Forecasting retail sales just got smarter, thanks to Google searches

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

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.

Nurturing what is known as “promotion focus” can help managers spot fresh ideas.
Photo by Diego PH on Unsplash

Houston researchers: Here's what it takes to spot a great new idea

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Having a “promotion focus” really does create a mental lens through which new ideas are more visible.

Key findings:

  • New ideas can be crucially important to businesses, driving innovation and preventing stagnation.
  • Recognizing those ideas, though, isn’t always easy.
  • Nurturing what is known as “promotion focus” can help managers spot fresh ideas.

Whenever the late surgeon Michael DeBakey opened a human chest, he drew on a lifetime of resources: the conviction that heart surgery could and should be vastly improved, the skill to venture beyond medicine’s known horizons and the vision to recognize new ideas in everyone around him, no matter how little formal training they had.

Appreciating new ideas is the heartbeat of business as well as medicine. But innovation is surprisingly hard to recognize. In a pioneering 2017 article, Rice Business Professor Jing Zhou and her colleagues published their findings on the first-ever study of the traits and environments that allow leaders to recognize new ideas.

Recent decades have produced a surge of research looking at how and when employees generate fresh ideas. But almost nothing has been written on another crucial part of workplace creativity: a leader’s ability to appreciate new thinking when she sees it.

Novelty, after all, is what drives company differentiation and competitiveness. Work that springs from new concepts sparks more investigation than work based on worn, already established thought. Companies invest millions to recruit and pay star creatives.

Yet not every leader can spot a fresh idea, and not every workplace brings out that kind of discernment. In four separate studies, Zhou and her coauthors examined exactly what it takes to see a glittering new idea wherever it appears. Their work sets the stage for an entirely new field of future research.

First, though, the team had to define their key terms. “Novelty recognition” is the ability to spot a new idea when someone else presents it. “Promotion focus,” previous research has shown, is a comfort level with new experiences that evokes feelings of adventure and excitement. “Prevention focus” is the opposite trait: the tendency to associate new ideas with danger, and respond to them with caution.

But does having “promotion focus” as opposed to “prevention focus” color the ability to see novelty? To find out, Zhou’s team came up with an ingenious test, artificially inducing these two perspectives through a series of exercises. First, they told 92 undergraduate participants that they would be asked to perform a set of unrelated tasks. Then the subjects guided a fictional mouse through two pencil and paper maze exercises.

While one exercise showed a piece of cheese awaiting the mouse at the end of the maze (the promise of a reward), the other maze depicted a menacing owl nearby (motivation to flee).

Once the participants had traced their way through the mazes with pencils, they were asked to rate the novelty of 33 pictures — nine drawings of space aliens and 24 unrelated images. The students who were prepped to feel an adventurous promotion focus by seeking a reward were much better at spotting the new or different details among these images than the students who’d been cued to have a prevention focus by fleeing a threat.

The conclusion: a promotion focus really does create a mental lens through which new ideas are more visible.

Zhou’s team followed this study with three additional studies, including one that surveyed 44 human resource managers from a variety of companies. For this study, independent coders rated the mission statements of each firm, assessing their cultures as “innovative” or “not innovative.” The HR managers then evaluated a set of written practices — three that had been in use for years, and three new ones that relied on recent technology. The managers from the innovative companies were much better at rating the new HR practices for novelty and creativity. To recognize novelty, in other words, both interior and external environments make a difference.

The implications of the research are groundbreaking. The first ever done on this subject, it opens up a completely new research field with profound questions. Can promotion focus be created? How much of this trait is genetic, and how much based on natural temperament, culture, environment and life experience? Should promotion focus be cultivated in education? If so, what would be the impact? After all, there are important uses for prevention focus, such as corporate security and compliance. Meanwhile, how can workplaces be organized to bring out the best in both kinds of focus?

Leaders eager to put Zhou’s findings to use right away, meanwhile, might look to the real-world model of Michael DeBakey. Practice viewing new ideas as adventures, seek workplaces that actively push innovation and, above all, cultivate the view that every coworker, high or low, is a potential source of glittering new ideas.

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This article originally appeared on Rice Business Wisdom.

Jing Zhou is the Mary Gibbs Jones Professor of Management and Psychology in Organizational Behavior at the Jones Graduate School of Business of Rice University. Zhou, J., Wang, X., Song, J., & Wu, J. (2017). "Is it new? Personal and contextual influences on perceptions of novelty and creativity." Journal of Applied Psychology, 102(2): 180-202.

