The career of a “Coder” is going the way of Webmaster or MySpace Influencer.
How did “learn to code” go from solid career advice to peak boomer cringe?
As you probably guessed, AI has accelerated the decline in demand for coders, but that’s not the whole story.
The deeper shift is that coding itself is no longer the main act.
Let’s explore why “learning to code” has lost its shine, even as technology professionals are more essential than ever.
From Vim to Vision Models
A “coder” used to be someone who owned massive O’Reilly books with woodland creatures on the cover, forged in the fires of toxic message boards and the trials of exiting Vim.
Those pioneers built the foundations of our digital world.
Today, things are different. Even someone like me can deploy a working app in almost any language, on any platform, after a few AI prompts. What once took weeks now takes an afternoon.
So, should you ditch tech and learn HVAC instead?
Well, sure, if you love wrenches.
But if you’re happier with a keyboard than a socket set, there’s still plenty of room for you in technology. The key is understanding how the field itself is changing, and how to evolve with it.
I base this on over a decade in software engineering, still building, breaking, and tinkering every day.
This Isn’t Your Dad’s Internet
As a millennial, I watched the internet evolve from a nerd’s playground to an AI-powered slop factory that runs half of modern life.
Technology now saturates everything.
Features need to ship instantly, and systems have grown so complex that human coders simply can’t keep pace with maintaining yesterday’s codebase while building tomorrow’s vibe code prompt.
According to a Gallup survey from June 2025:
“Across the last five years, organizational AI adoption moved from about 50% in 2020 to 78% in 2024. Among developers, planned use of AI tools rose from 70% in 2023 to 84% in 2025. Frequent AI use among individual workers nearly doubled from 11% in 2023 to 19% in 2025.”
In short, we’ve gone from AI as experiment to AI as default.
Software engineers are being recast, not as coders who write every line, but as designers, reviewers, and integrators of automated systems.
The promise of AI is still unfolding, but its impact is undeniable.
Waiting weeks or quarters for new features feels archaic in a post-OpenAI world. Teams must adapt faster, learn new tools, and redefine roles. The turbulence has been rough, the layoffs prove that, but the transformation is permanent.
Who Killed the Coder?
Before COVID, tech was already booming cloud migrations, mobile apps, DevOps, automation.
“Learn to code” was the mantra of a generation, the gateway to stability in a digital economy.
Then the pandemic hit, remote work exploded, and the hiring frenzy went nuclear.
By 2021, tech job postings were up more than 80% from pre-pandemic levels.
But when the boom cooled, hiring slowed, layoffs came, hybrid work stuck, and AI started handling the easy stuff.
Now, the best engineers aren’t just coding, they’re designing systems, integrating AI, and building smarter, faster, and more resilient architectures.
For years, media and academia treated “learning to code” as a silver bullet for career success.
But that narrative never evolved.
While bootcamps and textbooks still glorify syntax, the real value now lies in systems thinking, automation, and human-AI collaboration.
As Jeff Atwood famously wrote in, Please Don’t Learn to Code:
“I would no more urge everyone to learn programming than I would urge everyone to learn plumbing.”
A decade later, that advice hits harder than ever, since AI has made plumbing the system a literal job description.
The Profession That Forgot to Professionalize
Tech moves at breakneck speed, but its professional standards never caught up.
Unlike medicine or law, we have no universal credentials, ethics boards, or continuing education.
A “developer” might have a few months of bootcamp experience or decades of architecture, yet both hold the same title.
Companies often chase velocity over depth, hiring for speed instead of mastery.
Training remains fragmented: online tutorials, side projects, and trial by fire.
The result?
A profession that’s vital to society but structured like a hobby.
As AI reshapes our work, this lack of structure risks widening the gap between competent and careless engineering, and the consequences of that gap have never been higher.
The Fault Line in the Digital Age
The way we teach, govern, and value technology has fractured.
Media still sells “learn to code” nostalgia.
Academia teaches yesterday’s frameworks.
Industry, meanwhile, keeps sprinting toward the next release with no agreed-upon standards or ethics.
We’ve built a profession that’s perpetually reactive, racing to stay relevant, while society grows more dependent on systems few people truly understand.
We can’t afford that anymore.
It’s time to give technology the same rigor we give civil or biomedical engineering: clear pathways, ongoing accreditation, and ethical accountability.
The tools have grown up.
Now it’s our turn.
Where Do We Go From Here?
The age of the dedicated “coder” is fading.
AI now writes boilerplate, generates APIs, and even reviews pull requests.
Entry-level coding roles are shrinking, replaced by tools that automate what once took teams of humans.
But this isn’t the end of software work, it’s the start of a more human-centered era.
The future developer isn’t someone who just writes code.
They’re a system designer, an integrator, and a guide for intelligent machines.
Breaking into tech might feel daunting, but curiosity and adaptability now matter more than syntax memorization.
Don’t grind LeetCode, build something you care about.
Start a public GitHub repo.
Contribute to open source.
Join online communities or attend meetups (yes, even virtual ones).
These small, consistent steps prove what AI can’t: that you’re self-motivated, creative, and finish what you start.
The Industry’s Turn
For organizations, the mandate is just as clear:
Invest in mentorship.
Treat AI as a partner, not a threat.
Build teams around judgment, not just output.
The companies that prioritize design, reliability, and ethics over raw velocity will define the next decade.
Academia and government also have roles to play.
Universities must stop teaching yesterday’s tools for tomorrow’s world.
Focus on systems thinking, data literacy, and AI collaboration as foundational skills.
Governments should create frameworks for technical accreditation, apprenticeships, and retraining, because technology now impacts public safety and infrastructure as deeply as any traditional engineering field.
The Real Revolution
Coding jobs may be vanishing, but opportunity isn’t.
The tools are evolving faster than ever, and so must we.
By rethinking how we educate, hire, and define what it means to be a technologist, we can build a profession that’s not just reactive to change, but responsible for it.
The real revolution isn’t AI replacing humans,
it’s humans learning to lead alongside it.