Is Vibe Coding Dying? (garymarcus.substack.com)

The hype that "vibe coding" — amateurs using LLMs to spit out production apps without engineering teams — is replacing software development is cooling. Chamath Palihapitiya shared usage data showing a months-long decline after an early spike, and prominent voices (Mike Judge, Andrej Karpathy and others) have been blunt: these tools make flashy prototypes but struggle in real-world, novel systems. Customers are discovering that initial success often evaporates once apps face edge cases, scale, security needs, or ongoing maintenance. Technically, the failure mode is predictable: models generalize poorly outside their training distribution, so they excel at familiar scaffolding but produce brittle, incorrect or insecure code for unfamiliar requirements. Resulting artifacts are hard to test, debug, integrate, or maintain. The implication for the AI/ML community is twofold: temper investor and product expectations, and pivot research and tooling toward robustness — retrieval-augmented generation, fine-tuning, better spec-to-code pipelines, formal checks, CI, observability and human-in-the-loop guardrails. Vibe coding isn’t dead, but its role is shifting from “engineer replacement” to rapid prototyping and developer augmentation, reinforcing the continued need for software engineering discipline.