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What a $151 billion demand signal teaches economic and workforce developers about certainty

Economic and workforce developers live by an unforgiving rule: before a region can train anyone, someone must say which jobs, how many, where, and when. The sharpest current test of that rule comes from an unexpected direction, the federal government’s new homeland missile defense initiative and the contract vehicle behind it. The story is set in defense, but the lesson travels to any region, any industry, and any training dollar that has to be spent before the future announces itself.

On January 15, the Missile Defense Agency completed the last of three award tranches under SHIELD, the Scalable Homeland Innovative Enterprise Layered Defense contract, the $151 billion vehicle that will carry most of the Trump Administration’s Golden Dome missile defense initiative. More than 2,400 companies now hold a position on it, and across the missile defense industrial base the hiring has already begun. The workforce development system cannot answer this demand signal, and the reason is not the one usually given. The problem is not only that the workers are missing. The problem is that the demand has no shape, and training systems can only aim at shapes.

The Shortage Everyone Can See

The familiar half of the story is well documented. The most recent Aerospace Industries Association (AIA) and McKinsey (2025) workforce study counts 2.21 million workers in the American aerospace and defense sector and reports attrition holding near 15 percent, roughly double the average across other United States industries. Seventy-six percent of member firms report sustained difficulty hiring engineering talent; 56 percent report the same for the skilled trades. Against the production surge now underway, an analysis published by Voyager Technologies (2026) puts the sector’s skilled technical deficit above 200,000 workers and estimates that the labor pool serving programs like Golden Dome would need to grow 30 to 40 percent to meet demand.[1] These figures are contested at the margins, but no serious observer disputes the direction. If Golden Dome were an ordinary program, this would be an ordinary, if large, shortage story.

The Structure Few Are Reading

Golden Dome is not an ordinary program, and the difference sits in the acquisition mechanics. SHIELD is a ten-year, indefinite-delivery, indefinite-quantity contract. The $151 billion is a ceiling, not an appropriation; the agency obligated no funds at the base award, and revenue flows to a vendor only when it wins a task order. The awards themselves went out in three tranches over six weeks: 1,014 companies on December 2, another 1,086 on December 18, and a final 340 on January 15 (Defense One, 2025; Defense Security Monitor, 2026). The money actually in hand is real but far smaller: roughly $24.4 billion provided through the 2025 reconciliation act and $13.4 billion in the fiscal year 2026 defense appropriation, with the administration planning to seek some $17 billion more through reconciliation next year (Federal News Network, 2026a, 2026b). Congressional appropriators, meanwhile, complain that they have received no master deployment schedule, no cost schedule, and no finalized system architecture (Federal News Network, 2026a; see also Defense One, 2026). If the members who fund the program cannot learn where and when the work will occur, a community college dean has no chance.

What Training Systems Need to Aim At

This matters because every workforce program that has ever delivered at scale was built against knowable demand. The scholarship names the same failure mode: MIT’s Task Force on the Work of the Future concluded that American training institutions are capable but fragmented, and that workers and firms alike underinvest in skills when the return is uncertain (Autor et al., 2022).[2] Consider the counterexample from submarine country. The Navy awarded Electric Boat a $15.38 billion contract modification in March (Reed, 2026); the yard committed to 8,000 hires this year (InsideDefense, 2026); and the New England training partnership that feeds it passed 10,000 workers trained on July 1 (turnto10, 2026; What’s Up Newp, 2026).[3] That pipeline works because five things are known: the employer, the occupations, the counts, the location, and the dates. Golden Dome inverts every term. The employer is any of 2,400 firms. The occupational mix depends on which architecture survives, and there is no finalized architecture. The locations follow task orders that have not been issued. The dates are whatever the ceiling becomes when it turns into orders. A regional consortium can train welders against a submarine contract. No one can train anyone against a ceiling.

What Firms Do in the Meantime

In the absence of shape, firms that can afford to pre-position are doing so with their own capital. True Anomaly raised $650 million in April, plans to nearly double its workforce to 500 employees by the end of the year, and intends to grow its factory footprint from 140,000 square feet toward two million over four years (CNBC, 2026).[4] Northrop Grumman has pointed part of a $1.65 billion capital expenditure budget at readying its Space Park campus for rapid production cycles (Voyager Technologies, 2026). These are rational hedges, and they have a predictable labor market consequence: firms that cannot see demand hire ahead of it, and firms that cannot hire ahead of it recruit from one another. With sector attrition already near 15 percent, much of the apparent hiring boom is the same cleared, experienced workers cycling among badge colors at rising wages. The deficit does not close, it circulates.

