From a motorcycle and first-aid kit in 2019, Manali Strays has grown into a 15-person animal rescue operation serving stray animals across the Kullu valley.

Manali draws visitors from across the world for its snow-line, its apple orchards and its rivers. What most of them never see is what happens to the town’s street dogs once the tourist season empties out, or what it takes to treat an animal with no owner and nowhere to go when it is injured.

Manali Strays: Building Animal Rescue Services in the Himalayas

That gap is what Manali Strays was built to close. The organisation was founded on World Rabies Day — 28 September — in 2019, by four people who had each, in different ways, run up against the same problem: there was no veterinary infrastructure in the Kullu valley for animals that belonged to no one. Kamlesh, an Indian animal welfare worker with a diploma in veterinary sciences, had spent years handling rescue cases from Manali and its surrounding villages for another organisation and had seen how little could be done for them locally. Becky, a British national who visited Manali often, shared his concern. They were joined by Irma, a German national who had also spent time in Manali and had been trying to bring together people willing to set up a rescue, and by Buddhi Ram, a local resident. Together the four registered Manali Strays as a charitable trust in India in January 2020; a UK-registered arm followed a few months later to support fundraising and awareness abroad. The organisation is recognised by the Animal Welfare Board of India and holds 80G tax-exemption status, and describes itself as the first dedicated animal rescue operation in the Kullu district.

The work began in Haripur village, just outside Manali, with almost nothing but a motorcycle and a first-aid kit. Kamlesh would ride out to wherever a call came in — a dog hit on the road, an animal too weak to move — and treat it on the spot, because there was no facility to bring it back to. A helpline set up in the early days took two to five calls a day at first. It now takes 50 to 60, according to the organisation, a rough measure of how far word has spread and how much the service is now relied on.

The pandemic tested the operation almost as soon as it began. Manali’s street dogs survive largely on food discarded by hotels and restaurants, and when tourism shut down in 2020, that food source disappeared within weeks. Kamlesh’s team responded by running organised feeding drives across the town, alongside the rescue and treatment work they were already doing — and it was around this period that the need for a permanent facility became impossible to ignore. The trust leased land in Haripur and built a treatment centre where injured animals could be vaccinated, treated and held for recovery.

Six years on, that centre has grown into a proper veterinary facility, with a recovery shelter, an X-ray unit and a functioning operating theatre for sterilisation surgeries and other procedures. Three ambulances now run out of it, reaching animals in villages that would otherwise have no access to care at all. The team itself has grown from two founders to around 15 staff. According to the organisation, it has vaccinated more than 9,000 animals to date and sterilised more than 5,000 stray dogs — the latter figure central to keeping the street dog population in check without resorting to culling.

Himachal Pradesh’s monsoon floods, in 2023 and again in 2025, disrupted the region and its communities severely; the organisation says its rescue and treatment work continued through both.

Beyond Manali itself, the trust runs vaccination drives, sterilisation camps and rescue missions in villages further into the mountains that have no veterinary access of their own, and it runs an adoption programme for rescued dogs once they are treated, vaccinated and sterilised. It has also become a training ground for veterinary students: through a partnership with the Indian Veterinary Students’ Association, students travel to Manali for placements at the centre, working alongside the resident team on live rescue and surgical cases.

What began as one man on a motorcycle carrying bandages has become, over six years, an operation that a stray dog anywhere in the Kullu valley now stands a real chance of being reached by.

At the Tiruppur Knit Show, decarbonisation took centre stage as textile exporters confronted CBAM, DPP, clean energy and global market pressures.

Tiruppur, Tamil Nadu, India: The Knit Show has run in Tiruppur, India’s textile hub, since 2000, and its script rarely changes: rows of machinery, dye and chemical stalls, garment accessory vendors, buying agents working the floor at the Toplight Trade Center. This year — 21 to 23 August — the show drew its usual crowd, showcasing new machinery and advanced digital printing technology, the kind of incremental modernisation a 25-year-old trade fair reports on every edition.

But running alongside it this year was something the Knit Show doesn’t usually host: a two-day conference titled “Decarbonising the Textile Supply Chain,” which included a workshop and panel discussions, organised by Auroville Consulting in partnership with the Tiruppur Exporters’ Association (TEA), SIDBI, Guidance Tamil Nadu, MCCI, and the UK High Commission — built specifically for media professionals covering Tamil Nadu’s textile and export sector.