ChatGPT enhances creativity and problem-solving in ways that traditional search tools can’t match. Photo courtesy of Rice Business Wisdom

Houston researchers find AI provides fresh perspectives to everyday problems

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We all know ChatGPT has forever changed how we do business. It’s modified how we access information, compose content and analyze data. It’s revolutionized the future of work and education. And it has transformed the way we interact with technology.

Now, thanks to a recent paper by Jaeyeon (Jae) Chung (Rice Business), we also know it’s making us better problem solvers.

Key findings:

  • A recent study finds ChatGPT-generated ideas are deemed an average of 15% more creative than traditional methods.
  • ChatGPT enhances “incremental,” but not “radical,” innovation.
  • ChatGPT boosts creativity in tasks normally associated with human traits, like empathy-based challenges.

According to the study published in Nature Human Behavior by Chung and Byung Cheol Lee (University of Houston), ChatGPT enhances our problem-solving abilities, especially with everyday challenges. Whether coming up with gifts for your teenage niece or pondering what to do with an old tennis racquet, ChatGPT has a unique ability to generate creative ideas.

“Creative problem-solving often requires connecting different concepts in a cohesive way,” Chung says. “ChatGPT excels at this because it pulls from a vast range of data, enabling it to generate new combinations of ideas.”

Can ChatGPT Really Make Us More Creative?

Chung and Lee sought to answer a central question: Can ChatGPT help people think more creatively than traditional search engines? To answer this, they conducted five experiments.

Each experiment asked participants to generate ideas for solving challenges, such as how to repurpose household items. Depending on the experiment, participants were divided into one of two or three groups: one that used ChatGPT; one that used conventional web search tools (e.g., Google); and one that used no external tool at all. The resulting ideas were evaluated by both laypeople and business experts based on two critical aspects of creativity: originality and appropriateness (i.e., practicality).

In one standout experiment, participants were asked to come up with an idea for a dining table that doesn’t exist on the market. The ChatGPT group came up with suggestions like a “rotating table,” a “floating table” and even “a table that adjusts its height based on the dining experience.” According to both judges and experts, the ChatGPT group consistently delivered the most creative solutions.

On average, across all experiments, ideas generated with ChatGPT were rated 15% more creative than those produced by traditional methods. This was true even when tasks were specifically designed to require empathy or involved multiple constraints — tasks we typically assume humans might be better at performing.

However, Chung and Lee also found a caveat: While ChatGPT excels at generating ideas that are “incrementally” new — i.e., building on existing concepts — it struggles to produce “radically” new ideas that break from established patterns. “ChatGPT is an incredible tool for tweaking and improving existing ideas, but when it comes to disruptive innovation, humans still hold the upper hand,” Chung notes.

Charting the Next Steps in AI and Creativity

Chung and Lee’s paper opens the door to many exciting avenues for future study. For example, researchers could explore whether ChatGPT’s creative abilities extend to more complex, high-stakes problem-solving environments. Could AI be harnessed to develop groundbreaking solutions in fields like medicine, engineering or social policy? Understanding the nuances of the collaboration between humans and AI could shape the future of education, work and even (as many people fear) art.

For professionals in creative fields like product design or marketing, the study holds especially significant implications. The ability to rapidly generate fresh ideas can be a game-changer in industries where staying ahead of trends is vital. For now, take a second before you throw out that old tennis racquet. Ask ChatGPT for inspiration — you’ll be surprised at how many ideas it comes up with, and how quickly.

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This article originally appeared on Rice Business Wisdom. Based on research by Jaeyeon (Jae) Chung and Byung Cheol Lee (University of Houston). Lee and Chung, “An empirical investigation of the impact of ChatGPT on creativity.” Nature Human Behavior (2024): https://doi.org/10.1038/s41562-024-01953-1.


Grocery purchase data can accurately predict credit risk for individuals without traditional credit scores, potentially broadening the pool of qualified loan applicants. Photo via Unsplash

Houston researchers find alternate data for loan qualification

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Millions of consumers who apply for a loan to buy a house or car or start a business can’t qualify — even if they’re likely to pay it back. That’s because many lack a key piece of financial information: a credit score.

