Hiring specialists made sense before AI — now generalists win
Tony Stoyanov is CTO and co-founder of Elise AI
In the 2010s, tech companies chased staff-level specialists: backend engineers, data scientists, systems architects. That model worked when technology was slow to evolve. Specialists knew their trade, could achieve results quickly and built careers on predictable foundations such as cloud infrastructure or the latest JS framework
Then AI became mainstream.
The pace of change has exploded. New technologies emerge and mature in less than a year. You can’t hire someone who has been building AI agents for five years because the technology hasn’t been around that long. The people who are doing well today aren’t the ones with the longest resumes; they are the ones who learn quickly, adapt quickly and act without waiting for directions. Nowhere is this transformation more evident than in software engineering, which has probably undergone the most dramatic change of all and developed faster than almost any other field.
How AI rewrites the rules
AI has lowered the barriers to performing complex technical work and skills, and has also raised expectations for what counts as true expertise. McKinsey estimates that by 2030 up to 30% of US working hours could be automated and that 12 million workers may have to switch roles completely. Technical depth is still important, but AI favors humans who can figure things out as they go.
I see this every day at my company. Engineers who have never worked with front-end code are now building user interfaces, while front-end developers are moving to back-end work. The technology is becoming easier to use, but the problems are more difficult because they involve more disciplines.
In such an environment, it is not enough to be good at something. What matters is the ability to connect technology, product and business operations, so that you can quickly make good decisions, 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 age – layers of approval, rigid roles and an over-reliance on specialists who can’t get outside their own path.
The qualities of a strong generalist
A strong generalist has breadth without losing depth. They go deep in one or two domains, but remain very fluid. As David Epstein puts it 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 to think or reason.” True expertise comes from connecting the dots, not just gathering information.
The best generalists share these traits:
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Property: End-to-end accountability for results, not just tasks.
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First principles thinking: Challenge assumptions, focus on purpose, and rebuild when necessary.
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Adaptability: Quickly learn new domains and switch smoothly between them.
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Office: Act without waiting for approval and adapt as new information arrives.
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Soft skills: Communicate clearly, align teams and keep customer needs in focus.
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Range: Solve different types of problems and learn lessons from different contexts.
I try to make accountability a priority for my teams. Everyone knows what they own, what success looks like and how it fits in with the mission. Perfection is not the goal, forward movement is.
Embracing the shift
Focusing on customizable builders changed everything. These are the people with the reach and curiosity to use AI tools to learn quickly and execute with confidence.
If you’re a builder who thrives on ambiguity, this is your time. The AI era rewards curiosity and initiative more than credentials. When you’re hiring, look ahead. The people who move your business forward may not be the ones with the perfect resume for the job. They are the ones who can grow into what the company needs as it evolves.
The future belongs to generalists and the companies that trust them.
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