郑庆生

郑庆生把二十年中国科技周期归结为“流量方式”的连续重构，AI正在争夺新一代分配入口。 Before 2005, portals completed the first wave of moving information online. From 2005 to 2010, Tencent, Baidu, Shengda and others held the high ground in PC traffic; after 2010, mobile reshuffled the gateways; short video took off from 2015, and by around 2018 the new traffic had largely been divided up. ChatGPT has reopened the To C window. In his analogy, great internet products are “huge towns”: only after traffic converges do new commercial forms emerge.

内容平台的终局并非视频替代另一种媒体，而是短视频直接“挑战文字本身”，AI则进一步把效率产品从流程推向结果。 Text requires years of learning, while images are more immediate. Short video comes close to taking people to the scene for a look, making it a basic mode of cognition; as long as the information density is high enough, not reading books is not inherently shameful. AI is moving in the same direction: “tool products are results, entertainment products are processes.” In any efficiency scenario, users want as little involvement in the process as possible.

AI网络同时带来三项结构性差异：边际成本不再趋近于零、产品直接对结果负责、底层原理仍存在黑箱。 Every additional user creates token costs, turning subscriptions, pricing and user selection into real unit-economics questions. AI solutions may also bypass structured workflows and deliver results directly from raw information. More uncertain still are emergence and the “mysticism” in prompting: technology and products are evolving in sync for the first time, and model advances can create new applications while also changing the advantages of existing products.

软硬一体的核心机会不是再造一块屏幕，而是完成AI驱动的“深层次数字化”，把过去会随风飘散的信息变成可加工资产。 One hundred hours of recordings or tens of thousands of automatically captured photos a month were previously impossible to organize even if they were collected. AI makes dedicated recording, imaging and continuous-sensing devices possible as new data and traffic nodes. The key advantage of dedicated hardware over phones is that it is “always present,” but whether data, habits and usage patterns can become moats—and where the privacy boundary lies when recording in public—remain questions for law and social norms.

大模型如果胜出，可能成为入口，但应用公司的护城河、显性双边网络效应和AI社交形态仍没有答案。 Models may have an implicit flywheel: the more data and interactions they accumulate, the better the feedback they deliver to new users. But most users still talk to a model in a “many-to-one” structure. If a competing model overtakes it in quality, a product without network relationships could be highly vulnerable. OpenAI has accumulated usage habits and data and has strong product-company characteristics. Its ultimate value boundary will depend on how much technological progress offsets nontechnical product advantages.

郑庆生将当前阶段定义为应用“爆发的前端”，认为泡沫正常且目前可以接受，但最终必须以交付检验。 Unlike previous cycles, when companies first burned money to build scale and had little revenue, this cycle can hit pain points, charge customers and generate decent cash flow from the start. Strictly speaking, rapidly changing models can still invalidate existing PMF, while massive infrastructure costs remain mismatched with revenue. He compares 2025 with 2010 in mobile internet and 2026 with 2011, and expects applications to flourish further.

这一轮最重要的地域性投资判断是“华人大航海时代的开始”：AI产品天然适合全球部署，中国与海外华人团队可以从第一天服务世界市场。 Sequoia China did not treat foundation models as a capital-allocation race to cover wholesale, but entered Kimi, MiniMax, 智谱, Manus and other projects one by one. MiniMax was on its radar before ChatGPT took off, while Manus was tracked from the team’s capabilities and products during the Monica period. Since establishing its seed fund in 2018, Sequoia has moved further upstream; projects where it is the first institutional investor now account for more than half of its investments.

真正最大的未知不是AI产品还会增加什么功能，而是使用AI之后的人类会变成什么样。 If users eventually stop caring whether their conversational counterpart has independent consciousness or a “soul,” the default assumptions behind socializing, intimacy, education and consumption will all change. Education may place more weight on a global view, taxonomy, logic and precise expression, because learning also includes learning how to ask questions. For investors, the corresponding epistemic guardrail is to reconstruct the full set of options available at the time of a decision, rather than accept the faith stories written by survivors after the fact.

