Houston-based Complete Intelligence was just recognized by Capital Factory as the "Newcomer of the Year." Photo via completeintel.com

The business applications of artificial intelligence are boundless. Tony Nash realized AI's potential in an underserved niche.

His startup, Complete Intelligence, uses AI to focus on decision support, which looks at the data and behavior of costs and prices within a global ecosystem in a global environment to help top-tier companies make better business decisions.

"The problem that were solving is companies don't predict their costs and revenues very well," says Nash, the CEO and founder of Complete Intelligence. "There are really high error rates in company costs and revenue forecasts and so what we've done is built a globally integrated artificial intelligence platform that can help people predict their costs and their revenues with a very low error rate."

Founded in 2015, Complete Intelligence is an AI platform that forecasts assets and allows evaluation of currencies, commodities, equity indices and economics. The Woodlands-based company also does advanced procurement and revenue for corporate clients.

"We've spent a couple years building this," says Nash. "We have a platform that is helping clients with planning, finance, procurement and sales and a host of other things. We are forecasting equity markets; we are forecasting commodity prices, currencies, economics and trades. We built a model of the global economy and transactions across the global economy, so it's a very large, very detailed artificial intelligence platform."

That platform, CI Futures, has streamlined comprehensive price forecasting and data analysis, allowing for sound, data-based decisions.

"Our products are pretty simple," says Nash. "We have our basic off the shelf forecast which is called CI Futures, which is currencies, commodities, equities and economics and trade. Its basic raw data forecasts. We distribute that raw data on our website and other data distribution websites. We also have a product called Cost Flow, which is our procurement forecasting engine, where we build a material level forecasting for clients.


completeintel.com

"Then we have a product that we'll launch next year called Revenue Flow, which is a sales forecasting tool that will use balance of both client data and publicly available data to forecast client sales by product, by geography and so on and so forth. So we really only do three things: revenues, costs and raw data forecasts."

Forecasting across industries

Complete Intelligence's Cost Flow and Revenue Flow products are specific to direct clients. They are working with clients in the food and beverage sector, the energy sector, the chemical sector, and the technology sector.

"Anybody that manufactures a tangible good, should use our product," says Nash. "Because we can take their historical data we can configure their bills of material and they can see the exact cost and exact revenue of those products by month over time."

CI is not a consulting firm, so they offer their clients an annual license, which allows them to receive updated forecasts every month to understand how markets will iterate over time.

"We're integrating with the client's enterprise data," says Nash. "Whether it's their ERP system or their procurement system or their CRM, we're integrating with client's enterprise data, and we're creating forecast outlooks that are perfectly contextually relevant for client buying decisions."

Called out by Capital Factory

As a business solution, CI has garnered widespread industry confidence and accolades, such as Capital Factory's coveted "Newcomer of the Year" award, which recognizes innovative companies from a pool of 110 startups in Texas.

"Honestly, I couldn't believe it because with a startup like ours, there's so much hard work that goes into it, there's so much time, there's so much persistence," says Nash.

"And the types of startups that Capital Factory attracts are very competitive startups, so for us to receive this award, it's given us a huge amount of credibility in the market and it's really encouraged the team inside the company to understand that what we're doing is being recognized, it's meaningful and we're really going places."

From consulting to billions of monthly calculations

Nash is no stranger to going places. Before setting up shop in his native Texas, he lived in Singapore for 15 years where he started his career in sourcing and procurement for American retail firms.

"I became very sensitive to costs, cost inflections and I got very involved in global sourcing and international trade and then I did a couple of corporate turnarounds and start ups and so with that you see costs as an issue with those types of firms," Nash says.

He then worked with the Economist running their global research business. There, he grew familiar with how clients and customers use data. At IHS Markit, a global information provider.

"When I was working with those firms, those firms helped companies with planning," says Nash. "The problem is that those firms have very large errors in their forecasts. It is not just the internal forecasts that have a 30 percent or higher error rate in their forecasts, even the industry forecasters typically have around a 20 percent error rates in their forecasts.

