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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.

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

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EIA Raises 2026 WTI Forecast, Lowers 2027 Projection

The U.S. Energy Information Administration (EIA) increased its 2026 West Texas Intermediate (WTI) crude oil average spot price forecast, and lowered its 2027 projection, in its latest short term energy outlook (STEO). According to the EIA’s February STEO, which was released on February 10, the EIA now sees the WTI spot price averaging $53.42 per barrel this year and $49.34 per barrel next year. In its previous STEO, which was released in January, the EIA projected that the WTI spot price would average $52.21 per barrel in 2026 and $50.36 per barrel in 2027. A quarterly breakdown included in the EIA’s latest STEO projected that the WTI average spot price will come in at $58.62 per barrel in the first quarter of this year, $53.65 per barrel in the second quarter, $51.69 per barrel in the third quarter, $50.00 per barrel in the fourth quarter, $49.00 per barrel in the first quarter of next year, $49.66 per barrel in the second quarter, $49.68 per barrel in the third quarter, and $49.00 per barrel in the fourth quarter of 2027. In its previous STEO, the EIA forecast that the WTI spot price would average $54.93 per barrel in the first quarter of this year, $52.67 per barrel in the second quarter, $52.03 per barrel in the third quarter, $49.34 per barrel in the fourth quarter, $49.00 per barrel in the first quarter of next year, $50.66 per barrel in the second quarter, $50.68 per barrel in the third quarter, and $51.00 per barrel in the fourth quarter of 2027. In a BMI report sent to Rigzone by the Fitch Group on Friday, BMI projected that the front month WTI crude price will average $64.00 per barrel in 2026 and $68.00 per barrel in 2027. Standard Chartered sees the NYMEX WTI nearby

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Arista laments ‘horrendous’ memory situation

Digging in on campus Arista has been clear about its plans to grow its presence campus networking environments. Last Fall, Ullal said she expects Arista’s campus and WAN business would grow from the current $750 million-$800 million run rate to $1.25 billion, representing a 60% growth opportunity for the company. “We are committed to our aggressive goal of $1.25 billion for ’26 for the cognitive campus and branch. We have also successfully deployed in many routing edge, core spine and peering use cases,” Ullal said. “In Q4 2025, Arista launched our flagship 7800 R4 spine for many routing use cases, including DCI, AI spines with that massive 460 terabits of capacity to meet the demanding needs of multiservice routing, AI workloads and switching use cases. The combined campus and routing adjacencies together contribute approximately 18% of revenue.” Ethernet leads the way “In terms of annual 2025 product lines, our core cloud, AI and data center products built upon our highly differentiated Arista EOS stack is successfully deployed across 10 gig to 800 gigabit Ethernet speeds with 1.6 terabit migration imminent,” Ullal said. “This includes our portfolio of EtherLink AI and our 7000 series platforms for best-in-class performance, power efficiency, high availability, automation, agility for both the front and back-end compute, storage and all of the interconnect zones.” Ullal said she expects Ethernet will get even more of a boost later this year when the multivendor Ethernet for Scale-Up Networking (ESUN) specification is released.  “We have consistently described that today’s configurations are mostly a combination of scale out and scale up were largely based on 800G and smaller ratings. Now that the ESUN specification is well underway, we need a good solid spec. Otherwise, we’ll be shipping proprietary products like some people in the world do today. And so we will tie our

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From NIMBY to YIMBY: A Playbook for Data Center Community Acceptance

