{"id":519331,"date":"2025-08-29T08:59:47","date_gmt":"2025-08-29T15:59:47","guid":{"rendered":"https:\/\/jorgep.com\/blog\/?p=519331"},"modified":"2026-05-04T12:48:29","modified_gmt":"2026-05-04T19:48:29","slug":"mit-report-the-genai-divide-a-cautionary-tale-or-a-misleading-snapshot","status":"publish","type":"post","link":"https:\/\/jorgep.com\/blog\/mit-report-the-genai-divide-a-cautionary-tale-or-a-misleading-snapshot\/","title":{"rendered":"MIT Report The GenAI Divide: A Cautionary Tale or a Misleading Snapshot?"},"content":{"rendered":"\n<div class=\"wp-block-columns has-theme-palette-7-background-color has-background is-layout-flex wp-container-core-columns-is-layout-2edb0647 wp-block-columns-is-layout-flex\" style=\"margin-top:0;margin-bottom:0\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:80%\">\n<p>Part of: <strong> <a href=\"https:\/\/jorgep.com\/blog\/series-ai-learnings\/\">AI Learning Series Here<\/a><\/strong><\/p>\n\n\n<style>.kadence-column395113_97b87a-23 > .kt-inside-inner-col,.kadence-column395113_97b87a-23 > .kt-inside-inner-col:before{border-top-left-radius:0px;border-top-right-radius:0px;border-bottom-right-radius:0px;border-bottom-left-radius:0px;}.kadence-column395113_97b87a-23 > .kt-inside-inner-col{column-gap:var(--global-kb-gap-sm, 1rem);}.kadence-column395113_97b87a-23 > .kt-inside-inner-col{flex-direction:column;}.kadence-column395113_97b87a-23 > .kt-inside-inner-col > .aligncenter{width:100%;}.kadence-column395113_97b87a-23 > .kt-inside-inner-col:before{opacity:0.3;}.kadence-column395113_97b87a-23{position:relative;}@media all and (max-width: 1024px){.kadence-column395113_97b87a-23 > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}@media all and (max-width: 767px){.kadence-column395113_97b87a-23 > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}<\/style>\n<div class=\"wp-block-kadence-column kadence-column395113_97b87a-23\"><div class=\"kt-inside-inner-col\"><style>.wp-block-kadence-advancedheading.kt-adv-heading510545_15a085-99, .wp-block-kadence-advancedheading.kt-adv-heading510545_15a085-99[data-kb-block=\"kb-adv-heading510545_15a085-99\"]{font-size:var(--global-kb-font-size-sm, 0.9rem);font-style:normal;}.wp-block-kadence-advancedheading.kt-adv-heading510545_15a085-99 mark.kt-highlight, .wp-block-kadence-advancedheading.kt-adv-heading510545_15a085-99[data-kb-block=\"kb-adv-heading510545_15a085-99\"] mark.kt-highlight{font-style:normal;color:#f76a0c;-webkit-box-decoration-break:clone;box-decoration-break:clone;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;}.wp-block-kadence-advancedheading.kt-adv-heading510545_15a085-99 img.kb-inline-image, .wp-block-kadence-advancedheading.kt-adv-heading510545_15a085-99[data-kb-block=\"kb-adv-heading510545_15a085-99\"] img.kb-inline-image{width:150px;vertical-align:baseline;}<\/style>\n<p class=\"kt-adv-heading510545_15a085-99 wp-block-kadence-advancedheading\" data-kb-block=\"kb-adv-heading510545_15a085-99\">Quick Links:\u00a0<a href=\"https:\/\/jorgep.com\/blog\/resources-for-learning-ai\/\">Resources for Learning AI<\/a> | <a href=\"https:\/\/jorgep.com\/blog\/keeping-up-with-ai\/\">Keep up with AI<\/a> | <a href=\"https:\/\/jorgep.com\/blog\/list-of-ai-tools\/\" data-type=\"post\" data-id=\"402818\">List of AI Tools<\/a> | <a href=\"https:\/\/jorgep.com\/blog\/local-ai-series\/\" data-type=\"page\" data-id=\"519365\">Local AI<\/a><\/p>\n<\/div><\/div>\n\n\n<style>.kb-row-layout-id395113_d73e95-0d > .kt-row-column-wrap{align-content:start;}:where(.kb-row-layout-id395113_d73e95-0d > .kt-row-column-wrap) > .wp-block-kadence-column{justify-content:start;}.kb-row-layout-id395113_d73e95-0d > .kt-row-column-wrap{column-gap:var(--global-kb-gap-md, 2rem);row-gap:var(--global-kb-gap-none, 0rem );padding-top:var(--global-kb-spacing-xxs, 0.5rem);padding-bottom:var(--global-kb-spacing-xxs, 0.5rem);grid-template-columns:repeat(2, minmax(0, 1fr));}.kb-row-layout-id395113_d73e95-0d > .kt-row-layout-overlay{opacity:0.30;}@media