A frontier without an ecosystem is not stable
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Notes on advances in technology and real-world impact
关于技术进步和实际影响的说明
Notes on advances in technology and real-world impact
我一直在思考人工智能驱动经济中企业的未来。
I’ve been thinking a lot about the future of the firm in an AI-driven economy.
这一转变与以往任何平台变革都不同。过去，我们用数字系统来增强人力资本。这是第一次我们能够在人与数字系统之间建立真正的认知循环。这令人费解，因为它改变了我们甚至如何概念化企业内部的工作。
This transition is different than any previous platform shift. In the past, we used digital systems to enhance human capital. This is the first time we can create a real cognitive loop between people and digital systems. That is a mind-bender, because it changes how we even conceptualize work inside an enterprise.
关键不在于某种数字工具或系统及其使用，而在于组织如何在一个 AI 模型可以持续吸收人类和组织的专业知识并将其商品化的世界中继续学习、构建知识产权、差异化并蓬勃发展。
What is at stake is not some digital tool or system and its use, but how organizations continue to learn, build IP, differentiate, and thrive in a world where AI models can continuously absorb the expertise of humans and organizations and commoditize it.
每家公司都必须构建我所谓的人力资本和代币资本。人力资本包括其人员的知识、判断力、关系、创造力和模式识别能力，而代币资本则是公司构建和拥有的 AI 能力。
Every company is going to have to build what I think of as human capital and token capital. Human capital comprises the knowledge, judgment, relationships, ingenuity, and pattern recognition of its people, while token capital is the firm’s AI capability it builds and owns.
重要的是，随着代币资本的增长，人力资本并不会变得不那么有价值，反而会变得更有价值！我相信人类能动性将是代币资本增长的驱动力。人类将设定雄心勃勃的目标，跨领域连接点，建立关系，并识别最重要的模式。没有人类的指导，算力只会原地打转。
Importantly, human capital does not become less valuable as token capital grows. It only becomes more valuable! I believe human agency will be the driver of token capital growth. Humans will set ambitious goals, connect dots across domains, build relationships, and recognize patterns that matter most. Without human direction, you have compute running in circles.
这意味着真正的机会不在于选择最好的模型，而在于在模型之上构建一个学习循环，使人力资本和代币资本复合增长。你可以外包一项任务，甚至一份工作，但你永远无法外包你的学习。企业的未来在于能够将这种学习在人和 AI 之间复合增长。
This means the real opportunity is not in picking the best model but instead in building a learning loop on top of models where human capital and token capital compound. You can offload a task, or even a job, but you can never offload your learning. The future of the firm is the ability to compound that learning across people and AI.
这需要一种新的架构方法，使每个企业都能构建随时间改进的智能体系统，同时保持对其知识产权的控制。公司应该能够更换“通才”模型，而不会丢失其学习系统中构建的“公司老手”专业知识。这是未来时代你控制权和主权的关键“测试”。
This requires a new architectural approach where every business is able to build agentic systems that improve over time, while still retaining control over their IP. A company should be able to switch out a “generalist” model without losing the “company veteran” expertise built into their learning system. This is the key “test” of your control and sovereignty in the era ahead.
公司需要将其工作流程、领域知识和积累的判断力转化为每次使用都会改进的 AI 系统。私有评估应捕捉模型是否在真正改进对企业重要的结果（而不仅仅是外部基准！）。私有强化学习环境应让模型在组织内部的真实轨迹上变得更强大。其知识库使机构记忆可查询，并使代币的使用更高效。
Companies need to turn their workflows, domain knowledge, and accumulated judgment into AI systems that improve with each use. Private evals should capture whether a model is actually improving against outcomes that matter to the business (not just external benchmarks!). Private reinforcement learning environments should let models grow stronger on real traces from inside the organization. Its knowledge base makes institutional memory queryable and use of tokens more efficient.
这个循环成为公司新的知识产权。我将其视为一个爬山机器。与大多数资产不同，它会复合增长。每个改进的工作流程都会产生更好的训练信号，从而加速公司独有的隐性知识的积累。早期构建这一点的公司将拥有难以复制的优势，无论任何新模型的能力如何。
This loop becomes the new IP of the firm. I think of it as a hill climbing machine. And unlike most assets, it compounds. Every improved workflow generates better training signal, which accelerates the accumulation of tacit knowledge unique to the firm. The companies that build this early will have an advantage that is hard to replicate, regardless of any new individual model capability.
我们最不希望看到的是，每个行业的每家公司都将价值让渡给少数几个吞噬所见一切的模型。如果所有价值只被少数模型积累，政治经济将无法容忍。社会不会允许一个掏空整个行业的 AI 未来。
The last thing any of us want is a world where every company across every sector is ceding value to a few models that eat everything they see. If all the value is accrued by only a few models, the political economy will simply not tolerate it. There is no societal permission for an AI future that hollows out entire industries.
想想全球化第一阶段发生的情况，整个工业经济因外包而被掏空。GDP 数字表面上看起来不错，但流离失所是真实的，其后果至今仍在感受。让我们不要将这种动态带入 AI 时代，让少数 AI 系统捕获所有经济回报，而整个行业的知识在它们脚下被商品化。
Think about what happened in the first phase of globalization where entire industrial economies were hollowed out by outsourcing. The GDP numbers looked fine on the surface, but the displacement was real and the consequences are still being felt. Let us not bring that dynamic into the AI era, with a small number of AI systems capturing all the economic returns, while entire industries find their knowledge commoditized right out from underneath them.
在我看来，我们的首要任务是构建一个前沿生态系统，而不仅仅是前沿模型，这样价值就能广泛流向每家公司、每个行业和每个国家。一个每个组织都能拥有编码其机构知识的学习循环，复合其人力资本和代币资本的世界。
In my view, our priority has to be building a frontier ecosystem, not just a frontier model, so value flows broadly across every company, every industry, and every country. One where every organization can own the learning loop that encodes its institutional knowledge, compounding its human and token capital.
这是我成长过程中所秉持的理念：平台使在其之上创造的价值多于其内部捕获的价值，每家公司都能持续创新并构建自己的价值。
This is the ethos I’ve grown up with where platforms enable more value on top than is captured inside, and where every company can continuously innovate and build value of its own.
当这种情况发生时，公司将为自己和周围的经济创造价值。员工将看到自己的专业知识得到放大，他们的判断力成为可复制和可扩展的系统的一部分，而收益则归于他们所在的公司和社区。
When that happens, companies will create value for themselves and for the economy around them. Employees will see their expertise amplified and their judgment become part of systems that make it replicable and scalable and the benefits accrue to the companies and communities around them.
这就是公司为自己和更广泛经济创造价值的方式。这也是我们应该共同构建的稳定均衡。
That is how companies drive value for themselves and the broader economy. And it is the stable equilibrium we should build together.
关于技术进步和现实影响的笔记
Notes on advances in technology and real-world impact