Generative AI and Civic Institutions

Stay Ahead, Stay ONMINE

Generative AI and Civic Institutions

Different sectors, different goals Recent events have got me thinking about AI as it relates to our civic institutions — think government, education, public libraries, and so on. We often forget that civic and governmental organizations are inherently deeply different from private companies and profit-making enterprises. They exist to enable people to live their best lives, protect people’s rights, and make opportunities accessible, even if (especially if) this work doesn’t have immediate monetary returns. The public library is an example I often think about, as I come from a library-loving and defending family — their goal is to provide books, cultural materials, social supports, community engagement, and a love of reading to the entire community, regardless of ability to pay. In the private sector, efficiency is an optimization goal because any dollar spent on providing a product or service to customers is a dollar taken away from the profits. The (simplified) goal is to spend the bare minimum possible to run your business, with the maximum amount returned to you or the shareholders in profit form. In the civic space, on the other hand, efficiency is only a meaningful goal insomuch as it enables higher effectiveness — more of the service the institution provides getting to more constituents. In the civic space, efficiency is only a meaningful goal insomuch as it enables higher effectiveness — more of the service the institution provides getting to more constituents. So, if you’re at the library, and you could use an Ai Chatbot to answer patron questions online instead of assigning a librarian to do that, that librarian could be helping in-person patrons, developing educational curricula, supporting community services, or many other things. That’s a general efficiency that could make for higher effectiveness of the library as an institution. Moving from card catalogs to digital catalogs is a prime example of this kind of efficiency to effectiveness pipeline, because you can find out from your couch whether the book you want is in stock using search keywords instead of flipping through hundreds of notecards in a cabinet drawer like we did when I was a kid. However, we can pivot too hard in the direction of efficiency and lose sight of the end goal of effectiveness. If, for example, your online librarian chat is often used by schoolchildren at home to get homework help, replacing them with an AI chatbot could be a disaster — after getting incorrect information from such a bot and getting a bad grade at school, a child might be turned off from patronizing the library or seeking help there for a long time, or forever. So, it’s important to deploy Generative Ai solutions only when it is well thought out and purposeful, not just because the media is telling us that “AI is neat.” (Eagle-eyed readers will know that this is basically similar advice to what I’ve said in the past about deploying AI in businesses as well.) As a result, what we thought was a gain in efficiency leading to net higher effectiveness actually could diminish the number of lifelong patrons and library visitors, which would mean a loss of effectiveness for the library. Sometimes unintended effects from attempts to improve efficiency can diminish our ability to provide a universal service. That is, there may be a tradeoff between making every single dollar stretch as far as it can possibly go and providing reliable, comprehensive services to all the constituents of your institution. Sometimes unintended effects from attempts to improve efficiency can diminish our ability to provide a universal service. AI for efficiency It’s worth it to take a closer look at this concept — AI as a driver of efficiency. Broadly speaking, the theory we hear often is that incorporating generative AI more into our workplaces and organizations can increase productivity. Framing it at the most Econ 101 level: using AI, more work can be completed by fewer people in the same amount of time, right? Let’s challenge some aspects of this idea. AI is useful to complete certain tasks but is sadly inadequate for others. (As our imaginary schoolchild library patron learned, an LLM is not a reliable source of facts, and should not be treated like one.) So, AI’s ability to increase the volume of work being done with fewer people (efficiency) is limited by what kind of work we need to complete. If our chat interface is only used for simple questions like “What are the library’s hours on Memorial Day?” we can hook up a RAG (Retrieval Augmented Generation) system with an LLM and make that quite useful. But outside of the limited bounds of what information we can provide to the LLM, we should probably set guard rails and make the model refuse to try and answer, to avoid giving out false information to patrons. So, let’s play that out. We have a chatbot that does a very limited job, but does it well. The librarian who was on chatbot duty now may have some reduction in the work required of them, but there are still going to be a subset of questions that still require their help. We have some choices: put the librarian on chatbot duty for a reduced number of hours a week, hoping the questions come in when they’re on? Tell people to just call the reference desk or send an email if the chatbot refuses to answer them? Hope that people come in to the library in person to ask their questions? I suspect the likeliest option is actually “the patron will seek their answer elsewhere, perhaps from another LLM like ChatGPT, Claude, or Gemini.” Once again, we’ve ended up in a situation where the library loses patronage because their offering wasn’t meeting the needs of the patron. And to boot, the patron may have gotten another wrong answer somewhere else, for all we know. I am spinning out this long example just to illustrate that efficiency and effectiveness in the civic environment can have a lot more push and pull than we would initially assume. It’s not to say that AI isn’t useful to help civic organizations stretch their capabilities to serve the public, of course! But just like with any application of generative AI, we need to be very careful to think about what we’re doing, what our goals are, and whether those two are compatible. Conversion of labor Now, this has been a very simplistic example, and eventually we could hook up the whole encyclopedia to that chatbot RAG or something, of course, and try to make it work. In fact, I think we can and should continue developing more ways to chain together AI models to expand the scope of valuable work they can do, including making different specific models for different responsibilities. However, this development is itself work. It’s not really just a matter of “people do work” or “models do work”, but instead it’s “people do work building AI” or “people do work providing services to people”. There’s a calculation to be made to determine when it would be more efficient to do the targeted work itself, and when AI is the right way to go. Working on the AI