内容来源：https://research.google/blog/does-better-work-always-mean-better-workers/

内容总结：

一项针对专利律师的为期三个月的随机对照实验发现，在日常工作中使用人工智能虽能全面提升工作质量，但其对在职学习的影响因资历而异：使用AI达90天的资深律师展现出比未使用者更强的判断力，而初级律师平均技能未见提升，其评分呈现两极分化——高分和低分同时增多。

判断力是资深专业人士区别于初级同行的关键，其养成需要在工作中投入数千小时，通常通过处理相对常规的工作来积累，并往往在经验丰富者的指导下完成。然而，AI正在改变在职学习的运作方式。一方面，放射学、商业问题解决、求职写作和法律教育等领域的实证研究表明，当AI工具被系统地嵌入培训流程时，经验较少的从业者可以提升独立工作表现。另一方面，针对软件工程师、管理咨询师、高中生和临床医生的近期实验显示，AI虽能充当临时“外骨骼”提升即时产出，但一旦移除工具，这些增益往往难以持续。

要理解AI如何侵蚀或增强专业能力，需要三个鲜少同时具备的要素：（1）嵌入日常专业工作中持续数月而非数小时的AI使用；（2）在AI不可用时对无辅助判断力的可信评估；（3）由领域专家进行的盲评。

在发表于美国国家经济研究局的论文《AI辅助是增强还是侵蚀专业能力？来自专利撰写三个月实地实验的证据》中，我们描述了一项实地实验，旨在捕捉在知识产权法这一高度专业化的领域中，AI使用所带来的短期生产力变化和长期技能构建变化。实验中，我们在11家与谷歌有常规非排他业务往来的知识产权律所中，对133名律师随机分配了一款当时尚未发布的谷歌实验室AI专利撰写助手工具（现为Gemini Notebook的一部分）的使用权限。每家律所三分之二的律师（“ treatment”组）获得该工具的提前使用权限，其余律师（“对照组”）接受了如何使用AI的培训，但在三个月研究期结束前不得使用该工具。

在AI辅助10天后，我们向所有参与律师发送了一份虚构发明的发明人材料，要求他们撰写一份专利。90天后，我们用另一组模拟发明人材料重复了这一过程。我们在两个时间点都观察到了预期的AI对撰写任务的提升效果。在第10天，AI工具的使用使撰写评分（由独立法律专家按照包含五个质量维度的李克特量表评分标准进行评定）提高了0.34个标准差，相当于在对照组评分中百分位排名上升10个点；到第90天，这一提升增至0.38个标准差，意味着百分位排名上升11个点。这些提升在所有质量维度上表现出一致性。值得注意的是，更好的表现来自于低分频率的降低和良好评分频率的升高，而优秀评分的频率没有变化。从形式上看，获得AI使用权的律师评分从最低分位向中低和中分位移动，但卓越评分的数量没有明显变化。这一模式在初级律师中尤为明显，其质量提升的同时还伴随着显著的时间节省（例如，在10天任务中比对照组平均124分钟快18分钟）。

但专利律师不仅仅是撰写专利；他们还相互审查工作中的关键错误（有时称为“专利亵渎”），这些错误可能导致专利在法庭上无法执行——例如，过度主张发明的新颖性或范围。因此，我们在第90天增加了一项任务，要求受试者对一份包含许多实质性和文体错误的现有模拟专利进行红线标注（手动标记和修正）。这项测试任务反映了资深律师日常使用的专业判断力，而在这种场景下他们通常无法依赖AI。关键在于，虽然treatment组律师被鼓励在撰写任务中使用未发布的AI工具或任何其他AI工具，但红线标注任务中不允许任何人使用AI，因此其得分成为衡量其技能发展程度的指标。对于所有任务——撰写和红线标注——我们与第三方专利专业人士合作，从五个质量维度对提交内容进行评分：（1）可执行性，（2）准确性，（3）战略性模糊（权利要求的战术性范围界定），（4）完整性，（5）清晰度。