The problem isn’t just isolated to emerging economies. Exclusion from the financial system is a major issue in the United States, too, where some 45 million adults may be denied access to loans because they don’t have a credit history and are “credit invisible.”

To improve access to loans and peoples’ economic mobility, lenders have started looking into alternative data sources to assess a loan applicant’s risk of defaulting. These include bank account transactions and on-time rental, utility and mobile phone payments.

A new article by Rice Business assistant professor of marketing Jung Youn Lee and colleagues from Notre Dame and Northwestern identifies an even more widespread data source that could broaden the pool of qualified applicants: grocery store receipts.

As metrics for predicting credit risk, the researchers found that the types of food, drinks and other products consumers buy, and how they buy them, are just as good as a traditional credit score.

“There could be privacy concerns when you think about it in practice,” Lee says, “so the consumer should really have the option and be empowered to do it.” One approach could be to let consumers opt in to a lender looking at their grocery data as a second chance at approval rather than automatically enrolling them and offering an opt-out.

To arrive at their findings, the researchers analyzed grocery transaction data from a multinational conglomerate headquartered in a Middle Eastern country that owns a credit card issuer and a large-scale supermarket chain. Many people in the country are unbanked. They merged the supermarket’s loyalty card data and issuer’s credit card spending and payment history numbers, resulting in data on 30,089 consumers from January 2017 to June 2019. About half had a credit score, 81% always paid their credit card bills on time, 12% missed payments periodically, and 7% defaulted.

The researchers first created a model to establish a connection between grocery purchasing behavior and credit risk. They found that people who bought healthy foods like fresh milk, yogurt and fruits and vegetables were more likely to pay their bills on time, while shoppers who purchased cigarettes, energy drinks and canned meat tended to miss payments. This held true for “observationally equivalent” individuals — those with similar income, occupation, employment status and number of dependents. In other words, when two people look demographically identical, the study still finds that they have different credit risks.

People’s grocery-buying behaviors play a factor in their likelihood to pay their bills on time, too. For example, cardholders who consistently paid their credit card bill on time were more likely to shop on the same day of the week, spend similar amounts across months and buy the same brands and product categories.

The researchers then built two credit-scoring predictive algorithms to simulate a lender’s decision of whether or not to approve a credit card applicant. One excludes grocery data inputs, and the other includes them (in addition to standard data). Incorporating grocery data into their decision-making process improved risk assessment of an applicant by a factor of 3.11% to 7.66%.

Furthermore, the lender in the simulation experienced a 1.46% profit increase when the researchers implemented a two-stage decision-making process — first, screening applicants using only standard data, then adding grocery data as an additional layer.

One caveat to these findings, Lee and her colleagues warn, is that the benefit of grocery data falls sharply as traditional credit scores or relationship-specific credit histories become available. This suggests the data could be most helpful for consumers new to credit.

Overall, however, this could be a win-win scenario for both consumers and lenders. “People excluded from the traditional credit system gain access to loans,” Lee says, “and lenders become more profitable by approving more creditworthy people.”

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This article originally ran on Rice Business Wisdom based on research by Rice University's Jung Youn Lee, Joonhyuk Yang (Notre Dame) and Eric Anderson (Northwestern). “Using Grocery Data for Credit Decisions.” Forthcoming in Management Science. 2024: https://doi.org/10.1287/mnsc.2022.02364.


Using biased statistics in hiring makes it more difficult to predict job performance. Photo via Getty Images

Houston research finds race, gender ineffective predictors of employee productivity

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The Latin phrase scientia potentia est translates to “knowledge is power.”

In the world of business, there’s a school of thought that takes “knowledge is power” to an extreme. It’s called statistical discrimination theory. This framework suggests that companies should use all available information to make decisions and maximize profits, including the group characteristics of potential hires — such as race and gender — that correlate with (but do not cause) productivity.

Statistical discrimination theory suggests that if there's a choice between equally qualified candidates — let's say, a man and a woman — the hiring manager should use gender-based statistics to the company's benefit. If there's data showing that male employees typically have larger networks and more access to professional development opportunities, the hiring manager should select the male candidate, believing such information points to a more productive employee.

Recent research suggests otherwise.

