India’s AI Boom Is a User Land-Grab: Why Firms Are Sacrificing Near-Term Revenue (and What Happens Next)

India has become the world’s most enthusiastic downloader of generative AI apps—and the world’s most stubborn monetization puzzle. If you’re an AI company, the country looks like a dream: hundreds of millions of smartphone users, a young population, and a national ambition to become an AI powerhouse. If you’re a CFO, it looks like a stress test: tons of users, lots of usage, and comparatively little money.

That tension sits at the heart of a recent TechCrunch report by Jagmeet Singh, which argues that major AI players have been trading near-term revenue for user growth in India—often through extended free promotions, telecom bundles, and ultra-low-price plans. citeturn0search0

In this article, I’ll expand on what’s really happening underneath those promos: why India is a uniquely important battleground for AI adoption, how pricing experiments are reshaping product strategy, what infrastructure and policy moves are attempting to fix the “AI compute gap,” and what a realistic monetization path could look like through 2026 and beyond.

India: the biggest GenAI funnel on Earth (but not the biggest checkout line)

Let’s start with the math that makes investors both excited and slightly nauseous.

According to the TechCrunch piece (citing Sensor Tower data shared with the publication), India became the largest market for generative AI app downloads in 2025, with installs up 207% year-over-year. Yet India contributed only about 1% of global genAI in-app purchase revenue while driving roughly 20% of downloads. That’s the gap: India is an acquisition machine, not (yet) a monetization machine. citeturn0search0

Other reporting based on Sensor Tower’s “State of Mobile” findings puts India’s genAI downloads for 2025 at roughly 602 million (up from 198 million in 2024), reinforcing the picture of hyper-growth in adoption. citeturn2search5

So why the mismatch?

Price sensitivity is real. India is one of the world’s most value-conscious digital markets. “Free” is not a teaser here—it’s the default expectation until proven otherwise.
Payments behavior differs. India has world-class consumer payments rails (UPI), but recurring subscription habits vary dramatically by category. Many users are comfortable paying in small bursts (top-ups, one-time buys, bundles) rather than committing to $20/month equivalents.
Competition is intense. When multiple “good enough” AI assistants exist, the willingness to pay a premium for any single one drops.

In other words: India is where AI apps go to win mindshare—and where they learn humility about conversion.

The promo era: how AI firms bought adoption

The core tactic in 2025 was straightforward: remove friction, then pray retention is strong enough to survive the hangover when free access ends.

Telecom bundles: the Airtel–Perplexity playbook

One of the most visible growth hacks was the Airtel partnership with Perplexity. Airtel announced in July 2025 that it would provide a year of Perplexity Pro at no extra cost to its customer base, describing the offer as worth roughly ₹17,000 annually. citeturn1search3

As TechCrunch noted, that bundled offer ended for new redemptions in January 2026. citeturn0search0 A Perplexity help-center page states the promotion ended on January 16, 2026. citeturn1search6 Airtel’s own FAQ similarly notes the redemption window expired in mid-January 2026 (with existing claimants continuing for their 12-month period). citeturn1search5

From a strategy perspective, telco bundles do three things at once:

Mass distribution: you ride the telco’s scale and marketing channels.
Reduced churn risk: users don’t have to repeatedly re-enter payment details (or decide monthly whether to keep paying).
Category legitimation: “your telco gave you this” makes AI feel like a normal utility, not a risky new app.

The catch is obvious: if the bundle trains users to expect “premium AI” to be included with everything else, it becomes very hard to untrain them later. Bundles are great at creating users; they’re less great at creating stand-alone ARPU.

OpenAI’s ChatGPT Go promotion: subsidize now, convert later

OpenAI used a similar adoption lever via ChatGPT Go in India. An OpenAI Help Center page describes a limited-time promotion making ChatGPT Go available at no cost for 12 months for eligible users in India, starting November 4, 2025. citeturn1search1

TechCrunch reports that free access is no longer available—setting up a clearer conversion test as users decide whether the product is worth paying for after the free ride ends. citeturn0search0

Promos like this are less about short-term revenue and more about building habitual usage. The bet is that once a user has integrated an AI assistant into study routines, job searching, coding practice, or content creation, cancellation becomes psychologically harder. That’s the “sticky workflow” thesis.

