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.

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

houston voices

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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Houston lab explores how AI bots can help the elderly

AI for aging

The University of Houston’s Empathetic Lifespan AI & Robotics for Aging (ELARA) Lab is currently conducting research into how AI bots may be able to help the elderly live more social and independent lives through several ongoing initiatives.

The lab officially launched last month as part of the Gerald D. Hines College of Architecture & Design under the leadership of Assistant Professor Chorong Park. Part of the lab’s mission is tackling ongoing problems with aging, such as dealing with disabilities and social isolation. Researchers’ current work is focused on designing a new AI companion bot specifically tailored to the needs of older people.

“We need to take all the needs of older adults seriously,” Park said in a news release. “They won't use the robot if they don't feel at ease or if they feel they are being constantly watched.”

The field testing of new AI bots in this population hopes to overcome several traditional obstacles in technology use among the elderly. A study by Park shows that many older people have a fear of overt surveillance when using advanced AI. There is also ageism to consider. Most new technologies are designed with younger and employed buyers in mind, not retirees who may need help remembering daily tasks or accessing important information.

“The more older adults are excluded from technology development, the worse those technology gaps will become,” Park said. “AI and the majority of technologies are created for younger people, so my research method integrates older adults directly into the design process.”

ELARA recently collaborated with the Mamie George Community Center in Richmond, Texas, to track seniors’ response to desktop AI bots like Emo and Cupboo. Researchers also had participants use air-dry modeling clay to create their ideal robotic companion.

While the eventual AI bot may be able to help the elderly feel less isolated and more supported, there are concerns to consider. A study published in the Asian Journal of Psychology charted the development of delusional thinking in a 72-year-old woman who became convinced the empathic-response bot was in love with her. The rise of “AI psychosis” has the potential to exacerbate mental health problems, particularly in socially isolated people, which a quarter of Americans over the age of 65 are.

ELARA’s research is focused on creating “pet-like” AI models with enhanced trust cues. If it can overcome the dangers of socially isolated people relying on AI for companionship, it could be a big step forward for independent aging.

SpaceX IPO set to be biggest ever and could make Elon Musk a trillionaire

IPO News

SpaceX says it plans to raise up to $75 billion when it goes public this month, setting the stage for the largest-ever stock market debut and putting Elon Musk on course to becoming the world's first trillionaire.

The company, formally known as Space Exploration Technologies Corp., said Wednesday it will sell 555.6 million shares at $135 a piece in an initial public offering. The estimated proceeds would easily top the $26 billion raised by oil giant Saudi Aramco in 2019. The offering would also give SpaceX a market value of $1.77 trillion. Only six companies in the S&P 500 are currently worth more, with Nvidia tops at $5.2 trillion.

Besides the size of the offering and the expected proceeds, SpaceX's amended prospectus updates details about how much control of the company Musk will have. As SpaceX's CEO, chief technical officer and chairman, Musk's voting power will come primarily through his ownership of 5.22 billion Class B shares, which give the holder 10 votes for every share held. According to the filing, Musk would have 82.4% of the voting power in the company.

Forbes currently values Musk's net worth at $826 billion and his stake in SpaceX at $542 billion. The estimated value of his SpaceX holdings was based on an overall value for the company of $1.25 trillion. Based on those numbers, a $1.77 trillion valuation for SpaceX would boost Musk's net worth by $223 billion, making him a trillionaire. However, much of Musk's worth is in stock that he has yet to cash in.

Even as it makes a bid for a blockbuster market debut, SpaceX is currently losing billions of dollars a year. The filing shows that the company lost $2.6 billion from operations last year on $18.7 billion in revenue, and the losses kept piling up at the start of this year, too.

Fantastical plans

Time will tell how SpaceX fares on the market. Musk's plans for the company are as fantastical as the money he hopes raise in the sale.

Colorful, even frightening in parts, the IPO document strikes a contrast with the typically dry, technical prose in IPO documents, detailing plans to use proceeds from the sale to help put men on the moon again and perhaps even Mars. In one section, it talks of a need to build "a permanent human colony" on the red planet with "at least one million inhabitants" as existential threats loom that could consign man to "the same fate as the dinosaurs."

