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Instead, it\u2019s making us work harder. Copy link Facebook Email Notes More (Close) (User) (User's avatar) Discover more from After Burnout After Burnout is a quiet, personal newsletter about what happens when you step out of survival mode and start coming home to yourself. Over 1,000 subscribers (Enter your email...) Subscribe By subscribing, I agree to Substack's (https://substack.com/tos) Terms of Use , and acknowledge its (https://substack.com/ccpa#personal-data-collected) Information Collection Notice and (https://substack.com/privacy) Privacy Policy . Already have an account? Sign in AI promised efficiency. Instead, it\u2019s making us work harder. AI tools were supposed to free up our time\u2014but they\u2019re increasing our cognitive load and making us less productive. Here\u2019s what\u2019s really happening (and how to use them without burning out). (https://substack.com/@kvlly) (Kelly Vaughn) (Kelly Vaughn's avatar) (https://substack.com/@kvlly) Kelly Vaughn Aug 04, 2025 24 Share this post () (After Burnout) After Burnout AI promised efficiency. Instead, it\u2019s making us work harder. Copy link Facebook Email Notes More (https://afterburnout.co/p/ai-promised-to-make-us-more-efficient/comments) 1 2 Share A note: This is a different kind of post than I usually send from After Burnout. Instead of something raw, short, and personal, this is more of a deep-dive article. But given how much this topic connects to burnout and our relationship with productivity tools, it felt important to share. (https://substackcdn.com/image/fetch/$s_!fAI2!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Feed0168b-d82f-46c1-a3c6-bbe4d44879b0_3218x2189.jpeg) () We were promised a productivity revolution. AI tools would handle the boring stuff, freeing us to focus on creative, strategic work. We\u2019d finally have time to think, to innovate, to maybe even leave the office before dark. Instead, something strange is happening. We\u2019re working harder than ever. But here\u2019s the kicker: we were told AI would free us up for \u201chigher-level work.\u201d What actually happened? We just found more work to fill the space. That two-hour block AI created by automating your morning reports? It\u2019s now packed with three new meetings. The 30 minutes you saved on data analysis? You\u2019re using it to manage two more AI tools and review their outputs. We instinctively expand our work to fill available time\u2014it\u2019s like a productivity version of Parkinson\u2019s Law (work expands so as to fill the time available for its completion). AI promised to elevate us to strategic thinking, but instead we\u2019re just doing more stuff, faster, with less mental bandwidth to actually think strategically about any of it. I see it everywhere\u2014in Slack channels exploring AI tool recommendations, in LinkedIn posts about \u201cgame-changing workflows,\u201d in the exhausted faces of colleagues who\u2019ve spent their morning cleaning up code AI wrote for them. The promise was simple: AI saves time. The reality is messier. The data doesn\u2019t lie (even if we do) A (https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study) recent study of experienced software developers working on their own repositories found something shocking: when developers used AI tools, they took 19% longer to complete tasks than without AI . Even more telling: the developers estimated they were 20% faster with AI\u2014they were completely wrong about their own productivity. A 25% increase in AI adoption correlated with a 1.5% drop in delivery throughput and a 7.2% decrease in delivery stability. This isn\u2019t an isolated finding. The (https://cloud.google.com/devops/state-of-devops?hl=en®ion=US) 2024 DORA report \u2014the gold standard for measuring software delivery performance\u2014revealed that while 75% of developers reported feeling more productive with AI, the system-level results told a different story. A 25% increase in AI adoption correlated with a 1.5% drop in delivery throughput and a 7.2% decrease in delivery stability. Even more striking, the DORA researchers found that \u201cindividuals are reporting a decrease in the amount of time they spend doing valuable work as AI adoption increases\u201d\u2014exactly the opposite of what AI tools promise to deliver. (https://mitsloan.mit.edu/ideas-made-to-matter/productivity-paradox-ai-adoption-manufacturing-firms) Manufacturing research shows similar patterns\u2014companies adopting industrial AI see immediate productivity losses before eventual