Joules-to-GDP paper: GLM 5.2 nets $72.27/task; small models 2.5x energy efficiency
rohanpaul_ai · x · 2026-11-05
An arXiv paper (authors include John Hennessy, Christopher Re, Azalia Mirhoseini) introduces Joules-to-GDP, measuring Net Value per Joule (NVJ) and Time Saved per Joule (TSJ) across 220 GDPval tasks in 44 occupations:
- Most valuable ≠ most efficient: CAD-design work yields 10% more net value per task than web-dev tasks, but the latter yields 27% more per joule.
- Defaulting to the biggest model wastes energy budgets: Of five models tested, GLM 5.2 delivers the most net value ($72.27/task, 4 hours of laptop energy), while Gemma 4 31B achieves 56% of that with 23% of the energy — 2.5x NVJ and 2.8x TSJ.
- NVJ/TSJ for the same model also vary widely across accelerators, so task-model-accelerator matching should be energy-aware.
The framework offers a concrete way to reason about AI's economic value per unit of energy.
Related event: Stanford's Joules-to-GDP Framework Quantifies AI Energy-Economic Efficiency(5 posts)→
More from AGI Musings
- AI2 exec Bindu Reddy: AI's reputation is unfairly bad and the industry should celebrate it — bindureddy · 2026-11-05
- Many grant solicitations now flagged as 100% AI-generated by detector Pangram — analisereal · 2026-11-05
- Why AI research keeps looking under the lamppost: stale problem frames inherited from Kant — fkasummer · 2026-11-05
- Michael Nielsen on why scientists can't tell an anomaly from a revolution — michael_nielsen · 2026-11-05
- Anthropic's S-1 Paints an Autonomous Labor Future—and an Oversight Problem — asusarla · 2026-11-05
- New paper uses 288M job postings to measure where AI raises the value of work it can't do — robseamans · 2026-11-05