Illustration from the New York Times Magazine article 'Coding After Coders: The End of Computer Programming as We Know It'

The New York Times Magazine recently published a provocative essay titled:

“Coding After Coders: The End of Computer Programming as We Know It.”

You can read the article here.

Programming is not disappearing. It is changing.

The article explores a question that is increasingly on the minds of students, engineers, and executives alike. If AI systems can write software, what happens to programmers?

It is a fair question. But when you read the article carefully, a different story begins to emerge.

“The realms of programmers and everyday people… are drifting closer together.”

One of the most interesting passages in the New York Times article describes how conversational AI tools are reshaping who writes code.

“The realms of programmers and everyday people, separated for decades by an ocean of arcane know-how, are drifting closer together.”

That observation is hard to dispute. Tools like ChatGPT, Claude, and Copilot now make it possible for non-programmers to generate working code for the first time.

But there is an important detail in that shift. The number of people creating software will likely increase dramatically. Turning that code into reliable software systems is a different challenge altogether.

Those are different roles, and they require different skills.

Writing code is one thing. Designing a system that works at scale, survives failure, and evolves over time is another.

“Maybe they don’t label themselves as software engineers, but they’re creating code.”

The NYT article quotes economist Erik Brynjolfsson describing this dynamic:

“Maybe they don’t label themselves as software engineers, but they’re creating code.”

In other words, coding itself is becoming far more widespread. That does not mean professional developers disappear. It means that code generation is becoming easier.

The real question is what happens after that shift: who designs the systems, who evaluates the outputs, and who understands the domain where the software is deployed?

Those tasks depend on a deeper layer of knowledge. Large software systems rely on ideas that cannot simply be improvised by a prompt: algorithms, data structures, databases, distributed systems, and the architectural decisions that connect them.

Even when AI helps generate code, someone still has to decide how the system should work.

That is where computer science comes in.

Why Computer Science Matters More

Ironically, the rise of AI may make computer science education more important, not less.

When code becomes easier to generate, the limiting factor shifts from typing syntax to understanding systems. Someone still has to decide how software should behave, how data should be structured, and how a system should perform under load or failure.

A computer science curriculum focuses on exactly those foundations:

  • Algorithms and data structures, which determine whether software runs in milliseconds or minutes
  • Computer systems, which explain how memory, processes, and networks actually behave
  • Databases, which determine how information can be stored and retrieved reliably
  • Object-oriented design, which allows complex systems to remain maintainable
  • Software engineering methodologies, which allow teams to build and evolve systems over time

These ideas rarely appear in AI-generated snippets of code. But they determine whether the resulting software actually works.

As one Forbes analysis puts it:

“Employers aren’t just looking for programmers anymore. They need computer scientists who can bridge the gap between technical capability and real-world application; who understand both the code and the context in which it operates.”

AI may help write programs.

Computer science teaches you how to understand them.

The Interdisciplinary Programmer

But technical depth alone is not the whole story.

The most valuable developers increasingly combine computer science with expertise in another domain. Software only matters when it interacts with the real world, and that means understanding the systems where the software is deployed.

Education researcher Aviva Legatt makes this point clearly in the same Forbes article:

“Those who combine computer science with domain expertise in healthcare, finance, or other fields are finding significantly better opportunities.”

She goes on to recommend a specific educational path:

“I would recommend a double major in computer science and another subject such as systems engineering, business, or healthcare.”

The logic is straightforward. Healthcare systems require engineers who understand clinical workflows. Financial systems require engineers who understand markets. Educational technologies require engineers who understand how people learn.

The most effective developers increasingly combine technical foundations with domain expertise.

The Real Question

So perhaps the real question is not whether AI will eliminate programmers.

The more interesting question is what kind of programmer the AI era will reward.

If the evidence from both industry and education is any guide, the answer is becoming clearer.

The most valuable programmers may be the ones who were never just programmers to begin with. They are biologists building simulation engines, philosophers working on AI governance, economists designing financial systems, and teachers building educational technology.

They understand both the technical foundations of computing and the domains where software actually matters.

In other words, they bring two ways of thinking to the same problem.

Final Thought

The New York Times article is correct that programming is changing.

But the deeper transformation is not about replacing programmers.

It is about expanding what it means to be a programmer.

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