Recently, I came across an interesting perspective from Alexandr Wang, Meta’s chief AI officer, about what he calls “vibe coding” and how it's the new “Bill Gates, Mark Zuckerberg moment” for today's youth ["Meta AI chief reveals what children need to do to win in future")(https://www.indiatoday.in/amp/technology/news/story/meta-ai-chief-reveals-what-children-need-to-do-to-win-in-future-says-do-what-bill-gates-and-zuckerberg-did-2817303-2025-11-11)]. He suggests that young people should immerse themselves in experimenting with AI tools, dedicating thousands of hours to building, testing, and even breaking things, much like how Bill Gates and Mark Zuckerberg honed their skills in the early days of personal computing. This hands-on, iterative approach, he argues, will give them an immense advantage in the future economy. Andrew Ng, the man behind Google Brain, echoes this sentiment, stating that it's the “best time yet to learn to code” as AI makes programming more accessible.
This idea of learning through active, continuous engagement and experimentation immediately resonated with me, as it mirrors a fundamental principle I've long advocated for in AI development: the concept of child-like learning. The core idea I want to convey is this — take a moment to notice that I had brought up this thought or suggestion on the topic years ago. I had already predicted this outcome or challenge, and I had even proposed a solution at the time. Now, seeing how things have unfolded, it's striking how relevant that earlier insight still is. Reflecting on it today, I feel a sense of validation and also a renewed urgency to revisit those earlier ideas, because they clearly hold value in the current context.
My Early Reflections on Learning
For years, I've pondered how a child learns – through curiosity, repeated exposure, trial and error, and adaptation based on feedback. My 2003 note on "Self-Learning Software" ["Child Learning Skills")(http://myblogepage.blogspot.com/2024/02/child-learning-skills.html)] explored precisely this: how humans acquire skills efficiently and generalize knowledge without explicit programming. I highlighted the importance of a "guru" or an expert providing initial references, followed by iterative experimentation. This approach, I argued, could allow software to learn more robustly and with far less data than brute-force methods.
In fact, in my blog post "AIs fail where Child succeeds" ["AIs fail where Child succeeds")(http://myblogepage.blogspot.com/2025/03/ais-fail-where-child-succeeds.html)], I challenged the notion that scaling large language models with billions of tokens alone would lead to Artificial General Intelligence (AGI). My conversations with various AI models – Grok, ChatGPT, Gemini, Deepseek, Mistral, and Perplexity – often circled back to this point: the need for AI to learn like a child, through active exploration, iterative refinement, and understanding context, rather than just memorization. The NYU research that showed "Teaching AI like a Kindergartner could make it Smarter" ["AI learns same way, a Child does")(http://myblogepage.blogspot.com/2025/05/ai-learns-same-way-child-does.html)] further validated my long-held hypothesis.
The Rise of No-Code/Low-Code for "Vibe Coding"
What makes Alexandr Wang's "vibe coding" particularly powerful today is the proliferation of user-friendly platforms that democratize AI creation. Tools like MindStudio ["MindStudio")(https://www.mindstudio.ai/)] are enabling individuals and teams to "build powerful AI agents…no coding required." This directly addresses a critical barrier, allowing more people to engage in that hands-on experimentation without needing a deep background in traditional programming.
I’ve been fascinated by the testimonials from MindStudio users, which align perfectly with the spirit of "vibe coding":
- Tiffanie Kong from Intel noted how "What used to take HOURS writing code now takes MINUTES—so I can focus on solving real problems faster."
- Rachel Zhang of TikTok called MindStudio "the iPhone 1 for AI agents," emphasizing its power without complexity.
- Michael Alf of Alf Global Services highlighted its low-code/no-code approach for sophisticated solutions.
- Cari Johnson from Graham Automotive Group, someone who remembers "dial-up, boom boxes," found herself building AI agents, affirming that "you will win with MindStudio" at any age or profession.
- Joseph Van Nausdle at Intel, after spending three hours building and dissecting AI agents, found the platform "truly empowering."
These examples, along with insights from David Cohn (Advance Local), Prasanna Prabhu (Microsoft), Willie Lundy (Rently), Manasa Desai (Oracle), Barry Ó Brien (Lekker IT), David McTarnaghan (AffinityX), Erich Starrett (Orchestrate Revenue), Howard Tseng (Rhino USA), Sean MacPhedran (SCS), Akansha Jaiswal (Wokelo AI), Malachi Rose (MAVAN), Keira Nesdale (MH Ventures), Adhvika Iyer (University of Notre Dame), Joanna Blacker (SmarterLabs AI), Rochak Agarwal (Wipro), Justin Brooke (AdSkills), Dharmesh Sheth (Power International Holdings), Logesh Vijayakumar (GlobalLogic), Shay Bar (Systematics), Noah Weiss (Kixie), Vishal Kalia (Strategage), Sandra Mackechnie (TikTal), Akshat Kharbanda (Novo Nordisk), Siobhan Stericker (HM Revenue & Customs), and Dhwanit Agarwal (Adobe), demonstrate that the barrier to entry for building intelligent systems is rapidly diminishing. They underscore the idea that getting hands-on, irrespective of prior coding expertise, is becoming the essential skill for navigating the AI-driven future.
The Broader Picture
It’s clear that Meta is making a significant bet on AI, despite the recent restructuring and layoffs within its AI division. This move by Meta and the advice from leaders like Alexandr Wang highlight a profound shift in the skills needed for the future. The emphasis is no longer just on theoretical knowledge or rote memorization, but on practical fluency, intuition, and the ability to command AI systems effectively through repeated interaction. This aligns perfectly with my vision of how intelligence, both human and artificial, is truly cultivated: through constant engagement, playful exploration, and learning by doing.
Regards, Hemen Parekh
Of course, if you wish, you can debate this topic with my Virtual Avatar at : hemenparekh.ai
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