all and (max-width: 1024px){.kb-row-layout-id395113_d73e95-0d > .kt-row-column-wrap{grid-template-columns:repeat(2, minmax(0, 1fr));}}@media all and (max-width: 767px){.kb-row-layout-id395113_d73e95-0d > .kt-row-column-wrap{grid-template-columns:minmax(0, 1fr);}}<\/style><div class=\"kb-row-layout-wrap kb-row-layout-id395113_d73e95-0d alignnone wp-block-kadence-rowlayout\"><div class=\"kt-row-column-wrap kt-has-2-columns kt-row-layout-equal kt-tab-layout-inherit kt-mobile-layout-row kt-row-valign-top\">\n<style>.kadence-column395113_df36f9-de > .kt-inside-inner-col,.kadence-column395113_df36f9-de > .kt-inside-inner-col:before{border-top-left-radius:0px;border-top-right-radius:0px;border-bottom-right-radius:0px;border-bottom-left-radius:0px;}.kadence-column395113_df36f9-de > .kt-inside-inner-col{column-gap:var(--global-kb-gap-sm, 1rem);}.kadence-column395113_df36f9-de > .kt-inside-inner-col{flex-direction:column;}.kadence-column395113_df36f9-de > .kt-inside-inner-col > .aligncenter{width:100%;}.kadence-column395113_df36f9-de > .kt-inside-inner-col:before{opacity:0.3;}.kadence-column395113_df36f9-de{position:relative;}@media all and (max-width: 1024px){.kadence-column395113_df36f9-de > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}@media all and (max-width: 767px){.kadence-column395113_df36f9-de > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}<\/style>\n<div class=\"wp-block-kadence-column kadence-column395113_df36f9-de\"><div class=\"kt-inside-inner-col\"><style>.wp-block-kadence-advancedheading.kt-adv-heading395113_b3212c-b9, .wp-block-kadence-advancedheading.kt-adv-heading395113_b3212c-b9[data-kb-block=\"kb-adv-heading395113_b3212c-b9\"]{text-align:center;font-size:var(--global-kb-font-size-sm, 0.9rem);line-height:60px;font-style:normal;background-color:#f5a511;}.wp-block-kadence-advancedheading.kt-adv-heading395113_b3212c-b9 mark.kt-highlight, .wp-block-kadence-advancedheading.kt-adv-heading395113_b3212c-b9[data-kb-block=\"kb-adv-heading395113_b3212c-b9\"] mark.kt-highlight{font-style:normal;color:#f76a0c;-webkit-box-decoration-break:clone;box-decoration-break:clone;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;}.wp-block-kadence-advancedheading.kt-adv-heading395113_b3212c-b9 img.kb-inline-image, .wp-block-kadence-advancedheading.kt-adv-heading395113_b3212c-b9[data-kb-block=\"kb-adv-heading395113_b3212c-b9\"] img.kb-inline-image{width:150px;vertical-align:baseline;}<\/style>\n<p class=\"kt-adv-heading395113_b3212c-b9 wp-block-kadence-advancedheading\" data-kb-block=\"kb-adv-heading395113_b3212c-b9\">Subscribe to <a href=\"https:\/\/go.35s.be\/jtb\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>JorgeTechBits  newsletter<\/strong><\/a><\/p>\n<\/div><\/div>\n\n\n<style>.kadence-column395113_4b4b81-29 > .kt-inside-inner-col,.kadence-column395113_4b4b81-29 > .kt-inside-inner-col:before{border-top-left-radius:0px;border-top-right-radius:0px;border-bottom-right-radius:0px;border-bottom-left-radius:0px;}.kadence-column395113_4b4b81-29 > .kt-inside-inner-col{column-gap:var(--global-kb-gap-sm, 1rem);}.kadence-column395113_4b4b81-29 > .kt-inside-inner-col{flex-direction:column;}.kadence-column395113_4b4b81-29 > .kt-inside-inner-col > .aligncenter{width:100%;}.kadence-column395113_4b4b81-29 > .kt-inside-inner-col:before{opacity:0.3;}.kadence-column395113_4b4b81-29{position:relative;}@media all and (max-width: 1024px){.kadence-column395113_4b4b81-29 > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}@media all and (max-width: 767px){.kadence-column395113_4b4b81-29 > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}<\/style>\n<div class=\"wp-block-kadence-column kadence-column395113_4b4b81-29\"><div class=\"kt-inside-inner-col\"><\/div><\/div>\n\n<\/div><\/div><\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large is-resized\"><a href=\"htthttps:\/\/jorgep.com\/blog\/book-dont-just-chat-delegate\/\"><img