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

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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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Here's how Houston ranks among the best U.S. cities to start a career

New Horizons

College graduates staying in Houston are in the right place to be, according to a new WalletHub study. Houston has emerged on a new list of the 100 best places in America for starting a career.

Houston ranked 51st out of 182 U.S. cities based on its quality of life and vast opportunities for new college graduates transitioning into the workforce. The study compared each city based on 25 relevant metrics, like the availability of entry-level jobs, each city's annual job growth rate, workforce diversity, median annual income, housing affordability, and others.

Atlanta, Orlando, and Austin respectively comprised the top three best places to start a career.

Houston ranked 48th overall for its quality of life, and appeared No. 51 for its professional opportunities for new college graduates. Whether its starting a new business or entering a high-earning job field, Houston has many more opportunities than the vast majority of other cities on the list.

"The best cities for starting a career not only have a lot of job opportunities but also provide substantial income growth potential and satisfying work conditions," said WalletHub analyst Chip Lupo. "It’s also important to consider factors such as how fun a city is to live in or how good of a place it is for raising a family, to ensure life satisfaction outside of your career."

Other Texas hotspots for early career professionals
Austin boasts the best quality of life out of all 182 cities in the report, and the 10th best professional opportunities. The state capital also outperformed all other U.S. cities with the highest monthly average starting salaries for early career workers after being adjusted for the city's cost of living. Austin also offers the 15th highest number of entry level jobs per capita, the report said.

In a separate comparison of the cities with the largest share of residents aged 25 to 34, Austin ranked No. 5 nationally.

"In addition, Austin’s median annual household income is the 10th-highest in the nation, providing strong earning potential for those starting a career or a business," the report said. "Austin is also the sixth best city for singles, offering a vibrant social scene alongside strong career opportunities for young professionals."

Elsewhere in Texas, Dallas ranked as the second-best city in Texas for new grads to start a career and 12th nationally. Additional cities that made it into the top 100 best U.S. cities for early career professionals include Plano (No. 32), Irving (No. 42), Fort Worth (No. 64), Amarillo (No. 73), and San Antonio (No. 85).

The top 10 best cities for starting a career are:

  • No. 1 – Atlanta, Georgia
  • No. 2 – Orlando, Florida
  • No. 3 – Austin, Texas
  • No. 4 – Tampa, Florida
  • No. 5 – Miami, Florida
  • No. 6 – Charleston, South Carolina
  • No. 7 – Pittsburgh
  • No. 8 – Knoxville, Tennessee
  • No. 9 – Salt Lake City, Utah
  • No. 10 – Columbia, South Carolina
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This article first appeared on CultureMap.com.

Persona AI teams with Under Armour to protect next-gen robots

Future Fabrics

Houston-based Persona AI has cemented a partnership with sportswear manufacturer Under Armour to provide materials to protect future robots operating in dangerous conditions.

Through the partnership, Persona AI and Under Armour will launch a research initiative to explore whether advanced performance textiles can improve the durability and resilience of humanoid robots operating in harsh industrial environments.

“This is an opportunity to apply our innovation expertise in a new context,” Kyle Blakely, senior vice president of innovation, design studio, development, and testing at Under Armour, said in a news release. “Robotics presents a fascinating new design challenge, and we aim to play a leading role in shaping performance solutions for these environments. As humanoid systems take on more physically demanding roles, we see real potential to create new market opportunities, and we’re exploring how concepts like thermal management, abrasion resistance, and flexibility translate beyond sport."

Founded in June 2024 by former NASA engineer Nicolaus Radford and former Figure AI CTO Jerry Pratt, Persona AI has quickly risen to be a top name in the development of humanoid robotic systems. Radford previously was the principal investigator at NASA’s Dexterous Robotics Lab before becoming CEO of Nauticus Robotics. While at NASA, he was the chief engineer behind Robotnaut 2, the first humanoid robot on the International Space Station.

Persona AI raised preseed funding to develop humanoid robots designed to operate in shipyards and other industrial sites. The company has inked partnerships with HD Korea Shipbuilding & Offshore Engineering, HD Hyundai Robotic, and Korean manufacturing firm Vazil Company to create and deploy humanoid robots for complex welding tasks in shipyards.

These environments often involve exposure to dangerous chemicals, harsh weather and other potential hazards. The partnership between Persona AI and Under Armour will combine the clothing manufacturer’s development of resilient but flexible materials with the humanoid design of Persona AI.

Though best known for its sportswear, Under Armour produces a wide range of specialist fabrics and clothing, including an entire line used by the U.S. military. The company’s track record of developing high-performance fabrics built to withstand war zones and desert conditions makes it a strong partner in Persona AI’s latest endeavor.

“We chose to work with Under Armour because of their track record of innovation with these types of performance materials,” Radford said. “As we develop humanoids for intense and potentially hazardous environments, this collaboration helps us understand how advanced materials can enhance long-term reliability, thereby informing solutions to better protect workers in the field.”

Waymo suspends robotaxi service in Houston due to weather concerns

Transportation news

Waymo has suspended driverless car services in Houston and other major Texas cities, and in Atlanta, after one of its vehicles was stranded by flooding during heavy rains that will likely also hinder travel in a large swath of the U.S over the holiday weekend.

Severe thunderstorms with large hail and gusty winds were possible Friday, May 22 in Texas and other parts of the Southern and Central Plains, the National Weather Service said.

Forecasters warned of possible flash flooding along the Gulf Coast of Texas and Louisiana on Saturday, when rain and thunderstorms were expected across much of the central and eastern U.S.

The Waymo vehicle got stuck during a downpour in Atlanta on Wednesday that flooded streets and even part of a downtown highway. The vehicle was not occupied and was later recovered, the company said in a statement. At least one other Waymo vehicle was waylaid during the storm.

Waymo serves only the city of Atlanta in Georgia, and services Dallas, Houston, Austin, and San Antonio in Texas.

The company paused service in Texas “out of an abundance of caution for the forecasted severe weather,” the statement said.

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