During my time at JPMorgan Chase, I watched financial institutions grapple with the push toward Scope 3 carbon disclosure — first through voluntary international frameworks, and eventually under mandatory regimes anticipated by Europe’s CSRD.
Scope 3 forced organizations to quantify indirect, value-chain emissions using porous boundaries and proxy estimates. An entire consulting industry emerged, yet the outputs remained imprecise, extremely difficult to audit, and endlessly complex.
Now, as policy conversations turn toward regulating AI data centers based on "inference capacity" or "effective model output," we are setting up the exact same trap.
Why measuring inference capacity is a fool’s errand:
Software optimization moves too fast: Engineers optimize code daily, tweaking model architecture and precision in ways that could drastically change how much work each chip can do as quickly as overnight. Static government baselines become obsolete before a compliance report could even be filed.
The system boundary problem: A single user request query often jumps between edge models, cloud clusters, and third-party APIs. Tracing exact energy or compute across these complex chains creates the same porous, audit-challenged boundaries that plague Scope 3.
The Solution: Keep regulation precise, physical, and pared down.
Instead of trying to audit fluid software execution, policy should stick to direct, metered "Scope 1 & 2" physical baselines:
Utility Interconnect Approvals (MW): Hard electrical boundaries metered directly at the substation.
Thermal Design Power (TDP): Clear energy-draw baselines for facility cooling and power architecture.
Physical Silicon Tallies: Verifiable hardware counts at the facility wall.
Rule #1 of practical policy: If a metric can’t be measured deterministically at the physical boundary wall, don't build a mandate around it.
Regulators should focus oversight on concrete, physical inputs, but keep fluid, software-defined inference metrics off the ledger.
#AIRegulation #TechPolicy #DataCenters #CorporateGovernance #AIInfrastructure
