How Tech Hubs Transform Local Economies

I traveled more than 2,000 miles to see the frontier of America's tech boom up close. It wasn't a billion-dollar chip factory or a secretive Silicon Valley AI lab, but rather a city of just 8,000 people in northeast Oregon. Umatilla was not long ago known for a former chemical-weapons depot nearby, a state prison on the city’s outskirts and the strip clubs once dotting its main drag. But a growing fleet of Amazon data centers has turned the surrounding region into an unlikely nerve center for one of the most expensive infrastructure build-outs in U.S. history. Thousands of construction workers have descended on RV parks and hotels in recent years to build a regional data-center hub for Amazon Web Services. The project pipeline is so long that some newcomers put down roots. Local budgets ballooned with Amazon-linked revenues and taxes from new residents. Business surged for suppliers of concrete and fencing. The need for technicians, electricians and more has helped mint new members of the middle class in a job market previously geared toward manual labor in fields and warehouses and factory work turning the region’s bountiful potato harvests into french fries. Growth has also brought some pain. The costs of housing and child care are rising beyond reach for many blue-collar people. New demand for services and infrastructure has left local governments rushing to keep up. In Umatilla, a public spat over how to manage a deluge of new revenue devolved into a legal fight between the mayor and other city leaders. I had a great time visiting folks in Umatilla and nearby Hermiston, who let me into their lives for a few days to learn about the opportunities that come with a boom--as well as the challenges it might pose for communities elsewhere now vying for a piece of the action. Read my latest The Wall Street Journal feature here: https://lnkd.in/d6GJEgdm

Research Triangle Park (RTP) is one of the best examples of how intentional collaboration can reshape a regional economy. Founded in 1959, RTP helped transform the Raleigh–Durham–Chapel Hill area from a declining textile and tobacco economy into a globally recognized hub for research, technology, and life sciences. By deliberately positioning itself between Duke University, UNC–Chapel Hill, and NC State, RTP created a powerful university–industry ecosystem that: • Retained top academic talent • Attracted global companies and federal research labs • Generated tens of thousands of high-wage jobs • Fueled sustained population and urban growth across the Triangle What’s especially notable is the long-term stability of the model. Rather than chasing short-term booms, RTP built a diversified, research-driven economy that has proven resilient across multiple economic cycles. As regions think about the future of innovation, RTP remains a compelling case study in how education, public leadership, and private investment can align to create durable growth. #ResearchTrianglePark #EconomicDevelopment #InnovationEcosystems #LifeSciences #Tech #HigherEducation #TriangleNC #RegionalGrowth

The fastest-growing wealth hubs of the last decade include 𝐒𝐡𝐞𝐧𝐳𝐡𝐞𝐧 (+142%), 𝐁𝐞𝐧𝐠𝐚𝐥𝐮𝐫𝐮 (+120%), 𝐒𝐜𝐨𝐭𝐭𝐬𝐝𝐚𝐥𝐞 (+125%), and 𝐖𝐞𝐬𝐭 𝐏𝐚𝐥𝐦 𝐁𝐞𝐚𝐜𝐡 (+112%). 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧 𝐝𝐫𝐢𝐯𝐞𝐬 𝐰𝐞𝐚𝐥𝐭𝐡: Shenzhen’s surge stems from a dense tech ecosystem—global giants, startups, venture capital, and policy support fueling millionaire creation. 𝐏𝐨𝐥𝐢𝐜𝐲 𝐬𝐡𝐚𝐩𝐞𝐬 𝐨𝐮𝐭𝐜𝐨𝐦𝐞𝐬: Beijing’s decision to make Shenzhen a “model city” shows how public–private alignment can redefine a region’s trajectory. 𝐖𝐞𝐚𝐥𝐭𝐡 𝐟𝐨𝐥𝐥𝐨𝐰𝐬 𝐥𝐢𝐟𝐞𝐬𝐭𝐲𝐥𝐞 + 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬: Affluent individuals are migrating to cities with quality of life, business friendliness, and rising tech/service sectors. Why it matters: The geography of wealth is shifting faster than most strategies. For executives, this isn’t just demographics—it’s a roadmap to where influence, investment, and demand will concentrate next.    #Leadership #Innovation #GlobalMarkets   

