Tony Stoyanoff is CTO; Elise AI
In the 2010s, technology companies pursued staff-level specialists such as back-end engineers, data scientists, and systems architects. This model worked when technology evolved slowly. Specialists know their craft and can quickly build a career on predictable foundations like cloud infrastructure and modern JS frameworks.
Then AI became mainstream.
The pace of change is exploding. New technology emerges and matures within a year. AI agents are a technology that hasn’t been around that long, so you can’t hire someone who’s been building AI agents for five years. The people who are successful today are not the ones with the longest resumes. They are people who learn quickly, adapt quickly, and act without waiting for instructions. Nowhere is this change more evident than in software engineering. Software engineering has probably experienced the most dramatic changes and is evolving faster than almost any other field.
How is AI rewriting the rules?
AI has lowered the barriers to performing complex technical tasks and technical skills, and has also raised expectations for what we call true expertise. McKinsey estimates that by 2030, up to 30% of U.S. work hours may be automated and 12 million workers may need to completely change roles. Technical depth is still important, but AI prioritizes people who can understand things as they go along.
I see scenes like this every day at my company. Engineers who have never touched front-end code are building the UI, and front-end developers are transitioning to back-end work. As technology continues to become easier to use, the problem becomes more difficult as it crosses more disciplines.
In such an environment, being good at one thing is not enough. The key is the ability to bridge engineering, product, and operations to make good decisions quickly, even with incomplete information.
Despite all this excitement, only 1% of companies believe their use of AI is truly mature. Many still rely on structures built in later times, such as overlapping approvals, rigid roles, and an overreliance on experts who can’t get out of their lane.
Characteristics of a strong generalist
Strong generalists have breadth without losing depth. They dig deep into one or two areas but remain fluent across many areas. As David Epstein says range“There are people walking around with all the knowledge about humanity on their phones, but they don’t know how to integrate it. We don’t train people to think and reason.” True expertise comes from connecting the dots, not just collecting information.
Great generalists share the following characteristics:
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Owned by: End-to-end responsibility for results, not just tasks.
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First principles thinking: Question your assumptions, focus on your goals, and reframe as needed.
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Adaptability: Learn new domains quickly and move smoothly between domains.
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Agency: Act without waiting for approval and adjust as new information comes in.
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Soft skills: Communicate clearly, align your team, and stay focused on customer needs.
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range: Solve different types of problems and draw lessons from different situations.
I strive to prioritize accountability to my team. Everyone knows what they own, what success looks like, and how it ties into the mission. Perfection is not the goal, progress is the goal.
embrace change
Focusing on adaptable builders changed everything. These are people who have the scope and curiosity to use AI tools to learn quickly and execute with confidence.
If you’re a builder who thrives on ambiguity, now’s your chance. In the AI era, curiosity and initiative are valued over qualifications. If you’re hiring, look ahead. The person moving your company forward may not be the one with the best resume for the job. These are the people who can grow into the people you need as your company evolves.
The future belongs to generalists and the companies that trust them.
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