🗒️我的笔记
非线性时间
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Time isn’t as linear as you think. It has ripples and folds like smooth silk. It doubles back on itself, and if you know where to look, you can catch the future shimmering in the present.
时间并不像你想象的那么线性。它像光滑的丝绸一样有涟漪和褶皱。它自己加倍，如果你知道去哪里看，你就能捕捉到现在闪闪发光的未来。 (This is what people don’t understand about visionaries: They don’t need to predict the future. They learn to snatch it out of the folds of time and wear it around their bodies like a flowing cloak.)
（这就是人们对有远见的人不理解的地方：他们不需要预测未来。他们学会了从时间的褶皱中抢走它，并像飘逸的斗篷一样戴在身上。 -
As Every’s Evan Armstrong argued several months ago, “AI is an abstraction layer over lower-level thinking.” That lower-level thinking is, largely, summarizing.
正如Every的埃文·阿姆斯特朗（Evan Armstrong）几个月前所说，“人工智能是低级思维之上的抽象层。这种低层次的思维在很大程度上是总结性的。
If I’m using ChatGPT in this way today, there’s a good chance this behavior—handing off summarizing to AI—is going to become widespread in the future. That could have a significant impact on the economy.
如果我今天以这种方式使用 ChatGPT，那么这种行为——将总结交给 AI——很有可能在未来会变得普遍。这可能会对经济产生重大影响。
This is what I mean by catching the future in the present and the non-linearity of time. If we extrapolate my experience with ChatGPT, we can glean what the next few years of our work lives might look like.
这就是我所说的在当下捕捉未来和时间的非线性的意思。如果我们推断我使用 ChatGPT 的经历，我们可以收集到我们未来几年的工作生活可能是什么样子。
The end of the knowledge economy 知识经济的终结
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We live in a knowledge economy. What you know—and your ability to bring it to bear in any given circumstance—is what creates economic value for you. This was primarily driven by the advent of personal computers and the internet, starting in the 1970s and accelerating through today.
我们生活在一个知识经济中。你所知道的——以及你在任何特定情况下运用它的能力——都是为你创造经济价值的东西。这主要是由个人电脑和互联网的出现推动的，从 1970 年代开始，一直持续到今天。
But what happens when that very skill—knowing and utilizing the right knowledge at the right time—becomes something that computers can do faster and sometimes just as well as we can?
但是，当这种技能——在正确的时间了解和利用正确的知识——成为计算机可以做得更快，有时甚至和我们一样好的东西时，会发生什么？
We’ll go from makers to managers, from doing the work to learning how to allocate resources—choosing which work to be done, deciding whether work is good enough, and editing it when it’s not.
我们将从制作者到管理者，从做工作到学习如何分配资源——选择要完成哪些工作，决定工作是否足够好，并在不够好时进行编辑。
It means a transition from a knowledge economy to an allocation economy. You won’t be judged on how much you know, but instead on how well you can allocate and manage the resources to get work done.
这意味着从知识经济向分配经济的过渡。评判你的不是你知道多少，而是你分配和管理资源以完成工作的能力。
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There’s already a class of people who are engaged in this kind of work every day: managers. But there are only about 1 million managers in the U.S., or about 12% of the workforce. They need to know things like how to evaluate talent, manage without micromanaging, and estimate how long a project will take. Individual contributors—the people in the rest of the economy, who do the actual work—don’t need that skill today.
已经有一类人每天都在从事这种工作：经理。但美国只有大约100万名管理人员，约占劳动力的12%。他们需要知道如何评估人才、如何在没有微观管理的情况下进行管理，以及估计一个项目需要多长时间。个人贡献者——经济中其他部门的人，他们从事实际工作——今天不需要这种技能。
But in this new economy, the allocation economy, they will. Even junior employees will be expected to use AI, which will force them into the role of manager—model manager. Instead of managing humans, they’ll be allocating work to AI models and making sure the work gets done well. They’ll need many of the same skills as human managers of today do (though in slightly modified form).
