[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-global-ai-diffusion-q1-2026-microsoft-zh":3,"article-related-global-ai-diffusion-q1-2026-microsoft-zh":31,"series-research-51fd97a1-f7fd-44f8-9d54-b2d85fe74461":84},{"id":4,"slug":5,"title":6,"content":7,"summary":8,"source":9,"source_url":10,"author":11,"image_url":12,"cover_image":12,"category":13,"language":14,"translated_content":11,"related_article_id":15,"keywords":16,"key_takeaways":23,"views":27,"created_at":28,"published_at":29,"topic_cluster_id":30},"51fd97a1-f7fd-44f8-9d54-b2d85fe74461","global-ai-diffusion-q1-2026-microsoft-zh","17.8% 全球用上 AI","\u003Cp data-speakable=\"summary\">\u003Ca href=\"https:\u002F\u002Fblogs.microsoft.com\u002F\" target=\"_blank\" rel=\"noopener\">Microsoft\u003C\u002Fa> 表示，2026 年 Q1 全球 AI 使用率升到 17.8%，比前一季多 1.5 個百分點。\u003C\u002Fp>\u003Cp>這份 \u003Ca href=\"https:\u002F\u002Fblogs.microsoft.com\u002Fon-the-issues\u002F2026\u002F05\u002F07\u002Fthe-state-of-global-ai-diffusion-in-2026\u002F\" target=\"_blank\" rel=\"noopener\">Global AI Diffusion Report\u003C\u002Fa> 在 5 月 7 日發布，追蹤的是全球工作年齡人口的 AI 採用情況。報告同時指出，已有 26 個經濟體的 AI 使用率超過 30%，\u003Ca href=\"https:\u002F\u002Fu.ae\u002Fen\u002Fabout-the-uae\" target=\"_blank\" rel=\"noopener\">UAE\u003C\u002Fa> 仍以 70.1% 排名第一。\u003C\u002Fp>\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>項目\u003C\u002Fth>\u003Cth>數值\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>全球 AI 使用率（Q1 2026）\u003C\u002Ftd>\u003Ctd>17.8%\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>季增幅\u003C\u002Ftd>\u003Ctd>+1.5 個百分點\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>UAE 使用率\u003C\u002Ftd>\u003Ctd>70.1%\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>美國排名\u003C\u002Ftd>\u003Ctd>第 21 名\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>美國使用率\u003C\u002Ftd>\u003Ctd>31.3%\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>全球北方使用率\u003C\u002Ftd>\u003Ctd>27.5%\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>全球南方使用率\u003C\u002Ftd>\u003Ctd>15.4%\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Git pushes 年增\u003C\u002Ftd>\u003Ctd>78%\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>美國軟體工程師人數\u003C\u002Ftd>\u003Ctd>約 220 萬\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>美國工程師職位年增\u003C\u002Ftd>\u003Ctd>8.5%\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003Ch2>發生了什麼\u003C\u002Fh2>\u003Cp>\u003Ca href=\"\u002Ftag\u002Fmicrosoft\">Microsoft\u003C\u002Fa> 這次把焦點放在「擴散速度」而不是單一模型能力。數字顯示，AI 已從少數市場快速往外鋪開，但全球採用率還不到 2 成，代表多數地區仍在起步階段。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779567358340-iloe.png\" alt=\"17.8% 全球用上 AI\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>區域差異很明顯。報告說，亞洲是這一季變化最強的地區，南韓、泰國、日本的增幅都較大，部分原因是 AI 對亞洲語言的支援變好。這也解釋了為\u003Ca href=\"\u002Fnews\u002Fwhy-la-county-fair-plans-like-theme-park-zh\">什麼\u003C\u002Fa>同樣是產品上線，有些市場會先跑起來，有些市場卻卡在語言與本地化。\u003C\u002Fp>\u003Cp>報告還列出幾\u003Ca href=\"\u002Fnews\u002F5-things-to-know-about-la-county-fair-art-zh\">個關\u003C\u002Fa>鍵排名。全球北方的 AI 使用率為 27.5%，全球南方只有 15.4%，差距再次拉開；UAE 以 70.1% 穩居第一，美國則從第 24 名升到第 21 名，使用率為 31.3%。\u003C\u002Fp>\u003Cul>\u003Cli>26 個經濟體已超過 30% AI 使用率。\u003C\u002Fli>\u003Cli>UAE 仍是全球第一，且遠高於平均值。\u003C\u002Fli>\u003Cli>美國雖然上升，但排名仍落後多個中小型市場。