How creativity became the reigning value of our time

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How creativity became the reigning value of our time

Americans don’t agree on much these days. Yet even at a time when consensus reality seems to be on the verge of collapse, there remains at least one quintessentially modern value we can all still get behind: creativity. We teach it, measure it, envy it, cultivate it, and endlessly worry about its death. And why wouldn’t we? Most of us are taught from a young age that creativity is the key to everything from finding personal fulfillment to achieving career success to solving the world’s thorniest problems. Over the years, we’ve built creative industries, creative spaces, and creative cities and populated them with an entire class of people known simply as “creatives.” We read thousands of books and articles each year that teach us how to unleash, unlock, foster, boost, and hack our own personal creativity. Then we read even more to learn how to manage and protect this precious resource. Given how much we obsess over it, the concept of creativity can feel like something that has always existed, a thing philosophers and artists have pondered and debated throughout the ages. While it’s a reasonable assumption, it’s one that turns out to be very wrong. As Samuel Franklin explains in his recent book, The Cult of Creativity, the first known written use of creativity didn’t actually occur until 1875, “making it an infant as far as words go.” What’s more, he writes, before about 1950, “there were approximately zero articles, books, essays, treatises, odes, classes, encyclopedia entries, or anything of the sort dealing explicitly with the subject of ‘creativity.’” This raises some obvious questions. How exactly did we go from never talking about creativity to always talking about it? What, if anything, distinguishes creativity from other, older words, like ingenuity, cleverness, imagination, and artistry? Maybe most important: How did everyone from kindergarten teachers to mayors, CEOs, designers, engineers, activists, and starving artists come to believe that creativity isn’t just good—personally, socially, economically—but the answer to all life’s problems? Thankfully, Franklin offers some potential answers in his book. A historian and design researcher at the Delft University of Technology in the Netherlands, he argues that the concept of creativity as we now know it emerged during the post–World War II era in America as a kind of cultural salve—a way to ease the tensions and anxieties caused by increasing conformity, bureaucracy, and suburbanization. “Typically defined as a kind of trait or process vaguely associated with artists and geniuses but theoretically possessed by anyone and applicable to any field, [creativity] provided a way to unleash individualism within order,” he writes, “and revive the spirit of the lone inventor within the maze of the modern corporation.” Brainstorming, a new method for encouraging creative thinking, swept corporate America in the 1950s. A response to pressure for new products and new ways of marketing them, as well as a panic over conformity, it inspired passionate debate about whether true creativity should be an individual affair or could be systematized for corporate use.INSTITUTE OF PERSONALITY AND SOCIAL RESEARCH, UNIVERSITY OF CALIFORNIA, BERKELEY/THE MONACELLI PRESS I spoke to Franklin about why we continue to be so fascinated by creativity, how Silicon Valley became the supposed epicenter of it, and what role, if any, technologies like AI might have in reshaping our relationship with it. I’m curious what your personal relationship to creativity was growing up. What made you want to write a book about it? Like a lot of kids, I grew up thinking that creativity was this inherently good thing. For me—and I imagine for a lot of other people who, like me, weren’t particularly athletic or good at math and science—being creative meant you at least had some future in this world, even if it wasn’t clear what that future would entail. By the time I got into college and beyond, the conventional wisdom among the TED Talk register of thinkers—people like Daniel Pink and Richard Florida—was that creativity was actually the most important trait to have for the future. Basically, the creative people were going to inherit the Earth, and society desperately needed them if we were going to solve all of these compounding problems in