gains. This article talks of a \u201cJ-curve trajectory,\u201d indicating that a small dip in productivity is expected before gains can be seen. More established companies see a greater dip than newer companies, which is to be expected. The pattern is consistent across studies: AI tools create an illusion of efficiency while often making systems work harder, not smarter. Individual developers feel faster, but teams deliver slower. People report saving time, but organizations see increased coordination costs. Before I continue on, I\u2019m not saying AI tools are bad and that you shouldn\u2019t use them. I\u2019m pointing out where we realistically are in this current phase of AI innovation and what it means for us moving forward. If you enjoyed this post, you can support my writing by becoming a paid subscriber. Thank you! (https://afterburnout.co/p/ai-promised-to-make-us-more-efficient) () (https://afterburnout.co/p/ai-promised-to-make-us-more-efficient) (https://afterburnout.co/p/ai-promised-to-make-us-more-efficient) () (subscribe-widget-preamble) (Type your email...) Subscribe The cognitive load nobody talks about Here\u2019s what I think is really happening: AI tools aren\u2019t just saving us time\u2014they\u2019re shifting where we spend our mental energy. And that shift is exhausting in ways we haven\u2019t fully recognized yet. Instead of writing that email from scratch, you\u2019re now crafting the perfect prompt, reviewing AI output for accuracy, and fixing the weird formatting it generated. Instead of doing research yourself, you\u2019re fact-checking AI summaries and cross-referencing sources you don\u2019t trust. Researchers and practitioners increasingly describe this as a new kind of cognitive load, requiring effort just to filter and discern AI outputs, which might detract from deeper, more focused work. We\u2019ve traded implementation work for constant quality control\u2014and quality control is mentally draining. For developers, AI has shifted the job from writing code to \u201ccritical specification, orchestration, and post-hoc validation\u201d ( (https://sdtimes.com/agile/the-ai-productivity-paradox-in-software-engineering-balancing-efficiency-and-human-skill-retention/) source ). The code gets written faster, but now you need entirely new meta-skills to manage AI outputs you didn\u2019t create and can\u2019t fully trace. How this relates to burnout This isn\u2019t really about AI. It\u2019s about our relationship with productivity culture itself. The pressure to constantly optimize, to adopt every new tool, to squeeze efficiency from every moment\u2014it\u2019s just another version of the \u201cgrind harder\u201d mentality that leads to burnout. We\u2019ve taken the (https://fortune.com/2025/08/01/ai-startups-996-china-working-model-silicon-valley/) 996 work culture debate (working 9a-9p 6 days a week) and dressed it up in Silicon Valley clothing. Sometimes the most productive thing you can do is not try to be more productive. I keep thinking about my A- running rating and how that small drop from A+ still managed to break something inside me. We\u2019re doing the same thing with AI\u2014chasing perfect optimization at the expense of our mental health and actual effectiveness. Maybe the real productivity hack is learning to recognize when tools serve us versus when we\u2019re serving the tools. Maybe it\u2019s giving ourselves permission to work at a sustainable pace, even if the AI influencers on LinkedIn think we\u2019re falling behind. Maybe being \u201cless efficient\u201d but more intentional is actually the win. So what can we do about it? The solution isn\u2019t to abandon AI entirely \u2014some of these tools genuinely are helpful. But we need to fundamentally change our expectations and approach. 1. Reframe \u201cproductivity\u201d entirely Stop measuring success by time saved. Start measuring by cognitive energy preserved. The real question isn\u2019t \u201chow fast can I do this?\u201d but \u201ccan I actually absorb, process, and act effectively on what\u2019s being produced?\u201d If an AI tool saves you 30 minutes but leaves you mentally drained and second-guessing everything, that\u2019s not productivity\u2014that\u2019s cognitive debt. And if you\u2019re just filling those saved 30 minutes with another 30 minutes of less valuable work, you\u2019re going to eventually resent your work. 2. Give yourself permission to be selective You don\u2019t have to adopt every AI tool that promises efficiency. You don\u2019t have to optimize every workflow. You don\u2019t have to keep up with every \u201cgame-changing\u201d prompt strategy your colleagues are sharing. Even experienced professionals are wrong about which AI tools actually help them. Trust your own experience over the hype. If a tool makes you feel more scattered, more anxious, or more exhausted\u2014even if it technically saves time\u2014you have permission to stop using it. Exploration is healthy; forced adoption of tools that don\u2019t feel right to your workflow only increases stress. 