Tiruppur Knit Show turns spotlight on textile decarbonisation

That a trade show built around machinery and commerce now carries a parallel track built around carbon compliance and climate finance is, in some ways, the story. Tiruppur accounts for roughly 90 percent of India’s cotton knitwear exports and, by workshop participants’ count, is home to 2,500 exporters and 25,000 standalone units. Decarbonisation, until recently, was a compliance conversation, not a competitive one. This year, at least in this room, that seemed to be shifting — driven less by domestic regulation than by external pressure: the EU’s Carbon Border Adjustment Mechanism (CBAM) and Digital Product Passport (DPP) requirements, which are steadily raising the bar for market access into Europe.

Compliance as the opening argument

The first panel — moderated by Saraswathi, Secretary-General of MCCI — set the terms early: CBAM and DPP are no longer abstractions for Tiruppur’s exporters, but live conditions for keeping a buyer.

N. Thirukkumaran of ESSTEE Exports India framed compliance as a collective, ongoing discipline rather than a one-time fix. “It should not be something done only once; it has to be a continuous process,” he said, adding that Tiruppur’s companies aren’t competing against one another on this front so much as collectively meeting the bar buyers now demand.

He also pushed back on the idea that Tiruppur is only playing defence on emissions. Referencing EU reporting requirements he referred to as SEPA, Thirukkumaran argued the cluster already outperforms what’s being asked of it: Tiruppur, as a district, produces roughly 1,950 megawatts of renewable energy against a consumption of about 360 megawatts — a substantial surplus. On pollution specifically, he said 98 percent of Tiruppur’s dyeing units are now classified non-hazardous, and that the cluster follows zero liquid discharge “religiously.”

The technology pitch

The second panel turned to the shop floor: efficient utilities, clean power, low-carbon process heat, and EV logistics. It was moderated by Ramesh Matham, Consulting Editor at The Hindu BusinessLine, alongside Sujith Thomas of the UK High Commission, Martin Scherfler of Auroville Consulting, and Manikandan of Aspiration Cleantech Ventures.

Scherfler’s pitch centred on many topics including electrification. Industrial heat demand — the energy-intensive business of heating water and process fluids — has traditionally run on fossil fuels, he said, but that heat “can be replaced by electricity as a fuel.” The same logic, he pointed out, is already familiar to most people from their own lives: the shift from a petrol scooter to an electric one is electrification too.

He argued electrification carries a double advantage: electric processes are typically more efficient than their fossil-fuel equivalents, and because wind and solar are now the cheapest sources of electricity available, pairing electrification with clean power multiplies the emissions cut across a facility’s transport and heating load at once. Scherfler called it “not the silver bullet,” but estimated it could address roughly 70 to 80 percent of an industry’s total emissions.

Financing the transition

A third panel, moderated by Karunamayi of Auroville Consulting, turned to the money behind the shift — climate-linked finance instruments, government schemes, and the institutional support needed to de-risk early adoption for smaller units, with SIDBI’s Ramachandran, TEA Joint Secretary and Eastern Global Clothing CEO Kumar Duraiswamy, and Dyers Association Treasurer Madheswaran on the panel.

On the ground

The conference’s second day moved from panel discussion to the shop floor, with visits arranged to a dyeing unit at Jeyavishnu Clothing, the Veerapandi Common Effluent Treatment Plant, and the exporter floor at Esstee Exports India — structured to let visiting journalists see the compliance and technology conversation from the previous day translated into plant-level practice.

Why this belongs at a trade show

That a decarbonisation conference found a home inside a machinery trade fair, rather than staying confined to policy seminars or NGO reports, is itself worth noting. It suggests Tiruppur’s industry is beginning to treat emissions and compliance as commercially load-bearing — tied directly to buyer retention and market access — rather than only a regulatory box to tick. Whether that shift holds up under scrutiny, beyond the room in which it was pitched, is the question worth returning to.

Between July and September 2025, I interviewed 42 high school students as part of ongoing research into career aspirations and problem-solving. One pattern kept recurring, and it unsettled me more each time I saw it.

Students who regularly used AI tools gave curiously superficial answers to everyday problems. They would address the obvious aspect of a question, then stop, almost as if waiting for a “next suggestion” that never came. But when I asked simple follow-up questions such as “What other possibilities exist?” or “Is there an alternative perspective?”, something shifted. They began weighing trade-offs, generating options, thinking out loud in ways they hadn’t a moment before. The capacity for deeper analysis was clearly there. They just weren’t reaching for it on their own.