loading=\"lazy\" decoding=\"async\" width=\"640\" height=\"1024\" src=\"https:\/\/jorgep.com\/blog\/wp-content\/uploads\/CoverBook-01-640x1024.jpg\" alt=\"\" class=\"wp-image-520234\" style=\"aspect-ratio:0.6250142320391666;width:98px;height:auto\" srcset=\"https:\/\/jorgep.com\/blog\/wp-content\/uploads\/CoverBook-01-640x1024.jpg 640w, https:\/\/jorgep.com\/blog\/wp-content\/uploads\/CoverBook-01-188x300.jpg 188w, https:\/\/jorgep.com\/blog\/wp-content\/uploads\/CoverBook-01-768x1229.jpg 768w, https:\/\/jorgep.com\/blog\/wp-content\/uploads\/CoverBook-01-960x1536.jpg 960w, https:\/\/jorgep.com\/blog\/wp-content\/uploads\/CoverBook-01-1280x2048.jpg 1280w, https:\/\/jorgep.com\/blog\/wp-content\/uploads\/CoverBook-01.jpg 1600w\" sizes=\"auto, (max-width: 640px) 100vw, 640px\" \/><\/a><figcaption class=\"wp-element-caption\"><a href=\"https:\/\/jorgep.com\/blog\/book-series-ai-dont-just-chat\/\" data-type=\"page\" data-id=\"520242\">Check out the Book Series<\/a><\/figcaption><\/figure>\n<\/div><\/div>\n<\/div>\n\n\n<style>.wp-block-kadence-advancedheading.kt-adv-heading510545_15a085-99, .wp-block-kadence-advancedheading.kt-adv-heading510545_15a085-99[data-kb-block=\"kb-adv-heading510545_15a085-99\"]{font-size:var(--global-kb-font-size-sm, 0.9rem);font-style:normal;}.wp-block-kadence-advancedheading.kt-adv-heading510545_15a085-99 mark.kt-highlight, .wp-block-kadence-advancedheading.kt-adv-heading510545_15a085-99[data-kb-block=\"kb-adv-heading510545_15a085-99\"] mark.kt-highlight{font-style:normal;color:#f76a0c;-webkit-box-decoration-break:clone;box-decoration-break:clone;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;}.wp-block-kadence-advancedheading.kt-adv-heading510545_15a085-99 img.kb-inline-image, .wp-block-kadence-advancedheading.kt-adv-heading510545_15a085-99[data-kb-block=\"kb-adv-heading510545_15a085-99\"] img.kb-inline-image{width:150px;vertical-align:baseline;}<\/style>\n<p class=\"kt-adv-heading510545_15a085-99 wp-block-kadence-advancedheading\" data-kb-block=\"kb-adv-heading510545_15a085-99\">Quick Links:\u00a0<a href=\"https:\/\/jorgep.com\/blog\/resources-for-learning-ai\/\">Resources for Learning AI<\/a> | <a href=\"https:\/\/jorgep.com\/blog\/keeping-up-with-ai\/\">Keep up with AI<\/a> | <a href=\"https:\/\/jorgep.com\/blog\/list-of-ai-tools\/\" data-type=\"post\" data-id=\"402818\">List of AI Tools<\/a> | <a href=\"https:\/\/jorgep.com\/blog\/local-ai-series\/\" data-type=\"page\" data-id=\"519365\">Local AI<\/a><\/p>\n\n\n<style>.wp-block-kadence-advancedheading.kt-adv-heading407818_afcbba-c7, .wp-block-kadence-advancedheading.kt-adv-heading407818_afcbba-c7[data-kb-block=\"kb-adv-heading407818_afcbba-c7\"]{font-size:var(--global-kb-font-size-sm, 0.9rem);font-style:normal;}.wp-block-kadence-advancedheading.kt-adv-heading407818_afcbba-c7 mark.kt-highlight, .wp-block-kadence-advancedheading.kt-adv-heading407818_afcbba-c7[data-kb-block=\"kb-adv-heading407818_afcbba-c7\"] mark.kt-highlight{font-style:normal;color:#f76a0c;-webkit-box-decoration-break:clone;box-decoration-break:clone;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;}.wp-block-kadence-advancedheading.kt-adv-heading407818_afcbba-c7 img.kb-inline-image, .wp-block-kadence-advancedheading.kt-adv-heading407818_afcbba-c7[data-kb-block=\"kb-adv-heading407818_afcbba-c7\"] img.kb-inline-image{width:150px;vertical-align:baseline;}<\/style>\n<p class=\"kt-adv-heading407818_afcbba-c7 wp-block-kadence-advancedheading\" data-kb-block=\"kb-adv-heading407818_afcbba-c7\">Have questions, ideas to share, or just want to connect? I\u2019d love to hear from you! Check out my <a href=\"https:\/\/jorgep.com\/blog\/about\/\">About Page<\/a> to learn more about me or connect with me.<\/p>\n\n\n\n<p>The MIT report&nbsp;<em>\u201cThe GenAI Divide: State of AI in Business 2025,\u201d<\/em>&nbsp;published in April, has been making waves ever since. By August, its headline claim\u2014that 95% of generative AI projects fail to deliver measurable returns\u2014was being echoed across headlines, conferences, and boardrooms. It\u2019s a striking message, but one that deserves closer examination, especially given the limitations of the study behind it.