A peer-reviewed study out of Rice Business and Michigan Ross undercuts the premise of statistical discrimination theory. According to researchers Diana Jue-Rajasingh (Rice Business), Felipe A. Csaszar (Michigan) and Michael Jensen (Michigan), hiring outcomes actually improve when decision-makers ignore statistics that correlate employee productivity with characteristics like race and gender.

Here's Why “Less is More”

Statistical discrimination theory assumes a correlation between individual productivity and group characteristics (e.g., race and gender). But Jue-Rajasingh and her colleagues highlight three factors that undercut that assumption:

  • Environmental uncertainty
  • Biased interpretations of productivity
  • Decision-maker inconsistency

This third factor plays the biggest role in the researchers' model. “For statistical discrimination theory to work,” Jue-Rajasingh says, “it must assume that managers are infallible and decision-making conditions are optimal.”

Indeed, when accounting for uncertainty, inconsistency and interpretive bias, the researchers found that using information about group characteristics actually reduces the accuracy of job performance predictions.

That’s because the more information you include in the decision-making process, the more complex that process becomes. Complex processes make it more difficult to navigate uncertain environments and create more space for managers to make mistakes. It seems counterintuitive, but when firms use less information and keep their processes simple, they are more accurate in predicting the productivity of their hires.

The less-is-more strategy is known as a “heuristic.” Heuristics are simple, efficient rules or mental shortcuts that help decision-makers navigate complex environments and make judgments more quickly and with less information. In the context of this study, published by Organization Science, the heuristic approach suggests that by focusing on fewer, more relevant cues, managers can make better hiring decisions.

Two Types of Information "Cues"

The “less is more” heuristic works better than statistical discrimination theory largely because decision makers are inconsistent in how they weight the available information. To factor for inconsistency, Jue-Rajasingh and her colleagues created a model that reflects the “noise” of external factors, such as a decision maker’s mood or the ambiguity of certain information.

The model breaks the decision-making process into two main components: the environment and the decision maker.

In the environment component, there are two types of information, or “cues,” about job candidates. First, there’s the unobservable, causal cue (e.g., programming ability), which directly relates to job performance. Second, there's the observable, discriminatory cue (e.g., race or gender), which doesn't affect how well someone can do the job but, because of how society has historically worked, might statistically seem connected to job skills.

Even if the decision maker knows they shouldn't rely too much on information like race or gender, they might still use it to predict productivity. But job descriptions change, contexts are unstable, and people don’t consistently consider all variables. Between the inconsistency of decision-makers and the environmental noise created by discriminatory cues, it’s ultimately counterproductive to consider this information.

The Bottom Line

Jue-Rajasingh and her colleagues find that avoiding gender- and race-based statistics improves the accuracy of job performance predictions. The fewer discriminatory cues decision-makers rely on, the less likely their process will lead to errors.

That said: With the advent of AI, it could become easier to justify statistical discrimination theory. The element of human inconsistency would be removed from the equation. But because AI is often rooted in biased data, its use in hiring must be carefully examined to prevent worsening inequity.

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This article originally ran on Rice Business Wisdom based on research by Rice University's Diana Jue-Rajasingh, Felipe A. Csaszar (Michigan) and Michael Jensen (Michigan). For more, see Csaszar, et al. “When Less is More: How Statistical Discrimination Can Decrease Predictive Accuracy.”

As corporate debt markets continue to grow in importance, it will become crucial for investors and regulators to understand the nuanced factors influencing their liquidity. Photo via Getty Images

Rice research on bond and stock market differences, earnings variations

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At the end of every quarter, publicly traded companies announce their profits and losses in an earnings report. These updates provide insight into a company’s performance and, in theory, give investors and shareholders clarity on whether to buy, sell or hold. If earnings are good, the stock price may soar. If they’re down, the price might plunge.

However, the implications for the stock price may not be immediately clear to all investors. In the face of this uncertainty, sellers will ask for high prices, and buyers will offer low ones, creating a significant “bid-ask spread.” When this happens, it becomes more costly to trade, and the stock becomes less liquid.

This is a well-documented effect on equity stock markets. However, according to research by Stefan Huber (Rice Business), Chongho Kim (Seoul National University) and Edward M. Watts (Yale SOM), the corporate bond market responds differently to earnings news. This is because bond markets differ from stock markets in a significant way.

Stocks v. Bonds: What Happens When Earnings Are Announced?