\u003C\u002Fli>\u003Cli>Microsoft 的估算會校正作業系統、裝置占比、網路滲透率與人口結構。\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>為什麼重要\u003C\u002Fh2>\u003Cp>對開發者來說，這不是單純的普及率新聞，而是需求結構的變化。當更多人\u003Ca href=\"\u002Fnews\u002Fzero-everclear-syndicate-labs-exit-l2s-consolidate-zh\">開始\u003C\u002Fa>用 AI，會直接推高對聊天助手、代理工具、翻譯、搜尋與程式開發產品的實際需求，尤其是能處理本地語言的產品。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779567356919-jf4t.png\" alt=\"17.8% 全球用上 AI\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>報告也把 AI 與工程產出連在一起。Microsoft 指出，Git pushes 年增 78%，並提到 \u003Ca href=\"https:\u002F\u002Fwww.anthropic.com\u002F\" target=\"_blank\" rel=\"noopener\">Anthropic\u003C\u002Fa> 的 \u003Ca href=\"\u002Ftag\u002Fclaude-code\">Claude Code\u003C\u002Fa>、\u003Ca href=\"https:\u002F\u002Fopenai.com\u002F\" target=\"_blank\" rel=\"noopener\">OpenAI\u003C\u002Fa> 的 \u003Ca href=\"\u002Ftag\u002Fcodex\">Codex\u003C\u002Fa>，以及 \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Ffeatures\u002Fcopilot\" target=\"_blank\" rel=\"noopener\">GitHub Copilot\u003C\u002Fa>。這代表 AI 工具已不只在「試用」，而是在進入日常開發流程。\u003C\u002Fp>\u003Cp>勞動市場的訊號則更複雜。Microsoft 說美國 2025 年約有 220 萬名\u003Ca href=\"\u002Ftag\u002F軟體開發\">軟體開發\u003C\u002Fa>者，年增 8.5%，2026 年 3 月的就業也比去年同期高出約 4%。這比較像是 AI 降低了建置成本，但還沒有明顯壓縮工程師需求。\u003C\u002Fp>\u003Cp>真正的分水嶺，會是哪些國家和產業能把「使用 AI」轉成「多做出東西」。語言覆蓋、基礎設施、產品本地化，現在看起來比模型名稱更重要。\u003C\u002Fp>\u003Cp>問題已經不是 AI 會不會擴散，而是誰能先把擴散變成生產力。Microsoft 這份報告給出的答案很直接：先補齊語言與基礎建設的人，先吃到紅利。\u003C\u002Fp>\u003Cp>如果 17.8% 代表的是起跑線，那下一題就是，剩下的 82.2% 會在什麼時候、以什麼方式跟上。\u003C\u002Fp>","Microsoft 5 月 7 日公布最新全球 AI 擴散報告，稱 2026 年 Q1 全球工作年齡人口的 AI 使用率升至 17.8%。UAE 仍居首位，美國排第 21，北南半球差距也再度拉大。","blogs.microsoft.com","https:\u002F\u002Fblogs.microsoft.com\u002Fon-the-issues\u002F2026\u002F05\u002F07\u002Fthe-state-of-global-ai-diffusion-in-2026\u002F",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779567358340-iloe.png","research","zh","fe8f7986-0534-4998-9db8-c3dcc821553d",[17,18,19,20,21,22],"Microsoft","AI adoption","global diffusion","developer tools","UAE","GitHub Copilot",[24,25,26],"全球 AI 使用率在 2026 年 Q1 升至 17.8%，季增 1.5 個百分點。","UAE 以 70.1% 續居第一，美國升到第 21 名但仍落後多個市場。","語言支援與本地化，正成為 AI 擴散速度的核心變數。",9,"2026-05-23T20:15:34.58614+00:00","2026-05-23T20:15:34.57+00:00","0c35a120-52fc-41fc-afa3-d404eb934158",{"tags":32,"relatedLang":43,"relatedPosts":47},[33,35,37,39,41],{"name":17,"slug":34},"microsoft",{"name":19,"slug":36},"global-diffusion",{"name":20,"slug":38},"developer-tools",{"name":21,"slug":40},"uae",{"name":18,"slug":42},"ai-adoption",{"id":15,"slug":44,"title":45,"language":46},"global-ai-diffusion-q1-2026-microsoft-en","17.8% of the world used AI in Q1 2026","en",[48,54,60,66,72,78],{"id":49,"slug":50,"title":51,"cover_image":52,"image_url":52,"created_at":53,"category":13},"f374155a-c29e-478c-b7a5-679cad1c51e4","crdts-keep-replicas-in-sync-without-locks-zh","CRDT 