3. Plan for the adjustment period (and that it might be long) As stated earlier, AI adoption follows a \u201cJ-curve\u201d\u2014temporary productivity decline followed by eventual gains, but only after significant organizational adjustment. Maybe we\u2019re all in that dip right now, and that\u2019s\u2026 normal. The companies that eventually see gains? They\u2019re the ones that invested in complementary changes\u2014new processes, training, infrastructure. They didn\u2019t just bolt AI onto existing workflows and hope for magic. But here\u2019s what the research really shows: even when productivity gains do happen \u201caccomplishing twice as much work in the same amount of time\u201d\u2014not working less, just cramming more into the same hours. And that\u2019s what we really need to be wary of. We shouldn\u2019t be trying to do more work in the same amount of time; we shouldn\u2019t be aiming to be more efficient with our time so we don\u2019t have to work 60+ hour weeks. Subscribe to After Burnout By Kelly Vaughn \u00b7 Launched 4 months ago After Burnout is a quiet, personal newsletter about what happens when you step out of survival mode and start coming home to yourself. (https://afterburnout.co/p/ai-promised-to-make-us-more-efficient) (https://afterburnout.co/p/ai-promised-to-make-us-more-efficient) (https://afterburnout.co/p/ai-promised-to-make-us-more-efficient) (post-end-cta) (Type your email...) Subscribe By subscribing, I agree to Substack's (https://substack.com/tos) Terms of Use , and acknowledge its (https://substack.com/ccpa#personal-data-collected) Information Collection Notice and (https://substack.com/privacy) Privacy Policy . (https://substack.com/profile/815471-tomas) (Tomas) (Tomas's avatar) (https://substack.com/profile/12319668-jurgen-richard-plasser) (J\u00fcrgen Richard Plasser) (J\u00fcrgen Richard Plasser's avatar) (https://substack.com/profile/8984621-paige-niedringhaus) (Paige Niedringhaus) (Paige Niedringhaus's avatar) (https://substack.com/profile/50738233-vinicius-mendes) (Vinicius Mendes) (Vinicius Mendes's avatar) (https://substack.com/profile/105937682-paul-mora) (Paul Mora) (Paul Mora's avatar) 24 Likes \u2219(https://substack.com/note/p-170002891/restacks?utm_source=substack&utm_content=facepile-restacks) 2 Restacks 24 Share this post () (After Burnout) After Burnout AI promised efficiency. Instead, it\u2019s making us work harder. Copy link Facebook Email Notes More (https://afterburnout.co/p/ai-promised-to-make-us-more-efficient/comments) 1 2 Share Previous Discussion about this post Comments Restacks (User) (User's avatar) (Write a comment...) (https://substack.com/profile/84603294-mike-mcbride?utm_source=comment) (Mike McBride) (Mike McBride's avatar) (https://substack.com/profile/84603294-mike-mcbride?utm_source=substack-feed-item) Mike McBride (https://afterburnout.co/p/ai-promised-to-make-us-more-efficient/comment/142080767) (Aug 4, 2025, 3:53 PM) 2d The unquestioned mantra of AI \"freeing workers to spend time on strategy\" has always rubbed me the wrong way. In a large business, how many people set the strategy in any meaningful way? It's not that large of a percentage. Most workers will simply be doing other tasks, likely much more mundane tasks, like fact-checking and editing the output of GenAI tools. 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Please (https://enable-javascript.com/) turn on JavaScript or unblock scripts " ], "output": "htmltotext.txt", "pwd": "/data/archive/1754493532.617981", "schema": "ArchiveResult", "start_ts": "2025-08-06T15:19:48.535231+00:00", "status": "succeeded" } ], "media": [ { "cmd": [ "/usr/local/bin/yt-dlp", "--restrict-filenames", "--trim-filenames", "128", "--write-description", "--write-info-json", "--write-annotations", "--write-thumbnail", "--no-call-home", "--write-sub", "--write-auto-subs", "--convert-subs=srt", "--yes-playlist", "--continue", "--no-abort-on-error", "--ignore-errors", "--geo-bypass", "--add-metadata", "--format=(bv*+ba/b)[filesize<=750m][filesize_approx<=?750m]/(bv*+ba/b)", "--skip-download", "--cache-dir=/data/yt-dlp-cache/", "--cookies=/data/yt-dlp-cache/cookies.txt", "--proxy=socks5://tor-socks-proxy:9150", "--no-playlist", "https://afterburnout.co/p/ai-promised-to-make-us-more-efficient" ], "cmd_version": "2024.10.7", "end_ts": "2025-08-06T15:19:56.670705+00:00", "index_texts": [], "output": "ArchiveError: