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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.
There are two basic explanations for this discrepancy:
(Signaling hypothesis) The STEM degree filters the set of people who can both learn and persist through four years of difficult math.
(Skills hypothesis) Struggling through math classes imparts some hard-to-quantify mathematical intuition that is valuable for operating today’s calculators.
As a formerly strong believer of the signaling hypothesis, I am now increasingly buying the skills hypothesis (let’s say ~50% attribution to each cause). It’s clear that senior engineers today are far more capable of using coding agents than their junior counterparts, and a large portion of this is due to having struggled through 5+ years of writing code manually.
A job market in flux
Currently, the level of computing intuition needed to additively prompt the coding agents sits at roughly 5 years’ experience level, and the gap grows as coding agents continue to improve.
In between coding agent improvements and natural variation in learning aptitude, maybe 50% of new CS graduates will not be able to catch up, ever. Today’s seniors are lucky enough to have been paid to build their computing intuition, but some of them, too, will also fall behind the curve despite their head start.
To answer the opening question of the essay: only some junior engineers are worth hiring, specifically, the ones who are good enough to reach some useful threshold of “coding intuition” within ~2-3 years of having graduated. Since there are not very many of these graduates, a small number of elite companies compete fiercely for this talent.
The second-class tier of software consultants will continue growing, expanding the total size of the job market, but I don’t anticipate that their salaries will grow anywhere close to as rapidly as today’s senior engineers.
Everyone should learn some coding
Even as the bar to get into software engineering rises, I still think everyone should learn some coding. Too often, I see people treat computers as appliances - capable of doing what they were built to do, but nothing more. If you don’t think of computers as scriptable or programmable, then you won’t ever think to ask AI to automate something for you! The same is also true for many other fields, too! Math, law, taxes, medicine, DIY home repair, etc… Abundant and cheap expertise is now available for just $20/month, if only you know how to ask.
I would say that the major unlocks are at:
1-2 weeks: Basic understanding of what the field is about and what general words to use when asking the AI to do something.
1-2 months: Basic understanding of how and when to ask the AI something.
4-6 months: Ability to check the output for correctness (using external sources as needed).
5+ years: Reliable, effective prompting, to the point where people are willing to pay you to prompt the AI instead of doing it themselves.
If you’re already a software engineer, you might consider dabbling in data science, frontend, backend, security, and performance optimization/profiling – all of which are distinct skillsets that you can benefit from, even if they aren’t your primary job function.
Here’s a data science example of a “how + when + correctness”: A coworker was running some correlational analysis on a dataset and found it difficult to understand what was going on. I suggested he literally ask Claude to “make it prettier using NMF” – and all of a sudden, useful clusters started appearing.
(The expanded version of this prompt: NMF on the pairwise distance matrix gives k cluster centroids and cluster membership scores. Reordering the original distance matrix according to argmax(cluster score) highlights the clusters. The “how” here is knowing the keyword “NMF”; the “when” is “clustering on distance matrices”, and the “correctness” is knowing the preconditions for using it.)
Conclusion
One weirdly common and nihilistic take on AI is that you should stop trying so hard, and just use AI to speedrun your classes. I think this is probably the worst possible response. Doing the work is the best way to build mastery, and just like you weren’t allowed to use a calculator on your middle school math classes, you should hold off on using AI to do your classwork. The calculator advice sounded condescending when I was a kid, and this AI advice probably sounds the same – but I really do believe it’s for your own good. This advice continues to hold after you graduate, too. Don’t use AI until you’ve done it by hand at least once.
Assuming no more drastic changes in coding agent capabilities, I expect educational timelines to lengthen (in both directions), to give people more time and space to reach an adequate level of coding intuition.
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