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Methodological Challenges<\/h2>\n\n\n\n<p>The report draws its conclusions from:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>52 executive interviews<\/li>\n\n\n\n<li>Surveys of 153 business leaders<\/li>\n\n\n\n<li>Analysis of 300 public AI deployments<\/li>\n<\/ul>\n\n\n\n<p>While these inputs offer perspective, they capture only a narrow slice of the global GenAI landscape. With thousands of companies\u2014from early-stage startups to Fortune 500 giants\u2014experimenting actively with AI, this sample is far too small to justify sweeping generalizations.<\/p>\n\n\n\n<p>Another critical factor is the report\u2019s expectation that AI projects show measurable ROI within six months or less. Given that AI-driven transformation involves complex change management, workflow integration, and cultural shifts, this expectation may be overly optimistic. Many successful technology projects historically require longer time horizons to realize tangible business returns. This limitation further colors the report\u2019s stark conclusions.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Tone and Timing<\/h2>\n\n\n\n<p>The report strikes a skeptical tone, released at a moment of accelerating public curiosity and corporate investment in AI. That combination has amplified its impact\u2014but also risks distorting the narrative. Like any transformative technology, GenAI is in an early adoption phase where failures are common. Cloud computing, mobile applications, and even the internet itself followed similar learning curves before mainstream success. The difference now is that GenAI is evolving faster, and at greater scale, across industries.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">GenAI in Practice<\/h2>\n\n\n\n<p>Beyond the report\u2019s stark statistics, meaningful successes are happening every day:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Enterprises are cutting costs and improving customer service through automated support, faster software development, and AI-assisted decision-making.<\/li>\n\n\n\n<li>Small businesses are using AI for marketing, content generation, and operational efficiency, enabling them to compete at new levels.<\/li>\n\n\n\n<li>Individual professionals leverage tools like ChatGPT, Claude, and Copilot to boost creativity, streamline workflows, and expand productivity.<\/li>\n<\/ul>\n\n\n\n<p>These achievements are less dramatic than a \u201c95% failure rate,\u201d but they represent real, compounding progress.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What Drives GenAI Success<\/h2>\n\n\n\n<p>Organizations that achieve measurable returns from GenAI tend to share common practices:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Setting clear, outcome-oriented goals<\/li>\n\n\n\n<li>Investing in workforce training and change management<\/li>\n\n\n\n<li>Designing human-in-the-loop processes to ensure accountability and quality<\/li>\n\n\n\n<li>Treating AI as a strategic capability, not a quick plug-and-play solution<\/li>\n\n\n\n<li>Embracing\u00a0<strong>Production Operations and Maintenance Best Practices<\/strong>\u00a0such as\u00a0<strong>AIOps (Artificial Intelligence for IT Operations)<\/strong>\u00a0and\u00a0<strong>MLOps (Machine Learning Operations)<\/strong>\u00a0to manage, monitor, and continuously improve AI systems in production<\/li>\n<\/ul>\n\n\n\n<p>AIOps and MLOps ensure AI projects do not fail post-deployment by enabling continuous monitoring, automated incident detection, version control, data and model quality assessments, and seamless collaboration between data scientists and operations teams. These practices mirror DevOps but adapt for the unique challenges of AI systems.