AI stays under human control only when four things hold, in this order.
There are two ways to build them. Government sets the floor. Local democracy raises it, and that is where we work.
Across the United States, communities are saying no to data centers: electricity bills, water, grid capacity, noise, tax deals, and the secrecy around all of it. The objection is not to AI. It is to AI nobody can see and nobody can answer to.
Earthonomy, our parent company, keeps a register of data center facilities compiled from permits, rate cases and public filings, whether or not the operator takes part. Its first dataset covers 40 facilities in Loudoun County, Virginia, each with a transparency index and a right of correction.
Of those 40 facilities, not one discloses its on-site generation. Purchased power can be clean on paper while the generators behind the meter are the largest source of nitrogen oxides in the county. The register asks what a facility burns, not only what it buys.
Pin the region a model runs in, verify the facilities behind it against the register, and fail the build when they drop below the floor you declared. Designed; not yet built. It is the first “where does my model physically run” guarantee anyone will be able to buy.
The plainest local decision there is is a community deciding whether a data center is built, and on what terms. The operator presents jobs and tax revenue. The community experiences water, grid capacity, noise, and the promises that were made. Governed AI gives that community the record it needs to decide. That is pillar four, and it is where every Earthonomy AI implementation ends up.
Earthonomy AI is a software services company. We build governed AI inside organizations, on the cloud you already run, Azure, AWS or Google Cloud, and we implement the governed stack within Databricks. Here is what an engagement leaves behind, labelled honestly: Built runs on Earthonomy’s platform today; Partly built, Specified and Roadmap mean what they say.
Every implementation is assessed against Earthonomy’s Seven Forces Framework at the start and again at the end. Each force is scored to a level from 0 to 12, and the organization’s level is its weakest force. The result is a record, not an opinion.
Validators are drawn at random from a conflict-filtered pool, see only what they are assigned, and vote blind; the review panel must be unanimous. Earthonomy AI never validates its own engagement.
Earthonomy’s facility register and transparency index cover the data centers behind the regions you deploy to, compiled from permits and rate cases whether or not the operator engages.
Pin the region a model runs in, verify the facilities behind it against the register in your build pipeline, and fail the build when they drop below the floor you declared.
Collection agents read the power and building systems, the accounting turns readings into reportable scopes, and thresholds bound to your published commitments trigger a public update when one is crossed.
Certifications issue as verifiable credentials with a public verification link that needs no login. A regulator, a customer, a union or a county board checks it themselves.
Unions can endorse the standard, see aggregate workforce results across organizations, and train their members through Earthonomy Learn™. Commission is refused for unions, so the endorsement cannot be bought.
The data-center assessment asks about power efficiency, carbon-free supply, water, grid impact, waste heat, noise, local hiring, electricity-rate and tax transparency, tenant emissions, siting and electronic waste.
OmniESG™, Earthonomy’s data-intelligence product, is in market on the Databricks Marketplace, reads and writes your catalog, and ships an audit bundle with every report. It is the first workload we govern in your workspace.
What we implement for each, in your workspace, in your cloud.
The workspace is deployed into your own cloud network, with private connectivity, identity federated to your directory, and infrastructure hardened, patched and monitored before any model runs.
Internal: who and what may reach which data and which models, and what a model may say and do, enforced in Unity Catalog and at the AI gateway. External: every control mapped to the EU AI Act, NIST AI RMF and ISO/IEC 42001, so an audit is a report you already hold.
End-to-end lineage from source data to model output, audit logs and inference records retained on your terms, and reports a regulator, a customer or a union can read.
Pillar four is yours, not ours. We do not decide how your workplace, customers or community use AI. We build the workforce-impact and footprint reporting, the human appeal route on every automated decision, and the record your people need to say yes, no, or not like this.
Earthonomy already certifies organizations through RISE™ and facilities through Earthos™. We are building the third: Earthos™ Certified Governed AI, a certification for an AI system, by the same rule that makes a facility a product. No system holds it yet, including ours. Here is what it will test.
A vendor certifies the product it sells; a deployer certifies the system as it runs in their organization; a company whose AI use is incidental is covered by a governance dimension in its organization assessment instead. Validation is independent, conflict-filtered and blind. Earthonomy AI will never certify its own work, and the first system through the standard will be Earthonomy’s own assistant, Sofia™, validated by people who did not build it. This is what responsible use of AI looks like in practice, written as evidence rather than intent.
Two ways to build the four pillars. Governments secure the network, set the external guardrails and mandate observability, and that line holds as long as it is enforced. Citizens, workplaces and communities set the internal guardrails, demand observability of the tools they use, and exercise democratic choice where they are, and that path leaves more capacity than it found. We work on the second path, on the foundation the first one lays.
| Dimension | Top-down | Bottom-up |
|---|---|---|
| Who acts | Legislatures, regulators, courts | Citizens, businesses, nonprofits, unions, communities |
| Speed | Years, then all at once | Today, one decision at a time |
| Reach | Ends at the jurisdiction’s border | Travels with the people who practise it |
| When it fails | Enforcement lapses and the line moves | One actor lapses and the others hold |
| Which pillars it carries | 1 · 2 external · 3 by mandate | 2 internal · 3 in practice · 4 |
| What it does to capacity | Preserves it | Grows it |
| Nature | Holds the line | Regenerative |
Employment produces income, income becomes purchases, purchases become every company’s revenue, and revenue funds employment. AI aimed solely at removing jobs cuts the first link and the other three follow. AI aimed at what each person can do gives a company an advantage that cannot be bought, because it has to be built into people, and it holds headcount steady while it compounds. Where a company nonetheless cuts its workforce for AI, everyone should know: disclosure is observability applied to the workforce, and buying, investing and working on the basis of it is democratic choice. Observability watches the system, never the people: surveillance of workers is prohibited in the guardrails we build, because it is exactly what AI would be most effective at.
Every implementation is designed and built by our own staff, certified in cloud architecture and engineering on Azure, AWS and Google Cloud, and on Databricks. Governance is a practice before it is a platform, and the people who carry it are ours, not subcontracted.
Design the governance: which pillar is carried where, what the internal guardrails permit, which external standards apply, what must be observable and to whom, and how your people exercise choice over the result.
Build and run it: the secure workspace in your cloud, the catalog permissions and gateway guardrails, the lineage and audit pipelines, and the reports that turn observability into something a person can act on.
The engagement follows the methodology of our sister company, Augmentio: evidence before commitment, structurally. No client funds a scaled build on an unproven premise, and no phase ends in a deck in a folder.
The AIM Assessment: two to four weeks, fixed fee. A scored AI-maturity level with governance weighted heaviest, every finding traceable to an examined artifact, alongside the Seven Forces measurement. You leave with an evidence log, a gap map and a prioritized specification, useful whether or not we do the follow-on work.
Your choice of proof: a Signal Test on synthetic seed data, or a Prototype on real governed data in your own workspace. Both end in evidence and a production path, never in a slide.
Productionized delivery of the governed stack: the secure workspace, the permissions and guardrails, the lineage and audit records. Spec-first, with the repository as the source of truth: no code before an approved specification, every decision logged, every release tagged.
Your platform and its AI maintained, controlled and continuously improved as ongoing operations: the reports, the disclosures, the appeal route, and a re-measurement on a cadence you set. Everything we build is yours to keep and run without us.