Sustainability
Frugality is the method,
not the marketing.
We don't offset our compute. We mostly don't use it. A model that synthesizes in about a minute on one CPU core makes the energy question nearly disappear — and that is a property of the mathematics, not a pledge.
One model build
The arithmetic, honestly stated.
For a comparable small classification workload, full RSN synthesis measured ≈60 seconds on a CPU (~35 W) — about 0.0006 kWh. A modest 2-hour fine-tune on one A100 (~400 W) is ≈0.8 kWh — three orders of magnitude more. We state the comparison narrowly: it holds for the discriminative workloads where synthesis competes, and we make no energy claims about workloads it cannot do.
Synthesis time measured in the RSN benchmarks; GPU figures are illustrative magnitudes from public hardware power ratings. The honest comparison class is small discriminative models — not frontier LLMs, which synthesis cannot replace.
No training loop
The energy of deep learning is dominated by optimization — thousands of gradient passes over the data on power-hungry accelerators. Synthesis makes one statistical pass on a CPU. The loop, and its megawatts, simply never exist.
No specialized hardware
Every RSN result we publish was produced on an ordinary consumer CPU — the research machine had 8 GB of RAM. No accelerator was manufactured, shipped, cooled, or retired for any of it.
No retraining debt
Because a rebuild costs seconds, models can follow drifting data without the standing energy cost of scheduled re-training pipelines. The greenest GPU-hour is the one never booked.
Models that run where data lives
Synthesis is light enough for the edge — a browser tab, a clinic laptop, a field device. No data shipped to a datacenter also means no datacenter on the bill, energetic or otherwise.
The Siddha view
Nature does not train its intelligence by brute force. A leaf computes its venation from local rules, in one growing season, on sunlight. We think at least some of machine intelligence can be grown the same way — and where it can't, we'll publish that too.