Your data center is either closer than you think or much farther away

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A map of U.S. data centers. Courtesy of Rice Businesses Wisdom

A new study shows why some facilities cluster in cities for speed and access, while others move to rural regions in search of scale and lower costs. Based on research by Tommy Pan Fang (Rice Business) and Shane Greenstein (Harvard).

Key findings:

  • Third-party colocation centers are physical facilities in close proximity to firms that use them, while cloud providers operate large data centers from a distance and sell access to virtualized computing resources as on‑demand services over the internet.
  • Hospitals and financial firms often require urban third-party centers for low latency and regulatory compliance, while batch processing and many AI workloads can operate more efficiently from lower-cost cloud hubs.
  • For policymakers trying to attract data centers, access to reliable power, water and high-capacity internet matter more than tax incentives.

Recent outages and the surge in AI-driven computing have made data center siting decisions more consequential than ever, especially as energy and water constraints tighten. Communities invest public dollars on the promise of jobs and growth, while firms weigh long-term commitments to land, power and connectivity.

Against that backdrop, a critical question comes into focus: Where do data centers get built — and what actually drives those decisions?

A new study by Tommy Pan Fang (Rice Business) and Shane Greenstein (Harvard Business School) provides the first large-scale statistical analysis of data center location strategies across the United States. It offers policymakers and firms a clearer starting point for understanding how different types of data centers respond to economic and strategic incentives.

Forthcoming in the journal Strategy Science, the study examines two major types of infrastructure: third-party colocation centers that lease server space to multiple firms, and hyperscale cloud centers owned by providers like Amazon, Google and Microsoft.

Two Models, Two Location Strategies

The study draws on pre-pandemic data from 2018 and 2019, a period of relative geographic stability in supply and demand. This window gives researchers a clean baseline before remote work, AI demand and new infrastructure pressures began reshaping internet traffic patterns.

The findings show that data centers follow a bifurcated geography. Third-party centers cluster in dense urban markets, where buyers prioritize proximity to customers despite higher land and operating costs. Cloud providers, by contrast, concentrate massive sites in a small number of lower-density regions, where electricity, land and construction are cheaper and economies of scale are easier to achieve.

Third-party data centers, in other words, follow demand. They locate in urban markets where firms in finance, healthcare and IT value low latency, secure storage, and compliance with regulatory standards.

Using county-level data, the researchers modeled how population density, industry mix and operating costs predict where new centers enter. Every U.S. metro with more than 700,000 residents had at least one third-party provider, while many mid-sized cities had none.

ImageThis pattern challenges common assumptions. Third-party facilities are more distributed across urban America than prevailing narratives suggest.

Customer proximity matters because some sectors cannot absorb delay. In critical operations, even slight pauses can have real consequences. For hospital systems, lag can affect performance and risk exposure. And in high-frequency trading, milliseconds can determine whether value is captured or lost in a transaction.

“For industries where speed is everything, being too far from the physical infrastructure can meaningfully affect performance and risk,” Pan Fang says. “Proximity isn’t optional for sectors that can’t absorb delay.”

The Economics of Distance

For cloud providers, the picture looks very different. Their decisions follow a logic shaped primarily by cost and scale. Because cloud services can be delivered from afar, firms tend to build enormous sites in low-density regions where power is cheap and land is abundant.

These facilities can draw hundreds of megawatts of electricity and operate with far fewer employees than urban centers. “The cloud can serve almost anywhere,” Pan Fang says, “so location is a question of cost before geography.”

The study finds that cloud infrastructure clusters around network backbones and energy economics, not talent pools. Well-known hubs like Ashburn, Virginia — often called “Data Center Alley” — reflect this logic, having benefited from early network infrastructure that made them natural convergence points for digital traffic.

Local governments often try to lure data centers with tax incentives, betting they will create high-tech jobs. But the study suggests other factors matter more to cloud providers, including construction costs, network connectivity and access to reliable, affordable electricity.

When cloud centers need a local presence, distance can sometimes become a constraint. Providers often address this by working alongside third-party operators. “Third-party centers can complement cloud firms when they need a foothold closer to customers,” Pan Fang says.

That hybrid pattern — massive regional hubs complementing strategic colocation — may define the next phase of data center growth.