AI and Critical Thinking: What Studies Reveal

This pattern aligns with what researchers call cognitive offloading, one of the more quietly consequential effects of living alongside AI. The term describes our long-standing habit of delegating mental tasks to external tools like notebooks, calculators, and calendars to lighten cognitive load. But AI changes the nature of the offload. A calculator stores a number. AI generates the finished thought. When a tool not only holds information but produces the answer itself, students can begin expecting ready-made solutions rather than exercising their own problem-solving capacity. What is meant to scaffold learning risks becoming a crutch that quietly atrophies the very skills it was meant to build.

This is not a fringe worry. A 2025 mixed-methods study of 666 participants across age groups found a significant negative relationship between frequent AI tool use and critical thinking performance, with cognitive offloading identified as the mediating mechanism, and the effect most pronounced among younger, heavier users. My 42 interviews cannot establish that kind of statistical relationship on their own, but they offered a close-up view of the same mechanism in action, most visibly in that pause where a student seemed to be waiting for a “next suggestion” that never came.

A 2026 study on generative AI and learner agency distinguishes between two very different modes of AI use, dependent offloading, where the tool substitutes for a student’s own thinking, and autonomous offloading, where AI scaffolds thinking without replacing it. The dependent form threatens autonomy by making choices on the student’s behalf, even when it may feel helpful. A parallel systematic review frames the same divide as amplification versus substitution, where guided, metacognitively aware AI use extends a student’s cognitive reach, while passive, unreflective use displaces the mental effort deep learning actually requires. My follow-up questions seemed to pull a few students from dependent mode into autonomous mode. The capacity for deeper analysis was clearly still there. They just were not reaching for it on their own.

From McDonald’s to the Algorithm

Thirty years ago, sociologist George Ritzer described what he called McDonaldization, the spread of fast-food logic, efficiency, calculability, predictability, and control, into hospitals, universities, even relationships. It was a troubling process, but at least it was visible. We could see the golden arches multiplying, see the assembly line replacing the artisan.

What we are living through now is harder to see, because it inverts the logic. Call it AI-zation. Where McDonaldization imposed visible uniformity, AI-zation offers something that feels like the opposite — personalisation. Our search results are tailored to us. Our feed reflects our interests. Our recommendations are, supposedly, uniquely ours.

This is the seduction of AI-zation. It feels personal while quietly manufacturing a new kind of conformity, not the visible sameness of a fast-food counter, but an invisible narrowing of thought, preference, and possibility. Recommendation systems are trained on existing patterns, so they inevitably steer people toward what is already popular, already “successful,” already proven. The menu looks infinite. The actual range of what people consume keeps narrowing.

I think of this as programmed spontaneity, the feeling of free choice operating inside an algorithmically constrained space. The pattern is easiest to notice somewhere trivial. Watch one dance reel, and the next fifty look almost identical to it. The platform has correctly identified what will keep you watching and, in doing so, has quietly narrowed the world to a single, endlessly repeated genre. Nobody decided this for us, exactly, and yet our options shrank the moment we engaged.

The same logic operates in domains that matter far more than reels, and it does not require any AI to appear at all. As a professor who has placed students in internships across dozens of organisations, I have watched a version of this play out for years. A student’s developmental need is exposure to something unfamiliar; if they have already worked in one area, the internship that would serve them best is often in a different one entirely, so they build range rather than repetition. But host organisations want the opposite. If a student has prior exposure to, say, employee engagement work, an organisation wants exactly that student, because they arrive already useful and need less onboarding. The organisation optimises for efficiency, minimum ramp-up, and maximum immediate output. The student needs breadth; the system rewards depth in a single, already-proven groove. Multiply that logic across a career, and a person can end up highly efficient at one narrow thing and never discover the other things they might have been.

This is the same tension recommendation algorithms formalise and accelerate, not invent. Efficiency, for any system, means doing more of what has already worked. Development, for a person, means doing something not tried yet. AI-zation is what happens when that older institutional logic gets encoded into infrastructure that operates continuously, at population scale, and largely out of sight.

Consider a career platform’s job suggestions, or a course recommendation engine, operating on the same principle as the internship market. It is optimising for patterns it has already seen. If your interests do not fit an existing category, the system is unlikely to help you find your way there, and the more you see what “people like you” are doing, the more that pattern starts to feel natural rather than constructed.

Why This Should Worry India Specifically

India has long been a civilisation organised around multiplicity, languages, philosophical schools, and ways of solving problems coexisting, often in productive tension. That diversity was never just decoration. It was, and is, epistemological, made of different ways of knowing and different definitions of a life well lived.

AI-zation threatens this precisely because it operates through standardisation disguised as personalisation. Search algorithms are globally standardised. Ed-tech platforms structure content around what optimises engagement metrics, not around pedagogical diversity. Jugaad, the distinctly Indian capacity for improvisation, depends on encountering a problem the “official” system has not already solved, and then building a workaround. But if an algorithm is always ready with the “right” answer before a student has fully sat with the question, where does that improvisational instinct come from?