"Even the people who should actually know where prices are going are not very good forecasters. With Complete Intelligence, we wanted to use data and use artificial intelligence to machine learning to create a better way to identify where costs and revenues will go for companies."

Every month, CI runs billions of calculations. They test their error rates and record them for clients that request them. With 700 assets that they show publicly, CI their average error rate is 3.7 percent, which is dramatically lower than both corporate procurement professionals and industry experts.

"With us doing billions of calculations, it allows us to run simulations and scenarios that your average analyst just can't do and most companies haven't even thought of. We're able to run a comprehensive view of activities in the world to understand how things directly and indirectly affect a cost. In Houston, for example, that could be crude oil or natural gas or something like that."

Proving its value

Last year, the company tested its platform with a natural gas trader. After reviewing the data, CI revealed to the client that natural gas would fall by 40 percent over the next year.

"They looked at our forecast and said they couldn't work with us because it didn't make sense," says Nash. "A 40 percent fall didn't make sense, so they didn't subscribe to us. That was 2018. What has happened over the past 12 months? Natural gas prices had fallen by 49 percent. You would look at our forecasts and say, 'Wow, that's a dramatic drop over 12 months.' But reality was even more dramatic than that and there weren't analysts out there saying what our model was telling us."

That natural gas trading company never admitted its faux pas, but if they had listened to CI, they could have positioned themselves to negotiate their vendors down for their cost base, which helps the margin of their business.

"Nobody ever admits mistakes," says Nash. "But when you think about the numerous materials that require natural gas, especially things that are manufactured in Houston, it affects a lot of costs."

Houston roots — by way of Asia

The missed opportunity with the natural gas trader notwithstanding, Nash is happy that he brought Complete Intelligence to Houston.

"I went to Texas A&M and grew up in Texas, so I moved back to Texas knowing how good Americans are with planning, with math and with data. I like Houston because people make stuff in Houston," Nash says. "We just found Houston to be perfect after spending 15 years in Asia given the global centrality of Houston. The industry's here and there's a lot of diversity in Houston."

Nash's expectation was that he would be able to work with Western multinationals to improve their analytics and their artificial intelligence processes because he has learned that there is a lot of pressure in American financial markets and analysts communities to really know what is happening within companies.

"We want companies to be able to really tightly plan their costs so they can better improve their profitability," says Nash. "That's what I wanted to do when we moved to the U.S. and we're finding that there's a lot of interest from companies."

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​Planned UT Austin med center, anchored by MD Anderson, gets $100M gift​

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The University of Texas at Austin’s planned multibillion-dollar medical center, which will include a hospital run by Houston’s University of Texas MD Anderson Cancer Center, just received a $100 million boost from a billionaire husband-and-wife duo.

Tench Coxe, a former venture capitalist who’s a major shareholder in chipmaking giant Nvidia, and Simone Coxe, co-founder and former CEO of the Blanc & Otus PR firm, contributed the $100 million—one of the largest gifts in UT history. The Coxes live in Austin.

“Great medical care changes lives,” says Simone Coxe, “and we want more people to have access to it.”

The University of Texas System announced the medical center project in 2023 and cited an estimated price tag of $2.5 billion. UT initially said the medical center would be built on the site of the Frank Erwin Center, a sports and entertainment venue on the UT Austin campus that was demolished in 2024. The 20-acre site, north of downtown and the state Capitol, is near Dell Seton Medical Center, UT Dell Medical School and UT Health Austin.

Now, UT officials are considering a bigger, still-unidentified site near the Domain mixed-use district in North Austin, although they haven’t ruled out the Erwin Center site. The Domain development is near St. David’s North Medical Center.

As originally planned, the medical center would house a cancer center built and operated by MD Anderson and a specialty hospital built and operated by UT Austin. Construction on the two hospitals is scheduled to start this year and be completed in 2030. According to a 2025 bid notice for contractors, each hospital is expected to encompass about 1.5 million square feet, meaning the medical center would span about 3 million square feet.

