AI让初级专利律师立刻好转但可能会悄悄地阻碍他们的职业成长
对133名专利律师显示进行了为期三个月的实地试验，使大赦国际的援助能够改善即时表现，特别是对初级律师。 但当大赦国际被带走时，只有高级律师才保留这些福利。
AI援助是建立还是削弱专业知识是研究中的一个有争议的问题。 美国一家非营利经济学研究组织国家经济研究局(NBER)的一份工作文件通过对11家美国知识产权律师事务所进行为期三个月的随机控制试验来解决该问题。
该研究将两件事分开，首先让律师在大赦国际的协助下工作，然后在没有律师协助的情况下评估结果。 这种设计使研究人员能够衡量人工智能辅助工作的质量以及律师们自己在事后的判断力。 一家独立公司的盲目专利律师将所有结果分为五类：可执行性、技术准确性、战略模糊性、完整性和清晰性。
Google资助研究的三分之二的参与者获得了"InFlow"，一位来自Google Labs的未释放AI助手，而剩下的第三位则在没有任何AI的情况下工作。 在两项专利请求起草任务中，只有处理组可以使用InFlow，控制组在没有帮助的情况下完成了同样的任务。
在称为 " 重排 " 的第三项任务中，所有参与者都必须严格审查一项没有AI的专利草案。 正如提交人所解释的，起草措施立即由大赦国际协助执行，同时重新调整措施，使判决内部化，专利律师通常在没有任何工具的情况下执行这项任务。
大赦国际主要改进初级律师的草稿
在起草任务中，处理组在10天和90天的分数上都明显地超过了控制组，其效果出现在了所有五个分数维上。 较高的分数大多来自于收视率较低的差分和中程分数较多的分数，而顶级分数则几乎没有移动。
经验不足7年的低年级学生所得比高年级学生多得多。 研究者认为，这与越来越多的研究表明AI缩小了经验较少的工人与经验较多的工人之间的性能差距相一致，这种影响在书面、客户服务和软件开发方面已经观察到。
只有高年级人才有AI的优势
红线产生了相反的结果。 治疗组整体得分较好，但优势完全出自于资深律师，虽然在人工智能协助起草期间，低等律师公布的成绩最大，却完全看不出有什么好处。
取而代之的是，初级成绩相去甚远，中程分数被更坏更好取而代之的分数要少得多，而顶级没有增长。 作者将这读作一个迹象，即AI加快了一些低年级学生的技能建设，而替代了另一些低年级学生的技能建设，尽管实验并没有解释为什么效果会有所不同。
从大赦国际获得最多的律师是那些保留最不持久技能的律师，这导致研究人员提出，基线专门知识可能是将大赦国际协助的做法变成持久能力的先决条件。
补充文本分析提供了一种可能的解释。 资深律师更经常地调整有缺陷的草案，将其修改建立在法律原则上，而初级律师经常发现严重缺陷，而没有加以纠正，使得指出像对AI助理的指示一样读取。 因为这种行为也出现在了控制组中，所以作者不把它归结为AI训练的习惯，而是将其视为AI使用的初级水平的基线缺陷，没有被纠正。
实际结果意味着什么
作者指出一些限制，包括由高层律师高收费率驱动的样本规模小，参与公司并不代表整个专业服务，缺乏基线技能衡量，与建立专利专业知识所需的时间相比，三个月的窗口短。
研究者认为，只要专业工作仍然需要未经大赦国际的判断，立即提高生产率和持久技能发展之间的紧张关系就很重要，而且很可能会在很长一段时间内发生。 即使AI最终支持重排，在客户会议和审判室诉讼期间，律师也可能无法实时使用，因为案件事关重大，当场判决。
类似的模式出现在了其他领域。 在软件开发中，Anthropic研究发现，用AI学习了一个新编程库的年轻开发者在后续知识测试中比没有AI学习的开发者得分更差17%. 在作文中，麻省理工学院媒体实验室从外包思维中发现了认知债务的迹象。 而在管理咨询方面，一项BCG的研究表明，2023年，较弱的顾问在GPT-4的改善远大于强力表现者。
斯坦福研究者解释跨多行业的入门级工作减少的原因，认为AI大多可以取而代之的是学校和大学的教科书知识，这是早期职业工人带入桌面的主要事物，而经验丰富的专业人士也携带着AI难以轻易取而代之的实践专业知识。
专利律师研究为更广泛的关于专业知识被侵蚀的辩论提供了素材，一些研究者称之为"认知常识的愤怒". 个别公司从自动化中受益，而发育迟缓的人才培养成本则在整个专业中相去甚远，本研究新增更能从现实世界的实践中获得实验证据来支撑这种担忧。
自2025年初起，工程部门的初级职位从21%降至14%。
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AI makes junior patent attorneys instantly better but may quietly stunt their professional growth
A three-month field experiment with 133 patent attorneys shows that AI assistance improves immediate performance, especially for junior lawyers. But when the AI is taken away, only senior attorneys retain the benefits.
