AI让初级专利律师立刻好起来但可能会悄悄地阻碍他们的职业成长
与133名专利律师显示进行为期三个月的实地试验，AI协助改善即时表现，特别是初级律师。 但是，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。 美国一个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审判来解决这一问题。
这项研究将两件事分开，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在事后的判断力。 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，而剩下的三分之一则在没有任何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。 正如提交人解释的那样，起草措施是立即由大赦国际协助执行，同时重新调整措施，将判决内部化，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，治疗组在10天和90天的分数上都明显超过了控制组，其效果出现在所有5个积分维度上。 较高的分数大多来自收视率较低的差分和中途的分数，而最高的分数几乎没有移动。
经验不足7年的低年级学生的收入比高年级学生高得多。 研究人员认为，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丰富的工人之间的业绩差距相一致，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。 治疗小组总体上得分较好，但优势完全来自资深律师，虽然在AI协助起草期间他们公布的最大收益，但初级律师却完全看不到任何好处。
取而代之的是，初级成绩四分五裂，中程分数少得多，取而代之的是更糟糕和更好的分数，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，同时取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有差异。
从大赦国际获得最多收益的律师是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不加以纠正，使得指出像给AI助理的指令一样阅读。 因为这种行为也出现在控制组中，所以作者不把它归结为AI训练的习惯，而是把它看作AI使用的初级水平的基线赤字，没有纠正。
实际结果意味着什么
作者指出了若干限制，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，缺乏基准技能衡量，与建立专利专门知识所需的时间相比，三个月窗口短。
研究者认为，只要专业工作仍需要在没有AI的情况下做出判断，立即提高生产率与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，在委托人会议和法庭诉讼期间，如果利害关系大，当场判决的呼声也会发生，它可能不会实时提供给律师。
类似的图案出现在其他领域。 在软件开发中，Anthropic研究发现，通过AI学习新编程库的较年轻的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%. 在散文写作中，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。 在管理咨询方面，一项卡介苗研究在2023年显示，较弱的顾问在GPT-4的改善远超过强力表现者。
Stanford研究者解释跨多个行业的入门级工作减少的原因，认为AI大多可以取代来自学校和学院的教科书知识，这是早期职业工人带入桌面的主要事物，而有经验的专业人士也携带着AI难以轻易替代的实践专业知识。
专利律师研究为更广泛的关于专业知识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，而人才发育迟缓的成本则在整个专业中分散，本研究新增从现实世界实践中得到更多的实验证据来支持这一关切。
自2025年初以来，Anthropic公司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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