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Congress used to evaluate emerging technologies. Let’s do it again.

At about the time when personal computers charged into cubicle farms, another machine muscled its way into human resources departments and became a staple of routine employment screenings. By the early 1980s, some 2 million Americans annually found themselves strapped to a polygraph—a metal box that, in many people’s minds, detected deception. Most of those tested were not suspected crooks or spooks.  Then the US Office of Technology Assessment, an independent office that had been created by Congress about a decade earlier to serve as its scientific consulting arm, got involved. The office reached out to Boston University researcher Leonard Saxe with an assignment: Evaluate polygraphs. Tell us the truth about these supposed truth-telling devices. And so Saxe assembled a team of about a dozen researchers, including Michael Saks of Boston College, to begin a systematic review. The group conducted interviews, pored over existing studies, and embarked on new lines of research. A few months later, the OTA published a technical memo, “Scientific Validity of Polygraph Testing: A Research Review and Evaluation.” Despite the tests’ widespread use, the memo dutifully reported, “there is very little research or scientific evidence to establish polygraph test validity in screening situations, whether they be preemployment, preclearance, periodic or aperiodic, random, or ‘dragnet.’” These machines could not detect lies.  Four years later, in 1987, critics at a congressional hearing invoked the OTA report as authoritative, comparing polygraphs derisively to “tea leaf reading or crystal ball gazing.” Congress soon passed strict limits on the use of polygraphs in the workplace.  Over its 23-year history, the OTA would publish some 750 reports—lengthy, interdisciplinary assessments of specific technologies that proposed means of maximizing their benefits and minimizing harms. Their subjects included electronic surveillance, genetic engineering, hazardous-waste disposal, and remote sensing from outer space. Congress set its course: The office initiated studies only at the request of a committee chairperson, a ranking minority leader, or its 12-person bipartisan board.  The investigations remained independent; staffers and consultants from both inside and outside government collaborated to answer timely and sometimes politicized questions. The reports addressed worries about alarming advances and tamped down scary-sounding hypotheticals. Some of those concerns no longer keep policymakers up at night. For instance, “Do Insects Transmit AIDS?” A 1987 OTA report correctly suggested that they don’t. The office functioned like a debunking arm. It sussed out the snake oil. Lifted the lid on the Mechanical Turk. The reports saw through the alluring gleam of overhyped technologies.  In the years since its unceremonious defunding, perennial calls have gone out: Rouse the office from the dead! And with advances in robotics, big data, and AI systems, these calls have taken on a new level of urgency.  Like polygraphs, chatbots and search engines powered by so-called artificial intelligence come with a shimmer and a sheen of magical thinking. And if we’re not careful, politicians, employers, and other decision-makers may accept at face value the idea that machines can and should replace human judgment and discretion.  A resurrected OTA might be the perfect body to rein in dangerous and dangerously overhyped technologies. “That’s what Congress needs right now,” says Ryan Calo at the University of Washington’s Tech Policy Lab and the Center for an Informed Public, “because otherwise Congress is going to, like, take Sam Altman’s word for everything, or Eric Schmidt’s.” (The CEO of OpenAI and the former CEO of Google have both testified before Congress.) Leaving it to tech executives to educate lawmakers is like having the fox tell you how to build your henhouse. Wasted resources and inadequate protections might be only the start.  A man administers a lie detector test to a job applicant in 1976. A 1983 report from the OTA debunked the efficacy of polygraphs.LIBRARY OF CONGRESS No doubt independent expertise still exists. Congress can turn to the Congressional Research Service, for example, or the National Academies of Sciences, Medicine, and Engineering. Other federal entities, such as the Office of Management and Budget and the Office of Science and Technology Policy, have advised the executive branch (and still existed as we went to press). “But they’re not even necessarily specialists,” Calo says, “and what they’re producing is very lightweight compared to what the OTA did. And so I really think we need OTA back.”   What exists today, as one researcher puts it, is a “diffuse and inefficient” system. There is no central agency that wholly devotes itself to studying emerging technologies in a serious and dedicated way and advising the country’s 535 elected officials about potential impacts. The digestible summaries Congress receives from the Congressional Research Service provide insight but are no replacement for the exhaustive technical research and analytic capacity of a fully staffed and funded think tank. There’s simply nothing like the OTA, and no single entity replicates its incisive and instructive guidance. But there’s also nothing stopping Congress from reauthorizing its budget and bringing it back, except perhaps the lack of political will.  “Congress Smiles, Scientists Wince” The OTA had not exactly been an easy sell to the research community in 1972. At the time, it was only the third independent congressional agency ever established. As the journal Science put it in a headline that year, “The Office of Technology Assessment: Congress Smiles, Scientists Wince.” One researcher from Bell Labs told Science that he feared legislators would embark on “a clumsy, destructive attempt to manage national R&D,” but mostly the cringe seemed to stem from uncertainty about what exactly technology assessment entailed.  The OTA’s first report, in 1974, examined bioequivalence, an essential part of evaluating generic drugs. Regulators were trying to figure out whether these drugs could be deemed comparable to their name-brand equivalents without lengthy and expensive clinical studies demonstrating their safety and efficacy. Unlike all the OTA’s subsequent assessments, this one listed specific policy recommendations, such as clarifying what data should be required in order to evaluatea generic drug and ensure uniformity and standardization in the regulatory approval process. The Food and Drug Administration later incorporated these recommendations into its own submission requirements.  From then on, though, the OTA did not take sides. The office had not been set up to advise Congress on how to legislate. Rather, it dutifully followed through on its narrowly focused mandate: Do the research and provide policymakers with a well-reasoned set of options that represented a range of expert opinions. Perhaps surprisingly, given the rise of commercially available PCs, in the first decade of its existence the OTA produced only a few reports on computing. One 1976 report touched on the automated control of trains. Others examined computerized x-ray imaging, better known as CT scans; computerized crime databases; and the use of computers in medical education. Over time, the office’s output steadily increased, eventually averaging 32 reports a year. Its budget swelled to $22 million; its staff peaked at 143.  