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The World Economic Forum reports that 41% of employers plan workforce reductions by 2030 due to AI. Amazon’s CEO just told employees that AI will mean “fewer people doing some jobs,” while Microsoft is cutting 6,000 workers after pushing AI adoption. They’re about to make the biggest strategic mistake of the decade. The real choice leaders face today isnt which AI model to buyits how to use it: as a layoff machine, or as a competitive advantage to eat their rivals’ lunch. Weve seen this before. At my last job at Cloudability, I witnessed a fascinating SaaS dynamic: businesses that innovate take cost savings and reinvest them back into efficiency, reach, and market share.Our cloud cost management product rarely saved businesses money on their bills. But it did give them insights to optimize spend and allowed them to reinvest savings into bigger bets and innovation. The companies that did so went on to become innovators and leaders; the ones that didnt became footnotes in someone elses growth story. Replacement vs. Redeployment The same pattern is emerging with AI agents, except this time the savings aren’t budget dollarsthey’re human time, capability, and potential. According to new research from Salesforce, AI agent adoption is expected to increase 327% over the next two years, leading to productivity gains of 30%. The shift isnt toward replacementits toward redeployment. When AI handles repetitive tasks like rerouting customer support tickets, summarizing meeting notes, or filling out compliance reports, it doesnt replace jobs, it frees up capacity. CHROs expect to redeploy 23% of their workforce to new roles and 89% believe AI will empower them to reassign employees to new, more relevant positions. While those findings offer promise and reassurance for the workforce, not every leader will see it that way. Based on my experience using AI as a force multiplier for my team, I believe the companies that thrive will be the ones that figure out how to integrate humans and digital teammates in ways that maximize both efficiency and the irreplaceable qualities machines cant replicatelike creativity, empathy, and judgment. The AI Divide This sets up a fundamental choice: cut or invest. But beneath that lies a deeper philosophical question: Do you see people as a cost to manage or a force to grow your business? In the case of a contact center, a leader with a cost-cutting mindset might say “Let’s replace our support team with chatbots to save money.” That decision might frustrate customers and drive up escalations, leading to short-term savings at the expense of long-term growth. The reinvestment approach sounds more like: “Let’s use AI for case classification and routing so customers get the right expert with all context preloaded.” The result is faster resolution times, happier customers, support agents focused on higher-value workand more sustainable business growth. The productivity gains are realwhat sets winning leaders apart is asking, “How do we reinvest this efficiency and grow?” “Where can we shift human focus from tasks to transformation?” and How do we redesign work to create more value, not just more output? Review, Reallocate, and Reimagine For those leaders ready to grow and build something better, I recommend the three Rs. Review: Audit employee time usage, identify underused tools, and diagnose process bottlenecks. Determine which tasks are repetitive or low-value and which ones can be automated with AI agents. Reallocate: Reclaim hours unlocked by AI and reinvest them in work that accelerates revenue, deepens customer value, or fuels innovation. Reimagine: Shift time toward high-impact interactions, retrain agents for sales roles, or embed support teams into product development to help eliminate root-cause customer issues. AI will amplify an existing leadership philosophy. If a leader is already shortsighted about people and customers, AI will make that worse. If leaders invest in their teams and customer experience, AI will become a force multiplier for both. This difference in approach explains why identical AI implementations can produce vastly different business outcomes. The technology is the same, but the leadership philosophy determines whether AI becomes a race to the bottom or a rocket to the top.
