India’s AI Boom: Progress or Cargo Cult?

India is experiencing a fast-paced AI revolution. With the IndiaAI Mission, new foundational models, and the announcement of 34,000 GPUs under national AI compute, there’s a sense that we’re racing ahead.

At a recent event in Delhi, the government highlighted:

✅ 34,000 national GPUs

✅ Launch of public compute facilities

✅ Three startups selected to build India's foundational models

But beyond the press releases and podiums, a tougher question remains:

🤔 Are we building AI power — or just mimicking it?

🛬 Cargo Cult Thinking in Tech?

India’s AI stack still rests on imported foundations:

• Chips and GPUs: Mostly from U.S.-based firms like NVIDIA

• Foundational models: GPT, Bard, Claude dominate — trained abroad, aligned abroad

• AI hardware and infrastructure: Not designed or fabricated in India

We are deploying AI tools across sectors — governance, education, language — without owning the core tech beneath them. This risks replicating the appearance of innovation, not the capability itself.

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🚫 34,000 GPUs: A Start, But Not Sovereignty

Yes, the 34,000 GPUs mark progress — compute access is vital.

But in context, this is still far behind the global frontier:

• OpenAI's GPT-4 and GPT-5 models reportedly use up to 50,000 GPUs just for training

• Microsoft Copilot infrastructure is said to run on over 50,000 H100 GPUs

• Other models like Claude, Gemini, and Meta's LLaMA also scale across tens of thousands of GPUs

India’s 34,000 GPUs are split across research, startups, and applications — and we’re still buying, not building.

It’s like importing bulldozers and calling it infrastructure development — useful, but not self-reliant.

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🔐 Security Risks Beneath the Hype

This imported foundation brings deep strategic vulnerabilities:

• Chips can carry backdoors or firmware-level compromises

• Foreign models may embed opaque alignment, bias, or behavioral controls

• Once deployed into public systems, damage from misaligned AI is often irreversible

The illusion of progress can blind us to the loss of control. The deeper we integrate opaque, imported AI systems, the harder it becomes to correct course.

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🧠 What Real AI Capability Looks Like

If India truly wants to “Make AI in India,” it needs to:

✅ Design and manufacture chips locally

✅ Develop foundational models with Indian data and oversight

✅ Open-source critical AI infrastructure

✅ Build regulatory + audit tools for AI safety

✅ Invest in long-term AI R&D, not just deployments

Right now, we’re mostly assembling, not innovating.

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🧭 Final Thought: Don't Mistake Access for Autonomy

India has reason to be proud — 34,000 GPUs and a national AI mission are real steps forward. But let’s not confuse procurement with progress.

We’re still:

• Dependent on foreign hardware

• Using models trained on non-Indian priorities

• Vulnerable to invisible controls and misalignments

If we don't own the compute, build the models, and audit the stack, we risk becoming users in a creator-driven world. 

It’s time to move from cargo cult thinking to core innovation — while we still can. This isn’t about sarcasm or criticism — it’s about clarity. Real progress begins when we stop celebrating specs we don’t own, and start building the capability to design them ourselves.