Image credit: Asianet Hindi

I love listening to the Bhagavad Gita. Especially this particular rendition. There's something wonderful about the rhythm, the precision of Sanskrit, and the fact that the way these have been recited has likely not changed in two thousand years. I barely understand Sanskrit, but listening to this over the years has made me pick up some. I've often thought it would be far easier to pick up if I could read along with minimal effort: an auto-scroll of verse, transliteration, translation interpretation. I've just never gotten around to doing it. Until now. It took us half a day to put this together with AI. And remarkably, AI can handle all aspects of it: site building, design, translations, transliterations, verse timestamping (a bit wonky currently), interpretations. Curmudgeons will say, oh it's just mashing up what's available. To that, I say, that's 95% of creativity and invention. And we are just two years in.

This is going to have enormous implications, but one I see clearly as someone who runs a school is the impact it will have on school and college. Schools and colleges were already woefully outdated. They are obsolete now.

An Isolation Bubble

Schools have evolved into artificial environments that trap kids in bubbles, sheltered from meaningful connection to the real world. We've created these strange holding pens where young people spend their formative years practicing for "real life" instead of participating in it. This separation leaches out meaning from learning and turns education into a bizarre game of hoop-jumping.

No wonder schools and colleges have done so little to change learning outcomes despite decades of reform attempts. The fundamental model is broken. Learning happens best when embedded in authentic contexts with real stakes and natural feedback loops.

Worse, conventional education actively defeats self-learning and autonomy through its hidden curriculum. What's the hidden curriculum? It's the unwritten, unspoken lessons schools teach alongside their official content:

• That learning happens on someone else's schedule

• That your curiosity should be confined to 42-minute periods

• That external validation (grades, praise) matters more than intrinsic motivation

• That expertise comes from authorities rather than experience

• That learning stops when assessment begins

This hidden curriculum trains students to be dependent, compliant, and externally motivated—precisely the opposite of what thriving in today's world requires. It creates people who ask "Will this be on the test?" instead of "How does this work?" The system is perfectly designed to produce the results it gets: learners who stop learning when they leave school.

The Return of Apprenticeships

For most of human history, we didn't learn this way. We learned through apprenticeships—direct immersion in communities of practice where novices worked alongside experts, gradually taking on more responsibility as their skills grew. This wasn't just for manual trades; it was how people learned everything from medicine to law to commerce.

The modern education system disrupted this natural learning process, replacing it with artificial environments, fragmented knowledge, and abstract assessments. We did this with good intentions—to democratize learning and scale education. But we lost something crucial in the transition.

Apprenticeships work because they honor how humans naturally learn. They provide:

• Context and meaning for knowledge

• Immediate application of skills

• Natural feedback loops

• Identity development through community membership

• Visible pathways to mastery

The industrial age made us believe apprenticeships couldn't scale to meet modern needs. The world became too complex, knowledge too vast, and expertise too specialized for the old ways. Traditional apprenticeships seemed like relics from a simpler time.

Cognitive Apprenticeships: The Missing Bridge

In the 1980s, researchers like Collins, Brown, and Newman developed a framework called "cognitive apprenticeship" to bridge this gap. They recognized that while traditional apprenticeships worked well for visible skills (carpentry, cooking), modern knowledge work involved invisible thinking processes that apprentices couldn't observe directly.

Cognitive apprenticeship aimed to make thinking visible. It proposed methods for experts to externalize their thought processes through:

• Modeling: Demonstrating while thinking aloud

• Coaching: Observing and offering hints or scaffolding

• Articulation: Having learners explain their reasoning

• Reflection: Comparing processes with experts and peers

• Exploration: Setting new challenges for independent problem-solving

This framework was revolutionary in theory but difficult to implement at scale. It required close relationships between masters and apprentices, substantial time for dialogue, and experts who could articulate their tacit knowledge. Most educational institutions couldn't provide these conditions.

So we remained stuck with industrial-age education despite knowing better alternatives existed. The bridge between theory and practice remained incomplete.

AI as the Apprenticeship Enabler

Now AI is changing the equation dramatically. For the first time, we have tools that can help scale the cognitive apprenticeship model by serving as bridges between experts and novices.

Consider how this transforms learning in practice: A student interested in developing low-cost cooling solutions for India's increasingly hot summers can now apprentice with experts across the globe, with AI tools helping translate complex engineering papers, visualize heat dissipation patterns, generate relevant practice problems, and connect abstract thermodynamics concepts to practical cooling designs for local homes. The AI doesn't replace the human expert but rather amplifies their ability to mentor effectively.