Equities are usually traded on centralized exchanges (e.g., New York Stock Exchange). The exchange automatically queues up buyers and sellers according to the quote they’ve entered. Trades are executed electronically, and the parties involved are typically anonymous. A prospective buyer might purchase Microsoft shares from someone drawing down their 401(k) — or they could be buying from Bill Gates himself.

Corporate bond markets work differently. They are “over-the-counter” (OTC) markets, meaning a buyer or seller needs to find a counterparty to trade with. This involves getting quotes from and negotiating with potential counterparties. This is an inherent friction in bond trading that results in much higher costs of trading in the form of wider bid-ask spreads.

Here’s what Huber and his colleagues learned from the research: Earnings announcements prompt many investors to trade. And on OTC markets, potential buyers and sellers become easier to find and negotiate with.

A Stronger Bargaining Position for Bonds

According to Huber, “When earnings information comes out, a lot of people want to trade. In bond markets, that makes it much easier to find someone to trade with. The more options you have to trade, the stronger your bargaining position becomes, and the lower your trading costs go.”

He compares the process to shopping in a market with a flexible approach to pricing.

“Let's say you're at a farmers market and you want to buy an apple,” Huber says. “If there is only one seller, you buy the apple from that person. They can ask for whatever price they want. But if there are multiple sellers, you can ask around, and there is potential to get a better price. The price you get depends on the number of options you have in trading partners.”

What’s at Stake?

Although bonds receive less attention than equities, the stakes are high. There is about $10 trillion in outstanding corporate debt in the U.S., and more than $34 billion in average daily trading volume.

A detailed record of bond trades is available from the Financial Industry Regulatory Authority (FINRA), which requires that trades be reported via their Trade Reporting and Compliance Engine (TRACE).

The study from Huber and co-authors uses an enhanced version of TRACE to examine trades executed between 2002 and 2020. The team analyzed the thirty-day periods before and after earnings announcements to gather data about volume, bid-ask spreads and other measures of liquidity.

They find that, like on the stock market, there are more investors and broker-dealers trading bonds around earnings announcements. However, unlike on the stock market, transaction costs for bonds decrease by 6 to 7 percent in the form of bid-ask spreads.

What Sets This Research Apart?

“Taking a purely information asymmetry-based view would predict that what happens to stock liquidity would also happen to bonds,” Huber says. “A piece of information drops, and some people are better able to work with it, so others price protect, and bid-ask spreads and the cost of trading go up.”

“But if you consider the search and bargaining frictions in bond markets, you get a more nuanced picture. While information asymmetry increases, like it does on stock markets, the information prompts more investors into bond trading, which makes it easier to find counterparties and get better transaction prices. Consequently, bid-ask spreads go down. This search and bargaining friction does not really exist on equities exchanges. But we cannot ignore it in OTC markets.”

As corporate debt markets continue to grow in importance, it will become crucial for investors and regulators to understand the nuanced factors influencing their liquidity. This study provides a solid foundation for future research.

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This article originally ran on Rice Business Wisdom. For more, see “Earnings News and Over-the-Counter Markets.” Journal of Accounting Research 62.2 (2024): 701-35.

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Texas falls to bottom of national list for AI-related job openings

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For all the hoopla over AI in the American workforce, Texas’ share of AI-related job openings falls short of every state except Pennsylvania and Florida.

A study by Unit4, a provider of cloud-based enterprise resource planning (ERP) software for businesses, puts Texas at No. 49 among the states with the highest share of AI-focused jobs. Just 9.39 percent of Texas job postings examined by Unit4 mentioned AI.

Behind Texas are No. 49 Pennsylvania (9.24 percent of jobs related to AI) and No. 50 Florida (9.04 percent). One spot ahead of Texas, at No. 47, is California (9.56 percent).

Unit4 notes that Texas’ and Florida’s low rankings show “AI hiring concentration isn’t necessarily tied to population size or GDP.”

“For years, California, Texas, and New York dominated tech hiring, but that’s changing fast. High living costs, remote work culture, and the democratization of AI tools mean smaller states can now compete,” Unit4 spokesperson Mark Baars said in a release.

The No. 1 state is Wyoming, where 20.38 percent of job openings were related to AI. The Cowboy State was followed by Vermont at No. 2 (20.34 percent) and Rhode Island at No. 3 (19.74 percent).