The Regional Gamble

The same uncertainty falls hardest on regions. Golden Dome work will eventually concentrate somewhere; the obvious candidates, given agency and prime contractor footprints, include Huntsville, Colorado Springs, Southern California, and Florida’s Space Coast, and firms like True Anomaly are already placing their factory bets in Colorado. But no governor can know today whether their state is on that list, and the list itself will be written one task order at a time. A steady-state shortage lets a region invest where its employers already are, the way southern Arkansas built training around a munitions cluster that has expanded sharply in the past three years, with Lockheed Martin, General Dynamics, and the Rafael-Raytheon joint venture R2S all adding capacity on a defense footprint dating to the 1940s (Arkansas Money & Politics, n.d.; RTX, 2025).[5] A shapeless shock forces the opposite calculation: gamble scarce training dollars on demand that may materialize three states away, or wait for certainty and forfeit the first-mover position that regional economic developers spend careers pursuing. Either choice is defensible. Both are expensive. And the cost is borne by exactly the institutions, community colleges and state workforce agencies, least able to absorb a wrong bet.

The Case for the Ceiling

The acquisition logic behind SHIELD deserves its due.  A flexible, broadly competed vehicle lets the government move at commercial speed, avoids betting the program on a single architecture that intelligence or technology may overturn, and keeps two thousand innovators in the game rather than three primes.  Judged purely as procurement reform, it may prove to be good design; that question belongs to others.  The point here is narrower.  Flexibility for the buyer is uncertainty for everyone downstream, and uncertainty does not disappear when it is exported.  It comes to rest on the smallest actors in the system: the supplier deciding whether to add a second shift, the community college deciding whether to stand up a program, the technician deciding whether the job will exist in three years.  The Harvard Project on Workforce has mapped that system as it actually exists: thousands of mostly small, local training providers, exactly the institutions least equipped to absorb the risk that flexibility exports (Project on Workforce at Harvard, 2023).  The federal government has spent decades asking training systems to be demand-driven.  Golden Dome is what happens when the demand declines to specify itself.

What Mobilization Has Always Required

There is precedent for both paths.  When community colleges and certification bodies aligned curricula and credentials to the Artemis and Commercial Crew programs, from the Louisiana college that mapped its coursework to the technician tasks at the plant building the Artemis core stage to the FAA-recognized certifications anchoring Space Coast apprenticeships, the alignment worked because there were named vehicles, named sites, and named dates to align to (NASA, n.d.; Nunez Community College, n.d.; SpaceTEC, n.d.).[6]  The Strategic Defense Initiative of the 1980s offers the other lesson: a loud national demand signal that never resolved into stable production, and left behind little durable workforce infrastructure.  Which precedent Golden Dome follows will be decided less by the appropriated billions than by the task orders, because task orders are where a ceiling acquires an address, an occupation, and a start date.  The industrial base has been asked to mobilize, and it is trying.  But mobilization has never been a function of enthusiasm or even of money.  It has always required someone to say what, where, and when.  Until the task orders say so, the 200,000-worker question is not hard to answer.  It is impossible to ask precisely.  You cannot train against a ceiling.  You can only train against an order.

Footnotes

• [1] Voyager Technologies is a defense technology firm with a commercial interest in Golden Dome, and its figures characterize Aerospace Industries Association and McKinsey data.  The underlying study reports a 2.21-million-person workforce, attrition near 15 percent, and hiring difficulty rates of 76 percent for engineering and 56 percent for the skilled trades; it does not itself state a 200,000-worker deficit.  Treat these estimates as the analyst’s synthesis, not a study finding.
• [2] The MIT Task Force on the Work of the Future reported in 2020; the book is cited here for its durable institutional findings, not for current labor market data.
• [3] The partnership, run by the Southeastern New England Defense Industry Alliance (SENEDIA), holds a $98.3 million contract to train roughly 8,600 more workers through 2029.
• [4] Company-announced targets, not audited filings or realized headcount.
• [5] The R2S joint venture opened its East Camden plant in November 2025 and holds a $1.25 billion Tamir production contract (RTX, 2025); General Dynamics and the Army opened a $110 million load, assemble, and pack facility in April 2025.  See also the companies’ pages: Lockheed Martin (n.d.), General Dynamics Ordnance and Tactical Systems (n.d.), and Rafael Advanced Defense Systems (n.d.).
• [6] The Space Coast credentials are anchored by SpaceTEC, host of the Space Coast Consortium Apprenticeship Program (n.d.).  The alignment was built by the colleges and certification bodies against the programs’ known requirements, not directed by NASA.