While it’s sometimes said that the future impact of a technology is beyond anyone’s imagination, several findings proved prescient. A 1982 report on electronic funds transfer, or EFT, predicted that financial transactions would increasingly be carried out electronically (an obvious challenge to paper currency and hard-copy checks). Another predicted that email, or what was then termed “electronic message systems,” would disrupt snail mail and the bottom line of the US Postal Service.  In vetting the digital record-keeping that provides the basis for routine background checks, the office commissioned a study that produced a statistic still cited today, suggesting that only about a quarter of the records sent to the FBI were “complete, accurate, and unambiguous.” It was an indicator of a growing issue: computational systems that, despite seeming automated, are not free of human bias and error.  Many of the OTA’s reports focus on specific events or technologies. One looked at Love Canal, the upstate New York neighborhood polluted by hazardous waste (a disaster, the report said, that had not yet been remediated by the Environmental Protection Agency’s Superfund cleanup program); another studied the Boston Elbow, a cybernetic limb (the verdict: decidedly mixed). The office examined the feasibility of a water pipeline connecting Alaska to California, the health effects of the Kuwait oil fires, and the news media’s use of satellite imagery. The office also took on issues we grapple with today—evaluating automatic record checks for people buying guns, scrutinizing the compensation for injuries allegedly caused by vaccines, and pondering whether we should explore Mars.  The OTA made its biggest splash in 1984, when it published a background report criticizing the Strategic Defense Initiative (commonly known as “Star Wars”), a pet project of the Reagan administration that involved several exotic missile defense systems. Its lead author was the MIT physicist Ashton Carter, later secretary of defense in the second Obama administration. And the report concluded that a “perfect or near-perfect” system to defend against nuclear weapons was basically beyond the realm of the plausible; the possibility of deployment was “so remote that it should not serve as the basis of public expectation or national policy.”  The report generated lots of clicks, so to speak, especially after the administration claimed that the OTA had divulged state secrets. These charges did not hold up and Star Wars never materialized, although there have been recent efforts to beef up the military’s offensive capacity in space. But for the work of an advisory body that did not play politics, the report made a big political hubbub. By some accounts, its subsequent assessments became so neutral that the office risked receding to the point of invisibility. From a purely pragmatic point of view, the OTA wrote to be understood. A dozen reports from the early ’90s received “Blue Pencil Awards,” given by the National Association of Government Communicators for “superior government communication products and those who produce them.” None are copyrighted. All were freely reproduced and distributed, both in print and electronically. The entire archive is stored on CD-ROM, and digitized copies are still freely available for download on a website maintained by Princeton University, like an earnest oasis of competence in the cloistered world of federal documents.  Assessments versus accountability Looking back, the office took shape just as debates about technology and the law were moving to center stage.  While the gravest of dangers may have changed in form and in scope, the central problem remains: Laws and lawmakers cannot keep up with rapid technological advances. Policymakers often face a choice between regulating with insufficient facts and doing nothing.  In 2018, Adam Kinzinger, then a Republican congressman from Illinois, confessed to a panel on quantum computing: “I can understand about 50% of the things you say.” To some, his admission underscored a broader tech illiteracy afflicting those in power. But other commentators argued that members of Congress should not be expected to know it all—all the more reason to restaff an office like the OTA. A motley chorus of voices have clamored for an OTA 2.0 over the years. One doctor wrote that the office could help address the “discordance between the amount of money spent and the actual level of health.” Tech fellows have said bringing it back could help Congress understand machine learning and AI. Hillary Clinton, as a Democratic presidential hopeful, floated the possibility of resurrecting the OTA in 2017.  But Meg Leta Jones, a law scholar at Georgetown University, argues that assessing new technologies is the least of our problems. The kind of work the OTA did is now done by other agencies, such as the FTC, FCC, and National Telecommunications and Information Administration, she says: “The energy I would like to put into the administrative state is not on assessments, but it’s on actual accountability and enforcement.” She sees the existing framework as built for the industrial age, not a digital one, and is among those calling for a more ambitious overhaul. There seems to be little political appetite for the creation of new agencies anyway. That said, Jones adds, “I wouldn’t be mad if they remade the OTA.”  No one can know whether or how future administrations will address AI, Mars colonization, the safety of vaccines, or, for that matter, any other emerging technology that the OTA investigated in an earlier era. But if the new administration makes good on plans to deregulate many sectors, it’s worth noting some historic echoes. In 1995, when conservative politicians defunded the OTA, they did so in the name of efficiency. Critics of that move contend that the office probably saved the government money and argue that the purported cost savings associated with its elimination were largely symbolic.  Jathan Sadowski, a research fellow at Monash University in Melbourne, Australia, who has written about the OTA’s history, says the conditions that led to its demise have only gotten more partisan, more politicized. This makes it difficult to envision a place for the agency today, he says—“There’s no room for the kind of technocratic naïveté that would see authoritative scientific advice cutting through the noise of politics.” Congress purposely cut off its scientific advisory arm as part of a larger shake-up led by Newt Gingrich, then the House Speaker, whose pugilistic brand of populist conservatism promised “drain the swamp”–type reforms and launched what critics called a “war on science.” As a rationale for why the office was defunded, he said, “We constantly found scientists who thought what they were saying was not correct.”  Once again, Congress smiled and scientists winced. Only this time it was because politicians had pulled the plug.  Peter Andrey Smith, a freelance reporter, has contributed to Undark, the New Yorker, the New York Times Magazine, and WNYC’s Radiolab.

