{"id":324481,"date":"2026-07-16T10:00:00","date_gmt":"2026-07-16T07:00:00","guid":{"rendered":"https:\/\/ceotudent.com\/ai-productivity-paradox-individual-gains-company-results"},"modified":"2026-07-16T10:00:00","modified_gmt":"2026-07-16T07:00:00","slug":"ai-productivity-paradox-individual-gains-company-results","status":"publish","type":"post","link":"https:\/\/ceotudent.com\/en\/ai-productivity-paradox-individual-gains-company-results","title":{"rendered":"The AI Productivity Paradox: Why Individual Gains Are Not Showing Up in Company Results"},"content":{"rendered":"<p><strong>TL;DR<\/strong><\/p>\n<ul>\n<li>Controlled studies keep finding large individual-task speedups from AI: about 14% more calls resolved per hour, tasks finished up to 55.8% faster, writing time cut 40%. Yet firm-level and economy-level output has barely moved.<\/li>\n<li>The most rigorous 2025 trial found experienced developers were 19% slower with AI, even though they believed they were 20% faster. The gap between felt productivity and measured productivity is the whole story.<\/li>\n<li>One credible macro estimate puts AI&rsquo;s total-factor-productivity gain at no more than 0.66% over ten years. National labor-productivity growth is still running below its 1947-2024 average.<\/li>\n<li>This is the Solow paradox returning: &ldquo;You can see the computer age everywhere but in the productivity statistics.&rdquo;<\/li>\n<li>The CEO-and-student takeaway: manage your own output the way a CEO reads a P&amp;L (measure delivered results, not activity), while learning the tool like a student. Activity is not output, and only output shows up in the numbers that matter.<\/li>\n<\/ul>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_84 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/ceotudent.com\/en\/ai-productivity-paradox-individual-gains-company-results\/#The-Two-Numbers-That-Do-Not-Agree\" >The Two Numbers That Do Not Agree<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/ceotudent.com\/en\/ai-productivity-paradox-individual-gains-company-results\/#What-the-Controlled-Studies-Actually-Found\" >What the Controlled Studies Actually Found<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/ceotudent.com\/en\/ai-productivity-paradox-individual-gains-company-results\/#The-Study-That-Broke-the-Consensus\" >The Study That Broke the Consensus<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/ceotudent.com\/en\/ai-productivity-paradox-individual-gains-company-results\/#Where-the-Gain-Leaks-Out\" >Where the Gain Leaks Out<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/ceotudent.com\/en\/ai-productivity-paradox-individual-gains-company-results\/#The-View-From-the-Whole-Economy\" >The View From the Whole Economy<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/ceotudent.com\/en\/ai-productivity-paradox-individual-gains-company-results\/#The-Adoption-Impact-Gap\" >The Adoption-Impact Gap<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/ceotudent.com\/en\/ai-productivity-paradox-individual-gains-company-results\/#How-to-Read-This-Like-a-CEO-Learn-It-Like-a-Student\" >How to Read This Like a CEO, Learn It Like a Student<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/ceotudent.com\/en\/ai-productivity-paradox-individual-gains-company-results\/#FAQ\" >FAQ<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/ceotudent.com\/en\/ai-productivity-paradox-individual-gains-company-results\/#Sources\" >Sources<\/a><\/li><\/ul><\/nav><\/div>\n<h2 id=\"the-two-numbers-that-do-not-agree\"><span class=\"ez-toc-section\" id=\"The-Two-Numbers-That-Do-Not-Agree\"><\/span>The Two Numbers That Do Not Agree<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>There are two ways to measure whether AI makes people more productive, and in 2025 they point in opposite directions.<\/p>\n<p>The first is the controlled experiment: give one group an AI tool, withhold it from another, hand both the same task, and measure who finishes faster or better. These studies are almost uniformly positive. The second is the aggregate statistic: national labor-productivity data from the Bureau of Labor Statistics, and macro models of total factor productivity. These have not detected a boom.