Cretex Medical says it has purchased select assets and capabilities of Holland Molds Inc. in Wadena, Minnesota. The deal closed June 10, Cretex Medical said. Cretex Medical is a subsidiary of Elk River, Minnesota-based Cretex Cos., providing medical device contract manufacturing and engineering services through Brooklyn Park, Minnesota-based Cretex Medical Component & Device Technologies (CDT).…
There are plenty of useful things a metric can reveal. There are even more it can obscure or corrupt. It took me well over a decade of tracking my own life in ever greater detail to fully appreciate this duality, which probably reveals something about both me and the nature of measurement.
Like a lot of people bitten by the self-quantifying bug, I initially started gathering personal data to pursue a nebulous collection of goals and desires. As a sedentary technology journalist, I wanted to feel better physically and emotionally, to get outside more, and—where possible—to bring order to some of the messiness and uncertainty of my daily existence. These all seemed to be things that could be improved with the cool clarity of numbers.
Self-quantifiers often get stereotyped as obsessive self-optimizers (and many of them are), but my reasons for producing and collecting personal data were less about life-maxxing and more about life meaning—at least at first. As most people who know me will attest, I do not have now, nor have I ever possessed, a “productivity mindset.” I’m also not all that interested in life hacks, shortcuts, or new ways to compare myself with other people. Instead, what I wanted out of metrics—what I hoped I could divine from a never-ending stream of numbers about my health, work, and social life—was something more elusive: self-knowledge. This was my first mistake.
The idea that the more we know, the better is so profoundly embedded in our culture that it feels weird to even point it out. Since at least as far back as the Enlightenment, the primary way we’ve all agreed to go about knowing more has been through measurement and quantification. After all, more knowledge—more data—leads to better decisions, which leads to happier, more fulfilled people. Or so we’re told, and with increasing frequency in the era of AI.
When two Wired magazine editors, Gary Wolf and Kevin Kelly, coined the term “quantified self” in 2007 and helped launch the movement we are all now helplessly a part of, they were essentially selling this very idea. “Unless something can be measured, it cannot be improved,” wrote Kelly in an early blog post, doing his best impression of Lord Kelvin. “So we are on a quest to collect as many personal tools that will assist us in quantifiable measurement of ourselves.” Almost 20 years later, that quest is easier than ever thanks to a flood of devices, apps, and websites all designed to help us build our self-knowledge through numbers.
My first tool was a small, plastic clip-on Fitbit I started using in 2011. It did one thing: count the number of steps I took in a day. As a lifelong video game player, I was already well acquainted with the motivational power of simple scoring systems, and I hoped my new gadget would offer the gentle numerical nudge I thought I needed to step away from my Twitter feed and, if not touch grass, at least walk next to some. Walking also seemed to be one of the few times I had what could charitably be called intelligent ideas, which seemed like another promising by-product of doing more of it.
Alas, that was short-lived. I can’t tell you precisely when “getting out into nature more” or “thinking smarter thoughts” stopped mattering to me as goals, but I suspect it took no more than a few weeks. What I can say with certainty is that my initial goal of 6,000 daily steps quickly turned into 10,000, which then jumped to 15,000 and eventually settled at 20,000 for years. Stories about becoming a “steps guy” are clichéd at this point, and they’ve earned that status for a reason.
It didn’t take long for me to trade in pedometers for heart-rate monitors (I also started running), smartwatches, sleep-tracking rings, and an embarrassing number of macronutrient-tabulating apps. Outside the health and fitness realm, my early career as a journalist also happened to coincide with the rise of social media and web analytics tools like Chartbeat, which promised to further quantify difficult-to-measure aspects of my life, like “job success” and “impact,” by tracking things like page views, followers, retweets, likes, and all sorts of other attentional metrics that now carry great weight.
Metrics inevitably redefine your core sense of what’s important, whether you’re aware of the trap or not.
Ultimately, during the 10-plus years I diligently tracked my heart rate, steps, active calories, sleep, story engagement time, stress levels, and other metrics, I gained virtually nothing in terms of greater self-knowledge. (I suppose I did learn that I liked to make numbers go up and down, but who doesn’t?) The swirl of data that followed me everywhere did not lend additional meaning or insight to the way I relate to myself, my work, or the important people in my life. In fact, the more I used numerical proxies, the worse I felt about pretty much everything.
What I did learn were two important lessons about what happens when you try to quantify the minutiae of your life. First and foremost, whatever the amount of data you’re currently collecting about yourself, it will never feel sufficient. There’s always a new metric around the corner, a better way for a tracker to remix its readings and more accurately measure what’s “important”: heart rate variability, daily stress, exercise “readiness,” cardiovascular or “fitness” ages. Measurement begets more measurement. You can count on it.