At about the time when personal computers charged into cubicle farms, another machine muscled its way into human resources departments and became a staple of routine employment screenings. By the early 1980s, some 2 million Americans annually found themselves strapped to a polygraph—a metal box that, in many people’s minds, detected deception. Most of those tested were not suspected crooks or spooks. 

Then the US Office of Technology Assessment, an independent office that had been created by Congress about a decade earlier to serve as its scientific consulting arm, got involved. The office reached out to Boston University researcher Leonard Saxe with an assignment: Evaluate polygraphs. Tell us the truth about these supposed truth-telling devices.

And so Saxe assembled a team of about a dozen researchers, including Michael Saks of Boston College, to begin a systematic review. The group conducted interviews, pored over existing studies, and embarked on new lines of research. A few months later, the OTA published a technical memo, “Scientific Validity of Polygraph Testing: A Research Review and Evaluation.” Despite the tests’ widespread use, the memo dutifully reported, “there is very little research or scientific evidence to establish polygraph test validity in screening situations, whether they be preemployment, preclearance, periodic or aperiodic, random, or ‘dragnet.’” These machines could not detect lies. 

Four years later, in 1987, critics at a congressional hearing invoked the OTA report as authoritative, comparing polygraphs derisively to “tea leaf reading or crystal ball gazing.” Congress soon passed strict limits on the use of polygraphs in the workplace. 

