Tea and AI: Same Marketing Strategy

From Tea to AI: How History Repeats in India — But With Higher Stakes

Table of Contents

Introduction: A Sip of Memory, A Glimpse of the Future

AI is being marketed in India much like the British marketed tea about a century ago. My grandfather told me two things about his youth: ‘Tea used to be distributed free at nook and corners of streets. And advertisements would say: Yeh budda aur uska ladka dono chai peete hain. (This old man and his son, both drink tea)’
This anecdote is more than just a charming slice of colonial marketing history—it reveals how history repeats itself in form and function. What tea was to the British Empire, AI is to the digital empire now unfolding in India. The tactics are familiar, but the stakes today are far higher.

Part I: The Colonial Tea Campaign — Engineering Habit, Not Just Taste

In the early 20th century, India had become the world’s largest tea producer, especially after the British intensified cultivation in Assam, Darjeeling, and the Nilgiris. During World War I, British government orders guaranteed the purchase of tea, leading to a rapid expansion of plantations. However, by the mid-1920s, a global downturn including the Great Depression created a surplus of unsold tea. This overproduction threatened the viability of many estates dependent on exports^[1]^[2].

To address this surplus, the British Tea Board shifted focus inward and launched a campaign to promote tea drinking within India itself—a country where tea was not yet a deep-rooted cultural habit. Measures included free distribution of tea at railway stations, street corners, and factory gates. Vendors were subsidized to serve tea publicly, and homemakers were taught to brew tea with milk and sugar, adjusting the flavor to local tastes. It was a deliberate effort to condition behavior and embed tea in Indian daily life^[1]^[3].

The marketing slogan “Yeh budda aur uska ladka dono chai peete hain” (This old man and his son, both drink tea) was part of this campaign. This phrase was clever and strategic—portraying tea as a cross-generational practice, familiar and familial, rather than foreign or elite. It built trust and normalized tea as a domestic, hospitality-oriented ritual^[1]^[4].

Public tea stalls at railway stations and factories played a key role as well. Subsidized by the Tea Board, they served millions of cups as a free or low-cost introduction to the product for working-class Indians. Colonial administrative records highlight this move as intentional behavioral conditioning, not philanthropy^[3]^[2]^[5].

Tea plantation labor was largely supplied by indentured workers often relocated from other parts of India under difficult conditions. These labor communities themselves also became new consumers, integrating tea deeply into the socio-economic fabric of the plantation regions — further extending tea’s cultural reach^[2]^[5].

Part II: AI in India — The New Cognitive Infrastructure

Fast forward to the 2020s, and India’s rollout of AI technologies is following a strikingly similar trajectory. From free and frictionless access to chat assistants, AI tutors, and automation tools for micro, small, and medium enterprises (MSMEs), the strategy has been to embed AI in everyday workflows, learning, and thought processes. The goal: behavioral embedding so that AI becomes indispensable^[2]^[4].

Now, as with tea after its initial free introduction, AI access is shifting. Free usage caps limit access and premium subscription tiers offer enhanced functionality. Government schemes to accelerate AI adoption often depend on private platform intermediaries^[4]. Messaging echoes the tea campaign’s generational framing: “AI for everyone,” targeting children, students, professionals, and CEOs alike. Institutional promotion involves public-private partnerships as the Tea Board once supported public stalls and demonstrations^[1]^[6].

Part III: Cognitive Colonialism — The New Frontier

Tea shaped Indian habits; AI has the potential to shape cognition itself. When access to AI becomes gated behind paywalls or platform monopolies, knowledge and thinking risk stratification: those who can pay access more powerful cognitive tools, while others receive limited versions. This narrowing of public discourse and uneven access to knowledge is a form of “cognitive colonialism”—where private corporations mediate how citizens think, learn, and engage with society^[4].

Unlike tea, a physical commodity, AI influences how students learn, how writers compose, and how citizens interpret law and policy. The implications are not only economic but deeply epistemic—affecting what and how knowledge is constructed and disseminated.

Part IV: “Where Generations Meet” — Reclaiming the Narrative

The colonial tea slogan’s generational messaging is repurposed here as a symbol of critique and resistance. The memory of freely distributed tea becomes a tool to expose inherited systems and institutional drift in AI’s rollout. From consumption to critique, from habit to analysis, this represents intergenerational resistance—mobilizing the past to shape a more equitable future in India’s technological landscape.

Part V: What Comes Next?

Public Education: Use historical analogies from tea’s history to demystify AI’s rollout and lifecycle from free access to monetization.
Legal and Institutional Reform: Develop AI governance rooted in public interest, with transparency mandates and guarantees of equitable access.
Cultural Reframing: Question who controls cognition and learning, and what values are embedded in AI tools deployed at scale.

Conclusion:

AI in India faces a distinct challenge: it consumes and learns from the data available, lacking an independent “mind” or judgment of its own. This makes the quality, source, and framing of data critical. Recognizing this, India has invested over a billion dollars to train its AI models on datasets rooted in the Indian context, aiming to reduce reliance on foreign or biased information. This strategic focus on data localization and sovereignty addresses concerns about cognitive bias and cultural relevance but adds complexity to AI’s integration nationwide.

Just as British colonialism shaped and often distorted Indian history to serve imperial interests, the data that trains AI could “pollute” the future if it carries biases, skewed narratives, or incomplete representations. AI systems trained on flawed or one-sided data risk perpetuating or amplifying misinformation and institutional biases, affecting everything from education to public policy. India’s effort to build a controlled, accountable data infrastructure and develop regulations around data localization and privacy aims to mitigate these dangers.

In sum, the parallel to the colonial tea strategy extends beyond distribution and habit formation to the core of knowledge itself. The way history was shaped to control perception in the past may find a new form through AI’s influence on cognition—making it imperative for India to steward datasets, governance, and ethics with a focus on safeguarding its epistemic future.

Sandeep Bhalla's Analysis