the world. On the one hand, as someone who liked to think of himself as creative, it was hard not to be flattered by this. On the other hand, it all seemed overhyped to me. What was being sold as the triumph of the creative class wasn’t actually resulting in a more inclusive or creative world order. What’s more, some of the values embedded in what I call the cult of creativity seemed increasingly problematic—specifically, the focus on self-realization, doing what you love, and following your passion. Don’t get me wrong—it’s a beautiful vision, and I saw it work out for some people. But I also started to feel like it was just a cover for what was, economically speaking, a pretty bad turn of events for many people. Staff members at the University of California’s Institute of Personality Assessment and Research simulate a situational procedure involving group interaction, called the Bingo Test. Researchers of the 1950s hoped to learn how factors in people’s lives and environments shaped their creative aptitude.INSTITUTE OF PERSONALITY AND SOCIAL RESEARCH, UNIVERSITY OF CALIFORNIA, BERKELEY/THE MONACELLI PRESS Nowadays, it’s quite common to bash the “follow your passion,” “hustle culture” idea. But back when I started this project, the whole move-fast-and-break-things, disrupter, innovation-economy stuff was very much unquestioned. In a way, the idea for the book came from recognizing that creativity was playing this really interesting role in connecting two worlds: this world of innovation and entrepreneurship and this more soulful, bohemian side of our culture. I wanted to better understand the history of that relationship. When did you start thinking about creativity as a kind of cult—one that we’re all a part of? Similar to something like the “cult of domesticity,” it was a way of describing a historical moment in which an idea or value system achieves a kind of broad, uncritical acceptance. I was finding that everyone was selling stuff based on the idea that it boosted your creativity, whether it was a new office layout, a new kind of urban design, or the “Try these five simple tricks” type of thing. You start to realize that nobody is bothering to ask, “Hey, uh, why do we all need to be creative again? What even is this thing, creativity?” It had become this unimpeachable value that no one, regardless of what side of the political spectrum they fell on, would even think to question. That, to me, was really unusual, and I think it signaled that something interesting was happening. Your book highlights midcentury efforts by psychologists to turn creativity into a quantifiable mental trait and the “creative person” into an identifiable type. How did that play out? The short answer is: not very well. To study anything, you of course need to agree on what it is you’re looking at. Ultimately, I think these groups of psychologists were frustrated in their attempts to come up with scientific criteria that defined a creative person. One technique was to go find people who were already eminent in fields that were deemed creative—writers like Truman Capote and Norman Mailer, architects like Louis Kahn and Eero Saarinen—and just give them a battery of cognitive and psychoanalytic tests and then write up the results. This was mostly done by an outfit called the Institute of Personality Assessment and Research (IPAR) at Berkeley. Frank Barron and Don MacKinnon were the two biggest researchers in that group. Another way psychologists went about it was to say, all right, that’s not going to be practical for coming up with a good scientific standard. We need numbers, and lots and lots of people to certify these creative criteria. This group of psychologists theorized that something called “divergent thinking” was a major component of creative accomplishment. You’ve heard of the brick test, where you’re asked to come up with many creative uses for a brick in a given amount of time? They basically gave a version of that test to Army officers, schoolchildren, rank-and-file engineers at General Electric, all kinds of people. It’s tests like those that ultimately became stand-ins for what it means to be “creative.” Are they still used? When you see a headline about AI making people more creative, or actually being more creative than humans, the tests they are basing that assertion on are almost always some version of a divergent thinking test. It’s highly problematic for a number of reasons. Chief among them is the fact that these