Over its 23-year history, the OTA would publish some 750 reports—lengthy, interdisciplinary assessments of specific technologies that proposed means of maximizing their benefits and minimizing harms. Their subjects included electronic surveillance, genetic engineering, hazardous-waste disposal, and remote sensing from outer space. Congress set its course: The office initiated studies only at the request of a committee chairperson, a ranking minority leader, or its 12-person bipartisan board. 

The investigations remained independent; staffers and consultants from both inside and outside government collaborated to answer timely and sometimes politicized questions. The reports addressed worries about alarming advances and tamped down scary-sounding hypotheticals. Some of those concerns no longer keep policymakers up at night. For instance, “Do Insects Transmit AIDS?” A 1987 OTA report correctly suggested that they don’t.

The office functioned like a debunking arm. It sussed out the snake oil. Lifted the lid on the Mechanical Turk. The reports saw through the alluring gleam of overhyped technologies. 

In the years since its unceremonious defunding, perennial calls have gone out: Rouse the office from the dead! And with advances in robotics, big data, and AI systems, these calls have taken on a new level of urgency. 

Like polygraphs, chatbots and search engines powered by so-called artificial intelligence come with a shimmer and a sheen of magical thinking. And if we’re not careful, politicians, employers, and other decision-makers may accept at face value the idea that machines can and should replace human judgment and discretion. 

A resurrected OTA might be the perfect body to rein in dangerous and dangerously overhyped technologies. “That’s what Congress needs right now,” says Ryan Calo at the University of Washington’s Tech Policy Lab and the Center for an Informed Public, “because otherwise Congress is going to, like, take Sam Altman’s word for everything, or Eric Schmidt’s.” (The CEO of OpenAI and the former CEO of Google have both testified before Congress.) Leaving it to tech executives to educate lawmakers is like having the fox tell you how to build your henhouse. Wasted resources and inadequate protections might be only the start. 

A man administers a lie detector test to a job applicant in 1976. A 1983 report from the OTA debunked the efficacy of polygraphs.
LIBRARY OF CONGRESS

No doubt independent expertise still exists. Congress can turn to the Congressional Research Service, for example, or the National Academies of Sciences, Medicine, and Engineering. Other federal entities, such as the Office of Management and Budget and the Office of Science and Technology Policy, have advised the executive branch (and still existed as we went to press). “But they’re not even necessarily specialists,” Calo says, “and what they’re producing is very lightweight compared to what the OTA did. And so I really think we need OTA back.”  

What exists today, as one researcher puts it, is a “diffuse and inefficient” system. There is no central agency that wholly devotes itself to studying emerging technologies in a serious and dedicated way and advising the country’s 535 elected officials about potential impacts. The digestible summaries Congress receives from the Congressional Research Service provide insight but are no replacement for the exhaustive technical research and analytic capacity of a fully staffed and funded think tank. There’s simply nothing like the OTA, and no single entity replicates its incisive and instructive guidance. But there’s also nothing stopping Congress from reauthorizing its budget and bringing it back, except perhaps the lack of political will. 

“Congress Smiles, Scientists Wince”

The OTA had not exactly been an easy sell to the research community in 1972. At the time, it was only the third independent congressional agency ever established. As the journal Science put it in a headline that year, “The Office of Technology Assessment: Congress Smiles, Scientists Wince.” One researcher from Bell Labs told Science that he feared legislators would embark on “a clumsy, destructive attempt to manage national R&D,” but mostly the cringe seemed to stem from uncertainty about what exactly technology assessment entailed. 

The OTA’s first report, in 1974, examined bioequivalence, an essential part of evaluating generic drugs. Regulators were trying to figure out whether these drugs could be deemed comparable to their name-brand equivalents without lengthy and expensive clinical studies demonstrating their safety and efficacy. Unlike all the OTA’s subsequent assessments, this one listed specific policy recommendations, such as clarifying what data should be required in order to evaluatea generic drug and ensure uniformity and standardization in the regulatory approval process. The Food and Drug Administration later incorporated these recommendations into its own submission requirements. 

From then on, though, the OTA did not take sides. The office had not been set up to advise Congress on how to legislate. Rather, it dutifully followed through on its narrowly focused mandate: Do the research and provide policymakers with a well-reasoned set of options that represented a range of expert opinions.