Price localization: Google’s sub-$5 India plan

Discounts and freebies are one thing. Permanent pricing changes are another—because they reset the entire market’s reference price.

In December 2025, TechCrunch reported that Google launched a lower-cost AI plan in India, offering an “AI Plus” tier at ₹199 per month for the first six months (then ₹399/month). The same report framed it as a move to compete directly with low-cost offerings like ChatGPT Go. citeturn1search2

This is important because it signals that the “India discount” is not a temporary campaign; it’s turning into a structural pricing reality. And once one hyperscaler sets a low anchor, the rest of the market feels it.

The numbers behind the strategy: users, engagement, and the monetization gap

TechCrunch’s reporting includes a set of metrics that explain why AI companies are still willing to play the long game in India:

India accounted for ~19% of global users of leading AI assistant apps in 2025, per Sensor Tower data cited by TechCrunch. citeturn0search0
ChatGPT dominated by monthly active users and (per TechCrunch) commanded more than 60% of genAI in-app revenue in India, meaning its pricing changes swing the whole market. citeturn0search0
Engagement trails the U.S.: U.S. users spent ~21% more time per week and logged ~17% more sessions than users in India (Sensor Tower data cited by TechCrunch). citeturn0search0

That last point is subtle but critical: revenue doesn’t just lag because users won’t pay; it also lags because usage intensity is lower. Even ad-supported or microtransaction models need engagement volume. The strategic question is whether India’s engagement gap is structural (device constraints, language friction, connectivity variability) or temporary (early-stage adoption curve).

Why companies are willing to “lose money” in India (for now)

On paper, running expensive AI models for non-paying users looks like lighting GPUs on fire. In practice, there are several rational reasons companies tolerate it.

1) India is where global scale gets tested

India forces products to work under constraints: cheaper devices, more diverse languages, inconsistent bandwidth, and a wide range of digital literacy levels. If your AI assistant survives that environment, it’s more likely to scale across the rest of the “next billion users” markets.

2) India is a distribution engine for the global south

At the AI Impact Summit in New Delhi, Prime Minister Narendra Modi pitched India as an AI hub, emphasizing inclusive and low-cost AI solutions aimed at broad accessibility. citeturn0news15 Whether you agree with the politics or not, the market logic is sound: India isn’t just a country-sized market; it’s a template for many markets.

3) Switching costs will rise over time

Today, switching assistants is easy: download another app, try it, forget the old one. But as assistants plug deeper into personal workflows—notes, email, documents, study plans, workplace tools—switching becomes more annoying. AI firms want to be the assistant that users build their habits around first.

4) Data and feedback loops matter (with caveats)

More users mean more prompts, more edge cases, more language coverage, more product telemetry. But there’s an important caveat: data collection is increasingly constrained by privacy regulation, and many top-tier models rely on a mixture of licensed, curated, and synthetic datasets rather than raw user prompts. Still, product feedback loops (what features are used, where users get stuck) remain valuable.

Infrastructure: India is racing to bring compute closer to home

There’s another reason India is strategically interesting to AI firms: the government and the private sector are actively trying to expand domestic AI compute and data-center capacity. That matters because local infrastructure can reduce latency, improve reliability, and address data residency and compliance concerns.

The IndiaAI Mission GPU ramp

In February 2026, The Indian Express reported that India’s installed GPU capacity could triple from about 38,000 to roughly 100,000 by the end of 2026, citing Abhishek Singh, CEO of the IndiaAI Mission. citeturn0search2

Separately, The Times of India reported that IT Minister Ashwini Vaishnaw said at the AI summit that over 50,000 GPUs would be deployed within six months, describing it as more than doubling current capacity and potentially taking the total beyond 100,000 before 2026 ends. citeturn0search1

Whether the exact numbers land perfectly on schedule is less important than the direction: India is attempting to make compute availability less of a bottleneck for startups, researchers, and enterprises.

Private-sector mega-clusters and AI-ready campuses

Compute isn’t only a government story. According to reporting from The Times of India, Yotta Data Services announced a plan to invest $2 billion for an AI hub using Nvidia Blackwell Ultra GPUs, with the facility expected to become operational by August 2026. citeturn0news13

Meanwhile, also per The Times of India, Gujarat signed an MoU with L&T Vyom for a 250MW green, AI-ready data center campus in Dholera SIR, aiming for operations by 2028. citeturn0news14

These projects don’t immediately fix the consumer-app monetization puzzle, but they do change the long-term economics of building and deploying AI services within India—especially for enterprise and government workloads.