<\/p>\n<p>A CEO reading a P&amp;L knows this pattern well. A sales team can be busier than ever, with more calls, more demos, more activity dashboards lit up green, while revenue stays flat. Activity is an input. Output is what lands in the accounts. The AI productivity paradox is the same problem at civilizational scale: the input meters are screaming, and the output line is quiet.<\/p>\n<h2 id=\"what-the-controlled-studies-actually-found\"><span class=\"ez-toc-section\" id=\"What-the-Controlled-Studies-Actually-Found\"><\/span>What the Controlled Studies Actually Found<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The individual-level evidence is real and should not be waved away. Three studies anchor the optimistic case.<\/p>\n<p>In the largest field study to date, Erik Brynjolfsson, Danielle Li and Lindsey Raymond tracked 5,179 customer-support agents who were given a generative-AI conversational assistant. Access raised productivity, measured as issues resolved per hour, by 14% on average. The effect was concentrated among the least experienced workers, who improved by 34%, while the most skilled agents saw almost no gain. The AI was, in effect, copying the tacit knowledge of the best agents and handing it to novices.<\/p>\n<p>In a randomized controlled trial by Sida Peng and colleagues, 95 developers were asked to build an HTTP server in JavaScript. The group with GitHub Copilot finished 55.8% faster, a result significant at p = 0.0017, though with a wide 95% confidence interval running from 21% to 89%.<\/p>\n<p>In a preregistered experiment published in Science, Shakked Noy and Whitney Zhang assigned incentivized writing tasks to 453 college-educated professionals and gave half of them ChatGPT. Average completion time fell 40% and rated output quality rose 18%. As in the call-center study, weaker performers gained the most.<\/p>\n<p>Read together, these are the strongest cards in the &ldquo;AI works&rdquo; hand. Every figure below is a real, published number.<\/p>\n<table>\n<thead>\n<tr>\n<th>Study (named)<\/th>\n<th>Setting \/ task<\/th>\n<th>n<\/th>\n<th>Measured individual gain<\/th>\n<th>Who gained most<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Brynjolfsson, Li, Raymond 2023 (NBER w31161)<\/td>\n<td>Customer support, issues resolved per hour<\/td>\n<td>5,179 agents<\/td>\n<td>+14% average<\/td>\n<td>Novices +34%; experts near 0<\/td>\n<\/tr>\n<tr>\n<td>Peng et al. 2023 (arXiv 2302.06590)<\/td>\n<td>Coding, build an HTTP server<\/td>\n<td>95 devs<\/td>\n<td>55.8% faster (95% CI 21-89%)<\/td>\n<td>Less-experienced developers<\/td>\n<\/tr>\n<tr>\n<td>Noy, Zhang 2023 (Science)<\/td>\n<td>Professional writing tasks<\/td>\n<td>453<\/td>\n<td>Time -40%, quality +18%<\/td>\n<td>Lower-skill writers<\/td>\n<\/tr>\n<tr>\n<td>Bick, Blandin, Deming 2024 (NBER w32966)<\/td>\n<td>Self-reported time saved, US workers<\/td>\n<td>Large survey<\/td>\n<td>5.4% of work hours saved (~2.2 hrs\/week)<\/td>\n<td>Frequent AI users<\/td>\n<\/tr>\n<tr>\n<td>METR 2025 (arXiv 2507.09089)<\/td>\n<td>Experienced open-source devs, real repos<\/td>\n<td>16 devs, 246 tasks<\/td>\n<td>19% SLOWER with AI<\/td>\n<td>No one; all slowed<\/td>\n<\/tr>\n<tr>\n<td>Acemoglu 2024 (NBER w32487)<\/td>\n<td>Macro TFP, 10-year projection<\/td>\n<td>Model<\/td>\n<td>No more than +0.66% TFP total<\/td>\n<td>Economy-wide ceiling<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><em>Table: verified figures, each cell sourced to the named study listed. Full citations in Sources.<\/em><\/p>\n<h2 id=\"the-study-that-broke-the-consensus\"><span class=\"ez-toc-section\" id=\"The-Study-That-Broke-the-Consensus\"><\/span>The Study That Broke the Consensus<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Notice the fifth and sixth rows. They are the reason &ldquo;AI makes workers more productive&rdquo; is now a genuinely contested claim rather than a settled one.<\/p>\n<p>In 2025, the research nonprofit METR ran a randomized controlled trial on 16 experienced open-source developers working on mature codebases they knew well, averaging five years of prior experience on those projects, across 246 real tasks. When allowed to use early-2025 AI tools such as Cursor Pro and Claude 3.5\/3.7 Sonnet, they took 19% longer to finish.