The Score: How to Stop Playing Somebody Else’s Game C. Thi Nguyen
PENGUIN PRESS, 2026
The second lesson was less obvious but no less significant. The more personal or nuanced your goals are when you set off on your self-quantifying journey, the more likely it is you will ultimately replace them with some simplified metric or ranking. Want to become a better journalist? Why not use page views and leaderboards as a proxy for success? Enjoy cooking and want to improve? Foodie metrics dictate that more complicated recipes with longer ingredient lists are the answer. Even when we know that the value of good journalism isn’t reflected in how many people read a given story or that the joys of cooking are as much about improvisation and experimentation as about successfully following some complex recipe, it’s hard to resist the allure of a simple score or stat. Metrics inevitably redefine your core sense of what’s important, whether you’re aware of the trap or not.
Over the years, people have invented various terms to describe this phenomenon. In his recent book The Score: How to Stop Playing Somebody Else’s Game, the philosopher C. Thi Nguyen calls it “value capture.” Value capture happens, he says, when you adopt external sources of measurement and then let them rule you without adapting them to suit your life. “In value capture, you’re essentially outsourcing your values,” Nguyen writes. “You’re letting an external metric or ranking set what’s important for you.” Crucially, you’re also outsourcing the process of figuring out your own sense of meaning. It’s why my walks quickly shifted from feeling meditative to prioritizing miles.
Individuals, institutions, and indeed entire societies can fall prey to value capture. In fact, once you start noticing it, you start seeing it everywhere—in journalism, education, and business, but also in our food, our hobbies, and, yes, the way we measure our health and happiness. Here’s how Nguyen puts it:
Value capture happens when a restaurant stops caring about making good food and starts caring about maximizing its Yelp ratings. It happens when students stop caring about education and start caring about their GPA. It happens when scientists stop caring about finding truth and start caring about getting the biggest grants. It even happens in religion. A pastor recently told me that his church had become completely obsessed with baptism rates. The higher-ups had established an internal leaderboard in which the pastors competed on monthly baptism rates, and it was starting to dominate everybody’s attention. He’d found himself caring less about the long-term spiritual development of his flock and focusing more on trying to deliver popular sermons that would up his baptism rates and move him up that leaderboard.
At its core, The Score is trying to untangle a mystery that Nguyen, a specialist in the philosophy of games at the University of Utah, has been thinking about for a long time: Why is it that numbers and scoring systems in games can be the source of so much joy and fluidity and play, but public measures and institutional metrics (i.e., scores that apply to the real world) seem to drain the life out of everything and thrust us all into a bleak mindset of grinding optimization?
Porter, a historian of science who specializes in the social power of numbers, has spent his career looking at why quantification has become so dominant, not just in political and bureaucratic life but everywhere. One of his key insights about the inherent attractiveness of quantification, which he calls “a technology of distance,” is that it “minimizes the need for intimate knowledge and personal trust.” Put another way, metrics travel extremely well between different contexts and are easy to grasp and aggregate.
Whether it’s a student’s GPA or a country’s GDP, these measures are understood by pretty much everyone. But that understanding comes at a price, Porter reminds us: To arrive at a clear metric, you inevitably need to simplify what you’re attempting to measure, often jettisoning heaps of nuanced, qualitative, or open-ended information so that others can find the resulting number legible.
No one (hopefully) believes that a GPA captures in any meaningful way a student’s entire educational experience or aptitude for learning, but we’ve agreed to use it because more qualitative assessments are onerous to wade through and require expertise to decipher and compare. Ditto for the economic metric of GDP, which politicians and societies are now compelled to drive higher and higher because a group of economists once concluded that this figure correlates with general economic well-being.
This is the essential tension at the heart of all data, argues Nguyen. Any institutional quantification, he says, requires that the evaluation procedure and its product be comprehensible across contexts. That profoundly limits what the metric can actually measure. “In value capture, you’re ultimately taking that decontextualized nugget and internalizing it,” he writes. “You’re guiding your life using an evaluative technology that has been engineered to travel between contexts, by stripping it of nuance.”
Every so often I’ll find myself in friendly debate with a “numbers person”—a statistician, an economist, or a friend who’s still a committed self-quantifier. After patiently listening to my measurement-gone-awry examples—the disastrous attempt to quantify pain as “the fifth vital sign” in the mid-1990s (which exacerbated the opioid epidemic), or any of the countless examples of the McNamara fallacy, where decisions in academia, medicine, and politics are based solely on what’s easily measured—many will insist that I’m misunderstanding or misinterpreting the whole point of measuring. Metrics, they’ll say, are simply a means, and the important questions concern the ends for which they are used. In other words, these unfortunate outcomes amount to user error, not something inherently dangerous or misleading about the nature of measurement.
At some point during these conversations, Goodhart’s Law will invariably come up, usually as an explanation the metrics-minded deploy for why the ends get all mucked up. The principle, which is attributed to the British economist Charles Goodhart, is often expressed as the following: “When a measure becomes a target, it ceases to be a good measure.” I have a profound dislike for Goodhart’s Law, not because I think it’s untrue, but rather for the way it gets interpreted.