Perhaps surprisingly, given the rise of commercially available PCs, in the first decade of its existence the OTA produced only a few reports on computing. One 1976 report touched on the automated control of trains. Others examined computerized x-ray imaging, better known as CT scans; computerized crime databases; and the use of computers in medical education. Over time, the office’s output steadily increased, eventually averaging 32 reports a year. Its budget swelled to $22 million; its staff peaked at 143. 

While it’s sometimes said that the future impact of a technology is beyond anyone’s imagination, several findings proved prescient. A 1982 report on electronic funds transfer, or EFT, predicted that financial transactions would increasingly be carried out electronically (an obvious challenge to paper currency and hard-copy checks). Another predicted that email, or what was then termed “electronic message systems,” would disrupt snail mail and the bottom line of the US Postal Service. 

In vetting the digital record-keeping that provides the basis for routine background checks, the office commissioned a study that produced a statistic still cited today, suggesting that only about a quarter of the records sent to the FBI were “complete, accurate, and unambiguous.” It was an indicator of a growing issue: computational systems that, despite seeming automated, are not free of human bias and error. 

Many of the OTA’s reports focus on specific events or technologies. One looked at Love Canal, the upstate New York neighborhood polluted by hazardous waste (a disaster, the report said, that had not yet been remediated by the Environmental Protection Agency’s Superfund cleanup program); another studied the Boston Elbow, a cybernetic limb (the verdict: decidedly mixed). The office examined the feasibility of a water pipeline connecting Alaska to California, the health effects of the Kuwait oil fires, and the news media’s use of satellite imagery. The office also took on issues we grapple with today—evaluating automatic record checks for people buying guns, scrutinizing the compensation for injuries allegedly caused by vaccines, and pondering whether we should explore Mars. 

The OTA made its biggest splash in 1984, when it published a background report criticizing the Strategic Defense Initiative (commonly known as “Star Wars”), a pet project of the Reagan administration that involved several exotic missile defense systems. Its lead author was the MIT physicist Ashton Carter, later secretary of defense in the second Obama administration. And the report concluded that a “perfect or near-perfect” system to defend against nuclear weapons was basically beyond the realm of the plausible; the possibility of deployment was “so remote that it should not serve as the basis of public expectation or national policy.” 

The report generated lots of clicks, so to speak, especially after the administration claimed that the OTA had divulged state secrets. These charges did not hold up and Star Wars never materialized, although there have been recent efforts to beef up the military’s offensive capacity in space. But for the work of an advisory body that did not play politics, the report made a big political hubbub. By some accounts, its subsequent assessments became so neutral that the office risked receding to the point of invisibility.

From a purely pragmatic point of view, the OTA wrote to be understood. A dozen reports from the early ’90s received “Blue Pencil Awards,” given by the National Association of Government Communicators for “superior government communication products and those who produce them.” None are copyrighted. All were freely reproduced and distributed, both in print and electronically. The entire archive is stored on CD-ROM, and digitized copies are still freely available for download on a website maintained by Princeton University, like an earnest oasis of competence in the cloistered world of federal documents. 

Assessments versus accountability

Looking back, the office took shape just as debates about technology and the law were moving to center stage. 

While the gravest of dangers may have changed in form and in scope, the central problem remains: Laws and lawmakers cannot keep up with rapid technological advances. Policymakers often face a choice between regulating with insufficient facts and doing nothing. 

In 2018, Adam Kinzinger, then a Republican congressman from Illinois, confessed to a panel on quantum computing: “I can understand about 50% of the things you say.” To some, his admission underscored a broader tech illiteracy afflicting those in power. But other commentators argued that members of Congress should not be expected to know it all—all the more reason to restaff an office like the OTA.

A motley chorus of voices have clamored for an OTA 2.0 over the years. One doctor wrote that the office could help address the “discordance between the amount of money spent and the actual level of health.” Tech fellows have said bringing it back could help Congress understand machine learning and AI. Hillary Clinton, as a Democratic presidential hopeful, floated the possibility of resurrecting the OTA in 2017. 

But Meg Leta Jones, a law scholar at Georgetown University, argues that assessing new technologies is the least of our problems. The kind of work the OTA did is now done by other agencies, such as the FTC, FCC, and National Telecommunications and Information Administration, she says: “The energy I would like to put into the administrative state is not on assessments, but it’s on actual accountability and enforcement.”

