Local AI models can already answer 89% of everyday chat & reasoning queries as well as a frontier cloud model, a result that holds broadly across more than a million real queries & 20+ local models tested.¹

That means we’re generating more intelligence per watt of electricity : more work from the same number of electrons.¹

Tracking computing efficiency over time is not new. Koomey’s law found that computing power per watt doubled roughly every 1.5 years for decades, a trend that shrank the power of a mainframe into a laptop’s chassis.²³

Just as performance-per-watt guided the mainframe-to-PC transition, intelligence-per-watt will guide AI’s transition to the edge.

— Jon Saad-Falcon, Avanika Narayan, et al., “Intelligence per Watt”

GPUs follow a more languid curve today, doubling efficiency roughly every 2.7 years over the last 15 years, not every 1.5 years.⁴

The impact is no less impressive : the best local model’s win/tie rate against a frontier model, rose from 23.2% in 2023 to 71.3% in 2025, adding roughly 20 percentage points a year. In 2026, a local model selected for the task by another local AI bumps that number to nearly 90%.⁵

Efficiency improved alongside the trend line : intelligence-per-watt rose 5.3x over the same period, split into a 3.1x gain from better models & a 1.7x gain from better chips. Computers are faster, models are smarter. And the end user benefits.

5

Cloud remains essential for long multi-step reasoning, the hardest technical domains, & workloads where scale & parallelization matter. Cloud hardware still holds an edge over local hardware in many use cases. Cloud inference delivers a 40% energy efficiency gain relative to local models.⁶ Datacenters batch queries, a trick local hardware serving one user at a time cannot use, yet.

But for much of everyday knowledge work, there is no reason to send the query to the data center at all. The combination of local models plus a router is sufficient for the supermajority of work.⁷

It also cuts energy 80%, compute 77%, & cost 74% against an all-cloud baseline.

Mainframes became personal. So will your data center.

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1. Jon Saad-Falcon, Avanika Narayan, et al., “Intelligence per Watt: Measuring Intelligence Efficiency of Local AI,” Stanford University & Together AI, November 2025. arXiv:2511.07885. See also the Stanford Hazy Research overview. ↩︎ ↩︎

2. Koomey’s law, Wikipedia. ↩︎

3. Watts measure power, the rate energy is drawn, & joules measure the energy itself; Koomey’s original metric was computations per joule, but the underlying trend is the same one intelligence per watt now tracks for AI models. ↩︎

4. Anson Ho, Ege Erdil & Tamay Besiroglu, “Limits to the Energy Efficiency of CMOS Microprocessors,” 2023. arXiv:2312.08595. ↩︎

5. 71.3% & 89% are two different measurements from the same 2025 data, not the same number at different times. 71.3% is the best single local model’s win/tie rate against a frontier model, the trend line this chart plots (23.2% in 2023, 48.7% in 2024, 71.3% in 2025). 89% is a separate, higher ceiling: routing each query to whichever of the 20+ local models tested handles it best beats any single model by 16.3 to 28.8 percentage points. That gain is a selection effect: the paper notes local routing draws from 20+ diverse models versus three frontier cloud models, so on some benchmarks the best-of-local ensemble even surpasses best-of-cloud. More candidates to choose from, not just smarter routing, is what raises accuracy. Both figures come from the same study & neither supersedes the other. ↩︎ ↩︎

6. Jon Saad-Falcon, Avanika Narayan, et al., “Intelligence per Watt: Measuring Intelligence Efficiency of Local AI,” Stanford University & Together AI, November 2025. Cloud accelerators deliver at least 1.4x higher intelligence-per-watt than local chips running the same models, roughly a 40% efficiency premium. arXiv:2511.07885. ↩︎

7. Most AI Work Can Wait, tomtunguz.com. ↩︎