So, you thought AI could replace your dev team? How’s that going? 😀

If you’ve spent the last few months trying to build your MVP with a prompt instead of a product team — congratulations, you’ve just learned what many founders are now quietly admitting: it’s harder, slower, and far more fragile than it looked in that demo.

The Klarna story—where the company fired 700 people in favor of AI and is now hiring them back—isn’t just about customer service. It’s a mirror for what’s happening in software development too. The same dream: faster, cheaper, automated everything. The same reality: brittle systems, wasted time, and a creeping sense that maybe, just maybe, humans weren’t such a bad idea after all.

In this piece, we’ll unpack why using AI agents to write production-level software often backfires—especially when it’s done as a cost-saving shortcut. Think of it as a PSA for anyone who’s ever said “why pay a dev if GPT can code?” and lived to regret it.

(Before we wrap things up, let’s break it down clearly. Because somewhere between the “AI will replace all developers” narrative and the “AI is just hype” backlash, there’s a practical middle ground—one that actually works.)

The Setup Tax: What AI Doesn’t Tell You Up Front

AI promises speed. What it doesn’t mention is the unpaid internship you’ll have to serve to get there.

If you’ve tried setting up GPT-powered workflows to replace actual software development, you’ve likely found yourself writing long-winded prompts, fine-tuning for edge cases, breaking things into subtasks, and wrapping it all in a logic layer that… well, starts looking suspiciously like programming.

Only now, you’re doing it in natural language, with none of the reliability or version control you’re used to.

The hidden cost here is what we’d call the setup tax—all the time spent:

• Building your own tooling around the AI
• Debugging output that “almost works”
• Rewriting prompts every time the model updates and suddenly decides GET /users should return a motivational quote

And no, it doesn’t get better at scale. More agents, more problems.

This isn’t to say AI tools are useless—far from it. But if your goal was to save time and money by skipping real developers, you’re probably discovering that prompt engineering is just another form of technical work, and often harder to test or document.

When the Model Shifts and Your System Breaks

You wouldn’t build a bridge on a moving foundation. But that’s exactly what happens when your product logic depends on a third-party AI model that updates itself — without telling you.

Let’s say you finally get your AI agent doing something useful. It wires up some API calls, formats a JSON correctly, maybe even writes a unit test or two. You go to sleep thinking you’ve hacked the system. Then a model update rolls out — and the next morning your agent responds to a database error with a haiku.

This isn’t hypothetical. Developers using GPT-based agents have seen carefully crafted workflows break overnight. Prompts that worked perfectly yesterday suddenly misfire, return inconsistent outputs, or — worse — seem to work, but introduce subtle bugs no one notices until it’s too late.

Why? Because large language models don’t come with changelogs. You don’t get a push notification saying, “Hey, by the way, we completely rewired how the model interprets nested logic. Good luck!”

If your software’s backbone depends on deterministic behavior — and let’s be honest, whose doesn’t? — then relying on nondeterministic models in production isn’t just risky. It’s operational debt disguised as innovation.

So yes, AI can be useful. But if you don’t have a team maintaining your prompts, validating outputs, and designing fallback mechanisms, you’re not building a product. You’re rolling dice.

When “Cheap” Starts to Get Expensive

At first glance, replacing developers with AI feels like a startup founder’s dream. No salaries. No onboarding. No tech leads pushing for refactoring sprints.

Just you, a chatbot, and the illusion that $20 a month gets you a product team.

But here’s how that usually ends:

• You spend weeks setting up workflows that a junior developer could write in a day.
• You end up hiring a developer anyway—to fix the AI’s output or rebuild the project entirely.
• You realize that whatever money you “saved” is now buried in the form of delays, tech debt, or pivoting your product idea because the AI couldn’t handle the one thing that made it unique.

And that’s before we get into security concerns, compliance requirements, and the classic “who owns this code?” conversation.

The irony? The founders who wanted to avoid paying for a proper dev team often end up paying twice — first in time, then in cleanup. It’s not uncommon to see MVP budgets balloon because the first version, built on AI fumes and hope, has to be scrapped.

Cheap AI development isn’t development. It’s an experiment.

If you want to test ideas quickly? Great. But if you want something reliable, scalable, and usable—you’ll still need people.

Where AI Actually Helps—With Humans in the Loop

Let’s be clear: AI isn’t useless in software development. Far from it. It’s just not the solo act some people hoped for. When used well — by actual developers — it can speed things up, improve quality, and take the edge off repetitive tasks.

Here’s where AI shines:

• Boilerplate generation. Nobody wants to write the same CRUD controller for the fifth time. AI handles it in seconds.
• Code reviews & documentation. GPT can help explain code, suggest tests, or even write initial comments — especially helpful in large teams or legacy systems.
• Pair programming. Tools like GitHub Copilot speed up writing code, spotting syntax errors, and suggesting improvements in real time.
• Rapid prototyping. Got an idea and want to sketch it out quickly? AI can help scaffold interfaces, APIs, or even pitch decks — as long as you know how to steer it.

But all of this assumes one key thing: someone experienced is still in charge.

AI can assist a developer. It cannot replace product strategy, architectural thinking, user empathy, or the judgement that tells you when not to build something. It can write code—it just doesn’t know if that code is a good idea.

So yes, use the tools. Embrace them. But if you’re planning to launch a real product, remember: AI is a power tool. Without skilled hands, it’s just noise.

The difference between using AI instead of developers vs. using AI with developers