Across many conversations at the start of this year, at PTC and other conferences alike, the word on everyone’s lips seems to be “community.” For the data center industry, that single word now captures a turning point from just a few short years ago: we are no longer a niche, back‑of‑house utility, but a front‑page presence in local politics, school board budgets, and town hall debates. That visibility is forcing a choice in how we tell our story—either accept a permanent NIMBY-reactive framework, or actively build a YIMBY narrative that portrays the real value digital infrastructure brings to the markets and surrounding communities that host it. Speaking regularly with Ilissa Miller, CEO of iMiller Public Relations about this topic, there is work to be done across the ecosystem to build communications. Miller recently reflected: “What we’re seeing in communities isn’t a rejection of digital infrastructure, it’s a rejection of uncertainty driven by anxiety and fear. Most local leaders have never been given a framework to evaluate digital infrastructure developments the way they evaluate roads, water systems, or industrial parks. When there’s no shared planning language, ‘no’ becomes the safest answer.” A Brief History of “No” Community pushback against data centers is no longer episodic; it has become organized, media‑savvy, and politically influential in key markets. In Northern Virginia, resident groups and environmental organizations have mobilized against large‑scale campuses, pressing counties like Loudoun and Prince William to tighten zoning, question incentives, and delay or reshape projects.1 Loudoun County’s move in 2025 to end by‑right approvals for new facilities, requiring public hearings and board votes, marked a watershed moment as the world’s densest data center market signaled that communities now expect more say over where and how these campuses are built. Prince William County’s decision to sharply increase its tax rate on

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Nomads at the Frontier: PTC 2026 Signals the Digital Infrastructure Industry’s Moment of Execution

Each January, the Pacific Telecommunications Council conference serves as a barometer for where digital infrastructure is headed next. And according to Nomad Futurist founders Nabeel Mahmood and Phillip Koblence, the message from PTC 2026 was unmistakable: The industry has moved beyond hype. The hard work has begun. In the latest episode of The DCF Show Podcast, part of our ongoing ‘Nomads at the Frontier’ series, Mahmood and Koblence joined Data Center Frontier to unpack the tone shift emerging across the AI and data center ecosystem. Attendance continues to grow year over year. Conversations remain energetic. But the character of those conversations has changed. As Mahmood put it: “The hype that the market started to see is actually resulting a bit more into actions now, and those conversations are resulting into some good progress.” The difference from prior years? Less speculation. More execution. From Data Center Cowboys to Real Deployments Koblence offered perhaps the sharpest contrast between PTC conversations in 2024 and those in 2026. Two years ago, many projects felt speculative. Today, developers are arriving with secured power, customers, and construction underway. “If 2024’s PTC was data center cowboys — sites that in someone’s mind could be a data center — this year was: show me the money, show me the power, give me accurate timelines.” In other words, the market is no longer rewarding hypothetical capacity. It is demanding delivered capacity. Operators now speak in terms of deployments already underway, not aspirational campuses still waiting on permits and power commitments. And behind nearly every conversation sits the same gating factor. Power. Power Has Become the Industry’s Defining Constraint Whether discussions centered on AI factories, investment capital, or campus expansion, Mahmood and Koblence noted that every conversation eventually returned to energy availability. “All of those questions are power,” Koblence said.

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Cooling Consolidation Hits AI Scale: LiquidStack, Submer, and the Future of Data Center Thermal Strategy

As AI infrastructure scales toward ever-higher rack densities and gigawatt-class campuses, cooling has moved from a technical subsystem to a defining strategic issue for the data center industry. A trio of announcements in early February highlights how rapidly the cooling and AI infrastructure stack is consolidating and evolving: Trane Technologies’ acquisition of LiquidStack; Submer’s acquisition of Radian Arc, extending its reach from core data centers into telco edge environments; and Submer’s partnership with Anant Raj to accelerate sovereign AI infrastructure deployment across India. Layered atop these developments is fresh guidance from Oracle Cloud Infrastructure explaining why closed-loop, direct-to-chip cooling is becoming central to next-generation facility design, particularly in regions where water use has become a flashpoint in community discussions around data center growth. Taken together, these developments show how the industry is moving beyond point solutions toward integrated, scalable AI infrastructure ecosystems, where cooling, compute, and deployment models must work together across hyperscale campuses and distributed edge environments alike. Trane Moves to Own the Cooling Stack The most consequential development comes from Trane Technologies, which on February 10 announced it has entered into a definitive agreement to acquire LiquidStack, one of the pioneers and leading innovators in data center liquid cooling. The acquisition significantly strengthens Trane’s ambition to become a full-service thermal partner for data center operators, extending its reach from plant-level systems all the way down to the chip itself. LiquidStack, headquartered in Carrollton, Texas, built its reputation on immersion cooling and advanced direct-to-chip liquid solutions supporting high-density deployments across hyperscale, enterprise, colocation, edge, and blockchain environments. Under Trane, those technologies will now be scaled globally and integrated into a broader thermal portfolio. In practical terms, Trane is positioning itself to deliver cooling across the full thermal chain, including: • Central plant equipment and chillers.• Heat rejection and controls

