Hiring specialists made sense before AI – now generalists win

Tony Stoyanov is CTO and co-founder of EliseAI

In the 2010s, technology companies looked for specialists: back-end engineers, data scientists, system architects. This model worked when technology evolved slowly. Specialists knew their stuff, could deliver quickly, and build their careers on predictable foundations like cloud infrastructure or the latest JS framework.

Then AI became widespread.

The pace of change has exploded. New technologies appear and mature in less than a year. You cannot hire someone who has been build AI agents for five years, because the technology hasn’t been around that long. The people who thrive today aren’t the ones with the longest resumes; they are the ones who learn quickly, adapt quickly and act without waiting for direction. Nowhere is this transformation more evident than in software engineering, which has probably experienced the most radical change of all, evolving more rapidly than almost any other field of work.

How AI is rewriting the rules

AI has lowered the barriers to performing complex technical work and acquiring technical skills, and has also raised expectations for what constitutes true expertise. McKinsey estimates that by 2030, up to 30% of working hours in the United States could be automated and 12 million workers may have to change roles entirely. Technical depth still matters, but AI favors people who can figure things out as they go.

In my business, I see this every day. Engineers who have never touched front code now build user interfaces, while front-end developers shift to back-end work. The technology is becoming easier to use, but the problems are more difficult because they span more disciplines.

In this kind of environment, being good at one thing is not enough. What matters is the ability to connect 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 the way they use AI. Many still rely on structures built for a slower era – approval levels, rigid roles, and an over-reliance on specialists who can’t get out of their way.

Traits of a good generalist

A strong generalist has breadth without losing depth. They delve deeper into one or two areas but remain comfortable in several areas. As David Epstein says Range“You have people walking around with all the knowledge of humanity on their phones, but they don’t know how to integrate it. We don’t train people to think or reason.” True expertise comes from connecting the dots, not just gathering information.

The best generalists share these traits:

• Possession: End-to-end accountability for results, not just tasks.

• Thinking about first principles: Question assumptions, focus on the goal, and rebuild if necessary.

• Adaptability: Learn new areas quickly and move easily from one area to another.

• Agency: Act without waiting for approval and adjust as new information arrives.

• General skills: Communicate clearly, align teams and stay focused on customer needs.

• Range: Solve different types of problems and learn lessons in different contexts.

I try to make accountability a priority for my teams. Everyone knows what they have, what success looks like, and how it relates to the mission. Perfection is not the goal, forward movement is.

Accept change

Focusing on adaptable manufacturers changed everything. These are the people with the range and curiosity to use AI tools learn quickly and perform with confidence.

If you’re a builder who thrives on ambiguity, this is your time. The AI ​​era rewards curiosity and initiative more than qualifications. If you are recruiting, plan ahead. The people who will move your company forward may not be the ones with the perfect resume for the job. They are the ones who can become what the business will need as it evolves.

The future belongs to general practitioners and the companies who trust them.

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