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📢【新聞標題】
AI coding tools may not speed up every developer, study shows
AI編碼工具可能無法加速每位開發人員的工作，研究顯示
📰【摘要】
A new study by METR calls into question the extent to which today’s AI coding tools enhance productivity for experienced developers. The study found that allowing AI actually increases completion time by 19%. However, the authors note that AI progress has been substantial and other studies have shown that AI coding tools do speed up software engineer workflows.
🗝️【關鍵詞彙表】
📝 enhance (v.)
- 提高、增強
- 例句: The tools promise to enhance productivity.
- 翻譯: 這些工具承諾提高生產力。
📝 randomized controlled trial (n.)
- 隨機對照試驗
- 例句: METR conducted a randomized controlled trial for this study.
- 翻譯: METR 為這項研究進行了一項隨機對照試驗。
📝 forecast (v.)
- 預測
- 例句: Before completing their assigned tasks, the developers forecasted that using AI coding tools would reduce their completion time by 24%.
- 翻譯: 在完成分配的任務之前，開發人員預測使用 AI 編碼工具會將他們的完成時間縮短 24%。
📝 skeptical (adj.)
- 懷疑的
- 例句: The research offers yet another reason to be skeptical of the promised gains of AI coding tools.
- 翻譯: 該研究提供了另一個理由，讓人們對 AI 編碼工具所承諾的收益持懷疑態度。
📝 vulnerabilities (n.)
- 漏洞、弱點
- 例句: Other studies have shown that today’s AI coding tools can introduce mistakes and, in some cases, security vulnerabilities.
- 翻譯: 其他研究表明，當今的 AI 編碼工具可能會引入錯誤，在某些情況下，還可能引入安全漏洞。
📝 workflow (n.)
- 工作流程
- 例句: Software engineer workflows have been transformed in recent years by an influx of AI coding tools.
- 翻譯: 近年來，隨著大量 AI 編碼工具的湧入，軟體工程師的工作流程發生了轉變。
✍️【文法與句型】
📝 call into question
- 說明: To cast doubt on something.
- 翻譯: 對…產生疑問
- 例句: METR calls into question the extent to which today’s AI coding tools enhance productivity.
- 翻譯: METR 質疑當今 AI 編碼工具在多大程度上提高了生產力。
📝 tend to
- 說明: To usually do or be something.
- 翻譯: 傾向於、往往
- 例句: AI also tends to struggle in large, complex code bases.
- 翻譯: AI 也往往難以應付大型、複雜的程式碼庫。
📖【全文與翻譯】
Software engineer workflows have been transformed in recent years by an influx of AI coding tools like Cursor and GitHub Copilot, which promise to enhance productivity by automatically writing lines of code, fixing bugs, and testing changes.
近年來，隨著 Cursor 和 GitHub Copilot 等 AI 編碼工具的湧入，軟體工程師的工作流程發生了轉變，這些工具承諾通過自動編寫程式碼、修復錯誤和測試變更來提高生產力。
The tools are powered by AI models from OpenAI, Google DeepMind, Anthropic, and xAI that have rapidly increased their performance on a range of software engineering tests in recent years.
這些工具由 OpenAI、Google DeepMind、Anthropic 和 xAI 的 AI 模型提供支持，這些模型近年來在各種軟體工程測試中的性能迅速提高。
However, a new study published Thursday by the non-profit AI research group METR calls into question the extent to which today’s AI coding tools enhance productivity for experienced developers.
然而，非營利 AI 研究組織 METR 週四發表的一項新研究質疑了當今 AI 編碼工具在多大程度上提高了經驗豐富的開發人員的生產力。
METR conducted a randomized controlled trial for this study by recruiting 16 experienced open source developers and having them complete 246 real tasks on large code repositories they regularly contribute to.
METR 為這項研究進行了一項隨機對照試驗，招募了 16 位經驗豐富的開源開發人員，讓他們在經常貢獻的大型程式碼庫上完成 246 個實際任務。
The researchers randomly assigned roughly half of those tasks as “AI-allowed,” giving developers permission to use state-of-the-art AI coding tools such as Cursor Pro, while the other half of tasks forbade the use of AI tools.
