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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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How enterprises are rethinking online AI tools

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Chevron Chooses West Texas for 1st AI Data Center Power Project

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Energy Department Awards Contracts to Begin Refilling the Strategic Petroleum Reserve

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Gulf Energy Plans Production from Tullow Fields End-2026

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Xcel, Colorado agencies propose extending life of Comanche 2 coal unit

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Venture Global Bags Long-Term LNG Supply Contracts

United States liquefied natural gas (LNG) producer Venture Global Inc this week announced multiple long-term contracts to supply three Greek, Japanese and Spanish companies. Atlantic-See LNG Trade SA, formed this month by Greek companies Aktor Group and DEPA Commercial SMSA, committed to at least 0.5 million metric tons per annum (MMtpa) of United States-produced LNG for 20 years from 2030. “Under the SPA [sale and purchase agreement], Atlantic-See has the potential to expand its purchase commitment”, Venture Global said in a press release. “This deal marks Greece’s first ever long-term LNG supply agreement with a U.S. exporter, launching a dynamic and growing partnership between Atlantic-See LNG and Venture Global”, Venture Global said. The agreement between Venture Global and Atlantic-See LNG Trade was signed at the Partnership for Transatlantic Energy Cooperation summit between Central and Eastern European countries and the U.S. Separately Venture Global said it had bagged a contract to supply Tokyo-based Mitsui & Co Ltd one MMtpa for 20 years from 2029. “This collaboration between our two companies will strengthen energy security, enhance the balance of trade and deepen the long-standing ties between our nations”, said Venture Global chief executive Mike Sabel. “This agreement builds upon our existing long-term relationships with Japanese companies”. Spain’s Naturgy also contracted Venture Global for one MMtpa for 20 years from 2030. “The agreement represents Spain’s first long-term contract for American LNG since Venture Global’s first contract in 2018”, Venture Global said in another statement. “To date, Venture Global has supplied Spain with 35 cargos from its Calcasieu Pass and Plaquemines facilities”. Sabel said, “This contract will positively impact the U.S. balance of trade with Spain and enhance energy security across the region”. “The signing of this agreement, along with the strong commercial momentum we’ve achieved over the past six months, reflects the continued customer

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When the Cloud Leaves Earth: Google and NVIDIA Test Space Data Centers for the Orbital AI Era

On November 4, 2025, Google unveiled Project Suncatcher, a moonshot research initiative exploring the feasibility of AI data centers in space. The concept envisions constellations of solar-powered satellites in Low Earth Orbit (LEO), each equipped with Tensor Processing Units (TPUs) and interconnected via free-space optical laser links. Google’s stated objective is to launch prototype satellites by early 2027 to test the idea and evaluate scaling paths if the technology proves viable. Rather than a commitment to move production AI workloads off-planet, Suncatcher represents a time-bound research program designed to validate whether solar-powered, laser-linked LEO constellations can augment terrestrial AI factories, particularly for power-intensive, latency-tolerant tasks. The 2025–2027 window effectively serves as a go/no-go phase to assess key technical hurdles including thermal management, radiation resilience, launch economics, and optical-link reliability. If these milestones are met, Suncatcher could signal the emergence of a new cloud tier: one that scales AI with solar energy rather than substations. Inside Google’s Suncatcher Vision Google has released a detailed technical paper titled “Towards a Future Space-Based, Highly Scalable AI Infrastructure Design.” The accompanying Google Research blog describes Project Suncatcher as “a moonshot exploring a new frontier” – an early-stage effort to test whether AI compute clusters in orbit can become a viable complement to terrestrial data centers. The paper outlines several foundational design concepts: Orbit and Power Project Suncatcher targets Low Earth Orbit (LEO), where solar irradiance is significantly higher and can remain continuous in specific orbital paths. Google emphasizes that space-based solar generation will serve as the primary power source for the TPU-equipped satellites. Compute and Interconnect Each satellite would host Tensor Processing Unit (TPU) accelerators, forming a constellation connected through free-space optical inter-satellite links (ISLs). Together, these would function as a disaggregated orbital AI cluster, capable of executing large-scale batch and training workloads. Downlink

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Cloud-based GPU savings are real – for the nimble

