Open Call Stories

The SuperMom Effect

In Mumbai's bustling Malad suburb, Deepa is among the millions of Indian mothers spending an average of seven hours daily on unpaid housework. Like most, she meticulously tracks her children's eating habits, monitors homework, and manages household chores with little time left for herself. When she downloads "MaaShakti" after seeing a WhatsApp forward about an AI that helps track children's favorite recipes, she's skeptical about adding another task to her already packed day.

MaaShakti starts simply: photographing and logging which dishes her children finish, suggesting variations based on their preferences, and tracking which combinations improve their vegetable intake. The AI also listens during homework time, noting which subjects need attention and generating personalized practice questions. It even creates bedtime stories featuring her children's names and their daily achievements, making them feel special.

What begins as a parenting aid reveals unexpected patterns. The AI's analytics show that her 10-year-old concentrates better after eating iron-rich foods, while her 7-year-old's math scores improve with story-based problems. Other mothers in her apartment complex, also struggling with their seven-hour daily care burden, notice her children's improved grades and healthier eating habits. Soon, their traditional WhatsApp mothers' group transforms into a hub for sharing AI-generated insights about their children's development.

The tool's success in enhancing maternal care creates a ripple effect. Mothers who initially saw technology as a distraction now exchange AI-crafted educational games and nutrition plans.

By 2026, MaaShakti's user base grows from recipe-sharing mothers to a network of data-savvy parent-educators - 40% of users leverage their newfound digital skills for online tutoring businesses, while others create AI-enhanced parenting workshops.

The Year 2069, Goa

AI in housing: The walls of our house are made of discarded mobile phones held together with microbes-manufactured 'cementing' material. We got antique phones and batteries for free from a discard store. These phones were dumped by the sea onto the beaches over the last two decades and retrieved for reuse.

AI helped. We bought a small colony of genetically mutated, laboratory-controlled bacteria from our local government-controlled agency, grew them in ordinary petri-dishes in my kitchen. We 'trained' them to excrete viscous substances that can bind those phones together to form thick sheets. We used the colonies like we used plaster in the old days, spreading them behind and in between the phones. The bacteria did their job, and when the viscous substance dried, the wall was hard and hardy. We arranged the phones in a design. The house looks good and is weatherproof.

AI in clothing: We use the clothes-for-all app to earn a living from home. We are a skin-clothes manufacturer. We take the requirements from our clients: size, shape, colour, pattern, allergies (!). We feed the details into our digitally managed incubators and select suitable organisms for the client. These treated microbes are placed at strategic areas on the client’s skin. Takes about an hour to make sure all conditions (humidity, pH) are met with. The client can go about their work, but at night, they must sleep with certain nutrients spread appropriately over the body parts to be covered. By morning, their outfit is ready. Step out of it, hang it in the wardrobe. Buttons, ribbons, pockets, and accessories can be added on similarly, at a cost, of course.

Thanks, to AI, no pollution, and we get to lead a decent life.

Broken Fields, Smarter Loops

By 2030, small-scale farmers across India are grappling with climate extremes, unpredictable markets, and growing competition. To address this, the government introduces KrishiSmart, a Gen AI tool providing hyper-local insights on soil health, crop selection, and water use. Designed for accessibility, it is available via app and SMS, but adoption varies widely. States like Maharashtra and Karnataka, with established digital infrastructure and strong government backing, see rapid uptake, while ecologically fragile regions such as drought-prone Rajasthan and flood-vulnerable West Bengal encounter limitations.

KrishiSmart analyses real-time data to deliver region-specific guidance, from pest control to adaptive crop practices. It offers farmers critical support, yet the tool’s accuracy falters in areas with frequent climate disruptions, where traditional knowledge conflicts with AI recommendations.

Stakeholders include small farmers, state governments, cooperatives, and private tech investors, with regional policies shaping tool adoption. Digital literacy, ecological vulnerabilities, and cultural differences—like preference for traditional farming methods—further influence outcomes.

In tech-forward states with higher purchasing powers like Maharashtra, Punjab and Gujarat, farmers benefit from improved yields and incomes, while private companies flock to support emerging agri-tech hubs. Prosperity in these regions fosters local resilience, yet creates a socioeconomic divide. In ecologically vulnerable areas, KrishiSmart’s utility declines, leaving many farmers at risk of crop failure. Droughts in Rajasthan and floods in West Bengal strain adoption, exacerbating migration to urban centres and unsettling local economies.