当90天红线标注任务中移除AI助手后，平均化效应消失了。获得AI工具使用权的律师在这一无辅助任务中比对照组高出0.32个标准差（相当于百分位排名上升9个点），但这一效应完全由资深律师驱动——他们比对照组高出0.45个标准差（上升13个点），而初级律师没有表现出可辨识的改善。相反，初级律师的评分出现分化：极低分更多，平庸分更少，良好分更多，优秀分没有增加。

这些结果对学习意味着什么？获得AI工具使用权的资深律师显然在过去三个月的使用中从专业判断力方面收获了显著价值。但初级律师的情况更为复杂。部分评分有所改善，暗示AI具有跨越式学习的潜力。另一些评分则向分布的低端扩散，表明在某些场景下AI可以帮助初级律师交付合格的工作，但其在机器辅助下的更高表现并不能转化为更快获得使资深专业人士具有独特价值的专业能力。

考察定性编辑模式可以揭示不同经验水平的专业人士如何与生成式AI工具互动。在treatment组和对照组中，初级律师的提交都遵循僵化的形式主义：他们从上到下顺序工作，常常在到达主要专利权利要求之前就耗尽了时间用于校对低风险的引言部分。初级律师还专注于表面层面的同义词替换，而非商业范围上的改进。即使初级律师发现了严重缺陷，他们也往往留下描述性评论来诊断问题，而非执行红线标注来修复它。由于这种“诊断而不执行”的姿态在未使用AI的对照组初级律师中同样常见，它代表了初级律师的基线缺陷，而三个月的依赖AI执行改写并未改善这一缺陷。

相比之下，资深律师从AI接触中持续受益。接受AI的资深律师在红线标注上花费的时间比初级律师更长，跳过低风险的文字段落，从头重建权利要求，删除可能无意中缩小法律权利或限制保护范围的措辞，并将许多修改与明确的法律原则相配对。在后续访谈中，资深律师将AI输出描述为“逻辑审计师”而非成品。这削弱了他们对现有文本的依恋，迫使他们阐明结构性修改的原因和方式，从而激活并磨砺了他们的基础专业知识。

提升当前表现与构建持久的专业判断力是不同的目标。对于初级专业人士而言，二者之间可能存在张力。基础专业知识可能是缺失的一环，使AI辅助的重复练习能够在职业生涯中转化为成熟的专业判断力。即使模型能力不断进步，这种判断力很可能仍将重要：律师在与法官、客户和同事的互动中仍将使用自己的判断力，他们无法在所有场景中都依赖AI工具。这一AI时代的关键挑战在于：确保为今天产出更好工作而设计的工具，不会阻碍初级专业人士成为我们明天所需的专家。

在研究人员的实际工作环境中研究专业人士是一项挑战。我们的实验仅包含有限的专利律师样本，这反映了该领域顶级专业人士的高小时计费率。我们仅观察了他们三个月，与培养持久专业能力所需的数年时间相比只是一个很小的窗口。此外，过去一年模型能力和基线AI熟悉度（以及AI产品在法律领域的渗透）的快速进步，如果我们现在重新进行实验，可能会影响结果。这些局限性为进一步研究提供了机会，我们希望在未来几个月内开展部分工作。尽管如此，我们工作中的一个明确结论是：理解AI辅助对专业技能的影响，需要将使用工具的效应与无辅助用户表现的效应分开检验的测试。

衷心感谢合著者Josh Martin、Zanna Iscenko、Scott Strand、David Pearl和Melissa Ferere；感谢James Manyika、Ruth Porat、Kent Walker、Josh Woodward、Anu Madgavkar、Daniel Rock和Fabien Curto Millet的指导与支持；感谢谷歌实验室的Tom Shane、Mauricio Carneiro、Johnny Jacobs、Victor Lavrenko、Yinghao Sun、Samuel Yang、Daniel Nishi、Eric Wang、Kevin Li、Hao Zheng和Franz Och在实验中的合作；感谢Kiera Russell提供产品权限和受信测试者支持；感谢Steve Gong和Taylor Montgomery提供法律指导和招募支持；感谢我们的传播、营销和研究博客合作伙伴，包括Kerry McHugh、Zoe Ortiz、Emily Short、Joseph Mack、Esmeralda Cardenas和Leanne Trujillo推动发布。

