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.

Jamie Dimon, the CEO of JP Morgan, is the latest big name in finance to issue a warning, telling the BBC last week. “The level of uncertainty should be higher in most people’s minds.”

The AI infrastructure gold rush is not entirely about building more capable models. OpenAI is effectively betting that demand for new generative AI tools will grow exponentially. The company may also be looking to decrease its dependence on Microsoft and Nvidia and turn its massive $500 billion valuation into infrastructure that it can design and customize.

Even so, it would seem prudent for the industry to use analysis like the one debuted from MIT to explore how algorithms and hardware may evolve in the next few years.

The building boom now propping up much of the US economy may also have consequences for American innovation. By investing so heavily in GPUs and other chips specialized for deep learning, AI companies might miss new opportunities that could come from exploring ideas from the fringes of academia, like alternatives to deep learning, novel chip designs, and even approaches like quantum computing. That is, after all, where today’s AI breakthroughs came from.

Are you worried about the money being poured into new AI infrastructure ? Send an email to ailab@wired.com to share your thoughts.

This is an edition of Will Knight’s AI Lab newsletter. Read previous newsletters here.