讓副本不用鎖也能同步","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781011086259-4p4k.png","2026-06-09T13:17:34.493426+00:00",{"id":55,"slug":56,"title":57,"cover_image":58,"image_url":58,"created_at":59,"category":13},"4b3b5a50-45b7-4238-a38b-160f82e323ff","post-deterministic-systems-autonomous-infra-zh","後決定性分散系：自治基礎設施新框架","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781010194792-5ogb.png","2026-06-09T13:02:32.717551+00:00",{"id":61,"slug":62,"title":63,"cover_image":64,"image_url":64,"created_at":65,"category":13},"04e45398-9814-4907-b416-fcb5b8d69508","causal-learnability-formal-language-tasks-zh","用因果法量化任務可學性","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780987696075-l4g0.png","2026-06-09T06:47:34.438642+00:00",{"id":67,"slug":68,"title":69,"cover_image":70,"image_url":70,"created_at":71,"category":13},"75bcc569-5e89-45c8-b809-6f169e929f4b","rl-training-hands-off-control-gradually-zh","RL 先接管再放手","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780986786312-03yo.png","2026-06-09T06:32:32.849589+00:00",{"id":73,"slug":74,"title":75,"cover_image":76,"image_url":76,"created_at":77,"category":13},"e3ecab4b-7cc7-4246-baf6-e1c170d86ca5","omnigamearena-vlm-game-agent-benchmark-zh","OmniGameArena 讓 VLM 遊戲代理更好比","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780985893022-70pl.png","2026-06-09T06:17:32.189729+00:00",{"id":79,"slug":80,"title":81,"cover_image":82,"image_url":82,"created_at":83,"category":13},"6f25a29c-cbb8-4f53-9af7-1656b394333a","turboquant-cuts-kv-cache-memory-6x-google-tests-zh","TurboQuant 在 Google 測試中省下 6x KV 快取","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780906682236-sqe2.png","2026-06-08T08:17:21.878314+00:00",[85,90,95,100,105,110,115,120,125,130],{"id":86,"slug":87,"title":88,"created_at":89},"f18dbadb-8c59-4723-84a4-6ad22746c77a","deepmind-bets-on-continuous-learning-ai-2026-zh","DeepMind 押注 2026 連續學習 AI","2026-03-26T08:16:02.367355+00:00",{"id":91,"slug":92,"title":93,"created_at":94},"f4a106cb-02a6-4508-8f39-9720a0a93cee","ml-papers-of-the-week-github-research-desk-zh","每週 ML 論文清單，為何紅到 GitHub","2026-03-27T01:11:39.284175+00:00",{"id":96,"slug":97,"title":98,"created_at":99},"c4f807ca-4e5f-47f1-a48c-961cf3fc44dc","ai-ml-conferences-to-watch-in-2026-zh","2026 AI 研討會投稿時程整理","2026-03-27T01:51:53.874432+00:00",{"id":101,"slug":102,"title":103,"created_at":104},"cf046742-efb2-4753-aef9-caed5da5e32e","adaptive-block-scaled-data-types-zh","IF4：神經網路量化的聰明選擇","2026-03-31T06:00:36.990273+00:00",{"id":106,"slug":107,"title":108,"created_at":109},"53a0dc54-0371-4e40-8d5e-74e94a73840c","geometry-aware-similarity-metrics-for-neural-representations-zh","超越距離測量：用微分幾何重新理解神經網路","2026-03-31T06:01:01.241968+00:00",{"id":111,"slug":112,"title":113,"created_at":114},"fee7d472-a775-4b1d-bbc2-1e8bca1bbf8b","on-the-fly-repulsion-in-the-contextual-space-for-rich-divers-zh","讓AI繪圖更有創意：用排斥力提升生成多樣性","2026-03-31T06:01:25.439673+00:00",{"id":116,"slug":117,"title":118,"created_at":119},"a9901203-d69b-447b-8854-15d14eab32b4","vision-aided-beam-prediction-cnn-eca-zh","影像輔助波束預測升級 CNN","2026-04-01T10:00:25.8073+00:00",{"id":121,"slug":122,"title":123,"created_at":124},"b55e7dd4-0a24-4b3d-804d-b0309a03f498","triple-band-fss-mimo-antenna-sub-6-ghz-zh","三頻 FSS MIMO 天線瞄準 sub-6 GHz","2026-04-01T13:18:36.857305+00:00",{"id":126,"slug":127,"title":128,"created_at":129},"f68290bd-e7f3-4b30-ba22-dcd4e0130a66","openclaw-1299-repos-eight-weeks-analysis-zh","OpenClaw 1299 個 Repo 的資料解讀","2026-04-02T05:03:45.208411+00:00",{"id":131,"slug":132,"title":133,"created_at":134},"ed9f80eb-eb02-4d35-8ad4-0ddf428751dd","beam-coherence-aware-combining-mmwave-mimo-zh","毫米波 MIMO 的雙階合併法","2026-04-02T05:27:26.897188+00:00"]