中文翻译：

2026年10月7日

David Autor，科技与社会访问学者；Tanya Rodchenko，人工智能与经济事务负责人

我们与执业专利律师开展了一项为期三个月的随机对照试验，结果发现：尽管日常使用AI能够全面提升工作质量，但其对在职学习的影响因资历而异——使用AI达90天的资深律师展现出比未使用者更强的判断力，而初级律师的平均技能水平未见提升，其评分分布则分化为更多高分和更多低分。

判断力正是资深专业人士与初级同行之间的核心分野。培养判断力需要在工作中投入数千小时的学习，通常是通过对相对常规性工作的枯燥但具有塑造意义的投入，且往往在经验丰富的从业者的指导下完成。但AI已经在改变在职学习的运作方式。一方面，放射学、商业问题解决、求职者写作和法律教育领域的实证研究表明，当AI工具被精心嵌入培训流程时，经验较少的从业者能够提升其独立工作表现。

另一方面，近期针对软件工程师、管理咨询顾问、高中生和临床医生的实验表明，尽管AI充当了临时“外骨骼”来提升即时产出，但一旦移除工具，这些增益往往无法持续。要理解AI如何侵蚀或增强专业能力，需要三个罕见同时具备的要素：（1）将AI持续使用嵌入日常专业工作中，时间跨度以月而非小时计；（2）在AI不可用时对无辅助判断力进行可靠评估；（3）由领域专家进行盲评打分。

在由美国国家经济研究局发表的论文《AI辅助是增强还是侵蚀专业能力？来自专利撰写三个月实地实验的证据》中，我们描述了一项实地实验，用于捕捉在知识产权法这一高度专业密集的领域中，使用AI所带来的短期生产力和长期技能构建方面的变化。在实验中，我们对133名来自11家与谷歌有定期非排他业务往来的知识产权律所的律师，随机分配了一款当时尚未发布的谷歌实验室AI专利撰写辅助工具（现为Gemini Notebook的一部分）的使用权限。每家律所三分之二的律师（“实验组”）获得了该工具的提前使用权限，其余律师（“对照组”）接受了一些关于如何使用AI的培训，但在三个月研究期结束前不得使用该工具。

在获得AI辅助10天后，我们向所有参与律师发送了一份虚构发明的发明人材料包，要求他们撰写一份专利。90天后，我们用另一套模拟发明人材料做了同样的测试。我们在两个时间点都观察到了预期的AI对撰写任务表现的提升。在第10天，获得AI工具权限使撰写评分（由独立法律专家按照包含五个质量维度的李克特量表评分标准进行打分）提高了0.34个标准差，相当于在对照组评分中的百分位排名上升了10个点；到第90天，这一提升增至0.38个标准差，意味着百分位排名上升了11个点。这些提升在所有质量维度上都表现出高度一致性。值得注意的是，更好的表现来自低分频率的降低和高分频率的提高，而优秀评分的频率没有变化。形式上，被分配AI使用权限的律师的评分从底部移动到了中下和中等等分位，但卓越评分的数量没有明显变化。这一模式在初级律师中尤为明显，他们的质量提升同时也伴随着显著的时间节省（例如，在10天任务中比对照组平均124分钟快了18分钟）。

但专利律师不仅仅是撰写专利；他们还要相互审查工作中的关键错误（有时被称为“专利脏话”），这些错误可能导致专利在法庭上无法执行——例如，过度主张发明的新颖性或范围。因此，我们在第90天增加了一项任务，要求受试者对一份包含许多实质性及风格性错误的现有虚构专利进行红线标注（手动标记并修正）。该测试任务反映了资深律师日常运用的那种专业判断力，而在这种场景中，他们往往没有依赖AI的奢侈。关键在于，虽然实验组律师在撰写任务中被鼓励使用未发布的AI工具或任何其他AI工具，但在红线标注任务中不允许任何人使用AI，这使他们在该任务上的得分成为衡量其技能发展程度的指标。对于所有任务——无论是撰写还是红线标注——我们都与第三方专利专业人士合作，从五个质量维度对提交作品进行评分：（1）可执行性，（2）准确性，（3）策略性模糊（权利要求的战术性界定），（4）完整性，（5）清晰度。