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E-Commerce
In 2011, Marc Andreessen boldly declared that software is eating the world. At the time, it was both a prediction and a provocationone that came true faster than most imagined. Nearly 15 years later, software has thoroughly disrupted most sectors, from media to utilities, embedding itself in all types of business. However, some industries that couldnt be easily improved (eaten) by software have been overlooked, including measurably from an investment perspective. For instance, AI startups pulled in $131 billion in global venture capital funding in 2024 alone. Meanwhile total global VC funding in the robotics space stood at $100.9 billion between 2018 and 2024. Why the disparity? Software is efficient, frictionless, scalableand, for a time, it was deeply inspiring. But somewhere along the line, we over-indexed. We hyper-focused on funding and developing software designed to optimize everythingto squeeze out every last bit of efficiency and revenue. All the while, we watched the real world fade into the background. But a new day is dawning. Different kinds of companies are stepping forwardcompanies trying to rebuild the world. From logistics to defense, energy to mobility, a new generation of organizations is tackling hard, foundational problemsmany of which were left behind in our rush to digitize and scale seemingly everything. These companies are developing unmanned drones, next-generation supply chains, smart robotics, and automated factories. Theyre not launching apps; theyre launching rockets. Call these companies The New Industrials. These firms and founders heed lessons from our industrial past, but they are not fixated on nostalgia. Rather, they are impatient with the present. They see the limits of a purely digital world, and theyre pushing toward something more consequential. Something that runs on power, motion, material and design. Because in this new wave, design isnt decoration. It is direction. Its the force turning intent into impact. From Optimization to Originality Much of the last two decades in design have been about making things more efficient: flatter UIs, faster onboarding, smarter defaults. But this optimization era came at a cost. We lost the muscle for originality. The New Industrials are bringing it back. Over the last several years, there has been increased funding and founder ambition across deep tech, defense, and frontier industries. From 2021 through mid-June 2024, venture capitalists invested a total of $130 billion in defense-tech startups alone. Sequoia, Andreessen Horowitz, Lux, and Founders Fund, leaders in the industry, all shifted more of their portfolios into these sectors. Companies like Anduril, Epirus, and Saronic pushed into spaces that for many years, most founders wouldnt touchlike defense or hardware. Today, companies in these hardware spaces are building with vision. They treat design not as polish, but as infrastructurea critical tool for functionality and performance. Other firmslike Auger in the supply chain sectorare blending deep operational complexity with elegant, user-first interfaces to solve physical problems. The companys approach feels closer to consumer product design than legacy enterprise software. Its brand isnt just a wrapperits embedded in the system itself, helping the product operate seamlessly. The tools themselves feel as familiar and legible as a smartphoneand as powerful as a factory floor. Patriotism Without Politics This movement isnt just industrial. Its cultural. In a fractured world, The New Industrials represent a unifying force: a return to building. These companies are not espousing politicsbut they are rebuilding what America was once recognized for: innovation, ingenuity, clarity of purpose. You can see it in their design choices. Theres a material honesty to their work. A rugged elegance. A preference for legibility over flash. It feels like a new kind of Americanaone built not on sentiment, but on capability. And this movement is starting to break into the mainstream. The recently concluded Reindustrialize 2025, held in Detroit, welcomed a growing coalition of investors, technologists, manufacturers, and policymakers rallying around a techno-industrial renaissance. Thousands attended and watched online. Participation, varying from top defense officials to frontier founders such as Palmer Luckey, reinforced what many in this space already know: this is no longer fringe. Why design is the lever In this new era, seeing through the lens of design is how the biggest problems we face are understood, explored, and solved. Its how we create solutions that are not just functionalbut fundamentally better than anything that came before. And today, an exciting generation of organizations is approaching these challenges through that lens. The New Industrials arent just designing productstheyre designing entire companies, systems, and strategies with cohesive intention. From supply chain platforms and electromagnetic weapons, to autonomous vehicles and national narratives, they are aligning product, brand, and interface. In this way, design becomes an innovation engine. Its how critical, often overlooked industries are boldly reimagined. How solutions to space travel, homeland security, automation, and so much more are conceived, crafted, and launched into the world. Software may have nearly eaten the world. But it couldnt digest the hard problemsthe physical ones, the foundational ones. Now, the people who solve those problemsthe engineers, the builders, the designersare taking it back.
