An Industrious Workforce for the AI Decade

Reindustrialization requires a comprehensive workforce development strategy. In critical sectors like manufacturing, construction, health care, and energy, productivity gains depend on workers who can implement, oversee, and integrate new systems into production processes and workflows. That pairing is not automatic, and it requires more than just acquiring new technology. It requires recognizing that effective workforce development is a vital facet of successful technological development, and redesigning workforce development accordingly.

To work effectively in a technological environment that develops rapidly (especially in the age of AI and robotics), workforce development needs to be embedded in the disruptive innovation that the American tech sector pursues. That requires a compact: policymakers must design the right incentives; employers must invest in pipelines and hiring from them; and workers must be able to access paid pathways into real, durable careers. Policymakers can lay the groundwork by targeting support to firms where the needs are most acute and that are closest to the deployment of new technologies, small- and medium-sized enterprises in the tech sector.

It is imperative to reindustrialize the American workforce, not just the nation’s physical capital, so the United States can rebuild a labor market where rapid innovation with rising wages go hand-in-hand. As AI technologies transform the economy, the central question is not whether AI displaces work, but whether AI still depends on people. Done right, redesigned workforce development will be a critical mechanism for ensuring that can happen.

An Industrious Workforce for Critical Industry

Speed matters for competitiveness and resilience. America’s adversaries are mounting national initiatives—China’s 2025–2027 vocational plan targets calls for training more than 30 million workers as part of its strategy to build a workforce capable of deploying AI across its economy. Moving with the necessary swiftness on a smarter American workforce strategy will require challenging two camps that often talk past each other: tech optimists who assume labor supply will adjust on its own, and workforce traditionalists who assume reskilling alone will be enough to get displaced workers back on their feet. Both miss the imperative—and the opportunity—to move workers into higher-productivity roles at the same speed companies deploy new technologies.

In both the tech sector and across industry, labor remains a persistent production constraint, with manufacturing facing 2.1 million unfilled jobs by 2030 and tech job demand projected to grow at twice the rate of the overall workforce. Traditional “train first, employ later” strategies—marked by semester calendars, multi-year credentials—cannot keep pace with six-month product cycles. The cycle time of technology deployment in critical sectors essential to national security, including manufacturing, construction, health care, and energy, systemically outpaces workforce development cycles. This creates a structural mismatch where AI deployment cycles of three to six months do not align with workforce development timelines that remain anchored to traditional education models.

Countries like Singapore have developed six-to-nine month AI apprenticeships with job placement rates of over 90%, while the United States still relies primarily on academic programs and boot camps that range from 12 weeks to two years and lack direct employer integration. This mismatch can become self-reinforcing. The further workforce development falls behind, the stronger the claim that workers “do not have the skills,” and the weaker the incentive to invest in them. Workers can spend years accumulating debt in postsecondary programs, only to find the target has moved by the time they complete training. A redesigned system must connect emerging applications directly to the workers who will deploy and sustain them.

The need spans software and the physical economy. Skilled trades build and maintain data centers and energy systems, where electrical work accounts for 45-70% of construction budgets—a concentration that makes projects uniquely dependent on skilled electrical workers. In advanced manufacturing and defense, operators still commission, test, and certify autonomous systems before they scale—Boeing’s experience with its 777X fuselage robots, which required abandonment after four years when skilled mechanics proved more reliable, exemplifies this continued dependence on human expertise. These are not the jobs most imagine when they hear “tech,” yet labor markets tied to them remain the production constraint—the people and skills that make deployment productive.

How the Tech Sector Perceives Labor

These workforce bottlenecks are most acute when technology is first developed and deployed. To design responsive workforce systems, we must understand how different players in the tech sector engage with labor.

Inside the tech sector, two poles matter for workforce design: Big Tech (platforms and hyperscalers that set standards and buy at scale) and Little Tech (described by some as startups that are small, fast-moving, and innovation-led). The proving ground lives with Little Tech; the purchasing power that can scale what works lives with Big Tech. Startups begin with a fresh slate and every other disadvantage; that is why they are ground zero for establishing new roles and standards.

The tech sector has often scaled products first and managed externalities later: social media companies shipped engagement mechanics before they got a handle on how to manage their platforms or address child safety concerns; ridesharing upended local transport and labor standards, then fought city by city over safety and classification; autonomous-vehicle pilots ran on public streets until serious incidents forced hard resets. Building complements in advance is the wiser approach so adoption runs faster with fewer fractures. This is especially true of workforce development.

Yet training and apprenticeships have not been treated as core strategies in the last decade. Where tech has attempted to engage in training, results have been disappointing. Buyers are fragmented, and paybacks are slow; boot camps scaled marketing more than outcomes (especially when not tightly integrated with employers); and many firms have treated workforce spending as philanthropy, public relations, and an extension of corporate social responsibility rather than a core talent strategy. The path forward must be different: make workforce development a core business function, tie public support to verified wage and retention outcomes, and require employer co-investment. To capture AI’s productivity gains, employers and policymakers must elevate this approach to training and apprenticeships to strategic priorities.