Musk has almost equally ambitious plans for his other publicly traded company, Tesla. His goal is to transform the maker of electric vehicles into a producer of robotaxis and humanoid robots. Dan Ives of Wedbush Securities wrote in a research note that he expects Tesla and SpaceX to merge next year.

AI plays a key role

Key to the success of both companies — and any merged entity — is artificial intelligence. In its IPO filing, SpaceX says it sees potential revenue from AI of up to $26.5 trillion. But that depends on another lofty Musk ambition — putting data centers in space, which is not technologically possible at the moment.

Transforming his space company into a primarily AI-focused company will be a challenge for Musk, who started xAI in 2023 with 11 other co-founders who have all since left. Some were recruited away by rivals.

Its main AI product, the chatbot Grok, is "less impressive than anything that we see from any other major player in the space, whether that's OpenAI, or Anthropic, or (Google's) Gemini," said IDC analyst Arnal Dayaratna.

Dayaratna said that doesn't mean SpaceX doesn't have potential as a major AI player, thanks in part to its computing partnership with Anthropic and Musk's recent deal that gave SpaceX the rights to buy AI coding tool Cursor for $60 billion later this year. Folding in Cursor's capabilities would give SpaceX access to the coveted business customers now using Anthropic's Claude or OpenAI's ChatGPT.

SpaceX plans to use the net proceeds from the IPO to fund the expansion of infrastructure for its AI and rocket businesses, and to beef up the constellation of satellites that power Starlink Mobile, among other investments.

The company plans to list on the Nasdaq under the symbol "SPCX" and could begin trading as soon as the end of next week.

And SpaceX isn't the only colossal market debut investors are now bracing for. Earlier this week, Anthropic submitted a confidential filing with the U.S. Securities and Exchange Commission to officially start its own IPO clock.

OpenAI has not yet reported filing the initial SEC paperwork, but an IPO from the ChatGPT maker is widely expected.

"This listing represents the first major test for public markets after years of muted IPO activity with SpaceX paving the way for AI giants Anthropic and OpenAI to follow soon after," Ives wrote.

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Associated Press Technology Writer Matt O'Brien contributed.

New UH survey reveals concerns over AI data center growth in Houston

data findings

A new report out of the University of Houston shows that area residents remain wary of the long-term effects of operating data centers.

The recent survey from the University of Houston’s latest SPACE City Panel, conducted by the Center for Public Policy at the Hobby School of Public Affairs, shows that while 85 percent of Houston-area residents use AI, nearly 63 percent oppose the construction of AI data centers within 1 mile of their homes.

Respondents’ concerns centered around data centers’ high energy demand and the area’s power grid reliability. According to the survey, 32 percent of residents who oppose local data center projects would be more likely to support the centers if they relied on renewable energy over fossil fuels.

“Respondents understand that AI can bring economic and educational benefits, but they are also concerned about the physical infrastructure needed to fuel AI, especially data centers,” Soran Mohtadi, post-doctoral fellow at the Hobby School and a researcher on the report, said in a news release. “This physical infrastructure demands more electricity and water, leading to environmental impacts.”

Experts estimate that 6.5 gigawatts of data center capacity will be added to the Texas grid by 2030. And Houston’s data center capacity is predicted to more than double by 2028.

The Electric Reliability Council of Texas also projects electricity demand could reach 218 gigawatts by 2031, which would be more than double the record peak set in August 2023. Data centers are expected to account for 86 gigawatts of that new demand.

Survey respondents also said they are concerned about the state's future water supply, given the large amounts of water that data centers need to stay cool.

In terms of who’s responsible for that issue, 57.6 percent of respondents said they put the onus on Texas lawmakers, while 31.5 percent say tech companies should be responsible.

Additionally, more than 75 percent of respondents believed that data center developers and technology companies—not residents—should bear the cost of infrastructure upgrades to support data centers.

“Every decision legislators make has implications on residents’ everyday lives and local infrastructure now and in the future,” Maria P. Perez Arguelles, lead researcher on the report and research assistant professor at the Hobby School, added in the news release. “This issue is going to become more important in years to come, so this is just the beginning.”

Read the full report here.

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