The CIVIC Forum is building the civic discourse infrastructure that connects hardware measurements to policy frameworks, so communities can make evidence-based decisions about AI infrastructure that affects their energy, water, and future.
When a datacenter requests grid connection, utilities conduct interconnection studies over 6 to 18 months at roughly $500K, producing reports comprehensible only to power engineers. What follows is predictable: utilities request billions for grid upgrades, consumer advocates resist ratepayer burdens, communities mobilize against "mystery power users," and datacenter operators promise economic benefits. This pattern is incapable of sustaining AI growth.
We have identified four systematic gaps preventing evidence-based deliberation:
Energy estimates for AI systems vary 2 to 10x between calculations and direct measurement. Datacenter operators report figures without independent verification. Without verified measurements, stakeholders operate from incompatible assumptions.
Interconnection studies treat datacenters as rigid loads, ignoring that AI workloads can shift training by hours or defer inference by seconds. This flexibility could reduce infrastructure costs by billions if coordinated.
Current analyses assume a single future: this datacenter operating at this capacity for this duration. Yet uncertainties around datacenter counts, AI efficiency, and energy sources can swing infrastructure costs by billions.
Technical outputs remain incomprehensible to non-experts. Residents cannot ask "how will this affect my electricity bill or water supply?" and get evidence-based answers. Discourse defaults to worst-case fears versus corporate reassurances.
Democratic infrastructure governance requires vertical integration: hardware measurements must connect to policy frameworks through validated layers that preserve technical accuracy while enabling public participation. Each layer builds on the one below it.
Direct measurement of AI energy consumption and water use intensity establishes shared factual ground truth. Our measurement database provides the first independently verified, publicly accessible data for AI infrastructure impacts, building the factual foundation that evidence-based deliberation requires.
Our testbed measures how temporal flexibility in AI training and inference workloads affects distribution infrastructure: voltage profiles, transformer loading, and power quality. By showing datacenters can be grid assets rather than burdens, we shift the conversation from "how much will this cost?" to "how should we coordinate this resource?"
Macro-energy system models explore thousands of potential grid-AI futures under deep uncertainty. Rather than single-point forecasts, communities can evaluate how decisions perform across scenarios, identify adaptive strategies, and understand which uncertainties matter most.
AI agents synthesize outputs from all layers into interfaces where regulators, citizens, utilities, and datacenter operators explore tradeoffs between grid stress, electricity bills, water consumption, and emissions. Technical complexity becomes legible to all stakeholders through transparent uncertainty quantification.
This is not a hypothetical exercise. Michigan communities are facing datacenter siting decisions right now, and the university has the interdisciplinary depth to address them.
A proposed datacenter development prompted community concern about energy costs, water use, and environmental impact. Residents lacked access to verified data about actual resource demands, forcing deliberation based on estimates and competing narratives rather than evidence.
Datacenter siting decisions here highlighted the gap between technical interconnection studies and public understanding. The community engagement process revealed how inaccessible technical outputs prevent meaningful democratic participation in infrastructure choices.
The University of Michigan uniquely combines ML systems research, power systems laboratories, energy planning expertise, water systems knowledge, AI capabilities, energy law scholarship, and community engagement experience. Existing partnerships with utilities, regulators, and community organizations provide the relationships needed to translate research into practice.
Within five years, we will demonstrate that democracies can govern planetary-scale AI infrastructure through legitimate deliberative processes, creating replicable models for infrastructure decisions nationwide.
Deploy verified energy and water measurement infrastructure. Establish ground truth database with independently verified AI consumption data. Launch community engagement with Ypsilanti and Saline stakeholders.
Demonstrate datacenter workload flexibility on physical grid testbed. Publish first benchmark results showing coordination potential. Begin scenario model development with utility partners.
Connect all four CIVIC Stack layers into working prototype. Pilot AI-powered stakeholder interfaces with Michigan communities. Launch certificate program for practitioners.
Expand to multi-state regulatory contexts. Pursue federal funding alignment with DOE programs. Establish sustainable revenue through certificate programs and philanthropic partnerships.
Building the measurement and modeling infrastructure for evidence-based energy and water governance in the age of AI.
Creating verified benchmarks and open tools that establish Michigan as the national leader in AI infrastructure assessment.
Demonstrating that democratic processes can govern planetary-scale technology decisions through informed civic discourse.
Whether you are a policymaker, community member, researcher, utility, or datacenter operator, the CIVIC Forum is building tools and frameworks for you.
The CIVIC Forum
University of Michigan
hello@thecivic.forum