AI can make thinking visible in ways that were previously impossible:

• Externalizing expert thinking: AI can help experts articulate their tacit knowledge by suggesting analogies, identifying patterns in their work, and translating complex ideas into simpler language. A thermal engineer can use AI to translate technical concepts about passive cooling systems into explanations a newbie can immediately grasp and apply when designing clay refrigerators for rural homes without electricity.

• Scaffolding for novices: AI can provide just-in-time support that gradually fades as learners develop proficiency, mimicking the natural scaffolding that occurs in traditional apprenticeships. When testing different evaporative cooling materials, AI can initially provide step-by-step guidance on measurement protocols, gradually reducing prompts as the apprentice gains confidence.

• Connecting domains: AI can help learners see connections between their existing knowledge and new contexts, facilitating transfer in ways that siloed education rarely achieves. An apprentice might see how principles of thermodynamics apply both to the cooling panels they're designing and to urban heat island mitigation strategies for Mumbai neighborhoods.

• Providing deliberate practice: AI can generate infinite variations of challenges tailored to a learner's current capacity, creating the conditions for deliberate practice that builds expertise. AI can generate customized design challenges for cooling solutions at different price points based on the apprentice's growing understanding of materials and airflow dynamics.

(And of course, I used AI to come up with these examples, without any domain expertise. Whatever hallucinations there might be in year 2.0 where we are now, will be gone in a few tens of months.)

The result is a new kind of apprenticeship that combines the best of traditional learning-by-doing with the scalability of modern technology. We're not replacing human mentors—we're extending their reach and impact.

This shift has profound implications for traditional educational institutions. When AI-enabled apprenticeships become viable alternatives, the monopoly of schools and colleges on credentialing and knowledge transmission weakens substantially.

We're already seeing early signs of this shift:

• Coding bootcamps partnered with industry mentors outperforming a vast number four-year computer science degrees (at least in India)

• YouTube creators with millions of students teaching everything from mathematics to music

• Professional communities on platforms like Discord becoming de facto learning environments

• Companies like Google and IBM dropping degree requirements in favor of skills-based hiring

These examples share a common thread: they collapse the artificial boundary between learning and doing. They recognize that expertise develops through active participation in authentic work, not passive consumption of information.

AI accelerates this collapse by making expertise more accessible outside institutional walls. When a young 3D printing enthusiast can get feedback from an AI trained on advanced manufacturing techniques, or an aspiring architect can have complex structural engineering concepts translated into terms they understand, the gatekeeping function of educational institutions becomes less relevant.

The New Model: Connected Apprenticeships

What emerges isn't a return to the isolated master-apprentice relationships of the past, but rather connected apprenticeships—learning communities where:

• Learners work on authentic projects with real-world impact

• Multiple experts provide guidance across different domains

• AI tools bridge knowledge gaps and make thinking visible

• Progress is measured by capabilities rather than credentials

• Learning pathways are personalized to individual interests and aptitudes

In this model, a 14-year-old tackling India's urgent need for affordable cooling might simultaneously apprentice with a materials engineer, test terracotta-based evaporative cooling designs with peers in their neighborhood, and connect with sustainable architecture experts across the globe—all supported by AI tools that help translate complex thermodynamic principles into practical experiments they can run with locally available materials.

The boundaries between "student" and "professional" blur when contributions are valued based on quality rather than the contributor's age or credentials. Young people can make meaningful contributions to fields they care about years before they would complete a traditional education path.

We must also intentionally design for community. While apprenticeships naturally embed learners in professional communities, we need to nurture peer relationships too. Learning isn't just cognitive—it's deeply social and most enjoyable when done with the right group. The best parts of college aren't lectures or exams, but the relationships and shared discoveries with people you connect with. This doesn't require formal institutions; it requires thoughtful community building around shared interests and challenges. When young people tackle real problems together, they forge bonds that educational institutions often claim credit for but don't actually create. In our connected apprenticeship model, these communities form organically around meaningful work, with AI helping match peers with complementary skills and interests.

The great thing about apprenticeships is every problem and felt-need becomes a learning opportunity. I could rattle out a hundred different problems we can solve for in Mumbai or rural Maharashtra. In Mumbai, apprentices could develop tools for tracking and visualizing municipal spending data to improve government transparency. In rural areas, they might create affordable water quality testing systems that connect to smartphone apps. Or they could design modular solar-powered cooling units for small businesses that struggle with frequent power outages during summer months. Each real problem becomes not just a project, but a gateway to deeper understanding.

The First Self-Made 15-Year-Old Billionaire?

Throughout history, extraordinary young people have accomplished remarkable things when given access to the right resources and mentorship.