I'm noticing the trend from "AI sandboxes" to "AI hubs" to "AI zones". But what's the social impact?   The #UK government has just announced its AI Opportunities Action Plan which will commit £14 billion towards various data centre and #AI related projects and create 13,250 jobs (funded by major tech companies Vantage Data Centres, Nscale and Kyndryl). It includes plans for: 1️⃣ new 'AI Growth Zones' (starting in Culham, Oxfordshire) to speed up planning proposals and build more AI infrastructure   2️⃣ increasing public compute capacity by 20x + developing a new supercomputer   3️⃣ a new National Data Library to unlock the value of public data and support AI development   4️⃣ a dedicated AI Energy Council to work with energy companies to address energy challenges of AI.   On 1️⃣, I noticed that the UK started with "AI regulatory sandboxes" in various sectors (e.g. medtech, fintech) since 2023, then "AI research hubs" in 2024, to now "AI growth zones" in 2025.   Each development is more encompassing than the previous. The sandbox is an isolated environment for testing and experimentation; the hub is a space for R&D and sharing; and a zone is a larger-scale ecosystem (perhaps even based in a physical city). The 'zone' phase also tends to be where #AIpolicy intersects with data centre, quantum/super computing and energy policies. So expect to see that intersection a lot in 2025 AI policies. Anyways, this sandbox-hub-zone trend is similarly happening all over the world (at different timelines), including US, China, EU, Russia, Japan, South Korea, India, Singapore, Brazil, etc (see my tracker in my 'Visit my website' link) In theory, you could call this is an example of economies of scale - specifically the "localisation economy" - where resources and production are pulled into the same region for efficiency. While localisation is a natural trend…does it conflict with the democratisation of AI?   After all, AI democratisation aims to ensure wide and inclusive access to AI. But how does that work if AI resources, talent and industry are increasingly concentrated into zones? This is not new, but an age old issue with any #tech revolution.   Perhaps the nuance is that any such conflict will mostly impact the *development* and *talent distribution* of AI, rather than the *use* of it (as AI gets increasingly cheaper and scalable).   Even so, I'm thinking about what would be the socio-economic impact if AI-related jobs are concentrated in certain regions. I don't know what that would look like yet. Perhaps we could look to current analogies of tech hub concentration in Silicon Valley, Shenzhen, Seoul, etc and their broader social impact. One interesting thesis to test is whether AI localisation will worsen #housingaffordability and create a more competitive 'bottleneck' society (like what we're already seeing in East Asia). In any case, it's an important factor for anyone wanting to make any AI investment, policy, legal predictions.

Big Tech loves big numbers. “80,000 jobs created.” “Billions invested.” “Local growth unlocked.” Except most of it isn’t real. Take Microsoft and Google in Chile: both tout huge economic impact from their new data centers. The paperwork tells another story — a few dozen long-term jobs, mostly in security and maintenance. That’s not transformation. That’s housekeeping. This is the part we keep getting wrong. Infrastructure ≠ growth. Buildings don’t create ecosystems. Servers don’t create skills. “Investment” doesn’t automatically mean value retained. We keep mistaking presence for participation. A hyperscale data center sitting in your country isn’t the same as a digital economy taking root. The real multiplier only appears when you design for local leverage — supply chains, education, SME participation, energy integration, exportable know-how. Without that, it’s extractive growth with a green paint job. The value flows out; the PR stays in. If we’re serious about building digital economies, stop counting construction helmets and start tracking capability transfer. How much knowledge stayed? How many new businesses formed? How many locals can now build the next one themselves? Because in the end, “thousands of jobs” is just a line in a press release — and press releases don’t build economies. https://lnkd.in/eWssgUSq #Business #Growth #Strategy #Jobs

