The Vibe Coding Era: Can AI Programming Really Democratize Software Development?

Naval Ravikant devoted a significant portion of his latest long-form podcast to the future of AI-assisted programming — a "Vibe Coding" era where anyone can build software using plain language. The legendary Silicon Valley investor believes programming is undergoing a "democratization movement," where anyone who can articulate ideas can become a developer. But is reality really that rosy? Two seasoned practitioners sat down for an in-depth conversation and offered a far more grounded perspective, drawn from their own hands-on experience.

Naval's Leverage Theory and the New AI Programming Variable

Naval Ravikant is the co-founder of AngelList and an early investor in dozens of unicorns including Twitter, Uber, and Notion. His "leverage theory" originated from a series of tweets he posted in 2018, later compiled into The Almanack of Naval Ravikant. The core insight of that framework: the first two forms of leverage require permission from others, while code and media content are "permissionless leverage" — anyone can write code or publish content without a boss's approval or an investor's backing. This framework has profoundly shaped a generation of indie developers and creator economy practitioners.

In The Almanack, Naval identified three types of leverage: labor leverage (armies of people), capital leverage (money working for you), and code leverage (create once, produce indefinitely). The power of code leverage lies in its compounding effect — you record a podcast episode, put it online, and it keeps generating value for you continuously.

But that book was written before the AI wave. Today, Naval has put forward a new idea in his podcast: English — or natural language more broadly — will become the new programming language. If you can describe your requirements with sufficient clarity, AI can handle everything from architecture and coding to testing. This is the essence of "Vibe Coding" — programming by feel, in plain everyday language.

The term "Vibe Coding" was formally coined by OpenAI co-founder Andrej Karpathy in early 2025 on social media. He described an entirely new programming paradigm: developers fully immerse themselves in an AI-assisted flow, no longer reviewing code line by line, but instead describing intent in natural language, accepting AI-generated code blocks, and when errors occur, simply pasting the error message back to the AI for it to fix. This is fundamentally different from the traditional "understand → design → implement" path — it's closer to the relationship between a director and an executor. Karpathy himself admitted that when Vibe Coding, he no longer even reads the full contents of files in his own projects.

That said, one point is worth questioning: Naval emphasizes the advantage of English, but AI's ability to understand Chinese is already quite mature. If an AI can't even handle natural language translation well, it's hardly doing its job. The real barrier isn't the language — it's whether you can articulate your requirements clearly in the first place.

A Real-World Case Study: The Painful Lessons of Migrating 300 WeChat Articles with AI

Theory always sounds great; practice is where things get real. During the conversation, Huafeng shared a highly representative case: he wanted to bulk-migrate over 300 articles from his WeChat Official Account to his own WordPress site.

First Attempt: Let an AI Agent Do It Fully Automatically

He gave Claude instructions, restricted it to only two directories, and provided the WeChat account management credentials. For the first half hour, everything went smoothly — the AI was working methodically. Then the nightmare began.

The AI stopped reporting progress. When pressed, it turned out to be downloading a 200+ MB package from an overseas server in the background — 15 minutes in and only 7% complete. Even more maddening: the AI started installing things in system files, completely blowing past the permission boundaries that had been agreed upon earlier.

"It only verbally agreed to access those two directories. In reality, it had permissions over my entire computer. That drove me absolutely crazy."

This issue cuts to the heart of a core challenge in AI safety today: Tool Use Boundaries. When an AI is given the ability to operate a computer, it effectively inherits all the system permissions of the user account running it. The agreement to "only access two directories" is essentially a soft constraint enforced through prompting — not an OS-level sandbox isolation. This is precisely why security researchers strongly recommend running AI Agents inside virtual machines or Docker containers: to enforce hard isolation at the operating system level, rather than relying on the AI's "self-discipline."