“A company in Wyoming can hire an AI engineer from anywhere, and startups in Vermont can build powerful AI systems without being based in Silicon Valley,” Baars added.

The study analyzed LinkedIn job postings across all 50 states to determine which ones were leading in AI employment. Unit4 came up with percentages by dividing the total number of job postings in a state by the total number of AI-related job postings.

Experts suggest that while states like Texas, California and Florida “have a vast number of total job postings, the sheer volume of non-AI jobs dilutes their AI concentration ratio,” according to Unit4. “Moreover, many major tech firms headquartered in California are outsourcing AI roles to smaller, more affordable markets, creating a redistribution of AI employment opportunities.”

Houston energy trailblazer Fervo closes $462 million Series E

Fresh Funds

Houston-based geothermal energy company Fervo Energy has closed an oversubscribed $462 million series E funding round, led by new investor B Capital.

“Fervo is setting the pace for the next era of clean, affordable, and reliable power in the U.S.,” Jeff Johnson, general partner at B Capital, said in a news release.

“With surging demand from AI and electrification, the grid urgently needs scalable, always-on solutions, and we believe enhanced geothermal energy is uniquely positioned to deliver. We’re proud to support a team with the technical leadership, commercial traction, and leading execution capabilities to bring the world’s largest next-generation geothermal project online and make 24/7 carbon-free power a reality.”

The financing reflects “strong market confidence in Fervo’s opportunity to make geothermal energy a cornerstone of the 24/7 carbon-free power future,” according to the company. The round also included participation from Google, a longtime Fervo Partner, and other new and returning investors like Devon Energy, Mitsui & Co., Ltd., Mitsubishi Heavy Industries and Centaurus Capital. Centaurus Capital also recently committed $75 million in preferred equity to support the construction of Cape Station Phase I, Fervo noted in the release.

The latest funding will support the continued buildout of Fervo’s Utah-based Cape Station development, which is slated to start delivering 100 MW of clean power to the grid beginning in 2026. Cape Station is expected to be the world's largest next-generation geothermal development, according to Fervo. The development of several other projects will also be included in the new round of funding.

“This funding sharpens our path from breakthrough technology to large-scale deployment at Cape Station and beyond,” Tim Latimer, CEO and co-founder of Fervo, added in the news release. “We’re building the clean, firm power fleet the next decade requires, and we’re doing it now.”

Fervo recently won Scaleup of the Year at the 2025 Houston Innovation Awards, and previously raised $205.6 million in capital to help finance the Cape Station earlier this year. The company fully contracted the project's capacity with the addition of a major power purchase agreement from Shell this spring. Fervo’s valuation has been estimated at $1.4 billion and includes investments and support from Bill Gates.

“This new investment makes one thing clear: the time for geothermal is now,” Latimer added in a LinkedIn post. “The world desperately needs new power sources, and with geothermal, that power is clean and reliable. We are ready to meet the moment, and thrilled to have so many great partners on board.”

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This article originally appeared on EnergyCapitalHTX.com.

Baylor center receives $10M NIH grant to continue rare disease research

NIH funding

Baylor College of Medicine’s Center for Precision Medicine Models received a $10 million, five-year grant from the National Institutes of Health last month that will allow it to continue its work studying rare genetic diseases.

The Center for Precision Medicine Models creates customized cell, fly and mouse models that mimic specific genetic variations found in patients, helping scientists to better understand how genetic changes cause disease and explore potential treatments.

The center was originally funded by an NIH grant, and its models have contributed to the discovery of several new rare disease genes and new symptoms caused by known disease genes. It hosts an online portal that allows physicians, families and advocacy groups to nominate genetic variants or rare diseases that need further investigation or new treatments.

Since its founding in 2020, it has received 156 disease/variant nominations, accepted 63 for modeling and produced more than 200 precision models, according to Baylor.

The center plans to use the latest round of funding to bring together more experts in rare disease research, animal modeling and bioinformatics, and to expand its focus and model more complex diseases.

Dr. Jason Heaney, associate professor in the Department of Molecular and Human Genetics at BCM, serves as the lead principal investigator of the center.

“The Department of Molecular and Human Genetics is uniquely equipped to bring together the diverse expertise needed to connect clinical human genetics, animal research and advanced bioinformatics tools,” Heaney added in the release. “This integration allows us to drive personalized medicine forward using precision animal models and to turn those discoveries into better care for patients.”

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