Expertise in the Age of AI

2026-05-12

Tagged: llms

Does it make sense to hire junior engineers in the age of coding agents?

Junior engineers are expensive, both in salary and seniors engineers’ time. This cost was partially recouped through code contributions, but today, it’s more effective to directly maximize the output of your senior engineers. The hiring market reflects this trend: senior engineers have an easy time finding jobs, while fresh CS grads are having their worst years ever. And yet, OpenAI, Anthropic, and many top companies continue to compete fiercely for junior talent. What’s going on?

In this essay, I’ll explore the changing nature of expertise in the age of AI.

Math as an analogy

I think it helps to think about the impact of AI in terms of math, which had its AI moment half a century ago.

There used to be a job called “calculator”, which was a human who could do math calculations accurately and quickly. These people balanced books, calculated artillery firing angles based on distance and wind adjustments, calculated optimal hull shapes for ships and aircraft bodies, and so on. This job doesn’t exist anymore, and the last serious use of abaci and slide rules was in the 1970s, due to the invention of the scientific calculator. Calculators have only become more sophisticated over time, with today’s numerical modeling software running full scale physics and engineering simulations. (For the purpose of this essay, I’ll use “calculator” to mean everything from basic calculators to modeling software.)

Despite the existence of calculators, we teach and expect people to learn algebra, geometry, and calculus in high school. Continuing into the college level, we expect STEM majors to learn multivariable calculus, ODEs, PDEs, statistics, and linear algebra. Upon graduation, the vast majority of them use calculators every day and forget how to do all but the most basic mental math.

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