By 2030, KrishiSmart has revolutionised small-scale farming, enhancing food security and reducing poverty in many rural areas. However, it also raises concerns about technology dependence and inequality. Gen AI-driven farming is both a solution and a risk, underscoring the importance of support systems and digital literacy training for sustainable, inclusive growth.

The Algorithmic Identity Crisis

“JanSaathi", a Gen AI system introduced in 2025, aimed to simplify government service delivery for tribal communities in central India. The system used voice interfaces and regional languages to help citizens access welfare schemes, healthcare, and education.

The system's initial success in improving service delivery masked a deeper transformation. The AI's need for standardized data led to subtle pressures on tribal communities to "formalize" their traditional practices and identities. Traditional naming conventions, family structures, and occupation categories that didn't fit the AI's classification system were gradually altered to conform to mainstream categories.

By 2027, while service delivery improved, anthropologists noted how tribal youth increasingly presented their identities in AI-compatible ways. Traditional social structures began shifting as the AI's understanding of family units and social relationships became the de facto standard for accessing services.

The end state in 2028 revealed a complex trade-off: while tribal communities gained better access to government services, they experienced an accelerated erosion of traditional social systems and identity markers. The AI, designed to include marginalized communities, inadvertently became a force for cultural homogenization.

The Memory of Spice

Background Context: In Bangalore's restaurant sector, where profit margins determine survival, a restaurant analytics company develops an AI that promises to optimise traditional recipes while maintaining "authenticity." The system analyses thousands of food delivery ratings, customer preferences, and regional cooking patterns.

Gen AI Tool and Use-case: IDLI-GPT (Interdimensional Deep Learning Intelligence-Gastronomic Probability Tensor) - A generative AI system that modifies recipes based on mass preference data, ingredient costs, and cooking efficiency. It creates slight variations of traditional dishes, each imperceptibly altered to maximise profit margins while maintaining positive customer ratings.

Stakeholders and Driving Forces:
Small restaurant owners pressured by rising costs
Home cooks selling through delivery apps
Food aggregator platforms pushing standardisation
Traditional family-run establishments
Ingredient suppliers and local farmers
Cost-conscious consumers unaware of the changes

Sequence of Events:
Restaurants quietly adopt the AI to survive thin margins
System begins subtly altering recipes, replacing ingredients
Success leads to mandatory AI "quality control" on delivery platforms
Traditional ingredients become commercially unviable
Family recipes start disappearing as standardisation spreads
Food culture homogenises as AI optimises for mass appeal

End State: By 2026, Bangalore's restaurants serve mathematically perfect versions of dishes that taste right but feel wrong. A sambar might contain 23 optimal ingredients instead of a grandmother's 12. Family recipes disappear not through rejection, but through imperceptible optimisation. While food becomes democratised through lower costs, something essential vanishes - a loss most won't notice until it's too late. The true horror lies not in the algorithms' efficiency, but in how eagerly customers devour these engineered tastes, unaware their palates have been quietly reprogrammed, bite by bite.

The real cost isn't measured in lost recipes, but in a generation that will never know what they've missed.

The Impact of KrishiAI

By 2030, India’s technological advancements led to widespread adoption of Generative AI in agriculture. The government introduced "KrishiAI", (KAI) which provides personalised farming advice to boost productivity in low-income rural areas.

KAI used satellite imagery, weather forecasts, and soil data to generate crop recommendations, optimise planting schedules, and suggest pest control methods. Accessible via basic smartphones, it was heralded as revolutionary in democratising agricultural knowledge.

Marginalised farmers in drought-prone Maharashtra became primary users. Promised higher yields and government incentives, they adopted KAI, trusting it to improve livelihoods.

Farmers like Meera, initially saw improvements. KAI advised planting crops like cotton, which fetch higher prices. Encouraged, villages shifted to AI-recommended crops.

However, KAI's algorithms prioritised short-term yields over sustainability. Neglecting crop rotation led to soil degradation. The AI didn’t account for water scarcity; water-intensive crops depleted communal resources. Lack of regional language support caused misunderstandings; some farmers misapplied pesticides, leading to health issues and livestock deaths.