Features of the MD Anderson hospital will include:

  • Inpatient care
  • Outpatient clinics
  • Surgery suites
  • Radiation, chemotherapy, cell, and proton treatments
  • Diagnostic imaging
  • Clinical drug trials

UT says the new medical center will fuse the university’s academic and research capabilities with the medical and research capabilities of MD Anderson and Dell Medical School.

UT officials say priorities for spending the Coxes’ gift include:

  • Recruiting world-class medical professionals and scientists
  • Supporting construction
  • Investing in technology
  • Expanding community programs that promote healthy living and access to care

Tench says the opportunity to contribute to building an institution from the ground up helped prompt the donation. He and others say that thanks to MD Anderson’s participation, the medical center will bring world-renowned cancer care to the Austin area.

“We have a close friend who had to travel to Houston for care she should have been able to get here at home. … Supporting the vision for the UT medical center is exactly the opportunity Austin needed,” he says.

The rate of patients who leave the Austin area to seek care for serious medical issues runs as high as 25 percent, according to UT.

New Rice Brain Institute partners with TMC to award inaugural grants

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The recently founded Rice Brain Institute has named the first four projects to receive research awards through the Rice and TMC Neuro Collaboration Seed Grant Program.

The new grant program brings together Rice faculty with clinicians and scientists at The University of Texas Medical Branch, Baylor College of Medicine, UTHealth Houston and The University of Texas MD Anderson Cancer Center. The program will support pilot projects that address neurological disease, mental health and brain injury.

The first round of awards was selected from a competitive pool of 40 proposals, and will support projects that reflect Rice Brain Institute’s research agenda.

“These awards are meant to help teams test bold ideas and build the collaborations needed to sustain long-term research programs in brain health,” Behnaam Aazhang, Rice Brain Institute director and co-director of the Rice Neuroengineering Initiative, said in a news release.

The seed funding has been awarded to the following principal investigators:

  • Kevin McHugh, associate professor of bioengineering and chemistry at Rice, and Peter Kan, professor and chair of neurosurgery at the UTMB. McHugh and Kan are developing an injectable material designed to seal off fragile, abnormal blood vessels that can cause life-threatening bleeding in the brain.
  • Jerzy Szablowski, assistant professor of bioengineering at Rice, and Jochen Meyer, assistant professor of neurology at Baylor. Szablowski and Meyer are leading a nonsurgical, ultrasound approach to deliver gene-based therapies to deep brain regions involved in seizures to control epilepsy without implanted electrodes or invasive procedures.
  • Juliane Sempionatto, assistant professor of electrical and computer engineering at Rice, and Aaron Gusdon, associate professor of neurosurgery at UTHealth Houston. Sempionatto and Gusdon are leading efforts to create a blood test that can identify patients at high risk for delayed brain injury following aneurysm-related hemorrhage, which could lead to earlier intervention and improved outcomes.
  • Christina Tringides, assistant professor of materials science and nanoengineering at Rice, and Sujit Prabhu, professor of neurosurgery at MD Anderson, who are working to reduce the risk of long-term speech and language impairment during brain tumor removal by combining advanced brain recordings, imaging and noninvasive stimulation.

The grants were facilitated by Rice’s Educational and Research Initiatives for Collaborative Health (ENRICH) Office. Rice says that the unique split-funding model of these grants could help structure future collaborations between the university and the TMC.

The Rice Brain Institute launched this fall and aims to use engineering, natural sciences and social sciences to research the brain and reduce the burden of neurodegenerative, neurodevelopmental and mental health disorders. Last month, the university's Shepherd School of Music also launched the Music, Mind and Body Lab, an interdisciplinary hub that brings artists and scientists together to study the "intersection of the arts, neuroscience and the medical humanities." Read more here.

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

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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.