She sees the existing framework as built for the industrial age, not a digital one, and is among those calling for a more ambitious overhaul. There seems to be little political appetite for the creation of new agencies anyway. That said, Jones adds, “I wouldn’t be mad if they remade the OTA.” 

No one can know whether or how future administrations will address AI, Mars colonization, the safety of vaccines, or, for that matter, any other emerging technology that the OTA investigated in an earlier era. But if the new administration makes good on plans to deregulate many sectors, it’s worth noting some historic echoes. In 1995, when conservative politicians defunded the OTA, they did so in the name of efficiency. Critics of that move contend that the office probably saved the government money and argue that the purported cost savings associated with its elimination were largely symbolic. 

Jathan Sadowski, a research fellow at Monash University in Melbourne, Australia, who has written about the OTA’s history, says the conditions that led to its demise have only gotten more partisan, more politicized. This makes it difficult to envision a place for the agency today, he says—“There’s no room for the kind of technocratic naïveté that would see authoritative scientific advice cutting through the noise of politics.”

Congress purposely cut off its scientific advisory arm as part of a larger shake-up led by Newt Gingrich, then the House Speaker, whose pugilistic brand of populist conservatism promised “drain the swamp”–type reforms and launched what critics called a “war on science.” As a rationale for why the office was defunded, he said, “We constantly found scientists who thought what they were saying was not correct.” 

Once again, Congress smiled and scientists winced. Only this time it was because politicians had pulled the plug. 

Peter Andrey Smith, a freelance reporter, has contributed to Undark, the New Yorker, the New York Times Magazine, and WNYC’s Radiolab.

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Harbour Energy to add North Sea assets through Waldorf acquisition

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EIA: US oil inventories drop 1.8 million bbl

US commercial crude inventories for the week ended Dec. 5, excluding those in the Strategic Petroleum Reserve, dropped 1.8 million bbl from the previous week to 425.7 million bbl, which is about 4% below the average range for this time of year, according to the US Energy Information Administration’s (EIA) Weekly Petroleum Status Report. Total motor gasoline inventories gained 6.4 million bbl last week and are about 1% below the 5-year average range for this time of year. Finished gasoline inventories and blending components inventories rose. Distillate fuel inventories increased by 2.5 million bbl but are 7% below the 5-year average for this time of year. EIA reported that US crude refinery inputs last week averaged 16.9 million b/d, down 17,000 b/d from the previous week’s average. Refineries operated at 94.5% of their operable capacity. Gasoline production decreased to 9.6 million b/d, while distillate fuel production increased by 380,000 b/d, averaging 5.4 million b/d. US crude imports averaged 6.6 million b/d, up 609,000 b/d from the previous week’s average. Over the last 4 weeks, crude imports averaged 6.2 million b/d, down 7.7% from the same 4-week period last year. Total motor gasoline imports, including both finished gasoline and gasoline blending components, averaged 659,000 b/d. Distillate fuel imports averaged 181,000 b/d last week.

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Executive Roundtable: Converging Disciplines in the AI Buildout

At Data Center Frontier, we rely on industry leaders to help us understand the most urgent challenges facing digital infrastructure. And in the fourth quarter of 2025, the data center industry is adjusting to a new kind of complexity.  AI-scale infrastructure is redefining what “mission critical” means, from megawatt density and modular delivery to the chemistry of cooling fluids and the automation of energy systems. Every project has arguably in effect now become an ecosystem challenge, demanding that electrical, mechanical, construction, and environmental disciplines act as one.  For this quarter’s Executive Roundtable, DCF convened subject matter experts from Ecolab, EdgeConneX, Rehlko and Schneider Electric – leaders spanning the full chain of facilities design, deployment, and operation. Their insights illuminate how liquid cooling, energy management, and sustainable process design in data centers are now converging to set the pace for the AI era. Our distinguished executive panelists for this quarter include: Rob Lowe, Director RD&E – Global High Tech, Ecolab Phillip Marangella, Chief Marketing and Product Officer, EdgeConneX Ben Rapp, Manager, Strategic Project Development, Rehlko Joe Reele, Vice President, Datacenter Solution Architects, Schneider Electric Today: Engineering the New Normal – Liquid Cooling at Scale  Today’s kickoff article grapples with how, as liquid cooling technology transitions to default hyperscale design, the challenge is no longer if, but how to scale builds safely, repeatably, and globally.  Cold plates, immersion, dielectric fluids, and liquid-to-chip loops are converging into factory-integrated building blocks, yet variability in chemistry, serviceability, materials, commissioning practices, and long-term maintenance threatens to fragment adoption just as demand accelerates.  Success now hinges on shared standards and tighter collaboration across OEMs, builders, and process specialists worldwide. So how do developers coordinate across the ecosystem to make liquid cooling a safe, maintainable global default? What’s Ahead in the Roundtable Over the coming days, our panel

