Expertise in the Age of AI

I get the analogy of the calculator. The thing however, is that in college, we had dedicated time to learn how to not use it: classes without it, exams without it, etc.

In current job market and pressure, we doesn't have time anymore. You need to be constantly delivering the new jira ticket, and the time expected to perform a task now decreased, as it's expected of the workers that now they are "more productive with AI".

I think 'expertise' is a bit of a red herring when what is being discussed is experience.

I've always believed that coding and development is an art and something analogous is the experience of a visual arts student. There's a level of experience required when one applies to an art school. The student builds a portfolio of passion projects and demonstrates a passion and skill along with creativity and other beneficial traits. If they are accepted, they learn the deeper theory, techniques, and more that will aide them in their career. This increases their exposure and overall experience.

Experience for a young developer is going to start with passion projects and be supplemented and bolstered through education in a similar way. You can take shortcuts as an arts student or a developer but you really just end up hurting yourself.

The calculator analogy is more warning than reassurance. Human calculators went extinct. What survived was mathematical reasoning — a different skill entirely. The question isn't whether "coding education" will persist, it's which parts of coding are computation (syntax, boilerplate, API lookup) and which are reasoning (systems design, tradeoff analysis, debugging intuition).

I think the article underweights one skill that becomes more valuable with AI: verification. Knowing when AI output is subtly wrong — the off-by-one that compiles, the race condition that passes 90% of tests, the architectural choice that crumbles at scale. This isn't the same muscle as writing code. It's closer to adversarial testing, code review, and production debugging.

On teams I've managed, I've seen senior engineers miss AI-generated bugs because they trust the output, while methodical juniors catch them precisely because they don't trust it. The junior who builds a reputation as "the person who finds what the AI missed" becomes more valuable than the senior who produces volume without verification.

The pipeline problem is real, but the solution isn't necessarily "more manual coding." It might be training for verification fluency — the ability to read, test, and challenge AI output at a level deeper than surface correctness. That's a teachable skill that doesn't require 5 years of writing code from scratch.

Sorry for the off-topic comment, but what happened to the front page? At the time I’m writing this, 11/30 submissions are related to AI. Maybe my comment is cliché too, but I’m honestly tired of all the AI stuff.

Actually - to disillusion yourself from AI, try dabbling into something you do not know. Try writing a production quality 3D engine. Trust me, a 3D engine has its own domain knowledge besides just graphics. No, seriously. And then see how helpless you feel when you yourself do not have the expertise to judge whether the direction being taken is the right or wrong.

At that time, you wish if there were some pipe through which you could reach John Carmack, Tim Sweeney, Gabe Nawell, Jonathan Blow some Casey Muratori and just ask one thing:

Sir, is this really the right direction?

These tools feel good when you yourself are a domain expert. I have written backend systems and designed REST APIs all my life in multiple languages in Java, Python, Go, Ruby for multiple verticals I'd say I am damn expert at API design including all the layers that go under it and I can confidently give a shut up call to an LLM knowing what I know.

Fuck the bean counters and the greedy parasite execs and VPs. Hug a junior today, society will need them tomorrow because I was a clueless junior once and my seniors were very kind to me that I am able to put bread for my family on the table.

I think that the universities have an opportunity here to be the places where manual code is written so that juniors can gain the coding expertise necessary to become effective with AI.

Many universities are not set up to take advantage of this opportunity because they lean heavily into theory and look down on coding, but some departments will make the pivot well. I hope that ours (Montana State) is one of them.

AI is cheap right now. Let's re-ask this question when it's priced to recover profit and ROI.

> And yet, OpenAI, Anthropic, and many top companies continue to compete fiercely for junior talent.

Are they? I would imagine they have the luxury to pick the brightest candidates, and set them to work on jobs for which their models don't have training data for, such as developing new models. Not writing React code.