tests have never been shown to have predictive value—that’s to say, a third grader, a 21-year-old, or a 35-year-old who does really well on divergent thinking tests doesn’t seem to have any greater likelihood of being successful in creative pursuits. The whole point of developing these tests in the first place was to both identify and predict creative people. None of them have been shown to do that. Reading your book, I was struck by how vague and, at times, contradictory the concept of “creativity” was from the beginning. You characterize that as “a feature, not a bug.” How so? Ask any creativity expert today what they mean by “creativity,” and they’ll tell you it’s the ability to generate something new and useful. That something could be an idea, a product, an academic paper—whatever. But the focus on novelty has remained an aspect of creativity from the beginning. It’s also what distinguishes it from other similar words, like imagination or cleverness. But you’re right: Creativity is a flexible enough concept to be used in all sorts of ways and to mean all sorts of things, many of them contradictory. I think I write in the book that the term may not be precise, but that it’s vague in precise and meaningful ways. It can be both playful and practical, artsy and technological, exceptional and pedestrian. That was and remains a big part of its appeal. The question of “Can machines be ‘truly creative’?” is not that interesting, but the questions of “Can they be wise, honest, caring?” are more important if we’re going to be welcoming [AI] into our lives as advisors and assistants. Is that emphasis on novelty and utility a part of why Silicon Valley likes to think of itself as the new nexus for creativity? Absolutely. The two criteria go together. In techno-solutionist, hypercapitalist milieus like Silicon Valley, novelty isn’t any good if it’s not useful (or at least marketable), and utility isn’t any good (or marketable) unless it’s also novel. That’s why they’re often dismissive of boring-but-important things like craft, infrastructure, maintenance, and incremental improvement, and why they support art—which is traditionally defined by its resistance to utility—only insofar as it’s useful as inspiration for practical technologies. At the same time, Silicon Valley loves to wrap itself in “creativity” because of all the artsy and individualist connotations. It has very self-consciously tried to distance itself from the image of the buttoned-down engineer working for a large R&D lab of a brick-and-mortar manufacturing corporation and instead raise up the idea of a rebellious counterculture type tinkering in a garage making weightless products and experiences. That, I think, has saved it from a lot of public scrutiny. Up until recently, we’ve tended to think of creativity as a human trait, maybe with a few exceptions from the rest of the animal world. Is AI changing that? When people started defining creativity in the ’50s, the threat of computers automating white-collar work was already underway. They were basically saying, okay, rational and analytical thinking is no longer ours alone. What can we do that the computers can never do? And the assumption was that humans alone could be “truly creative.” For a long time, computers didn’t do much to really press the issue on what that actually meant. Now they’re pressing the issue. Can they do art and poetry? Yes. Can they generate novel products that also make sense or work? Sure. I think that’s by design. The kinds of LLMs that Silicon Valley companies have put forward are meant to appear “creative” in those conventional senses. Now, whether or not their products are meaningful or wise in a deeper sense, that’s another question. If we’re talking about art, I happen to think embodiment is an important element. Nerve endings, hormones, social instincts, morality, intellectual honesty—those are not things essential to “creativity” necessarily, but they are essential to putting things out into the world that are good, and maybe even beautiful in a certain antiquated sense. That’s why I think the question of “Can machines be ‘truly creative’?” is not that interesting, but the questions of “Can they be wise, honest, caring?” are more important if we’re going to be welcoming them into our lives as advisors and assistants. This interview is based on two conversations and has been edited and condensed for clarity. Bryan Gardiner is a writer based in Oakland, California.

