhipu refused to sell vertical, industry-specific models: “an industry model is essentially rebuilding the wheel of traditional algorithms inside the shell of a large model,” and “only a general model of a certain scale can produce human-like cognitive emergence”.

The five-level map to AGI

Zhipu is the Chinese firm that has most openly published an AGI roadmap. Tang Jie’s five-level scheme, which Zhang Peng also uses publicly, defines the climb. Zhipu places today’s frontier around L1 to L3, which is why agents, reasoning, and self-improving training keep recurring in its releases.

Reframing AGI as a business

The one place Zhang Peng translates the creed into unit economics is the capital-markets story. After Zhipu’s first post-IPO results in March 2026, he offered what he called the “first principle” of AGI commercialization: AGI commercial value equals the intelligence ceiling multiplied by token consumption.

Tang Jie’s radicalism

Tang Jie supplies the harder edge. He halted the team’s internationally recognized graph-neural-network and knowledge-graph research and moved the whole group to large language models before the bet was safe. After DeepSeek’s emergence in early 2025, he judged the “chat paradigm” to be near its ceiling and reframed the goal as “making AI actually do things.”

Strip away the stock drama and Zhipu is running a live experiment on three questions that matter well beyond one company.

• Can open weights be a durable strategy at the frontier? Zhipu is betting that giving away SOTA models builds a moat through developers and ecosystem rather than eroding one.
• Does an Entity List placement bind a software company? So far the designation has reshaped Zhipu’s supply chain toward domestic chips without visibly slowing its model releases.
• Can a research-first culture survive public markets? The Touch High plan is a direct refusal of the quarterly logic that public listing usually imposes. Whether shareholders tolerate a two-year monetization pause after a 2,200% run is the open question the next eight quarters will answer.

The company’s own history is the reason to take the gamble seriously. The last time Zhipu made a large, unpopular, long-horizon bet — trillion-scale open pre-training in 2021–2022 — it was early rather than wrong. Touch High asks the market to extend the same benefit of the doubt one more time.