<\/p>\n<p>The most important finding was not the slowdown. It was the misperception. Before starting, the developers forecast that AI would make them 24% faster. After finishing, after actually being slowed down, they still believed AI had made them about 20% faster. The felt sense of productivity moved in the exact opposite direction from the measured result.<\/p>\n<p>This is the mechanism of the whole paradox in miniature. AI reliably makes work feel faster and easier: the blank page disappears, boilerplate writes itself, the first draft arrives in seconds. That felt fluency is genuine, and it is exactly what surveys capture when workers report time savings. The St. Louis Fed&rsquo;s survey work by Alexander Bick, Adam Blandin and David Deming found workers reporting that AI saved 5.4% of their work hours, roughly 2.2 hours a week. But self-reported saved time is a feeling, not an audited output number. The METR trial is what happens when you audit it.<\/p>\n<h2 id=\"where-the-gain-leaks-out\"><span class=\"ez-toc-section\" id=\"Where-the-Gain-Leaks-Out\"><\/span>Where the Gain Leaks Out<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>If individuals really do finish some tasks faster, why does none of it reach the firm&rsquo;s output or the nation&rsquo;s productivity statistics? Because a task speedup has to survive a long journey before it becomes measured output, and it leaks at every stage. The table below is a diagnostic framework built for this article, mapping each leak to the discipline that plugs it.<\/p>\n<table>\n<thead>\n<tr>\n<th>Leak point<\/th>\n<th>Why the gain disappears<\/th>\n<th>CEO-and-student fix<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Time saved is reinvested in more activity, not banked<\/td>\n<td>Faster drafting just means more drafts, more meetings, more messages<\/td>\n<td>Bank the saved hour against a real output target, the way a CEO banks a cost cut; do not spend it on motion<\/td>\n<\/tr>\n<tr>\n<td>Rework and verification<\/td>\n<td>AI output looks finished but needs checking; senior time is spent reviewing junior-plus-AI work<\/td>\n<td>Count net time including review; measure the task end-to-end, not the first draft<\/td>\n<\/tr>\n<tr>\n<td>Coordination drag<\/td>\n<td>One person is faster, but the team, approvals, and handoffs are not<\/td>\n<td>Optimize the bottleneck, not your own step; the P&amp;L reflects the slowest link<\/td>\n<\/tr>\n<tr>\n<td>Skill atrophy and over-trust<\/td>\n<td>Over-reliance erodes the judgment that catches AI errors<\/td>\n<td>Stay in student mode: keep doing hard tasks unaided to preserve the taste that quality control requires<\/td>\n<\/tr>\n<tr>\n<td>Wrong task automated<\/td>\n<td>Speeding up work that should not be done at all<\/td>\n<td>CEO question first: is this output worth producing? Efficiency at a useless task is zero<\/td>\n<\/tr>\n<tr>\n<td>Measurement illusion<\/td>\n<td>Felt speed (surveys) diverges from measured speed (trials), as METR showed<\/td>\n<td>Trust your own audited numbers over your sense of momentum<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><em>Table: CEOtudent editorial framework (synthesis of public research).<\/em><\/p>\n<h2 id=\"the-view-from-the-whole-economy\"><span class=\"ez-toc-section\" id=\"The-View-From-the-Whole-Economy\"><\/span>The View From the Whole Economy<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Zoom out to the macro level and the leaks add up to a striking flatness. Daron Acemoglu&rsquo;s task-based model, published as an NBER working paper, estimates that AI will raise total factor productivity by no more than 0.66% in total over ten years, roughly 0.064% a year. That is not zero, but it is nowhere near the transformation the individual-task numbers might imply.<\/p>\n<p>The observed data has not contradicted him. US nonfarm-business labor productivity rose 2.0% from the fourth quarter of 2023 to the fourth quarter of 2024, and 2.4% in the second quarter of 2025. Those are respectable numbers, but they are not a breakout. Over the current business cycle since late 2019, productivity has grown at an annualized 1.8%, which is below the long-run rate of 2.1% since 1947. Whatever AI is doing, it has not yet pushed the aggregate above its own historical trend.