Z47
January 14, 2026

The formula for India’s AI moment is DPI^AI

This February, India hosts the India AI Impact Summit. It now sits alongside Bletchley Park (2023), Seoul (2024), and Paris (2025) as the conversation around AI shifts from theory to application. As the world moves forward from talking about what might go wrong with AI, it is replaced with a focus on what (actually) gets built, especially in countries where scale is not an option. 

That makes it the right moment to launch our new series, Intelligent Indians! with Shri Abhishek Singh, CEO of the India AI Mission. Intelligent Indians is a living chronicle of India’s AI rise, its builders, its breakthroughs, and our national AI ambition.  

Even before AI, India never had the luxury of building for everyone. With 1.4 billion people, the average is often the conventional edge case. Aadhaar, UPI, DigiLocker, and the DPI stack were all born from that constraint. The IndiaAI Mission carries the same DNA. The national AI bet is straightforward: build AI on top of DPI. Abhishek Singh calls it DPIAI

Across seven pillars, compute, datasets, skills, innovation, applications, startup financing, and safe, trusted AI, the IndiaAI strategy is clearly scoped and built for execution, with public-private partnerships.  

India’s language diversity makes the case for AI in governance and public sector applications obvious. We have twenty-two official languages, hundreds of dialects. If AI is to work for India, it must speak first. Voice is not a feature; it is the interface. Language and accessibility sit at the foundation of the Indian AI Mission.  

Compute is where most AI ambitions stall. India is trying to break that constraint early. Through incentives that pull in private capital and subsidize access for builders, the program has created capacity for roughly 38,000 GPUs at under $1 per GPU hour, well below global benchmarks. This lowers the barrier when deciding who gets to train models. 

The mission is opening up data too. AI Kosh now hosts more than 3,000 curated datasets for anyone building models or applications. Alongside this, teams are working on foundation models tuned to Indian languages and contexts. At population scale, that work can’t be outsourced. 

IndiaAI’s approach to applications follows the same logic. Healthcare. Agriculture. Governance. Climate. Education. Learning disabilities. These are not pilot projects; they are pressure points. Skilling runs in parallel, support for researchers across engineering, medicine, law, and public policy, and funding for data labs that employ the people who make large-scale AI possible. Annotation and evaluation are not footnotes. They are the next large opportunities. 

Agriculture shows why these matter. Farmers who lived near Krishi Vigyan Kendras always had an edge: better inputs, better advice, better timing. AI makes that advantage accessible to everyone. Any farmer should be able to ask a question by voice and get guidance in their own language. No travel. No hearsay. Agricultural guesswork gets replaced with scientific wisdom.  

What enables this is India’s structural edge: DPI paired with a consent-based data layer. Under the DPDP Act, applications can access the right data, with permission, to deliver services that are specific and timely. Farmers can see which subsidies or insurance schemes apply to them. Workers can understand healthcare or employment benefits they’re already eligible for. The answers exist because the rails are already in place. 

Education and healthcare mirror the pattern. Teacher shortages persist even in major cities. Most classrooms end up teaching to the top slice of students. AI tutors change the dynamic. They slow down. They repeat. They speak the student’s language. In healthcare, platforms like the Bharat Health Stack and Jan Aushadhi already cover millions. AI makes them usable, by voice, at scale, without friction. And this is what makes India's AI moment unique, that DPIAI rails exist for founders to build on.

The AI Summit isn’t just IndiaAI’s story, it is a landmark in India’s AI story. Nation first. Infrastructure-forward. Scaled to a billion users by default. Built to run in the real world, by Intelligent Indians!  

For more information, write to us: namaste@Z47.com.
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January 14, 2026

The formula for India’s AI moment is DPI^AI

Article
Listen to article

This February, India hosts the India AI Impact Summit. It now sits alongside Bletchley Park (2023), Seoul (2024), and Paris (2025) as the conversation around AI shifts from theory to application. As the world moves forward from talking about what might go wrong with AI, it is replaced with a focus on what (actually) gets built, especially in countries where scale is not an option. 

That makes it the right moment to launch our new series, Intelligent Indians! with Shri Abhishek Singh, CEO of the India AI Mission. Intelligent Indians is a living chronicle of India’s AI rise, its builders, its breakthroughs, and our national AI ambition.  

Even before AI, India never had the luxury of building for everyone. With 1.4 billion people, the average is often the conventional edge case. Aadhaar, UPI, DigiLocker, and the DPI stack were all born from that constraint. The IndiaAI Mission carries the same DNA. The national AI bet is straightforward: build AI on top of DPI. Abhishek Singh calls it DPIAI

Across seven pillars, compute, datasets, skills, innovation, applications, startup financing, and safe, trusted AI, the IndiaAI strategy is clearly scoped and built for execution, with public-private partnerships.  

India’s language diversity makes the case for AI in governance and public sector applications obvious. We have twenty-two official languages, hundreds of dialects. If AI is to work for India, it must speak first. Voice is not a feature; it is the interface. Language and accessibility sit at the foundation of the Indian AI Mission.  

Compute is where most AI ambitions stall. India is trying to break that constraint early. Through incentives that pull in private capital and subsidize access for builders, the program has created capacity for roughly 38,000 GPUs at under $1 per GPU hour, well below global benchmarks. This lowers the barrier when deciding who gets to train models. 