🔗 Original source & video: Technology Cycles, Traffic, and Artificial Intelligence — 张小珺 x 郑庆生

2025’s inflection point was not the release of a single model, but AI moving from “picking up the next line” to “going straight for the outcome”: DeepSeek lifted market sentiment, Devin made tasks executable asynchronously, Claude 3.5 opened up coding and Agent workflows, and Nano Banana Pro gave ordinary consumers an immediately tangible “iPhone 4 moment.” 刘英俊 was shaken before DeepSeek: Devin was “the first product that let you leave the screen while it completed a task.” Models and products are still evolving rapidly on parallel tracks; betting on a static capability lead is not enough.

The potential business-model shift this cycle is not merely the emergence of another attention platform, but a move from “real-estate businesses” to “labor businesses.” The internet standardized and distributed goods, services, and content; 郑庆生 imagines a future in which white-collar labor is extremely abundant and platform value shifts toward connecting people’s full intent with labor. 刘英俊 believes the supply-side revolution has not truly arrived yet, but “it’s getting close.”

Total Record and the hardwaring of AI could create a long-term data flywheel: meetings, calls, operations, and life processes that were never previously recorded are becoming raw material for analysis. 刘英俊 calls the internet’s past digitization “shallow digitization” and this wave “deep digitization”; his principle is “record it first, figure out why later.” One of 郑庆生’s clearest expectations for 2026 is that “both data and intelligence become hardware,” solving collection and execution at the same time.

AIGC has not directly wiped out UGC as originally expected; the more realistic path is “humans driving AI,” with human territory continuing to shrink while people still own intent, professional judgment, and the finishing touch. Recommendation data tells models what outcomes are popular, but may not contain the scripts, storyboards, lighting, or performances that produced them; 郑庆生 compares AI to “a doctoral student who excels academically” or “the greatest common denominator.” 刘英俊 leaves open another possibility: know-how may eventually sink into the infrastructure layer, and humans may no longer need to understand the process at all.

The adoption inflection point for AI content is not whether it can convincingly pass as human-made, but whether users still care: Sora 2 videos carry watermarks and are obviously AI-generated, yet comment sections discuss only the plot and the memes. This resembles humanity’s transition from not understanding film editing to accepting flashbacks, close-ups, and complex montage; the next content paradigm may merge drama, anime, and games into one class of real-time, interactive, generated experiences—“as long as you enjoy it.”

The biggest danger in early-stage investing is not having no methodology, but mistaking the necessary conditions written by survivors for sufficient conditions that can predict the future. During periods of upheaval, be extremely optimistic about broad trends while remaining open-minded about specific products; 刘英俊 says “our descriptions are often toxic,” and that there is no such thing as “the Douyin, WeChat, or Xiaohongshu of the AI era.” Model capability is a rising sea level: startups must build “boats” that rise with it, not “pillars” that will eventually be submerged.

Incumbent follow-on is not necessarily bearish; it may validate that the market is large enough, but each cycle still offers only a handful of tickets into the next generation of giants—few enough to count on one hand. Midjourney still has roughly $700M in ARR, while 科瑟 and ChatGPT were discussed as non-consensus phenomena; this suggests that brand, habit, recognition, and founder traits may run deeper than short-term technical differences. The hardest edge to replicate may ultimately be “an individual’s passion and resonance with the times.”

The guests do not deny that many people will drown in the short term, but believe “is it a bubble?” is not the most useful question: canals, railways, telecom, e-commerce, and the Hundred团大战 all came with bubbles, and liquidity itself is a benefit of innovation. 郑庆生 believes that if demand for compute approaches infinity while the unit cost of compute keeps falling, the industry’s five- to ten-year scale could still be materially underestimated by linear extrapolation. 刘英俊 predicts that by late 2026 or early 2027, early-stage investors may stop discussing AI specifically; he also predicts Doubao could reach 500M DAU, rank third overseas, and that WeChat will develop substantive AI capabilities.

