Another week, another trillion-dollar AI forecast. Morgan Stanley pegs the upside at $16 trillion in added market value for the S&P 500, while McKinsey estimates AI could contribute $4.4 trillion in annual value to the global economy. These headline numbers are eye-popping. But behind them lies a harder truth: Productivity gains don’t automatically translate into shared prosperity.
If history is any guide, it won’t be the cashier, warehouse associate, or health care call-center representative who reaps the early rewards. Nor the 70 million Americans skilled through alternative routes rather than a bachelor’s degree. The first beneficiaries are more likely to be highly paid professionals who can purchase these tools, along with firms and shareholders with the resources to deploy them at scale. For all the talk of AI productivity, without guardrails, this technology risks amplifying the inequities already shaping our economy.
And indeed, this divide is already emerging. The International Monetary Fund estimates that 40% of jobs worldwide could be affected by AI. In advanced economies, some roles will be augmented and grow more productive, while others may face reduced demand and wage erosion. That’s a recipe for uneven gains.
Real-world patterns bear this out as well. In its first Economic Index, Anthropic found AI use clustering in mid- to high-wage occupations such as programmers and copywriters, while workers in low-wage jobs showed far less uptake. In other words, the people best positioned to benefit from AI are the ones already using it to enhance their productivity.
State governments are scrambling to respond too. Code for America’s recent Government AI Landscape Assessment found that while states like North Carolina, New Jersey and Utah are advancing in governance, workforce training remains underdeveloped nationwide. In too many places, we’re piloting AI tools without building the human capability to use them well and widely.
Pennsylvania’s pilot with OpenAI illustrates both promise and pitfalls. Hundreds of employees across agencies used ChatGPT to automate drafting, summarizing, and search. Staff reported saving about 90 minutes per day. Yet the biggest gains accrued to analytical workers already fluent with technology. Entry-level and lower-wage staff saw little benefit — and in some cases felt new pressure to “keep up” without adequate support.
Here lies the paradox. On one hand, research shows AI can significantly boost productivity, especially for less-experienced workers who adapt quickly with a digital co-pilot. On the other hand, the very entry-level tasks through which entry-level workers build skills are also the easiest to automate, thinning the rungs of the career ladder just as more workers are asked to climb it.
So how do we ensure AI accelerates, rather than undermines, economic mobility?
First, treat worker capability as infrastructure. The White House’s new AI Action Plan and recent OMB guidance aim to scale responsible AI use, grow the workforce, and reduce procurement barriers. Those are essential steps. But mandates alone won’t deliver broad-based benefits. Public sector leaders should pair adoption with investments in frontline training, hands-on mentorship and paid time to practice — especially in service, care and clerical roles that rarely get early access. Public reporting on where AI saves time (and whose time) would help ensure gains don’t pool at the top.
Second, design adoption around learning, not just speed. In Pennsylvania, the biggest time savings came from summarizing and drafting — the very tasks through which entry-level employees often learn. If agencies strip those tasks away, they may hit efficiency targets while starving the pipeline of tomorrow’s experts. Guardrails could help: Require peer-review steps where AI drafts become teachable moments, or reserve certain first-pass tasks for entry-level staff with AI as a coach. Federal support for AI Centers of Excellence and regulatory sandboxes should be used not only to test new tools, but also to test new approaches to workforce development specifically.
Third, invest earlier in the pipeline. Exposing students to AI through pre-apprenticeships in middle and high school could create pathways into fields where entry-level jobs are at risk of vanishing. If traditional rungs of the career ladder disappear because of AI, we’ll need to build new ones. Philanthropy can help accelerate this shift by continuing to fund programs in schools, nonprofits and community colleges that expand AI exposure while cultivating durable skills.
Finally, we need to rethink what productivity means. Saving 90 minutes with a chatbot is useful. But converting those minutes into better services, faster case resolution, warmer classrooms or more humane care is what genuine progress should look like. In the AI economy, the metric that matters shouldn’t be keystrokes avoided; it should be increased economic mobility.
AI can help us build that kind of economy. But it won’t happen by default. If leaders in government, philanthropy, and business put equity at the center of adoption — funding capability where it’s thinnest, preserving pathways into skilled work, and measuring who benefits — we can ensure productivity leads to economic opportunity. If we don’t, the outcome will feel familiar: enormous gains accruing to the few, while too many others will see their prospects narrow.
The AI productivity story is real. Whether it multiplies opportunity or inequality is up to us.
Jonathan Hasak leads U.S. public-sector partnerships at Coursera, helping governments, workforce boards, community colleges, nonprofits, and philanthropies scale AI-enabled upskilling. With 15+ years at the intersection of workforce, policy, and technology, he has led 7-figure workforce initiatives and advised governors on future-of-work policy. His writing has appeared in The Washington Post, Forbes, The Hill, and EdSurge.
This article first appeared on August 25, 2025.
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