Whether AI assistance builds or erodes professional expertise is a contested question in research. A working paper from the National Bureau of Economic Research (NBER), a nonprofit U.S. economics research organization, tackles it with a three-month randomized controlled trial across eleven U.S. intellectual property law firms.
The study separated two things by having attorneys first work with AI assistance and then evaluate results without it. This design let researchers measure both the quality of AI-assisted work and how well the attorneys' own judgment held up afterward. Blinded patent attorneys at an independent firm scored all results across five categories: enforceability, technical accuracy, strategic ambiguity, completeness, and clarity.
Two-thirds of participants in the Google-funded study got access to "InFlow," an unreleased AI assistant from Google Labs, while the remaining third worked without any AI. In two patent claim drafting tasks, only the treatment group could use InFlow, and the control group completed the same tasks unaided.
In a third task called redlining, all participants had to critically review an existing patent draft without AI. As the authors explain, drafting measures immediate AI-assisted performance, while redlining measures internalized judgment, a task patent attorneys routinely perform without any tools.
AI improves drafts mainly for junior attorneys
In the drafting tasks, the treatment group clearly outperformed the control group at both the 10-day and 90-day marks, with effects showing up across all five scoring dimensions. Higher scores came mostly from fewer bad ratings and more mid-range ones, while top scores barely moved.
Gains were much larger for juniors with fewer than seven years of experience than for seniors. According to the researchers, this aligns with a growing body of studies showing that AI narrows performance gaps between less and more experienced workers, an effect already observed in writing, customer service, and software development.
Only seniors keep AI advantages
Redlining produced the opposite result. The treatment group scored better overall, but the advantage came entirely from senior attorneys, and juniors saw no benefit at all, even though they had posted the biggest gains during AI-assisted drafting.
Instead, junior results split apart, with far fewer mid-range scores replaced by more bad and more good ones, and no growth at the top. The authors read this as a sign that AI sped up skill-building in some juniors while replacing it in others, though the experiment doesn't explain why the effects diverged.
The attorneys who gained the most from AI were the same ones who retained the least lasting skill, which leads the researchers to suggest that baseline expertise may be a prerequisite for turning AI-assisted practice into durable ability.
A supplementary text analysis offers one possible explanation. Senior attorneys more often restructured the flawed draft and grounded their changes in legal principles, while juniors frequently spotted serious flaws without fixing them, leaving notes that read like instructions to an AI assistant. Because this behavior appeared in the control group too, the authors don't attribute it to an AI-trained habit and instead see it as a baseline deficit at the junior level that AI use didn't fix.
What the results mean in practice
The authors point to several limitations, including the small sample size driven by top attorneys' high billable rates, the fact that participating firms aren't representative of professional services as a whole, the lack of a baseline skills measurement, and the three-month window being short compared to the years it takes to build patent expertise.
The tension between immediate productivity gains and lasting skill development matters as long as professional work still requires judgment without AI, the researchers argue, and it likely will for a long time. Even if AI eventually supports redlining, it probably won't be available to attorneys in real time during client meetings and courtroom proceedings, where stakes are high and judgment calls happen on the spot.
Similar patterns show up in other fields. In software development, an Anthropic study found that younger developers who learned a new programming library with AI help scored 17 percent worse on a follow-up knowledge test than those who learned without it. In essay writing, the MIT Media Lab found signs of cognitive debt from outsourced thinking. And in management consulting, a BCG study showed back in 2023 that weaker consultants improved far more with GPT-4 than strong performers did.
Stanford researchers explain the decline in entry-level jobs across multiple industries by arguing that AI can mostly replace textbook knowledge from school and college, which is the main thing early-career workers bring to the table, while experienced professionals also carry hands-on expertise that AI can't easily substitute.
The patent attorney study feeds into a broader debate about the erosion of professional expertise, what some researchers call the "tragedy of the cognitive commons." Individual firms benefit from automation while the cost of stunted talent development spreads across an entire profession, and this study adds more experimental evidence from real-world practice to back up that concern.
Anthropic recently said that junior positions in its engineering departments dropped from 21 percent to 14 percent since early 2025.
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