Looking ahead, shifts in remote work, climate resilience, energy prices and AI-driven computing may reshape where new facilities go. Some workloads may move closer to users, while others may consolidate into large rural hubs. Emerging data-sovereignty rules could also redirect investment beyond the United States.

“The cloud feels weightless,” Pan Fang says, “but it rests on real choices about land, power and proximity.”

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This article originally appeared on Rice Business Wisdom. Written by Scott Pett.

Pan Fang and Greenstein (2025). “Where the Cloud Rests: The Economic Geography of Data Centers,” forthcoming in Strategy Science.

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Search trends predict future retail revenue up to three quarters in advance. Photo via Pexels

Forecasting retail sales just got smarter, thanks to Google searches

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Imagine being able to predict the future of retail sales — not with months-old economic reports, but with real-time digital breadcrumbs left behind by consumers. Thanks to new research from K. Ramesh (Rice Business) and Gary Lind (University of Pittsburgh), that future is already here.

Key findings

  • Search trends predict future retail revenue — up to three quarters ahead.
  • Markets don’t fully price in search-based insights, especially before earnings announcements.
  • Trading strategies using search data outperform traditional asset-pricing models by 2–3%.
  • Analysts often underuse this data, resulting in predictable forecast errors.
  • At the macro level, search trends closely track — and often lead — official retail sales and GDP.

Their study shows that Google search trends are powerful predictors of consumer demand, capable of forecasting U.S. retail sales up to three quarters in advance. The implications span industries and audiences — from Wall Street traders to Main Street retailers, and from data-driven CFOs to curious business students.

A smarter, faster crystal ball for retail demand

Retail sales drive nearly 70% of U.S. gross domestic product (GDP), making them one of the most important indicators of economic health. Yet traditional forecasting relies heavily on backward-looking data and broad economic indicators. Ramesh and Lind’s work offers a leap forward: search data from Google Trends — publicly available and easy to use — can offer a faster, more precise signal of where sales are headed.

“If you want to understand consumer demand,” says Ramesh, the Herbert S. Autrey Professor of Accounting at Rice Business, “look at what people are searching for — not just what companies report after the fact. These digital footprints can tell us where revenue is going, often before the market catches up.”

This low-cost approach stands in stark contrast to expensive alternatives, such as satellite images of parking lots, in-store foot traffic from mobile data, or retail scanner feeds — tools often reserved for hedge funds and large institutional investors. Google Trends offers a scalable, accessible option for anyone seeking insight into consumer behavior.

Why this matters for investors and analysts

The researchers analyzed search patterns for nearly 200 publicly traded U.S. retail companies between 2004 and 2019. They found that firms with rising search volumes tend to report higher revenues — often well before analysts or investors react.

In fact, investment strategies built around these trends earned 2–3% higher returns than traditional models would predict. And most of these gains were concentrated around earnings announcements — a sign that markets were slow to recognize what search data had already revealed.

The advantage is even more striking for companies that fly under Wall Street’s radar. For firms with limited analyst coverage, the predictive power of search data on stock performance was more than 80% stronger.

A new tool for analysts — if they’ll use it

Despite the clear signal in the data, the study finds that analysts routinely fail to incorporate search-based insights into their forecasts. That gap translates into missed revenue predictions and market inefficiencies — which sophisticated investors can exploit.

“The search data are out there,” says Lind, a Rice Business Ph.D. ’19 graduate now on faculty at Pitt Business. “They’re public, powerful, and free. Analysts who ignore them are missing an opportunity.”

The impact of this research extends well beyond equity markets. By aggregating Google search data across the retail sector, the authors found strong alignment with U.S. Census retail sales figures and GDP trends. In other words, these digital signals reflect not just firm-level interest, but macroeconomic momentum.

A data wake-up call

This makes search trends a potentially valuable early-warning system for policymakers, central banks, and anyone responsible for interpreting the economic landscape. During volatile or uncertain periods, such tools could offer precious foresight into shifting consumer behavior.

In a digital era where consumers research before they shop — whether online or in-store — it’s no surprise that their Google queries leave valuable clues. What’s surprising is how often businesses, investors, and even economists still overlook them, Ramesh says. “Search data is a goldmine. And it's just sitting there, waiting to be used.”

