Oct 30, 2025
Pentatonic AI and Autonomous Systems
Executive Summary
Energy Intelligence Yield (EIY) measures how much real-world energy is saved per joule of AI compute energy consumed. It reframes artificial intelligence from an energy consumer into an energy amplifier—a system where digital intelligence produces measurable efficiency gains in manufacturing, logistics, and circular economy systems in the physical world.
Formula:
EIY = Joules of Energy Avoided ÷ Joules of Compute Consumed In our reference example, analyzing a single smartphone consumed 3.2 kJ of compute and avoided 300 MJ of manufacturing energy—achieving an Energy Intelligence Yield of 94,000Å~.
At market scale, Physical AI systems processing just 10% of smartphones entering the secondary market could avoid 0.63 TWh of manufacturing energy annually—equivalent to powering 60,000 homes for a year, or running 5,029 DGX B200 AI systems continuously for 12 months.
Key Findings:
• Single device EIY: ~61,000Å~ energy leverage (conservative baseline)
• Market-scale EIY: ~14,300Å~ across 50 million devices
• Addressable market: 39 billion products annually (electronics, fashion, sports gear, automotive, packaging)
• Total embodied energy: 365 TWh across high-applicability product categories
• Full industry potential: 795 TWh addressable across all Physical AI applications
2. Concept Definition — What is Energy Intelligence Yield?
2.1 Definition
Energy Intelligence Yield (EIY) quantifies the efficiency of intelligent systems by comparing energy avoided in the physical world to the energy consumed during computation.
Formula:
EIY = E_avoided / E_compute
Where:
• E_avoided = Energy prevented from being consumed (manufacturing, logistics, disposal)
• E_compute = Energy consumed during AI inference and decision-making
2.2 Interpretation Thresholds
EIY | Value Interpretation |
|---|---|
EIY < 1 | Net energy cost (compute uses more energy than it saves) |
EIY = 1 | Break-even (compute energy equals saved energy) |
EIY > 1 | Net-positive intelligence (compute saves more than it uses) |
EIY > 100 | High-leverage intelligence (typical for circular economy applications) |
EIY > 10,000 | Extreme leverage (achievable in reuse-vs-manufacture scenarios) |
3. Reference Case Study — Smartphone Trade-In Analysis
3.1 Scenario
A consumer brings a used smartphone to a retail location. A Physical AI system:
• Scans the device using computer vision (condition, model, functionality)
• Infers optimal route: resale, refurbishment, or component recovery
• Routes the device to the highest-value/lowest-energy outcome
3.2 Data Sources & Assumptions
Parameter | Value | Source |
|---|---|---|
Device | iPhone 13 Pro | Representative smartphone |
Manufacturing Energy | 300 MJ (83 kWh) | Conservative estimate based on Ecoinvent 3.8 LCA Database |
Compute Hardware | LLM-based routing | - |
Probability of Reuse | 65% | Conservative estimate based on |
Energy Avoided | 195 MJ | 300 MJ Å~ 0.65 = 195 MJ |
3.3 Calculation
EIY = E_avoided / E_compute
EIY = 195 MJ / 0.0032 MJ
EIY = 60,938Å~
Conservative estimate: ~61,000Å~ energy leverage
This means every joule spent on intelligent routing saves approximately 61,000 joules of manufacturing energy.
4. Market-Scale Energy Impact
4.1 Smartphones: The Starting Point
At 10% market penetration (50 million smartphones entering secondary markets):
• Energy avoided: 0.63 TWh/year
• Homes powered: 60,000 for one year
• DGX B200 systems: 5,029 running 24/7 for 12 months
• CO₂e avoided: 313,000 tonnes
• Economic value: $63 million
4.2 Full Industry Scaling
Scope | Energy (TWh) | DGX Systems | Homes | vs Phone |
|---|---|---|---|---|
Smartphones only (10%) | 0.6 | 5,029 | 60,000 | 1x |
All consumer products (10%) | 5.5 | 43,694 | 524,000 | 8.7x |
Full industry potential (10%) | 11.9 | 95,196 | 1,133,000 | 19x |
At 25% penetration across full industry:
• 29.8 TWh energy saved annually
• 237,990 DGX B200 systems running 24/7 for one year
• 2.8 million homes powered for one year
• Equivalent to powering all of the world's largest AI companies combined
5. Conclusion — Toward an Intelligent Energy Economy
Energy Intelligence Yield reframes artificial intelligence as an energy amplifier, proving that computation can actively decarbonize the material world.
Every joule spent on intelligence yields tens of thousands of joules saved in the real world.
This is not abstract efficiency—it is measurable, auditable, and scalable.
5.1 The Path Forward
Physical AI systems are emerging at the intersection of:
• Mature edge inference hardware (efficient, real-time, low-power)
• Urgent decarbonization imperatives (net-zero commitments, regulatory pressure)
• Economic incentives (material scarcity, supply chain resilience)
EIY provides the quantitative foundation to unlock this convergence.
5.2 Call to Action
We invite:
• Researchers to validate and extend EIY across domains
• Industry partners to pilot EIY measurement in live systems
• Policymakers to incorporate EIY into circular economy frameworks
• Investors to recognize energy leverage as a sustainability metric
The intelligence economy is inevitable. With EIY, we can ensure it is also a regenerative energy economy.
5. Contact & Collaboration
Pentatonic AI and Autonomous Systems
Volodymyr Nesin
vova.nesin@pentatonic.com
www.pentatonic.com/research
For pilot partnerships, methodology inquiries, or policy consultation, please contact us directly.
