Reliance Industries Limited, India’s largest business group, has announced plans to build the country’s largest artificial intelligence–ready data centre in Jamnagar, a coastal industrial city in the western Indian state of Gujarat, as part of a broader push to expand access to AI technologies at population scale.

The announcement was made by Mukesh Ambani, chairman and managing director of Reliance Industries, during the Vibrant Gujarat Regional Conference for the Kutch and Saurashtra region, a government-led investment and development forum focused on regional economic growth.

Ambani said the Jamnagar facility is being developed with a single objective: “Affordable AI for every Indian.” He positioned the project as a foundational investment in India’s digital infrastructure, aimed at enabling large-scale adoption of artificial intelligence across sectors including industry, services, education and public administration.

“As part of making Gujarat India’s artificial intelligence pioneer, in Jamnagar we are building India’s largest AI-ready data centre,” Ambani said, adding that the facility is intended to support widespread access to AI tools for individuals, enterprises and institutions.

Reliance also announced that its digital arm, Jio, will launch a “people-first intelligence platform,” designed to deliver AI services in multiple languages and across consumer devices. According to Ambani, the platform is being built in India for both domestic and international users, with a focus on everyday productivity and digital inclusion.

The AI initiative forms part of Reliance’s broader commitment to invest approximately Rs 7 trillion (about USD 85 billion) in Gujarat over the next five years. The company said the investments are expected to generate large-scale employment while positioning the region as a hub for emerging technologies.

The Jamnagar AI data centre is being developed alongside what Reliance describes as the world’s largest integrated clean energy manufacturing ecosystem, encompassing solar power, battery storage, green hydrogen and advanced materials. Ambani said the city, historically known as a major hub for oil refining and petrochemicals, is being re-engineered as a centre for next-generation energy and digital technologies.

The announcements were made in the presence of Indian Prime Minister Narendra Modi and Gujarat Chief Minister Bhupendra Patel, underscoring the alignment between public policy and private investment in India’s long-term technology and infrastructure strategy.

Jio Platforms Limited, owned by India’s top  billionaire Mukesh Ambani, has emerged as India’s largest filer of international patents in 2024–25, according to the Annual Report of the Office of the Controller General of Patents, Designs & Trade Marks, published by the Government of India. The scale of the lead is striking — and it highlights a deeper structural imbalance in India’s innovation ecosystem.

Jio Platforms Limited is the digital and technology arm of Reliance Industries Limited, India’s oil-to-telecom conglomerate. It functions as a holding company for Reliance Jio Infocomm and other digital businesses, with a focus on telecom services, broadband connectivity, cloud computing, artificial intelligence, Internet of Things (IoT), and advanced 5G solutions for both consumers and enterprises.

During the year, Jio Platforms filed 1,037 international patent applications, more than four times the filings of the next-ranked private company and over fourteen times those of India’s leading publicly funded research institution.

For comparison:

• TVS Motor Co filed 238 international patents
• CSIR filed 70 international patents
• IIT Madras filed 44 international patents
• Ola Electric Mobility filed 31 international patents

Other Indian universities and firms each filed in the low double or single digits.

Taken together, the combined international patent filings of entities ranked second through tenth still amounted to less than half of Jio Platforms’ total for the year.

Including domestic filings, Jio Platforms filed 1,654 patents in 2024–25. As of March 31, 2025, the company held 485 granted patents, largely concentrated in telecom networks, 5G and emerging 6G technologies, artificial intelligence, and digital infrastructure.

What the data shows — and what it doesn’t

The patent numbers reflect a sustained increase in corporate R&D activity at Jio Platforms, part of the broader Reliance Industries group. Reliance spent ₹4,185 crore on R&D in FY25, up from ₹3,643 crore the previous year. Over three years, its annual R&D expenditure has increased by more than ₹1,500 crore.

However, the patent data also raises uncomfortable questions. India’s publicly funded research ecosystem — including IITs, national laboratories and government-backed innovation programmes — contributes only a small fraction of the country’s internationally filed intellectual property.

For instance, all IITs combined filed fewer international patents than a single large private company, despite decades of public investment in technical education and research infrastructure.

Corporate deep tech versus public research

Reliance Industries’ leadership has framed this push as a transition toward deep technology development. At the company’s 2025 AGM, Mukesh Ambani said, “We are resolutely transforming our operating model to become a Deep-Tech company with advanced manufacturing capabilities.”

Akash Ambani added that Jio’s core technology stack — including its 5G core — has been developed by in-house engineering teams in India.

While these claims align with the patent data, they also underscore a broader shift: cutting-edge applied research in India is becoming increasingly concentrated within a few large corporate groups, rather than distributed across universities, startups and public labs.

How far is India from global R&D leaders?

Even with its rising patent output, Jio Platforms’ R&D spending remains small compared to global technology leaders:

• Alphabet (Google) spends over USD 40 billion annually on R&D
• Microsoft spends approximately USD 27–30 billion
• Amazon spends more than USD 80 billion on technology and research

In comparison, Reliance’s FY25 R&D spend translates to roughly USD 500 million, a fraction of what global firms invest each quarter.