<\/p>\n<p>None of this is new in kind. In 1987 the economist Robert Solow wrote that &ldquo;you can see the computer age everywhere but in the productivity statistics.&rdquo; It took roughly a decade, and the diffusion of the internet and enterprise software, before the computer revolution finally showed up in the late-1990s productivity numbers. The AI version of the Solow paradox may resolve the same way, through slow reorganization of how work is done rather than through a tool that instantly makes everyone more productive.<\/p>\n<h2 id=\"the-adoption-impact-gap\"><span class=\"ez-toc-section\" id=\"The-Adoption-Impact-Gap\"><\/span>The Adoption-Impact Gap<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The corporate data tells the same story from the inside. Stanford HAI&rsquo;s 2025 AI Index reports that organizational AI adoption jumped to 78% in 2024, up from 55% the year before, and that use of generative AI in at least one business function more than doubled to 71%. Adoption is nearly universal.<\/p>\n<p>Impact is not. McKinsey&rsquo;s 2025 State of AI survey found that the large majority of organizations have not yet seen a material effect on enterprise-level earnings from their AI use, with only a small minority of high performers attributing meaningful bottom-line impact to it. The gap between &ldquo;we use AI&rdquo; and &ldquo;AI changed our results&rdquo; is the corporate face of the productivity paradox. Buying seats is easy. Rewiring the workflow so the time savings survive to the P&amp;L is the hard part, and most firms have not done it.<\/p>\n<h2 id=\"how-to-read-this-like-a-ceo-learn-it-like-a-student\"><span class=\"ez-toc-section\" id=\"How-to-Read-This-Like-a-CEO-Learn-It-Like-a-Student\"><\/span>How to Read This Like a CEO, Learn It Like a Student<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The paradox is not a reason to ignore AI. It is a reason to measure yourself honestly. Three operating principles follow from the evidence.<\/p>\n<p>First, measure output, not activity. The METR developers felt 20% faster while being 19% slower. Your own sense of momentum is an unreliable instrument. Define the unit of output that actually matters in your role, such as shipped features, closed deals, published analyses, or decisions made, and track it before and after AI, end-to-end including review time. A CEO does not report &ldquo;we were very busy this quarter.&rdquo; Do not report it to yourself either. This is exactly the discipline behind running <a href=\"https:\/\/ceotudent.com\/en\/personal-operating-system\/\">your life on a personal operating system<\/a>.<\/p>\n<p>Second, bank the saved time deliberately. The single biggest leak is that saved minutes get spent on more motion. If AI genuinely gives you back two hours a week, the CEO move is to allocate those hours to a specific higher-value output, not to let them dissolve into a fuller inbox.<\/p>\n<p>Third, stay a student where it counts. The gains in the field studies were largest for novices precisely because AI encodes expertise they lacked. But the METR slowdown fell on experts working in domains they knew deeply, partly because reviewing and correcting AI output cost more than doing the work themselves. The lesson is to keep building the judgment that lets you tell good AI output from confident-sounding wrong output, and to keep <a href=\"https:\/\/ceotudent.com\/en\/how-to-audit-your-job-for-ai-replaceability\/\">auditing your own job for what AI can and cannot replace<\/a>. That judgment is the one asset AI cannot yet hand you, and it is what turns a task speedup into real, bankable output.<\/p>\n<h2 id=\"faq\"><span class=\"ez-toc-section\" id=\"FAQ\"><\/span>FAQ<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><strong>Is the claim that AI does not improve productivity?<\/strong><br \/>\nNo. Controlled studies show clear individual-task gains, especially for less-experienced workers. The paradox is that these gains have not yet translated into firm-level or economy-wide output growth.<\/p>\n<p><strong>Why would AI make experienced developers slower?<\/strong><br \/>\nIn the METR 2025 trial, experienced developers working on codebases they knew well spent extra time prompting, waiting, and reviewing AI output that needed correction. For work you already do fluently, that overhead can exceed the time the AI saves.