We Think Small. Intelligently.
The AI industry is in an energy crisis of its own making. Data centers consumed an estimated 415 terawatt-hours of electricity globally in 2024—roughly 1.5% of all electricity used worldwide. By 2030, that figure is projected to double. In the United States alone, data centers now account for over 4% of national electricity consumption, and AI-specific servers used between 53 and 76 terawatt-hours last year—enough to power more than 7 million homes.
The prevailing assumption is that better AI requires bigger models, and bigger models require more power. Frontier-class systems now run on 600 billion to over one trillion parameters, with corresponding compute demands that strain electrical grids and drive utility costs up for everyone. A Carnegie Mellon study estimates data centers could increase average U.S. electricity bills by 8% by 2030—and over 25% in high-demand markets like northern Virginia.
We asked a different question: what if intelligence isn't in the scale, but in the structure?
Cognitive Agent Framework runs on ~70 billion parameters—roughly 7% of frontier scale. On validated theory-of-mind benchmarks, it scored 100% where GPT-4's documented ceiling is 88%. Not "good enough" performance. Not a compromise for efficiency. Better results, at a fraction of the compute.
The energy implications are significant. Vastly better performance per watt means less demand on GPUs and lower power requirements per response. It also means infrastructure flexibility that trillion-parameter models simply can't offer. Smaller models can run in facilities powered by Canadian hydro, EU wind farms, or regional grids that couldn't support hyperscale deployments. Geographic and energy-source choices become possible that are foreclosed at frontier scale.
The industry is treating AI's energy footprint as an externality to be managed later. We think it's a design problem to be solved now.