References

The race to build data centers is reshaping local economies faster than almost any other real estate trend. From 2023 to 2024, data center construction spending increased by 70%, more than triple the rate of any other property type. [i] The sector is drawing billions in venture capital and now consumes up to eight percent of total U.S. electricity.[ii]  

For local communities, that growth is arriving with both a windfall and a set of tradeoffs: improved fiber and broadband networks, short-term and permanent job gains, and large tax receipts on one side, with infrastructure demands, public health concerns, and general local opposition on the other. Data centers are coming; the question is how economic developers can help communities capture the benefits and manage the tradeoffs.

Why are there so many new data centers?

A data center is a facility used to house large computer systems.[iii] There are many types of data centers hosting different services, but today’s headline centers are mostly used for cloud computing, including massive hyper scalers, which host at least 5,000 servers and can be 60,000 square feet in size.[iv] Cloud computing is an on-demand computing service, which can be rented by consumers. These centers are used for various IT purposes like storage, networking, and software deployment.

Separately, AI training data centers use cloud-computing’s on-demand infrastructure with specialized hardware and storage to train Large Language Models (LLMs), like ChatGPT. The AI market is booming and driving data center growth through investments by such companies as Microsoft, OpenAI, Google, and AWS. As these companies search for sites, EDO leaders are often the ones asked to explain what a data center will mean for a community. This piece is built for those conversations: a guide to the benefits, costs, and tradeoffs that come up when a project is proposed.

What are the potential benefits?

New tax revenues are the most significant benefit of hosting a data center. Depending on state and local tax regimes, new centers may pay real property taxes, personal property taxes, business income taxes, and sales and use taxes. Revenue can particularly surge in states which impose a personal property tax.

Some communities also benefit from developer philanthropy. Though unlike tax receipts, this giving is rarely guaranteed. A good example is Meta’s Data Center Community Action Grants program, which funds local STEM education and community projects.[v] A Meta facility in Huntsville, Alabama contributed to broadband expansions that brought internet access to nearby rural communities.[vi] While some communities benefit from data center philanthropy, , most charitable giving is contingent on specific Public Benefit or Community Benefit Agreement (PBA, CBA). These agreements typically stipulate investments in local workforce, agreements to limit the use of resources like water and energy, and investment in local funds. One such agreement in Cedar Rapids, Iowa required data center developers to invest in the local Community Betterment Fund, administered by the city council.[vii]

While data centers offer job gains, the highest number are related to the construction stage with permanent jobs, while high-paying, typically number less than 100 per site. Moreover, the scale depends on facility type and may take years to materialize. A Brookings analysis found that counties receiving their first large data center saw total private employment grow by 2,000 to 4,000 jobs over six years. Construction and IT sectors see the sharpest gains, but the overall employment effect is modest relative to the scale of investment these facilities represent.[viii]

What are the drawbacks?

Perhaps the most urgent challenge posed by data centers is energy use. A single modern AI data center can use energy equivalent to 100,000 homes over a year, and even larger data centers are entering the pipeline further increasing the demand.[ix] This creates a major challenge for utilities, community leaders, and economic developers, especially if this power demand results in higher costs for other consumers.

Data centers can also affect local air quality. To avoid using congested municipal power grids, some data centers utilize on-site natural gas turbines for power. These turbines – as well as diesel backup generators[x] at grid-connected centers – produce fine particulate matter and nitrogen oxides.[xi] The U.S. Environmental Protection Agency identifies both[xii]^{, [xiii]} as causes of harm to human health, but it remains difficult to isolate how much of these pollutants any single facility adds to a community’s air.[xiv]

As with energy, data centers need large amounts of water to cool their servers. In some cases, the water is taken from municipal sources which can raise water prices for residents. However, water consumption depends heavily on the type of cooling a data center uses. Centers that use closed-loop or immersion cooling consume less than those using evaporative cooling.[xv] Large data centers, like those that train LLMs for AI, can use as much as 5 million gallons per day.[xvi]

Taken together, the energy, air quality, and water demands of data centers represent costs that often fall on residents rather than developers. That doesn’t mean communities should turn data centers away, but it does mean the terms of any agreement matter enormously.