A Big Workforce Development Redesign Should Start Small

The conversation is starting to change as firms realize that making AI productive depends as much on operators and technicians as on software engineers. Across manufacturing and energy, founders are recruiting a different type of startup workforce that resembles the skilled trades more than traditional tech companies.

The intersection of the blue-collar workforce with technology is happening across multiple domains and is deeply connected to the proliferation of AI. The growing demand for electricians, pipefitters, sheet metal workers, and finishing trades to develop, build, and maintain data centers across the United States is creating space for a different technology sector workforce development conversation than in recent decades. Big Tech’s capital expenditures are already pulling skilled trades into data center build-outs via registered apprenticeships; both Amazon and Google have announced noteworthy training programs tied to data center investments.

But policy should start where tools most immediately meet work: small- and medium-sized enterprises (SMEs) and startups. Little Tech—startups in the sense of small, fast-moving, innovation-led firms—is where a reindustrialized workforce can take off through a new generation of firm-embedded training and apprenticeships that will move operators, technicians, and line supervisors into AI-enabled roles that evolve alongside the technology they deploy.

Policymakers and industry leaders must focus on Little Tech, where the next generation of startups applying these technologies to manufacturing, healthcare, and energy offers fertile ground for designing and testing new workforce development strategies that embed workers at the point of adoption.

Starting small may sound counterintuitive, but it is critical. SMEs employ nearly half of American workers and account for approximately 43% of the nation’s GDP. They also tend to move faster and are in closer proximity to local labor markets. In other nations, they anchor workforce systems; in the United States, they can serve as the chassis for reindustrializing the American workforce, as they move quickly, hire locally, and can standardize what works.

Rather than a lagging institution to help workers recover from dislocation, a redesign of American workforce development should implement policies and incentives to put more workforce development within firms themselves on the front end of innovation, in partnership with workers and education and training institutions. Starting with startups, where the needs are most acute and the bulk of technical workforce employment exists, makes the most sense.

A Blueprint for a New Workforce Strategy

There are several key steps that industry, policymakers, and worker organizations can take to bring a new workforce development strategy to life for the coming AI moment. Matching rhetoric to results cannot be achieved solely through cuts and consolidation. The Trump administration’s executive actions to articulate a national AI strategy, as well as a recent joint workforce development strategy by the Departments of Labor, Commerce, and Education, offer a good start, but more must be done. Targeted, smart public investments and proactive policymaking to align tech sector incentives are needed to realize the reindustrialization of the American worker. Below are a few options.

• Building employer collaboratives. In critical sectors, SMEs and startups should pool demand, set and signal skill standards, sponsor shared apprenticeships, and co-own outcomes with public co-funders. These collaboratives make small firms big enough to act on the workforce at scale. Reinvigorating federally supported Manufacturing Extension Partnerships and Manufacturing Institutes will offer platforms for collaborative formation among smaller firms, and could be modeled for other critical industry sectors.

• Paying for outcomes. The federal government should offer refundable training credits for firms under 500 employees that run cohort apprenticeships or traineeships in defined roles. These credits could advance a portion at the cohort’s start, and vest the balance based on outcomes: training completion, employee retention and wage lift. Senator Tom Cotton’s American Workforce Act offers a directionally similar model, tying federal vouchers and employer bonuses to verified job placement and wage gains. Enabling states and regions that have committed industry partners and real strategies to more flexibly use workforce dollars under a reauthorized Workforce Innovation and Opportunity Act to support these outcomes-based strategies will also help.

• Making apprenticeship the default. The Department of Labor should streamline its apprenticeship paperwork requirements, ensure its templates are vendor-neutral, and approve applications far more rapidly. It should also offer incentives for intermediaries that deliver program enrollments and completion for small firms. These sensible reforms, alongside continued investment, can make apprenticeship expansion goals real, and should be elements of a National Apprenticeship Act.

• Using the power of the public purse to scale. Government procurement decisions and federal, state, and local public investments decisions should prefer successful workforce strategies and outcomes. The Small Business Administration should provide working-capital lines with interest rebates for smaller firms to compete for major public procurements and investments that deliver clear workforce outcomes.

• Embracing higher education to build domestic expertise and capacity. Community colleges and extension partners should adopt a focus on firm-embedded pathways, and deploy instructors and master technicians accordingly. Guidelines for newly authorized Workforce Pell Grants should support these ends; aligning educational missions to workforce outcomes through Career and Technical Education Experimentation grants can help as well.

Conclusion: A Compact for an AI Decade

America is rebuilding its industrial capital base; now it must reindustrialize its workers. The compact is simple. Policymakers provide the funding, establish procurement preferences for skills-first vendors, and maintain public transparency regarding both. Employers and startup founders publish outcomes and hire from pipelines validated by completion, wage lift, and retention. Workers get paid pathways into real roles as adoption accelerates.

We can start with startups and institutionalize what works, publishing outcomes so that the best models can scale and the weakest ones are retired. The result will be a more resilient economy where AI adoption simultaneously increases throughput and wages.