As Nguyen notes, Goodhart’s Law says very little about why metrics fail to capture what’s important—or what to do about it. Find better measures, some will conclude. Don’t let metrics become targets, others will insist. These are not helpful takeaways. All measurements, I would argue, are in fact targets, whether you intend them to be or not. Metrics inevitably present one direction or option as better, Nguyen writes in The Score—“longer lifespans, faster student graduation rates, more page views, higher customer satisfaction scores.” What people are talking about when they bring up Goodhart’s Law isn’t human error; it’s actually a fundamental problem with measurement itself.
I want to be clear here: Measurement can and does serve a number of vital functions. It has in a very literal sense made the modern world possible, with all its life-saving, suffering-reducing, and awe- inspiring scientific breakthroughs. When used with care and diligence, metrics can make our progress (or lack of it) clearer and more transparent. Are we decreasing carbon dioxide emissions or not? They can also introduce accountability into formerly opaque systems, such as by measuring whether a company is complying with state and federal regulations. They can even make us more objective, reduce biases, and galvanize us to act.
But as Nguyen points out throughout The Score, the fundamental weakness of metrics comes when we use them to pursue subtler, more personal goals. What I think many of us miss—what I know I certainly missed—is that there are always trade-offs when you try to distill something important down to a data point. When we turn to metrics to understand ourselves, our social world, and culture as a whole, they will never come close to capturing what matters. Even worse, they’ll often actively obscure it.
Today, I find that numbers have very little to offer when it comes to my daily work, my physical or mental fitness, my relationships, or any other part of my life I consider important. Granted, I’m lucky enough to be in relatively good health at the moment. I don’t have to track my glucose levels or monitor my blood pressure. As a freelance writer, I also have the luxury of not having numbers foisted on me in the form of key performance indicators (KPIs), objectives and key results (OKRs), or any of the endless quantitative evaluations that come baked into pretty much every corporate and gig economy job.
Still, in a very real sense, there is no escaping metrics or, especially, the logic that accompanies them. Knowing has become numeric, and we all live in a world that increasingly sees us as a collection of numbers—as “data subjects.” The first and most urgent challenge, I’d suggest, is finding a way to keep us from seeing ourselves and each other that way.
This won’t be easy. As Porter, Nguyen, and countless other philosophers, anthropologists, and historians have already observed, the language of numbers is largely how we ascribe value today—as well as how we digest and metabolize our relationships to ourselves, to others, and to the world around us. Indeed, many of us have accepted not only that metrics have a natural existence in human affairs but that there are in fact no aspects of human life that cannot be somehow translated into data.
Knowing has become numeric, and we all live in a world that increasingly sees us as a collection of numbers— as “data subjects.”
So how do we push back? Nguyen’s book offers a useful first step. As he notes again and again in The Score, believing that numbers say something real or useful about human needs and desires gives them power. We can, at the very least, start to seriously question that belief, to ask what meaning and pleasure we might be giving up in pursuit of a metric.
Doing so will hopefully lead to another realization: that playing the numbers game is ultimately a losing proposition for humans. If we insist on expressing our worth through attentional metrics and productivity scores, if we continue to turn intelligence and creativity into a series of benchmarks for AI to surpass, we’ve already lost. Of course machines will surpass us in a world built around metrics. That is literally what we create them to do. The answer is not to turn ourselves into machines too.
If there’s one thing that keeps me up at night, it is that we’ve become so accustomed to seeing and understanding the larger world and ourselves through numbers that it has deprived us of the language to express what’s fundamental and valuable about our own humanity. We need this ability now more than ever, especially if we’re going to adequately answer two of the most important questions of our era: What are humans for? And what is AI for?
As part of my own attempts to disentangle myself from a life of numbers—efforts that started shortly before covid—I’ve abandoned most of the tools of measurement I spent a decade collecting. I’ve largely given up on social media. I stopped using apps to track my health and well-being. The watch I currently wear tells me the time and the date and nothing else.
In fact, the only holdover from my days of obsessive self-quantification is a dogmatic devotion to walking—without all the step counting, of course. These days, I walk when I’m feeling disillusioned or overwhelmed; I walk when I can’t figure out how to finish an essay; I also walk because I enjoy spending time outdoors with my dog and catching up on the details of my neighbors’ lives. The benefits of pursuing this daily activity are as clear and obvious to me as anything could be in life. I just can’t express them in a number.
Bryan Gardiner is a writer based in Oakland, California.
A pregnant woman in rural America may have to drive two hours — sometimes more — to reach a hospital that can deliver her baby. If labor comes early or complications arise, that distance becomes dangerous.
This is happening in the United States in 2026 — not because we lack medical knowledge or technology, but because we have failed to train and place the physicians where they are most needed.
On May 31, at the American Society of Clinical Oncology (ASCO) meeting in Chicago, an international study co-led by a UCLA research team reported that patients with pancreatic cancer who took the drug daraxonrasib lived substantially longer, for an average of 13.2 months, compared with 6.6 to 6.7 months for patients who had chemotherapy alone.
This is welcome news, and in anticipation of these results, the FDA, just a month earlier, announced it was granting early access to the drug for selected patients who had failed guideline-directed treatments for this lethal malignancy.
