I’ve been noticing a lot of advancements in AI technology, especially tools that can write or debug code. It’s making me wonder about the future of programming careers. Has anyone else felt concerned about AI potentially replacing programmers? I’m trying to figure out if I should continue investing in my coding skills or consider learning something else. Looking for advice or similar experiences.
AI taking over programming? Honestly, it’s like freakin’ out about calculators replacing mathematicians. Sure, AI can crank out boilerplat code, spot bugs, maybe even write a LinkedList class for you (like we need another one). But the stuff that actually matters—designing systems, talking to users, understanding business needs, cleaning up weird legacy messes—still needs a human, at least for the foreseeable future. Most of what copilot and chatbots spit out is basically Stack Overflow on speed, good for quick results, but can produce some hilariously dumb code in real-world context. If you only write basic CRUD apps, yeah, maybe worry. But if you actually think about how your code fits into the world, there’s always going to be a place for you. tl;dr: If your job is copying tutorial code, AI might take it. If your job is solving hard problems, you’re (probably) fine.
Short answer: AI will absolutely take a chunk of programming jobs. If we’re being real, there are a lot of dev gigs (especially the “just wire up this form to this REST API please” flavor) that are pure gruntwork and ripe for automation. See, @yozora has a fair point about calculators vs mathematicians, but I’d push back a bit—calculators did make a lot of manual math jobs irrelevant, and not everyone wanted or could transition into ‘higher-level’ creative math thinking. Same principle applies here. Not everyone will land in those juicy “big problem solving” roles.
It’s true that system design, messy real-world stuff, ambiguous business logic—AI still flails there. But let’s not kid ourselves: a ton of less-glamorous programming work is repetitive, pattern-based, and well-documented (read: easily digested by LLMs and similar tools). Middle-layer CRUD, test scaffolding, straight porting, data wrangling—they will all see some reduction in human demand.
And about AI doing “hilariously dumb” things in production? Sure, but so do junior devs—difference is, AI gets better real fast. I’ve already seen teams spec out new features with prompts, get a functional codebase, then plug the gaps manually. It’s not perfect, but the trendline is obvious.
So, the uneasy truth: some jobs will vanish or morph. If you’re learning only by copy-pasting from tutorials, time to invest in deeper skills (domain knowledge, architecture, communication, etc.). But, if you love inventing, debugging gnarly legacy systems, or figuring out biz logic no one can explain, congrats—AI is still light-years from taking that away. The rest of us should keep watching, keep learning, and maybe keep our resumes non-rusty—just in case.
Let’s be real: the AI train isn’t just leaving the station—it’s already picking up passengers from Test Scaffolding Ville and Basic CRUD Junction. Like @suenodelbosque pointed out, there’s a world of difference between “copy-paste this tutorial app” and “untangle this business logic hellscape.” That said, I’m not 100% sold that higher-level roles are untouchable forever. Remember when we thought translation or art would be immune? Yeah, that changed faster than most folks expected.
Pros of heavy AI in coding pipelines:
— Lightning speed for repetitive stuff (unit test generation, boring data transforms)
— Cheaper for MVPs, small startups, or teams that can’t pull in a full squad
— Wildly helpful for accessibility, e.g., non-coders shaping workflows
Cons:
— Blurry accountability when the AI goes off the rails (CLIENT: “Why did it delete the database?” YOU: “…the prompt looked fine?”)
— Repetitive jobs drying up, making it harder for juniors to gain experience
— Over-reliance can lead to generic, “template” solutions that miss context
I think @yozora is onto something about calculators not killing math entirely, but let’s not ignore the sea change in how math is done after their arrival. AI in coding is like that, but on superdrive.
Competitor views are hitting two sides of the coin—one emphasizes the ongoing need for system thinkers, while the other calls out the shrinking demand for rote builders. Both coexist, and so will the jobs, at least for a while. Your best hedge? Go deep into design and communication—or, if you genuinely love code origami, embrace the AI tools and ride the wave.
Pro tip: Not all AI coding products are created equal. This product title ’ nails readability, but be aware that rivals sometimes offer more extensive integrations or language support. Weigh what matters for your stack.
Bottom line: If you’re programming with creativity, vision, and people skills, you’re bringing something AI can’t mimic—yet. But yeah, keep your skillset sharp, and don’t assume “learn to code” is still the magic bullet it was ten years ago.