AI Research
House bill targets rising rural utility costs from AI data centers
Surging utility bills linked to artificial intelligence data centers would get a closer look from a trio of federal agencies under a new bipartisan bill in the House.
The Unleashing Low-Cost Rural AI Act from Reps. Jim Costa, D-Calif., and Blake Moore, R-Utah, would require the Energy, Interior and Agriculture departments to examine the effect AI data center buildouts are having on rural America.
“AI Data Centers are expanding rapidly and using more energy and water than entire cities. That energy demand is driving up utility costs for consumers,” Costa said in a press release Thursday. “My legislation ensures we take a hard look at how this growth impacts rural communities that are powering the AI industry, and make sure families aren’t left paying the price.
“But at the same time,” he continued, “it’s important that rural communities are not left behind in the new opportunities that AI data centers will provide for agricultural sciences and an improved ability to compete in this modern era.”
The rapid construction of AI data centers across the country — especially in rural areas — has led to a spike in energy demand that has dramatically driven up utility costs for consumers. The lawmakers’ press release cited a stat from PJM — the world’s largest energy market, spanning 13 states — that said data centers have led to an additional $9.3 billion in costs for ratepayers.
The AI Action Plan released by President Donald Trump in July featured several callouts to the importance of expanded energy capacity through streamlined permitting and fewer environmental regulations. The plan also sought to make federal lands “available for data center construction and the construction of power generation infrastructure for those data centers.”
Moore said in the press release that Utah is “a prime location” for AI infrastructure and data centers, but “cementing” the state’s innovation bona fides “will require identifying rural areas ready for data expansion, streamlining permitting for new energy projects, and promoting the co-location of data centers with energy facilities.”
“These efforts will power our growing digital demands without passing costs on to families,” he added. “I’m grateful to partner with Representative Costa to introduce the Unleashing Low-Cost Rural AI Act to identify other areas of the country, like Utah, that will advance solutions to meet our energy needs.”
Under the bill, the Energy, Interior and Agriculture would team up to study the impact of AI data center expansions in rural parts of the country, in addition to identifying areas that appear to be strong candidates for tech expansion. They would also assess the impact data center expansion might have on consumer costs, as well as energy supply and reliability.
The agencies would also be charged with examining ways current energy infrastructure may be upgraded to allow AI data centers to coexist alongside those power facilities. There will also be reviews of nuclear and geothermal energy, solar, wind and hydro power, battery storage, and carbon capture.
According to a piece published last month in the Tech Policy Press, global energy use by data centers has jumped 12% annually over the past seven years, with projections that it will more than double by 2030.
“As providers of the largest and most compute-intensive AI models keep adding them into more and more aspects of our digital lives with little regard for efficiency (and without giving users much of a choice), they grow increasingly dependent on a growing share of the existing energy and natural resources, leading to rising costs for everyone else,” the authors warned.
A new global reviewer survey from IOP Publishing (IOPP) reveals a growing divide in attitudes among reviewers in the physical sciences regarding the use of generative AI in peer review. The study follows a similar survey conducted last year showing that while some researchers are beginning to embrace AI tools, others remain concerned about the potential negative impact, particularly when AI is used to assess their own work.
Currently, IOPP does not allow the use of AI in peer review as generative models cannot meet the ethical, legal, and scholarly standards required. However, there is growing recognition of AI’s potential to support, rather than replace, the peer review process.
Key Findings:
- 41% of respondents now believe generative AI will have a positive impact on peer review (up 12% from 2024), while 37% see it as negative (up 2%). Only 22% are neutral or unsure—down from 36% last year—indicating growing polarisation in views.
- 32% of researchers have already used AI tools to support them with their reviews.
- 57% would be unhappy if a reviewer used generative AI to write a peer review report on a manuscript they had co-authored and 42% would be unhappy if AI were used to augment a peer review report.
- 42% believe they could accurately detect an AI-written peer review report on a manuscript they had co-authored.
Women tend to feel less positive about the potential of AI compared with men, suggesting a gendered difference in the usefulness of AI in peer review. Meanwhile, more junior researchers appear more optimistic about the benefits of AI, compared to their more senior colleagues who express greater scepticism.
When it comes to reviewer behaviour and expectations, 32% of respondents reported using AI tools to support them during the peer review process in some form. Notably, over half (53%) of those using AI said they apply it in more than one way. The most common use (21%) was for editing grammar and improving the flow of text and 13% said they use AI tools to summarise or digest articles under review, raising serious concerns around confidentiality and data privacy. A small minority (2%) admitted to uploading entire manuscripts into AI chatbots asking it to generate a review on their behalf.
Interestingly, 42% of researchers believe they could accurately detect an AI-written peer review report on a manuscript they had co-authored.
“These findings highlight the need for clearer community standards and transparency around the use of generative AI in scholarly publishing. As the technology continues to evolve, so too must the frameworks that support ethical and trustworthy peer review”, said Laura Feetham-Walker, Reviewer Engagement Manager at IOP Publishing and lead author of the study.
“One potential solution is to develop AI tools that are integrated directly into peer review systems, offering support to reviewers and editors without compromising security or research integrity. These tools should be designed to support, rather than replace, human judgment. If implemented effectively, such tools would not only address ethical concerns but also mitigate risks around confidentiality and data privacy; particularly the issue of reviewers uploading manuscripts to third-party generative AI platforms,” adds Feetham-Walker.