Widespread media coverage of this regulatory pivot in drug access highlights the intense, enduring interest among the general public, as well as in the scientific and medical communities, for identifying treatments that can move the survival needle lethal for complex, heterogeneous malignancies, thereby derailing effective treatment, especially when only single drugs are deployed to improve clinical outcomes.
<strong>Background:</strong> Use of in-home video telehealth rapidly expanded in response to the COVID-19 pandemic, including at Veterans Affairs (VA), a forerunner in telehealth. Despite this uptick, differences in use by patient age and rurality created a digital divide that persists to this day. While clinicians frequently cite patients’ older age and lack of technical skills as barriers to in-home video telehealth, it remains unclear how clinicians decide whether to offer video visits to patients and to what extent these beliefs may hinder offering video visits to older adults. Gathering perspectives from clinician users of in-home video telehealth may illuminate opportunities to ensure continued access to care through solutions such as telehealth. <strong>Objective:</strong> This study aimed to examine clinician decision-making around the offer of in-home video telehealth to understand how interprofessional clinicians determine to whom they offer in-home video telehealth and what factors (organizational, personal, or attitudinal) influence their decision. <strong>Methods:</strong> We conducted a qualitative study by using semistructured interviews. Participants were interprofessional clinicians (N=16) employed by 11 different VA hospitals and included 1 clinical pharmacist, 6 medical doctors, 2 nurse practitioners, 1 occupational therapist, 3 psychologists, 1 physical therapist, 1 speech-language pathologist, and 1 social worker. All the participants had at least some experience using in-home video telehealth from locations across VA (the largest integrated health care system in the United States) and were interviewed over a 6-month period. Interviews focused on clinicians’ use of video telehealth and the decision-making process involved in offering in-home video telehealth. We used directed content analysis with a rapid analytic approach, given the time-pressured nature of our project. <strong>Results:</strong> This study revealed that clinician decision-making around offering in-home video visits is complex and influenced by several domains, namely, (1) clinician factors, including experience with video and perceived benefits of video; (2) appointment factors, including the visit’s clinical goal; (3) clinician-reported patient factors, including age and willingness to try video; (4) patient social context, including caregiver availability; (5) geographical factors, such as availability of reliable high-speed internet and patient distance from the medical center; and (6) health system factors, including technical support and clinician ability to work from home. Access to in-home video telehealth may be facilitated by clinician familiarity and confidence with telehealth technology, strategies to improve patients’ technical skills, and support for caregivers. Infrastructure also plays an important role, including device availability, broadband reliability, and clear protocols for matching services to video visits. <strong>Conclusions:</strong> Findings highlight the importance of clinician competency in telehealth, patient and caregiver digital readiness, and a supportive technology infrastructure to equitable in-home video care. In addition, improved guidance for specific clinical services and awareness of potential biases may enable consistent, accessible telehealth delivery for older adults and medically complex populations. <strong>Trial Registration:</strong>
Making good on its threat, Eli Lilly has begun eliminating mandated price breaks to a few dozen hospitals that participate in a federal drug discount program after failing to receive comprehensive claims data.
The move comes after the company warned earlier this month it would take such a step as part of a policy announced in January in order to reduce what it calls duplicate discounts paid to the hospitals. Trade groups representing hospitals, however, argue the move is unlawful and want Congress to intervene.
At the time, Lilly maintained that more than 2,300 hospitals had complied with its demand, but some larger hospitals systems around the U.S. refused to do so, despite recent follow-up letters regarding the policy, which went into effect Feb. 1. Up to 1,000 had so far not complied, and Lilly indicated it was pressing about 50 larger hospitals to provide data.
On this week’s episode of “The Readout LOUD”: New hope for people living with Huntington’s disease, and a report card on RFK Jr.’s effort to remake health in America.
The Food and Drug Administration reversed its opposition to a closely watched gene therapy for Huntington’s disease, clearing a path for its maker, the biotech company UniQure, to file for U.S. approval. Joining us to discuss this new development and what it means for the Huntington’s community is Lauren Holder, a Huntington’s disease advocate who is living with the early stages of the disease.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
The search for dark matter has been blown wide open
For decades, physicists have hunted for weakly interacting massive particles (WIMPs), a leading candidate for dark matter. But their search has run into a new problem: neutrinos.
These tiny particles from the sun and other stars can create a “neutrino fog” that drowns out any signal of dark matter. Hitting the neutrino fog does not, however, mean an end to the search. Researchers just have to shift the focus of their hunt.
They’re now casting a much wider net. New proposals include quantum sensors, liquid-helium detectors, and even searches in Jupiter’s atmosphere.
This story is from the next edition of our magazine, which is all about engineering. Subscribe now to get a copy when it lands!
Entrepreneurs in Nairobi are making the case for going solar
Shops with diesel-powered grain mills are common in Nairobi. Milcah Wanjiru’s is different: it runs on either solar energy or the grid.
