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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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Energy Department Aligns Award Criteria for For-profit, Non-profit Organizations, and State and Local Governments, Saving $935 Million Annually

WASHINGTON — The U.S. Department of Energy (DOE) today announced three new policy actions that are projected to save more than $935 million annually for the American taxpayer, while expanding American innovation and scientific research. In three new policy memorandums, the DOE announced that it will follow best practices used by fellow grant providers and limit “indirect costs” of DOE funding to 10% for state and local governments, 15% for non-profit organizations, and 15% for for-profit companies. The Energy Department expects to generate over $935 million in annual cost savings for the American people, delivering on President Trump’s commitment to bring greater transparency and efficiency to federal government spending. Estimated savings are based on applying the new policies to 2024 fiscal year spending. “This action ensures that Department of Energy funds are supporting state, local, for-profit and non-profit initiatives that make energy more affordable and secure for Americans, not funding administrative costs,” U.S. Secretary of Energy Chris Wright said. “By aligning our policy on indirect costs with industry standards, we are increasing accountability of taxpayer dollars and ensuring the American people are getting the greatest value possible from these DOE programs.” These policy actions follow an announcement made in April to limit financial support of “indirect costs” of DOE research funding at colleges and universities to 15%, saving an estimated additional $405 million annually. By enacting indirect cost limits, the Department aligns its practices with those common for other grant providers. The full three memorandums are available below: POLICY FLASH SUBJECT: Adjusting Department of Energy Financial Assistance Policy for State and Local Governments’ Financial Assistance Awards BACKGROUND: Pursuant to 5 U.S.C. 553(a)(2), the Department of Energy (“Department”) is updating its policy with respect to Department financial assistance funding awarded to state and local governments. Through its financial assistance programs (which include grants and cooperative agreements),

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Tech CEOs warn Senate: Outdated US power grid threatens AI ambitions

The implications are clear: without dramatic improvements to the US energy infrastructure, the nation’s AI ambitions could be significantly constrained by simple physical limitations – the inability to power the massive computing clusters necessary for advanced AI development and deployment. Streamlining permitting processes The tech executives have offered specific recommendations to address these challenges, with several focusing on the need to dramatically accelerate permitting processes for both energy generation and the transmission infrastructure needed to deliver that power to AI facilities, the report added. Intrator specifically called for efforts “to streamline the permitting process to enable the addition of new sources of generation and the transmission infrastructure to deliver it,” noting that current regulatory frameworks were not designed with the urgent timelines of the AI race in mind. This acceleration would help technology companies build and power the massive data centers needed for AI training and inference, which require enormous amounts of electricity delivered reliably and consistently. Beyond the cloud: bringing AI to everyday devices While much of the testimony focused on large-scale infrastructure needs, AMD CEO Lisa Su emphasized that true AI leadership requires “rapidly building data centers at scale and powering them with reliable, affordable, and clean energy sources.” Su also highlighted the importance of democratizing access to AI technologies: “Moving faster also means moving AI beyond the cloud. To ensure every American benefits, AI must be built into the devices we use every day and made as accessible and dependable as electricity.”

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Networking errors pose threat to data center reliability

Still, IT and networking issues increased in 2024, according to Uptime Institute. The analysis attributed the rise in outages due to increased IT and network complexity, specifically, change management and misconfigurations. “Particularly with distributed services, cloud services, we find that cascading failures often occur when networking equipment is replicated across an entire network,” Lawrence explained. “Sometimes the failure of one forces traffic to move in one direction, overloading capacity at another data center.” The most common causes of major network-related outages were cited as: Configuration/change management failure: 50% Third-party network provider failure: 34% Hardware failure: 31% Firmware/software error: 26% Line breakages: 17% Malicious cyberattack: 17% Network overload/congestion failure: 13% Corrupted firewall/routing tables issues: 8% Weather-related incident: 7% Configuration/change management issues also attributed for 62% of the most common causes of major IT system-/software-related outages. Change-related disruptions consistently are responsible for software-related outages. Human error continues to be one of the “most persistent challenges in data center operations,” according to Uptime’s analysis. The report found that the biggest cause of these failures is data center staff failing to follow established procedures, which has increased by about 10 percentage points compared to 2023. “These are things that were 100% under our control. I mean, we can’t control when the UPS module fails because it was either poorly manufactured, it had a flaw, or something else. This is 100% under our control,” Brown said. The most common causes of major human error-related outages were reported as:

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Liquid cooling technologies: reducing data center environmental impact

“Highly optimized cold-plate or one-phase immersion cooling technologies can perform on par with two-phase immersion, making all three liquid-cooling technologies desirable options,” the researchers wrote. Factors to consider There are numerous factors to consider when adopting liquid cooling technologies, according to Microsoft’s researchers. First, they advise performing a full environmental, health, and safety analysis, and end-to-end life cycle impact analysis. “Analyzing the full data center ecosystem to include systems interactions across software, chip, server, rack, tank, and cooling fluids allows decision makers to understand where savings in environmental impacts can be made,” they wrote. It is also important to engage with fluid vendors and regulators early, to understand chemical composition, disposal methods, and compliance risks. And associated socioeconomic, community, and business impacts are equally critical to assess. More specific environmental considerations include ozone depletion and global warming potential; the researchers emphasized that operators should only use fluids with low to zero ozone depletion potential (ODP) values, and not hydrofluorocarbons or carbon dioxide. It is also critical to analyze a fluid’s viscosity (thickness or stickiness), flammability, and overall volatility. And operators should only use fluids with minimal bioaccumulation (the buildup of chemicals in lifeforms, typically in fish) and terrestrial and aquatic toxicity. Finally, once up and running, data center operators should monitor server lifespan and failure rates, tracking performance uptime and adjusting IT refresh rates accordingly.

