Intelligence Per Watt

Intelligence Per Watt

Stanford 2025

Intelligence<br>Per Watt

A Unified Metric for the AI Era

01. Vision

02. Agenda

03. Papers + Code

04. Blog

05. Related

06. People

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Updated: 01/07/2025

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01

Vision Statement

From 1946 to 2009, computing efficiency—performance per watt—doubled every 1.5 years. This trend, documented by Koomey and colleagues, transformed where computing could happen. Workloads migrated from mainframe rooms to desktops, then laptops, then pockets. The transition from centralized time-sharing to personal computing didn't occur because PCs surpassed mainframes in raw performance. It occurred when efficiency gains made computing capable enough within the power constraints of personal devices.

We're at the same inflection point for artificial intelligence.

Today, most AI queries flow through centralized datacenters while demand grows at steep rates: 1300× increases in token processing, year-over-year scaling that strains power grids. Yet telemetry shows that 77% of requests are practical tasks—writing emails, summarizing documents, seeking information—that don't require frontier-scale models.

We propose INTELLIGENCE PER WATT (IPW) —task accuracy per unit of power—as a unified metric for understanding this transition. Just as performance-per-watt guided the mainframe-to-PC shift, intelligence-per-watt clarifies the path from centralized AI to distributed intelligence. IPW provides a common framework for studying three questions shaping AI's future:

Workload Redistribution: From Cloud to Edge

Local language models (≤20B parameters) now accurately answer 88.7% of single-turn queries, and consumer accelerators run them at interactive latencies. IPW improved 5.3× from 2023–2025—3.1× from model advances, 1.7× from hardware gains. By measuring intelligence efficiency across the model-hardware landscape, we can identify which queries belong on which devices. Hybrid systems that route queries appropriately cut energy, compute, and cost by 60–80% while preserving quality. IPW tracks this redistribution as it unfolds.

Economic Value: Measuring AI's Real-World Impact

Not all intelligence is equal. A model that handles graduate-level physics but fails at email drafting delivers different economic value than one with the opposite profile. By weighting IPW against GDP-relevant task distributions, we can quantify how much economic value AI systems generate per watt consumed. This lens reveals where current systems create value, where gaps remain, and how efficiency gains translate into productivity across economic sectors.

National Competitiveness: The Global AI Race

The nation that most efficiently converts energy into deployed intelligence gains advantage. We introduce Gross Domestic Intelligence (GDI) —the product of intelligence-per-watt and accessible power—as a framework for AI competition. China and the United States face inverse constraints: China is compute-bound by export controls on advanced chips; America is energy-bound by grid limitations and datacenter bottlenecks. IPW reveals an asymmetric American asset: hundreds of millions of local accelerators already deployed in homes and offices. This installed base could boost effective AI capacity 2–4× without new datacenter construction.

The path forward: Intelligence per watt should be a north star metric for model architecture, hardware design, and national strategy. We're building the measurement infrastructure, benchmarks, and systems to make this concrete—and releasing our tools for others to use.

© Avanika Narayan & Jon Saad-Falcon<br>Stanford University, CA

02

The IPW Research Agenda

We're pursuing a coordinated research program to understand and maximize intelligence efficiency across the full stack.

Category<br>Initiative<br>Objective

Measurement &<br>Benchmarking<br>GDP-Weighted Evaluation<br>Quantifying economic value generated per watt on real-world, GDP-relevant tasks.

Measurement &<br>Benchmarking<br>IPW Attribution<br>Decomposing efficiency gains into algorithmic versus hardware contributions through continuous benchmarking.

National<br>Competitiveness<br>Gross Domestic Intelligence<br>Identifying high-impact interventions across inference systems, power grids, and model architectures.

Models &<br>Systems<br>Post-training for IPW<br>Training local models to use frontier models as tools for verification and sophisticated assistance.

Models &<br>Systems<br>Hybrid Inference Engine<br>Building systems that automatically route work between local and cloud compute to maximize IPW subject to latency, privacy, and cost constraints.

© Avanika Narayan & Jon Saad-Falcon<br>Stanford University, CA

03

Papers + Code

Publications

📰 Article

China's AI Heist

Jared Dunnmon, Avanika Narayan, Jon Saad-Falcon

A Foreign Affairs essay on how the United States should respond to Beijing's unauthorized "distillation" of frontier AI models, and what safeguarding America's lead in AI will...