🔗 Original source & video: Breaking Down 2025: AI’s Bubble, Inflection Points, and Survivors — 郑庆生 x 刘英俊 x 李彦男 x 李广平

郑庆生 uses “traffic” to rewrite economic history: roads, canals, railways, telegraphs, radio, television, PCs, mobile phones and AI all change how people, information and attention move. Towns were once the biggest social products—“a city had as many DAUs as people”; today’s best To C platforms are “markets capable of rivaling nation-states.” The core investment question is what the next technology will turn into the next super-node.

AI is not a simple sequel to the mobile internet because it changes cost structures, delivery models and the explainability of technology at the same time. Every additional user generates Token costs, so the network’s marginal cost no longer approaches zero; efficiency products are shifting from walking users through a process to taking them “straight to the result.” More unusually, emergence remains a black box, while Prompting carries an element of “art and metaphysics”—meaning technology, products and business models must be considered together.

Beyond foundation models, the next generation of traffic gateways may come from integrated hardware-software devices that continuously capture the physical world. In the past, even if a hundred hours of audio or tens of thousands of automatically captured photos were digitized, they could not be organized; AI is making it possible for the “information that used to scatter in the wind” to be processed for the first time. If the amount of personal information owned by each person expands by hundreds, thousands or even tens of thousands of times, hardware may build moats through data, usage habits and new ways to play—a shift 郑庆生 calls “deeper digitization.”

The credibility of precise predictions about AI product forms is very low because humans will co-create behaviors with products that did not previously exist. Short video began as a minor video category but ultimately “challenged text itself”; Twitter’s 140-character limit and Musical.ly’s early usage patterns also exceeded what early users could have extrapolated. Investors can preserve product intuition and, once new forms emerge, identify whether they have the potential to become “classics.”

Value allocation between models and applications has not converged, but AI’s commercialization starting point is better than that of the previous 2 internet cycles. Subscription models have been normalized by SaaS, most products generate revenue relatively early and can benchmark ARPU, and early-stage cash flows are healthier than the “burn for scale first, monetize later” model; the cost is that rapid model upgrades may flatten application advantages. Conversely, user habits, accumulated data and non-technical product capabilities may still draw boundaries that technology cannot penetrate.

The biggest structural change with Agents is that they are “global by birth,” giving Chinese and overseas Chinese founders their first chance to serve the entire world directly. 郑庆生 likens 2025 to 2010 in the mobile internet and 2026 to 2011: after the underlying models, applications, Agents and intelligent hardware are entering a more prosperous competitive phase. His core bet today is “the beginning of the great overseas expansion of Chinese founders,” but whether incumbent platforms will be replaced, enhanced or continue expanding still requires an ecosystem-by-ecosystem judgment.

郑庆生 does not deny that AI has a bubble, but believes the bubble itself provides liquidity for early innovation; the only question is whether products can ultimately be delivered. AI can already hit pain points and charge for them, making it different from speculation built on pseudo-concepts; the current moment looks more like the front end of an application boom than a countdown to a burst. “Bubbles exist in the ocean”—rising and falling tides are acceptable as long as products backed by real money and real revenue remain.

For founders moving from product manager to large-company CEO, the job is to move beyond understanding users and become the organization’s “personified symbol.” From 1 to 10, product sensitivity matters more; beyond 10, leaders must maintain institutions, morale and a shared imagination like generals—“as long as you play that CEO, regardless of what your own nature is.” MBTI can help start a conversation but cannot be an investment criterion; the more reliable discipline is to resist survivorship bias and reconstruct all available options and sufficient conditions at the time decisions were made.

🔗 Original source & video: 126. A Conversation with Sequoia’s 郑庆生: The Traffic Revolution in Economic History, the Unpredictability of Human Behavior, and Founders’ Personalities