AI will democratize education, but it may also create a new class divide — where human mentorship and connection becomes the ultimate privilege.
Imagine two classrooms in 2035. In one, students absorb lessons from AI tutors: efficient, tireless, optimized for results. They ace tests, master coding, and solve equations with machine-like precision. In another, a handful of students engage with human mentors: debating philosophy, exploring big questions, and learning how to think, not just what to know.
Unless we're proactive, thats the future we're heading to.
The rise of a productivity caste
AI-driven education will produce a generation of hyper-competent, technically skilled individuals. Perfect for productivity. But leadership? Vision? Emotional intelligence? Over time, we’ll see the formation of a two-tiered system:
The erosion of social mobility
AI can teach you Python. But it can’t introduce you to a venture capitalist. Mentors don’t just teach — they open doors. A student in a low-income district might master coding with an AI tutor but never meet someone who says, “You should start a company”. Meanwhile, their affluent peer, equally skilled, lands funding through a mentor’s network.
Cultural homogenization vs. curated depth
AI systems reflect the data they’re trained on: dominant cultures, mainstream narratives, sanitized histories. Students raised on AI will inherit a flattened, algorithm-approved worldview. Meanwhile, the elite will preserve niche, human-curated knowledge. Indigenous folklore, avant-garde art, and philosophical traditions passed down like heirlooms.
AI promised to level the playing field, but instead, it may deepen the divide — not between humans and machines, but between those who can afford the richness of human connection and those who can’t.
We’re entering an era where “being human” will be a luxury good.
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If you have any questions or thoughts, don't hesitate to reach out. You can find me as @viksit on Twitter.
The internet runs on human attention, but AI agents won’t have any.
Ads work because people see them: scrolling, clicking, impulse buying. But AI agents don’t get distracted. They parse, compare, and execute, stripping out everything that doesn’t directly serve their task. When products like OpenAI’s operator replace a lot of human browsing, the internet’s ad economy is going to collapse. And reinvent itself.
I. Most ads will be written for robots, not humans
Forget catchy slogans. Agents don’t care about “luxurious silk” or “artisanal bread.”
They process structured data: “100% mulberry silk, $99, 12h delivery, 37% cheaper than Sandro". Agent Optimization (AO) — the SEO of the AI agent era — will become a critical new discipline. But branding isn’t going anywhere. Instead of emotional persuasion, companies will structure their data, and compete on trust signals, and machine-readable credibility.
Verified seller badges, return policies, shipping speed, and service reliability will matter as much as price and quality.
II. The companies that control AI agents will control the ad economy
Your agent isn’t just “searching” for Tokyo hotels. It’s booking one. The question is: did it pick the best deal, or the brand that paid for placement? Agent platforms will monetize in two ways:
• First-party ads: Sponsored results baked into agent recommendations.
• Agent 'SEO': Brands will optimize for AI just like they do for Google, structuring their data to appear in agent-driven choices.
But here’s the issue: consumers won’t even know when they’re being sold to.
III. Users will win — until they don’t
At first, agents will feel like magic: saving time, cutting noise, negotiating deals! But agents learn from you. If you always pick the second-cheapest flight, expect a well-placed sponsored option in slot #2.
Ultimately, the future of advertising won’t just be about grabbing your attention. It’ll also be about gaming AI agents. The real question isn’t whether AI will shop for you. It’s who your AI will really be working for.
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If you have any questions or thoughts, don't hesitate to reach out. You can find me as @viksit on Twitter.
In 2025, AI is centralized in the hands of a few western companies that control everything — training data, compute power, access, and distribution. The path to decolonizing AI isn’t just about forcing them to comply with national laws. It’s about building technology to offer real alternatives to the status quo. Here are three upcoming shifts that have the power to wrestle away the control from Silicon Valley and make AI truly global.
I. Ownership of training data
Asking OpenAI to pay for data access isn’t enough! It still controls the outputs. Countries need infrastructure where data ownership stays with its creators. This is a great use case for decentralized storage systems like Arweave. With permanent, verifiable storage and in-built licensing technology at scale, creators can set explicit terms on how their data is used, ensuring fair attribution and payment.
2. Federated and localized AI models
Today’s AI assumes one-size-fits-all, but intelligence isn’t universal. Governments and organizations need to train sovereign LLMs that reflect their language, culture and laws. Imagine India’s models trained on Tamil poetry and Indian case law, or Brazil’s models deeply embedded in their journalism. Decentralized federated learning, where models are trained across jurisdiction without sharing raw data, can make this possible.
