Over the last two years, AI has become part of the toolkit of anyone building products. Cursor, v0, Lovable, GitHub Copilot. What used to take days now takes hours. That's real and there's no going back.
The problem isn't the tool. It's the confusion about what it does.
There's a growing belief that AI replaces the experienced developer. It doesn't. It makes them much faster. The difference matters because people who believe in replacement make decisions that cost a lot three months later.
What AI really speeds up
Repetitive code generation is where AI shines. CRUD endpoints, standard authentication setup, interface components from text descriptions. A senior dev who spent two days on these tasks now spends two hours. That gain is concrete.
Visual prototyping too. Tools like v0 and Lovable generate clickable interfaces in minutes. For testing a hypothesis with real users before building, that is hugely valuable. You show up with something navigable without having written a line of real code.
Review and documentation are other areas where AI helps a lot. Spotting problematic patterns in isolated snippets, generating code documentation, suggesting readability improvements. All of it speeds things up without changing who is in command of the decisions.
Where AI stops being enough
System architecture is where the lack of human experience shows. Which services should be split? Does a message queue make sense at this point in the product? How does the database schema need to evolve when the product grows? These questions need context AI doesn't have: the business, the users, the roadmap. AI executes well what you describe. It won't tell you when you're describing the wrong thing.
Security is another critical point. AI generates code that works. Code that works isn't necessarily secure code. SQL injection, exposure of sensitive data, wrong CORS settings, tokens without expiry. AI won't warn you about these flaws unless you know exactly what to ask. And founders building alone rarely know what to ask.
Scalability too. The prototype works for ten users. Does it work for a thousand? It depends on decisions about database indexes, caching strategy, query design. A senior dev looks at the data model and already knows where volume will cause problems. AI doesn't look at the future. It solves the present.
How AI works when it's in the right place
AI as accelerator, experienced dev as referee.
At devexperience, AI is part of all our sprints. Discovery, component generation, code review. But every architecture, security and product decision goes through someone with enough technical and business context to evaluate what the AI suggested.
The result is shipping MVPs in six to eight weeks with the code quality of a production product. Your users don't know we used AI. The dev who comes in after us won't hate what they inherit.
What happens when AI is in command
Founders who come to us after three to six months of building alone with AI always describe the same experience. The first weeks felt like magic. Code appeared. The interface took shape.
Then the first real user arrived. The first bug in production. The first attempt to add a new feature. That's when they realized the code had no tests, the schema needed a complex migration, the authentication had a flaw, and nobody could understand what the code was doing.
AI without an experienced dev creates the illusion of progress. The product looks finished. It isn't.
If you're thinking about using AI to build your MVP, the right question isn't which tool to use. It's who will be in command of what the AI is doing.