Growing up in the Bay Area, I watched tech companies transform entire communities. Not just through jobs, but through waves of investment that rippled into schools, infrastructure, and neighborhoods that needed it most. Now it’s happening in the City of Memphis. XAI’s recent $6 million donation to Memphis-Shelby County Schools is just the beginning. If the pattern holds, many more millions will follow. But here’s what the Bay Area taught me: the money itself isn’t the answer. Six million dollars can transform education or disappear into bureaucracy. It’s the tip of the ice berg. The difference comes down to the people distributing it; whether they truly understand their community’s needs and can think strategically about long-term impact. I hope Memphis administrators are studying what worked (and what didn’t) in other tech boom towns. I hope they’re listening to students, parents, and teachers in the most impacted neighborhoods. I hope they’re building systems that scale, because if xAI follows the pattern, this is just the start of tech’s influence. The Bay Area’s boom created tremendous opportunity but also revealed how quickly inequality can accelerate without intentional planning. Memphis has a chance to learn from both the wins and the mistakes. History is repeating itself. The question is whether we repeat all of it or just the parts worth keeping. #partnershipuniversity

Foxconn’s semiconductor push in Uttar Pradesh is being seen by many as a factory announcement, but it’s actually much more than that. When global technology players choose a region, they’re not just evaluating land parcels or incentives. They’re assessing whether the ecosystem is ready in terms of infrastructure, policy clarity, talent availability, connectivity, and the ability to execute at global quality standards. The HCL–Foxconn OSAT unit coming up in Jewar reflects that readiness. It signals a shift in how Uttar Pradesh is positioning itself, from purely industrial execution to enabling deep-tech capabilities. This move goes beyond job creation and points toward the creation of skills corridors, innovation capacity, and long-term technological depth. What makes this moment important is the underlying belief it validates that world-class tech ecosystems don’t always emerge organically in a few legacy hubs. They can be intentionally designed through the right mix of policy, infrastructure, and institutional support. That’s a powerful idea for any region looking to move up the value chain. As this momentum builds, the next phase will be crucial. Strengthening talent and skilling pipelines, encouraging R&D partnerships, enabling startup participation, and maintaining strong quality and standards frameworks will determine how durable this ecosystem becomes. Deep tech isn’t just a buzzword here; it’s a strategic lever for growth. Uttar Pradesh’s trajectory is starting to show how thoughtful policy can translate into technology-led economic transformation not overnight, but in a way that compounds over time. #SiliconSwaraj  #AI_Pradesh #TechUPgrade

The standard data center siting model—with its sketchy promises of local economic development—is breaking down. Local leaders are questioning Big Tech’s claims of spillover benefits, even as frustration grows over rising electricity demands--and bills. Which is why it’s time to rethink the hundreds of data center siting deals still to come. As outlined in new work at The Brookings Institution by my colleagues Daniel Goetzel, Shriya Methkupally, and myself, developers, regions, and states need a more mutualistic approach. They need one that links massive data center projects to real investments in regional tech ecosystems. Here's our post: https://lnkd.in/eDhaneiu Today, data centers’ local economic contributions remain modest, despite extravagant promises. Under the standard model, long-term employment is limited once the construction phase ends. The ChatGPT era, however, is disrupting this model. AI firms’ race to scale ever-larger models has driven unprecedented demand for computing infrastructure, forcing rapid deal-making with communities—often amid growing concerns about energy use, noise, and other impacts. These pressures are shifting the balance of power. Regions with land, electricity, water, and permitting authority now have leverage to shape AI-focused data center deals to be more beneficial. And so regions should seize that leverage, and work harder to align deals with ambitious agendas for tech development, R&D, innovation, and entrepreneurship. They should ask for more. And developers facing site scarcity, backlash, and fierce competition may find they need to give more to get the deals done. Given that, we suggest a playbook for higher-order deal-making with hyperscalers and AI upstarts, including: 📈 Structuring negotiations to unlock regional economic development 🏭 Creating regional testbeds with universities, startups, and operators (e.g., CoreWeave, Microsoft, Princeton University, The Johns Hopkins University, University of Wisonsin/Madison) 💵 Converting AI investment into local wealth creation through shared-prosperity models (e.g., @O.H.I.O. Fund, Emerson Collective) 💡 Turning AI energy demand into a regional R&D focus in energy, grids, and emerging technologies (e.g., EmeraldAI Technologies, Fervo Energy, Thintronics®, ComEd) Can it work? We believe it can. Regions are rediscovering that their land, infrastructure, water, approvals, and electricity are precious. They should insist these resources be shared only through grand bargains that deliver lasting, high-value development in return--and they may find their AI partners eventually agree. The Brookings Institution Michael Hicks Paul Kedrosky Maria Messick Joe Parilla Mayu Takeuchi Sanjay Patnaik Amy Liu Karen Hao Brad Henderson Jason Hall Liat Krawczyk Francesca Gabriella Ioffreda

