India’s artificial intelligence story is beginning to look less like a software race and more like an industrial one.
Mumbai: The government is increasingly presenting AI, semiconductor manufacturing, advanced packaging, chip design, compute infrastructure and indigenous models as parts of the same strategic technology stack. That shift matters because an AI ecosystem cannot become truly sovereign if the models are local but the critical hardware underneath them remains overwhelmingly imported.
India has already committed serious money to that proposition. The IndiaAI Mission carries an outlay of ₹10,371.92 crore, while Semicon 2.0 has been approved with ₹1,27,500 crore. Separately, 12 semiconductor manufacturing projects have attracted committed investment of more than ₹1.64 lakh crore, with three already in commercial production.
The message is becoming obvious: India does not only want to use AI. It wants to own more of the machinery that makes AI possible.
A modest ambition, then.
The Real AI Race Starts Below The Software Layer
Public attention naturally gravitates toward chatbots, foundation models and flashy applications. But every serious AI system depends on less glamorous infrastructure: GPUs, memory, advanced packaging, high-speed interconnects, power systems and, ultimately, semiconductor manufacturing.
That is why India’s policy architecture is widening.
Under Semicon 2.0, the government is targeting six areas: chip design, semiconductor equipment and materials, additional fabs, advanced packaging, R&D and talent development. The programme also notes that 105 startups are already working on chip development, while around 68,000 students have been trained through semiconductor-design programmes across 315 universities.
This is not yet technological independence, but it is a move away from treating chipmaking as somebody else’s problem.
AI Compute Is Growing Quickly, But Dependence Remains
On the AI side, the numbers are already substantial.
By June 2026, India’s shared AI compute capacity had crossed 45,000 GPUs. By August, 237 projects had received subsidised compute support covering 93.18 lakh GPU hours. The government has also selected 20 indigenous foundation-model proposals from 506 applications, including 12 large multimodal models and eight small language models.
That creates a useful domestic platform for startups, universities and researchers that otherwise could struggle to afford large-scale compute.
There is, however, an inconvenient footnote. India’s current compute ecosystem still relies heavily on globally sourced GPUs. The government itself acknowledges this dependence.
So while India may be building sovereign AI models, much of the silicon doing the thinking still arrives from elsewhere.
Digital sovereignty, apparently, comes with an import invoice.
Manufacturing Is Finally Moving Beyond Announcements
The semiconductor programme is at least beginning to produce physical output.
The government says Micron, Kaynes and CG Semi have started commercial production, while the first large-scale fab under the newer manufacturing push is scheduled for commissioning in 2028. Approved projects now span silicon, silicon carbide, gallium nitride and packaging technologies.
That distinction matters. Semiconductor ecosystems are not built by announcing fabs; they are built when wafers, packages and usable chips start leaving factories consistently.
India still has a long road ahead in advanced-node fabrication, equipment, specialty chemicals and manufacturing depth. Countries that dominate this industry spent decades building supplier networks, engineering expertise and intellectual property.
Money helps.
Time remains annoyingly unavailable for purchase.
Why SEMICON India 2026 Matters
The next major checkpoint comes later this month.
SEMICON India 2026 will be held from September 17 to 19 at Yashobhoomi in New Delhi, under the theme “Silicon to Systems: Building the Ecosystem.” The fifth edition is expected to bring together government, semiconductor companies, investors, researchers and global technology players.
The theme itself is revealing.
Earlier policy conversations were heavily focused on attracting individual fabs and packaging plants. “Silicon to Systems” suggests a broader objective: connect component manufacturing, design, electronics, AI infrastructure and downstream products into one value chain.
That is a more difficult strategy.
It is also the one that actually matters.
The Upside Is Bigger Than Chips
If even part of this ecosystem scales successfully, the benefits extend beyond AI.
Domestic semiconductor capability supports automobiles, telecom, aerospace, consumer electronics, defence, power electronics and industrial systems. It can create high-skilled employment, attract supply-chain investment and reduce exposure to geopolitical disruption.
For AI specifically, stronger local capability could eventually lower infrastructure costs, give Indian companies more control over hardware optimisation and allow models to be designed alongside the systems that run them.
The downside is equally clear: enormous capital requirements, long construction cycles, global competition for talent and continued dependence on foreign equipment and advanced manufacturing technology.
India is therefore not yet challenging the world’s semiconductor leaders on equal footing.
But the strategy has evolved in an important way.
The country is beginning to recognise that the AI race is not won only by building a clever model.
It is won by controlling enough of the stack underneath it that somebody else cannot switch off the future.
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