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This article originally appeared on Rice Business Wisdom.

Lind and Ramesh, “Using Internet Search Data to Predict Aggregate Retail Sales and Enhance Firm-Level Revenue Expectations,” Contemporary Accounting Research (April 2025): 1-32. https://doi.org/10.1111/1911-3846.13043.

There's no crystal ball, but this researcher from Rice University is trying to see if some metrics work for economic forecasting. Photo via Getty Images

Houston researcher tries to crack the code on the Fed's data to determine economic outlook

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Research by Rice Business Professor K. Ramesh shows that the Fed appears to harvest qualitative information from the accounting disclosures that all public companies must file with the Securities and Exchange Commission.

These SEC filings are typically used by creditors, investors and others to make firm-level investing and financing decisions; and while they include business leaders’ sense of economic trends, they are never intended to guide macro-level policy decisions. But in a recent paper (“Externalities of Accounting Disclosures: Evidence from the Federal Reserve”), Ramesh and his colleagues provide persuasive evidence that the Fed nonetheless uses the qualitative information in SEC filings to help forecast the growth of macroeconomic variables like GDP and unemployment.

According to Ramesh, the study was made possible thanks to a decision the SEC made several years ago. The commission stores the reports submitted by public companies in an online database called EDGAR and records the IP address of any party that accesses them. More than a decade ago, the SEC began making partially anonymized forms of those IP addresses available to the public. But researchers eventually figured out how to deanonymize the addresses, which is precisely what Ramesh and his colleagues did in this study.

"We were able to reverse engineer and identify those IP addresses that belonged to Federal Reserve staff," Ramesh says.

The team ultimately assembled a data set containing more than 169,000 filings accessed by Fed staff between 2005 and 2015. They quickly realized that the Fed was interested only in filings submitted by a select group of industry leaders and financial institutions.

But if Ramesh and his colleagues now had a better idea of precisely which bellwether firms the Fed focused on, they still had no way of knowing exactly what Fed staffers had gleaned from the material they accessed. So the team decided to employ a measure called "tone" that captures the overall sentiment of a piece of text – whether positive, negative, or neutral.

Building on previous research that had identified a set of words with negatively toned financial reports, Ramesh and his colleagues examined the tone of all the SEC filings accessed by Fed staff between one meeting of the Federal Open Markets Committee (FOMC) and the next. The FOMC sets interest rates and guides monetary policy, and its meetings provide an opportunity for Fed officials to discuss growth forecasts and announce policy decisions.

The researchers then examined the Fed's growth forecasts to see if there was a relationship between the tone of the documents that Fed staff examined in the period between FOMC meetings and the forecasts they produced in advance of those meetings.

The team found close correlations between the tone of the reports accessed by the Fed and the agency’s forecasts of GDP, unemployment, housing starts and industrial production. The more negative the filings accessed prior to an FOMC meeting, for example, the gloomier the GDP forecast; the more positive the filings, the brighter the unemployment forecast.

Ramesh and his colleagues also compared the Fed's forecasts with those of the Society of Professional Forecasters (SPF), whose members span academia and industry. Intriguingly, the researchers found that while the errors in the SPF's forecasts could be attributed to the absence of the tonal information culled from the SEC filings, the errors in the Fed’s forecasts could not. This suggests both that the Fed was collecting qualitative information that the SPF was not—and that the agency was making remarkably efficient use of it.

"They weren’t leaving anything on the table," Ramesh says.

Having solved one mystery, Ramesh would like to focus on another; namely, how does the Fed identify bellwether firms in the first place?

Unfortunately, the SEC no longer makes IP address data publicly available, which means that Ramesh and his colleagues can no longer study which companies the Fed is most interested in. Nonetheless, Ramesh hopes to use the data they have already collected to build a model that can accurately predict which firms the Fed is most likely to follow. That would allow the team to continue studying the same companies that the Fed does, and, he says, “maybe come up with a way to track those firms in order to understand how the economy is going to move.”

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This article originally ran on Rice Business Wisdom and was based on research from K. Ramesh is Herbert S. Autrey Professor of Accounting at Jones Graduate School of Business at Rice University.

