For years, artificial intelligence has been governed by a simple creed: Bigger is better. Feed a model more data, more chips and more electricity, and it will become smarter.
That has been true to a certain extent, especially in an industry obsessed with building “superintelligence,” or all-knowing computer systems. But this scaling approach has also produced a hefty environmental toll, one that is fast becoming a political liability as communities from Malaysia to the MAGA heartland push back against new data centers.
But what if we’re thinking about it all wrong? The more important question is whether most businesses actually need these God-like AI systems, and whether they are even the right tools for the tasks that matter most.
For all the hype, much of AI’s practical, commercial value will come from automating narrow, repetitive tasks, not from building an omniscient machine. For that kind of work, smaller, specialized tools may be the best option. They’re cheaper to run, easier to secure, less demanding on water and energy resources, and often just as effective. If AI is to become genuinely useful without becoming politically and environmentally untenable, the future may belong not to the biggest models, but to the smartest uses of more modest ones.
Take agentic AI, long touted as the next big breakthrough for productivity. Its promises are some of the most seductive for business leaders: software that can act on a users’ behalf and handle routine tasks to free people up for more valuable work. But many of these chores are so narrow and mundane that they don’t require supreme intelligence, or the energy appetites of giant, cloud-based systems.
The way we have been thinking about AI for decades now has been shaped by same bigger-is-better approach, Daniela Rus, the director of the MIT Computer Science and Artificial Intelligence Laboratory, told me. “The end result,” she said, “is that we have these huge models that have a very large energy costs and also very large water costs, and this translates into a big environmental footprint.”
That realization pushed Rus toward alternatives. Her academic experimentation eventually led to LiquidAI, an MIT spinout that builds small, task-specific models for enterprises and researchers and that can run on devices. The company has done this by using only 1,000 GPUs, or advanced AI processers. (OpenAI, by contrast, said last July that it expected to bring more than one million GPUs online by the end of the year.)
Even simple courtesies expose the inefficiencies of the current paradigm. Saying “please” and “thank you” to ChatGPT is reportedly costing OpenAI tens of millions of dollars in energy and power costs. To Ramin Hasani, Rus’ former student and the co-founder of LiquidAI, that’s evidence of how much waste is baked into today’s systems. Smaller, specialized AI, he told me, can match their larger cloud-based counterparts on specific tasks while using “orders of magnitude less amounts of energy consumption.”
Rus and Hasani are hardly alone. Researchers from Nvidia Corp. and the Georgia Institute of Technology argued in a paper last year that insisting on large models for agentic tasks “reflects a misallocation of computational resources” that is “economically inefficient and environmentally unsustainable at scale.” In their view, shifting such workloads to smaller ones is not merely a technical refinement, but a “moral” obligation.
So why are companies still pouring so much money into tapping giant systems for routine work? One answer is inertia. The same Nvidia-led researchers point to the enormous capital already committed to the existing, centralized system (US tech giants are collectively estimated to invest some $650 billion in AI infrastructure this year alone). Once that much money is on the table, it becomes harder to question whether the underlying approach still makes the most sense.
Then there is hype. Small models do not attract the same marketing frenzy or media attention, the paper noted, even when they are better suited for many business uses. It’s easier to sell the fantasy of an AI that knows everything than one that quietly processes documents or handles back-office work more cheaply and securely.
This all matters especially here in Asia, where many middle powers are mulling how to keep pace in the AI race without the capital for vast data-center buildouts or the energy required to power them. If the future of AI depends entirely on massive infrastructure, most countries will be left buying access to someone else’s system. But the real impact may be less about superintelligence and more about humble systems that do specific jobs well.
Which makes now a good moment for a rethink. 2025 was supposed to be the year of AI agents. It didn’t go as planned. The economics were too punishing and the cybersecurity risks too obvious. Smaller models — including ones that can run entirely on a device — could change that calculus. They promise lower costs, tighter security, and a much more compact environmental footprint.
AI doesn’t have to be inherently unsustainable. But getting to a different future will require more pluralism in research, more willingness to challenge Big Tech’s incentives, and less concentration of technological power.
The smartest thing policymakers and business leaders can do now may be to think smaller. For this tech revolution to scale, it may first have to shrink.
This report is auto-generated from Bloomberg news service. ThePrint holds no responsibility for its content.
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