About a quarter of Kenya’s population still lacks centralized electricity, and off-grid solar is being promoted as a route to universal access by 2030. In Wanjiru’s case, it cuts operating costs and can improve profits once the upfront investment is recovered.
Geoengineering still faces major practical challenges
—Casey Crownhart
Solar geoengineering is often portrayed as a sort of emergency brake. Something along the lines of “Pull in case of climate emergency to scatter light-reflecting particles to bounce sunlight out of the atmosphere and cool the planet.”
But it might be less like a simple brake the more like a complicated, entirely unsolved puzzle. My colleague James Temple dug into these engineering challenges in his latest feature story. My biggest takeaway? This all looks a lot harder than I thought.
This article is from The Spark, our weekly newsletter giving you the inside track on all things climate. Sign up to receive it in your inbox every Wednesday.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 The Pentagon says it used Grok in strikes on Iran Its AI chief said it helped fire over 2,000 munitions. (Le Monde) + He spoke in defense of xAI in a data center pollution lawsuit. (NYT $) + Officials claim the company is essential to national security. (AP News) + Conversational AI has entered the war room. (MIT Technology Review)
2 Apple will raise prices due to the memory chip shortage Tim Cook said price increases are “unavoidable.” (WSJ $) + AI’s demand for data centers has led to dwindling supplies. (Reuters $) + iPhone prices could rise by $200 or more. (WSJ $)
3 Strikes beyond battlefields are pumping demand for counter-drone tech The market for airport and infrastructure defenses is booming. (Reuters $) + Worried by China, Taiwan is teaching its citizens to fly drones. (Guardian) + Europe has a drone-filled vision for future war. (MIT Technology Review)
4 Anthropic and DeepMind’s CEOs have called for a US-led AI coalition They want the alliance to shape AI rules and standards. (CNBC) + Anthropic’s CEO told G7 leaders to “resist the temptation to splinter.” (FT $)
5 American developers are turning to cheaper Chinese AI They say DeepSeek is good enough for a fraction of the cost. (Rest of World) + What’s next for Chinese open-source AI? (MIT Technology Review)
6 Two-thirds of Americans think AI is advancing too quickly Pew Research found increasing use but negative views. (The Verge) + AI is sprinting, and we’re struggling to keep up. (MIT Technology Review)
7 Elon Musk’s next move may be a megamerger of SpaceX and Tesla Shareholders might object, but there’s little they could do. (NYT $)
8 White House aims for Anthropic to block jailbreaks may be impossible Security experts say it simply isn’t technically feasible. (Wired $)
9 Ancient DNA is rewriting the history of plague Genomic data suggests it emerged thousands of years earlier. (Economist $)
10 AI image generator Midjourney is shifting to full-body ultrasound scans It also plans to build a spa in San Francisco. (The Verge)
Quote of the day
“We had a great meeting with AI.”
—President Trump says negotiations with Anthropic over restoring access to the company’s latest AI models are going well, the Wall Street Journal reports.
One More Thing
GETTY IMAGES
Why can’t tech fix its gender problem?
Women remain grossly underrepresented in the technology industry. At the core of the problem is money: tech has generated enormous personal fortunes, and most of that wealth has gone to men.
White and Asian men manage 93% of venture dollars. In 2021, only 2% of venture capital funding went to startups founded solely by women.
The lack of investor and founder diversity doesn’t only determine who gets rich. It also shapes the kinds of problems technology companies set out to solve.
A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)
+ Meet one of the world’s most wonderfully weird animals: the aardwolf. + Escape into this dreamy, minimalist photo collection about the Pacific surf. + Admire the casual football skills of this Venetian gondolier executing a stylish backheel while on the job. + Explore an interactive prehistoric globe simulation and browse an endless timeline at The Dinosaur Database.
In high school, Ashley Delgado dreamed of becoming a doctor and one day buying her father a Rolls-Royce. “She wanted to heal people,” said her father, James Taylor. She had a high GPA, Taylor added, and did especially well in science and Latin.
In her mid-20s, Ashley suffered a leg injury and was prescribed OxyContin. The painkiller marked the beginning of a yearslong descent through addiction — from prescription opioids to methamphetamine, then heroin, and finally, fentanyl.
Underneath an Apennine massif, below the Jinping Mountains of Sichuan, and at the bottom of a South Dakota mine, there is a cosmic hunt afoot.
Isolated deep beneath these rocky shields, massive detectors filled with liquid xenon aim to make the first direct detections of dark matter, the long-sought invisible substance whose gravity has sculpted our universe.
The hope is that someday, a bit of dark matter called a weakly interacting massive particle (a WIMP, for short) will collide with a xenon atom, creating a burst of light and electric charge. After running for years, these experiments have recently begun seeing infrequent blips from a particle that glides ethereally through ordinary matter until it crashes into the detectors. Unfortunately, the new signal is not produced by dark matter. Instead, the detectors are picking up on something similarly insubstantial but much more mundane: neutrinos, the featherweight subatomic particles that the sun and other stars produce in massive quantities.
