{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Agents and Engineers","title":"The AI Skill Flip","html":"","width":"100%","height":180,"duration":3795,"description":"Sheamus McGovern founded ODSC roughly twelve years ago and now splits his time between the conference business and a role as venture partner and Head of AI at Cortical Ventures. His book, The AI Skill Flip, came out of a pattern he kept hitting: data scientists and software engineers coming to him asking whether AI was going to take their jobs and what they should do about it. He wanted to write something that sat between the doom narrative and the utopian one, both of which he thinks are wrong. The \"flip\" in the title is the observation that the balance of skills has shifted rather than disappeared. Five years ago a software engineer spent most of their time writing raw code. Now much of that time goes to judging and evaluating what the model produced and thinking further up the stack. The same flip applies in marketing, where the skill becomes knowing what good looks like and what persona you're targeting rather than producing the asset yourself.\nAsked what separates people who get real value from AI from people who don't, Sheamus lands on three things. First is passion, the plain will to get a good outcome, which he compares to what separates a strong startup founder from an average one. Second is creativity, which he argues AI increases rather than eliminates, because models are sycophantic and will happily build exactly what you asked for. His example is watching people reach for Replit, Base44, or Lovable and build a dashboard, when the real question is whether a dashboard is even the right artifact in a world of agentic workflows. Dan pushes the point further, noting that dashboards existed because software was expensive to build, so you built one thing and maintained it. Third is judgment. AI is excellent at producing output and terrible at judging its value, which Sheamus frames as another instance of the automation paradox.\nOn whether judgment can be taught, Sheamus starts at the engineering level with evaluations. Traditional numeric metrics still...","thumbnail_url":"https://img.transistorcdn.com/TK0Nqa_Yt1Nidvhw7SvORku00Quhyrr-EpS6aCekMzA/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9hMmU5/YmJlYjNlY2E2ZThh/OGYwZmExY2M5MGMz/MDQyNC5wbmc.webp","thumbnail_width":300,"thumbnail_height":300}