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Venus Aerospace closes $91 million Series B to scale hypersonic engine

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Houston-based Venus Aerospace has closed a $91 million Series B round and plans to scale the production of its hypersonic engine.

The round was led by Houston-based Mercury Fund with participation from Lockheed Martin Ventures, MESH, PEAK6, Draper Associates, Starboard Star Venture Capital, Green Sands Equity and other investors, according to a news release.

The investment comes about a year after Venus completed the first U.S. flight test of its high-thrust rotating detonation rocket engine (RDRE). The engine is expected to enable vehicles to travel four to six times the speed of sound from a conventional runway and is about 15 percent more efficient than traditional alternatives, according to the company.

Venus Aerospace says the latest round of funding will allow it to move the RDRE from demonstration to deployment and meet customer requirements for the near-term defense and space industries. The company says that the reusable RDRE is designed with a "common propulsion architecture" that can work for multiple industries and mission types.

“This financing marks an important step in moving Venus from breakthrough demonstration to scaled capability,” Sassie Duggleby, co-founder and CEO, said in the news release. “Our customers need propulsion systems that go farther, can be produced reliably and are built on supply chains they can trust. We are advancing that capability with American engineering and manufacturing talent to strengthen U.S. defense, expand space access and support the future of high-speed flight.”

Venus Aerospace raised a $20 million Series A in 2022, led by Wyoming-based Prime Movers Lab. At the time, the company said it would put the funding toward three main technologies: a next-generation rocket engine, aircraft shape and leading-edge cooling system.

The company also picked up an investment from Lockheed Martin Ventures, the investment arm of aerospace and defense contractor Lockheed Martin, in November 2025—in addition to funding from other investors over the years.

“Since our initial investment, Venus has progressed very quickly in its technology development," Chris Moran, vice president and general manager of Lockheed Martin Ventures, added in the release. "Our reinvestment in Venus recognizes Venus’ accomplishments to date and focus on speed to manufacture, cost management and reduction of supply chain constraints. Venus is working effectively to position its propulsion system for the production scale required by defense programs.”

"Venus is exactly the kind of company Houston capital should be backing," Blair Garrou, co-founder and managing partner at Mercury Fund, added in the release. "It combines multiple frontier technologies, domestic manufacturing and clear commercial and national security relevance. We believe this team is positioned to lead an important new chapter in defense and space, and we are proud to support a company building breakthrough technology here in Texas."

Venus Aerospace and Houston clean tech startup Vaulted Deep were named to the World Economic Forum's Technology Pioneers community earlier this summer. Read more here.

Intuitive Machines lands $148M as part of NASA Moon Base funding

to the moon

Houston-based Intuitive Machines has been awarded $148.3 million to deliver its Nova-C lander to the moon by 2028. The funding is part of $600 million that NASA recently awarded to three companies as part of the agency’s Moon Base Program.

The contracts aim to support sustained human presence and commercial operations on the Moon. Austin-based Firefly Aerospace was awarded $144.2 million by NASA for one mission and Pittsburgh-based Astrobotic netted $297.9 million for two lunar landings. Intuitive Machine's award is the company's sixth task order under NASA's Commercial Lunar Payload Services (CLPS) program.

“We’re building a proving ground for Moon Base operations,” Ryan Stephan, NASA’s Moon Base acting director of cargo landers, said in a news release. “Accelerating our Moon mission ordering cadence and launch opportunities enable us to move quickly to learn, iterate, and improve.”

Under the latest task order, Intuitie Machines will deliver three scientific and operational payloads to the moon, which include a:

  • Linear Energy Transfer Spectrometer (LETS) radiation monitor to gather critical environmental safety data
  • Advanced stereo cameras to analyze surface-plume interactions (SCALPSS)
  • Laser retroreflector array (LRA) for precise cislunar positioning

The funding breakdown includes a $68.6 million base contract and a $79.7 million performance incentive for Intuitive Machines.

The company says the funding will allow it to create a standardized and repeatable "lunar utility pipeline" for delivering cargo to the moon.