Are data centers right for your community?

Like every expansion project, communities should treat every transaction as a negotiation ensuring that any tradeoffs are more than offset by project benefits. Because demand for sites is high, developers need communities as much as communities need them, and that gives community leaders room to negotiate. Some Community Benefit Agreements have required developers to invest in local workforce programs, infrastructure, and community funds. Others have secured commitments to reduce grid electricity demand and water consumption.

What undermines this leverage is the incentive package. Many communities are offering tax exemptions and other concessions to attract developers, which can minimize potential tax revenue that makes data centers attractive. This is a particular risk given the relatively low job creation for residents.[xvii]  

The financial case for data centers rests heavily on their tax contributions, and those contributions can erode quickly when lofty incentives are on the table.

Major Takeaways

Data center development is moving fast, and the communities best positioned to benefit are those that have assessed their leverage, tax structure, and infrastructure capacity before entering negotiations.

• Tax revenue can be substantial, but incentives offered to attract developers can offset those gains
• Data centers are not major job creators and generate modest local wage increases, so financial benefits largely depend on tax structure
• Energy and water demands can raise costs and health risks for residents, particularly in densely populated areas
• Community Benefit Agreements that require developers to invest in the community including workforce programs and infrastructure
• The type of data center matters: cooling systems, on-site power generation, and facility size all affect local impact

Data centers are less about job creation and more about tax base expansion, infrastructure investment, and long-term economic positioning. For communities, the real value comes when leaders align these projects with broader development goals while carefully managing tradeoffs like energy demand, land use, and community concerns. EDO leaders can use this information to help communities enter negotiations with a clear picture of what a given project will actually cost and deliver for their community before extending incentives.

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Sources

[i] https://americanedgeproject.org/wp-content/uploads/2025/12/Americas-AI-Surge-Powering-Growth-in-Every-State.pdf

[ii] https://americanedgeproject.org/wp-content/uploads/2025/12/Americas-AI-Surge-Powering-Growth-in-Every-State.pdf

[iii] https://www.congress.gov/crs-product/R48646

[iv] https://www.ibm.com/think/topics/data-centers

[v] https://about.fb.com/news/2025/11/expanding-meta-data-center-community-action-grants-program/

[vi] https://www.brookings.edu/articles/why-community-benefit-agreements-are-necessary-for-data-centers/

[vii] https://ipmnewsroom.org/how-do-data-centers-benefit-the-places-where-theyre-built-local-mayors-give-mixed-reviews/

[viii] https://www.brookings.edu/articles/new-evidence-on-data-center-employment-effects/

[ix] https://www.wri.org/insights/us-data-center-growth-impacts

[x] https://ecology.wa.gov/air-climate/air-quality/data-centers

[xi] https://www.epa.gov/sites/default/files/2020-10/documents/c03s01.pdf

[xii] https://assessments.epa.gov/isa/document/&deid=310879

[xiii] https://www.ncbi.nlm.nih.gov/books/NBK588512/

[xiv] https://www.vpm.org/news/2025-12-17/virginia-data-centers-diesel-backup-generators-deq-loudoun-turner-dowd

[xv] https://www.brookings.edu/articles/ai-data-centers-and-water/

[xvi] https://www.brookings.edu/articles/ai-data-centers-and-water/

[xvii] https://www.brookings.edu/articles/new-evidence-on-data-center-employment-effects/

Artificial intelligence (AI) is being adopted faster than any technology in modern history.  Within two years of widespread availability, 39.4 percent of American adults were using generative AI tools, a pace that outstripped both the personal computer and the Internet at comparable stages (Deming, 2024).  Yet the defense industrial base (DIB), the network of manufacturers that produces everything from fighter jet components to submarine hull plates, has no structured workforce training program to match this adoption curve.  The technology is arriving.  The people who need to use it are not ready.

That disconnect is the central finding of a 32-source analysis conducted by the Center for Regional Economic Competitiveness (CREC) under a cooperative agreement with the Department of Defense (DoD) Manufacturing Technology (ManTech) Program Office.  The research draws on peer-reviewed studies, policy documents, industry analyses, and workforce data to answer a straightforward question: what does AI actually mean for the defense industrial base over the next five years?  The answer is more nuanced, and more urgent, than most of the conversation around AI in defense would suggest.

