Vibe coding demos are everywhere: one clever prompt, a wall of code, and suddenly it looks like AI has “solved” software engineering. But here’s the paradox 👇 Getting from 0 → 1 (an idea into a working prototype) has never been easier. AI has radically compressed that step in the SDLC. A weekend hacker can now do what once took a small team. The problem? Going from 1 → Production (5) -> robust, secure, scalable systems that enterprises can trust and still takes a lot of engineering effort. Testing, integrations, infra hardening, compliance, observability are often ignored, but they don’t disappear because a model can draft code. That’s what the curve below reminds us: enterprise value grows fast early, then flattens as engineering effort piles into reliability, security, and scale. Yet this is precisely where the bulk of real-world value is created. So when people claim “AI has taken over coding,” they’re only seeing the left-hand side of the curve. The real world runs on the right-hand side. However let’s not dismiss the disruption. Some of the world’s sharpest minds are attacking this exact gap. If (or when) AI cracks the 1 → Production leap, the entire definition of software engineering shifts. Until then: vibe coding ≠ production software. And clowns claiming otherwise are missing the plot. #AI #SoftwareEngineering #DeveloperTools #Productivity #AIinTech #Coding #TimeToProduction #Engineering #FutureOfWork #VC
Aniruddh Muley
1mo
Perfectly articulated Anishkumar SS !
LTIMindtree | Oracle | Product | Leadership | AI-Curious | Vibe Coding
1moWell put Anishkumar SS. AI is really helping on daily basis. however, is it production ready? Never. There is always cleanup, end goal alignment needed.