“The question isn’t whether to use AI in public services. It’s whether to use it in a way that can be verified, measured, and held accountable.”
The Scale Problem California Already Has
Governor Newsom’s April 2025 executive order deploying first-in-the-nation GenAI technologies across state government marked a turning point. California is no longer piloting AI — it is scaling it. The California Department of Tax and Fee Administration uses AI to assist staff across 16,000 pages of reference materials. Caltrans processes continuous real-time traffic data. Statewide rollouts of Microsoft 365 Copilot and the State Digital Assistance AI mean every email draft and document summary generates multiple inference calls.
The numbers are significant. A medium-to-large California agency in 2026 generates an estimated 10,000 to 50,000+ AI inference calls per day. High-volume automated systems — cybersecurity monitoring, high-traffic web portals — exceed 100,000 calls per day. The fiscal impact for major departments to manage these systems is already estimated in the millions of dollars annually.
At 50,000 calls per day with a 62% token reduction, a mid-size California agency using the IGM would eliminate the computational equivalent of 31,000 wasted inference calls daily — saving energy, reducing cost, and generating a verifiable audit trail that SB 53 now requires.
The Core Problem: Current AI governance in California operates at the prompt layer — system instructions that ask models to comply. Nothing mechanically enforces compliance. No audit trail verifies it. SB 53 requires agencies to report detailed inventories and risk assessments. Without infrastructure-layer governance, those reports are estimates, not facts.
What I See From the Veterans Treatment Court
I am a Veterans Treatment Court Liaison at the Los Angeles County Department of Military and Veterans Affairs. Every week I work with veterans who have other-than-honorable discharges, recent incarceration, housing instability, and mental health crises — often simultaneously. These are the exact populations California’s public service AI is being deployed to serve.
Here is what ungoverned AI looks like in my work: a veteran in crisis asks a benefits eligibility system a question. The ungoverned AI produces a 400-token response — detailed, comprehensive, and completely unusable by a case manager with three other calls on hold. The governed AI produces a 150-token response: call 988 Veterans Crisis Line, file VA Form 21-526EZ, contact SSVF for emergency housing. Actionable. Bounded. Auditable.
That 62% token reduction is not an abstraction. It is the difference between a case manager who can act and one who is still reading. Multiply that across 10,000 daily inference calls in a public-facing agency and the operational impact is measurable — in staff time, in response quality, and in cost.
The IGM: Governance That Holds
The Inference Governance Module (IGM) is a constraint-first architecture that sits between the user request and the language model — enforcing governance mechanically before inference occurs. It does not ask the model to comply. It enforces compliance as an architectural property.
The IGM pipeline consists of six stages:
- Input Processing — PII detection · language classification · hard rejection
- GATE — 22-language matrix · domain routing · token ceiling assignment
- Inference Engine — Hard token ceiling · model cannot exceed limit
- TTP Monitor — Real-time energy measurement · joules per inference
- Output Gateway — Cryptographic signing · immutable catalogue logging
- Admin Dashboard — Live audit log · governance calendar · compliance interface
The IGM Admin Dashboard is the compliance interface SB 53 and AB 1018 require but don’t specify. Every inference generates a cryptographically signed record: timestamp, language, token counts, energy consumption in joules, and governance status (PASS or FAIL). The dashboard displays this in real time — a governance calendar showing daily activity, a scrollable audit log with every inference record, and system health monitoring across all eight modules.
This is not a periodic audit. It is continuous, automated, verifiable governance at the moment AI inference occurs.
What Ungoverned Inference Actually Costs California
Token waste is not a technical abstraction — it is a budget line. At current Anthropic API pricing, each unnecessary token has a cost. At scale, across California’s enterprise AI deployments, that cost compounds daily.
| Agency Scale | Daily Calls | Ungoverned Tokens | Governed Tokens | Daily Savings |
|---|---|---|---|---|
| Small / Pilot | 1,000 | 400,000 | 152,000 | −248,000 tokens |
| Mid-Size Agency | 10,000 | 4,000,000 | 1,520,000 | −2,480,000 tokens |
| Large Agency | 50,000 | 20,000,000 | 7,600,000 | −12,400,000 tokens |
| High-Volume System | 100,000 | 40,000,000 | 15,200,000 | −24,800,000 tokens |
At $0.003 per 1,000 output tokens (Anthropic claude-sonnet pricing), a large agency running 50,000 calls per day saves approximately $37,200 per day in token costs alone — over $13.5 million annually — from governance enforcement. That does not include energy savings, staff time reclaimed from verbose outputs, or compliance cost avoidance.
SB 53 Compliance: What the Law Requires, What IGM Delivers
SB 53, signed by Governor Newsom in September 2025, requires covered AI developers to implement safety and security protocols and maintain documentation of AI system capabilities and limitations. AB 1018 extends accountability requirements to automated decision systems in public services. The fiscal impact for major departments to manage these compliance obligations is estimated in the millions annually.
| SB 53 / AB 1018 Requirement | IGM Implementation | Verified |
|---|---|---|
| Safety and security protocols for AI systems | 8-module pipeline with hard enforcement at each stage | ✓ |
| Documentation of AI capabilities and limitations | Cryptographic audit trail — every inference catalogued | ✓ |
| Risk assessment and incident reporting | PASS/FAIL governance status per inference — real time | ✓ |
| Transparency in automated decision systems | Admin dashboard — governance calendar and audit log | ✓ |
| Language access for diverse populations | 22-language DHCS-aligned governance matrix | ✓ |
| Environmental impact accountability | Per-inference energy measurement — joules recorded | ✓ |
The Compliance Argument: SB 53 establishes the obligation. The IGM provides the architecture to fulfill it — not through periodic audits or self-reported compliance, but through continuous, mechanical enforcement at the moment AI inference occurs. Every inference. Every time.
See It Running — Live, Right Now
The IGM is not a white paper. It is a working system, publicly accessible, empirically validated across ten California public service scenarios. The live demonstration runs real inference calls — governed versus ungoverned — in real time. The Admin Dashboard shows the governance calendar, audit log, and system health updating with every inference.
Every time you run a governed scenario, it flows through the real IGM pipeline, gets catalogued with a cryptographic signature, and appears in the Admin Dashboard within seconds. The response changes with each run because it is live inference, not a cached demo. What does not change is the governance.
- Live Demo: igm-demo-production.up.railway.app
- GitHub: github.com/debacconexus/igm-demo
A Proposal for CalCompute
California is the only state with the combination of legislative foundation (SB 53, AB 1018), compute infrastructure mandate (CalCompute), and operational diversity (22 DHCS languages, 58 counties, every major public service domain) to make governed AI inference a standard rather than an exception.
The IGM is patent-pending and open for research and public sector deployment. A CalCompute partnership could establish the IGM architecture — or an architecture that implements its principles — as the reference standard for governed AI inference in California state agencies. The compliance infrastructure that SB 53 demands and that agencies are spending millions to approximate already exists. It is running. It is auditable. It is measurable.
The missing governance layer is not missing anymore.
James DeBacco, MSW, DSW(c) is Founder & CEO of DeBacco Nexus LLC, Veterans Treatment Court Liaison at LA County DMVA, Executive Director of Bridges2Freedom 501(c)(3), Member of the CalCompute Consortium, and DSW Candidate at USC Suzanne Dworak-Peck. USPTO Provisional Application 19/571,156 · [email protected]