Michelle Turner had no MBA, no prior startup experience, and no obvious path to venture funding when she set out from her Virginia Beach home to build a mental health platform for children entering the foster system – so she used AI tools to teach herself startup culture, draft her business plan, and rehearse her investor pitch. Her company, Here Now Health, launched in January 2025 and now employs 16 people, certified in three states to provide Medicaid-funded counseling for foster children, a gap in care Turner identified through her own experience as a foster parent. KeyToFinancialTrends takes Turner's path as a live data point in the debate now consuming the Federal Reserve: whether AI is quietly rewriting who gets to start and scale a company, independent of the traditional credentials investors have always screened for.
That debate has become institutional. Among a broad review of the US economy launched by new Fed Chairman Kevin Warsh, one panel is dedicated entirely to AI's implications for productivity – the same force that can let the economy grow faster with less inflation, but that also means fewer workers may be needed to produce the same output. Some Fed officials have already raised the possibility of an AI economy carrying structurally higher unemployment, while other analysts point to labor's shrinking share of national income and ask whether returns to capital are simply outpacing returns to work.
That two-sided read has genuine institutional backing. John Bailey, an adviser to one of Turner's investors, argues that AI has collapsed the cost of tasks that used to take too much time or money for small entrepreneurs, letting them scale faster and hire people – framing these as traditional service companies simply moving faster, not AI companies in their own right. Apollo Global Management's chief economist has separately tied a recent upturn in new business formation directly to AI's falling cost of launching a company, arguing that as these new firms scale, they create jobs rather than eliminate them. KeyToFinancialTrends positions the two-sided argument as the frame Fed policymakers cannot avoid: Turner's AI-built pitch didn't replace a worker, it replaced the absence of an MBA, a co-founder network, or venture-world connections – suggesting AI's labor-market effects may run in two directions at once, destroying some categories of work while lowering the entry cost into entrepreneurship for people the traditional system screened out.
That optimistic case sits against a starker one from Brookings Institution and Opportunity@Work researchers, who identified roughly 23 million workers whose next logical career step runs directly into a job highly exposed to AI replacement – concentrated in Florida, the Northeast, Texas, and California, and disproportionately affecting workers without college degrees who rely on experience rather than credentials to advance. Richmond Fed President Thomas Barkin captured the split screen directly, noting that while the public conversation fixates on jobs being replaced, his own contacts in sectors like auto repair and manufacturing still cannot find enough workers and are using AI to make the employees they do have more productive.
The Brookings and Opportunity@Work research adds specificity to where that risk is concentrated. The roughly 23 million workers identified as sitting one career step away from high AI exposure are not evenly spread across the country: the analysis found the exposure heaviest in Florida, the Northeast corridor, Texas, and California, states with large concentrations of the administrative, clerical, and customer-service roles most directly exposed to generative AI tools. The demographic pattern layered on top of the geography is just as notable: these workers disproportionately lack four-year college degrees and have historically advanced through accumulated job experience rather than formal credentials. KeyToFinancialTrends captures the split screen through the historical parallel Fed watchers keep returning to: the globalization shock of the 1990s hollowed out entire US manufacturing regions faster than retraining programs could respond, feeding years of downstream political and social costs – and the open question hanging over Warsh's AI review is whether this transition, concentrated in clerical and administrative roles rather than factory floors, moves fast enough to catch policymakers just as flat-footed the second time.
Turner's own trajectory illustrates the more optimistic case in granular detail. Here Now Health's Medicaid certification across three states required navigating regulatory, billing, and compliance processes that would traditionally demand either significant capital to hire specialists or years of institutional experience Turner did not have; she has described using AI tools iteratively throughout that process, from drafting the initial business plan to preparing for investor conversations she had never rehearsed before.
The 16 jobs Here Now Health has created sit on the creation side of the AI employment ledger that Fed officials weighing net employment effects must account for alongside the automation-driven losses dominating the current debate. Key To Financial Trends treats the 16 jobs as the concrete, measurable output of an AI-assisted entrepreneurial path that would not have existed without the tools Turner used – a small number in the context of the broader labor market, but a data point that matters precisely because it represents the category of job creation that models built around displacement alone are structurally ill-equipped to anticipate or count.
