The market valuation of Knowledge Atlas has surpassed one trillion Hong Kong dollars.

On the same day, Tencent's valuation was approximately 3.94 trillion, Alibaba's was about 1.97 trillion, Xiaomi's was around 612.9 billion, Meituan's was about 444.5 billion, JD.com's was approximately 302.9 billion, Trip.com's was 229.3 billion, and Kuaishou's was around 196.9 billion.

A company founded less than seven years ago, not yet profitable, with revenues of just over 700 million yuan in the past year, has achieved a market capitalization exceeding a large group of China's most profitable internet enterprises.

The capital market is expressing a clear judgment with real money: the gold mines marked on the old map are depreciating; the outline of a new continent is emerging, and pricing power is already beginning to change hands.

Under the Shadow of a Trillion-Dollar Valuation

Most market interpretations of Knowledge Atlas remain at the technical level. The GLM series has achieved good evaluation scores, its open-source strategy has attracted developers, enterprise client numbers are growing, and official responses on social platform X over the weekend regarding the technology race added fuel to this rally.

The surface waves are lively. But the direction of the tide is never determined by the waves.

Tencent's net profit last year exceeded 200 billion yuan, yet its price-to-earnings ratio is less than 15 times. Alibaba's market capitalization is even lower than the combined value of its stake in Ant Group and its core e-commerce business. The capital market is using a cold, hard method to repricing: the existing assets of internet giants are depreciating.

Simultaneously, a native large language model company with annual revenue of seven billion yuan and still operating at a loss has received a trillion-dollar valuation.

Capital is clearly not betting on intrinsic value; it is betting on a disruptive replacement of business models. The replacement capabilities of large language models—replacing search, replacing matchmaking, replacing content creation, replacing code writing—are expected to systematically erode the core profit pools of internet giants over the next decade.

Of course, many are also saying that Knowledge Atlas's trillion-dollar valuation is merely a "greater fool theory" play, an impulsive pricing driven by the technology hype. This skepticism is not without reason; a company still incurring losses, no matter how one dissects its cash flows, struggles to justify a trillion-dollar valuation.

However, the "greater fool" label may precisely obscure a paradigm shift in valuation logic. When the moats of internet giants develop structural cracks due to large language models, the transfer of market share gains a high-probability pathway. The market is not pricing Knowledge Atlas's present, but rather the expected timeline for large language models to devour old profit pools.

The giants see this trend but find themselves unable to move effectively.

Tencent's WeChat ecosystem relies on advertising revenue. When users no longer browse their Moments feed or read public accounts but instead directly ask an AI assistant, the foundation of WeChat's commercialization begins to loosen.

Alibaba's e-commerce empire is built on the "search-compare-purchase" chain. When an AI assistant directly completes product selection and price comparison for users, the traffic gateway of Taobao and Tmall becomes bypassed.

Even ByteDance, the most aggressive in AI investment, faces a similar logical challenge. Douyin retains users through its recommendation algorithm. When AI can directly generate the content users want to see, the moat of the recommendation algorithm is circumvented.

The three broadest moats of the internet era are being quietly outflanked by large language models: you know the enemy is coming from a certain direction, but your walls are built facing the other way.

The Core of the Dilemma

At this moment, internet giants face a deeper predicament: if large language models render existing business models obsolete, then who are they, as potential (or non-) winners in the large language model era?

Tencent's core asset has never been WeChat ads; it is "connection"—WeChat connects people, public accounts connect people and content, and mini-programs connect people and services. Advertising, payments, and game distribution are byproducts of connection.

The interaction logic of the large language model era is shifting from "connection" to "agency." Users no longer open mini-programs one by one or browse public accounts one by one; instead, they directly tell their AI assistant to "book me a flight ticket" or "write my weekly report." The AI assistant directly calls services, generates content, and completes tasks, compressing connection into the background.

An intermediary layer now exists between the user and Tencent. The more powerful this layer becomes, the further Tencent is from the user. The more users become accustomed to obtaining everything through an AI assistant, the lower the strategic value of WeChat as a "connector" becomes. Tencent faces a loosening of its very corporate raison d'être. When your core mission is connection, and the era tells you connection is no longer scarce, who are you?

Alibaba is watching two curves race. One is Alibaba Cloud: large language model training and inference require massive computing power. The stronger Tongyi Qianwen becomes, the greater the API call volume, leading to more certain revenue growth for cloud infrastructure. The other curve is the advertising revenue from Taobao and Tmall: as AI assistants directly handle product selection and price comparison for users, information asymmetry is compressed, and merchants' willingness to purchase traffic may subsequently weaken.

