Post-00s Entrepreneurs Take Center Stage: 'Go Big or Go Home' | 36Kr Offline Gathering Season 2
What has always hindered the discovery of truth is not bare-faced lies, but those "profound yet incorrect insights." This quip by the German Lichtenberg over two centuries ago is being played out vividly in today's AI field. Over the past two years, the industry has been enveloped by a set of neat, "profound insights": larger parameters, more GPUs, and massive token consumption have become the yardstick for measuring everything and the totem of belief. A Silicon Valley-style "Token arms race" ha
Analysis
This is precisely the most paradoxical landscape of the current AI ecosystem. We are obsessed with measuring "input" yet reluctant to examine the substance of the "output." Consequently, the industry is filled with two opposing voices: on one side, the clamor of grand narratives, constantly predicting comprehensive replacement of humanity; on the other, the silence of pragmatists, who in their corners strive to ground AI in reality, solving those specific and gritty real-world problems. A recent "offline gathering" hosted by 36Kr acted like a touchstone. It did not indulge in the former, but instead invited several founders born after 2000 to the stage, pressing them on more fundamental, even somewhat "clumsy" questions: What real productivity has AI actually created? How does it change a company's organization? How should we, amidst the bubbles and hype, anchor our own course?
The three young founders on stage constituted, in unison, a corrective to the "Token frenzy." They all chose "non-consensus" paths, venturing beyond the hottest wave of large models to tackle AI's toughest challenges—the physical world and scientific laws. Zheng Jiaxi, who works on underwater robots, has no interest in generic applications; he focuses on the meticulous inspection needs of offshore energy platforms amidst typhoon-force waves. Jin Ruofan, developing multi-agent systems for biopharmaceuticals, was already thinking back in 2022 when ChatGPT dazzled the world—not about how smooth the dialogue was, but why it couldn't invoke tools and enter laboratories to solve those complex, long-cycle scientific discovery loops. And the youngest humanoid robotics founder, Huang Yi, uses a controversial "full-stack open-source" approach, attempting to break the curse of slow hardware iteration. He wants the pace of robotic evolution to keep up with this heart-pounding era.
Their choices are essentially a collective rebellion against the industry's "profound incorrect insights." While mainstream consensus chases "smarter brains," they are working on "more dexterous bodies" and "more specialized domain knowledge." While others compare the parameter scale of models, they compete on the speed of iteration in interacting with the real world. The example from Jin Ruofan's team on virus research is stunning: a vague input leads to a genuine scientific hypothesis. This is not the merit of piling up model parameters, but of the Agent architecture behind it understanding and restructuring the research workflow. This signals a key turning point: AI's productivity is moving from the single dimension of "generating text" to the composite dimension of "driving processes." It is no longer just a mouth that can chat, but is becoming a pair of hands that can operate lab equipment, a pen that can draw design blueprints, and eyes and limbs that can plunge into the deep sea.
This also raises a deeper anxiety: Why hasn't AI, which theoretically promises huge efficiency revolutions, triggered a leap in organizational efficiency within many enterprises? The problem may not lie in the technology itself, but in our use of an old map to find a new continent. The bureaucracy of the industrial age and the flat structures of the internet age have not directly adapted to AI-native demands. AI, as "productivity itself," requires a thorough restructuring of an organization's decision-making processes, knowledge management, and collaboration models. What it needs is not just a few tools labeled with AI, but a data-driven, intelligent organizational form capable of real-time perception, rapid decision-making, and agile response. This is far more difficult and profound than simply introducing a large model API.
Youth is not a privilege in this transformation. On the contrary, it means a more rigorous scrutiny. Capital and the market are no longer paying for grand narratives in a PPT; they need to see solid progress, verifiable closed loops, and precise targeting of industry pain points. Huang Yi securing over 120 orders isn't because the "humanoid robot" story is sexy, but because his open-source strategy genuinely lowers the barrier to ecosystem participation, allowing universities and developers to enter the field at an affordable cost. Zheng Jiaxi and Jin Ruofan's projects are also rooted in critical areas like energy safety and biomedicine. No matter how advanced the technology, it cannot bear fruit if it is detached from the soil of industrial reality.
Perhaps this is the picture the second half of AI entrepreneurship should look like: bid farewell to the blind worship of "token consumption" and return to the devout pursuit of "problem-solving effectiveness." Young entrepreneurs are forced to prove in a shorter time that they not only understand AI, but also understand industry, understand organizations, and understand those non-linear pain points in a complex world. They need to be simultaneously technical experts, product managers, and industry insight leaders. This challenge is immensely difficult, but that is precisely where the opportunity lies. When everyone crowds into the same lane to compare whose horse-drawn carriage is faster, those quietly starting to develop internal combustion engines, though initially clumsy and misunderstood, may be truly touching the pulse of the era.
The future of AI will not belong to those who are best at explaining it, but to those who are best at applying it to solve concrete problems. The process of discovering truth ultimately cannot be completed in studies and conference halls; it requires repeated trial and error and iteration in muddy factories, turbulent waves, and complex laboratories. Those "profound incorrect insights" will still proliferate because they are easy and effortless to spread, but true progress always belongs to the "literal-minded" who dare to question consensus and blaze trails in the real world through action. This group of young entrepreneurs is becoming such "literal-minded" people. They may be unpolished, but they are closer than ever to the essence of AI as a productivity—not a triumph of rhetoric, but a triumph of action.
Disclaimer: The above content is generated by AI and is for reference only.