Can Lovable replace full stack engineers?
What tools like GitHub Spark and Lovable mean for the future of development.
Note: This post was originally published on Medium.
Last week, Naeem and I tested out Lovable.
It’s a cool platform that markets itself as “the world’s first AI full-stack engineer” — which is quite the claim. Lovable promises to turn natural language prompts into full-stack apps, from backend APIs to frontend components. (It’s in the same arena as the new GitHub Spark, but louder and more ambitious).
We gave it a fairly standard challenge:
“Build an e-commerce app with authentication, cart logic, product pages, and Stripe checkout.”
Within 20 minutes, we had a live, clickable product in the browser.
It was impressive.
But we wouldn’t ship it.
There are a lot of people out there claiming that Lovable can replace their developers. They “created entire apps in 20 minutes” too.
The truth isn’t that simple. Even with a powerful resource like Lovable, it takes strategy, finessing, and iteration to really make an app ready to ship.
And while Lovable is great for reducing elbow grease, I’d never let it replace a full-stack engineer. Let me tell you exactly why that is.
An accelerator, not a shortcut
When we dug into what Lovable had actually built, the shape was familiar. It had:
An Express backend with standard REST endpoints
A basic React frontend styled with Tailwind
Stripe’s quickstart integration copy-pasted from docs
A MongoDB layer with basic persistence
To be clear: this is great for a jumpstart. A junior dev might spend days to stitch that much together.
But it also lacked rate limiting on payment endpoints, CSRF protection on session-bound actions, as well as logging or monitoring hooks.
In short, it looked like an intern’s MVP — fast, but naive.
To get this to prod, we’d need to:
Replace the payment logic with a properly stateful, idempotent flow
Add input validation and auth checks everywhere
Wrap all third-party calls in retry/circuit breaker patterns
Insert observability for everything async (queues, events, payments)
As much as Lovable gets you ahead, there’s a lot of building to do from there. There’s no immediate solution to a complex problem — and scalability, resilience, and availability are complex.
AI creates features. It doesn’t design.
AI tools like Lovable help you go from zero to “something that runs” faster than ever.
But getting to demo isn’t the same as getting to production.
That’s where System Design comes into play. System Design is the only route to building software that works well at scale and under stress. It’s about structure, reliability, and evolution.
You have to consider questions like:
How should services communicate — and what happens if one goes down?
Where does state live? And who owns it?
How do we isolate failures, scale bottlenecks, and observe behavior?
What tradeoffs are we making, and why?
These are the considerations that inform sound design choices.
And if you ship a Lovable-generated app without design guardrails, it only looks great until one flaky dependency or usage spike brings it all crashing down.
AI can’t help with design.
AI mimics design patterns it’s seen before, but it doesn’t know why they were used, what tradeoffs they made, or whether they’re right for your team, your users, or your infrastructure.
That’s a developer’s job.
If you want to build systems that last, scale, and recover, that still takes real engineering.
Lovable can’t replace a real full-stack dev
Lovable is a great productivity tool. It gets you from idea to something-clickable in record time by:
Prototyping flows before architecture is locked down
Translating ideas into working boilerplate
Spinning up MVPs or internal tooling fast
Saving hours of repetitive wiring work
But is it a full-stack developer?
Not exactly.
Sure, from the outside, it looks like the work of a full-stack engineer. It can generate a working app with auth, a shopping cart, and Stripe integration.
But it can never replace a full stack developer. Working with tools like Lovable isn’t just a click-and-ship situation.
The process is collaborative:
You prompt Lovable
It gives you a rough but functional structure
You review and assess
You re-prompt to iterate upon changes
You rewrite parts
Lovable is a fast assistant. And like any assistant, it still needs supervision.
AI helps you skip the blank page, but the illusion breaks as soon as you step off the happy path. You’re still debugging, refactoring, and making design decisions the AI didn’t — or couldn’t — consider.
What engineers still own
If an app runs in the browser in 20 minutes, what’s left?
Everything that makes software last.
1. System Design
Lovable doesn’t know whether this feature is customer-critical or a throwaway experiment. It doesn’t see how your services talk to each other, or what happens when one fails. That’s your job: to make tradeoffs, weigh risk, and design for failure.
2. Observability and operability
The AI won’t ask “how will we debug this in production?” It won’t include logs, metrics, or traces unless you specifically prompt for them. Even then, it won’t wire them correctly. You’re still on call when things go sideways — not the AI.
3. Security
The generated code will skip threat modeling, abuse scenarios, and access boundaries. It will trust too much, validate too little, and assume the user is always right. You still need to threat-test what you ship.
4. Test strategy
Lovable might give you a few unit tests — mostly happy-path. It won’t write load tests, contract tests, chaos experiments, or state transition verifications. You still have to verify that the system behaves correctly in the weird corners where bugs live.
5. Code Review
AI-generated code isn’t vetted. It can pull in outdated patterns, unsafe libraries, or subtle bugs — and it won’t tell you why it made those choices. You’re still responsible for reviewing, refactoring, and approving everything that goes to production.
AI goes faster, engineering goes further
With AI tools, we’ll spend less time wiring up CRUD logic or styling buttons. But the core of software engineering remains.
Making apps that really last for years comes down to design choices.
At the end of the day, we should be embracing tools like Lovable. But their marketing slogan isn’t something that should keep devs up at night.
So, what can we expect as more tools like Lovable emerge?
Developers:
System Design as table stakes: You can’t ship a successful app without designing it well (and AI won’t do it for you).
If you’re new to System Design, you may want to check out our courses on Frontend System Design and Modern System Design.
Full stack as the default: Tools like Lovable are lowering the barrier to working across the stack like never before. If you’re still working on one end of the stack, companies will be expecting you to start broadening your scope.
Leaders:
Don’t expect AI tools to replace engineers: These tools boost productivity, but they don’t replace experience or judgment.
Have you built anything with Lovable? What was your experience like? Hit reply and let me know.
Happy learning!
- Fahim