has an advantage in that it will hopefully render the task reproducible, so it will lead to efficiency, but let’s remember that AI engineering is vastly different from the work of the reference librarian. We’re not interchanging the same workers, tasks, or skill sets here, and in our contemporary economy, the AI engineer’s time costs a heck of a lot more. So if we did want to measure this efficiency all in dollars and cents, the same amount of time spent working at the reference desk and doing the chat service will be much cheaper than paying an AI engineer to develop a better agentic AI for the use case. Given a bit of time, we could calculate out how many hours, days, years of work as a reference librarian we’d need to save with this chatbot to make it worth building, but often that calculation isn’t done before we move towards AI solutions. We need to interrogate the assumption that incorporating generative AI in any given scenario is a guaranteed net gain in efficiency. Externalities While we’re on this topic of weighing whether the AI solution is worth doing in a particular situation, we should remember that developing and using AI for tasks does not happen in a vacuum. It has some cost environmentally and economically when we choose to use a generative AI tool, even when it’s a single prompt and a single response. Consider that the newly released GPT-4.5 has increased prices 30x for input tokens ($2.50 per million to $75 per million) and 15x for output tokens ($10 per million to $150 per million) just since GPT-4o. And that isn’t even taking into account the water consumption for cooling data centers (3 bottles per 100 word output for GPT-4), electricity use, and rare earth minerals used in GPUs. Many civic institutions have as a macro level goal to improve the world around them and the lives of the citizens of their communities, and concern for the environment has to have a place in that. Should organizations whose purpose is to have a positive impact weigh the possibility of incorporating AI more carefully? I think so. Plus, I don’t often get too much into this, but I think we should take a moment to consider some folks’ end game for incorporating AI — reducing staffing altogether. Instead of making our existing dollars in an institution go farther, some people’s idea is just reducing the number of dollars and redistributing those dollars somewhere else. This brings up many questions, naturally, about where those dollars will go instead and whether they will be used to advance the interests of the community residents some other way, but let’s set that aside for now. My concern is for the people who might lose their jobs under this administrative model. For-profit companies hire and fire employees all the time, and their priorities and objectives are focused on profit, so this is not particularly hypocritical or inconsistent. But as I noted above, civic organizations have objectives around improving the community or communities in which they exist. In a very real way, they are advancing that goal when part of what they provide is economic opportunity to their workers. We live in a Society where working is the overwhelmingly predominant way people provide for themselves and their families, and giving jobs to people in the community and supporting the economic well-being of the community is a role that civic institutions do play. [R]educing staffing is not an unqualified good for civic organizations and government, but instead must be balanced critically against whatever other use the money that was paying their salaries will go to. At the bare minimum, this means that reducing staffing is not an unqualified good for civic organizations and government, but instead must be balanced critically against whatever other use the money that was paying their salaries will go to. It’s not impossible for reducing staff to be the right decision, but we have to bluntly acknowledge that when members of communities experience joblessness, that effect cascades. They are now no longer able to patronize the shops and services they would have been supporting with their money, the tax base may be reduced, and this negatively affects the whole collective. Workers aren’t just workers; they’re also patrons, customers, and participants in all aspects of the community. When we think of civic workers as simply money pits to be replaced with AI or whose cost for labor we need to minimize, we lose sight of the reasons for the work to be done in the first place. Conclusion I hope this discussion has brought some clarity about how really difficult it is to decide if, when, and how to apply generative AI to the civic space. It’s not nearly as simple a thought process as it might be in the for-profit sphere because the purpose and core meaning of civic institutions are completely different. Those of us who do machine learning and build AI solutions in the private sector might think, “Oh, I can see a way to use this in government,” but we have to recognize and appreciate the complex contextual implications that might have. Next month, I’ll be bringing you a discussion of how social science research is incorporating generative AI, which has some very intriguing aspects. As you may have heard, Towards Data Science has moved to an independent platform, but I will continue to post my work on my Medium page, my personal website, and the new TDS platform, so you’ll be able to find me wherever you happen to go. Subscribe to my newsletter on Medium if you’d like to ensure you get every article in your inbox. Find more of my work at www.stephaniekirmer.com. Further reading “It’s a lemon”-OpenAI’s largest AI model ever arrives to mixed reviews: GPT-4.5 offers marginal gains in capability and poor coding performance despite 30x the cost. arstechnica.com Using GPT-4 to generate 100 words consumes up to 3 bottles of water: New research shows generative AI consumes a lot of water – up to 1,408ml to generate 100 words of text. www.tomshardware.com Environmental Implications of the AI Boom: The digital world can’t exist without the natural resources to run it. What are the costs of the tech we’re using… towardsdatascience.com Economics of Generative AI: What’s the business model for generative AI, given what we know today about the technology and the market? towardsdatascience.com

Different sectors, different goals

Recent events have got me thinking about AI as it relates to our civic institutions — think government, education, public libraries, and so on. We often forget that civic and governmental organizations are inherently deeply different from private companies and profit-making enterprises. They exist to enable people to live their best lives, protect people’s rights, and make opportunities accessible, even if (especially if) this work doesn’t have immediate monetary returns. The public library is an example I often think about, as I come from a library-loving and defending family — their goal is to provide books, cultural materials, social supports, community engagement, and a love of reading to the entire community, regardless of ability to pay.

