Tony Stoyanov is CTO and Co-Founder EliseAI
In the 2010s, tech companies pursued staff-level specialists: backend engineers, data scientists, systems architects. That model came in handy as technology slowly evolved. Experts knew their craft, could deliver results quickly and built careers on predictable foundations like cloud infrastructure or the latest JS frameworks
Then AI became mainstream.
The pace of change has accelerated. New technologies emerge and become 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 existed that long. The affluent people today aren’t the ones with the longest resumes; They are people who learn fast, adapt quickly and act without waiting for direction. Nowhere is this change more evident than in software engineering, which has experienced perhaps the most dramatic change, evolving faster than almost any other field of work.
How AI is rewriting the rules
AI has lowered the barriers to performing complex technical tasks, technical skills, and has also raised expectations for what counts as true expertise. McKinsey estimates that by 2030, up to 30% of work hours in the US could be automated and 12 million workers may need to change roles entirely. Technical depth still matters, but AI favors those who can understand things as they go.
At my company, I see this every day. Engineers who had never touched front-end code are now building UIs, while front-end developers are moving into back-end work. Technology is becoming easier to use but the problems are harder as they span more disciplines.
In that kind of environment, being great at one thing is not enough. The ability to connect engineering, product, and operations to make good decisions quickly, even with incomplete information, matters.
Despite all the excitement, only 1% of companies consider themselves truly mature in using AI. Many still rely on structures built for the slow era – layers of approval, rigid roles and over-reliance on experts who can’t move outside their field.
Characteristics of a Strong Generalist
A strong generalist has breadth without losing depth. They go deep in one or two domains but remain proficient in many areas. As David Epstein says Category“You have people walking around with all the knowledge of humanity on their phones, but they have no idea how to integrate it. We don’t train people to think or reason.” True expertise comes not from simply collecting information, but from connecting the dots.
The best generalists share these traits:
- ownership: End-to-end accountability not just for actions, but also for results.
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First-Principles Thinking: Question assumptions, focus on the goal, and reframe when needed.
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Compatibility: Learn new domains quickly and move between them with ease.
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agency: Act without waiting for approval and make adjustments as new information arrives.
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soft skills: Communicate clearly, align teams and keep customer needs in mind.
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Category: Solve a variety of problems and learn lessons from contexts.
I try to make accountability a priority for my teams. Everyone knows what they have, what success looks like and how it connects to the mission. Perfection is not the goal, moving forward is.
embracing change
Focusing on adaptable builders changed everything. These are the people who have the range and curiosity to use AI tools to learn fast and execute with confidence.
If you’re a builder who thrives on ambiguity, this is your time. The AI age rewards curiosity and initiative more than credibility. If you’re hiring, look no further. The people who will lead your company may not have the perfect resume for the job. They are the only ones who can grow as the company grows and as per its needs.
The future belongs to generalists and the companies that rely on them.
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