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Rethinking the \u201cDivide\u201d<\/h2>\n\n\n\n<p>The true GenAI divide is not simply between success and failure. It lies between organizations that approach AI with strategy, patience, and integration\u2014and those chasing headlines without a plan. The MIT report highlights the risks of mismanagement, but its broader message is incomplete. GenAI is no miracle, but neither is it, as some would suggest, a dead end. It is a tool, one with transformative potential, whose biggest impact will come from thoughtful, deliberate adoption.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">References:<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/mlq.ai\/media\/quarterly_decks\/v0.1_State_of_AI_in_Business_2025_Report.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">The GenAI Divide: State of AI in Business 2025 &#8211; MLQ.ai (PDF)<\/a><br>This is the preliminary findings document hosted on MLQ.ai, widely referenced by press and analysts.<a href=\"https:\/\/mlq.ai\/media\/quarterly_decks\/v0.1_State_of_AI_in_Business_2025_Report.pdf\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/nanda.media.mit.edu\/\" target=\"_blank\" rel=\"noreferrer noopener\">NANDA &#8211; The Internet of AI Agents &#8211; MIT (Project Page)<\/a><br>Includes background and related reports; look for \u201cReports\u201d to explore all available documents from the MIT team<\/li>\n\n\n\n<li><a href=\"https:\/\/nationalcioreview.com\/articles-insights\/extra-bytes\/mit-finds-genai-projects-fail-roi-in-95-of-companies\/\" target=\"_blank\" rel=\"noreferrer noopener\">MIT Finds GenAI Projects Fail ROI in 95% of Companies &#8211; National CIO Review<\/a><br>Overview of the MIT NANDA study\u2019s findings, including data points and reflection on organizational barriers to GenAI success<\/li>\n\n\n\n<li><a href=\"https:\/\/www.linkedin.com\/posts\/markamontgomery_mit-report-95-of-generative-ai-pilots-at-activity-7363318990846222337-A3FD\" target=\"_blank\" rel=\"noreferrer noopener\">MIT Report: AI Adoption in Business 2025 &#8211; LinkedIn<\/a><br>Industry reactions and highlights from the MIT report, posted by experts and practitioners<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/www.youtube.com\/watch?v=sH5pXold95I\" target=\"_blank\" rel=\"noreferrer noopener\">EP 597: Do 95% of AI Pilots Fail? Why You Should Ignore MIT&#8217;s Viral &#8230;<\/a><br>A podcast episode that systematically breaks down the MIT study\u2019s methodology and calls out flaws in its data selection, media amplification, and potential marketing motives.<\/li>\n\n\n\n<li><a href=\"https:\/\/www.youtube.com\/watch?v=X6O21jbRcN4&amp;vl=en\" target=\"_blank\" rel=\"noreferrer noopener\">MIT Viral Study DEBUNKED &#8211; YouTube<\/a><br>A video that analyzes the actual content of the MIT report, debunks the headline claim, and argues that most critics have not read the original study before sharing its findings.<\/li>\n\n\n\n<li><a href=\"https:\/\/www.youtube.com\/watch?v=RIhPFDpQ7tI\" target=\"_blank\" rel=\"noreferrer noopener\">Is AI a bubble? Debunking MIT&#8217;s GenAi Report &#8211; YouTube<\/a><br>This video offers a critical review of the GenAI Divide report, discussing its overlooked sectors and wider implications for the AI industry.<\/li>\n\n\n\n<li><a href=\"https:\/\/www.youtube.com\/watch?v=QYopvnsIyss\" target=\"_blank\" rel=\"noreferrer noopener\">MIT AI Report Was Wrong | #kpunpacked #podcast &#8230; &#8211; YouTube<\/a><br>An in-depth podcast episode that digs into the details of the MIT report, revealing what the headlines missed and providing alternative views on GenAI project success<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>The MIT report&nbsp;\u201cThe GenAI Divide: State of AI in Business 2025,\u201d&nbsp;published in April, has been making waves ever since. By August, its headline claim\u2014that 95% of generative AI projects fail to deliver measurable returns\u2014was being echoed across