{"id":176,"date":"2025-12-21T03:00:00","date_gmt":"2025-12-20T19:00:00","guid":{"rendered":"https:\/\/connectword.dpdns.org\/?p=176"},"modified":"2025-12-21T03:00:00","modified_gmt":"2025-12-20T19:00:00","slug":"hiring-specialists-made-sense-before-ai-now-generalists-win","status":"publish","type":"post","link":"https:\/\/connectword.dpdns.org\/?p=176","title":{"rendered":"Hiring specialists made sense before AI \u2014 now generalists win"},"content":{"rendered":"

Tony Stoyanov is CTO and co-founder of <\/i>EliseAI<\/i><\/a><\/p>\n

In the 2010s, tech companies chased staff-level specialists: Backend engineers, data scientists, system architects. That model worked when technology evolved slowly. Specialists knew their craft, could deliver quickly and built careers on predictable foundations like cloud infrastructure or the latest JS framework<\/p>\n

Then AI went mainstream.<\/p>\n

The pace of change has exploded. New technologies appear and mature in less than a year. You can\u2019t hire someone who has been building AI agents<\/a> for five years, as the technology hasn\u2019t existed for that long. The people thriving today aren\u2019t those with the longest r\u00e9sum\u00e9s; they\u2019re the ones who learn fast, adapt fast and act without waiting for direction. Nowhere is this transformation more evident than in software engineering, which has likely experienced the most dramatic shift of all, evolving faster than almost any other field of work.<\/p>\n

How AI Is rewriting the rules<\/b><\/h2>\n

AI has lowered the barrier to doing complex technical work, technical skills and it’s also raised expectations for what counts as real expertise. McKinsey estimates that by 2030, up to 30% of U.S. work hours<\/a> could be automated and 12 million workers may need to shift roles entirely. Technical depth still matters, but AI favors people who can figure things out as they go.<\/p>\n

At my company, I see this every day. Engineers who never touched front-end code<\/a> are now building UIs, while front-end developers are moving into back-end work. The technology keeps getting easier to use but the problems are harder because they span more disciplines.<\/p>\n

In that kind of environment, being great at one thing isn\u2019t enough. What matters is the ability to bridge engineering, product and operations to make good decisions quickly, even with imperfect information.<\/p>\n

Despite all the excitement, only 1% of companies<\/a> consider themselves truly mature in how they use AI. Many still rely on structures built for a slower era \u2014 layers of approval, rigid roles and an overreliance on specialists who can\u2019t move outside their lane.<\/p>\n

The traits of a strong generalist\u00a0<\/b><\/h2>\n

A strong generalist has breadth without losing depth. They go deep in one or two domains but stay fluent across many. As David Epstein puts it in Range<\/i>, \u201cYou have people walking around with all the knowledge of humanity on their phone, but they have no idea how to integrate it. We don\u2019t train people in thinking or reasoning.\u201d True expertise comes from connecting the dots, not just collecting information.<\/p>\n

The best generalists share these traits:<\/p>\n