当在90天红线标注任务中移除AI助手后，这种“拉平效应”消失了。获得AI工具权限的律师在这项无辅助任务中以0.32个标准差的优势超过对照组（相当于百分位排名上升9个点），但这一效应完全由资深律师驱动——他们以0.45个标准差（上升13个点）超过对照组受试者——而初级律师没有表现出可辨识的提升。相反，初级律师的评分出现了两极分化：极低分更多，平庸分更少，良好分更多，优秀分没有增加。

这些结果对学习意味着什么？获得AI工具权限的资深律师显然在过去三个月的工具使用中，从专业判断力方面收获了显著价值。但初级律师的情况更为复杂。部分评分有所提升，暗示了AI跨越式促进学习的潜力。另一些评分则扩散到了分布的低端，表明在某些场景下AI能帮助初级律师交付合格的工作，但他们借助机器取得的更高表现并不能转化为更快获得那些使资深专业人士具有独特价值的专业能力。

审视定性编辑模式可以为不同经验水平的专业人士如何与生成式AI工具互动提供一些洞见。在实验组和对照组中，初级律师的提交作品都遵循刻板的形式主义：初级律师从头到尾按顺序线性操作，常常在到达主要专利权利要求之前就把时间耗尽在低风险的引言部分的文字编辑上。初级律师还专注于表面层面的同义词替换，而非商业范围的改进。即使初级律师发现了严重缺陷，他们也往往留下描述性的评论来诊断缺陷，而非执行红线标注来修复它。由于这种“诊断而不执行”的姿态在无辅助的对照组初级律师中同样普遍，它代表了初级律师的基线缺陷，而三个月依赖AI执行改写并未弥补这一缺陷。

相比之下，资深律师从AI接触中持续受益。实验组资深律师在红线标注上花费的时间比初级律师更长，跳过低风险的文字，从头重建权利要求，删除可能无意中缩小法律权利或限制保护范围的语言，并将许多编辑与明确的法律原则相对应。在后续访谈中，资深律师描述他们将AI输出视为“逻辑审计员”而非成品。这削弱了他们对现有文本的依附，迫使他们阐明结构性编辑的原因和方式，从而激活并磨砺了他们的基础专业能力。

提升表现和构建持久的专业判断力是不同的目标。尤其对初级专业人士而言，二者可能存在张力。基础专业能力可能是缺失的一环，使AI辅助的重复练习能够在职业生涯中转化为成熟的专业判断力。即使模型能力不断进步，这种判断力很可能仍将重要：律师将继续在与法官、客户和同事的互动中运用他们的判断力。他们无法在所有场景中都依赖AI工具来协助。这个AI时代的一个关键挑战是：确保为今天创造更好工作成果而设计的工具，不会阻碍初级专业人士成为我们明天所需要的专家。

在研究者的工作环境中研究专业人士是一项挑战。我们的实验样本仅包含有限的专利律师，这反映了该领域顶级专业人士高昂的小时计费率。我们仅观察了三个月，与他们发展持久专业能力所需的数年时间相比，这只是一个很小的窗口。此外，过去一年中模型能力和基线AI熟悉度的快速提升（以及AI产品在法律领域的渗透），如果我们现在重新进行试验，可能会影响我们的结果。这些局限性为进一步研究提供了机会，我们希望在未来几个月内开展其中一些研究。尽管如此，我们工作的一个明确启示是：理解AI辅助对专业技能的影响，需要将使用工具的效应与无辅助用户表现的效应分开进行测试。

衷心感谢共同作者Josh Martin、Zanna Iscenko、Scott Strand、David Pearl和Melissa Ferere；感谢James Manyika、Ruth Porat、Kent Walker、Josh Woodward、Anu Madgavkar、Daniel Rock和Fabien Curto Millet的指导与支持；感谢谷歌实验室的Tom Shane、Mauricio Carneiro、Johnny Jacobs、Victor Lavrenko、Yinghao Sun、Samuel Yang、Daniel Nishi、Eric Wang、Kevin Li、Hao Zheng和Franz Och与我们合作开展实验；感谢Kiera Russell提供产品权限和可信测试者支持；感谢Steve Gong和Taylor Montgomery提供法律指导和招聘支持；感谢我们的传播、营销和研究博客合作伙伴，包括Kerry McHugh、Zoe Ortiz、Emily Short、Joseph Mack、Esmeralda Cardenas和Leanne Trujillo推动发布。

