Why Devs Still Doubt AI—and Why They're Dead Wrong

I’ve noticed a lot of skepticism about AI from developers, and it often stems from bias and a lack of real understanding. Let’s unpack why so many devs are still hesitant to embrace it.

You're in a room with a bunch of seasoned developers, most of them sharp, experienced, and deadly efficient. One of them casually starts talking about using AI for coding, and you'll feel the energy shift. Smirks, eye-rolls, the occasional comment like "It's good for boilerplate but that's it." Or worse: "I tried it once, didn’t work, waste of time." That sentence right there is the core problem.

In the past year, I’ve spoken to dozens of devs—bootstrappers, full-stack engineers, indie hackers—and the pattern is always the same. Most of them tried AI. Few of them committed to it. They either gave up too soon, expected it to magically deliver perfect code, or treated it like a Google replacement instead of what it actually is: a coding assistant that requires process. Not magic.

The funny thing is most of the failed experiences I hear about fall into one of three categories:

No workflow

They tried ChatGPT or Claude once or twice. Didn’t like the result. Closed the tab. No iterative prompting. No context sharing. No scaffolding or plugin usage. Just raw unstructured input and surprise when the results sucked, anyone that has used AI knows extensively, you put in shit, you get shit out…

You wouldn’t give a real engineer a crappy set of instructions if you want a good result, so why leave it up to the AI to guess what you want…

One-shot syndrome

They threw a massive prompt at it—"build me a social media scheduler with notifications, background jobs, and a React frontend"—and got back generic, unscalable nonsense. Obviously.

We don’t have super intelligence yet, right now, AI works best when you’re tackling a specific problem, try instead breaking down your problems into smaller digestible chunks, you’ll find you’ll get exponentially better results.

Bespoke everything

Instead of working within an established framework or pattern (like Laravel, Rails, Django, Vue, React, etc…), they wanted it to generate totally custom solutions from scratch. This makes it 10x harder for the AI to generate quality output. Just like humans, LLMs work better with conventions.

The funny thing is, the devs who’ve actually invested in building a workflow are getting insane returns. I’ve seen one-man teams ship SaaS products in weeks using GPT-4. I've watched backend services built with clear architectural layers—AI-assisted every step of the way. I personally use it to scaffold components, refactor legacy code, explain obscure error messages faster than Stack Overflow ever could, and even model database schemas that fit neatly into my domain logic.

Here’s a simple example:

I needed a custom event publisher architecture in Go. I asked GPT-4 to generate a clean interface-driven publisher pattern with a Google Pub/Sub adapter. It nailed the interface layer, even helped break out the adapter logic into its own package. Saved me at least 3 hours—and that’s just on boilerplate and wiring alone. Would it have done as well if I gave it no context or just said “build event system”? Probably not.

Developers who are understand architecture and know the nomenclature of architectural design patterns are in a much better position than those who don’t, If you don’t know what the adapter pattern is or SOLID, your chosen AI might not use it when generating results, so knowing what you want is essential for good returns.

Another example? I was working on a Laravel CMS and needed a dynamic store-driven page builder architecture. Rather than piecing it all together from scratch, I got GPT to walk me through a viable pattern, iterating on the Vue component structure, the store architecture, and the schema for saving layout data in a single content field.

I still had to tweak, test, and debug—but the core scaffolding was done in minutes. The end result was something pretty much identical to what I would have written without an LLM.

I've also used AI to write custom backtesting scripts for my trading bots. Instead of spending hours building and testing loops around different indicators, I just fed it the logic and asked for an optimised tester script. A few iterations later, I had something that gave me real data. That kind of development cycle used to take a day. Now it takes 30 minutes.

I’ve even started using AI to write tests for legacy codebases where the original logic is barely documented. Feed it a chunk of code, ask for test cases, and suddenly I’ve got PHPUnit or Jest specs ready to go. Is it perfect?

No. But it’s enough to get moving, and far better than staring at a blinking cursor trying to remember how the old module even worked.

Let’s talk about onboarding. I recently brought someone onto a project and used GPT to generate tailored onboarding docs, explain the folder structure, and summarise the logic behind some key modules. That kind of clarity used to take days to prepare.

Now I can generate it in minutes, revise it in context, and hand a junior dev a well-lit path instead of just a Git repo and a shrug.

The secret is to treat AI like a junior dev who can write ultra-fast, but needs good direction. Be specific. Use structure. Iterate. You wouldn’t give your intern a vague task with no context and expect gold. Why are you doing that with AI?

And I get it—some devs are just allergic to change. The same way some people resisted version control, unit testing, or even frameworks when they first dropped.

But resisting AI is a bad idea if you want to stay competitive in the ever evolving market. AI is becoming more than a productivity hack, It has become the foundation of modern developer leverage. It’s how you 10x output without burning out, and how you can focus on the problems you’re solving rather than the semantics of programming.

The world is moving toward higher levels of abstraction, and AI is part of that trajectory. We went from assembly to C, from C to high-level languages, from high-level to frameworks and libraries. Now we’re stepping into prompt-driven engineering.

If you refuse to ride that wave, you’re signing your own death warrant, someone else will outperform and get that contract, or create that SaaS you’ve been working on for 2 years.

So if you’re a skeptic, try again. But this time, try with intent. Try with a workflow. Try using a framework the AI knows well. Try prompting iteratively, like you're guiding a team member.

Make peace with the fact that AI isn't perfect. Neither are you… So combining your knowledge with AI is where you will see magic happen.

The truth is, AI isn’t replacing good devs.

It’s turning them into great ones.

Those who learn to wield it early position themselves to gain the most.

Embracing it opens doors to new opportunities and long-term advantage.

Staying current is the only way to remain relevant.