张津剑

张津剑 identifies the rightest decision of the past 3 years as announcing “All in AI” to LPs in November 2022, but says the real regret in hindsight is that “we could have gone even harder, and been even more open.” Oasis saw the paradigm shift through Stability AI and then studied Transformer; typical startup valuations at the time were just RMB100M–RMB200M, with RMB1B already considered expensive, while MiniMax completed its investment in early 2023. The costliest counterexample was Figure AI: Oasis passed on a round at roughly $800M because it believed “robotics projects must belong to China”; the company later reached a $40B valuation.

If he could do it again, 张津剑 would not simply use money to buy commodities and equity, but would convert money into compute and then “use compute to invest in founders.” His framework is that money represented industrial-era control over commodities, while compute represents AI-era control over intelligence; as intelligence scales, compute could become scarce, quota-based and regulated like electricity. Institutions should therefore consider building their own compute clusters, while technology philanthropy could shift from giving money to providing free compute.

Large models and embodied intelligence are not mutually exclusive tracks, but start from the “south slope” and “north slope,” move through multimodality and ultimately converge at AGI. MiniMax’s early All in MOE and multimodal push, followed by the M series, M2 and M2.1 and feedback from developers, led 张津剑 to revise his belief that a single modality could follow Transformer to AGI; on the embodied side, the key question is not whether machines can run and jump, but that “Robotics is not Embodied AI; Robotics is AI.” The real value lies in the AI trained through machines interacting with the environment.

张津剑 is explicitly optimistic about the 2026 fundraising market, not on sentiment but because frontier technology ultimately comes down to talent, energy and supply chains—and only China and the US have the full stack. He believes global capital has been “overallocated” to the US, leaving China as the only major destination for redeployed funds; innovations from DeepSeek, MiniMax, 宇树 and 《黑神话：悟空》 are also making Southeast Asian and Middle Eastern investors more willing to buy Chinese technology. On exits, Hong Kong is becoming a new channel for Chinese tech companies, while the RMB-heavy capital structures of 智谱, MiniMax and others are forming a domestic growth system less dependent on foreign capital.

The faster AI changes, the more investment judgment should shift from “things” to “people,” especially by re-rating execution. Models already express themselves, think and even form a global view better than humans, but they cannot access the embodied feedback of execution; a founder’s remaining core tasks are to define the problem, hold onto first principles and act quickly. More than 40 TS in a week is high-frequency noise; the low-frequency signal remains whether the problem matters and whether PMF has been found: “In the AI era, becoming less valuable is easy; doing becomes exceptionally valuable.”

张津剑 sees the central question of the next decade not as a particular application, but as “finding and constructing agency” for both humans and Agents. Agents need identity, accounts, payments, authorization, anti-fraud mechanisms, and eventually legal and insurance systems; humans, meanwhile, must escape the external agency conferred by teachers, bosses and market approval. As personal Agents continuously verify language, behavior and microexpressions over time, “being real” and “living as yourself” will shift from matters of values to survival strategies: “Soon, you won’t even have the chance to pretend.”

The next 3-5 years may be a “Great Science era,” led by a cohort of 90s- and 95s-born scientists who know how to use AI. 张津剑 also sees deep AI-Science integration, Agent infrastructure built on technologies such as x402 and ZK, and a global opportunity for China’s “north slope” of embodied intelligence. His advice to founders is a Day One “Global Vision, Global Ambition”: participate in global innovation on the basis of one’s own understanding, while living as oneself.

🔗 Original source & video: All in AI’s First Three Years | A Conversation with 张津剑, Partner at Oasis Capital

Zhang Jinjian’s core view is that the world is moving faster, becoming harder, and splitting apart more rapidly—not because information is scarce, but because human attention is out of control. The human eye can take in roughly (10^9) bits per second, while the brain can process only about (10^6) bits and actual action output is just 10 bits; when people give up independent perception and follow others’ actions, the world starts to howl like a microphone placed near a speaker: crowded assets get even more crowded, while the unvisited zones grow colder.

Lack of attention is not, by itself, an investment thesis; a genuine left-field opportunity requires both taste on your side and a market that is not paying attention. Responding to 曲凯’s question that “no one is watching” may simply mean “there is no market,” Zhang cited embodied intelligence: from June to September 2023, while peers warned that companies they had backed in 2015 still had not broken even, Oasis invested in nearly 10 companies in the space. His principle: “If no one in the industry is paying attention to this, that is precisely where we should direct ours.”

The long-term form of an Agent is not an intern inside a computer, but a closed loop combining sensors, full-spectrum models, and embodied intelligence. The frequencies humans can perceive account for only roughly one-billionth of the observable spectrum, while data from X-rays, gamma rays, brain alpha waves, and more remain largely unused; “we’ve run out of data” is true only within humanity’s existing text, image, and audio modalities. The real opportunity may lie in touch, temperature, smell, and many more sensors.

In the AI era, organizational relationships will invert from “AI fits into my workflow” to “I fit into AI’s workflow.” Zhang imagines a future work group with a CEO, CFO, chief product officer, and a dozen other Agents—but only one human. What people retain as their core value is not compute, but articulating real needs, supplying taste, and writing high-quality prompts: “AI bro, you take this Research project. See what I can do.”

Human limitations are not purely a disadvantage; they are precisely what generate strategy, taste, and a commitment to facts. The brain predicts the next input to save computation, but when reality diverges from the prediction, many people refuse to recalculate and dress wishful thinking up as idealism. Zhang’s counter-definition is that “sticking to the facts is actually hard work,” because it requires recomputing from the present.

The foundational pieces most worth revaluing over the next five years, in Zhang’s view, are AR/VR, blockchain, and Agent-native transaction and trust networks. Once the number of Agents equals or exceeds the number of humans, buying wine might involve a butler Agent retrieving memories, a finance Agent allocating 30% of next month’s RMB10,000 budget, and the Agents on both sides negotiating automatically. He believes this identity, information, and trust layer will not continue to be built on the traditional internet.

Intelligence will spread like electricity; the most meaningful startup opportunities may not belong to “large-model power plants,” but to the next generation of products that embed intelligence in specific contexts. Figure’s valuation rose from roughly $800M in October 2023 to about $30B in May 2025, showing how violently consensus can accelerate once it forms in an era of divergence. The cost of individual experimentation is rising accordingly: “When no one is paying attention to you, you have to pay attention to yourself.”

🔗 Original source & video: Where Are Our Opportunities as the World Accelerates and Splits Apart? | A Conversation with Zhang Jinjian, Partner at Oasis Capital