In the private sector, efficiency is an optimization goal because any dollar spent on providing a product or service to customers is a dollar taken away from the profits. The (simplified) goal is to spend the bare minimum possible to run your business, with the maximum amount returned to you or the shareholders in profit form. In the civic space, on the other hand, efficiency is only a meaningful goal insomuch as it enables higher effectiveness — more of the service the institution provides getting to more constituents.

In the civic space, efficiency is only a meaningful goal insomuch as it enables higher effectiveness — more of the service the institution provides getting to more constituents.

So, if you’re at the library, and you could use an Ai Chatbot to answer patron questions online instead of assigning a librarian to do that, that librarian could be helping in-person patrons, developing educational curricula, supporting community services, or many other things. That’s a general efficiency that could make for higher effectiveness of the library as an institution. Moving from card catalogs to digital catalogs is a prime example of this kind of efficiency to effectiveness pipeline, because you can find out from your couch whether the book you want is in stock using search keywords instead of flipping through hundreds of notecards in a cabinet drawer like we did when I was a kid.

However, we can pivot too hard in the direction of efficiency and lose sight of the end goal of effectiveness. If, for example, your online librarian chat is often used by schoolchildren at home to get homework help, replacing them with an AI chatbot could be a disaster — after getting incorrect information from such a bot and getting a bad grade at school, a child might be turned off from patronizing the library or seeking help there for a long time, or forever. So, it’s important to deploy Generative Ai solutions only when it is well thought out and purposeful, not just because the media is telling us that “AI is neat.” (Eagle-eyed readers will know that this is basically similar advice to what I’ve said in the past about deploying AI in businesses as well.)

As a result, what we thought was a gain in efficiency leading to net higher effectiveness actually could diminish the number of lifelong patrons and library visitors, which would mean a loss of effectiveness for the library. Sometimes unintended effects from attempts to improve efficiency can diminish our ability to provide a universal service. That is, there may be a tradeoff between making every single dollar stretch as far as it can possibly go and providing reliable, comprehensive services to all the constituents of your institution.

Sometimes unintended effects from attempts to improve efficiency can diminish our ability to provide a universal service.

AI for efficiency

It’s worth it to take a closer look at this concept — AI as a driver of efficiency. Broadly speaking, the theory we hear often is that incorporating generative AI more into our workplaces and organizations can increase productivity. Framing it at the most Econ 101 level: using AI, more work can be completed by fewer people in the same amount of time, right?

Let’s challenge some aspects of this idea. AI is useful to complete certain tasks but is sadly inadequate for others. (As our imaginary schoolchild library patron learned, an LLM is not a reliable source of facts, and should not be treated like one.) So, AI’s ability to increase the volume of work being done with fewer people (efficiency) is limited by what kind of work we need to complete.

If our chat interface is only used for simple questions like “What are the library’s hours on Memorial Day?” we can hook up a RAG (Retrieval Augmented Generation) system with an LLM and make that quite useful. But outside of the limited bounds of what information we can provide to the LLM, we should probably set guard rails and make the model refuse to try and answer, to avoid giving out false information to patrons.

So, let’s play that out. We have a chatbot that does a very limited job, but does it well. The librarian who was on chatbot duty now may have some reduction in the work required of them, but there are still going to be a subset of questions that still require their help. We have some choices: put the librarian on chatbot duty for a reduced number of hours a week, hoping the questions come in when they’re on? Tell people to just call the reference desk or send an email if the chatbot refuses to answer them? Hope that people come in to the library in person to ask their questions?

I suspect the likeliest option is actually “the patron will seek their answer elsewhere, perhaps from another LLM like ChatGPT, Claude, or Gemini.” Once again, we’ve ended up in a situation where the library loses patronage because their offering wasn’t meeting the needs of the patron. And to boot, the patron may have gotten another wrong answer somewhere else, for all we know.

I am spinning out this long example just to illustrate that efficiency and effectiveness in the civic environment can have a lot more push and pull than we would initially assume. It’s not to say that AI isn’t useful to help civic organizations stretch their capabilities to serve the public, of course! But just like with any application of generative AI, we need to be very careful to think about what we’re doing, what our goals are, and whether those two are compatible.

Conversion of labor

Now, this has been a very simplistic example, and eventually we could hook up the whole encyclopedia to that chatbot RAG or something, of course, and try to make it work. In fact, I think we can and should continue developing more ways to chain together AI models to expand the scope of valuable work they can do, including making different specific models for different responsibilities. However, this development is itself work. It’s not really just a matter of “people do work” or “models do work”, but instead it’s “people do work building AI” or “people do work providing services to people”. There’s a calculation to be made to determine when it would be more efficient to do the targeted work itself, and when AI is the right way to go.

