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.

Teylers Museum in Haarlem, the Netherlands’ oldest museum, offers a rare journey through 250 years of science, from giant fossils and early physics to its historic Oval Room.

As I walked along the peaceful banks of the Spaarne River in Haarlem, a historic Dutch city located just fifteen minutes by train from Amsterdam, an elegant neoclassical facade caught my eye. To a casual passerby, the grand entrance might look like just another historic manor. Stepping through its heavy doors, I felt like I had walked right into another century. This is the Teylers Museum, the oldest museum in the Netherlands, founded in 1778.

At a time when science and art were seen as sister disciplines rather than opposing worlds, Pieter Teyler van der Hulst, a wealthy cloth merchant and banker, decided to do something extraordinary. Inspired by Enlightenment ideals that people should discover the world independently through reason and hands-on investigation, he left his immense fortune to establish a public center for knowledge. Teylers Museum was designed not as a dusty storehouse for old relics, but as a living “temple of the muses.” It became a welcoming space where researchers, students, and everyday curious visitors could gather under one roof to witness live physics experiments, study fossilized secrets of the Earth, and admire master drawings.

The Heart of the Enlightenment

Inside the Oval Room Stepping into the museum’s historic core, the Oval Room, felt like walking directly into an eighteenth century laboratory. Completed in 1784, this double-tiered hall features carved wooden showcases, brass scientific instruments, and a balcony library filled with leather bound encyclopedias, all bathed in soft natural light flowing through an ornate ceiling skylight.

In the late 1700s, this room served as a high tech science hub. Martinus van Marum, the museum’s legendary first director, used the space to host public demonstrations that fascinated scholars and visitors alike. Van Marum firmly believed that science needed to be seen to be truly understood. To explore the mysterious nature of electricity, he commissioned John Cuthbertson in 1784 to build the largest electrostatic generator in the world.

From giant electrostatic machines and rare fossils to Hendrik Lorentz’s physics cabinet, Teylers Museum preserves the history of science in a remarkably intimate setting.

Equipped with two massive glass discs over five feet in diameter, Van Marum’s generator could produce sparks over two feet long, generating artificial lightning that left audiences completely amazed. As I stood before this colossal machine, I couldn’t help but think back to Van Marum’s original notes from his high voltage trials. He noticed that these massive electrical discharges left behind a distinct, sharp smell, an observation that quietly laid crucial groundwork for the later discovery of ozone gas.

Fossils, Physics, and the Foundations of Modern Science

Moving beyond the Oval Room led me into the scientific galleries, where cabinet after cabinet reveals the real origins of modern paleontology and physics. Long before Charles Darwin published his theories on evolution, early naturalists were struggling to make sense of prehistoric remains.

In 1802, Van Marum purchased a famous fossil, originally unearthed in Öhningen in southern Germany, known at the time as “Homo diluvii testis”, or “the witness of the Flood.” Theologians of the era believed it to be the skeletal remains of a human who perished in Biblical waters. Years later, French naturalist Georges Cuvier examined the specimen and identified it as the fossilized giant salamander ‘Andrias scheuchzeri’. That discovery helped overturn centuries of religious assumptions, proving that entire species could actually become extinct over time.

Teylers Museum also houses one of the rare specimens of Archaeopteryx, the famous primeval bird fossil that provided the crucial missing link between feathered dinosaurs and modern birds. Walking past these display cases felt like watching the early building blocks of science come together.

The museum’s dedication to physics continued well beyond the 18th century. In 1910, theoretical physicist and Nobel laureate Hendrik Lorentz was appointed Curator of Teylers Physics Cabinet. Lorentz, whose mathematical equations laid the groundwork for Albert Einstein’s theory of special relativity, conducted experiments on electromagnetism, optics, and atomic physics within these very walls for nearly two decades. When Einstein visited his friend Lorentz in Haarlem, he described the city and its scientific atmosphere as a sanctuary of pure thought.

A Center for Curiosity

Dutch Museum Culture Across Generations Exploring the galleries, I was repeatedly struck by an aspect of the experience that feels deeply rooted in Dutch culture. In the Netherlands, museums are rarely treated as rigid, solemn monuments reserved only for academics. Instead, they are active, community centered gathering places designed to spark curiosity across every stage of life.

Around me, multi-generational discovery was happening in real time. I watched a young child look wide-eyed at a display of polished mineral specimens, pointing out bright colors to a grandparent who was patiently explaining how crystals form. A few yards away, a group of students stood engrossed near a collection of early optical instruments, casually debating how light bends through glass lenses.

This spirit of accessibility gives Dutch museum culture its vitality. From toddlers interacting with physical phenomena to lifelong learners examining centuries old manuscripts, people of all ages come together to ask questions and explore. Teylers Museum reflects this philosophy naturally. It doesn’t feel like a dusty home for old artifacts, but a place where centuries old ideas still inspire people today.

Timeless Wonder in a Physical World

What makes Teylers Museum stand out today is its complete preservation. While modern science centers rely heavily on interactive touchscreens and digital simulations, Teylers offers something far rarer: authentic, untouched history. The brass dials of the barometers, the hand blown vacuum tubes, the polished mahogany cases, and the handwritten labels remain virtually untouched, arranged exactly as they were over two centuries ago.

Standing among these collections, the experience feels less like viewing a static display and more like walking into a researcher’s active workplace, as if the scientists have merely stepped out for a short break.

As I walked out into the quiet streets of Haarlem, I couldn’t help but feel that the real magic of the place was not just in its old collection. It was in the reminder that science is not about having all the answers, but about never losing the urge to keep looking.