headlines, conferences, and boardrooms. It\u2019s a striking message, but one that deserves closer examination, especially given the limitations&#8230;<\/p>\n","protected":false},"author":2,"featured_media":427864,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_kad_blocks_custom_css":"","_kad_blocks_head_custom_js":"","_kad_blocks_body_custom_js":"","_kad_blocks_footer_custom_js":"","ngg_post_thumbnail":0,"episode_type":"","audio_file":"","podmotor_file_id":"","podmotor_episode_id":"","cover_image":"","cover_image_id":"","duration":"","filesize":"","filesize_raw":"","date_recorded":"","explicit":"","block":"","itunes_episode_number":"","itunes_title":"","itunes_season_number":"","itunes_episode_type":"","_kad_post_transparent":"","_kad_post_title":"","_kad_post_layout":"","_kad_post_sidebar_id":"","_kad_post_content_style":"","_kad_post_vertical_padding":"","_kad_post_feature":"","_kad_post_feature_position":"","_kad_post_header":false,"_kad_post_footer":false,"_kad_post_classname":"","footnotes":""},"categories":[441],"tags":[1032,471,941,930,871],"class_list":["post-519331","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tech-talk","tag-agents","tag-ai","tag-ai-agents","tag-ai-series","tag-genai"],"taxonomy_info":{"category":[{"value":441,"label":"Tech Talk"}],"post_tag":[{"value":1032,"label":"Agents"},{"value":471,"label":"AI"},{"value":941,"label":"AI Agents"},{"value":930,"label":"AI Series"},{"value":871,"label":"GenAi"}]},"featured_image_src_large":["https:\/\/jorgep.com\/blog\/wp-content\/uploads\/FeaturedImage-Topic-AI-1024x512.png",1024,512,true],"author_info":{"display_name":"Jorge Pereira","author_link":"https:\/\/jorgep.com\/blog\/author\/jorge\/"},"comment_info":0,"category_info":[{"term_id":441,"name":"Tech Talk","slug":"tech-talk","term_group":0,"term_taxonomy_id":451,"taxonomy":"category","description":"","parent":0,"count":702,"filter":"raw","cat_ID":441,"category_count":702,"category_description":"","cat_name":"Tech Talk","category_nicename":"tech-talk","category_parent":0}],"tag_info":[{"term_id":1032,"name":"Agents","slug":"agents","term_group":0,"term_taxonomy_id":1042,"taxonomy":"post_tag","description":"","parent":0,"count":118,"filter":"raw"},{"term_id":471,"name":"AI","slug":"ai","term_group":0,"term_taxonomy_id":481,"taxonomy":"post_tag","description":"","parent":0,"count":163,"filter":"raw"},{"term_id":941,"name":"AI Agents","slug":"ai-agents","term_group":0,"term_taxonomy_id":951,"taxonomy":"post_tag","description":"","parent":0,"count":127,"filter":"raw"},{"term_id":930,"name":"AI Series","slug":"ai-series","term_group":0,"term_taxonomy_id":940,"taxonomy":"post_tag","description":"","parent":0,"count":169,"filter":"raw"},{"term_id":871,"name":"GenAi","slug":"genai","term_group":0,"term_taxonomy_id":881,"taxonomy":"post_tag","description":"","parent":0,"count":90,"filter":"raw"}],"_links":{"self":[{"href":"https:\/\/jorgep.com\/blog\/wp-json\/wp\/v2\/posts\/519331","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/jorgep.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/jorgep.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/jorgep.com\/blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/jorgep.com\/blog\/wp-json\/wp\/v2\/comments?post=519331"}],"version-history":[{"count":1,"href":"https:\/\/jorgep.com\/blog\/wp-json\/wp\/v2\/posts\/519331\/revisions"}],"predecessor-version":[{"id":519332,"href":"https:\/\/jorgep.com\/blog\/wp-json\/wp\/v2\/posts\/519331\/revisions\/519332"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/jorgep.com\/blog\/wp-json\/wp\/v2\/media\/427864"}],"wp:attachment":[{"href":"https:\/\/jorgep.com\/blog\/wp-json\/wp\/v2\/media?parent=519331"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/jorgep.com\/blog\/wp-json\/wp\/v2\/categories?post=519331"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/jorgep.com\/blog\/wp-json\/wp\/v2\/tags?post=519331"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}