If you thought the value of the AI ecosystem was going to only accrue to a few companies, the past few months have represented a turning point in what the future of AI might look like. It's clear that there's going to be incredible innovation and growth coming out of the frontier AI labs, and they will continue to push the limits of what model progress looks like. They have the scale of compute, large revenue streams and customer bases, incredible researchers, large data pipelines, and more, to continue to stay at the forefront. And at the same time, there’s an amazing ecosystem starting to play out to diffuse AI into the real world, taking on a variety of approaches that build on top of these frontier models or offer alternative visions that can credibly work as well. Here are just a few of the categories that seem to be working right now: * There’s an ecosystem of companies that will help enterprises and applied AI companies develop their own models tuned for specific use cases, and run the inference for them. This can drive additional performance gains and cost effective approaches to getting AI into workloads across different domains. * Applied AI companies that are delivering the end-user experience and business process tools necessary to actually driving enterprise adoption - including legal, IT, security, HR, customer support, coding, and more. These companies can work with any model and act as a routing layer, and deeply understand the enterprise workflow, can drive change management, actually get to the data necessary to work with, and more. * New labs are emerging that go deep in particular domains that are either not the focus areas of frontier labs or require a deep level of vertical expertise to stay ahead. Life sciences, financial services, healthcare, and more all have labs that will be able to bring completely new approaches to large enterprises across the economy. * There’s all new infrastructure emerging to run models and agents effectively, protect and govern how they operate, store and secure the data they work with, and help enterprises orchestrate their activities. Multiple layers of this stack all are being built up right now that will help drive enterprise adoption. * New services firms that can actually drive the change management in enterprises necessary to the diffusion of AI. There will be hundreds or even thousands of new firms that emerge in lines of business or specific industries that can enable agents to be adopted. I’m probably even missing a few categories, but this is what an incredible healthy technology ecosystem looks like. Way too early to call the winning architectures, and in reality it’s going to be a heterogenous environment as all other tech markets have become. Very exciting.
如果你原本以为 AI 生态系统的价值只会集中到少数几家公司手里，那么过去几个月已经成为一个转折点，显示出 AI 的未来可能会是什么样子。很明显，前沿 AI labs 将继续释放出惊人的创新与增长，并持续推动 model 进步的边界。他们拥有大规模 compute（算力）、可观的收入来源和客户基础、顶尖研究人员、庞大的数据管道等等，因此会继续站在最前沿。与此同时，一个令人惊叹的生态系统也正在展开，把 AI 扩散到现实世界中；其中既有建立在这些前沿 models 之上的各种路线，也有能够可信地同样奏效的替代性愿景。以下只是目前看起来正在奏效的几个类别：* 有一类公司会帮助企业和 applied AI 公司开发针对特定 use case（使用场景）调优的自有 models，并为它们运行 inference（推理）。这能够带来额外的性能提升，并以更具成本效益的方式把 AI 引入不同领域的工作负载。* 还有 applied AI 公司，负责提供真正推动企业采用所必需的终端用户体验和业务流程工具——包括 legal、IT、security、HR、customer support、coding 等等。这些公司可以与任何 model 协作并充当 routing layer（路由层），而且它们深刻理解企业工作流，能够推动 change management（变革管理），真正获取开展工作所需的数据，等等。* 新的 labs 也正在出现，它们在某些特定领域深耕，这些领域要么不是前沿 labs 的重点，要么需要深厚的垂直专业知识才能保持领先。Life sciences、financial services、healthcare 等领域都将拥有自己的 labs，能够为整个经济中的大型企业带来全新的方法。* 还有全新的基础设施正在出现，用于高效运行 models 和 agents（智能体），保护并治理它们的运行方式，存储并保障它们处理的数据安全，并帮助企业编排它们的活动。这个技术栈中的多个层次现在都在建设中，将有助于推动企业采用。* 还会出现新的服务型公司，真正推动企业内部为 AI 扩散所必需的变革管理。未来会有数百家、甚至数千家新公司在不同业务线或特定行业中涌现，帮助 agents 被采用。我可能甚至还漏掉了几个类别，但这就是一个极其健康的技术生态系统应有的样子。现在就断言哪种架构会胜出还为时过早，而现实中它会像其他技术市场一样，成为一个 heterogenous（异构的）环境。非常令人兴奋。
It’s fairly obvious that gate keeping models will not work at scale. Competing in AI is too economically and strategically important for China at this point, and we’ve now crossed the rubicon where it’s clear that they can compete at near frontier levels. If this were even just 1 month ago and we saw kimi k3 our brains would be broken. The solution to this isn’t to get more locked down and slow your own ecosystem. If that happens, you can guarantee that America loses the global battle (or just fights with more and more regulation to mixed success). The solution is to -safely- ensure that you keep a high rate of progress and drive diffusion of the technology, build out infrastructure, enable US OSS, and more.
很明显，对 models 进行 gate keeping（把关、封锁）不可能在大规模上奏效。现阶段，对 China 来说，在 AI 上竞争在经济和战略上都太重要了，而我们现在已经越过了那个临界点：很清楚，他们能够在接近前沿的水平上竞争。哪怕只是放在 1 个月前，如果我们看到 kimi k3，我们都会震惊到说不出话。解决办法不是把系统锁得更死、让你自己的生态系统变慢。如果那样发生，你几乎可以肯定 America 会输掉这场全球竞争（或者只能靠越来越多的监管去打这场仗，效果却喜忧参半）。真正的解决办法是——在安全前提下——确保你保持高速进展，推动这项技术的扩散，建设基础设施，赋能 US OSS（美国开源软件），等等。