Policy and governance: India wants AI growth with guardrails (and leverage)

AI monetization is not just “how do we charge users?” It’s also “how do we operate legally and competitively in a market where governments increasingly shape the rules of data, compute, and platform distribution?”

In late February 2026, Economic Times reported on the “New Delhi Frontier AI Impact Commitments,” described as a voluntary framework unveiled at the AI Impact Summit 2026 to promote inclusive and responsible AI development. citeturn2news15

For global AI firms, this matters because India can become both:

A growth market they want access to, and
A governance market whose norms could influence other countries, especially those looking for “responsible AI” templates.

In short: if you want to build a truly global AI consumer product, you don’t get to ignore Indian policy debates—because they’re increasingly entangled with infrastructure, data localization expectations, and public-sector adoption.

So how do you actually make money in India with AI?

Charging $20/month for a chatbot is the simplest business model—and it’s also the one most likely to stall in India at mass scale. The interesting part of 2026 will be the experimentation with alternative monetization structures that match Indian consumer behavior.

Model 1: low-cost tiers with meaningful differentiation

Low-cost tiers only work if they feel like an upgrade, not a donation. That means clear benefits: higher limits, faster responses, better models, multimodal tools, and practical perks like storage or bundled services (Google’s plan bundles storage across Photos/Drive/Gmail, for example). citeturn1search2

The danger: low-cost tiers can cannibalize higher tiers globally if users learn how to route purchases through India pricing (e.g., via VPN or account-region games). Platforms will keep tightening regional enforcement if that becomes material.

Model 2: bundles (telcos, device makers, banks)

Bundles can remain a major path to monetization, but with a twist: instead of “free for everyone,” we may see bundles targeted to premium customer segments—postpaid plans, business users, or high-ARPU households. This shifts AI from mass giveaway to retention tool for the bundle partner, who then subsidizes part of the cost.

Airtel’s Perplexity partnership shows how this can scale quickly, even if redemption windows are time-boxed. citeturn1search3turn1search5

Model 3: microtransactions and task-based pricing

India has a strong history of prepaid behavior. It wouldn’t be shocking to see AI products adopt pricing like:

₹X for 100 “premium messages”
₹Y for 10 image generations
₹Z for a one-time “Deep Research” report

This approach aligns with users who don’t want subscriptions but will pay for a specific outcome (exam prep, resume rewrite, visa document draft, product comparison, etc.).

Model 4: ads (carefully) and commerce

Ad-supported AI assistants are controversial—because ads can degrade trust and raise questions about neutrality. But some hybrid models are plausible, especially if ads are clearly labeled, or if monetization comes through affiliate-like commerce flows (shopping recommendations, ticketing, local services). India’s massive e-commerce ecosystem provides an obvious “intent monetization” opportunity, though platforms will have to avoid turning the assistant into a spam cannon.

Model 5: enterprise subsidizes consumer (the “B2B2C” play)

In India, large employers, training institutes, and coaching platforms have enormous reach. If AI assistants become embedded in education platforms, workplace productivity suites, or call-center workflows, enterprise contracts could subsidize mass consumer access.

There’s also a strong public-sector angle: regional language interfaces and citizen services can drive enormous usage volumes. The Times of India reported on an Andhra Pradesh “Swadeshi AI stack” initiative partnering with IBM, BharatGen, and NxtGen, designed for multilingual citizen services hosted on sovereign cloud or on-prem infrastructure. citeturn2news16

Case study lens: content creation is the Trojan horse

One reason AI adoption spiked in India in 2025 is that generative AI fits neatly into the creator economy. TechCrunch noted viral interest in AI-generated content and said content creation/editing tools accounted for a large slice of the most downloaded genAI apps. citeturn0search0

This matters for monetization because creators are more likely than casual users to pay—if the tool drives income or saves serious time. In India, even modest productivity gains can justify small monthly fees. The key is to package AI as a livelihood tool, not a novelty.