Failed to save media", "pwd": "/data/archive/1754493532.617981", "schema": "ArchiveResult", "start_ts": "2025-08-06T15:19:50.258638+00:00", "status": "failed" } ], "mercury": [ { "cmd": [ "/home/archivebox/.npm/bin/postlight-parser", "https://afterburnout.co/p/ai-promised-to-make-us-more-efficient" ], "cmd_version": "2.2.3", "end_ts": "2025-08-06T15:19:48.502438+00:00", "index_texts": null, "output": "mercury/", "pwd": "/data/archive/1754493532.617981", "schema": "ArchiveResult", "start_ts": "2025-08-06T15:19:45.611226+00:00", "status": "succeeded" } ], "pdf": [], "readability": [ { "cmd": [ "/home/archivebox/.npm/bin/readability-extractor", "/tmp/tmppjawmvn5", "https://afterburnout.co/p/ai-promised-to-make-us-more-efficient" ], "cmd_version": "0.0.11", "end_ts": "2025-08-06T15:19:34.472613+00:00", "index_texts": [ "A note: This is a different kind of post than I usually send from After Burnout. Instead of something raw, short, and personal, this is more of a deep-dive article. But given how much this topic connects to burnout and our relationship with productivity tools, it felt important to share.We were promised a productivity revolution. AI tools would handle the boring stuff, freeing us to focus on creative, strategic work. We\u2019d finally have time to think, to innovate, to maybe even leave the office before dark.Instead, something strange is happening. We\u2019re working harder than ever.But here\u2019s the kicker: we were told AI would free us up for \u201chigher-level work.\u201d What actually happened? We just found more work to fill the space. That two-hour block AI created by automating your morning reports? It\u2019s now packed with three new meetings. The 30 minutes you saved on data analysis? You\u2019re using it to manage two more AI tools and review their outputs.We instinctively expand our work to fill available time\u2014it\u2019s like a productivity version of Parkinson\u2019s Law (work expands so as to fill the time available for its completion). AI promised to elevate us to strategic thinking, but instead we\u2019re just doing more stuff, faster, with less mental bandwidth to actually think strategically about any of it.I see it everywhere\u2014in Slack channels exploring AI tool recommendations, in LinkedIn posts about \u201cgame-changing workflows,\u201d in the exhausted faces of colleagues who\u2019ve spent their morning cleaning up code AI wrote for them.The promise was simple: AI saves time. The reality is messier.A recent study of experienced software developers working on their own repositories found something shocking: when developers used AI tools, they took 19% longer to complete tasks than without AI. Even more telling: the developers estimated they were 20% faster with AI\u2014they were completely wrong about their own productivity.A 25% increase in AI adoption correlated with a 1.5% drop in delivery throughput and a 7.2% decrease in delivery stability. This isn\u2019t an isolated finding. The 2024 DORA report\u2014the gold standard for measuring software delivery performance\u2014revealed that while 75% of developers reported feeling more productive with AI, the system-level results told a different story. A 25% increase in AI adoption correlated with a 1.5% drop in delivery throughput and a 7.2% decrease in delivery stability. Even more striking, the DORA researchers found that \u201cindividuals are reporting a decrease in the amount of time they spend doing valuable work as AI adoption increases\u201d\u2014exactly the opposite of what AI tools promise to deliver.Manufacturing research shows similar patterns\u2014companies adopting industrial AI see immediate productivity losses before eventual gains. This article talks of a \u201cJ-curve trajectory,\u201d indicating that a small dip in productivity is expected before gains can be seen. More established companies see a greater dip than newer companies, which is to be expected.The pattern is consistent across studies: AI tools create an illusion of efficiency while often making systems work harder, not smarter. Individual developers feel faster, but teams deliver slower. People report saving time, but organizations see increased coordination costs.Before I continue on, I\u2019m not saying AI tools are bad and that you shouldn\u2019t use them. I\u2019m pointing out where