Tony Stoyanov is CTO and co-founder of EliseAI
Within the 2010s, tech corporations 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 most recent JS framework
Then AI went mainstream.
The pace of change has exploded. Recent technologies appear and mature in lower than a 12 months. You may’t hire someone who has been constructing AI agents for five years, because the technology hasn’t existed for that long. The people thriving today aren’t those with the longest résumés; they’re those who learn fast, adapt fast and act without waiting for direction. Nowhere is that this transformation more evident than in software engineering, which has likely experienced probably the most dramatic shift of all, evolving faster than almost some other field of labor.
How AI Is rewriting the foundations
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, as much as 30% of U.S. work hours may very well be automated and 12 million staff may have to shift roles entirely. Technical depth still matters, but AI favors individuals who can figure things out as they go.
At my company, I see this on daily basis. Engineers who never touched front-end code are actually constructing UIs, while front-end developers are moving into back-end work. The technology keeps getting easier to make use of but the issues are harder because they span more disciplines.
In that type of environment, being great at one thing isn’t enough. What matters is the flexibility to bridge engineering, product and operations to make good decisions quickly, even with imperfect information.
Despite all the thrill, just one% of corporations consider themselves truly mature in how they use AI. Many still depend 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 robust generalist
A powerful generalist has breadth without losing depth. They go deep in a single or two domains but stay fluent across many. As David Epstein puts it in Range, “You will have people walking around with all of the knowledge of humanity on their phone, but they do not know integrate it. We don’t train people in pondering or reasoning.” True expertise comes from connecting the dots, not only collecting information.
The very best generalists share these traits:
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Ownership: End-to-end accountability for outcomes, not only tasks.
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First-principles pondering: Query assumptions, concentrate on the goal, and rebuild when needed.
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Adaptability: Learn recent domains quickly and move between them easily.
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Agency: Act without waiting for approval and adjust as recent information is available in.
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Soft skills: Communicate clearly, align teams and keep customers’ needs in focus.
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Range: Solve different sorts of problems and draw lessons across contexts.
I attempt to make accountability a priority for my teams. Everyone knows what they own, what success looks like and the way it connects to the mission. Perfection isn’t the goal, forward movement is.
Embracing the shift
Specializing in adaptable builders modified every thing. These are the individuals with the range and curiosity to make use of AI tools to learn quickly and execute confidently.
When you’re a builder who thrives in ambiguity, that is your time. The AI era rewards curiosity and initiative greater than credentials. When you’re hiring, look ahead. The individuals who’ll move your organization forward won’t be those with the right résumé for the job. They’re those who can grow into what the corporate will need because it evolves.
The long run belongs to generalists and to the businesses that trust them.
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