Working on the AI has an advantage in that it will hopefully render the task reproducible, so it will lead to efficiency, but let’s remember that AI engineering is vastly different from the work of the reference librarian. We’re not interchanging the same workers, tasks, or skill sets here, and in our contemporary economy, the AI engineer’s time costs a heck of a lot more. So if we did want to measure this efficiency all in dollars and cents, the same amount of time spent working at the reference desk and doing the chat service will be much cheaper than paying an AI engineer to develop a better agentic AI for the use case. Given a bit of time, we could calculate out how many hours, days, years of work as a reference librarian we’d need to save with this chatbot to make it worth building, but often that calculation isn’t done before we move towards AI solutions.

We need to interrogate the assumption that incorporating generative AI in any given scenario is a guaranteed net gain in efficiency.

Externalities

While we’re on this topic of weighing whether the AI solution is worth doing in a particular situation, we should remember that developing and using AI for tasks does not happen in a vacuum. It has some cost environmentally and economically when we choose to use a generative AI tool, even when it’s a single prompt and a single response. Consider that the newly released GPT-4.5 has increased prices 30x for input tokens ($2.50 per million to $75 per million) and 15x for output tokens ($10 per million to $150 per million) just since GPT-4o. And that isn’t even taking into account the water consumption for cooling data centers (3 bottles per 100 word output for GPT-4), electricity use, and rare earth minerals used in GPUs. Many civic institutions have as a macro level goal to improve the world around them and the lives of the citizens of their communities, and concern for the environment has to have a place in that. Should organizations whose purpose is to have a positive impact weigh the possibility of incorporating AI more carefully? I think so.

Plus, I don’t often get too much into this, but I think we should take a moment to consider some folks’ end game for incorporating AI — reducing staffing altogether. Instead of making our existing dollars in an institution go farther, some people’s idea is just reducing the number of dollars and redistributing those dollars somewhere else. This brings up many questions, naturally, about where those dollars will go instead and whether they will be used to advance the interests of the community residents some other way, but let’s set that aside for now. My concern is for the people who might lose their jobs under this administrative model.

For-profit companies hire and fire employees all the time, and their priorities and objectives are focused on profit, so this is not particularly hypocritical or inconsistent. But as I noted above, civic organizations have objectives around improving the community or communities in which they exist. In a very real way, they are advancing that goal when part of what they provide is economic opportunity to their workers. We live in a Society where working is the overwhelmingly predominant way people provide for themselves and their families, and giving jobs to people in the community and supporting the economic well-being of the community is a role that civic institutions do play.

[R]educing staffing is not an unqualified good for civic organizations and government, but instead must be balanced critically against whatever other use the money that was paying their salaries will go to.

At the bare minimum, this means that reducing staffing is not an unqualified good for civic organizations and government, but instead must be balanced critically against whatever other use the money that was paying their salaries will go to. It’s not impossible for reducing staff to be the right decision, but we have to bluntly acknowledge that when members of communities experience joblessness, that effect cascades. They are now no longer able to patronize the shops and services they would have been supporting with their money, the tax base may be reduced, and this negatively affects the whole collective.

Workers aren’t just workers; they’re also patrons, customers, and participants in all aspects of the community. When we think of civic workers as simply money pits to be replaced with AI or whose cost for labor we need to minimize, we lose sight of the reasons for the work to be done in the first place.

Conclusion

I hope this discussion has brought some clarity about how really difficult it is to decide if, when, and how to apply generative AI to the civic space. It’s not nearly as simple a thought process as it might be in the for-profit sphere because the purpose and core meaning of civic institutions are completely different. Those of us who do machine learning and build AI solutions in the private sector might think, “Oh, I can see a way to use this in government,” but we have to recognize and appreciate the complex contextual implications that might have.

Next month, I’ll be bringing you a discussion of how social science research is incorporating generative AI, which has some very intriguing aspects.

As you may have heard, Towards Data Science has moved to an independent platform, but I will continue to post my work on my Medium page, my personal website, and the new TDS platform, so you’ll be able to find me wherever you happen to go. Subscribe to my newsletter on Medium if you’d like to ensure you get every article in your inbox.

Find more of my work at www.stephaniekirmer.com.

Explore More Insights

Stay ahead with more perspectives on cutting-edge power, infrastructure, energy, bitcoin and AI solutions. Explore these articles to uncover strategies and insights shaping the future of industries.