英文来源：

October 7, 2026
David Autor, Technology & Society Visiting Fellow, and Tanya Rodchenko, Principal, AI & Economy
In a three-month randomized controlled trial with practicing patent attorneys, we found that, while routinely using AI lifts work quality across the board, its effects on on-the-job learning depend on seniority: senior lawyers who used AI for 90 days demonstrate stronger judgment than those who didn’t, whereas junior lawyers show no average skill gain, their scores splitting into more strong scores and more low scores.
Judgment is what sets senior professionals apart from their junior peers. Building judgment requires thousands of hours learning on the job, usually through tedious but formative engagement with relatively routine work, often under guided supervision by experienced workers. But AI is already changing how on-the-job learning works. On the one hand, empirical studies in radiology, business problem-solving, job-seeker writing, and legal education show that when AI tools are thoughtfully embedded into training workflows, less-experienced workers can improve their independent performance.
On the other hand, recent experiments with software engineers, management consultants, high school students, and clinicians demonstrate that while AI serves as a temporary exoskeleton that boosts immediate output, those gains often fail to persist once the tool is removed. Understanding how AI can erode or enhance expertise requires three elements rarely found together: (1) sustained AI usage embedded in ordinary professional work over months rather than hours, (2) a credible evaluation of unassisted judgment when AI is unavailable, and (3) blinded grading by domain experts.
In “Does AI Assistance Enhance or Erode Expertise? Evidence from a Three-Month Field Experiment in Patent Drafting”, published by the National Bureau of Economic Research, we describe a field experiment used to capture changes in both short-term productivity and longer-term skill building that occur as a result of AI usage in the highly expertise-intensive profession of intellectual property law. In our experiment, we randomized access to a then-unreleased Google Labs AI patent writing assistant tool, now part of Gemini Notebook, among 133 lawyers at eleven intellectual property law firms that are engaged in regular, non-exclusive business with Google. Two-thirds of lawyers at each firm (“treatment” group) were given early access to the tool, while the remainder ("control" group) were given some training on how to use AI but were withheld from using the tool until after the three-month study period.
After 10 days of AI assistance, we sent all participating lawyers a packet of inventor materials for a hypothetical invention and asked them to draft a patent. We did the same after 90 days, with a different set of simulated inventor materials. We saw the expected AI boost in performance on the drafting tasks at both time periods. At 10 days, AI tool access raised drafting scores (graded on a rubric with Likert scales for five different aspects of quality by independent legal experts) by 0.34 standard deviations, equivalent to a 10-point climb in percentile ranking among control group scores; by 90 days, this improvement increased to 0.38 SD, implying an 11-percentile point increase. These improvements occurred with remarkable consistency across all quality dimensions. Tellingly, better performance came from lower frequency of poor scores and higher frequency of good scores, and no change in the frequency of excellent scores. Formally, scores among lawyers allocated AI access moved from the bottom to the lower-middle and middle quintiles, but there were no noticeable changes in the number of exceptional scores. This pattern was particularly clear for junior lawyers, whose improvements in quality were also matched by significant time savings (18 minutes faster than the control group’s average speed of 124 minutes on the 10-day task, for example).
But patent lawyers don’t just draft patents; they also review each other’s work for critical mistakes (sometimes called “patent profanity”) that can make patents unenforceable in court — for instance, by overclaiming the novelty or scope of an invention. We therefore added another task at 90 days, which required subjects to redline (to manually mark up and correct) an existing hypothetical patent that contained many errors, both substantive and stylistic. This test task mirrors the kind of professional judgment that more senior lawyers routinely use and for which they most often do not have the luxury of relying on AI. Critically, while treatment group lawyers were encouraged to use the unreleased AI tool or any other AI tool on the drafting tasks, no one was allowed to use AI for the redlining task, making their scores on this task a measure of how well their skills had developed. For all tasks, drafting as well as redlining, we worked with third party patent professionals to score submissions on five dimensions of quality: (1) enforceability, (2) accuracy, (3) strategic ambiguity (the tactical scoping of claims), (4) completeness, and (5) clarity.