但在这个新经济中，分配经济，他们会的。即使是初级员工也会被期望使用人工智能，这将迫使他们扮演经理的角色——模型经理。他们不会管理人类，而是将工作分配给 AI 模型并确保工作顺利完成。他们需要许多与当今人类管理者相同的技能（尽管形式略有修改）。
Is the allocation economy good for humanity? 分配经济对人类有好处吗？
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A transition from a knowledge economy to an allocation economy is not likely to happen overnight. When we talk about doing “model management,” that’s going to look like replacing micro-skills—like summarizing meetings into emails—rather than entire tasks end to end, for a while, at least. Even if the capability is there to replace tasks, there are many parts of the economy that won’t catch up for a long time, if ever.
从知识经济向分配经济的转变不可能在一夜之间发生。当我们谈论“模型管理”时，这看起来像是取代微技能——比如将会议总结成电子邮件——而不是整个任务，至少在一段时间内是这样。即使有能力取代任务，经济的许多部分也不会在很长一段时间内赶上，如果有的话。
I recently got my pants tailored in Cobble Hill, Brooklyn. When I pulled out my credit card to pay for it, the lady behind the counter pointed at a paper sign taped to the wall: “No credit cards.” I think we’ll find a similar pace of adoption for language models: There will be many places where they could be used to augment or replace human labor where they are not. These will be for many different reasons: inertia, regulation, risk, or brand.
我最近在布鲁克林的鹅卵石山（Cobble Hill）定制了我的裤子。当我掏出信用卡付款时，柜台后面的女士指着贴在墙上的纸牌：“没有信用卡。我认为我们会发现语言模型的采用速度相似：在许多地方，它们可以用来增加或取代人类劳动力，而它们却没有。这些将有许多不同的原因：惯性、监管、风险或品牌。
This, I think, is a good thing. When it comes to change, the dose makes the poison. The economy is big and complex, and I think we’ll have time to adapt to these changes. And the slow handoff of human thinking to machine thinking is not new. Generative AI models are part of a long-running process.
我认为这是一件好事。当谈到变化时，剂量会产生毒药。经济庞大而复杂，我认为我们将有时间适应这些变化。人类思维向机器思维的缓慢交接并不是什么新鲜事。生成式 AI 模型是长期运行过程的一部分。
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In his 2013 book Average Is Over, economist Tyler Cowen wrote about a stratification in the economy driven by intelligent machines. He argued that there is a small, elite group of highly skilled workers who are able to work with computers that will reap large rewards—and that the rest of the economy may be left behind:
经济学家泰勒·考恩（Tyler Cowen）在2013年出版的《平均结束了》（Average Is Over）一书中，谈到了由智能机器驱动的经济分层。他认为，有一小群精英高技能工人能够使用计算机，从而获得丰厚的回报，而经济的其他部分可能会被抛在后面：
“If you and your skills are a complement to the computer, your wage and labor market prospects are likely to be cheery. If your skills do not complement the computer, you may want to address that mismatch. Ever more people are starting to fall on one side of the divide or the other. That’s why average is over.”
“如果你和你的技能是对计算机的补充，你的工资和劳动力市场前景可能会令人愉快。如果你的技能不能与计算机相辅相成，你可能想解决这种不匹配的问题。越来越多的人开始倒在鸿沟的一边或另一边。这就是为什么平均水平结束了。
At the time, he wasn’t writing about generative AI models. He was writing about iPhones and the internet. But generative AI models extend the same trend.
当时，他并没有写关于生成式人工智能模型的文章。他写的是关于iPhone和互联网的文章。但生成式人工智能模型延续了同样的趋势。
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在日常生活中更好地使用语言模型的人将在经济中处于显着优势。知道如何分配情报将获得巨大的回报。
People who are better equipped to use language models in their day-to-day lives will be at a significant advantage in the economy. There will be tremendous rewards for knowing how to allocate intelligence.
Today, management is a skill that only a select few know because it is expensive to train managers: You need to give them a team of humans to practice on. But AI is cheap enough that tomorrow, everyone will have the chance to be a manager—and that will significantly increase the creative potential of every human being.
今天，管理是一项只有少数人知道的技能，因为培训管理者的成本很高：你需要给他们一个团队来练习。但人工智能足够便宜，明天每个人都有机会成为经理，这将大大增加每个人的创造潜力。
It will be on our society as a whole to make sure that, with the incredible new tools at our disposal, we bring the rest of the economy along for the ride.
我们的整个社会将确保，借助我们掌握的令人难以置信的新工具，我们带动经济的其他部分。