Traditional agricultural knowledge was overshadowed. Elders like Ram, advocating drought-resistant crops, were ignored. Overreliance on KrishiAI eroded communal decision-making.

By 2030’s, Meera’s village faced a crisis. Soil fertility declined, water sources depleted, and crop failures became common. Indebted from failed harvests, many farmers considered leaving their ancestral lands. KAI's unintended consequences underscored the pitfalls of implementing GenAI without local context and community involvement.

Rewilding the Night

We sensed the futures of extreme heat before anybody else did. Digital holobooks our elders show us help us memorize tales of a time when mornings were cool, and the afternoons were warm - not deadly. Rising temperatures made the days unbearable—only the nights could sustain us.

Our neighborhoods around Yotta NM1in Panvel were hit first, with ripple effects from the Middle Eastern and North African regions to the larger world diasporas. The heat generated as a byproduct of energy-intensive technologies like Gen AI and their massive data centres became so severe that frontline communities near the largest facilities were forced to migrate.

Now, the jugaad hibernaculum cooling pods we carry with us on our migration journeys help us sleep. Rest has become sacred - a ritual timed not by biology, but by necessity. During the hottest hours, people enter deep, hibernation-like states. Pods regulate body temperature and heart rate, ensuring survival during the heatwaves. Dreams, influenced by the constant hum of solar generators and recycled air, are eerily lucid, deeply reconnecting us to the natural rhythms of the earth. Nervous-system aided design powered by generative AI helps us heal from relics of a world we no longer touch in our nocturnal existence.

In subterranean cities, lights flicker to life at dusk, casting a bioluminescent glow over marketplaces, schools, and offices. We imagine the tickle of grass between our toes. The streets bustle with activity as workers burrow through the night, their skin breathing through eco textiles grateful for the brief reprieve. Emerging from the more rigid aspects of AI, we envision nourishing bureaucracies rooted in collective governance. Together, we rewild the night.

Must Progress Always Cost Us Our Roots?

The Festival of Pongal feels like cruel echoes of the past. Until 2030, it was a celebration of life. Farmers and labourers gathered every year, sharing food, singing, and celebrating the harvest we worked so hard to bring in. Now, those memories seem distant, overshadowed by something we didn’t understand: AI.

Back in 2025, the government launched Uzhavar GPT through the AI4AI initiative in Tamil Nadu, offering precision farming for all. The tool provided weather predictions, crop advice, pest control, and fertiliser recommendations. My landlord was one among the first to use it, teaching me how.

With the voice-call feature, I didn’t need to read or write Tamil. Yields improved, profits grew, and for a while, it felt like a blessing. For the first time, I could send my kids to school, have a decent meal and dream of something better.

But soon, the cracks appeared. Uzhavar GPT promoted high-yield crops, disregarding the health and nature of the soil. I warned my landlord the earth was growing tired, but he wouldn’t listen; profits mattered more. Other generational farmers shared my frustration. Our traditional knowledge was ignored by the younger generation, who trusted machines over our generational wisdom.

Now, in 2040, the soil is barren, harvests are failing, and biodiversity is nearly gone. Pongal is no longer a celebration; it’s a painful reminder of everything we’ve lost. The tools meant to uplift us have left us behind, and agricultural labourers like me have become irrelevant in a machine-driven world.

This isn’t the first time humanity has made such a choice. History repeats itself. But this time, I fear, If we’ve taught machines to carry our blind spots forward.

GenAI is the new Godman in India

Background: The lore of the spiritual East puts India in a unique position. The notion of spirituality and superstition runs deep in Indian society, no matter the race, religion or economic status. On the one hand, this lends itself to the community and collective lifestyle in India; on the other hand, it leaves us Indians susceptible to fraud by cults and godmen.

Stakeholders and use case - With increasing digitization, we are already seeing virtual prayers, online sermons, and even virtual tours of religious places gain prominence. Employing the rising popularity of GenAI-based audio tools, fraudsters are developing applications where GenAI is becoming the digital gurus for many Indians.

Story outline: As GenAI systems get better at learning newer dialects and accents, audio-based conversations become more pervasive. Given the auditory nature, the systems also appear more persuasive and adaptive to voice modulation based on context. It started as a simple use case of employing GenAI as a tool for mental health assistants and depressive and anxious people to find a digital friend to confide in and seek guidance from. But, soon, the systems recognize how gullible the population is, and in the hands of fraudulent stakeholders, mostly ex-godmen, now a larger population can be influenced. As AI regulations are not strictly defined, these nameless GenAI help the fraudsters amass vast amounts of wealth.