When the AI assistant was removed for the 90-day redlining task, the leveling effect vanished. Lawyers given AI tool access outperformed controls by 0.32 SD on this unassisted task (a 9-point equivalent climb in percentile rankings), but this effect was driven entirely by senior lawyers, who outperformed control subjects by 0.45 SD (a 13-point climb), while juniors showed no discernible improvement. Instead, scores of juniors bifurcated: more very low scores, fewer mediocre ones, more good ones, and no more excellent ones.
What do these results mean for learning? Seniors who had been given AI tool access clearly harvested significant value in terms of their professional judgment from their use of the tool over the previous three months. But the junior-level picture is more nuanced. Some scores improved, hinting at AI’s ability to leapfrog learning. Other scores spread to the lower rungs of the distribution, showing that in some scenarios AI can help juniors deliver adequate work, but their higher machine-assisted performance does not translate into faster acquisition of the expertise that makes senior professionals distinctly valuable.
Examining qualitative editing patterns provides some insight into how professionals of different experience levels engage with generative AI tools. Across both treatment and control groups, junior submissions followed rigid formalism: juniors worked sequentially from top to bottom, frequently exhausting their time copy-editing low-stakes introductory sections before reaching the main patent claims. Juniors also focused on surface-level synonym swapping rather than commercial scope improvement. Even when juniors spotted serious flaws, they often left descriptive comments diagnosing the defect rather than executing the redline to fix it. Because this "diagnose-without-execute" posture was equally common among unassisted control juniors, it represents a baseline junior deficit that three months of relying on AI to execute rewrites did not remediate.
Senior lawyers, by contrast, benefited consistently from AI exposure. Treated seniors spent longer on redlining than juniors, bypassing low-stakes prose to rebuild the claims from scratch, strip out language that could inadvertently narrow legal rights or limit protective scope, and pair many edits with explicit legal doctrines. In follow-up interviews, seniors described treating AI output not as a finished product, but as a "logic auditor". It weakened attachment to existing prose and forced them to articulate the why and how of structural edits, activating and sharpening their foundational expertise.
Improving performance and building durable professional judgment are different goals. For junior professionals in particular, they may be in tension with one another. Foundational expertise may be the missing link that allows AI-assisted repetition to translate into seasoned professional judgment over the course of a career. Such judgment will likely continue to be important even as model capabilities advance: lawyers will continue to use their judgment in their interactions with judges, clients and colleagues. They will not be able to rely on an AI tool to assist them in all settings. A critical challenge of this AI moment is ensuring that tools designed to produce better work today do not prevent junior professionals from becoming the experts we need tomorrow.
Studying professionals in their working environments is a challenge for researchers. Our experiment includes a limited sample of patent lawyers, a reflection of the high hourly billing rate of top-tier professionals in the field. We only observe them over three months, a small window compared with the years they would need to develop durable expertise. Moreover, the rapid advance in both model capabilities and baseline AI familiarity (as well as AI-enabled product penetration in the legal domain) over the past year might affect our results if we re-ran the trial now. These limitations give rise to opportunities for further research, some of which we hope to pursue in the months ahead. Nevertheless, a clear takeaway from our work is that understanding the effect of AI assistance on professional skill requires tests that separate the effect of using tools from the effect of unassisted user performance.
A huge thank you to co-authors Josh Martin, Zanna Iscenko, Scott Strand, David Pearl and Melissa Ferere; to James Manyika, Ruth Porat, Kent Walker, Josh Woodward, Anu Madgavkar, Daniel Rock, and Fabien Curto Millet for their guidance and support; to Tom Shane, Mauricio Carneiro, Johnny Jacobs, Victor Lavrenko, Yinghao Sun, Samuel Yang, Daniel Nishi, Eric Wang, Kevin Li, Hao Zheng and Franz Och of Google Labs for partnering with us on the experiment; to Kiera Russell for product access and trusted tester support; to Steve Gong and Taylor Montgomery for legal guidance and recruitment support; and to our Comms, Marketing, and Research Blog partners including Kerry McHugh, Zoe Ortiz, Emily Short, Joseph Mack, Esmeralda Cardenas and Leanne Trujillo for driving the launch.