The future of software development is changing rapidly and teens are at the threshold of a unique opportunity. Alexandr Wang, chief AI officer at Meta, believes that young learners should invest their time in what he calls “vibe coding”. He draws a parallel between today’s AI revolution and the era when Bill Gates and Mark Zuckerberg started coding in their teens. Wang describes vibe coding as a hands-on, experimental way of learning AI: instead of following standard courses. He encourages teens particularly those around age 13 to use AI tools, run prompts, tweak inputs, and build things intuitively. He argues that thousands of hours spent this way will give a substantial edge in the future economy.
What really makes vibe coding stand out is its shift from writing lines of code to commanding AI tools. The user sets intent, defines what they want, and a sophisticated system turns that into functional software. Traditional learning paths emphasise syntax and algorithms, but vibe coding prioritises problem solving and creative experimentation.
Wang suggests the moment for grasping this shift is now. As AI becomes more integrated into workflows across industries, the ability to harness it becomes a skill worth mastering early. He suggests teens should make full use of their formative years by engaging deeply with AI platforms rather than only following conventional schooling. Yet vibe coding carries certain caveats. Experts caution that mastering prompts and tools does not replace understanding underlying systems. Even with AI assistance, the process requires discipline, critical thinking and a willingness to learn fundamentals.
From a broader perspective, this development signals a transformation in how education and skill-building are viewed. It challenges the notion that programming is reserved for those who master code syntax. Vibe coding suggests that the next generation can build and innovate faster if they focus on guiding AI rather than becoming syntax experts. For aspiring learners, starting points can include: exploring accessible AI tools, experimenting with simple projects, and iterating rapidly rather than aiming for perfection. The principle is to learn by doing and refine by experience. Encouragingly, this path does not require an engineering degree but demands curiosity, persistence and creative thinking.