The Workforce Problem Is the AI Problem

The bottleneck in defense manufacturing is not access to AI.  It is the availability of workers who can use it.  Manufacturing labor productivity has been nearly flat since the mid-2000s despite decades of automation investment.  The missing ingredient is not more technology but more skilled humans: process engineers, electricians, robotics specialists, maintenance technicians, and quality inspectors.  Deloitte projects 3.8 million net new manufacturing jobs by 2033.  The question is not whether humans are needed but whether enough of them will be available with the right skills.

AI tools produce a median 25 percent productivity improvement when used effectively (Sadun, 2025), but that word effectively carries enormous weight.  Without structured training and integration support, adoption produces uneven results and worker frustration rather than sustained gains.  The Computer Numerical Control (CNC) revolution of the 1970s and 1980s provides the relevant precedent.  Plants that adopted CNC were 75 percent more likely to use problem-solving teams, twice as likely to offer technical training, and held regular shop floor meetings at substantially higher rates than non-adopters (Deming, 2024).  Automation changed the nature of skilled work but did not eliminate the need for it.  AI will be no different.

AI Is Hollowing Out the Talent Pipeline

One of the most consequential findings in the research is a dynamic that almost no one in defense manufacturing is discussing: AI is absorbing the routine tasks that junior workers need to develop into senior experts.  Anthropic’s own internal study found that while engineer productivity gains grew from approximately 20 percent to 50 percent year over year, senior engineers reported growing concern about skill atrophy among junior staff.  Junior engineers stopped asking questions of mentors because AI answered faster (Orrell, 2025).

In defense manufacturing, this pattern is particularly dangerous.  Senior machinists, welders, quality inspectors, and maintenance technicians acquire their expertise through years of hands-on practice that begins with routine work.  If AI absorbs those tasks before workers have the chance to learn from them, organizations gain short-term productivity at the cost of a long-term senior talent shortage.  The pipeline narrows even as current output rises.

The Technology Is Not Ready for What Defense Demands

Enthusiasm for AI in manufacturing often outpaces what the technology can reliably deliver.  AI-powered Automated Optical Inspection (AOI) systems currently achieve 60 to 70 percent accuracy, a rate that is insufficient for defense applications where tolerances are measured in thousandths of an inch.  On the agentic AI front, the best-performing architecture for manufacturing decision support, a Retrieval-Augmented Generation (RAG) system, achieved 77.89 percent accuracy compared to 52.37 percent for a baseline Large Language Model (LLM).  Better, but not defense-grade.

The implication is straightforward: human-in-the-loop oversight will remain essential in defense manufacturing for the foreseeable future.  Planning for AI in this sector means planning for augmentation, not autonomy.  Workforce development strategies that assume AI will replace human judgment are building on a foundation that does not yet exist.

Implementation Matters More Than the Technology

The same AI technology can produce opposite outcomes depending on how it is deployed.  Where AI is implemented primarily for surveillance and performance monitoring, workers report reduced autonomy, lower morale, and increased turnover.  Where workers are involved early in design and deployment, 96 percent reported increased job satisfaction when freed from monotonous tasks to focus on higher-level work.  Collaborative robots (cobots) can increase worker productivity by up to 85 percent when paired with proper training (International Federation of Robotics).  For defense manufacturers, the management decision matters more than the technology decision.  Procurement of AI systems without parallel investment in organizational change is a misallocation.

The System Is Not Built for This Speed

AI capabilities evolve on timelines measured in months.  Defense acquisition spans years.  Secretary of Defense Pete Hegseth has publicly stated that the current acquisition system is archaic and must shift from decade-long development cycles to rapid iteration.  Small and mid-size defense suppliers cannot wait for multi-year contract modifications to adopt AI tools that may be obsolete by the time approval comes through.

Meanwhile, the data needed to make smarter workforce investments now exists.  CREC and RTI International, under the ManTech cooperative agreement, produced the first national dataset measuring the Critical Technology Area (CTA) workforce in aerospace and defense manufacturing, delivered in December 2025.  This dataset maps supply and demand for skills in AI, autonomous systems, additive manufacturing, and other priority technology areas across the top 30 aerospace manufacturers.  The data confirms significant gaps between CTA skill supply and employer demand.  Yet no federal workforce investment program currently uses it.  The approximately $6 billion the federal government spends annually on workforce development through the Workforce Innovation and Opportunity Act (WIOA), the Perkins Act, and grants from the Economic Development Administration (EDA) and the National Institute of Standards and Technology (NIST) is allocated without reference to defense-specific workforce intelligence.