This is not a case for rejecting technology. Digital tools have brought genuine benefit, access to information, connection, and efficiency gains that matter enormously in a country still building out infrastructure

This is not a case for rejecting technology. Digital tools have brought genuine benefit, access to information, connection, and efficiency gains that matter enormously in a country still building out infrastructure. The distinction that matters is between technology that augments human capability and technology that quietly programs human behaviour. Increasingly, we are getting more of the latter than the former.

The Illusion of Control

Defenders of algorithmic systems often point out that users retain control, that you can adjust settings, opt out, switch platforms. This misses something important. When algorithms mediate access to jobs, credit, education, and healthcare, individual opt-out becomes practically impossible for most people. And the “preferences” we express are themselves shaped by prior exposure. You keep choosing certain content partly because the algorithm keeps showing it to you. The system trains you as much as you train it.

The same illusion holds for the reasons AI systems offer when they do explain themselves. Research on explainable AI has repeatedly found that these explanations are often generated after the decision, plausible stories rather than faithful accounts of how the system actually arrived at its answer, and that even explanations carrying no real information can produce as much user trust as genuine ones. Demanding explanations from algorithms is necessary, but it is not sufficient. A system can learn to produce a persuasive explanation without that explanation being true.

Scale up the pattern from my student interviews and ask what happens when algorithms do our remembering (search), our navigating (maps), our reading choices (feeds), and increasingly our writing (generative AI). Each instance looks helpful in isolation. Together, they add up to something closer to the outsourcing of cognition itself.

What Can Actually Be Done

Four responses seem worth taking seriously, none of them rejecting technology.

First, algorithmic literacy needs to become collective, not just individual. When people understand that their “personalised” experience is shaped by hidden, profit-driven choices, they can begin to question the pattern rather than simply live inside it. For educators specifically, this means treating AI tools as objects of critical scrutiny in the classroom, not just productivity aids, and explicitly teaching students to pause and probe past the first answer, the way my follow-up questions did in those interviews.

Second, we need to protect spaces of deliberate non-optimisation. Not everything should be made efficient. Deep learning requires struggle. Creativity requires wandering. We need to consciously build and defend spaces, in classrooms, in workplaces, where algorithms do not intrude by default.

Third, India already has a structural alternative worth naming directly. The Open Network for Digital Commerce (ONDC) is public infrastructure built on the same logic that made UPI transform payments, an open protocol that keeps any single company from owning the whole stack of app, algorithm, and data. It has scaled fast, past 500 million transactions by mid-2026. Adoption is still uneven outside metro cities, but this is what building alternative infrastructure looks like in practice.

Cooperative ownership is a related, distinct model, workers or citizens owning the platform itself. Europe’s Smart cooperative serves over 100,000 freelancers; Switzerland’s MIDATA lets citizens govern their own health data. New York’s Drivers Cooperative is the cautionary case. Launched as a driver-owned Uber alternative, it now struggles because collective ownership has to compete with venture capital willing to lose money for years to win the market. Whether India’s cooperative tradition can extend into education technology or data governance, alongside infrastructure like ONDC, remains an open question.

Fourth, algorithmic systems that affect access to opportunity need to be explainable and challengeable. This requires regulation, but it also requires organised public demand, because voluntary transparency from platforms whose business model depends on opacity is not something to wait for.

The Stakes

Mahatma Gandhi’s idea of swadeshi, self-reliance rather than dependence on external systems, has an obvious digital-age analogue, something like cognitive sovereignty, the capacity to think, choose, and imagine outside the boundaries an algorithm has already drawn. In education specifically, this means treating AI as scaffolding to be gradually withdrawn, not a permanent support to lean on.

We are not choosing between technology and tradition. We are choosing who controls the cognitive infrastructure young people grow up inside, and whether that infrastructure preserves the messy, effortful, sometimes inefficient work of actually thinking something through. My conversations with those 42 students suggested the capacity for that kind of thinking has not gone anywhere. It just needs to be asked for.

EDITOR’S FACT-CHECK

Key claims independently verified against primary sources: Gerlich (2025), Societies 15(1):6, on cognitive offloading and critical thinking (n=666); Zhu et al. (2026), Frontiers in Psychology, on dependent vs. autonomous cognitive offloading; ONDC’s 500-million-transaction milestone (reported July 2026); Smart cooperative’s membership figures; and the trajectory of New York’s Drivers Cooperative.