The mission is opening up data too. AI Kosh now hosts more than 3,000 curated datasets for anyone building models or applications. Alongside this, teams are working on foundation models tuned to Indian languages and contexts. At population scale, that work can’t be outsourced. 

IndiaAI’s approach to applications follows the same logic. Healthcare. Agriculture. Governance. Climate. Education. Learning disabilities. These are not pilot projects; they are pressure points. Skilling runs in parallel, support for researchers across engineering, medicine, law, and public policy, and funding for data labs that employ the people who make large-scale AI possible. Annotation and evaluation are not footnotes. They are the next large opportunities. 

Agriculture shows why these matter. Farmers who lived near Krishi Vigyan Kendras always had an edge: better inputs, better advice, better timing. AI makes that advantage accessible to everyone. Any farmer should be able to ask a question by voice and get guidance in their own language. No travel. No hearsay. Agricultural guesswork gets replaced with scientific wisdom.  

What enables this is India’s structural edge: DPI paired with a consent-based data layer. Under the DPDP Act, applications can access the right data, with permission, to deliver services that are specific and timely. Farmers can see which subsidies or insurance schemes apply to them. Workers can understand healthcare or employment benefits they’re already eligible for. The answers exist because the rails are already in place. 

Education and healthcare mirror the pattern. Teacher shortages persist even in major cities. Most classrooms end up teaching to the top slice of students. AI tutors change the dynamic. They slow down. They repeat. They speak the student’s language. In healthcare, platforms like the Bharat Health Stack and Jan Aushadhi already cover millions. AI makes them usable, by voice, at scale, without friction. And this is what makes India's AI moment unique, that DPIAI rails exist for founders to build on.

The AI Summit isn’t just IndiaAI’s story, it is a landmark in India’s AI story. Nation first. Infrastructure-forward. Scaled to a billion users by default. Built to run in the real world, by Intelligent Indians!  

We are excited about the innovation and growth opportunities in this sector.

If you are considering building in the footwear space, we’d love to chat.
Drop us a line at consumer@matrixpartners.in

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Index Performance

+28.1%
Since Jan 2024
NIFTY 500
+19.0%
Since Jan 2024

Z47^fortyseven is up +23.9% since its January 2024 base date, versus Nifty 500's +18.4%, ahead by 550 bps.

The cohort moved +4.7% over the month versus Nifty 500's +2.5%, leading by 220 bps.

Anchored in domestic demand and rising digital adoption, the cohort remained resilient amid global headwinds.

Consumer Tech was the best-performing sector at +9.2% last month, driven by sustained growth in consumer demand and strength in consumer-internet platforms.

Largest Constituents  ·  The Names That Anchor The Index

1.
Eternal
Quick-commerce leadership and continued investment
▲ +12.8%
2.
Groww
Broking market-share gains and margin-funding growth.
▲ +10.4%
3.
Lenskart
Store densification and margin expansion.
▲ +2.4%

Top Gainers  ·  Key Drivers

1 MONTH RETURN
1.
CarTrade
Auto-marketplace dominance and a cash-rich balance sheet.
▲ +59.4%
2.
 Amagi Media Labs
Profitability turnaround and AI-led cloud media adoption.
▲ +31.4%

Top Laggards  ·  Key Drivers

1 MONTH RETURN
1.
Fractal Analytics
Enterprise AI spending trends and post-listing share supply.
▼ -10.8%
2.
MedPlus Health
Pharmacy-margin pressure and competitive intensity.
▼ -6.6%

Key Themes  ·  Latest Results

In Q4FY26, Z47^fortyseven's cohort grew top line ~39% YoY, more than 3x the broad market's ~12% growth.

Operating leverage lifted net margins around 500 bps into positive territory, even as broad-market net margins remained roughly flat.

With 40 of 47 companies now profitable, the cohort reflects a broader shift toward profitable growth over growth at any cost.

AI adoption runs deeper across this cohort than in the broader market, with companies using it to drive growth and reshape demand, not just improve efficiency.

Cash generation is increasingly defining the winners, enabling market leaders like Eternal, CarTrade, and PB Fintech to fund acquisitions and expansion from their own balance sheets.

Market & Macro Context

The cohort saw several block deals this month, including sizeable stake sales in Lenskart, Delhivery, Honasa, and Shadowfax.

Ownership continues to shift from foreign investors to domestic institutions, creating a more durable shareholder base.

AI remained the defining technology investment theme, driving capital deployment across both private and public markets.

IPO Takeaway · Kissht

Listed May 2026

A modest listing pop followed by strong post-listing gains reinforced the market's preference for asset quality and disciplined underwriting over pure loan-book growth.

The listing helped reset perceptions around unsecured lending, creating a constructive valuation anchor for the issuers that follow.

The buyer mix was a notable positive — strong participation from long-only domestic institutions supporting a durable post-listing ownership base.

Net Read

Fundamentals continued to strengthen across the cohort, with growth, margins, and cash generation improving in tandem.

Performance dispersion widened, with profitability and earnings quality increasingly distinguishing the strongest performers from the rest.

Disclaimer

Z47^fortyseven is published for informational purposes only and does not constitute investment advice, or any offer, solicitation, or recommendation to buy or sell securities. Index performance is historical and should not be construed as indicative of future results.

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