Physicists’ failure to find dark matter where they thought it was has led to a cornucopia of proposals for new ways to search: quantum sensors, liquid-helium-based detectors, searches in Jupiter’s atmosphere, and more.
Physicists have known for decades that this neutrino background was there; they were just hoping to discover WIMP dark matter first. Now the chance is looking slim. Some of today’s WIMP detectors are simply so large and sensitive that they are entering the so-called “neutrino fog,” in which the ordinary particles are likely to drown out any signal from the main target. There is no shielding these detectors from neutrinos, which easily slip through the Earth itself. That means the next experiment to use this long-standing approach for seeking WIMP dark matter may be the last.
Hitting the neutrino fog does not, however, mean an end to the search for dark matter. Researchers just have to shift the focus of their hunt. “We haven’t seen WIMP dark matter,” says Kathryn Zurek, a theoretical particle physicist at the California Institute of Technology. Nor, she says, have scientists found new particles in the Large Hadron Collider (LHC), the powerful proton-smashing facility that straddles the border between France and Switzerland. “And so people naturally broaden their scope,” Zurek says. As they do, there are plenty more candidates waiting in the wings
In other words, the hunt is transforming from a narrow probe into a kind of free-for-all. It’s a big shift. Today, particle physicists are less sure about dark matter’s identity than when they began looking for it. They’ll freely admit that they cannot presume the basics—for example, if the stuff that makes up dark matter is heavier than the Earth or lighter than a radio wave, or if dark matter is one kind of particle or a dozen.
The uncertainty can be frustrating, even humbling. “The potential range where the candidates could be is so enormous that the odds of any one small experiment finding it are very, very small,” says Hugh Lippincott, a dark matter experimentalist at the University of California, Santa Barbara.
But physicists’ failure to find dark matter where they thought it was has also led to a cornucopia of proposals for new ways to search: quantum sensors, liquid-helium-based detectors, searches in Jupiter’s atmosphere, and more. “Now there’s a great deal of excitement. And finally, there’s technology there,” says Gray Rybka, a University of Washington physicist who co-leads an experiment looking for axions, an ultra-lightweight dark matter candidate.
Still, with so many places to look, where does it make sense for physicists to begin again?
Astronomical ignorance
For starters: the birth of the universe. Dark matter has been with us since the beginning, and there’s much to learn from those early eons. Maps of the cosmic microwave background—the first light from the universe’s early years—are full of fluctuations caused by the clumpiness of underlying matter. Reading these cosmic dregs, researchers can tell that only 17% of the matter in the universe is made of ordinary particles like protons and neutrons. The remaining 83% is dark matter, which has little to no interaction with light or ordinary matter other than through gravity.
We can tell quite a bit about dark matter from those gravitational effects. We know that the Milky Way contains a halo of the stuff. Our own solar system orbits the galactic center far too quickly to be bound by the tug of ordinary matter alone: without dark matter’s gravitational tether, we would be flung off into intergalactic space. We can also see how the heft of a galaxy’s dark matter bends the path of light as it makes its way to Earth’s telescopes. And on the grandest scale, we can see how superclusters of galaxies are distributed in space like dewdrops on a spiderweb. No cosmological theory without dark matter can explain all these phenomena.
But all the astronomical and cosmological evidence has little to say about what dark matter is actually made of. “It does not tell you anything about the individual constituents. It just tells you the effect of a bunch of them together,” says Lippincott, who has led the LZ experiment, a WIMP dark matter detector currently in operation at the former Homestake Mine in South Dakota.
The idea of WIMPs emerged during the 1980s. At the time, theorists were exploring add-ons to the standard model, the overarching theory of particle physics that describes all the universe’s fundamental particles and their interactions. The standard model is powerful but doesn’t account for everything—notably, it omits gravity—so some adjustments seemed necessary. The most popular idea, a class of theories called supersymmetry (SUSY, informally), called for pairing each known particle type in the universe with an as-yet-unseen “superpartner.” To have avoided detection, superpartners would have to have a lot of mass (putting them outside the reach of existing colliders) and be weakly interacting, able to pass ghostlike through matter. That is to say, they would be WIMPs. It didn’t take too long for physicists to realize that the WIMP was also an excellent dark matter candidate: two problems, one particle.
The appeal of SUSY was so strong that many particle physicists expected to see WIMPs as soon as the LHC turned on in 2008. Instead, as the data came in from the LHC, the most promising SUSY theories were largely ruled out.
The PandaX-4T experiment in China’s Sichuan Province, which started up in 2020, is on the hunt for WIMP dark matter, using a detector filled with ultra-high purity liquid xenon.
ALAMY, GETTY IMAGES; IMAGING BY JANA HEIDENREICH
WIMPs, though, have lived on, no longer tied to the theory that birthed them. And the latest generation of dark matter detectors have kept the hunt alive. After all, Lippincott says, “the motivation to look for dark matter has not gotten any weaker, right?”