<\/p>\n<p><strong>Do workers just imagine the time savings?<\/strong><br \/>\nPartly. Workers in the St. Louis Fed survey genuinely reported saving 5.4% of their hours, and the METR developers believed AI sped them up. But when measured against a control group, the felt speedup did not always match the real result. Perceived and measured productivity can diverge sharply.<\/p>\n<p><strong>How large is AI&rsquo;s expected effect on the overall economy?<\/strong><br \/>\nAcemoglu&rsquo;s model projects no more than a 0.66% total-factor-productivity gain over ten years, about 0.064% annually. Other economists are more optimistic, but no credible estimate implies an instant productivity boom.<\/p>\n<p><strong>Has AI shown up in official productivity numbers yet?<\/strong><br \/>\nNot distinctly. US nonfarm-business productivity grew 2.0% over 2024 and 2.4% in the second quarter of 2025, but the current business cycle is still running below the long-run 2.1% average since 1947.<\/p>\n<p><strong>Is this the same as the old Solow paradox?<\/strong><br \/>\nIt rhymes with it. Solow observed in 1987 that computers were everywhere except in the productivity statistics. The gains eventually appeared once firms reorganized around the technology. AI may follow the same delayed path.<\/p>\n<p><strong>What should an individual do about it?<\/strong><br \/>\nTrack a concrete output metric before and after adopting AI, count review and rework time, deliberately reallocate any time saved to higher-value work, and keep practicing core skills unaided so you can catch AI errors.<\/p>\n<h2 id=\"sources\"><span class=\"ez-toc-section\" id=\"Sources\"><\/span>Sources<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li>Erik Brynjolfsson, Danielle Li and Lindsey Raymond, Generative AI at Work, NBER Working Paper 31161, 2023.<\/li>\n<li>Sida Peng, Eirini Kalliamvakou, Peter Cihon and Mert Demirer, The Impact of AI on Developer Productivity: Evidence from GitHub Copilot, arXiv 2302.06590, 2023.<\/li>\n<li>Shakked Noy and Whitney Zhang, Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence, Science, 2023.<\/li>\n<li>METR, Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity, arXiv 2507.09089, 2025.<\/li>\n<li>Daron Acemoglu, The Simple Macroeconomics of AI, NBER Working Paper 32487, 2024.<\/li>\n<li>Alexander Bick, Adam Blandin and David Deming, The Rapid Adoption of Generative AI, NBER Working Paper 32966 and Federal Reserve Bank of St. Louis, 2024.<\/li>\n<li>US Bureau of Labor Statistics, Productivity and Costs, nonfarm business sector releases, 2024-2025.<\/li>\n<li>Stanford Institute for Human-Centered Artificial Intelligence, The 2025 AI Index Report, 2025.<\/li>\n<li>McKinsey and Company, The State of AI: How Organizations Are Rewiring to Capture Value, 2025.<\/li>\n<\/ul>\n<hr>\n<p><em>This content was compiled with the support of AI following in-depth research, then written and prepared for publication by the CEOtudent editorial team.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Controlled studies show large individual speedups from AI, yet firm-level and economy-wide output has barely moved. A synthesis of NBER, MIT, METR and BLS evidence on the gap between felt productivity and measured results &#8211; and how to close it.<\/p>\n","protected":false},"author":1,"featured_media":324483,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[5,18],"tags":[],"class_list":["post-324481","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-is","category-strateji"],"_links":{"self":[{"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/posts\/324481","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/comments?post=324481"}],"version-history":[{"count":0,"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/posts\/324481\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/media\/324483"}],"wp:attachment":[{"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/media?parent=324481"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/categories?post=324481"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/tags?post=324481"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}