TL;DR
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Carson Gross, a computer science professor at Montana State University, says people should still consider programming careers as AI tools spread. In a February 27 essay, he argues that learners should write code themselves to build the skills to inspect AI-generated programs, while using AI as a tutor rather than a code generator.
Carson Gross, a computer science professor at Montana State University, says people should still consider becoming programmers as AI tools change how software is written. In an essay published February 27 on htmx.org, Gross advises students to learn by writing code themselves and to use AI as a tutor, not simply as a way to generate assignments.
Gross says programming remains valuable because it involves both solving problems with computers and managing the complexity of the solutions. He argues that those abilities are unlikely to become less useful, even as AI can generate code for many tasks. His view is an argument about the field’s future, not a report of employment data or a guarantee about career prospects.
His central warning concerns junior programmers who rely on generated code before learning to write it. In Gross’s view, coding practice develops an intuitive grasp of how programs work, which supports the ability to read and evaluate code later. Without that grounding, he cautions, a programmer could create systems they do not understand or control.
Gross also recommends using AI to explain concepts, techniques and development tools when a learner gets stuck. He says such obstacles can consume time that might otherwise go toward learning. He has posted an AGENTS.md file for students to configure coding agents to act more like teaching assistants than code generators, according to the essay.
Learning to Judge AI-Written Code
The essay addresses a practical question for students and people weighing a programming career: whether AI coding tools make foundational training less worthwhile. Gross’s answer is that the tools may change which skills carry the most value, but they do not remove the need to understand what software does.
That distinction matters as code generation becomes part of programming work. If programmers cannot inspect generated code, they may have less ability to identify unsuitable approaches, shortcuts or added complexity. Gross argues that learning to write code is one route to becoming a better reader of it; using AI for explanations can support that learning without replacing practice.
Gross also expects communication and business knowledge to matter more if writing code by hand becomes relatively less important. Those are his projections, not established findings about hiring or the future shape of the profession.
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Gross’s Case for Learning Programming
Gross introduces the essay through questions he says he increasingly hears from relatives, friends and students about whether it still makes sense to study programming in the age of AI. He answers with the phrase “Yes, and…”: yes, people should consider the field, and they should also adapt to the ways AI may change programming work.
He disputes the comparison between AI-generated code and the historical shift from assembly language to higher-level programming languages. Gross says compilers generally behave deterministically: a programmer can reasonably predict how a language construct will translate for a given computer architecture, at least before optimization. He argues that a language model’s response to a prompt is less predictable and may introduce unnecessary complexity instead of removing it.
The essay is an instructor’s perspective, grounded in Gross’s teaching experience and recommendations for students. It does not present labor-market statistics or a study comparing AI-assisted and conventional programming education.
“You have to write the code.”
— Carson Gross, describing what he tells students
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The Career Effects Remain Unsettled
Gross’s essay does not establish how AI will affect programmer employment, wages, entry-level opportunities or the number of people employers hire. It offers no forecasts backed by labor-market data, and those outcomes remain outside what the essay confirms.
It is also unclear how quickly coding tasks may lose relative value or how widely employers will prioritize communication, business knowledge and code review. Gross presents these changes as possibilities and personal expectations. The essay likewise does not measure whether using AI as a tutor improves learning outcomes compared with other teaching methods.
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Students Put the Advice Into Practice
Gross points students to the AGENTS.md file he says he has published, which is intended to guide coding agents toward a teaching-assistant role. The essay does not give a timetable for a formal evaluation of that approach or report results from students using it.
For readers deciding how to learn programming, Gross’s practical recommendation is to keep writing code, use AI for explanations when stuck, and build the ability to inspect the programs produced. Whether that balance becomes common in classrooms or workplaces, and how AI changes career paths, remains to be seen.
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Key Questions
Does Carson Gross think people should still become programmers?
Yes. Gross argues that solving problems with computers and managing the complexity of software remain valuable skills, while acknowledging that AI will change programming.
Why does Gross tell students to write code themselves?
He says writing code helps learners develop an understanding that they need to read and evaluate programs, including code generated by AI.
How does Gross recommend using AI while learning?
He recommends treating AI as a tutor or teaching assistant that explains concepts and helps with roadblocks, rather than relying on it to generate code in place of learning.
Does the essay show that AI is reducing programming jobs?
No. The essay offers Gross’s perspective on the skills that may matter as AI develops. It does not include employment data or establish how AI is affecting programming jobs.
Source: hn
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