Expect to see more India-specific feature localization in this segment:

Regional language scripting for short video formats
Captioning and dubbing tuned for Indian languages and accents
Social platform templates optimized for Instagram, YouTube Shorts, Moj-style formats, and emerging microdrama apps

What changes in 2026: the “conversion moment” arrives

The most interesting part of the TechCrunch story is timing. Many of the big promos started to wind down in early 2026. citeturn0search0 That creates a natural experiment: after the free period, do users churn, downgrade, or pay?

There are a few outcomes to watch:

Conversion is low, but retention remains high on free tiers: AI becomes an “ad-supported or subsidized utility,” and premium remains niche.
Conversion is moderate with heavy tiering: A stable “₹199–₹499 per month” segment emerges for power users.
Bundles dominate: Telcos and device makers become the main monetizers; AI firms become the suppliers.

In all cases, India remains strategically valuable because it shapes product design for massive scale. But the industry will stop pretending that “user growth alone” is enough—especially as model training and inference costs remain material.

How India’s AI boom intersects with the IT services story

There’s another layer here: India isn’t just a consumer market; it’s also the home base of a massive IT services industry that’s being reshaped by AI-native competitors and automation.

In February 2026 coverage, Nasscom projected India’s IT industry revenues would grow to about $315 billion in FY26, with growth around 6.1%, even amid AI-related disruptions. citeturn2news13turn2news12

That suggests a dual-track AI economy:

Consumer AI is battling monetization and pricing pressure.
Enterprise AI is embedding into services delivery, cybersecurity, and internal tooling, where budgets are larger and ROI is easier to quantify.

In other words: if consumer subscription revenue in India stays weak, enterprise adoption could still make the country financially meaningful for AI vendors—via cloud spend, enterprise seats, security tooling, and managed services.

Risks and frictions: what could slow the party?

No boom is complete without a few inconvenient realities.

1) Trust, safety, and misinformation

As AI assistants become default information tools, the stakes rise. Hallucinations aren’t just funny mistakes; they become consumer harm, regulatory risk, and brand damage—especially when AI is used for health, finance, or legal guidance.

2) Language diversity isn’t a checkbox feature

India’s linguistic diversity is a product challenge and a compute challenge. Better multilingual performance requires data, evaluation, and often model adaptations. The payoff is huge, but it’s not cheap.

3) Compute costs remain a strategic constraint

Even with GPU expansion plans, top-tier AI experiences remain expensive to deliver at scale. If monetization lags too far behind usage, firms will be forced to rate-limit or degrade free tiers, which could slow adoption.

4) The “free premium” trap

When a market experiences repeated “premium for free” promotions from multiple players, users can become permanently anchored to the idea that paying is optional. Companies must transition from promotions to sustainable value communication—or accept that India is primarily a volume play with lower monetization per user.

What I think happens next (a journalist’s forecast, not a prophet’s guarantee)

Here’s the most realistic direction of travel for India’s AI app economy through 2026:

Freemium stays dominant, but free tiers get tighter (more limits, more friction, more “wait until tomorrow”).
Low-cost paid tiers expand and become the main consumer revenue driver, rather than global-priced subscriptions.
Bundles professionalize: more targeted, more segmented, less “free for everyone.”
Enterprise + public sector adoption grows, helped by expanding domestic compute capacity and “AI-ready” data center investments. citeturn0search1turn0search2turn0news13turn0news14
Local language and localized workflows become the real differentiators: exam prep, small business marketing, customer support, and creator tooling.

And, inevitably, the phrase “unit economics” will make a comeback in boardrooms. Not because growth is over—but because the era of “just add users” is being replaced by “add users… profitably.”

Conclusion: India’s AI boom is not a bubble—it’s a pricing experiment at nation scale

India’s position as the world’s largest genAI app download market is not an accident. It’s the result of massive smartphone scale, cultural curiosity, and aggressive go-to-market tactics by AI firms that treated the country like the ultimate onboarding funnel.

But the next chapter is harder: converting that funnel into sustainable revenue without killing the momentum that made India so strategically valuable in the first place.

If you’re watching the global AI race, don’t treat India as a side plot. It’s where pricing models get stress-tested, where product localization becomes non-negotiable, and where “AI for everyone” collides with the simple fact that GPUs are not powered by good vibes.

Sources

Bas Dorland, Technology Journalist & Founder of dorland.org