An observation from the chats where vibe coders hang out: six months ago people were asking “which model should I use?” Now they’re discussing hexagonal architecture, database selection, queues, backups, and production.
Vibe coders grew up. And this isn’t just a fun fact - it’s a signal for everyone building products.
How the Questions Evolved
I’ve been watching several vibe-coder chats for the last 6 months. Here’s how the questions changed:
Month 1-2: Which model to pick? Claude or GPT? Cursor or Windsurf?
Month 3-4: How do I write a good prompt? How do I give context? Why does AI generate garbage?
Month 5-6: Is hexagonal architecture overkill for my project? PostgreSQL or SQLite for MVP? Do I need queues from day one? How do I set up backups?
See the shift? From “which tool” to “how to build it right.” Classic developer learning curve - just compressed into 6 months instead of several years.
Why It Happened So Fast
Vibe coding removes the hardest barrier - syntax. You don’t need to learn where to put a semicolon. Cursor writes the code, Claude explains the error, Vercel deploys it.
But architectural questions don’t disappear. They actually surface faster. When you’re not spending 80% of your energy debugging typos, you have mental bandwidth to think about structure.
According to AuthenticJobs, the vibe coding learning curve is 2-4 weeks to basic competency, 3-6 months to productive work. That matches what I see in the chats - the “grown-up” questions start appearing around month 3.
The Wall Everyone Hits
Frontend Mentor nailed it: “Developer who understands fundamentals uses AI to build faster. Developer who skipped fundamentals hits walls the moment something breaks.”
I saw this firsthand with my own projects. ProdSignal - a Product Hunt digest - I built the prototype over a weekend using Claude Code. It works. Data pulls in, digest gets formatted. Great.
Then I needed to scale it. And that’s when the questions AI can’t answer for you started: how do I organize the data? Where should I cache? Do I need a queue or will a cron job do?
Answering those requires context - how many users, what load, what budget. AI can propose options, but the choice is yours.
What This Means for PMs
Three concrete takeaways:
1. Prototypes become products. A PM prototype used to be a Figma mockup. Now it’s a working MVP. And when the CEO says “let’s launch this prototype” - you need to know the difference between a prototype and a production-ready solution.
2. PMs need to understand architecture. Not write code - but know why PostgreSQL and not SQLite. Why a queue and not a synchronous request. Vibe coders already ask these questions. PMs should too.
3. The learning curve is real. If you plan to prototype with AI - budget 3 months. Month one is “wow, it works.” Month three is “wait, how does this scale?”
My Experience
I’ve been vibe coding for about 10 months. Honest timeline:
- Week 1: Built a landing page in Cursor. Pure euphoria.
- Month 1: Built a Telegram bot with Claude Code. It works. I’m proud.
- Month 2: Bot crashed. Data wasn’t being backed up. Two days to recover.
- Month 3: Started thinking about architecture BEFORE writing the prompt.
- Month 4: Ask myself “do I need a queue here” before asking Claude to write code.
Same path as in the chats. Just lived through it myself.
The Vibe Coding Trade-off
Honest trade-off as I see it:
Upside: speed is an order of magnitude higher. Prototype in a day. MVP in a week. That used to take months.
Downside: tech debt accumulates invisibly. AI writes code that works - but not necessarily well-structured code. After 3 months it’s harder to refactor than to rewrite from scratch.
Bottom line: vibe coding is excellent for validating hypotheses. But for production you need someone who understands what’s happening under the hood. Doesn’t have to write code themselves - but needs to understand it.
What I Realized
Vibe coding isn’t a trend. It’s a new path into development. Fast, accessible, and it genuinely works. But foundational knowledge didn’t go anywhere - it just shows up later. At month three instead of year one.
For PMs this is an opportunity: build working prototypes independently. And a responsibility: know when a prototype needs to be rewritten properly.