Both versions are aligned block by block, in reading order: title, the essentials, then paragraph by paragraph. Where the translation merged or split a paragraph, the matching cell stays empty — we never pair two passages by guesswork.
Microsoftの社内で、ソフトウェアエンジニアの数万人が、2026年1月から4月にかけてAIコーディングツールを使い始めた。対象は、画面のボタンではなく黒い入力画面に文章で指示を出すタイプのツールで、AnthropicのClaude CodeとGitHubのCopilot CLIである。研究者らは、誰がツールを試し、誰が使い続け、仕事の成果がどう変わったかを追った。
Tens of thousands of software engineers at Microsoft began using AI coding tools between January and April 2026. The tools in question are not the button-driven kind but ones that take instructions as text typed into a black command-line window: Anthropic's Claude Code and GitHub's Copilot CLI. The researchers tracked who tried the tools, who kept using them, and how their work output changed.
最初に試すきっかけは、職場の人間関係だった。日常的にコードレビュー（他人が書いたコードを読んで確認し合う作業）を交わす同僚が使っていると、自分も試す確率が高まる。直属の上司が使っていれば82％、レビュー相手の4分の1以上が使っていれば54％、オッズ（試してみる確率の比）が上がった。2つ上の上長を共有する同僚の4分の1以上が直近2週間に使っていた場合は、最大で216％跳ね上がった。
Workplace relationships were what first prompted people to try them. An engineer was more likely to try the tools when a colleague with whom they regularly exchanged code reviews (the practice of reading and checking each other's code) was already using them. If their direct manager was using them, the odds of trying them rose by 82%; if more than a quarter of their review partners were using them, the odds rose by 54%, where odds refers to the ratio of the likelihood of trying the tools. When more than a quarter of colleagues who shared a manager two levels up had used the tools in the previous two weeks, the odds jumped by as much as 216%.
試すことと続けることは、別の話だった。続けて使うかどうかは、本人が普段どれだけコードを書いているかと関係していた。日頃からたくさんのコードを書き、頻繁にプルリクエスト（変更を本線に取り込むよう依頼する単位）を出す多忙なエンジニアほど、AIの恩恵を実感して定着した。一方、以前から別の開発画面で既存のAIツールを使っていた人は、新しいツールを試しても元の環境のほうがいいと感じるのか、定着しにくかった。
Trying the tools and continuing to use them were different matters. Whether people kept using them depended on how much code they typically wrote. Busy engineers who wrote a lot of code regularly and submitted pull requests (requests to merge changes into the main codebase) frequently were more likely to see the benefits of AI and stick with it. Conversely, people who had already been using other AI tools in a different development environment were less likely to stick with the new tools, perhaps because they felt their existing setup was better.
効果の数字はこうだ。導入初週にツールを使ったエンジニアは、使わなかった場合に比べ、マージされたプルリクエストの数が平均で24％増えた。この増加は導入直後の一時的な盛り上がりではなく、調査した4カ月間にわたって安定して続いた。ツール別に見ると、Claude Code利用者は11.4％、Copilot CLI利用者は24.9％の向上だった。
The effect on output was as follows. Engineers who used the tools in their first week saw the number of merged pull requests rise by an average of 24% compared with those who did not. The increase was not a short-lived spike right after adoption; it held steady across the four months studied. By tool, Claude Code users improved by 11.4% and Copilot CLI users by 24.9%.
何が変わるのか。ツールを導入したい組織にとって、この結果は、既に使っている同僚や上司の輪から広がるという見取り図になる。定着を見込むなら、コードを多く書く開発者から使ってもらうのが効きそうだと、数字は示唆している。ただし、これはMicrosoft社内の数字である。論文は査読前のプレプリント（arXivに掲載された論文）で、社外のエンジニアや別の職場で同じ結果が出るかは、まだ確かめられていない。24％という数字も、マージされたプルリクエストの数で測ったもので、コードの質そのものを測ったものではない。
What does this mean? For organizations thinking about adopting such tools, the results suggest that adoption spreads outward from the colleagues and managers who already use them. If the goal is for the tools to stick, the data suggest that getting them to developers who write a lot of code first is likely to be most effective. However, these are internal figures from Microsoft. The paper is a preprint, meaning it has not been peer-reviewed and was posted on arXiv, and it remains to be seen whether engineers outside Microsoft or at other workplaces would see the same results. The 24% figure, moreover, measures the number of merged pull requests, not the quality of the code itself.