What distinguishes Jio is not scale, but focus — a relatively high conversion of R&D spending into international patent filings, particularly in telecom infrastructure. Whether these patents translate into long-term technological leadership or commercial dominance remains to be seen.

A lopsided innovation landscape

The government recognition of Jio Platforms as India’s largest global IP creator is significant. But it also exposes a systemic challenge: India’s innovation pipeline is heavily skewed toward a small number of well-capitalised corporations, while public research institutions and startups struggle to compete at the global IP level.

If India’s ambition is to become a science and technology powerhouse, the data suggests that patent creation — especially internationally relevant IP — cannot remain so narrowly concentrated. A resilient innovation ecosystem depends not only on corporate R&D, but also on universities, independent labs and publicly funded science translating research into globally protected knowledge.

For now, Jio Platforms’ patent dominance stands less as a simple success story — and more as a mirror reflecting the structural gaps in India’s broader research and innovation system.

When cities build new subway lines, they face an impossible dilemma long before construction begins: how do you identify the cheapest, safest path through hundreds of city blocks without spending years on costly field surveys? Urban planners typically assume that the only way to make an optimal choice is to collect as much data as possible — often far more than budgets, timelines, or logistics allow.

A new study from researchers at the Massachusetts Institute of Technology (MIT) challenges that assumption with a radically different idea: for many complex decisions, you don’t need more data — you just need the right data.

The team has developed a mathematical and algorithmic framework that can identify the smallest possible dataset required to guarantee an optimal decision, even in problems involving thousands of uncertainties. Their findings suggest that a city planning a subway line under Manhattan, or a utility operator optimizing an electricity grid, may be able to cut their data collection needs dramatically.

And the breakthrough doesn’t just reduce the burden of field surveys — it rewrites long-held beliefs about AI and the data economy.

“Data are one of the most important aspects of the AI economy. Models are trained on more and more data, consuming enormous computational resources. But most real-world problems have structure that can be exploited. We’ve shown that with careful selection, you can guarantee optimal solutions with a small dataset,”Asu Ozdaglar, head of MIT’s EECS department, in a statement issued as part of this research.

Rethinking the ‘Big Data’ Era

For over a decade, modern AI has pushed the narrative that “more data is always better.” But many real-world optimization problems — from supply chains and transit networks to energy markets — have predictable structural patterns. The MIT researchers argue that this structure can be used to determine exactly which data points matter.

This is crucial in systems like:subway route selection, supply chain diversification, electricity network optimization, construction planning, logistics and resource allocation.

In all these cases, practitioners often drown in unnecessary data collection, hoping volume will compensate for uncertainty.

The new algorithm takes the opposite approach: start with no data, and add only what is provably essential.

The Key Breakthrough

The team’s method begins by mathematically defining what it means for a dataset to be “sufficient.” They break the decision space into “optimality regions” — scenarios in which a particular route, price, or configuration becomes the best choice.

A dataset is sufficient if it can accurately determine which region the real world belongs to. “When we say a dataset is sufficient, we mean that it contains exactly the information needed to solve the problem. You don’t need to estimate all parameters accurately; you just need data that can discriminate between competing optimal solutions,” said Amine Bennouna, co-lead author.

Once the structure is defined, the algorithm repeatedly asks a critical question: “Is there any scenario in which the optimal decision could change, and my current data would fail to detect it?”

If the answer is yes, the algorithm identifies precisely which new measurement would fill that gap. If no, the dataset is complete — and provably sufficient.

This approach can shrink data collection from thousands of measurements to a handful.

“The algorithm guarantees that, for whatever scenario could occur within your uncertainty, you’ll identify the best decision,” Omar Bennouna, co-lead author, added.

From Manhattan Tunnels to Global Supply Chains

Consider the example of a subway planned beneath New York City. Every city block might hide different soil conditions, underground utilities, or hazard profiles. The traditional assumption: investigate everything.

The MIT model: investigate only the blocks that can change the optimal route, and ignore the rest.

The same applies to: selecting shipping routes in a congested supply chain,  configuring electrical grid nodes during volatile energy prices,  deciding where to place sensors in large infrastructure projects. This could save millions in surveys, simulations, and engineering assessments.

The researchers were able to show not only that minimal datasets exist — but that their algorithm can find them systematically, preserving optimality with mathematical certainty.

“We challenge this misconception that small data means approximate solutions. These are exact sufficiency results with mathematical proofs,” Saurabh Amin, co-senior author, said.

Why This Matters: Cost, Carbon, and Computational Savings

If AI models can be trained with fewer, smarter data points: computation becomes cheaper, energy consumption drops, model training becomes faster and policymakers can rely on faster decision cycles.

For large infrastructure projects, this could mean shaving months — even years — off planning timelines.

The team plans to expand the framework to more complex scenarios, including situations where data are noisy or partially observable — a common challenge in the real world.

Their work will be presented at the Conference on Neural Information Processing Systems (NeurIPS) — a major stage for breakthroughs that redefine how AI systems learn and make decisions.

If successful, this research could push the global AI ecosystem to rethink its data obsession. Instead of hoarding massive datasets, the future may lie in asking sharper questions — and collecting only the data that truly matters.