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Cisco unveils prototype quantum networking chip

Clock synchronization allows for coordinated time-dependent communications between end points that might be cloud databases or in large global databases that could be sitting across the country or across the world, he said. “We saw recently when we were visiting Lawrence Berkeley Labs where they have all of these data sources such as radio telescopes, optical telescopes, satellites, the James Webb platform. All of these end points are taking snapshots of a piece of space, and they need to synchronize those snapshots to the picosecond level, because you want to detect things like meteorites, something that is moving faster than the rotational speed of planet Earth. So the only way you can detect that quickly is if you synchronize these snapshots at the picosecond level,” Pandey said. For security use cases, the chip can ensure that if an eavesdropper tries to intercept the quantum signals carrying the key, they will likely disturb the state of the qubits, and this disturbance can be detected by the legitimate communicating parties and the link will be dropped, protecting the sender’s data. This feature is typically implemented in a Quantum Key Distribution system. Location information can serve as a critical credential for systems to authenticate control access, Pandey said. The prototype quantum entanglement chip is just part of the research Cisco is doing to accelerate practical quantum computing and the development of future quantum data centers.  The quantum data center that Cisco envisions would have the capability to execute numerous quantum circuits, feature dynamic network interconnection, and utilize various entanglement generation protocols. The idea is to build a network connecting a large number of smaller processors in a controlled environment, the data center warehouse, and provide them as a service to a larger user base, according to Cisco.  The challenges for quantum data center network fabric

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Zyxel launches 100GbE switch for enterprise networks

Port specifications include: 48 SFP28 ports supporting dual-rate 10GbE/25GbE connectivity 8 QSFP28 ports supporting 100GbE connections Console port for direct management access Layer 3 routing capabilities include static routing with support for access control lists (ACLs) and VLAN segmentation. The switch implements IEEE 802.1Q VLAN tagging, port isolation, and port mirroring for traffic analysis. For link aggregation, the switch supports IEEE 802.3ad for increased throughput and redundancy between switches or servers. Target applications and use cases The CX4800-56F targets multiple deployment scenarios where high-capacity backbone connectivity and flexible port configurations are required. “This will be for service providers initially or large deployments where they need a high capacity backbone to deliver a primarily 10G access layer to the end point,” explains Nguyen. “Now with Wi-Fi 7, more 10G/25G capable POE switches are being powered up and need interconnectivity without the bottleneck. We see this for data centers, campus, MDU (Multi-Dwelling Unit) buildings or community deployments.” Management is handled through Zyxel’s NebulaFlex Pro technology, which supports both standalone configuration and cloud management via the Nebula Control Center (NCC). The switch includes a one-year professional pack license providing IGMP technology and network analytics features. The SFP28 ports maintain backward compatibility between 10G and 25G standards, enabling phased migration paths for organizations transitioning between these speeds.

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Engineers rush to master new skills for AI-driven data centers

According to the Uptime Institute survey, 57% of data centers are increasing salary spending. Data center job roles that saw the highest increases were in operations management – 49% of data center operators said they saw highest increases in this category – followed by junior and mid-level operations staff at 45%, and senior management and strategy at 35%. Other job categories that saw salary growth were electrical, at 32% and mechanical, at 23%. Organizations are also paying premiums on top of salaries for particular skills and certifications. Foote Partners tracks pay premiums for more than 1,300 certified and non-certified skills for IT jobs in general. The company doesn’t segment the data based on whether the jobs themselves are data center jobs, but it does track 60 skills and certifications related to data center management, including skills such as storage area networking, LAN, and AIOps, and 24 data center-related certificates from Cisco, Juniper, VMware and other organizations. “Five of the eight data center-related skills recording market value gains in cash pay premiums in the last twelve months are all AI-related skills,” says David Foote, chief analyst at Foote Partners. “In fact, they are all among the highest-paying skills for all 723 non-certified skills we report.” These skills bring in 16% to 22% of base salary, he says. AIOps, for example, saw an 11% increase in market value over the past year, now bringing in a premium of 20% over base salary, according to Foote data. MLOps now brings in a 22% premium. “Again, these AI skills have many uses of which the data center is only one,” Foote adds. The percentage increase in the specific subset of these skills in data centers jobs may vary. The Uptime Institute survey suggests that the higher pay is motivating workers to stay in the

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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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