A new study from MIT suggests the biggest and most computationally intensive AI models may soon offer diminishing returns compared to smaller models. By mapping scaling laws against continued improvements in model efficiency, the researchers found that it could become harder to wring leaps in performance from giant models whereas efficiency gains could make models running on more modest hardware increasingly capable over the next decade.

“In the next five to 10 years, things are very likely to start narrowing,” says Neil Thompson, a computer scientist and professor at MIT involved in the study.

Leaps in efficiency, like those seen with DeepSeek’s remarkably low-cost model in January, have already served as a reality check for the AI industry, which is accustomed to burning massive amounts of compute.

As things stand, a frontier model from a company like OpenAI is currently much better than a model trained with a fraction of the compute from an academic lab. While the MIT team’s prediction might not hold if, for example, new training methods like reinforcement learning produce surprising new results, they suggest that big AI firms will have less of an edge in the future.

Hans Gundlach, a research scientist at MIT who led the analysis, became interested in the issue due to the unwieldy nature of running cutting edge models. Together with Thompson and Jayson Lynch, another research scientist at MIT, he mapped out the future performance of frontier models compared to those built with more modest computational means. Gundlach says the predicted trend is especially pronounced for the reasoning models that are now in vogue, which rely more on extra computation during inference.

Thompson says the results show the value of honing an algorithm as well as scaling up compute. “If you are spending a lot of money training these models, then you should absolutely be spending some of it trying to develop more efficient algorithms, because that can matter hugely,” he adds.

The study is particularly interesting given today’s AI infrastructure boom (or should we say “bubble”?)—which shows little sign of slowing down.

OpenAI and other US tech firms have signed hundred-billion-dollar deals to build AI infrastructure in the United States. “The world needs much more compute,” OpenAI’s president, Greg Brockman, proclaimed this week as he announced a partnership between OpenAI and Broadcom for custom AI chips.

A growing number of experts are questioning the soundness of these deals. Roughly 60 percent of the cost of building a data center goes toward GPUs, which tend to depreciate quickly. Partnerships between the major players also appear circular and opaque.

Australia’s utility sector accounts for some 43.1% of the country’s carbon footprint, and some 37.2% of its direct emissions, new research from Edith Cowan University (ECU) has revealed.

Dr. Soheil Kazemian, from the ECU School of Business and Law, said the utilities sector included electricity generation, transmission and distribution, gas supply, water supply and waste collection and treatment.

Electricity generation and transmission were identified as the most significant contributors within the utilities sector, with commercial services and manufacturing emerging as substantial sources of embodied emissions within the sector.

The research, published in the Management of Environmental Quality: An International Journal, revealed that 71% of embodied emissions were attributed to electricity transmission, distribution, on-selling electricity, and electricity market operation. Electricity generation accounted for a further 15%, while gas supply accounted for 5%, water supply for 4%, and waste services and treatment for the remaining 5% of embodied emissions in the sector.

“The study highlights electricity transmission and generation as the subsectors with the highest potential for adopting low-carbon technologies. By pinpointing emission hotspots and offering detailed sectoral disaggregation, the results of the research provide actionable insights for prioritizing investment in emissions reduction strategies, advancing Australia’s sustainability goals and supporting global climate change mitigation,” Dr. Kazemian said.

He said that as with any other business, the pressure to reduce the carbon emissions footprint of the utility sector would need to originate from the consumer sector.

Unlike other sectors, however, increased investment into the utilities sector is likely to result in a smaller carbon footprint.

“This is a major difference between the different sectors in Australia. If you invest more in mining, that means the carbon footprint from that industry would increase, and the same can be said for manufacturing as the investment would result in expanded business.

“While new infrastructure development can generate temporary increases in emissions for the utility sector during construction, the long-term impact depends on where those dollars are spent. Investment in renewable energy systems or efficient delivery networks can significantly cut emissions, whereas continuing to fund carbon-intensive energy sources risks locking in higher emissions for decades to come.

“This complexity highlights a critical point that meaningful decarbonization will depend not only on policy or technology, but also on consumer choices. When households and businesses demand cleaner energy, utilities are more likely to channel investment into low-carbon solutions. By consciously choosing renewable energy options and supporting sustainable providers, consumers can send a powerful market signal that accelerates the transition to a cleaner grid,” Dr. Kazemian said.