--- title: Even "Experts" Get Stuff Wrong | daily.dev description: A transcript excerpt discusses Geoffrey Hinton's 2016 prediction that deep learning would surpass radiologists within five years and make training new... canonical: https://daily.dev/posts/even-experts-get-stuff-wrong-bs1o0wabz twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Even "Experts" Get Stuff Wrong | daily.dev og:description: A transcript excerpt discusses Geoffrey Hinton's 2016 prediction that deep learning would surpass radiologists within five years and make training new... og:url: https://daily.dev/posts/even-experts-get-stuff-wrong-bs1o0wabz og:image: https://api.daily.dev/og/posts/BS1O0WaBz.png og:image:alt: Even "Experts" Get Stuff Wrong og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Even "Experts" Get Stuff Wrong **[NeetCode](https://daily.dev/sources/neetcode)** · 1 min read · 3 upvotes · 0 comments ## Summary A transcript excerpt discusses Geoffrey Hinton's 2016 prediction that deep learning would surpass radiologists within five years and make training new radiologists pointless, noting this turned out wrong. It draws on Jevons paradox: faster AI-assisted scan turnaround increased demand for scans, which increased demand for radiologists rather than eliminating the role. The speaker parallels this to current AI hype, suggesting labs have realized they overestimated AI capabilities broadly and have refocused on coding as the area showing clearest value, while questioning the magnitude and long-term implications of that shift. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### Did deep learning end up replacing radiologists like Geoffrey Hinton predicted in 2016? No, Hinton's prediction that deep learning would outperform radiologists within five years and that people should stop training them turned out to be incorrect. Faster AI-assisted scan analysis reduced turnaround time, which increased the number of people getting scanned, which in turn increased demand for radiologists rather than eliminating the role, an example of Jevons paradox. _Anyone weighing bold AI-replaces-humans predictions can follow how these claims play out over time on daily.dev._ ### What is Jevons paradox and how does it apply to AI automation predictions? Jevons paradox describes how making a resource more efficient to use can increase, rather than decrease, total demand for it. Applied to radiology, faster deep-learning-assisted scan turnaround led more people to get scanned, which raised demand for radiologists instead of shrinking the field as automation predictions assumed it would. _Developers assessing automation claims in their own field can track this reasoning pattern on daily.dev._ ## Similar posts on daily.dev - [Fool's Expertise](https://daily.dev/posts/fool-s-expertise-bzvnuq28c) · Lobsters · 0 upvotes · 0 comments - [The steam engine lesson AI doomsayers keep missing](https://daily.dev/posts/the-steam-engine-lesson-ai-doomsayers-keep-missing-hhgwipqzw) · TechCentral · 0 upvotes · 0 comments - [Hinton, Bengio and AI lab scientists warn of an intelligence explosion](https://daily.dev/posts/hinton-bengio-and-ai-lab-scientists-warn-of-an-intelligence-explosion-tdfvju6dz) · The Next Web · 7 upvotes · 0 comments --- Tags: [#ai](https://daily.dev/tags/ai), [#deep-learning](https://daily.dev/tags/deep-learning) [View this post on daily.dev](https://daily.dev/posts/even-experts-get-stuff-wrong-bs1o0wabz) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Even \"Experts\" Get Stuff Wrong","url":"https://daily.dev/posts/even-experts-get-stuff-wrong-bs1o0wabz","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/even-experts-get-stuff-wrong-bs1o0wabz"},"datePublished":"2026-08-14T14:57:34.314Z","dateModified":"2026-09-14T06:42:46.340Z","description":"A transcript excerpt discusses Geoffrey Hinton's 2016 prediction that deep learning would surpass radiologists within five years and make training new...","image":"https://i.ytimg.com/vi/qrVW4UtOFYQ/sddefault.jpg","thumbnailUrl":"https://i.ytimg.com/vi/qrVW4UtOFYQ/sddefault.jpg","isAccessibleForFree":true,"articleSection":"NeetCode","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"NeetCode","logo":"https://media.daily.dev/image/upload/s--xfR6ZV0s--/f_auto/v1729363595/logos/neetcode","url":"https://daily.dev/sources/neetcode"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/even-experts-get-stuff-wrong-bs1o0wabz","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":3},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"ai,deep-learning","timeRequired":"PT1M","video":{"@type":"VideoObject","name":"Even \"Experts\" Get Stuff Wrong","description":"A transcript excerpt discusses Geoffrey Hinton's 2016 prediction that deep learning would surpass radiologists within five years and make training new...","thumbnailUrl":"https://i.ytimg.com/vi/qrVW4UtOFYQ/sddefault.jpg","uploadDate":"2026-08-14T14:57:34.314Z","duration":"PT1M","url":"https://api.daily.dev/r/BS1O0WaBz","embedUrl":"https://www.youtube.com/embed/qrVW4UtOFYQ"}} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"NeetCode","item":"https://daily.dev/sources/neetcode"},{"@type":"ListItem","position":3,"name":"Even \"Experts\" Get Stuff Wrong"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/even-experts-get-stuff-wrong-bs1o0wabz#faq","mainEntity":[{"@type":"Question","name":"Did deep learning end up replacing radiologists like Geoffrey Hinton predicted in 2016?","acceptedAnswer":{"@type":"Answer","text":"No, Hinton's prediction that deep learning would outperform radiologists within five years and that people should stop training them turned out to be incorrect. Faster AI-assisted scan analysis reduced turnaround time, which increased the number of people getting scanned, which in turn increased demand for radiologists rather than eliminating the role, an example of Jevons paradox. Anyone weighing bold AI-replaces-humans predictions can follow how these claims play out over time on daily.dev."}},{"@type":"Question","name":"What is Jevons paradox and how does it apply to AI automation predictions?","acceptedAnswer":{"@type":"Answer","text":"Jevons paradox describes how making a resource more efficient to use can increase, rather than decrease, total demand for it. Applied to radiology, faster deep-learning-assisted scan turnaround led more people to get scanned, which raised demand for radiologists instead of shrinking the field as automation predictions assumed it would. Developers assessing automation claims in their own field can track this reasoning pattern on daily.dev."}}]} ```