The pattern points to an evolving GPU ecosystem: while top-tier chips like Nvidia’s new GB200 Blackwell processors remain in extremely short supply, older models such as the A100 and H100 are becoming cheaper and more available. Yet, customer behavior may not match practical needs. “Many are buying the newest GPUs because of FOMO—the fear of missing out,” he added. “ChatGPT itself was built on older architecture, and no one complained about its performance.” Gil emphasized that managing cloud GPU resources now requires agility, both operationally and geographically. Spot capacity fluctuates hourly or even by the minute, and availability varies across data center regions. Enterprises willing to move workloads dynamically between regions—often with the help of AI-driven automation—can achieve cost reductions of up to 80%. “If you can move your workloads where the GPUs are cheap and available, you pay five times less than a company that can’t move,” he said. “Human operators can’t respond that fast automation is essential.” Conveniently, Cast sells an AI automation solution. But it is not the only one and the argument is valid. If spot pricing can be found cheaper at another location, you want to take it to keep the cloud bill down/ Gil concluded by urging engineers and CTOs to embrace flexibility and automation rather than lock themselves into fixed regions or infrastructure providers. “If you want to win this game, you have to let your systems self-adjust and find capacity where it exists. That’s how you make AI infrastructure sustainable.”

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Harnessing Gravity: RRPT Hydro Reimagines Data Center Power

At the 2025 Data Center Frontier Trends Summit, amid panels on AI, nuclear, and behind-the-meter power, few technologies stirred more curiosity than a modular hydropower system without dams or flowing rivers. That concept—piston-driven hydropower—was presented by Expanse Energy Corporation President and CEO Ed Nichols and Chief Electrical Engineer Gregory Tarver during the Trends Summit’s closing “6 Moonshots for the 2026 Data Center Frontier” panel. Nichols and Tarver joined the Data Center Frontier Show recently to discuss how their Reliable Renewable Power Technology (RRPT Hydro) platform could rewrite the economics of clean, resilient power for the AI era. A New Kind of Hydropower Patented in the U.S. and entering commercial readiness, RRPT Hydro’s system replaces flowing water with a gravity-and-buoyancy engine housed in vertical cylinders. Multiple pistons alternately sink and rise inside these cylinders—heavy on the downward stroke, buoyant on the upward—creating continuous motion that drives electrical generation. “It’s not perpetual motion,” Nichols emphasizes. “You need a starter source—diesel, grid, solar, anything—but once in motion, the system sustains itself, converting gravity’s constant pull and buoyancy’s natural lift into renewable energy.” The concept traces its roots to a moment of natural awe. Its inventor, a gas-processing engineer, was moved to action by the 2004 Boxing Day tsunami, seeking a way to “containerize” and safely harvest the vast energy seen in that disaster. Two decades later, that spark has evolved into a patented, scalable system designed for industrial deployment. Physics-Based Power: Gravity Down, Buoyancy Up Each RRPT module operates as a closed-loop hydropower system: On the downstroke, pistons filled with water become dense and fall under gravity, generating kinetic energy. On the upstroke, air ballast tanks lighten the pistons, allowing buoyant forces to restore potential energy. By combining gravitational and buoyant forces—both constant, free, and renewable—RRPT converts natural equilibrium into sustained mechanical power.

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Buyer’s guide to AI networking technology

Extreme Networks: AI management over AI hardware Extreme deliberately prioritizes AI-powered network management over building specialized hyperscale AI infrastructure, a pragmatic positioning for a vendor targeting enterprise and mid-market.Named a Leader in IDC MarketScape: Worldwide Enterprise Wireless LAN 2025 (October 2025) for AI-powered automation, flexible deployment options and expertise in high-density environments. The company specializes in challenging wireless environments including stadiums, airports and historic venues (Fenway Park, Lambeau Field, Dubai World Trade Center, Liverpool FC’s Anfield Stadium). Key AI networking hardware 8730 Switch: 32×400GbE QSFP-DD fixed configuration delivering 12.8 Tbps throughput in 2RU for IP fabric spine/leaf designs. Designed for AI and HPC workloads with low latency, robust traffic management and power efficiency. Runs Extreme ONE OS (microservices architecture). Supports integrated application hosting with dedicated CPU for VM-based apps. Available Q3 2025. 7830 Switch: High-density 100G/400G fixed-modular core switch delivering 32×100Gb QSFP28 + 8×400Gb QSFP-DD ports with two VIM expansion slots. VIM modules enable up to 64×100Gb or 24×400Gb total capacity with 12.8 Tbps throughput in 2RU. Powered by Fabric Engine OS. Announced May 2025, available Q3 2025. Wi-Fi 7 access points: AP4020 (indoor) and AP4060 (outdoor with external antenna support, GA September 2025) completing premium Wi-Fi 7 portfolio. Extreme Platform ONE:Generally available Q3 2025 with 265+ customers. Integrates conversational, multimodal and agentic AI with three agents (AI Expert, AI Canvas, Service AI Agent) cutting resolution times 98%. Includes embedded Universal ZTNA and two-tier simplified licensing. ExtremeCloud IQ: Cloud-based network management integrating wireless, wired and SD-WAN with AI/ML capabilities and digital twin support for testing configurations before deployment. Extreme Fabric: Native SPB-based Layer 2 fabric with sub-second convergence, automated macro and micro-segmentation and free licensing (no controllers required). Multi-area fabric architecture solves traditional SPB scaling limitations. Analyst Rankings: Market leadership in AI networking Foundry Each of the vendors has its