Now it’s looking as if those WIMPs—if they exist—may be beyond our current powers of detection. There are a range of difficulties, but the most pervasive is that when you’re looking for a needle in a haystack, even a few other needle-shaped objects can cloud the search. Interactions between neutrinos and the xenon inside the detectors, while astronomically rare, do just that.
A future, final WIMP experiment would investigate the rest of the places WIMPs could be hiding, even peering into the neutrino fog. An effort called XLZD (a somewhat ungainly acronym reflecting a mashup of existing collaborations) would use 60 to 80 metric tons of liquid xenon, which is about the yearly global production of that rare element and at least six times more xenon than the biggest current detector contains. But it may already have been scuttled, for reasons unrelated to the neutrino fog: At a particle physics meeting in December 2025, the US Department of Energy announced that the US would neither host XLZD nor pay its share of the price tag, which could be well over $300 million. “It may be that the project doesn’t happen at all,” Lippincott says. “And then the US pulling out would have effectively killed it.”
In the meantime, the hunting ground for dark matter has been expanding dramatically. In 2022, researchers developed an enormous plot showing various candidates for what dark matter is made of and their possible masses. The options fell mainly into two ranges that span about 50 orders of magnitude (that’s 1050, or 10 with 49 more zeroes). At the heavy end of the scale are primordial black holes, hypothetical asteroid-size objects that formed shortly after the Big Bang and might still be floating about the universe.
But many physicists are most interested in the lightest option: the axion.
Listen closely
Like the WIMP, the axion first emerged as a solution to problems with the standard model. In the axion’s case, it was to address an outstanding question about the strong nuclear force, the fundamental force that holds atomic nuclei together. Axions were proposed by theorists in the 1970s as a mathematical tweak. By adding a particle with a trillionth to a millionth the mass of an electron, they could explain why the strong force seems to behave precisely the same way when it comes to both matter and antimatter even though it doesn’t need to—an unanswered question known as the strong CP problem.
Axions would be abundant and interact infrequently with ordinary matter, two necessary features for dark matter. Detecting axions is no walk in the cosmic park, though. The delicate particles carry only a hint of energy—about as much as a radio wave. That makes them imperceptible to traditional particle detectors. (Proton collisions at the LHC, for example, pack about a quintillion times more energy.)
Physicists have developed strategies they hope will bear fruit. One of the most promising ideas is to use an ultracold chamber suffused with a strong magnetic field like a radio, tuning it to specific wavelengths. If an axion happens to be resonant, meaning it has the same wavelength as the chamber, it stands the chance of transforming into something far easier to detect: a particle of light. The first full-size detector, called a haloscope, was built at Lawrence Livermore National Laboratory in 1994; today there is a coterie of detectors around the world, with quirky acronymic names like MADMAX and ABRACADABRA.
Fiddling with a cosmic radio to listen for a rare invisible particle is tricky and requires quantum sensors cooled to millikelvin temperatures, a fraction of a degree above absolute zero. Even that doesn’t entirely negate background noise. “At one point, we detected a ‘message from God’ in our experiment,” Rybka tells me wryly. “We looked it up in the FCC allocation of spectrum. It was a religious programming station.”
So far, particle physicists have combed somewhere between 10% and 20% of the parameter space—the area on a chart showing how massive and interactive dark matter might be—where an axion that solves the strong CP problem might exist. But the search for axions might not end there. Physicists are also looking for axions that don’t address the strong CP problem but could still be dark matter. “More and more people are thinking about models specifically for dark matter, even without connecting that to any other problem,” says Stefania Gori, a theorist at the University of California, Santa Cruz. “Of course,” she says, “if one can solve more than one problem at once, it’s even nicer.”
Quiet revolution
As dark matter has continued to go undetected, physicists have dropped some of their theoretical desiderata. Modern candidates don’t need the convenience of WIMPs or the clean simplicity of axions; they need only fulfill the requirements for dark matter.
The poster child for these unpretentious candidates is low-mass dark matter, so named because it is somewhere between an electron and a proton in weight. If a WIMP is a billiard ball, low-mass dark matter is a ping-pong ball, explains Rouven Essig, a theorist at Stony Brook University. Like a ping-pong ball hitting bowling pins, low-mass dark matter wouldn’t have enough heft to produce a clear signal from a collision with atomic nuclei. “One needed to really come up with new ideas for how one could detect the signals, and then, of course, also new technologies,” says Essig.
Researchers have designed novel detectors and installed them in underground labs around the world—often right next to their WIMP-detecting forebears. Some of the new machines look for evidence that particles have collided with electrons and ionized them. Others peer into crystals whose lattices should jiggle subtly after a bump from such a particle. There are even prototypes that look for hints in liquid helium, a sensitive superfluid that should throw up a splash when hit by incident dark matter.
This array of light-sensing photomultiplier tubes was used in XENON1T, a dark matter detector at INFN Gran Sasso National Laboratory in Italy. Its successor, XENONnT, is now in operation and boasts more than eight metric tons of liquid xenon.
