India, Nov. 6 -- Big Tech is betting its future on bigger artificial intelligence (AI) data centres, with Alphabet disclosing more than $1 trillion in commitments. India is joining the spending frenzy, with the Adani group pledging $100 billion and Reliance $110 billion for AI infrastructure. These bets could become monuments to a disastrous assumption that growing demand for intelligence guarantees demand for the infrastructure being built today. AI use will surely increase exponentially, but much of that intelligencewill move into people's hands.Companies counting on keeping itcentralised could become toast, and India should study what happened to earlier computing pioneers before buying its own toaster. Exponential technologies become smaller, faster, and cheaper, opening markets that their pioneers often fail to capture. In the 1960s and 1970s, mainframes were synonymous with computing. IBM dominated, with Burroughs, Univac, Control Data, Honeywell, and later Amdahl competing for the rest of the market. Their machines occupied special rooms, cost fortunes, and required specialists to operate them. It was easy to assume thatan expanding appetite for computing would mean more demand for bigger mainframes. Yet IBM barely survived, and its rivals' computer businesses became toast. Then came minicomputers from companies such as DEC, Data General, and HP, followed by workstations from Sun Microsystems and others. Computing moved directly into departments and laboratories. People no longer had to beg thecomputer department for time on its expensive machines, and the market exploded as they discovered what they could do without the gatekeepers. Personal computers took that freedom much further, followed by laptops and smartphones. Computing spread across desks, into homes, and eventually into billions of pockets. Companies that opened those markets became toast too: DEC and Data General were swallowed by competitors, Wang went bankrupt, and Sun was sold to Oracle. Someone predicting this explosion in computing could have been completely right about the demand and disastrously wrong about the companies that would supply it. Today's AI giants are betting that the world will rent intelligence forever. As it becomes cheap enough to own and run, their customers will have less reason to keep paying the landlord. OpenAI and Anthropic sell accessto their models, while Google, Meta, and Microsoft are building the AI equivalent of mainframes, bettingthat customers will keep rentingintelligence from them. But intelligence will move out of those data centres, onto phonesand laptops, inside factories, hospitalsystems, and private clouds controlled by the organisations using them. Those systems will call on largerservices when needed, while handling everyday work themselves. The intelligence will stay close to its users,and more of the latter's money will stay in their pockets. Training a frontier model can require enormouscomputing resources, but using it does not require repeating that investment or choosing the largest model for every task. A shopkeeper managing accounts, a factory inspecting parts in Coimbatore, and a tutor helping a child in rural Bihar have different requirements. None needs to pay for the world's most powerful model when a smaller, cheaper one can do the job just as well. Such models already run on small devices. In 2025, Apple described a compact language model that runs on its iPhones, calling on server models for more demanding work. As hardware and software improve, more of that capability will fit into an affordable phone, putting intelligence within reach of people who cannot afford another monthly subscription. Privacy will be the key, because an assistant that works with me will come to know what I am researching, whom I trust, what worries me, and which ideas I am still afraid to say out loud. No corporation has a right to that window into my life. Science fiction understood thislong before chatbots arrived. R2-D2 in Star Wars and Rosie in The Jetsons were companions with intelligence of their own, living and working alongside the people they helped. That isthe future I want - an assistant that grows with me, remembers whatmatters, and helps me turn unfinished ideas into things I could never have built alone. Its accumulatedknowledge should remain mine for alifetime, surviving changes in devices, providers, and business models. For India, the opportunity extends far beyond constructing facilities for global platforms. A tutor that understands local culture and values, an assistant helping a kirana shopkeeper, or a medical assistant bringingtraditional knowledge to a village clinic could put useful intelligence within reach of millions. India'sentrepreneurs and researchers should be building those capabilities onplatforms they control. Locating a foreign platform's servers in India also does not give Indian users control over their knowledge or the freedom to change suppliers. India should measure its AI progress by what people can do with the technology, how affordable it becomes, and how much independence they retain. Distributed intelligence can bring capabilities once reserved for wealthy institutions to people who could never afford them before. Cloud infrastructure will remain important, just as mainframes did,and cheaper intelligence will create new uses and expand demand.But that growth will not guarantee higher profits for the companiesbuilding today's largest systems.Control will shift toward the people and organisations using AI. India would be wiser to watchSilicon Valley's trillion-dollar betsturn to toast than to volunteer asthe next slice. Its talent should besolving its people's unmet needs and putting affordable intelligence into their hands instead of copyingsomeone else's spending frenzy....