Very good post from the Head of Economics at Anthropic. They’re finding that jobs have been less negatively impacted by AI than expected, as we continue to see time and time again in the data. The reason for this is that AI - at least so far - still requires people to operate to produce value in most cases. Most jobs can’t be fully automated with AI, only certain tasks in those jobs. And when you automate specific tasks, you actually can get even more output from those jobs, raising the demand (or at least maintaining it) in many cases. “So far, AI is both skill-biased and labor-augmenting. It complements domain expertise. It relies on humans in the loop to direct and evaluate the most complex work. And it rewards AI proficiency. Model capabilities are improving fast, but remain stubbornly jagged. To fill in the pockets of the jagged frontier, expert oversight is needed to steer incredibly capable AI systems, and to recover when they falter.” I suspect that we continue to see this in a number of critical areas of work. It’s clearly happening in software engineering, where software produced is being multiplied, all of which still requires developers to manage the work agents are doing. Software engineers are needed in a wide variety of industries now and companies of all sizes can now light up software projects that would have been impractical before. But there will be plenty of other domains of work where demand remains strong in a world where agents can accelerate the output of that job. Jevons paradox is alive and well.
Anthropic 的 Head of Economics 这篇帖子非常好。他们发现，AI 对就业的负面影响比预期更小，而我们也一次又一次地从数据中看到了这一点。原因在于，AI——至少到目前为止——在大多数情况下仍然需要人来操作，才能创造价值。大多数工作都无法被 AI 完全自动化，只能自动化这些工作中的某些具体任务。而当你把特定任务自动化时，实际上往往还能让这些岗位产出更多成果，从而在很多情况下提高对这类工作的需求（或者至少维持需求）。“到目前为止，AI 既是 skill-biased（偏向高技能者）的，也是 labor-augmenting（增强劳动者能力）的。它补充的是领域专业知识。它依赖 humans in the loop（人在回路中）来指导和评估最复杂的工作。它也奖励 AI proficiency（AI 熟练度）。模型能力提升得很快，但仍然呈现出顽固而不平整的 jagged（锯齿状）特征。要填补这条 jagged frontier（锯齿状前沿）中的空白区域，就需要专家监督，来引导能力极强的 AI 系统，并在它们失误时进行补救。”我怀疑，我们会继续在许多关键工作领域看到这种现象。这显然正在 software engineering 中发生：产出的软件正在成倍增加，而这一切仍然需要开发者去管理 agent 正在执行的工作。现在，各行各业都需要 software engineers，而且各种规模的公司如今都可以启动过去原本并不现实的软件项目。但在许多其他工作领域中也会是如此：在一个 agent 能够加速该岗位产出的世界里，对这类工作的需求仍将保持强劲。Jevons paradox 依然真实存在，而且运转良好。