Hiring specialists made sense before AI — now generalists win

Tony Stoyanov is CTO and co-founder of EliseAIIn 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 frameworkThen AI went mainstream.The pace of change has exploded. New technologies appear and mature in less than a year. You can’t hire someone who has been building AI agents for five years, as the technology hasn’t existed for that long. The people thriving today aren’t those with the longest résumés; they’re 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.How AI Is rewriting the rulesAI 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 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.At my company, I see this every day. Engineers who never touched front-end code 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.In that kind of environment, being great at one thing isn’t enough. What matters is the ability to bridge engineering, product and operations to make good decisions quickly, even with imperfect information.Despite all the excitement, only 1% of companies consider themselves truly mature in how they use AI. Many still rely on structures built for a slower era — layers of approval, rigid roles and an overreliance on specialists who can’t move outside their lane.The traits of a strong generalist 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, “You have people walking around with all the knowledge of humanity on their phone, but they have no idea how to integrate it. We don’t train people in thinking or reasoning.” True expertise comes from connecting the dots, not just collecting information.The best generalists share these traits:Ownership: End-to-end accountability for outcomes, not just tasks.First-principles thinking: Question assumptions, focus on the goal, and rebuild when needed.Adaptability: Learn new domains quickly and move between them smoothly.Agency: Act without waiting for approval and adjust as new information comes in.Soft skills: Communicate clearly, align teams and keep customers’ needs in focus.Range: Solve different kinds of problems and draw lessons across contexts.I try to make accountability a priority for my teams. Everyone knows what they own, what success looks like and how it connects to the mission. Perfection isn’t the goal, forward movement is.Embracing the shiftFocusing on adaptable builders changed everything. These are the people with the range and curiosity to use AI tools to learn quickly and execute confidently.If you’re a builder who thrives in ambiguity, this is your time. The AI era rewards curiosity and initiative more than credentials. If you’re hiring, look ahead. The people who’ll move your company forward might not be the ones with the perfect résumé for the job. They’re the ones who can grow into what the company will need as it evolves.The future belongs to generalists and to the companies that trust them.Read more from our guest writers. Or, consider submitting a post of your own! See our guidelines here. Weiter zum vollständigen Artikel bei VentureBeat Weiter zum vollständigen Artikel bei VentureBeatWeiter zum vollständigen Artikel bei VentureBeat Weiter zum vollständigen Artikel bei VentureBeat