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DCF Trends Summit 2025: AI for Good – How Operators, Vendors and Cooling Specialists See the Next Phase of AI Data Centers

At the 2025 Data Center Frontier Trends Summit (Aug. 26-28) in Reston, Va., the conversation around AI and infrastructure moved well past the hype. In a panel sponsored by Schneider Electric—“AI for Good: Building for AI Workloads and Using AI for Smarter Data Centers”—three industry leaders explored what it really means to design, cool and operate the new class of AI “factories,” while also turning AI inward to run those facilities more intelligently. Moderated by Data Center Frontier Editor in Chief Matt Vincent, the session brought together: Steve Carlini, VP, Innovation and Data Center Energy Management Business, Schneider Electric Sudhir Kalra, Chief Data Center Operations Officer, Compass Datacenters Andrew Whitmore, VP of Sales, Motivair Together, they traced both sides of the “AI for Good” equation: building for AI workloads at densities that would have sounded impossible just a few years ago, and using AI itself to reduce risk, improve efficiency and minimize environmental impact. From Bubble Talk to “AI Factories” Carlini opened by acknowledging the volatility surrounding AI investments, citing recent headlines and even Sam Altman’s public use of the word “bubble” to describe the current phase of exuberance. “It’s moving at an incredible pace,” Carlini noted, pointing out that roughly half of all VC money this year has flowed into AI, with more already spent than in all of the previous year. Not every investor will win, he said, and some companies pouring in hundreds of billions may not recoup their capital. But for infrastructure, the signal is clear: the trajectory is up and to the right. GPU generations are cycling faster than ever. Densities are climbing from high double-digits per rack toward hundreds of kilowatts. The hyperscale “AI factories,” as NVIDIA calls them, are scaling to campus capacities measured in gigawatts. Carlini reminded the audience that in 2024,

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FinOps Foundation sharpens FOCUS to reduce cloud cost chaos

“The big change that’s really started to happen in late 2024 early 2025 is that the FinOps practice started to expand past the cloud,” Storment said. “A lot of organizations got really good at using FinOps to manage the value of cloud, and then their organizations went, ‘oh, hey, we’re living in this happily hybrid state now where we’ve got cloud, SaaS, data center. Can you also apply the FinOps practice to our SaaS? Or can you apply it to our Snowflake? Can you apply it to our data center?’” The FinOps Foundation’s community has grown to approximately 100,000 practitioners. The organization now includes major cloud vendors, hardware providers like Nvidia and AMD, data center operators and data cloud platforms like Snowflake and Databricks. Some 96 of the Fortune 100 now participate in FinOps Foundation programs. The practice itself has shifted in two directions. It has moved left into earlier architectural and design processes, becoming more proactive rather than reactive. It has also moved up organizationally, from director-level cloud management roles to SVP and COO positions managing converged technology portfolios spanning multiple infrastructure types. This expansion has driven the evolution of FOCUS beyond its original cloud billing focus. Enterprises are implementing FOCUS as an internal standard for chargeback reporting even when their providers don’t generate native FOCUS data. Some newer cloud providers, particularly those focused on AI infrastructure, are using the FOCUS specification to define their billing data structures from the ground up rather than retrofitting existing systems. The FOCUS 1.3 release reflects this maturation, addressing technical gaps that have emerged as organizations apply cost management practices across increasingly complex hybrid environments. FOCUS 1.3 exposes cost allocation logic for shared infrastructure The most significant technical enhancement in FOCUS 1.3 addresses a gap in how shared infrastructure costs are allocated and

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Aetherflux joins the race to launch orbital data centers by 2027