We teach it, measure it, envy it, cultivate it, and endlessly worry about its death. And why wouldn’t we? Most of us are taught from a young age that creativity is the key to everything from finding personal fulfillment to achieving career success to solving the world’s thorniest problems. Over the years, we’ve built creative industries, creative spaces, and creative cities and populated them with an entire class of people known simply as “creatives.” We read thousands of books and articles each year that teach us how to unleash, unlock, foster, boost, and hack our own personal creativity. Then we read even more to learn how to manage and protect this precious resource.

Given how much we obsess over it, the concept of creativity can feel like something that has always existed, a thing philosophers and artists have pondered and debated throughout the ages. While it’s a reasonable assumption, it’s one that turns out to be very wrong. As Samuel Franklin explains in his recent book, The Cult of Creativity, the first known written use of creativity didn’t actually occur until 1875, “making it an infant as far as words go.” What’s more, he writes, before about 1950, “there were approximately zero articles, books, essays, treatises, odes, classes, encyclopedia entries, or anything of the sort dealing explicitly with the subject of ‘creativity.’”

This raises some obvious questions. How exactly did we go from never talking about creativity to always talking about it? What, if anything, distinguishes creativity from other, older words, like ingenuity, cleverness, imagination, and artistry? Maybe most important: How did everyone from kindergarten teachers to mayors, CEOs, designers, engineers, activists, and starving artists come to believe that creativity isn’t just good—personally, socially, economically—but the answer to all life’s problems?

Thankfully, Franklin offers some potential answers in his book. A historian and design researcher at the Delft University of Technology in the Netherlands, he argues that the concept of creativity as we now know it emerged during the post–World War II era in America as a kind of cultural salve—a way to ease the tensions and anxieties caused by increasing conformity, bureaucracy, and suburbanization.

“Typically defined as a kind of trait or process vaguely associated with artists and geniuses but theoretically possessed by anyone and applicable to any field, [creativity] provided a way to unleash individualism within order,” he writes, “and revive the spirit of the lone inventor within the maze of the modern corporation.”

Brainstorming, a new method for encouraging creative thinking, swept corporate America in the 1950s. A response to pressure for new products and new ways of marketing them, as well as a panic over conformity, it inspired passionate debate about whether true creativity should be an individual affair or could be systematized for corporate use.

I spoke to Franklin about why we continue to be so fascinated by creativity, how Silicon Valley became the supposed epicenter of it, and what role, if any, technologies like AI might have in reshaping our relationship with it.

I’m curious what your personal relationship to creativity was growing up. What made you want to write a book about it?

Like a lot of kids, I grew up thinking that creativity was this inherently good thing. For me—and I imagine for a lot of other people who, like me, weren’t particularly athletic or good at math and science—being creative meant you at least had some future in this world, even if it wasn’t clear what that future would entail. By the time I got into college and beyond, the conventional wisdom among the TED Talk register of thinkers—people like Daniel Pink and Richard Florida—was that creativity was actually the most important trait to have for the future. Basically, the creative people were going to inherit the Earth, and society desperately needed them if we were going to solve all of these compounding problems in the world.

On the one hand, as someone who liked to think of himself as creative, it was hard not to be flattered by this. On the other hand, it all seemed overhyped to me. What was being sold as the triumph of the creative class wasn’t actually resulting in a more inclusive or creative world order. What’s more, some of the values embedded in what I call the cult of creativity seemed increasingly problematic—specifically, the focus on self-realization, doing what you love, and following your passion. Don’t get me wrong—it’s a beautiful vision, and I saw it work out for some people. But I also started to feel like it was just a cover for what was, economically speaking, a pretty bad turn of events for many people.

Staff members at the University of California’s Institute of Personality Assessment and Research simulate a situational procedure involving group interaction, called the Bingo Test. Researchers of the 1950s hoped to learn how factors in people’s lives and environments shaped their creative aptitude.

Nowadays, it’s quite common to bash the “follow your passion,” “hustle culture” idea. But back when I started this project, the whole move-fast-and-break-things, disrupter, innovation-economy stuff was very much unquestioned. In a way, the idea for the book came from recognizing that creativity was playing this really interesting role in connecting two worlds: this world of innovation and entrepreneurship and this more soulful, bohemian side of our culture. I wanted to better understand the history of that relationship.

When did you start thinking about creativity as a kind of cult—one that we’re all a part of?

Similar to something like the “cult of domesticity,” it was a way of describing a historical moment in which an idea or value system achieves a kind of broad, uncritical acceptance. I was finding that everyone was selling stuff based on the idea that it boosted your creativity, whether it was a new office layout, a new kind of urban design, or the “Try these five simple tricks” type of thing.

You start to realize that nobody is bothering to ask, “Hey, uh, why do we all need to be creative again? What even is this thing, creativity?” It had become this unimpeachable value that no one, regardless of what side of the political spectrum they fell on, would even think to question. That, to me, was really unusual, and I think it signaled that something interesting was happening.