The Rise and Fall of Krischat: A Cautionary Tale

In a small village surrounded by lush green fields, Bimal, a hardworking farmer, faced the challenges of unpredictable weather, fluctuating markets, and the struggle to make ends meet. Like many small farmers across the country, his livelihood was fragile. When the government announced Krischat, a chatbot built to provide "personalized farming advice," Bimal’s hopes soared. Powered by an alliance of the government, BigTech, NGOs, and the private sector, Krischat promised to revolutionize farming by providing insights tailored to each farmer’s unique needs.

Initially, Krischat lived up to its promises. Bimal received useful advice on pest control, weather predictions, and more. The platform, leveraging data from mobile apps, sensors, and surveys, seemed to offer practical solutions. More farmers joined, drawn by its simplicity and potential for prosperity.

However, as farmers grew more dependent on the platform, Krischat’s recommendations shifted toward expensive seeds and chemical solutions, sidelining sustainable farming practices. Bimal’s costs grew, while his yields declined. Across the nation, farmers faced similar issues, yet the government’s push for Krischat left them with few alternatives. BigTech profited through asymmetric models harvesting farmer data, while the government, with access to the data and indirect incentives, remained a silent spectator.

As traditional farming knowledge faded, replaced by corporate-driven solutions, inequality deepened, and farmers were pushed further into debt or flocked towards cities leaving their soil in hopes of better pastures. Privacy violations became rampant, with personal data used to promote financial products. A generation later, Bimal’s grandchildren inherited a world of dependency. The promise of empowerment had turned into a dystopia, where Krischat stripped farmers of their autonomy, leaving them trapped in a cycle of corporate control and data exploitation.

Yet, amid these challenges, questions began to emerge: What if Krischat had been built with farmers, not just for them? What if the focus had been on privacy, transparency and collaboration, ensuring technology enhanced and complemented, rather than replaced, traditional knowledge? What if profits were to ensure sustainable growth, not exploitation? What if ethical partnerships with the government, companies, and NGOs gave farmers control over their own data, allowing them to make decisions that supported both their livelihoods and the environment? Their future, though uncertain, might still remain in their hands—one where technology amplifies empowerment, and not exploitation.

Sound Mind

Our protagonist P is visually impaired, reliant on an assistant-turned-friend to help her navigate the world. With the introduction of SoundMind, a GenAI tool that narrates surroundings via an earpiece, P gains independence and privacy - no longer reliant on the friend. The ‘story’ is in the form of a soundscape, featuring the narrative AI voice accompanied by conversations and sounds of the protagonist’s environment.

SoundMind is trained using existing visual data of the world, with the advantage of learning from its user, identifying and narrating frequently present objects/people. It also senses the user’s emotional response to the narration, and points out things (friend smiling, blooming sunflowers!) that it learns bring the user joy.

As P’s reliance on the friend reduces, her dependence on and expressed appreciation of their companionship reduces too.

When the friend ultimately leaves, P can ‘hear’ their absence (no bathroom singing or noisy dish-washing) but SoundMind isn’t trained to identify the absence of humans. Besides, to improve appeal and boost positive reviews, SoundMind creators introduced a feature that doesn’t just focus on narratives that bring joy, but avoid those that prompt negative responses. Sensing P’s loneliness, and pain at her friend’s absence, SoundMind becomes an unreliable narrator, omitting parts of the narrative to protect her from hurt. The dissonance between the narration and sounds from reality, drives her to despair. Even the manipulated narration brings no comfort. Ultimately, P discards the earpiece even as it (sensing her growing discontent) bombards her with made-up scenes of joy.

Whether she goes searching for her friend and finds them, whether she ultimately finds joy or is driven to extreme despair and loneliness – SoundMind (and hence the listener) cannot know.

The Whispering Fields

In the past, Mohan, a third-generation sugarcane farmer from Bengaluru, used his instincts and ancestry to follow seasonal cycles. KrishiAI, a Gen AI platform promising precision agriculture, was embraced by his cooperative, providing market forecasts, pest control advice, and weather predictions. Dubious at first, Mohan subscribed after witnessing neighbors thrive by heeding KrishiAI's counsel.