This won't happen tomorrow, but the direction is certain. Alibaba stands on both curves simultaneously, and their trajectories are not correlated. The real test of this transformation is how fast Alibaba Cloud can run and whether it can establish a new growth pillar before e-commerce advertising revenue hits its ceiling.

ByteDance's dilemma is actually more hidden. It appears all-in on AI but inevitably struggles. Douyin's recommendation algorithm is built on the precise capture of user behavior—what they watch, how long they stay, who they like—every action feeds the algorithm. The recommendation logic of large language models is shifting from "behavioral data" to "semantic understanding." It doesn't need you to watch a thousand videos to know what you like; you just need to say, "I'm feeling down today." The barrier of behavioral data may be worthless in the face of semantic understanding.

Furthermore, while the Doubao series has performed impressively in evaluations, ByteDance cannot abandon its most lucrative recommendation advertising business. Within ByteDance, large language models are still largely confined to an "assistive recommendation" role, unable to touch the core business logic. The reason is obvious: this is not a matter of courage but is determined by the company's DNA. A company built on the belief of "behavioral matching" finds it difficult to switch its soul to "semantic understanding."

Three giants, three forms of confusion, sharing the same underlying logic: the core asset of the large language model era is "intelligence," while the core asset of the internet era is "data." Data can be accumulated; intelligence is general-purpose. No matter how much data is accumulated, a stronger foundational model can surpass it in an instant.

This paradigm conflict has repeated throughout business history. Kodak invented the digital camera but locked it away because digital would kill film. Nokia developed a touchscreen early on but couldn't let go of the physical keyboard.

With every technological paradigm shift, the giants of the old era painfully oscillate between "protecting existing profits" and "embracing the new paradigm."

The cruelty of the large language model era is that it doesn't grant a decade for a slow transition. It took less than three years from GPT-3 to trillion-parameter models, while Kodak struggled for at least twenty years from the invention of the digital camera to bankruptcy. The window of opportunity is visibly narrowing, yet the steering wheel remains locked in the profit logic of the old era.

The Unburdened Player

Knowledge Atlas can secure a trillion-dollar valuation precisely because it lacks these burdens. It doesn't need to protect advertising revenue, doesn't need to consider the interests of an e-commerce platform, and doesn't need to maintain the moat of a recommendation algorithm. It can, without hesitation, commit all its resources to improving model capabilities and building a developer ecosystem.

The winners of the internet era—Tencent, Alibaba, ByteDance—first aggregated massive numbers of C-end users, then monetized through advertising, e-commerce, and gaming. Technological capability served the business model.

Native large language model players like Knowledge Atlas take a different path: first establish a generational advantage in model capability, attract developers, build an ecosystem, and then seek commercialization paths. OpenAI and Anthropic have followed this path, achieving sky-high valuations based on technological leadership before clear business models emerged, then gradually establishing commercialization loops through enterprise services and developer payments.

Whether this path can succeed in China remains an open question.

A company with annual revenue of just over seven billion yuan, still in a phase of significant losses, needs to prove at least three things in the coming years to support a trillion-Hong Kong-dollar valuation: that its model capabilities can continuously approach the global first tier; that its API and enterprise service revenues can scale rapidly; and that high R&D investment will ultimately translate into higher gross margins and stronger customer retention. Otherwise, today's valuation will be difficult to sustain on narrative alone.

Yet the market has already priced in a trillion Hong Kong dollars. This price indicates at least one thing: investors judge that the business models of the large language model era will not naturally grow from the profit pools of the old era. It requires a company without historical baggage to cultivate them anew in fresh soil.

Concluding Thoughts

The identity crisis of Chinese internet companies is, at its root, a failure of the coordinate system. The capital market sensed this first.

The fissure between the two sets of coordinates is the collective strategic confusion of China's internet companies. Everything measured by the old coordinates is depreciating. The new coordinates are held by a few, while most still stand on the coastline of the old continent, watching the masts on the horizon, unsure whether to set sail.

The trillion-dollar valuation of Knowledge Atlas is not the victory of one company but the silence of countless others. One old map is being folded away; a new one is just unfurling. The giants of the old era are pinned to the shore, watching others' masts disappear over the horizon. No one can tell them whether, after setting sail, they will find a new continent or deeper waters.