Your book highlights midcentury efforts by psychologists to turn creativity into a quantifiable mental trait and the “creative person” into an identifiable type. How did that play out?

The short answer is: not very well. To study anything, you of course need to agree on what it is you’re looking at. Ultimately, I think these groups of psychologists were frustrated in their attempts to come up with scientific criteria that defined a creative person. One technique was to go find people who were already eminent in fields that were deemed creative—writers like Truman Capote and Norman Mailer, architects like Louis Kahn and Eero Saarinen—and just give them a battery of cognitive and psychoanalytic tests and then write up the results. This was mostly done by an outfit called the Institute of Personality Assessment and Research (IPAR) at Berkeley. Frank Barron and Don MacKinnon were the two biggest researchers in that group.

Another way psychologists went about it was to say, all right, that’s not going to be practical for coming up with a good scientific standard. We need numbers, and lots and lots of people to certify these creative criteria. This group of psychologists theorized that something called “divergent thinking” was a major component of creative accomplishment. You’ve heard of the brick test, where you’re asked to come up with many creative uses for a brick in a given amount of time? They basically gave a version of that test to Army officers, schoolchildren, rank-and-file engineers at General Electric, all kinds of people. It’s tests like those that ultimately became stand-ins for what it means to be “creative.”

Are they still used?

When you see a headline about AI making people more creative, or actually being more creative than humans, the tests they are basing that assertion on are almost always some version of a divergent thinking test. It’s highly problematic for a number of reasons. Chief among them is the fact that these tests have never been shown to have predictive value—that’s to say, a third grader, a 21-year-old, or a 35-year-old who does really well on divergent thinking tests doesn’t seem to have any greater likelihood of being successful in creative pursuits. The whole point of developing these tests in the first place was to both identify and predict creative people. None of them have been shown to do that.

Reading your book, I was struck by how vague and, at times, contradictory the concept of “creativity” was from the beginning. You characterize that as “a feature, not a bug.” How so?

Ask any creativity expert today what they mean by “creativity,” and they’ll tell you it’s the ability to generate something new and useful. That something could be an idea, a product, an academic paper—whatever. But the focus on novelty has remained an aspect of creativity from the beginning. It’s also what distinguishes it from other similar words, like imagination or cleverness. But you’re right: Creativity is a flexible enough concept to be used in all sorts of ways and to mean all sorts of things, many of them contradictory. I think I write in the book that the term may not be precise, but that it’s vague in precise and meaningful ways. It can be both playful and practical, artsy and technological, exceptional and pedestrian. That was and remains a big part of its appeal.

The question of “Can machines be ‘truly creative’?” is not that interesting, but the questions of “Can they be wise, honest, caring?” are more important if we’re going to be welcoming [AI] into our lives as advisors and assistants.

Is that emphasis on novelty and utility a part of why Silicon Valley likes to think of itself as the new nexus for creativity?

Absolutely. The two criteria go together. In techno-solutionist, hypercapitalist milieus like Silicon Valley, novelty isn’t any good if it’s not useful (or at least marketable), and utility isn’t any good (or marketable) unless it’s also novel. That’s why they’re often dismissive of boring-but-important things like craft, infrastructure, maintenance, and incremental improvement, and why they support art—which is traditionally defined by its resistance to utility—only insofar as it’s useful as inspiration for practical technologies.

At the same time, Silicon Valley loves to wrap itself in “creativity” because of all the artsy and individualist connotations. It has very self-consciously tried to distance itself from the image of the buttoned-down engineer working for a large R&D lab of a brick-and-mortar manufacturing corporation and instead raise up the idea of a rebellious counterculture type tinkering in a garage making weightless products and experiences. That, I think, has saved it from a lot of public scrutiny.

Up until recently, we’ve tended to think of creativity as a human trait, maybe with a few exceptions from the rest of the animal world. Is AI changing that?