"We are shifting the paradigm from custom aerospace engineering to commercial mass production of lunar infrastructure," Steve Altemus, CEO of Intuitive Machines, said in a separate news release. "Our flight-proven Nova-C platform allows us to build, test, and deploy multiple landers in parallel using Industry 4.0-powered manufacturing. This contract directly advances our core mission to provide persistent, reliable, and commercial baseline of transport, connectivity, and operations that allows our customers to stay longer and achieve more on the Moon."

NASA also shared that it is exploring plans to send PROMISE, a rover based on the Mars Perseverance and Curiosity rovers, to the moon and it plans to seek proposals for additional lunar lander missions, technology demonstrations, a communications and navigation satellite network, and new science payloads to support its lunar outpost. NASA is developing its Moon Base near the lunar South Pole. The agency expects it to come to fruition sometime after 2032.

Intuitive Machines had received its last CLPS award for $180.4 million in March 2026. It will be the first mission to utilize the company's larger cargo lunar lander, Nova-D. The company was also recently awarded a $1 million grant from Maryland Gov. Wes Moore to expand its robotics operations in the state.

UT team develops wearable technology for atmospheric water harvesting

In The Air

Engineers at the University of Texas at Austin have developed a prototype jacket that harvests clean drinking water directly from the atmosphere, and it works even in the driest desert conditions.

The research, published in Science Advances, marks the latest milestone in nearly a decade of work by materials scientist and chair professor Guihua Yu and his team at the Cockrell School of Engineering's Walker Department of Mechanical Engineering and Texas Materials Institute. The wearable technology marks a significant leap: instead of a bulky, stationary machine, this jacket does the work.

Photo courtesy of UT Austin

"We have been working on atmospheric water harvesting technology for a number of years," Yu says. "This current version is even more wearable. We're transitioning from conventional, more stationary water harvesting to something truly portable and personal."

Yu's lab first published work on hydrogel-based water harvesting around 2019, and the jacket is the latest evolution of that platform, now called AirGel. Last year, the broader AirGel invention won the top prize in the graduate category of the National Collegiate Inventors Competition.

The jacket is woven with specially engineered hydrogel fibers; ultra-porous materials that attract and absorb moisture from the surrounding air much like a household desiccant. Unlike a desiccant, the material doesn't require intense heat to release that water. The hydrogel is thermally responsive, meaning a modest rise in temperature — even from mild solar heating — is enough to release the water it has captured.

Condenser test in AustinSo, somebody would be wearing the jacket, or perhaps carrying this gel-like textile as a blanket, as it passively absorbs moisture from the air. Then they would detach the textile panels and place them into a small, portable collector unit; essentially a compact heater. The water evaporates out of the textile, condenses inside the collector, and drips out as clean, drinkable water.

"It immediately becomes drinkable because it already goes through the distillation process," Yu explains.

In trials, the jacket produced between 400 and 900 milliliters of water per day depending on humidity, or roughly 14-30 ounces, nearly a quart, depending on the air's humidity. With one kilogram of the textile, the researchers found they could generate approximately 3.7-4 liters of water in arid conditions, and potentially double that in humid ones. So far, the team has tried the jacket out in very dry, semi-dry, and humid areas, and the jacket was able to pull water from each climate.

Lead researcher Chuxin Lei, a postdoctoral researcher on Yu's team and co-author on the paper, says the goal was to rethink who this technology could serve.

Portable bag contents

"Many current [atmospheric water harvesting] systems are still built as rigid or stationary platforms, making them less suitable for people who are moving, working outdoors, or operating in some remote environment. This lead us to ask whether we could build a water harvesting system that could become more like clothing — light, wearable, flexible, and naturally suited for personal use," Lei says.

The potential applications are wide-ranging. Yu's team has previously worked with the Department of Defense on water solutions for soldiers, where water logistics can be dangerous and costly. The technology could also serve hikers, emergency responders, disaster relief workers, and agricultural and field workers. Anyone who needs clean water on the go and far from infrastructure.

The team also sees a potential future where the technology complements large-scale centralized water systems rather than replacing them.

"Our solution cannot be a universal solution for all," Yu acknowledges. "But I think it's an extremely important alternative."

For now, the jacket is still a laboratory prototype, but Yu and Lei are optimistic. With the right industry partnerships, they say, the technology could realistically reach commercial scale within three to five years.

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This article originally appeared on CultureMap.com, written by Natalie Grigson.

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