Amazon layoffs expected to disproportionately hit AWS and tech talent

Jassy explained: “If you grow as fast as we did for several years, the size of businesses, the number of people, the number of locations, the types of businesses you’re in, you end up with a lot more people than what you had before, and you end up with a

Rust 1.93 updates bundled musl library to boost networking

The Rust team has unveiled Rust 1.93, the latest version of the programming language designed to create fast and safe system-level software. This release improves operations involving the DNS resolver for the musl implementation of the C standard library. Linux binaries are expected to be more reliable for networking as

Intel nabs Qualcomm veteran to lead GPU initiative

Intel has struggled for more than two decades to develop a successful GPU/accelerated computing strategy, going all the way back to the aughts and the ill-fated Larrabee effort. Its most recent efforts centered around Ponte Vecchio and Gaudi chips, neither of which have gained any traction. Still, CEO Lip-Bu Tan

New Relic extends observability into ChatGPT-hosted apps

New Relic’s cloud-based observability platform monitors applications and services in real time to provide insights into software, hardware, and cloud performance. The new capability extends the platform’s browser agent into the GPT iframe environment. It captures standard telemetry data, including latency and connectivity of an application within the GPT iframe.

TenneT, Uniper Partner for Power Station for Frankfurt Region

Uniper SE and TenneT BV have agreed to develop a new central network node in Großkrotzenburg, Germany to serve data center-driven growth in power demand in the greater Frankfurt area, targeting to start operation earlier than planned. Under the partnership, German power and gas utility Uniper agreed to sell part of a plot of land at the Staudinger power plant to enable TenneT, a transmission system operator in the Netherlands and Germany, to build a switching station. “The core of the project is the construction of a new 380-kilovolt switchyard, which will create extensive additional capacity for urgently needed customer connections in the Rhine-Main area”, said a joint statement online. “The connection of new and existing extra-high-voltage lines will further strengthen electricity transmission to the Frankfurt metropolitan area and neighboring regions, particularly Lower Franconia. “At the same time, the new grid infrastructure will form the basis for connecting important Uniper transformation projects at the Staudinger site. Plans include a hydrogen-ready combined cycle gas turbine plant as part of the German government’s expected tender for flexible generation capacities, as well as the prospective establishment of battery storage facilities and data centers”. The station is scheduled to start service by 2030, Uniper said. “With this project, we are launching our extensive grid expansion program in the Rhine-Main region, one of Germany’s key growth areas”, said TenneT Germany chief operating officer Ina Kamps. In September 2025 Tennet said it had submitted a plan to authorities and thereby launched the approval process for a 380 kV extra-high-voltage overhead line stretching about nine kilometers (5.59 miles) between Bommersheim and Eschborn. The new line would replace a section of the 380-kV line between the Frankfurt Südwest and Karben substations. “This is a first, important step towards a demand-oriented and sustainable electricity grid in the region”, TenneT said

Energy Secretary Issues Emergency Order to Secure Texas Grid Amid Winter Storm Fern

WASHINGTON—The U.S. Department of Energy (DOE) today issued an emergency order for the deployment of backup generation resources in order to mitigate blackouts in Texas during Winter Storm Fern. Issued pursuant to Section 202(c) of the Federal Power Act, the order authorizes the Electric Reliability Council of Texas (ERCOT) to deploy backup generation resources at data centers and other major facilities. Today’s action follows a letter Secretary Wright sent Thursday to grid operators asking them to be prepared to use backup generation if needed to mitigate the risk of blackouts during the storm. DOE estimates more than 35 GW of unused backup generation remains available nationwide. The order will help ERCOT with the extreme temperatures and storm destruction across Texas and reduce costs for Americans during the winter storm. “The Trump administration is committed to unleashing all available power generation needed to keep Americans safe during Winter Storm Fern,” said Energy Secretary Wright. “Unfortunately, the last administration had the nation on track to lose significant amounts of baseload power, but we are doing everything in our power to reverse those reckless decisions. The Trump administration will continue taking action to ensure that the 35 GW of untapped backup generation that exists across the country can be deployed as needed during Winter Storm Fern and in the future.” On day one, President Trump declared a national energy emergency after the Biden administration’s energy subtraction agenda left behind a grid increasingly vulnerable to blackouts. According to the North American Electric Reliability Corporation (NERC), “Winter electricity demand is rising at the fastest rate in recent years,” while the premature forced closure of reliable generation such as coal and natural gas plants leaves American families vulnerable to power outages. The NERC 2025 – 2026 Winter Reliability Assessment further warns that areas across the continental United States have an elevated risk of

Energy Secretary Secures Mid-Atlantic Grid Amid Winter Storm Fern

WASHINGTON—The U.S. Department of Energy (DOE) today issued an emergency order to mitigate blackouts in the Mid-Atlantic during Winter Storm Fern. Issued pursuant to Section 202(c) of the Federal Power Act, the order authorizes PJM Interconnection, LLC (PJM) to run specified resources located within the PJM Region, regardless of limits established by environmental permits or state law. The order will help PJM with the extreme temperatures and storm destruction across the Mid-Atlantic and reduce costs for Americans during the winter storm. “As Winter Storm Fern brings extreme cold and dangerous conditions to the Mid-Atlantic, maintaining affordable, reliable, and secure power in the PJM region is non-negotiable,” said U.S. Secretary of Energy Chris Wright. “The previous administration’s energy subtraction policies weakened the grid, leaving Americans more vulnerable during events like Winter Storm Fern. Thanks to President Trump’s leadership, we are reversing those failures and using every available tool to keep the lights on and Americans safe through this storm.” On day one, President Trump declared a national energy emergency after the Biden administration’s energy subtraction agenda left behind a grid increasingly vulnerable to blackouts. According to the North American Electric Reliability Corporation (NERC), “Winter electricity demand is rising at the fastest rate in recent years,” while the premature forced closure of reliable generation such as coal and natural gas plants leaves American families vulnerable to power outages. The NERC 2025 – 2026 Winter Reliability Assessment further warns that areas across the continental United States have an elevated risk of blackouts during extreme weather conditions. Power outages cost the American people $44 billion per year, according to data from DOE’s National Laboratories. This order will help mitigate power outages in the Mid-Atlantic and highlights the commonsense policies of the Trump Administration to ensure Americans have access to affordable, reliable and secure electricity. The order is in effect from January 25—January 31, 2026.