Pascal AI Labs, a rapidly growing technology company focused on transforming how investment research is conducted, has announced the close of a $3.1 million seed funding round. The funding was led by Kalaari Capital, with additional participation from Norwest, Infoedge Ventures, Antler, and several prominent angel investors.
This funding marks a significant step in the company’s journey to bring advanced, AI-driven research capabilities to financial institutions worldwide.
The new capital will be used to speed up the development of Pascal AI’s autonomous investment workflows, expand its presence in the United States, and form strategic partnerships with key data providers.
The company’s platform is already in use by more than 25 financial firms across the U.S. and the Asia-Pacific region, including private equity funds managing $2 billion in assets and one of the world’s top three asset managers with over $1 trillion under management.
Pascal AI offers secure and native connections to data on over 16,000 publicly traded companies across 27 markets, giving investment teams a broad and reliable foundation for their work.
The problem that Pascal AI is addressing is one that many investment professionals are familiar with. Analysts and portfolio managers are inundated with vast amounts of data from company filings, earnings call transcripts, market reports, and internal research notes.
While existing platforms can surface this information, they often fail to capture the accumulated judgment and institutional knowledge that experienced investors rely on. As a result, analysts spend hours manually piecing together information, and chief investment officers often lack a clear, forward-looking view of their portfolios.
Pascal AI takes a different approach by automating the entire investment lifecycle. The platform learns from a firm’s proprietary history—its past decisions, research notes, and investment patterns so it can reason and act like a seasoned investor rather than simply retrieving data. This means it can proactively connect insights, identify risks, and suggest actions in a way that reflects the unique thinking of each firm.
Because the stakes in investment decision-making are high, trust and security are central to Pascal AI’s design. The platform is built on a proprietary Knowledge Graph that makes every action fully auditable and traceable. It supports enterprise-grade security features, including role-based permissions and the option for on-premise deployment, ensuring that sensitive information remains protected while still enabling robust AI-driven analysis.
Pascal AI was founded by Vibhav Viswanathan and Mithun Madhusudan, both of whom bring deep expertise in finance, artificial intelligence, and scaling technology products.
Viswanathan, a graduate of the University of Chicago Booth School of Business, previously led AWS Inferentia and Neuron in Silicon Valley and has hands-on investment experience from his time at Capital Group and NEA-IUVP.
Madhusudan, an alumnus of the Indian Institute of Management Bangalore, has led AI and product teams at Indian tech unicorns Apna and ShareChat, where he helped scale AI products to more than 100 million users.
KEY QUOTES:
“The future of investment management is autonomous investment research. Pascal AI is systematically automating complex investment workflows with the long-term vision of creating a fully autonomous investment research company. This funding allows us to accelerate that journey, moving from workflow automation to true autonomy, and giving analysts instant, auditable insights and CIOs a continuously updated view of exposures and performance”.
Vibhav Viswanathan, co-founder and CEO of Pascal AI
“At Kalaari, we believe the next decade will see a decisive shift toward autonomous research platforms that can scale human judgment with machine intelligence. Pascal AI is at the forefront of this transformation—building secure, auditable, and truly agentic workflows that don’t just process information, but reason like an investor. What stood out to us was the clarity and conviction with which Vibhav and Mithun are reimagining how investors and CIOs make decisions. With strong early traction from marquee global clients, the team has already validated the depth of the problem and the strength of their solution. We are excited to partner with them on this mission.”
Kalaari Capital Partner Sampath P
With all of the challenges facing health care — a shrinking workforce population, reduced funding, new technologies and pharmaceuticals — it’s no longer an option to change, but an imperative. In order to keep caring for our communities well into the future, we need to transform how we provide care to people. Technology, artificial intelligence and digital transformation can not only help us mitigate these trends but truly innovate and find new ways of making health better.
There are many exciting capabilities already making their way into our field. Ambient listening technology for providers and other automation and AI reduce administrative burden and free up people and resources to improve front-line care. Within the next five years, we expect hospital “smart rooms” to be the norm; they leverage cameras and AI-assisted alerting to improve safety, enable virtual care models across our footprint and allow us to boost efficiency while also improving quality and outcomes.
It’s easy to get caught up in shiny new tools or cutting-edge treatments, but often the most impactful innovations are smaller — adapting or designing our systems and processes to empower our teams to do what they do best.
That’s exactly what a new collaboration with the AHA and Epic is aiming to do. A set of point-of-care tools in the electronic health record is helping providers prevent, detect and treat postpartum hemorrhage, which is responsible for 11% of maternal deaths in the U.S. Early detection and treatment of PPH is key to a full recovery. One small innovation — incorporating tools into your EHR and labor and delivery workflows — is having a big impact: enhancing providers’ ability to effectively diagnose and treat PPH.
It’s critical to leverage technology advancements like this to navigate today’s challenging environment and advance health care into the future. However, at the same time, we also need to focus on how these opportunities can deliver measurable value to our patients, members and the communities we serve.
I will be speaking with Jackie Gerhart, M.D., chief medical officer at Epic, later this month for a Leadership Dialogue conversation. Listen in to learn more about how AI and other technological innovations can better serve patients and make actions more efficient for care providers.
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