The Untapped Pipeline

Approximately 200,000 service members transition out of the military each year with technical training, leadership experience, security clearances, and familiarity with defense systems and culture.  They represent the single most qualified talent pipeline available for defense manufacturing.  Yet persistent mismatches between military occupational specialties and civilian job classifications, inadequate credentialing bridges, and employer unfamiliarity with military skill sets prevent this pipeline from flowing at scale.  Community colleges, which should function as the primary bridge between military training and civilian manufacturing careers, vary widely in their alignment with local defense labor markets.  The best performers, such as Dallas College, Wake Tech, and Miami Dade, maintain active employer relationships and anticipate skill demand.  Most do not (Fuller, HBS).

AI is a necessary but insufficient condition for a competitive defense industrial base.  The binding constraints are workforce readiness, organizational capacity to integrate new tools, acquisition pathways that match the pace of technology change, and analytical infrastructure that connects federal investment to measurable outcomes.  Technology alone will not resolve any of them.

Deliberate action looks like this: structured AI training for the existing manufacturing workforce.  Redesigned development pathways that ensure junior workers still build expertise even as AI absorbs routine tasks.  Acquisition reform that matches technology timelines.  And federal investment guided by actual workforce data rather than assumptions about where the gaps are.  The data exists.  The talent pipeline exists.  The question is whether the institutions responsible for the defense industrial base will use them before the window closes.

Sources

Deming, D. (2024). “The Rapid Adoption of Generative AI.”  NBER Working Paper.

Deming, D. (2024). “How Computers Turned Machinists Into Problem-Solvers.”

Fuller, J. (HBS). “Why the Skills Gap Persists.”  Harvard Business School.

Linder, B. (2026). “AI Won’t Save Manufacturing.”  Forbes.

Orrell, B. (2025). “What Anthropic’s Internal Study Suggests About the Future of Work.”  American Enterprise Institute.

RTI International / CREC / ManTech (2025). “Measuring the Size and Dynamics of the CTA Workforce.”

Sadun, R. (2025). “Reskilling the Workforce With AI.”  Harvard Business School.

“Agentic AI for Smart Manufacturing” (2025).  SSRN.

“AI and Job Quality: Insights from Frontline Workers” (2024).  Partnership on AI / SSRN.

Global events are reshaping the environment in which manufacturers operate, accelerating defense production and increasing pressure across supply chains. Workforce systems must be ready to respond.

Demand signals are sharpening.
Defense suppliers are preparing for higher production levels, and many will need more technicians, machinists, quality specialists, and production workers. Intermediaries should be working with manufacturers now to identify surge occupations, map regional training capacity, and reduce the time between hiring demand and credential completion.

Training timelines must accelerate.
Defense production schedules move faster than most traditional workforce programs. Manufacturers are increasingly looking for short-cycle training, modular credentials, and employer-driven curriculum updates that can move workers into production roles quickly.

Workforce visibility is becoming a competitive advantage.
Manufacturers need real-time insight into workforce availability across regions and supply chains. Intermediaries that can provide labor market intelligence, talent pipeline data, and employer coordination will become essential partners in supply chain readiness.

Partnerships matter more than programs.
No single organization can meet the scale of emerging workforce demand. Intermediaries that connect manufacturers with community colleges, training providers, workforce boards, and regional economic development partners will help firms expand capacity and reduce risk as production ramps up.

For workforce intermediaries, the coming months represent a window to act:

• Work with manufacturers now to map surge occupations and talent gaps.
• Align training programs with emerging technologies and defense production needs.
• Strengthen partnerships across colleges, workforce boards, and industry associations.
• Improve access to workforce data so manufacturers can better understand where talent is available.

At CREC, we are hearing increasing interest from regions and intermediaries looking to strengthen the workforce backbone of defense and national security supply chains.

If you work with manufacturers or workforce programs supporting advanced manufacturing, what are you seeing on the ground right now? Are firms signaling increased demand, supply chain pressures, or new workforce challenges? Where are the biggest talent bottlenecks emerging?

Let’s compare notes and talk about practical next steps. Connect with us.