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Microsoft’s In-Chip Microfluidics Technology Resets the Limits of AI Cooling

Raising the Thermal Ceiling for AI Hardware As Microsoft positions it, the significance of in-chip microfluidics goes well beyond a novel way to cool silicon. By removing heat at its point of generation, the technology raises the thermal ceiling that constrains today’s most power-dense compute devices. That shift could redefine how next-generation accelerators are designed, packaged, and deployed across hyperscale environments. Impact of this cooling change: Higher-TDP accelerators and tighter packing. Where thermal density has been the limiting factor, in-chip microfluidics could enable denser server sleds—such as NVL- or NVL-like trays—or allow higher per-GPU power budgets without throttling. 3D-stacked and HBM-heavy silicon. Microsoft’s documentation explicitly ties microfluidic cooling to future 3D-stacked and high-bandwidth-memory (HBM) architectures, which would otherwise be heat-limited. By extracting heat inside the package, the approach could unlock new levels of performance and packaging density for advanced AI accelerators. Implications for the AI Data Center If microfluidics can be scaled from prototype to production, its influence will ripple through every layer of the data center, from the silicon package to the white space and plant. The technology touches not only chip design but also rack architecture, thermal planning, and long-term cost models for AI infrastructure. Rack densities, white space topology, and facility thermals Raising thermal efficiency at the chip level has a cascading effect on system design: GPU TDP trajectory. Press materials and analysis around Microsoft’s collaboration with Corintis suggest the feasibility of far higher thermal design power (TDP) envelopes than today’s roughly 1–2 kW per device. Corintis executives have publicly referenced dissipation targets in the 4 kW to 10 kW range, highlighting how in-chip cooling could sustain next-generation GPU power levels without throttling. Rack, ring, and row design. By removing much of the heat directly within the package, microfluidics could reduce secondary heat spread into boards and

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Designing the AI Century: 7×24 Exchange Fall ’25 Charts the New Data Center Industrial Stack

SMRs and the AI Power Gap: Steve Fairfax Separates Promise from Physics If NVIDIA’s Sean Young made the case for AI factories, Steve Fairfax offered a sobering counterweight: even the smartest factories can’t run without power—and not just any power, but constant, high-availability, clean generation at a scale utilities are increasingly struggling to deliver. In his keynote “Small Modular Reactors for Data Centers,” Fairfax, president of Oresme and one of the data center industry’s most seasoned voices on reliability, walked through the long arc from nuclear fusion research to today’s resurgent interest in fission at modular scale. His presentation blended nuclear engineering history with pragmatic counsel for AI-era infrastructure leaders: SMRs are promising, but their road to reality is paved with physics, fuel, and policy—not PowerPoint. From Fusion Research to Data Center Reliability Fairfax began with his own story—a career that bridges nuclear reliability and data center engineering. As a young physicist and electrical engineer at MIT, he helped build the Alcator C-MOD fusion reactor, a 400-megawatt research facility that heated plasma to 100 million degrees with 3 million amps of current. The magnet system alone drew 265,000 amps at 1,400 volts, producing forces measured in millions of pounds. It was an extreme experiment in controlled power, and one that shaped his later philosophy: design for failure, test for truth, and assume nothing lasts forever. When the U.S. cooled on fusion power in the 1990s, Fairfax applied nuclear reliability methods to data center systems—quantifying uptime and redundancy with the same math used for reactor safety. By 1994, he was consulting for hyperscale pioneers still calling 10 MW “monstrous.” Today’s 400 MW campuses, he noted, are beginning to look a lot more like reactors in their energy intensity—and increasingly, in their regulatory scrutiny. Defining the Small Modular Reactor Fairfax defined SMRs

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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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