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E-Commerce
The era of supersized data centers is upon us. As artificial intelligence dominates the agendas of the tech giants, the need for bigger and more powerful data centers is accelerating, and it’s leading to a building boom that could reshape the American landscape. “We aren’t seeing gigawatt buildings yet, but it’s really only a matter of time,” says Dan Drennan, data centers sector leader at Corgan, the top-ranking architecture firm on Building Design + Constructions annual list of revenue for data center design. These rising demands are creating new challenges for the design of data centers, from the power generation needed to the infrastructure to the buildings that contain the servers that make AI work. Right now, and for the foreseeable future, everything is getting bigger. Meta recently announced plans to build data centers that use up to 5 gigawatts of power. OpenAI, Oracle, and SoftBank announced plans earlier this year to invest up to $500 billion in a vast data center building spree. These and other so-called hyperscale data users like Google, Microsoft, and Amazon are expected to drive most of the growth in data centers in the U.S. and globally, according to an analysis by the Boston Consulting Group. While the average data center building uses 40 megawatts of power today, it’s not uncommon for the biggest companies to be relying on data centers that suck up 300 to 400 megawatts of power per building. And that number is only going up. More power, bigger buildings Vantage Data Center, Goodyear, Arizona [Photo: Courtesy Corgan] “We’re actually building several multi-GW clusters.” Mark Zuckerberg’s July 14 data center building announcement on Facebook put these plans into somewhat menacing perspective. He paired his post with a visualization of a massive rectilinear block smothering a large portion of New York City. “Just one of these covers a significant part of the footprint of Manhattan,” he wrote. Meta’s largest announced projectthe Louisiana-based Hyperion data centeris expected to use 2 gigawatts of power by 2030, with the potential to grow to 5 gigawatts of capacity. Now in its very early stages of construction, it sits on 2,250 acres of a former agricultural site. Manhattans total land area is more than 14,000 acres. “From a logistical standpoint, it just makes sense to build these things under one roof,” says Gordon Dolven, director of CBRE Americas data center research. The dominant paradigm of AI today is the large language model, which pulls its intelligence out of deep pools of data and information stored in numerous servers stacked in long rows of 8-foot-tall cabinets, like the aisles of a grocery store filled with nothing but black boxes and blinking blue lights. These servers connect and communicate with each other almost synaptically, so the closer they are to one another, the faster they can make those connections. The farther away they are, the slower the connections, and the more networking infrastructure and fiber optic cables required to keep them in communication. That’s why the building size of data centers is increasing, and also why the companies pushing the development of AI are trying to have more of these large buildings constructed near each other. For example, Meta’s Hyperion data center will be made up of 11 buildings covering more than 4 million square feet, according to a company spokesperson. Its Prometheus data center in Ohio is a vast campus that’s scaling up to run on 1 gigawatt of power by 2026, partly by gearing up servers in quickly built mega-tents. Meta Platforms CEO Mark Zuckerberg spoke at the Acquired Live event in San Francisco in September 2024. Listeners heard how Meta is playing a big role in defining the next decade of computing with AI. [Photo: David Paul Morris/Bloomberg via Getty Images] A bigger load More servers means more equipment to help them run efficiently, and that results in data center buildings surrounded by lots of large mechanical, cooling, and electrical equipment. “The big thing for data centers is they always have to have backup power. Then you usually need an extra, so there’s a backup to the backup. And those take up a lot of space,” says Rob LoBuono, a critical facilities leader at Gensler, another of the top architecture firms designing data centers. Backups are also being used for the data itself. “We’re seeing more of a trend toward multiple buildings, multiple points of redundancy, separated across the campus.” And because the server equipment is getting heavier, the buildings need more robust structures at the foundation, with more material-intensive construction. “Where we were planning for 200 or 250 pounds per square foot previously, we’re now talkig about 400, 500 pounds per square foot of loading on these floor plates,” Drennan says. “The loading that you’re planning for on the building goes up.” All these factors are combining to make the buildings enormous. It’s not uncommon for construction on the larger AI-focused campuses to cover 500,000 square feet or more, usually across a single story. And technically they can keep growing. “If you’re talking about a new building, assuming the land is such that we’re able to shape the building in a way that we can get all the gear around the building that’s needed to serve that compute in an efficient way, then there’s really no limit to how big these can go,” Drennan says. More efficient infrastructure Data center campuses don’t necessarily need to grow to Manhattan size, though, and almost certainly won’t. Experts say the equipment and infrastructure behind data centers, and AI data centers in particular, are