The Indic-Language Internet and the Vernacular Feed

For the first two decades of India’s internet, the default user was imagined as English-speaking, urban, and male. That image is now outdated. The country’s internet has become rural-majority, mobile-first, and—above all—Indic-language. The shift is a genuine access success: hundreds of millions of people can now read, watch, and speak in the languages they use at home. But the same infrastructure has also become a much larger surface for attention extraction.

Point C1 The shift to an Indic-language, mobile-first internet multiplied India’s online population, but it also multiplied the surface area for attention extraction, because the same engagement-optimized feed designs now operate across dozens of languages with weaker local safety, quality, and creator-economy supports.

The Language Flip

India had roughly 886 million active internet users in 2024, according to the Internet in India Report 2024 jointly prepared by IAMAI and Kantar. Nearly all of them—98 percent—accessed content in Indic languages. Point C2 The figure is not only a rural story: 57 percent of urban internet users said they prefer consuming content in regional languages.

The most popular languages were Tamil, Telugu, and Malayalam, according to the report, largely because content availability is already strong in those languages. Hindi and other major languages follow. The point is not that any one language dominates; it is that the internet is no longer a single English-dominant space with regional niches. It is a federation of vernacular feeds, each with its own creators, influencers, WhatsApp groups, and recommendation algorithms.

This matters for the attention-economy argument. When platforms expand from serving 150 million English-speaking users to serving 800 million-plus Indic-language users, the business model does not change—it scales. The same ad-revenue, time-on-platform, and engagement-maximization logic now reaches many more people, many of whom are newer to digital media and less likely to have encountered counter-programming such as media literacy, fact-checking, or privacy literacy in their own languages.

Rural, Mobile, and Shared

The language shift is tied to a geography shift. Rural India now accounts for 488 million internet users, or 55 percent of the total user base, and rural growth is running at roughly twice the pace of urban growth. Point C3 The average Indian internet user spent about 90 minutes online per day in 2024, with urban users slightly above that average.

Source: IAMAI-Kantar Internet in India Report 2024 (ICUBE 2024, n=90,000+ households). Urban/rural classification as defined by the survey; totals may not sum to 886 million due to rounding.

Mobile devices remain the primary access point, and shared-device use is common, especially among women and users under 19. That shared-device pattern matters: it can mean less individual privacy, more family-curated or algorithm-curated content, and fewer personalized controls. A teenager using a parent’s phone, or a family member using a single shared smartphone, may not have the same ability to set limits, manage notifications, or choose higher-quality defaults.

Vernacular Platforms at Scale

The demand showed up in platforms built for it. ShareChat, a social media platform designed around Indian languages, reported more than 350 million active users as of 2025, with 90 percent of them consuming content in local languages. Point C4 Dailyhunt, a vernacular news aggregator, has long positioned itself around the fact that nearly 70 percent of Indians speak non-English languages.

These are not small experiments. They are national-scale platforms. ShareChat and its short-video sibling Moj together operate across 15 Indian languages. The fact that a vernacular social platform can reach hundreds of millions of users means the design questions that apply to Meta, Google, and ByteDance also apply here: what the feed optimizes for, how long it keeps users watching, what kind of content gets algorithmically amplified, and what safeguards exist for hate speech, misinformation, and addictive use patterns.

The Extraction Multiplier

The vernacular internet did not invent a gentler business model. It imported the same one: ad-supported, engagement-optimized, infinite-scroll feeds. Point C5 Short-form video, autoplay, push notifications, streaks, and algorithmic recommendations work as well in Bhojpuri and Kannada as they do in English and Hindi. The psychological mechanisms—variable rewards, social comparison, fear of missing out—are language-agnostic.

What is different is the supply side of quality and safety. There are fewer fact-checkers fluent in smaller languages, smaller independent newsrooms with the resources to fight legal or algorithmic takedowns, and weaker creator-economy infrastructure outside the largest languages. Point C6 A misleading health video in English may be flagged quickly by multiple actors; the same video in a less-resourced language can circulate longer before anyone with authority notices.

This is not a claim that Indic-language internet users are more gullible. It is a claim about information-ecology resourcing. The same extraction pressure is applied to a landscape with fewer quality signals and fewer moderators who understand local context, dialect, and political nuance.

What Gets Lost in Translation

The gap between access and quality shows up in what is easy to find versus what is hard. Entertainment, religion, astrology, health claims, and political outrage translate easily into short video and memes. Nuanced public-interest reporting, educational scaffolding, mental-health resources, and civic information are harder to produce at scale in 22 officially recognized languages and hundreds of dialects.

The result is a skewed vernacular internet: high on emotion and low on structured knowledge. A student in a small town can watch a physics video in her own language, but she is more likely to encounter a feed optimized to keep her watching than one optimized to help her learn. The problem is not the language; it is the mismatch between the languages people speak and the incentives of the platforms that serve them.

The Opportunity: Bhashini and Beyond

There is a counter-narrative, and it is not fantasy. The Indian government’s Bhashini platform aims to make digital services available across Indian languages through translation, speech recognition, and text-to-speech. As of late 2024, Bhashini reported more than 100 million inferences per month and translation services in 22 scheduled Indian languages. Point C7

If these language tools mature, they could lower the cost of producing high-quality educational, civic, and health content in many more languages. AI translation and voice interfaces could make it easier for a farmer to access a government scheme, a student to ask a question in her mother tongue, or a local journalist to reach a wider audience. But access and translation are inputs, not outcomes. They make substance possible; they do not make it profitable. Without changes to platform incentives, the same translation technology can also be used to generate more low-quality content faster.

What This Article Does Not Claim

This article does not claim that Indic-language content is inherently lower quality than English content. Some of India’s most rigorous journalism, most creative art, and most effective education happens in Indian languages. The concern is about the structural conditions under which most vernacular feeds currently operate.

It also does not claim that users are passive. People select content, ignore recommendations, and teach one another how to spot false claims. But individual resistance is easier when the surrounding information environment has quality signals—trusted publishers, accessible fact-checkers, and platform designs that do not systematically reward outrage and novelty.

Sources and Method

This article draws on the IAMAI-Kantar Internet in India Report 2024, based on the ICUBE 2024 study covering over 90,000 households across India (except Lakshadweep). Secondary coverage from Entrepreneur India and BW Businessworld was used to verify headline figures. Platform-scale references draw on industry reporting about ShareChat and Dailyhunt. Government language-technology figures come from the Press Information Bureau year-end review for MeitY and the Bhashini website. Where a source is industry-reported rather than independently audited, the text says so.

Open Questions

• How much of the vernacular internet’s attention skew is driven by platform design versus by advertiser demand and creator supply?
• Which Indian languages have the strongest independent fact-checking and quality-journalism ecosystems, and which are underserved?
• Can AI translation and voice interfaces improve the ratio of substance to extraction, or will they mainly accelerate low-cost content production?
• What design changes would make vernacular feeds more useful for learning, health, and civic participation without being paternalistic?
• How should Indian regulators think about language-specific accountability when platforms operate across dozens of languages?

Related in This Series

• The Attention Extraction — the overview and thesis of the diagnosis.
• By the Numbers: What Indians Actually Do Online — the country’s digital time budget across entertainment, education, work, and communication.
• The Generational Bet — why the AI moment makes attention choices a national stake.
• The Substance Builder — practical paths for turning dead time into small acts of creation.
• Designing for Substance — how platform incentives and regulation could make substance the easier path.
• Attention, Substance, and the AI Moment: A Series Index — the full series guide and reading paths.