we realistically are in this current phase of AI innovation and what it means for us moving forward.Here\u2019s what I think is really happening: AI tools aren\u2019t just saving us time\u2014they\u2019re shifting where we spend our mental energy. And that shift is exhausting in ways we haven\u2019t fully recognized yet.Instead of writing that email from scratch, you\u2019re now crafting the perfect prompt, reviewing AI output for accuracy, and fixing the weird formatting it generated. Instead of doing research yourself, you\u2019re fact-checking AI summaries and cross-referencing sources you don\u2019t trust.Researchers and practitioners increasingly describe this as a new kind of cognitive load, requiring effort just to filter and discern AI outputs, which might detract from deeper, more focused work. We\u2019ve traded implementation work for constant quality control\u2014and quality control is mentally draining.For developers, AI has shifted the job from writing code to \u201ccritical specification, orchestration, and post-hoc validation\u201d (source). The code gets written faster, but now you need entirely new meta-skills to manage AI outputs you didn\u2019t create and can\u2019t fully trace.This isn\u2019t really about AI. It\u2019s about our relationship with productivity culture itself.The pressure to constantly optimize, to adopt every new tool, to squeeze efficiency from every moment\u2014it\u2019s just another version of the \u201cgrind harder\u201d mentality that leads to burnout. We\u2019ve taken the 996 work culture debate (working 9a-9p 6 days a week) and dressed it up in Silicon Valley clothing.Sometimes the most productive thing you can do is not try to be more productive.I keep thinking about my A- running rating and how that small drop from A+ still managed to break something inside me. We\u2019re doing the same thing with AI\u2014chasing perfect optimization at the expense of our mental health and actual effectiveness.Maybe the real productivity hack is learning to recognize when tools serve us versus when we\u2019re serving the tools. Maybe it\u2019s giving ourselves permission to work at a sustainable pace, even if the AI influencers on LinkedIn think we\u2019re falling behind.Maybe being \u201cless efficient\u201d but more intentional is actually the win.The solution isn\u2019t to abandon AI entirely\u2014some of these tools genuinely are helpful. But we need to fundamentally change our expectations and approach.1. Reframe \u201cproductivity\u201d entirelyStop measuring success by time saved. Start measuring by cognitive energy preserved. The real question isn\u2019t \u201chow fast can I do this?\u201d but \u201ccan I actually absorb, process, and act effectively on what\u2019s being produced?\u201dIf an AI tool saves you 30 minutes but leaves you mentally drained and second-guessing everything, that\u2019s not productivity\u2014that\u2019s cognitive debt. And if you\u2019re just filling those saved 30 minutes with another 30 minutes of less valuable work, you\u2019re going to eventually resent your work.2. Give yourself permission to be selectiveYou don\u2019t have to adopt every AI tool that promises efficiency. You don\u2019t have to optimize every workflow. You don\u2019t have to keep up with every \u201cgame-changing\u201d prompt strategy your colleagues are sharing.Even experienced professionals are wrong about which AI tools actually help them. Trust your own experience over the hype. If a tool makes you feel more scattered, more anxious, or more exhausted\u2014even if it technically saves time\u2014you have permission to stop using it. Exploration is healthy; forced adoption of tools that don\u2019t feel right to your workflow only increases stress.3. Plan for the adjustment period (and that it might be long)As stated earlier, AI adoption follows a \u201cJ-curve\u201d\u2014temporary productivity decline followed by eventual gains, but only after significant organizational adjustment. Maybe we\u2019re all in that dip right now, and that\u2019s\u2026 normal.The companies that eventually see gains? They\u2019re the ones that invested in complementary changes\u2014new processes, training, infrastructure. They didn\u2019t just bolt AI onto existing workflows and hope for magic.But here\u2019s what the research really shows: even when productivity gains do happen \u201caccomplishing twice as much work in the same amount of time\u201d\u2014not working less, just cramming more into the same hours.And that\u2019s what we really need to be wary of. 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