Enterprises will connect to and manage orbital workloads “the same way they manage cloud workloads today,” using optical links, the spokesperson added. The company’s approach is to “continuously launch new hardware and quickly integrate the latest architectures,” with older systems running lower-priority tasks to serve out the full useful lifetime of their high-end GPUs. The company declined to disclose pricing. Aetherflux plans to launch about 30 satellites at a time on SpaceX Falcon 9 rockets. Before the data center launch, the company will launch a power-beaming demonstration satellite in 2026 to test transmission of one kilowatt of energy from orbit to ground stations, using infrared lasers. Competition in the sector has intensified in recent months. In November, Starcloud launched its Starcloud-1 satellite carrying an Nvidia H100 GPU, which is 100 times more powerful than any previous GPU flown in space, according to the company, and demonstrated running Google’s Gemma AI model in orbit. In the same month, Google announced Project Suncatcher, with a 2027 demonstration mission planned. Analysts see limited near-term applications Despite the competitive activity, orbital data centers won’t replace terrestrial cloud regions for general hosting through 2030, said Ashish Banerjee, senior principal analyst at Gartner. Instead, they suit specific workloads, including meeting data sovereignty requirements for jurisdictionally complex scenarios, offering disaster recovery immune to terrestrial risks, and providing asynchronous high-performance computing, he said. “Orbital centers are ideal for high-compute, low-I/O batch jobs,” Banerjee said. “Think molecular folding simulations for pharma, massive Monte Carlo financial simulations, or training specific AI model weights. If the job takes 48 hours, the 500ms latency penalty of LEO is irrelevant.” One immediate application involves processing satellite-generated data in orbit, he said. Earth observation satellites using synthetic aperture radar generate roughly 10 gigabytes per second, but limited downlink bandwidth creates bottlenecks. Processing data in

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Here’s what Oracle’s soaring infrastructure spend could mean for enterprises

He said he had earlier told analysts in a separate call that margins for AI workloads in these data centers would be in the 30% to 40% range over the life of a customer contract. Kehring reassured that there would be demand for the data centers when they were completed, pointing to Oracle’s increasing remaining performance obligations, or services contracted but not yet delivered, up $68 billion on the previous quarter, saying that Oracle has been seeing unprecedented demand for AI workloads driven by the likes of Meta and Nvidia. Rising debt and margin risks raise flags for CIOs For analysts, though, the swelling debt load is hard to dismiss, even with Oracle’s attempts to de-risk its spend and squeeze more efficiency out of its buildouts. Gogia sees Oracle already under pressure, with the financial ecosystem around the company pricing the risk — one of the largest debts in corporate history, crossing $100 billion even before the capex spend this quarter — evident in the rising cost of insuring the debt and the shift in credit outlook. “The combination of heavy capex, negative free cash flow, increasing financing cost and long-dated revenue commitments forms a structural pressure that will invariably finds its way into the commercial posture of the vendor,” Gogia said, hinting at an “eventual” increase in pricing of the company’s offerings. He was equally unconvinced by Magouyrk’s assurances about the margin profile of AI workloads as he believes that AI infrastructure, particularly GPU-heavy clusters, delivers significantly lower margins in the early years because utilisation takes time to ramp.

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New Nvidia software gives data centers deeper visibility into GPU thermals and reliability

Addressing the challenge Modern AI accelerators now draw more than 700W per GPU, and multi-GPU nodes can reach 6kW, creating concentrated heat zones, rapid power swings, and a higher risk of interconnect degradation in dense racks, according to Manish Rawat, semiconductor analyst at TechInsights. Traditional cooling methods and static power planning increasingly struggle to keep pace with these loads. “Rich vendor telemetry covering real-time power draw, bandwidth behavior, interconnect health, and airflow patterns shifts operators from reactive monitoring to proactive design,” Rawat said. “It enables thermally aware workload placement, faster adoption of liquid or hybrid cooling, and smarter network layouts that reduce heat-dense traffic clusters.” Rawat added that the software’s fleet-level configuration insights can also help operators catch silent errors caused by mismatched firmware or driver versions. This can improve training reproducibility and strengthen overall fleet stability. “Real-time error and interconnect health data also significantly accelerates root-cause analysis, reducing MTTR and minimizing cluster fragmentation,” Rawat said. These operational pressures can shape budget decisions and infrastructure strategy at the enterprise level.

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Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs).  In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

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John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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