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.