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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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."