USA Is Canceling Almost $30B in Biden-Era Energy Loans

The Trump administration said it’s canceling almost $30 billion of financing from the Energy Department’s green bank after reviewing transactions approved under former President Joe Biden. The Energy Department said Thursday that its Loan Programs Office — now called the Office of Energy Dominance Financing — also plans to revise another $53 billion of funding. In a statement, the department said it has eliminated about $9.5 billion in financing for wind and solar projects as part of the adjustment and plans to redirect the funding toward natural gas and nuclear projects. The Energy Department, which didn’t immediately respond to a request for comment, didn’t provide specific details on which other deals were affected. The Energy Department’s loans office swelled into a $400 billion green bank under Biden, partly due to an infusion in funds from his signature Inflation Reduction Act. The office has been used to finance Tesla Inc.’s Model S sedan and some of the first new nuclear reactors built in the US in decades by Southern Co. But the program drew criticism from Energy Secretary Chris Wright, who said the department closed or offered $85 billion of financing in the final months of the Biden presidency after Donald Trump’s election. “We found more dollars were rushed out the door of the Loan Programs Office in the final months of the Biden Administration than had been disbursed in over 15 years,” Wright said in Thursday’s statement. While Trump once proposed killing the Energy Department program — arguing during his first term that the government had no business picking winners and losers — his administration later sought to tap the bank to pay for its own energy priorities. The administration has laid out plans to use the program, which has more than $289 billion in loan authority remaining, to finance

Crude Closes Higher on Iran, Cold Weather

Oil rose as traders factored in the possibility of US military action in Iran that could upend supplies from one of OPEC’s leading producers, and a massive winter storm in the US pushing up the price of refined products. West Texas Intermediate rose 2.9% to settle above $61, posting a fifth weekly gain. Prices rose after President Donald Trump revived his threats to use military force against Iran’s senior leadership, with a US Navy carrier strike group moving toward the Middle East. While Trump previously walked back pledges to attack the country, a renewed pressure campaign could add to oil’s geopolitical risk premium given Iran’s strategic importance to the industry. Adding to the concern and the geopolitically driven bullish momentum, the US is also pressuring Iraq to disarm Iran-backed militias, the Financial Times reported. Meanwhile, the Kremlin poured cold water on hopes of a breakthrough to end Russia’s war in Ukraine. An end to the conflict could limit supply disruptions and sanctions on Moscow’s crude. “The bottom line is that geopolitical headlines remain plentiful and uncertainty remains exceptionally high. Heading into the weekend, crude is likely to trade in whichever direction the headlines push it,” said Rebecca Babin, a senior energy trader at CIBC Private Wealth Group. “For now, recent shifts in military assets and official commentary appear to be leaning back toward renewed concerns over potential military action involving Iran,” Babin added. If the US strikes Iran, prompting a retaliation, it is unlikely but possible that the conflict will impact oil supplies, according to Rapidan Energy Group. The geopolitical analysis firm assigned a 20% probability to a “sustained and severe interruption” in energy production and flows in the region. Oil products such as diesel, which can be used as heating oil in the US Northeast, are also pushing higher

SLB Predicts Worst Is Behind Global Oil Market

SLB, the world’s largest oilfield-services provider, raised its dividend and posted fourth-quarter earnings that beat estimates as activity in the Middle East and other key regions accelerated and its data-center business rapidly expanded. The worst may be behind the global oil market, Chief Executive Officer Olivier Le Peuch said in a statement, predicting a gradual ramp-up in drilling activity in major regions including OPEC countries after a supply glut sent crude prices tumbling last year. Deriving the bulk of its revenue from overseas markets, Houston-based SLB is often regarded as a bellwether for the global oil industry and its financial health. Shares rose by as much as 4.8% to $51.67, briefly hitting the highest price since April 2024 before paring gains. “As we move into 2026, we believe that the headwinds we experienced in key regions in 2025 are behind us,” Le Peuch said. “In particular, we expect rig activity in the Middle East to increase compared to today’s level, and our footprint in the region puts us in a strong position to benefit from this recovery.” The data-center business, which grew 121% from a year earlier, helped to shield the company from lower oil prices and geopolitical uncertainty, he added. SLB has also increased its focus on production and recovery services, which help drillers to boost efficiency and extract more crude at lower cost. SLB has been expanding into oilfield tech and other ancillary business lines to offset muted growth in traditional drilling and US shale activity. SLB posted adjusted fourth-quarter earnings of 78 cents a share, surpassing analysts’ estimates of 74 cents. The company increased its quarterly divided 3.5% to 29.5 cents a share. The company’s global footprint positions it to benefit from US government efforts to revive Venezuelan oil production, Bloomberg Intelligence analyst Scott Levine wrote in