In the Renaissance, teenage apprentices created works we now consider masterpieces. Mozart composed his first symphony at age eight while apprenticing under his father. The Wright brothers built a modestly successful printing business as teenagers with no formal technical education before revolutionizing aviation.

Increasing technological complexity made apprenticeship impractical at scale and lengthened schooling. As fields like medicine, engineering, and science grew more sophisticated, the knowledge required to practice them expanded beyond what could be efficiently transmitted through direct mentorship. Medical training evolved from apprenticeships lasting a few years to over a decade of formal education. However, AI can now digest that complexity and reverse this trend. For instance, an aspiring prosthetics designer can now use AI to rapidly understand complex materials science data that would have previously required years of specialized education, allowing them to focus on creative design solutions immediately.

Our current system holds back talented young people by forcing them to wait years before applying their skills to real problems. AI-enhanced apprenticeships open doors by providing immediate access to expertise and opportunity.

A teen, in today's system, might be stuck in general science class despite having the capacity to contribute to cutting-edge research. In an AI-enhanced apprenticeship model, they could be working alongside architects, with AI bridges helping translate complex thermal efficiency calculations and material specifications into terms they can understand and apply immediately.

Is it so hard to imagine this young person developing a revolutionary passive cooling system for affordable housing by 15? And with the accelerating pace of technological change and investment, the financial rewards for such innovation could indeed reach billions.

The question isn't whether it's possible—it's whether we're ready to reimagine learning systems that enable rather than constrain such potential.

While the headline-grabbing stories might be about exceptional prodigies, this apprenticeship model isn't just for future billionaires or rare geniuses. It's for everyone. Each person has unique potential that standardized education often fails to nurture. One student might discover they have a gift for designing intuitive solutions that help elderly residents navigate the chaotic, dense urban landscape of Indian cities. Another might find meaning in applying technology to traditional agricultural practices, creating sensor networks that optimize water usage while preserving indigenous crop varieties—a project we're currently developing with a few bright college students in rural Telangana.

This approach offers everyone a path to meaning, discovery, and purpose—helping each person find their place in the web of life. When learning is connected to real contributions and communities, it transforms from a chore into a fulfilling journey. 

Conveyor Belts to Exploration

Our current educational model is essentially a conveyor belt: standardized, sequential, and designed for efficiency rather than effectiveness. Students move along at a predetermined pace, receiving the same inputs regardless of their interests or aptitudes, until they reach the end and receive a stamp of completion.

AI-enhanced apprenticeships offer a different metaphor: exploration with expert guides. Learners chart their own courses through territory that matters to them, supported by mentors who've traveled similar paths before and AI tools that help navigate unfamiliar terrain.

This exploration model recognizes that learning isn't linear. It's messy, interconnected, and driven by curiosity and purpose. It acknowledges that expertise develops not through memorization but through progressive participation in authentic practices.

Most importantly, it restores meaning to education by embedding it in contexts that matter. When a young person can see the direct impact of their learning on problems they care about, motivation becomes intrinsic rather than imposed.

The most profound transformation might not be about what we learn or how we learn—but about why we learn and do.

PS: In many ways, the Gita is the original apprenticeship!

Interested in making this happen? Talk to us

We are going to pilot a version of this where we provide bright young talent from across India, especially rural India training and internships with companies we are in touch with. We use real-world work on such projects, open-source initiatives, research projects in cutting edge AI and ML, and startup experiments. In the early days we will work with interns who are in their 1st, 2nd, or 3rd year in college. We'll probably move to a model where they can even join us directly after 12th. We strongly believe there are more Vaibhav Suryavanshis waiting to be unearthed across domains. Let's do this together!

Please fill out this form if you are interested in either an apprenticeship or mentoring or taking on apprentices.

(If you don't follow cricket: Vaibhav Suryavanshi is a 14-year-old prodigy who recently scored the second-fastest century in the IPL, competing against seasoned professionals.)

Notes

[1] The hidden curriculum concept was developed by sociologists like Philip Jackson, who observed that schools teach far more than their explicit content—they also teach compliance, hierarchy, and the separation of knowledge from application.

[2] Cognitive apprenticeship as described by Collins, Brown, and Newman emphasized the importance of making thinking visible to learners. Their 1989 paper "Cognitive Apprenticeship: Teaching the Crafts of Reading, Writing, and Mathematics" remains relevant despite being written decades before AI tools could help implement their vision.

Update: Some readers have asked if I'm suggesting AI will replace teachers. Absolutely not. I'm arguing that AI can enhance the human-human connection that makes apprenticeship learning so powerful. The goal isn't to automate education but to humanize it by removing artificial constraints and enabling more meaningful relationships between learners and mentors.