One of the biggest misconceptions about data centers is that they drain resources and contribute little back to the communities they operate in. That picture hasn’t been accurate for a long time. In the early days, data centers were inefficient. Cooling systems consumed large amounts of water, designs weren’t optimized for energy use, and the facilities themselves didn’t create many long-term jobs. But the industry has evolved dramatically. Today’s data centers are engineered to use a fraction of the water and energy they once required. Closed-loop cooling, alternative fluids, and more efficient chips have reduced resource consumption to levels comparable to a Class A office building. On the power side, the shift to behind-the-meter generation means facilities are now building their own energy plants and, in many cases, providing stability back to the grid instead of pulling from it. And the idea that data centers don’t create jobs is simply outdated. Mega-campuses measured in gigawatts require thousands of workers for multi-year construction and more than a thousand permanent roles to operate and maintain them. Entire local economies grow around these facilities. The industry isn’t taking from communities, it’s investing in them. And the scale of that investment is only going to increase as demand accelerates.

AI, Power, and Geography — The Infrastructure Shift The world’s infrastructure is being rebuilt in real time. Artificial intelligence and data centers are reshaping economies, labor, and the electric grid. Across the United States, AI growth is fast but uneven. A few metropolitan regions — San Francisco, New York, Seattle, Boston, and Austin — hold about two-thirds of all AI-related job postings and much of the private investment (Brookings Institution, 2024). Most other regions lack the trained workforce, digital infrastructure, or capital to compete. Data centers are among the most energy-intensive facilities ever built. A single hyperscale site draws 100 to 500 megawatts — enough to power a small city. Global data-center electricity use will more than double by 2030, from about 460 terawatt-hours in 2022 to over 1,000 TWh (International Energy Agency, 2024; Goldman Sachs, 2024). The surge comes from AI workloads, GPU clusters, and 24-hour cooling systems. These forces expose a divide: 1. Opportunity: Regions that plan and invest early capture jobs, research centers, and new industries. 2. Risk: Regions that delay lose economic ground as leading hubs consolidate talent and infrastructure. 3. Connection: Energy, computation, and community are interdependent. Grid stability, water use, and local trust determine where this expansion succeeds. Leadership, public and private, must focus on essentials: • Regional strategy. Identify local strengths — manufacturing, agriculture, defense, education — and apply AI where it adds measurable value. • Infrastructure capacity. Expand grid headroom, water systems, and transmission planning before approving major compute facilities. • Human capital. Build technical education pipelines. Mississippi offers a model, training 2,800 teachers in AI fundamentals and linking those programs to its industrial base. • Transparency. Publish power, water, and emissions data for large facilities so communities can weigh costs and benefits. • Accountability. Track measurable outcomes: employment, energy efficiency, resilience, and equity of access. In 2023, northern Virginia narrowly avoided grid shortages after data center loads spiked beyond forecast levels (Virginia State Corporation Commission, 2024). Similar pressures are emerging in Ohio, Texas, and Oregon. Digital expansion is outpacing power and planning. AI, energy, and geography now form a single system. Their integration determines economic stability, environmental limits, and public confidence. The outcome depends on balance, reliability, and shared benefit. — #AI #Energy #Infrastructure #Data #Leadership #Policy #Resilience #Regions #Systems #Technology #Workforce