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Infrastructure Maturity Defines the Next Phase of AI Deployment

The State of Data Infrastructure Global Report 2025 from Hitachi Vantara arrives at a moment when the data center industry is undergoing one of the most profound structural shifts in its history. The transition from enterprise IT to AI-first infrastructure has moved from aspiration to inevitability, forcing operators, developers, and investors to confront uncomfortable truths about readiness, resilience, and risk. Although framed around “AI readiness,” the report ultimately tells an infrastructure story: one that maps directly onto how data centers are designed, operated, secured, and justified economically. Drawing on a global survey of more than 1,200 IT leaders, the report introduces a proprietary maturity model that evaluates organizations across six dimensions: scalability, reliability, security, governance, sovereignty, and sustainability. Respondents are then grouped into three categories—Emerging, Defined, and Optimized—revealing a stark conclusion: most organizations are not constrained by access to AI models or capital, but by the fragility of the infrastructure supporting their data pipelines. For the data center industry, the implications are immediate, shaping everything from availability design and automation strategies to sustainability planning and evolving customer expectations. In short, extracting value from AI now depends less on experimentation and more on the strength and resilience of the underlying infrastructure. The Focus of the Survey: Infrastructure, Not Algorithms Although the report is positioned as a study of AI readiness, its primary focus is not models, training approaches, or application development, but rather the infrastructure foundations required to operate AI reliably at scale. Drawing on responses from more than 1,200 organizations, Hitachi Vantara evaluates how enterprises are positioned to support production AI workloads across six dimensions as stated above: scalability, reliability, security, governance, sovereignty, and sustainability. These factors closely reflect the operational realities shaping modern data center design and management. The survey’s central argument is that AI success is no longer

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AI’s New Land Grab: Meta’s Indiana Megaproject and the Rise of Europe’s Neocloud Challengers

While Meta’s Indiana campus anchors hyperscale expansion in the United States, Europe recorded its own major infrastructure milestone this week as Amsterdam-based AI infrastructure provider Nebius unveiled plans for a 240-megawatt data center campus in Béthune, France, near Lille in the country’s northern industrial corridor. When completed, the campus will rank among Europe’s largest AI-focused data center facilities and positions northern France as a growing node in the continent’s expanding AI infrastructure map. The development repurposes a former Bridgestone tire manufacturing site, reflecting a broader trend across Europe in which legacy industrial properties, already equipped with heavy power access, transport links, and industrial zoning, are being converted into large-scale digital infrastructure hubs. Located within reach of connectivity and enterprise corridors linking Paris, Brussels, London, and Amsterdam, the site allows Nebius to serve major European markets while avoiding the congestion and power constraints increasingly shaping Tier 1 data center hubs. Industrial Infrastructure Becomes Digital Infrastructure Developers increasingly view former industrial sites as ideal for AI campuses because they often provide: • Existing grid interconnection capacity built for heavy industry• Transport and logistics infrastructure already in place• Industrial zoning that reduces permitting friction• Large contiguous parcels suited to phased campus expansion For regions like Hauts-de-France, redevelopment projects also offer economic transition opportunities, replacing legacy manufacturing capacity with next-generation digital infrastructure investment. Local officials have positioned the project as part of broader efforts to reposition northern France as a logistics and technology hub within Europe. The Neocloud Model Gains Ground Beyond the site itself, Nebius’ expansion illustrates the rapid emergence of neocloud infrastructure providers, companies building GPU-intensive AI capacity without operating full hyperscale cloud ecosystems. These firms increasingly occupy a strategic middle ground: supplying AI compute capacity to enterprises, startups, and even hyperscalers facing short-term infrastructure constraints. Nebius’ rise over the past year

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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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