When people started defining creativity in the ’50s, the threat of computers automating white-collar work was already underway. They were basically saying, okay, rational and analytical thinking is no longer ours alone. What can we do that the computers can never do? And the assumption was that humans alone could be “truly creative.” For a long time, computers didn’t do much to really press the issue on what that actually meant. Now they’re pressing the issue. Can they do art and poetry? Yes. Can they generate novel products that also make sense or work? Sure.

I think that’s by design. The kinds of LLMs that Silicon Valley companies have put forward are meant to appear “creative” in those conventional senses. Now, whether or not their products are meaningful or wise in a deeper sense, that’s another question. If we’re talking about art, I happen to think embodiment is an important element. Nerve endings, hormones, social instincts, morality, intellectual honesty—those are not things essential to “creativity” necessarily, but they are essential to putting things out into the world that are good, and maybe even beautiful in a certain antiquated sense. That’s why I think the question of “Can machines be ‘truly creative’?” is not that interesting, but the questions of “Can they be wise, honest, caring?” are more important if we’re going to be welcoming them into our lives as advisors and assistants.

This interview is based on two conversations and has been edited and condensed for clarity.

Bryan Gardiner is a writer based in Oakland, California.

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This week, there has been a whole lot of doom and gloom in the UK energy market. Harbour Energy announced 250 job cuts in Aberdeen, Ørsted discontinued its Hornsea 4 offshore wind project, and Drax opted not to bid for its Cruachan II in the Cap and Floor mechanism. Up first, news reporter Mat Perry discusses the UK’s largest oil and gas operator slashing 25% of its UK onshore headcount as it downgrades spending in the country. Aberdeen Features Lead Ryan Duff gives his two cents on the state of job losses in the north-east of Scotland and the wider North Sea. Brace yourself for explicit language. Next up, Mat and renewables reporter Michael Behr discuss why Ørsted has pulled the hand brake on a major offshore wind project. Pumped storage hydro was also mentioned, as Cruachan II gives the cap and floor scheme a swerve after battery firms argued that the mechanism was biased towards such projects. And finally, Michael chats with Guy Newey, chief executive officer at the Energy Systems Catapult, about the ever-divisive zonal pricing debate and how it could help drive innovation in the energy sector. Listen to Energy Voice Out Loud on your podcast platform of choice.

Delayed Blenheim Palace solar project seeks £800m finance package

Planning for the UK’s largest solar farm has hit minor delays, as local opposition to the project mounts despite the huge biodiversity net gain touted by developers. German developer Photovolt Development Partners (PVDP) is seeking nearly £1 billion in debt and equity to build what would be the UK’s largest solar farm on land owned by the Blenheim estate, near Blenheim Palace, bordering Jeremy Clarkson’s farm. The project is due to enter a critical stage of planning next week, a month after originally expected. “The final decision to approve the project will be made by energy security and net zero secretary, Ed Miliband,” a spokesperson said. “The examination period, run by the planning inspectorate and the next stage in the process for Botley West, is due to commence on 13 May 2025. The decision on when the examination period begins is entirely down to the planning inspectorate.” The Botley West solar farm could meet at least 840 megawatts (MW) of the UK’s energy needs and play a huge role in decarbonising the local energy supply in Oxfordshire, according to developers. The Oxfordshire region is one of the country’s dirtiest fossil fuel systems, supplied mainly by the Didcot gas power station since the closure of an adjacent coal plant, but Botley West could change that. The public examination period for the project, which is expected to last for about six months, was expected to start in the middle of April – but has since been pushed back to May. Two specific hearings are expected to take place where heritage issues would be addressed and local venues could hold examinations, including open floor hearings. “The applicant themselves submitted two change requests on 28 March 2025, which the examining authority has now considered and determined to incorporate into the examination,” the planning inspectorate

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.”

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:

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.

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

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.

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

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

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

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

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

2025 playbook for enterprise AI success, from agents to evals

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

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

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

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