A.I. Is Changing What “High Potential” Looks Like

The startup hiring process is changing faster than the systems we use to prepare people for work. It’s becoming clear in interviews. Candidates for nontechnical roles are increasingly asked to build something: a small tool, a new workflow or an analysis using A.I. Companies want to see whether someone can take an ambiguous problem and turn it into a working solution.

The shift is just as pronounced on the technical side. If A.I. can write much of the code, understanding how that code fits together—architecture, security, technical hygiene and system design—becomes more important. More technical interviews resemble interviews for engineering managers because the job is becoming less about writing every line of code and more about directing, evaluating and improving the work of coding agents.

These changes point to a larger shift in how startups define talent. A.I. is making it easier to demonstrate technical proficiency and produce work that once required years of specialized training. As execution becomes cheaper and faster, the traits companies need most are moving upstream: judgment, curiosity, initiative, systems thinking and the ability to figure out what should be built in the first place. That should initiate a reevaluation of both hiring and how we prepare people for work in the first place.

Universities and recruiting systems still rely heavily on signals that are easy to measure: GPA, credentials, internships, resumes and technical interviews. Those signals have never been perfect, but they made more sense when the ability to execute a defined task was a meaningful proxy for future performance.

In an A.I.-enabled workplace, the harder question is whether someone can identify the right problem, make good decisions with incomplete information and create something that did not previously exist. The ability to produce an answer is becoming less distinctive, while knowing which question to ask—and what to do with the answer—is becoming more so.  

I have never been convinced that candidates should be judged primarily by how well they produce polished resumes. The gap between what schools measure and what startups need, however, is becoming harder to ignore. If I were designing the way we evaluate students today, I would place far more weight on evidence of creative thinking, initiative and positive impact. What did someone notice that others missed? What did they start without being asked? What did they build, change or improve? How did they respond when the first attempt failed? Those are harder qualities to reduce to a transcript, but they are largely the qualities that determine whether someone can create substantive value. 

At an early-stage company, every hire has always had to wear multiple hats. But founders are now thinking more deliberately about the division of labor between people and A.I. before they hire at all. They are asking: What does this business need a human to do that A.I. cannot, and what should A.I. handle?

A company might need someone who can define a product’s direction, build prototypes, evaluate A.I.-generated work, talk to customers and coordinate a collection of A.I. tools. 

That job may not fit neatly into an existing title. At the earliest stages, founders are creating roles from scratch based on the needs of the business rather than inheriting an organizational chart. That has important implications for people entering the workforce—and for those already in it.

The advantage will not necessarily go to the person who knows the most about one tool or has the most technically impressive credentials. Tools will change too quickly for that to be a durable advantage. The people who stand out will be those who can keep learning, question assumptions and use A.I. to extend their capabilities without outsourcing their judgment to it.

That last part matters. Working effectively with A.I. means knowing how to break a problem into pieces, give clear direction, evaluate an imperfect output and recognize when the answer is wrong. It means understanding a system well enough to know what to delegate and what to keep under human control. A.I. makes judgment more valuable precisely because execution is becoming easier.

This is also why experienced workers have an opportunity that is easy to miss amid the anxiety about A.I. replacing and reducing jobs. People who have spent years learning how organizations work, how customers behave and how decisions get made have something that cannot be generated by simply prompting a model. But experience remains valuable only if paired with a willingness to keep learning. 

The most valuable experienced workers will be those who understand why the old system worked, recognize where it no longer does and have the curiosity to build something better. That is a harder standard than simply acquiring a new technical skill. It also happens to be a much more human one.

Our educational institutions, meanwhile, are still built around a world in which people followed relatively predictable paths: learn a body of knowledge, earn a credential, apply for a defined role and gradually accumulate expertise.

A.I. is disrupting every step of that sequence. Students can learn new skills on demand. Workers can perform tasks that once required specialists. Companies can create roles that did not exist a year ago. The traditional sequence of education, followed by employment, followed by specialization, is becoming less predictable, and education has to catch up.

Universities should give students more opportunities to demonstrate initiative, creative problem-solving and real-world impact. Employers should rethink hiring processes that over-index on credentials and test whether candidates can actually solve problems in the ways their businesses need them to. And individuals at every stage of their careers should treat learning as an ongoing part of the job, rather than something that ends when a degree is earned. The people who thrive in this new world will be the people who can look at a changing system, figure out what matters and take the initiative to build what comes next.