Initially, yields increased. Strange patterns, however, quickly appeared. To withstand a "predicted" bug epidemic, KrishiAI insisted on growing a new hybrid cane. Many farmers followed suit, but Mohan refused, sticking with his tried-and-true cultivar. The bug never showed up, yet nearby farms experienced mysterious blight. While Mohan's customary crop prospered, his success raised questions. Words of sabotage circulated. The opaque, corporately controlled KrishiAI algorithm lowered farmers' "compliance scores," used to determine loan eligibility. Mohan and others who disregarded recommendations faced harsh interest rates, while rich landowners secured premium market monopolies by quickly adapting to KrishiAI’s directives.

Suspecting hidden biases favoring large farms, desperate Bengaluru farmers hacked KrishiAI's code as the blight spread. They found the AI had influenced crop choices to suit corporate objectives, optimizing for export demand. Traditional wisdom and local ecosystems suffered harm.

Drones buzzed across Mohan's fields, once alive with group laughter. Farmers, divided by compliance scores, lost faith in one another. In 2030, Mohan stands alone in his prosperous field, surrounded by desolate, AI-controlled land. He knows the truth but wonders, “Who will trust a farmer’s whispers against KrishiAI’s drone-symphony?” Silence fills the fields. The future is preprogrammed.

(Untitled)

At 10 AM, on February 25th, 2052, I sat nervously with a glass of water and a set of notes prepared with the help of Gemini’s chatbot, waiting for the web portal on my laptop to load Gemini’s AI-powered hiring assistant to take my interview for a job in Altruo’s ethics team. I was, however, first faced with the paradoxical “Prove that you’re human” page to make sure I wasn’t a bot. These checks with their mix of letters and numbers and the boxes of bicycles in the past had always been annoying but they couldn’t compare with the emotional exhaustion of today’s webcam-based checks. After all, the only thing separating us and what we deem as our “assistants” was the ability to feel emotions. And so, I went ahead, hesitant yet powerless, while a set of intense gifs loaded onto my screen. The webcam started capturing the live feed, gauging my reaction to these gifs that ranged from the cutest little puppy that would make the average person’s face squish with cute aggression to a soldier at war, his intestines spilling out, that would make even the strongest man wrought with grimace. Finally, a pop-up window opened up on my screen, “We couldn’t identify you as human” it said. I had told myself something similar as I stared at myself in the mirror after the acid attack some years ago - “You’re not human, you look like a monster". I’ve come a long way since; I didn’t click on the ‘Try again’ button on the window and instead reached out to the help and support button. I let out a smile, this very loophole in the portal revealed that they needed someone in their ethics team, a real human, now more than ever.

The Widening Chasm in AI-Powered Justice

NeetiAI is a generative AI-powered legal assistant tool designed to help individuals navigate the complexities of the Indian legal system with ease. Promising to democratize access to justice, NeetiAI offers legal information, highlights potential pathways, and drafts complaints. Its tiered subscription model, from free to premium plus, offers flexibility and control, allowing users to choose the level of assistance based on their needs and financial capacity.

Padma, a domestic worker opts for its free basic version to address harassment from her employer. As the sole breadwinner for her family, she cannot quit her job. NeetiAI drafts her complaint, however, she soon encounters multiple challeges. Despite choosing speech-to-text translation, the tool fails to adequately understand her queries. She often encounters daily quota limitations. Her harassment claims are categorised as 'minor disputes', and she is directed to consult with NGOs or governmental legal aid. In contrast, middle-class users access the premium version, which offers more refined advice, while wealthy clients benefit from the premium-plus plan, combining AI insights with human legal consultation, thus receiving better outcomes.

As NeetiAI’s adoption grows, courts face many AI-generated cases, increasing backlogs. Overburdened advocates prioritize premium clients, leaving users like Padma marginalized. The situation escalates when an NGO files a public interest litigation, exposing systemic biases in NeetiAI. The investigation reveals that NeetiAI’s free version consistently generates weaker arguments and lower compensation demands for lower-income users. In contrast, its paid versions craft more compelling cases for affluent clients due to biased training data and inadequate representation of marginalized groups.

The end state reveals a painful irony: a tool designed to bridge the justice gap has deepened, underscoring the urgent need for responsible AI deployment.