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AAndrea Chiarelli

I'm Software Engineer and Technical Author with over 20 years of experience in software design and implementation.<br>Throughout my career, I've had the opportunity to use a variety of programming languages and technologies on many different projects. In the last few years, I've been focusing on simplifying the developer experience with Identity and related topics as a Developer Advocate at Auth0 by Okta.

div>p:first-child]:mt-0 [&>div>p:first-child]:pt-0 min-w-0 wrap-break-word [&_a]:break-all **:max-w-full">For a while now, an idea has been gaining traction: with artificial intelligence, anyone can build an app without knowing how to code. The promise is incredibly seductive: with just a few prompts, we can generate code and instantly turn an idea into a product.

It’s no coincidence that this vision took hold so quickly and gave rise to services like Lovable.dev, Blot.new, v0, and others. Every new technological evolution that narrows the gap between an idea and software tends to make developers' work look like an arcane ritual waiting to be dismantled by a simpler formula.

There is something deeply familiar about all of this. Something that reminds me of a line from a song many of us grew up with, with its slightly childish enthusiasm: everybody wants to be a cat!

https://youtu.be/9FOilw55tmQ?list=RD9FOilw55tmQ

Today, it seems like everybody wants to be a dev. The real question is whether everybody can be a dev.

Joking aside, the attempt to make programming accessible to everyone is an old story, one that certainly didn't start with the advent of AI.

A World Without Developers

The idea that technological evolution can democratize programming is a recurring theme in the history of computer science. Every time a new abstraction emerges, someone proclaims that the job of writing software is about to become obsolete . Sometimes the promise is alluring; other times, it's just a clever way to sell a new tool. Yet, the core premise remains the same: if computers get closer and closer to understanding human language, then perhaps those seemingly indispensable technical skills are no longer needed .

I’ve seen this pattern repeat itself multiple times. A demo takes half an hour to build, a prototype seems to work, and suddenly, the idea of building an app feels within anyone's reach. It’s fascinating, but the problem is that what you see at the beginning is often just the surface-level work: the interface, the screens, the user flow. What remains hidden is the hardest part, the work that determines whether the application will actually hold up when it goes live in production.

Promises of the Past

Looking back, the history of computing is full of waves that announced the end of developers. These waves didn't eliminate the profession; they transformed it. And that transformation should serve as a lesson to help us understand exactly what is happening today with AI.

COBOL and Quasi-Natural Language

In the 1950s and '60s, when programming meant working directly with hardware, assembly, and mathematical logic, COBOL was born. Its goal was clear: to bring programming closer to everyday language so that business managers could express rules more naturally. The idea was that a manager could describe a process's logic in English, and the computer would handle the rest.

That promise didn't pan out the way people imagined. We didn't end up in a world where everyone wrote software the way they wrote letters. Instead, a vast ecosystem of specialists emerged who knew how to use that language rigorously, efficiently, and sustainably. In other words, the barrier to computer programming didn't disappear; it shifted.

SQL and Fourth-Generation Languages

In the 1970s and '80s, with the rise of databases, fourth-generation languages (4GLs) and SQL arrived. The concept was simple: instead of explaining every procedural step to the computer, you just had to declare what you wanted to achieve . In theory, a non-technical user could query a database and get a result.

In practice, however, writing correct queries, managing complex schemas, and understanding how data connects requires a much deeper level of reasoning than it appears at first glance.

As a result, the language became more accessible, but the need for expertise didn't vanish. If anything, it became more specialized. New roles, new professionals, and new problems to manage emerged. The computer kept doing its part, but the ability to think in a structured and precise way remained essential.

HyperCard and the Dream of Democratic Programming

In the 1980s, HyperCard truly felt like a revolution. With a simple card-based metaphor and a highly readable language, it promised to put software creation into the hands of anyone . Teachers, artists, students, everyday people: everyone could build interactive apps,...