3. Breaking Silicon Valley’s AI compute monopoly
The US controls most high-performance AI compute, and OpenAI decides who gets access to cutting-edge models. Even open-source AI isn’t truly open if it still relies on Big Tech’s cloud. Breaking this chokehold means investing in national AI grids — state-backed compute clusters that reduce dependence on US infrastructure. Decentralized compute networks like AO Computer take it further, enabling models to run outside corporate control. AI independence isn’t just about open models. It's about ensuring they can operate without Silicon Valley’s permission.
The fight against algorithmic imperialism won’t be won in courtrooms. It’ll be won by shifting the balance of power: who owns the data, who trains the models, and who controls the infrastructure that runs them.
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If you have any questions or thoughts, don't hesitate to reach out. You can find me as @viksit on Twitter.
OpenAI’s dismissal of India’s copyright lawsuit as a “jurisdictional mismatch” reveals a fatal blind spot — India’s case isn’t about payments. It’s about dismantling the neocolonial data economy that fuels AI.
OpenAI’s defense hinges on two colonial era tactics: extraction, and the dismissal of local governance. By scraping India’s newspapers, books, and films without payment, it replicates the logic of empires that mined resources but left colonies impoverished. When Indian publishers protested, OpenAI shrugged: “Your laws don’t apply to us.” Sound familiar?
India’s 1.4 billion people represent the largest AI user base outside China — a country that has recently proven it can outpace Silicon Valley. If courts rule against OpenAI, they will set a legal precedent: AI trained on a nation’s culture must pay tribute to its laws.
Brazil is drafting similar legislation for Portuguese-language data. Kenya's AI regulation has already landed. The Global South isn’t just suing, it’s unionizing.
ChatGPT’s ability to summarize The Indian Express’s investigations verbatim doesn’t democratize: it plagiarizes, diverting readers, ad revenue, and trust from the outlets that fund reporting. Meanwhile, U.S. publishers like The New York Times secure licensing deals. Why is Indian content “free” but western journalism worthy of payment?
The answer lies in what we might term "algorithmic imperialism" — a system where AI mimics the Global South’s voice, stories, and labor, but funnels profits and control back to Silicon Valley. When ChatGPT generates Hindi poetry using centuries-old Indian texts, it monetizes a legacy OpenAI never paid to learn.
India’s lawsuit forces a reckoning: Should OpenAI’s Hindi outputs, trained on Indian texts, be governed by California’s “fair use”, or New Delhi’s copyright courts?
This isn’t just about copyright. It’s about who gets to shape the mind of AGI.
If India wins, it proves data isn’t the new “oil.” It’s the new land, and the Global South is reclaiming it.
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If you have any questions or thoughts, don't hesitate to reach out. You can find me as @viksit on Twitter.
The future of AI isn’t about bigger models; it’s about smarter apps that solve real problems. The winners will understand business pain points better than anyone, AND stay ahead of AI’s rapid shifts. Here are 5 trends I think builders should think about to make something that lasts.
Local models and hybrid compute.
The future isn’t entirely in the cloud. Distilled LLMs, like those emerging from Meta and DeepSeek, are making local computation viable. Picture a lightweight model summarizing local documents on your phone while syncing with a cloud model to generate an investor deck.
Automation as a co-pilot.
AI is moving from standalone apps to embedded operators across workflows. Take Excel: instead of Googling how to write a VLOOKUP, imagine an assistant instantly recognizing your intent and creating the formula for you. It’s Clippy, but actually useful. Open-source tools like Goose hint at what’s next.
Privacy-first AI.
Privacy is shifting from a feature to a foundation. Apple’s on-device processing keeps your data local, ensuring security while delivering AI insights. In healthcare, sensitive patient data could be handled by local models, while appointment scheduling is offloaded to the cloud. Privacy is now a critical differentiator.
Decentralized compute.
As open-source models proliferate, centralized infrastructure becomes a bottleneck. Enter decentralized systems like AO computer, designed to host large models independently. For truly autonomous agents to thrive, they’ll need infrastructure that doesn’t rely on centralized cloud providers. This ensures resilience for the next wave of AI.
UX innovation.
The interface layer is the next frontier. Imagine a collaborative workspace like Miro where AI dynamically suggests workflows or an email client that crafts the perfect response in your tone. Multimodal, real-time collaboration will make AI feel less like a tool and more like a teammate.
The tools are here, the costs are nosediving, and the only thing standing between you and the next great AI-powered app is your imagination!
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If you have any questions or thoughts, don't hesitate to reach out. You can find me as @viksit on Twitter.