In a world where there are strong open source alternatives that are only just behind the frontier models, you make yourself less secure and competitive by gatekeeping access to frontier model capabilities. If you play this out, even if America could fully ban access to open source models, other ecosystems wouldn't have that ban and they'd have a leg up on access to those models. They could use them against us -or just use them to better defend themselves- and our own companies wouldn't be able to keep up. The calculus of what you do about AI regulation simply must change to account for the fact that open weights models are far stronger than many expected.
在一个已经存在强大开源替代方案、而它们只比前沿模型稍微落后一点的世界里，对前沿模型能力进行 gatekeeping（把控访问）只会让你自己在安全性和竞争力上处于更不利的位置。把这件事推演下去看，即便 America 真能彻底禁止获取开源模型，其他生态系统也不会有这种禁令，因此它们在获取这些模型方面反而会占优。它们既可以用这些模型来对付我们——或者只是更好地保护自己——而我们自己的公司却无法跟上。对于 AI 监管该怎么做，其判断逻辑必须改变，因为 open weights models 的能力已经比很多人预期的强得多。
A lot of people make the mistake of thinking that when AI costs drop, that spend on AI drops with it. Usually the opposite happens. When you make AI cheaper, it will get consumed more. Because now you can afford to use for a wider of tasks than you did before. You start to write more code. You review more code for bugs and security. You run agents on large data sets you couldn’t process before. And so on. Thus, for the foreseeable future, anything that lowers the cost of tokens will drive up inference demand. This also gives you some insight also into why even open source business models work in AI. No one is running these models on their devices; they’re running them in infra. Great time to be one of those providers.
很多人都会犯一个错误，以为当 AI 成本下降时，AI 支出也会随之下降。通常情况恰恰相反。你把 AI 做得更便宜，它就会被消费得更多。因为现在你能负担得起把它用在比以前更广泛的任务上。你会开始写更多代码。你会审查更多代码中的 bug 和安全问题。你会在以前无法处理的大型数据集上运行 agents（智能体）。等等。因此，在可预见的未来，任何降低 token 成本的因素，都会推高 inference（推理）需求。这也能让你理解，为什么连开源商业模式在 AI 领域也行得通。没有人是在自己的设备上运行这些模型；他们是在 infra（基础设施）上运行。对这类提供商来说，这是个绝佳时期。
Good post if you’re trying to understand AI diffusion. Progress driven by AI will ultimately be rate limited by its interaction with the real world. The reason coding, for instance, has been adopted so quickly is you can write a near infinite amount of code, test it, and run it -and it can add value- without anyone in the outside world ever having to do anything differently. A single person, from a computer, can just make something work end-to-end differently. This is not true for life sciences, where new drug development eventually needs to be tested for years. Doing a sale, which requires going back and forth with a prospect. Or even a contract, which has to be negotiated on the other side by your counterparty. “An AI will hand you a genuinely clever design for a jet turbine blade. It might be far more likely to work than anything your engineers came up with. It'll still probably fail, because that's the base rate at the edge of what anyone knows. The only way to find out is to build the blade and try to break it. That's the real limit on learning, and it doesn't care how smart you are. Coming up with ideas was never the hard part. The hard part is how fast reality answers them.” Incidentally, this is why you need an applied AI that actually takes intelligence and makes it useful within the workflows of existing industries. Model outputs -alone- are not enough in most cases. You need to actually change the underlying workflows, and you need systems to deal with the realities of the real world feedback loops that are in these industries. This is why there’s so much opportunity in the applied AI layer.
如果你想理解 AI diffusion（扩散），这是一篇很好的文章。由 AI 驱动的进步，最终会受限于它与现实世界的交互速度。比如，coding 之所以被如此迅速地采用，是因为你可以写出几乎无限量的代码，对它进行测试并运行——而且它还能创造价值——整个过程中，外部世界里任何人都不需要改变任何做事方式。一个人坐在电脑前，就能把某个东西从端到端彻底改成另一种工作方式。但在 life sciences 中这就不成立了，因为新药开发最终需要经过多年的测试。销售也是如此，因为它需要与你的潜在客户反复来回沟通。甚至合同也是如此，因为它还需要交易对手方在另一边进行谈判。“AI 也许会给你一个真正巧妙的 jet turbine blade 设计。它可能比你的工程师提出的任何方案都更有希望成功。但它大概率仍然会失败，因为在任何人知识边界的前沿，失败本来就是基础概率。要想知道答案，唯一的办法就是把这个叶片造出来，然后尝试把它弄坏。这才是学习的真正限制，而它并不在乎你有多聪明。提出想法从来都不是最难的部分。难的是，现实会以多快的速度回应这些想法。” 顺带一提，这也正是为什么你需要 applied AI：它必须真正把 intelligence（智能）带入现有行业的工作流中，并使之变得有用。仅有模型输出——单靠这一点——在大多数情况下是不够的。你必须真正改变底层工作流，而且你还需要系统来处理这些行业中现实世界反馈回路所带来的种种现实约束。这就是为什么 applied AI 这一层存在如此巨大的机会。