getting denser, more efficient, and smaller. As a result, data center operators are packing more servers into these spaces, boosting their computing capacity as well as their electricity demands. Just a few years ago, data centers could expect servers to use about 200 watts per square foot of space, Drennan says. A 10,000-square-foot building would pull about 2 megawatts of power in total. But server sizes have gone down and data centers can pack more of them into the same amount of space. “Now you’ve got three, four, five times that density. So that same 10,000 square feet that used to be 2 megs is now 8 megs or 10 megs of power, Drennan says. Scale the building to 100,000 square feet or 500,000 square feet, or even build multiple buildings at that size, and the capacity of the data center goes up significantly. The cooling question A lot of that efficiency is driven by the support systems that keep data centers running, especially the all-important cooling equipment that allows servers to run 24 hours a day without overheating. Dolven says data centers used to rely solely on air cooling (think dozens of giant air conditioners running nonstop). Now new technologies like closed-loop coolant systems, direct liquid cooling, and even immersion systems that keep servers under water are lessening the power demands of the cooling side of data centers, allowing more of that power to flow to more servers. These technologies may also help cut down some of the extreme resource use data centers require. One study, for example, found that a midsize data center used 300,000 gallons of water per day for cooling. That’s about the amount of water used daily by 1,000 homes. Drennan expects data centers to get more efficient over time, making even “older” ones built just five years ago able to see their power capacities increase. “What they do with that increment of power gets more productive,” he says. “The compute gets better, the algorithms get better, the systems get better, and so the output goes up, even though the required support for that density is the same.” The limiting factors of data center size are the heat they produce and the power they require. Expelling heat from data centers is a significant part of what makes their footprint so large. This requires giant air-conditioning units that can number in the hundreds, with refrigerator-size condensers lined up outside or on the roof, and boxy air chillers pumping cool water into a network of pipes in the building. Outside there are other cooling tower boxes, coolant processors, exhaust filtration units, power transformers, and backup power generators. This equipment ends up in long rows and stacks on the periphery of data center buildings, with room in between for natural airflow and human maintenance. Though cooling technology is improving, the size of the equipment behind that cooling is getting bigger. According to Drennan, just a few years ago a data center building would need extra space equivalent to about half its footprint to house all the cooling equipment required. “Now it’s more like the yard is four times the size of the building footprint,” he says. “You’ve got three or four times the amount of compute inside the building, so you’ve got to have three or four times the amount of equipment to reject that heat and back up the power associated with that.” Hot and power-hungry In the past, data center power demands were manageable. Dolven says a 5-megawatt project could pop up and simply request the power from a utility that was more than happy to sell it. “You could interconnect to the existing grid, you could tap into an adjacent substation that may have already been constructed,” he says. “But when you request 500 megawatts, the scenario shifts dramatically.” New power generation has to be developed. Miles of high-voltage transmission lines have to be constructed, crossing through existing communities and private land. This brings its own permitting and approvals challenges, not to mention community opposition. A recent report found that the data center hot spot of Virginia is expecting its energy demands to double as more data centers come on line. This is leading to higher energy costs for regular consumers in the region. Dolven says many hyperscale data center users are looking at building their own power generation facilities within their data center campuses, essentially making the power they need to operate without relying on, or impacting, the surrounding community’s infrastructure. [Photo: Wonder Valley, courtesy Gensler] That’s the approach at Wonder Valley, a data center in development in rural Alberta, Canada, that bills itself as the largest AI data center industrial park in the world. Planned to have its own off-grid natural gas and geothermal plants on-site while pulling from existing “stranded” sources of natural gas, Wonder Valley aims to be a 7.5-giawatt data center within the next 5 to 10 years. Gensler is the design firm behind the project, and LoBuono says it’s being designed to be as sustainable as possible, utilizing local timber and in a style that reflects the natural surroundings. Wonder Valley’s developer, O’Leary Ventures, argues that by generating much of its own power, the center will offer a net positive to the region, through jobs, tax revenue, and a jolt to the local economy. “The whole point of what we’re trying to pivot toward in this industry is making these buildings more of an asset,” LoBuono says. “Optics are huge in this industry. We shouldn’t be thinking about destroying Manhattan. The buildings get bigger, but the bigger has a benefit.”
Category:
E-Commerce
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