Intel’s AI pivot could make lower-end PCs scarce in 2026

However, he noted, “CPUs are not being cannibalized by GPUs. Instead, they have become ‘chokepoints’ in AI infrastructure.” For instance, CPUs such as Granite Rapids are essential in GPU clusters, and for handling agentic AI workloads and orchestrating distributed inference. How pricing might increase for enterprises Ultimately, rapid demand for higher-end offerings resulted in foundry shortages of Intel 10/7 nodes, Bickley noted, which represent the bulk of the company’s production volume. He pointed out that it can take up to three quarters for new server wafers to move through the fab process, so Intel will be “under the gun” until at least Q2 2026, when it projects an increase in chip production. Meanwhile, manufacturing capacity for Xeon is currently sold out for 2026, with varying lead times by distributor, while custom silicon programs are seeing lead times of 6 to 8 months, with some orders rolling into 2027, Bickley said. In the data center, memory is the key bottleneck, with expected price increases of more than 65% year over year in 2026 and up to 25% for NAND Flash, he noted. Some specific products have already seen price inflation of over 1,000% since 2025, and new greenfield capacity for memory is not expected until 2027 or 2028. Moor’s Sag was a little more optimistic, forecasting that, on the client side, “memory prices will probably stabilize this year until more capacity comes online in 2027.” How enterprises can prepare Supplier diversification is the best solution for enterprises right now, Sag noted. While it might make things more complex, it also allows data center operators to better absorb price shocks because they can rebalance against suppliers who have either planned better or have more resilient supply chains.

Reports of SATA’s demise are overblown, but the technology is aging fast

The SATA 1.0 interface made its debut in 2003. It was developed by a consortium consisting of Intel, Dell, and storage vendors like Seagate and Maxtor. It quickly advanced to SATA III in 2009, but there never was a SATA IV. There was just nibbling around the edges with incremental updates as momentum and emphasis shifted to PCI Express and NVMe. So is there any life to be had in the venerable SATA interface? Surprisingly, yes, say the analysts. “At a high level, yes, SATA for consumer is pretty much a dead end, although if you’re storing TB of photos and videos, it is still the least expensive option,” said Bob O’Donnell, president and chief analyst with TECHnalysis Research. Similarly for enterprise, for massive storage demands, the 20 and 30 TB SATA drives from companies like Seagate and WD are apparently still in wide use in cloud data centers for things like cold storage. “In fact, both of those companies are seeing recording revenues based, in part, on the demand for these huge, high-capacity low-cost drives,” he said. “SATA doesn’t make much sense anymore. It underperforms NVMe significantly,” said Rob Enderle, principal analyst with The Enderle Group. “It really doesn’t make much sense to continue make it given Samsung allegedly makes three to four times more margin on NVMe.” And like O’Donnell, Enderle sees continued life for SATA-based high-capacity hard drives. “There will likely be legacy makers doing SATA for some time. IT doesn’t flip technology quickly and SATA drives do wear out, so there will likely be those producing legacy SATA products for some time,” he said.

DCN becoming the new WAN for AI-era applications

“DCN is increasingly treated as an end-to-end operating model that standardizes connectivity, security policy enforcement, and telemetry across users, the middle mile, and cloud/application edges,” Sanchez said. Dell’Oro defines DCN as platforms and services that deliver consistent connectivity, policy enforcement, and telemetry from users, across the WAN, to distributed cloud and application edges spanning branch sites, data centers and public clouds. The category is gaining relevance as hybrid architectures and AI-era traffic patterns increase the operational penalty for fragmented control planes. DCN buyers are moving beyond isolated upgrades and are prioritizing architectures that reduce operational seams across connectivity, security and telemetry so that incident response and change control can follow a single thread, according to Dell’Oro’s research. What makes DCN distinct is that it links user-to-application experience with where policy and visibility are enforced. This matters as application delivery paths become more dynamic and workloads shift between on-premises data centers, public cloud, and edge locations. The architectural requirement is eliminating handoffs between networking and security teams rather than optimizing individual network segments. Where DCN is growing the fastest Cloud/application edge is the fastest-growing DCN pillar. This segment deploys policy enforcement and telemetry collection points adjacent to workloads rather than backhauling traffic to centralized security stacks. “Multi-cloud remains a reality, but it is no longer the durable driver by itself,” Sanchez said. “Cloud/application edge is accelerating because enterprises are trying to make application paths predictable and secure across hybrid environments, and that requires pushing application-aware steering, policy enforcement, and unified telemetry closer to workloads.”

