How AI Changed Software Development (From a Developer Who Ships With It Daily)

AI didn't replace software development. It moved the hard part. Typing code got cheap. Deciding what to build, checking that it actually works and owning it after launch got more valuable. I use AI coding tools every working day, on client websites and on factory software at Devloom. This is my honest account of how AI changed software development, what didn't change, and what it means if you're hiring a developer in 2026.

The short timeline (as I lived it)

For most of programming history, "help" meant documentation, Stack Overflow and a colleague who'd seen the bug before. Then AI-assisted development moved fast:

• Autocomplete got smart. Editors went from completing variable names to completing whole lines. GitHub Copilot's technical preview, launched on June 29, 2021, was the first time many of us watched a tool write a function from a comment.
• Chat arrived. ChatGPT launched on November 30, 2022, and suddenly you could paste an error and get an explanation in plain English. For me this was the biggest jump. Explaining things is where beginners lose the most time.
• Agents showed up. By 2025, tools could read a whole repository, edit several files, run the tests and try again. Instead of "complete this line", I could say "add pagination to the blog and keep the existing styles", then review a diff.
• "Vibe coding" got a name. Andrej Karpathy coined the term in February 2025 for building software by describing what you want and mostly accepting what the AI produces. On November 6, 2025, Collins Dictionary made it its Word of the Year for 2025 ("programming by vibes, not variables"), which tells you how mainstream it got.

What actually got faster

Here's where AI coding tools save me real hours, week after week:

• Boilerplate and setup. Config files, form validation, API route scaffolding, test skeletons. Work I understand completely but don't enjoy typing.
• Translating between things. Converting a design into components, a SQL query into an ORM call, or an old jQuery snippet into React.
• First drafts of unfamiliar code. When I touch a library I haven't used, AI gets me to a working example faster than the docs alone.
• Explaining legacy code. On factory projects we often inherit old scripts or spreadsheets full of formulas. Asking an AI "what does this do?" is a good first pass, which I then verify.

What got riskier

The same speed creates new failure modes:

• Code that is almost right. In Stack Overflow's 2025 Developer Survey, 66% of developers named "AI solutions that are almost right, but not quite" as their top frustration. Almost-right is dangerous: it passes a quick glance and fails in production.
• Confidence without understanding. The same survey found more developers distrust the accuracy of AI tools (46%) than trust it (33%), and only 3% "highly trust" the output. Experienced developers were the most cautious, with the lowest "highly trust" rate (2.6%) and the highest "highly distrust" rate (20%). That matches my experience: the more you know, the more you check.
• Feeling fast isn't being fast. METR ran a randomised trial with 16 experienced open-source developers in early 2025. With AI tools allowed, tasks took 19% longer, yet the developers had expected a 24% speedup and, even after the slowdown, still believed AI had sped them up by 20%. That's one study with a narrow setup, and the tools have improved since, but the gap between feeling productive and being productive is real.
• Security and data. AI happily suggests outdated packages, weak auth patterns or code that logs sensitive data. Someone has to know better.

What vibe coding is good for (and where it breaks)

I like vibe coding for prototypes, internal tools with no sensitive data, landing pages and "is this idea even possible?" experiments. It breaks down when:

1. The software touches money, personal data or a production line.
2. Requirements live in someone's head, not in a prompt (most real businesses).
3. The code has to be maintained by a person in two years.

My rule: if I can't explain every line I'm shipping, I'm not done. AI writes drafts. I'm the one accountable for what goes live.

How my workflow looks now

A typical feature on a client project goes like this:

1. Clarify the job in writing. What problem, for whom, and what "done" means. AI can't do this part for me, because it requires talking to the client.
2. Sketch the approach myself. Data model, edge cases, which existing components to reuse.
3. Let AI draft. Small, reviewable chunks, never "build the whole app".
4. Review like it came from a stranger. Read the diff, run it, try to break it.
5. Test the boring paths. Empty states, slow networks, bad input, mobile.
6. Ship and watch. Logs, Core Web Vitals and real user feedback.

This blog is a good example. AI drafted the Markdown pipeline, the structured data helpers and the generated cover images in a fraction of the time they'd have taken by hand. But every decision about what to build, which pages search engines should index and how drafts stay out of production was mine, and I reviewed every line before it went live.

What this means if you're hiring a developer

• Prototypes are cheaper and faster than ever. If someone quotes you months for a simple marketing site in 2026, ask why.
• Responsibility isn't cheaper. Discovery, architecture, security, testing and maintenance still take a human who understands your business.
• Ask how they use AI. A good answer sounds like "for drafts and boilerplate, and I review everything." A worrying answer is "AI builds it, I just deploy."

For factory and operations software, where "almost right" is costly, read why custom factory software still needs a real team.

FAQ

Can AI replace software developers?

Not the job, but parts of the work. AI is very good at producing code. Developers are paid to decide what code should exist, make sure it's correct and secure, and keep it running. That part has become more important, not less.

What is vibe coding?

A term coined by Andrej Karpathy in February 2025 for building software mainly by describing what you want to an AI and accepting its output, often without reading the code closely. It's great for experiments and risky for production software.

Is it still worth learning to code in 2026?

Yes. You need to understand code to judge AI output. I wrote about learning to code at 15, before AI could write the code for me, and how I'd learn today.

---

Want a site or tool built by someone who uses AI for speed but doesn't skip the thinking? Tell me what you're building.