The AI Productivity Paradox人工智能生产力悖论
We’ve been writing a lot lately about product teams that are clearly leveraging AI to deliver faster, yet their outcomes are not improving.最近我们写了很多关于产品团队的文章，他们显然正在利用人工智能来提高交付速度，但最终成果却并未改善。
Today, this phenomenon, known as the “AI Productivity Paradox,” has been recognized by people from across the industry.如今，这种被称为“人工智能生产力悖论”的现象已得到业内人士的广泛认可。
From the latest McKinsey Quarterly: “The business world is grappling with an AI paradox: Adoption of generative and agentic AI is growing, investment is accelerating, but sustained impact on performance is elusive.”最新一期《麦肯锡季刊》指出：“商业世界正陷入一场人工智能悖论：生成式和代理式人工智能的采用率在增长，投资在加速，但对绩效的持续影响却难以捉摸。”
From the Atlassian’s State of Teams 2026 Report: “89% of executives say AI has increased the speed of work, but only 6% feel confident they can point to specific organization-wide AI ROI.”Atlassian 的《2026 年团队状况报告》显示：“89% 的高管表示人工智能提高了工作速度，但只有 6% 的人有信心指出人工智能在全公司范围内的具体投资回报率。”
Yet while it may be easy to agree that AI increases productivity but not necessarily results, there’s much less agreement on why this is.尽管人们很容易认同人工智能能提高生产力却未必能改善结果，但对于其背后的原因，却鲜有共识。
For those of us that have been studying this problem of accelerating output without the corresponding improvement in outcomes since long before AI, this has not been a surprise.对于我们这些早在人工智能出现之前就开始研究“产出加速但成果未见提升”这一问题的人来说，这并不令人惊讶。
And it isn’t really much of a paradox either.而且，这其实算不上什么真正的悖论。
We continue to see most people utilizing AI to simply speed up their old, project model way of working.我们依然看到大多数人只是在使用人工智能来加速他们旧有的项目制工作模式。
As AI product leader Hilary Gridley argues, “It’s never been faster to build, which means it’s never been easier to run 10 times faster in the wrong direction.”正如人工智能产品领袖希拉里·格里德利（Hilary Gridley）所言：“构建速度从未如此之快，这意味着以错误的方向全速奔跑也从未如此容易。”
As Chip Huyen, the author of the bestselling AI Engineering points out, “AI makes building easier, but the hardest part remains knowing what to build.”正如畅销书《AI工程》（AI Engineering）作者 Chip Huyen 所指出的：“人工智能让构建变得更容易，但最难的部分依然是搞清楚到底该构建什么。”
The real problem with the project model was never that it was too slow (although it is often slow). The larger problem is that it’s designed to deliver output, rather than outcomes.项目模式的真正问题从来不是速度太慢（尽管它确实经常很慢）。更大的问题在于，它的设计初衷是交付产出，而非达成成果。
When generative AI emerged, especially as it pertained to building products, I was optimistic that it would serve as the great equalizer, and companies with the best engineers would no longer have such a strong advantage over the rest.当生成式人工智能出现时，尤其是在产品构建领域，我曾乐观地认为它将成为伟大的平衡器，拥有顶尖工程师的公司将不再对其他公司拥有如此巨大的优势。
With the benefit of hindsight, it’s pretty clear now that those with the best engineers also often had the best product people, and that the true advantage was less their delivery skills, and more their culture, strategy and discovery skills.事后看来，现在很清楚，那些拥有顶尖工程师的公司往往也拥有顶尖的产品人才，而他们真正的优势不在于交付能力，而在于文化、战略和发现能力。
So the result today is literally the opposite of what I had initially expected. Rather than closing the gap, the strong product companies are increasing the distance between themselves and the majority of the market.因此，今天的结果与我最初的预期截然相反。优秀的科技产品公司不仅没有缩小差距，反而正在拉大与市场大多数公司之间的距离。
The product model is what is enabling these companies to leverage AI for improved outcomes and not just output.正是产品模式使这些公司能够利用人工智能来改善成果，而不仅仅是增加产出。
Recently I wrote about how strong product teams use AI very differently when they are building to learn (product discovery) versus building to earn (product delivery). 最近我写过关于优秀产品团队如何在使用人工智能时，区分“通过构建来学习”（产品发现）和“通过构建来获利”（产品交付）这两种不同目的的文章。
While so many are using AI to accelerate the creation of the artifacts of the old project model (e.g. business cases, roadmaps, PRD’s, code) the strong teams are using AI to accelerate the discovery of a solution that solves for both customers (value) and their own company (viability), and test those proposed solutions with users, customers, and the impacted stakeholders. 当许多人还在利用人工智能加速旧项目模式下的产物（如商业计划书、路线图、产品需求文档、代码）时，优秀的团队正在利用人工智能加速发现既能满足用户（价值）又能满足公司（可行性）的解决方案，并与用户、客户及相关利益方共同验证这些方案。
Once they have the evidence and confidence that they have a solution worth building, then they use AI to accelerate their building to earn – focusing on building a commercial quality product – a solution that is reliable, accurate, scalable, performant, and, more generally, something that their customers can depend on.一旦他们有证据并确信所选方案值得构建，便会利用人工智能加速“通过构建来获利”的过程——专注于打造商业级产品，即那种可靠、准确、可扩展、高性能，且更重要的是能让客户信赖的解决方案。
You might wonder why a team can’t just generate something quickly and launch it to customers and see what happens? They absolutely can, and that’s precisely what so many are doing today. The problem is the outcome. Hence the AI productivity paradox.你可能会问，为什么团队不能直接快速生成并发布给客户看看效果呢？他们当然可以，而这正是今天许多人正在做的事情。问题在于结果。这就是所谓的“人工智能生产力悖论”。
For so many company leaders, it doesn’t matter when I show them the data, or even point to their own results. They are deeply convinced that if they could just get their ideas built faster, the results will surely follow.对于许多公司领导者来说，无论我向他们展示多少数据，甚至指出他们自己的结果，都无济于事。他们深信，只要能更快地实现想法，结果自然会随之而来。
For many of these leaders, I expect we will simply have to wait until they can no longer deny the evidence that the issue is not time and cost of building; the real issue is that their ideas so often prove to be not worth building (they are simply not an effective solution to whatever problem they are trying to solve).对于这些领导者，我想我们只能等待，直到他们无法再否认这样一个事实：问题不在于构建的时间和成本，而在于他们的想法往往被证明不值得构建（它们根本无法有效解决所面对的问题）。
But for those who embrace the different purposes, tools and techniques of build to learn versus build to earn, there has never been a better time to be creating products powered by technology.但对于那些能够理解“通过构建来学习”与“通过构建来获利”在目的、工具和技术上差异的人来说，现在是创造科技驱动型产品的最佳时机。