XENON COLLABORATION, GETTY IMAGES; IMAGING BY JANA HEIDENREICH
There is, however, a rub: noise. While all experiments in dark matter detection are hampered by noise from the outside world, low-mass searches also suffer from the intrinsic din of their detector mediums. Atomic lattices like those inside crystals are like a crowded subway car, naturally prone to shaking and jostling their electron passengers. It’s not the quiet space you want if you’re looking for dark matter.
This noise has created challenges. In 2020, a surprising number of what physicists call “excess events” cropped up across a range of detectors looking for low-mass dark matter. Could they, some physicists wondered, be a signal of dark matter? Unfortunately, the noise causing most of these readings has been identified, and the answer is a pretty firm “no.”
Background noise can come from anywhere: impurities in silicon-based detectors, materials that have spent too long on Earth’s surface (cosmic rays make them lightly radioactive). In one experiment, a crystalline detector was clamped too hard; the extra pressure caused vibrations that looked like evidence of dark matter. “It’s always been true that understanding those backgrounds has been difficult,” says Dan McKinsey, an experimentalist at Lawrence Berkeley National Laboratory. “But we’ve shifted our regime so quickly that suddenly we don’t understand, as a community, what the key backgrounds are.”
Getting a handle on that background noise is key, especially as experiments to detect low-mass dark matter get bigger. In a few years, for example, McKinsey and his colleagues plan to install a number of tabletop experiments in Modane, France, underneath 1,700 meters of solid rock on the Italian border. One of them is a vessel with about a tablespoon of ultracold liquid helium. If a particle of low-mass dark matter impacts the liquid, it will generate a vibration that sprays thousands of helium atoms upward, where silicon detectors will look for microscopic changes in voltage. Other setups will work with sapphire-silica crystals and gallium arsenide, a semiconductor.
These experiments will help researchers identify the best approaches to use for bigger, more sensitive—and more expensive—detectors. “There’s still lots of ideas, and it’s still not quite clear what’s going to be the best one to really scale up,” Essig says. For now, if it fits on a table and can plausibly detect dark matter, physicists are willing to try it.
The utmost gravity
The hunting ground for dark matter extends past the surface of any table, or even Earth itself. Some researchers have suggested we look for the stuff not in underground laboratories but on planets, stars, and moons. If dark matter particles occasionally annihilate when they encounter one another, they could ionize hydrogen in the atmosphere of a planet, creating an ultraviolet aurora visible from space. This self-annihilation could also be a strong source of heat—enough to melt a planet’s core. The fact that Earth’s core is solid provides limits for dark matter’s characteristics, and measuring the temperatures of planetary cores more precisely could set even more stringent constraints.
Searching for astrophysical signs of dark matter is not new, but in recent years physicists have become almost artistic with their proposals. One particularly evocative suggestion: looking at the icy ocean of Jupiter’s moon Ganymede. If dark matter is indeed really heavy—possibly a primordial black hole—it could punch through the surface and leave a crater that looks very different from one caused by an asteroid.
Some particle physicists think it would be better to drop all the existing assumptions and refocus. “Everything we know about dark matter has come from its interaction with gravity,” says Caltech’s Zurek. If you concentrate more specifically on that interaction, she says, at least “you’re guaranteed to learn something.” We know how dark matter behaves on the scale of the observable universe as far down as galaxies. “Below that, we really know very little about how dark matter collapses under the weight of gravity,” Zurek says. How, for example, does it clump up on the level of a solar system?
In recent years physicists have become almost artistic with their proposals. One particularly evocative suggestion: looking at the icy ocean of Jupiter’s moon Ganymede.
This isn’t a “next year” or “next five years” project. The technologies physicists have now are simply not sensitive enough to search for these gravitational interactions. So Zurek is thinking long term. Really long term. “It’s going to take decades, like probably 100 years,” she acknowledges. “It may not be something that I see in my lifetime.”
Someday—perhaps by monitoring the timing of distant pulsars (the spinning remnants of dead stars) or measuring slight perturbations in gravitational interaction between atoms suspended in lasers—physicists could learn more about dark matter’s true nature.
For now, the scale of the problem seems daunting, especially compared with the ones particle physicists have tackled in the past. Before the LHC even turned on, for example, those hunting for the Higgs boson had hemmed their quarry in. They knew, from rigorous theoretical calculations and robust experimental measurements, that if the boson existed, it had to weigh between 120 times and 150 times the mass of a proton. Soon after the LHC began smashing protons, the Higgs popped out of the data, right at 133 times the mass of a proton.
Dark matter, by contrast, remains an almost total mystery. Rybka likens guessing about its mass or its interaction strength to “drawing numbers out of a hat.” “We literally don’t even know what the hat looks like,” he adds.
With this many unknowns, success is far from certain, and the researchers have no illusions. “There’s no discovery guaranteed. You might be wasting your time completely,” Essig says.
None of this has dissuaded him or others. “That’s just the nature of the problem. We have to search far and wide and explore lots of things,” Essig says. “If you don’t like it, do something else.”
Dan Garisto is a freelance physics journalist based in Syracuse, New York.