Edged US Builds Waterless, High-Density AI Data Center Campuses at Scale

Edged US is targeting a narrow but increasingly valuable lane of the hyperscale AI infrastructure market: high-density compute delivered at speed, paired with a sustainability posture centered on waterless, closed-loop cooling and a portfolio-wide design PUE target of roughly 1.15. Two recent announcements illustrate the model. In Aurora, Illinois, Edged is developing a 72-MW facility purpose-built for AI training and inference, with liquid-to-chip cooling designed to support rack densities exceeding 200 kW. In Irving, Texas, a 24-MW campus expansion combines air-cooled densities above 120 kW per rack with liquid-to-chip capability reaching 400 kW. Taken together, the projects point to a consistent strategy: standardized, multi-building campuses in major markets; a vertically integrated technical stack with cooling at its core; and an operating model built around repeatable designs, modular systems, and readiness for rapidly escalating AI densities. A Campus-First Platform Strategy Edged US’s platform strategy is built around campus-scale expansion rather than one-off facilities. The company positions itself as a gigawatt-scale, AI-ready portfolio expanding across major U.S. metros through repeatable design targets and multi-building campuses: an emphasis that is deliberate and increasingly consequential. In Chicago/Aurora, Edged is developing a multi-building campus with an initial facility already online and a second 72-MW building under construction. Dallas/Irving follows the same playbook: the first facility opened in January 2025, with a second 24-MW building approved unanimously by the city. Taken together with developments in Atlanta, Chicago, Columbus, Dallas, Des Moines, Kansas City, and Phoenix, the footprint reflects a portfolio-first mindset rather than a collection of bespoke sites. This focus on campus-based expansion matters because the AI factory era increasingly rewards developers that can execute three things at once: Lock down power and land at scale. Standardize delivery across markets. Operate efficiently while staying aligned with community and regulatory expectations. Edged is explicitly selling the second

CBRE’s 2026 Data Center Outlook: Demand Surges as Delivery Becomes the Constraint

The U.S. data center market is entering 2026 with fundamentals that remain unmatched across commercial real estate, but the nature of the dominant constraint has shifted. Demand is no longer gated by capital, connectivity, or even land. It is gated by the ability to deliver very large blocks of power, on aggressive timelines, at a predictable cost. According to the CBRE 2026 U.S. Real Estate Market Outlook as overseen by Gordon Dolven and Pat Lynch, the sector is on track to post another record year for leasing activity, even as vacancy remains at historic lows and pricing reaches all-time highs. What has changed is the scale at which demand now presents itself, and the difficulty of meeting it. Large-Block Leasing Rewrites the Economics AI-driven workloads are reshaping leasing dynamics in ways that break from prior hyperscale norms. Where 10-MW-plus deployments once commanded pricing concessions, CBRE now observes the opposite behavior: large, contiguous blocks of capacity are commanding premiums. Neocloud providers, GPU-as-a-service platforms and AI startups, many backed by aggressive capital deployment strategies, are actively competing for full-building and campus-scale capacity. For operators, this is altering development and merchandising strategies. Rather than subdividing shells for flexibility, owners increasingly face a strategic choice: hold buildings intact to preserve optionality for single-tenant, high-density users who are willing to pay for scale. In effect, scale itself has become the scarce asset. Behind-the-Meter Power Moves to the Foreground For data centers, power availability meaning not just access, but certainty of delivery, is now the defining variable in the market. CBRE notes accelerating adoption of behind-the-meter strategies as operators seek to bypass increasingly constrained utility timelines. On-site generation using natural gas, solar, wind, and battery storage is gaining traction, particularly in deregulated electricity markets where operators have more latitude to structure BYOP (bring your own power) solutions.

Blue Origin targets enterprise networks with a multi-terabit satellite connectivity plan

“It’s ideal for remote, sparse, or sensitive regions,” said Manish Rawat, analyst at TechInsights. “Key use cases include cloud-to-cloud links, data center replication, government, defense, and disaster recovery workloads. It supports rapid or temporary deployments and prioritizes fewer customers with high capacity, strict SLAs, and deep carrier integration.” Adoption, however, is expected to largely depend on the sector. For governments and organizations operating highly critical or sensitive infrastructure, where reliability and security outweigh cost considerations, this could be attractive as a redundancy option. “Banks, national security agencies, and other mission-critical operators may consider it as an alternate routing path,” Jain said. “For most enterprises, however, it is unlikely to replace terrestrial connectivity and would instead function as a supplementary layer.” Real-world performance Although satellite connectivity offers potential advantages, analysts note that questions remain around real-world performance. “TeraWave’s 6 Tbps refers to total constellation capacity, not per-user throughput, achieved via multiple optical inter-satellite links and ground gateways,” Rawat said. “Optical crosslinks provide high aggregate bandwidth but not a single terabit-class pipe. Performance lies between fiber and GEO satellites, with lower intercontinental latency than GEO but higher than fiber.” Operational factors could also affect network stability. Jitter is generally low, but handovers, rerouting, and weather conditions can introduce intermittent performance spikes. Packet loss is expected to remain modest but episodic, Rawat added.

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.

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

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

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

Stay Ahead, Stay ONMINE