HomeCountiesArticlesMethodologyContactResearch and commentary on why we do what we do: AI's impact on local economies, how communities can prepare, and where StrataHelm stands.
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Federal AI-workforce policy is still largely deciding whether and how to study the problem: a handful of bills, nearly all stalled at introduction, proposing measurement rather than response. Waiting for that process is itself a decision. When federal help does arrive it will demand exactly the local capacity communities should build now: measure your position, identify your vulnerable points, build the institutional muscle, strengthen the fundamentals, and stay ready for federal resources.
The unemployment rate, GDP, the Air Quality Index, the flood map — the great public measures are coordination infrastructure, not just data. AI is reshaping local labor markets with no such shared measure. The principles a legitimate one must meet: open methodology, universal coverage, public access, honesty about uncertainty, credible grounding, and maintenance. We built our index to be judged against them — and a standard is earned, not declared.
A county has not one exposure number but two: the exposure of the people who live there, and the exposure of the jobs located there. Where residents and workers are not the same people — which is nearly everywhere — the two can point in opposite directions, and they call for completely different plans.
Entrepreneurship after an anchor-employer loss: the evidence against real-estate-first incubators and public venture funds, and the ecosystem-building approaches that actually deliver.
What if we prepare for AI disruption and it never comes? The question stops a great deal of sensible action cold. Climate adaptation has wrestled with it for thirty years and has an answer: the no-regret investment, worth making under every plausible future. Most of what a community should do to prepare — diversify, strengthen the workforce, build entrepreneurial capacity, shore up finances, measure its own position — pays off whether or not AI disrupts a single job.
The archetypal vulnerable job used to be on a factory floor. Its white-collar successor is the back office — claims processors, bookkeepers, office clerks and administrative support — the routine cognitive work current AI does most readily, and which federal projections now expect to shed hundreds of thousands of jobs this decade with AI named as a cause. Like manufacturing, it clusters: insurance capitals, financial-processing hubs, and the suburban office campuses that quietly anchor whole regions.
A data-driven review of government programs after mass job loss: sectoral training's standout record, the mixed federal retraining story, when place-based investment works, and what does not.
You have your county's AI exposure data; now you have to brief a council or commission on it without causing panic or a shrug. A practical guide drawn from decades of risk-communication research — Sandman's Risk = Hazard + Outrage, the EPA's Seven Cardinal Rules — translated into a concrete five-slide deck, the language that calms versus the language that panics, and what to do before you build a single slide.
Every field protects itself with vocabulary, and the economics of AI borrows from labor economics, computer science, urban planning and climate science at once — then uses the same words to mean different things. Twelve terms a municipal leader actually encounters, in plain language with the research behind each: what exposure does and does not claim, why augmentation and automation are a choice rather than a destiny, and why creative destruction is a national comfort but a local emergency.
"Just learn a trade" is real advice at 19 and a mirage at 48. The arithmetic of apprenticeship pipelines and nursing-school bottlenecks, and what an honest mid-career strategy looks like.
If you write a CEDS or a WIOA workforce plan, federal requirements already oblige you to analyze resilience, regional labor-market conditions, and threats to your economy — and AI-driven labor disruption fits inside each of those existing obligations. The specific regulatory hooks in EDA and Department of Labor rules, drop-in language a planner can adapt, and the sourced data needed to satisfy a federal reviewer.
CAERI is a flood map for AI’s effect on local labor markets: it doesn’t forecast the storm, it shows where a community sits relative to the water. What the five-pillar index measures, how to read a score without misreading it, and how municipal leaders can use position — not prophecy — to plan.
The default response to a proposed data center is a moratorium — a posture, not a plan. This briefing argues for the harder, more valuable move: architecting the deal. Closed-loop cooling, over-provisioned on-site clean generation, transparent public reporting, and enforceable remediation deadlines — written into the community's own terms — turn an anxiety into an asset.
The economy can grow while your county falls apart. Why national averages mislead, which counties experience a boom as a bust, and what to measure when "the economy is fine" is not true on Main Street.
Youngstown, Flint, Gary, Rochester: what the historical record shows about towns built around one employer, the repeatable four-stage pattern of collapse, and why the damage lasts forty years.
"Will AI take our jobs?" has no honest answer without a date attached. A three-horizon framework for municipal leaders — the next 24 months, years two to seven, and beyond — and what each horizon asks of a city.
If slowing AI down delays a cure, the people who die in the gap are real. An essay on the identifiable-victim bias and the moral arithmetic the AI-slowdown debate keeps skipping.
StrataHelm measures AI-driven job disruption county by county — yet we are unapologetically optimistic about AI itself. Our position is not "slow the technology down." It is "speed up the conversation."