研究人員隨機將大約一半的任務分配為“允許 AI”，允許開發人員使用最先進的 AI 編碼工具，例如 Cursor Pro，而另一半任務則禁止使用 AI 工具。
Before completing their assigned tasks, the developers forecasted that using AI coding tools would reduce their completion time by 24%. That wasn’t the case.
在完成分配的任務之前，開發人員預測使用 AI 編碼工具會將他們的完成時間縮短 24%。但事實並非如此。
“Surprisingly, we find that allowing AI actually increases completion time by 19% — developers are slower when using AI tooling,” the researchers said.
研究人員表示：“令人驚訝的是，我們發現允許使用 AI 實際上會使完成時間增加 19%——開發人員在使用 AI 工具時速度較慢。”
Notably, only 56% of the developers in the study had experience using Cursor, the main AI tool offered in the study. While nearly all the developers (94%) had experience using some web-based LLMs in their coding workflows, this study was the first time some used Cursor specifically.
值得注意的是，研究中只有 56% 的開發人員有使用 Cursor（研究中提供的主要 AI 工具）的經驗。雖然幾乎所有開發人員 (94%) 都有在其編碼工作流程中使用某些基於 Web 的 LLM 的經驗，但這是他們第一次專門使用 Cursor。
The researchers note that developers were trained on using Cursor in preparation for the study.
研究人員指出，開發人員在準備研究時接受了使用 Cursor 的培訓。
Nevertheless, METR’s findings raise questions about the supposed universal productivity gains promised by AI coding tools in 2025. Based on the study, developers shouldn’t assume that AI coding tools — specifically what’s come to be known as “vibe coders” — will immediately speed up their workflows.
儘管如此，METR 的研究結果引發了人們對 2025 年 AI 編碼工具所承諾的普遍生產力提升的質疑。根據該研究，開發人員不應假設 AI 編碼工具（尤其是現在所謂的“vibe coders”）會立即加速他們的工作流程。
METR researchers point to a few potential reasons why AI slowed down developers rather than speeding them up: Developers spend much more time prompting AI and waiting for it to respond when using vibe coders rather than actually coding. AI also tends to struggle in large, complex code bases, which this test used.
METR 的研究人員指出了一些潛在原因，解釋了為什麼 AI 會減慢開發人員的速度，而不是加快他們的速度：開發人員在使用 vibe coders 時花費更多時間提示 AI 並等待它響應，而不是實際編碼。AI 也往往難以應付大型、複雜的程式碼庫，而這正是本次測試所使用的。
The study’s authors are careful not to draw any strong conclusions from these findings, explicitly noting they don’t believe AI systems currently fail to speed up many or most software developers. Other large-scale studies have shown that AI coding tools do speed up software engineer workflows.
該研究的作者謹慎地沒有從這些發現中得出任何強烈的結論，明確指出他們不認為 AI 系統目前未能加速許多或大多數軟體開發人員的速度。其他大規模研究表明，AI 編碼工具確實可以加速軟體工程師的工作流程。
The authors also note that AI progress has been substantial in recent years and that they wouldn’t expect the same results even three months from now. METR has also found that AI coding tools have significantly improved their ability to complete complex, long-horizon tasks in recent years.
作者還指出，近年來 AI 的進步是巨大的，即使在三個月後，他們也不會期望得到相同的結果。METR 還發現，近年來，AI 編碼工具在完成複雜、長期任務方面的能力顯著提高。
However, the research offers yet another reason to be skeptical of the promised gains of AI coding tools. Other studies have shown that today’s AI coding tools can introduce mistakes and, in some cases, security vulnerabilities.
然而，該研究提供了另一個理由，讓人們對 AI 編碼工具所承諾的收益持懷疑態度。其他研究表明，當今的 AI 編碼工具可能會引入錯誤，在某些情況下，還可能引入安全漏洞。
🔗【資料來源】
文章連結：https://techcrunch.com/2025/07/11/ai-coding-tools-may-not-speed-up-every-developer-study-shows/