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Weekend reads: Computer science conferences impose caps; Lancet strikes to continue; Is this the ‘mathocalypse’?
If your week flew by — we know ours did — catch up here with what you might have missed. The week at Retraction Watch featured: A company named Springer that isn’t the publisher is offering authors…
A scoping review of bias in large AI models for medicine and healthcare
Communications Health - A scoping review of bias in large AI models for medicine and healthcare
Closing surveillance detection gaps for emerging digital drug discourse with ontology-enhanced large language models
npj Digital Public Health - Closing surveillance detection gaps for emerging digital drug discourse with ontology-enhanced large language models
Smart home device use influences older adults’ mental health through social interaction and digital benefits
Scientific Reports - Smart home device use influences older adults’ mental health through social interaction and digital benefits
Building a science of biodiversity recovery
Nature Reviews Biodiversity - Biodiversity science has built powerful systems for detecting decline, but must now pivot towards defining, explaining and scaling recovery. The 17th Conference of the...
A photoinduced, FeX3-catalyzed, stereo- and regio-selective halofunctionalization of olefins with MXn via a radical-polar crossover reaction
Communications Chemistry - The classical reaction for the halofunctionalization of olefins involves hazardous and highly toxic molecular halogens. Here, the authors report the FeX3-catalyzed...
Photonic circuits become trainable AI hardware
Nature Computational Science - A physics-based in situ learning method is demonstrated that directly measures optical gradients with photonic integrated circuits, allowing diverse circuit...
Epidemiological modeling in the age of artificial intelligence
Nature Computational Science - Artificial intelligence is enabling epidemic models to learn across outbreaks. The next challenge is ensuring that these models learn and transfer not mere...
Steering complex rhythms
Nature Computational Science - A recent study reports a property-first computational strategy, identifying coordinated parameter changes that can tune several features of a complex oscillator at...
Structural fragility in digital public health communication
Communications Health - Structural fragility in digital public health communication
Oral health status and functional outcomes in Japanese adults with hemophilia: an exploratory cross-sectional study stratified by HIV status
Scientific Reports - Oral health status and functional outcomes in Japanese adults with hemophilia: an exploratory cross-sectional study stratified by HIV status
Patriots ST coach reacts to underrated rookie's instant success
Patriots special teams coordinator Jeremy Springer has been impressed by rookie linebacker Namdi Obiazor.
New Open-Access Journal Puts Sustainable Polymers at the Center of Materials Science
Polymers are quietly the most successful materials story of the modern era, and the scientific community is now giving them a dedicated stage. Springer Nature has launched the Journal of Materials Science: Polymers, a new open-access title created to advance the science,
Intuitive enzyme design with LLM agents
Nature Computational Science - A collaborative system of large language model agents brings protein engineering closer to natural-language interaction, lowering the barrier between human intent and...
The Big Holes in Wearable Heart Rate Variability And Readiness Scores
On September 9th, Apple announced it was revamping its Apple Watch Health Sensing System, rolling out a Readiness score (0 to 10), and increasing the frequency of heart rate variability (HRV) outputs 24-fold. This can be viewed as upping its competition with various other consumer wearable sensors. These “readiness scores” as a composite of multiple metrics, with heart rate variability (HRV) being front and center for most. The majority of Americans are now using wearable sensors, which equates to well over 100 million adults. HRV and Readiness scores are increasingly being marketed as a measurement of autonomic nervous system health, a digital marker for future disease, a clock for biological age, and a holistic metric to promote healthspan and even longevity. (Apple also introduced a new longevity tab and “Health Age”.) None of this has been proven. In this edition of Ground Truths I am going to review what we know about heart rate variability and readiness scores. Heart Rate Variability HRV is the variation in normal heart cycle timing. The variability of the heart rate, the barely perceptible millisecond changes in time between consecutive heart beats (see R-R intervals in the Figure below, left panel), is due to interplay between the sympathetic and parasympathetic (vagal nerve) inputs. Distinct from heart rate, individuals with the same heart rate can have very different HRVs. It is a rough reflection of the autonomic nervous system (ANS) activity, inadequate to say whether a person’s ANS function is abnormal. For more than three decades, heart rate variability (HRV) has been measured and several studies have found an association of low HRV and clinical outcomes, particularly a link with higher all-cause and cardiovascular mortality. There have also been less well established links of low HRV to risk of early cognitive impairment, dementia, mental illness, Type 2 diabetes and substance abuse. An important reminder is that HRV is a surrogate marker without any established cause-and-effect relationship. If you increase your HRV, that doesn’t mean it will improve health outcomes. In fact, there is no hard evidence for that. All that work linking to health outcomes was done with electrocardiogram (ECG) derived HRV. Now, in the era of consumer wearables, this is getting assessed differently, by optical pulse (yes, the lights you see) plethysmography (PPG) or what is called pulse rate variability (PRV). They are not the same, as shown below (right panel) and only concordant when the delay between the ECG and pulse is kept constant, which basically means at rest. I should mention there’s also what I will call MPV, a mattress mechanical movement sensor, a derived heart rate variability, that companies like Eight Sleep use, even further away from directly measuring HRV. HRV has not one uniform measurement but many different types of quantification, such as RMSSD, the magnitude of difference between successive R-R intervals of normal sinus beats (N-N) or SDNN, the standard deviation of NN intervals, both in milliseconds. SDNN is one of the so-called frequency domain HRVs (others are LF, HF, LF/HF). Different wearable sensors use different metics; Apple has relied on SDNN and nearly all of the others use RMSSD, which is generally considered the more accurate metric. There’s also the different length of time measured, such as for a matter of minutes, all day, or an overnight’s sleep. Short measurements are especially problematic since they don’t capture enough of respiratory modulation and other factors that influence HRV. Share Ground Truths How well does HRV correlate with PRV? There are very limited studies, especially independently done. One that is commonly cited was conducted by Air Force researchers in only 13 healthy adults assessing Oura ring 3 and 4, Whoop 4.0, and Garmin Fenix 6 and showed a correlation coefficient of 0.88 to 0.97 and a mean absolute percentage error from 6 to 10%. The correlation is not a perfect 1.0, but there’s at least a fairly high level of correlation. HRV is supposed to increase during the night due to takeover of the parasympathetic nervous system, and higher during deep sleep. A recent example of my 1 week, all day “HRV,” and one during sleep is shown below. As you can see, the N of 1 data are inconsistent for the same days from different sensors (Oura, AppleWatch, Fitbit Air, and Eight Sleep) by patterns, absolute numbers, and comparison with prior days and weeks. The largest study in over 8 million Fitbit users (the old version, not Google Fitbit Air, introduced in May 2026) gives you a sense of the effect of age, sex, and the 2 different main HRV (here PRV) metrics, with RMSSD on the left and SDRR (=SDNN) on the right below. That study, from data collected in 2018, is a major outlier, since all the more recent ones are tiny with respect to sample size. Many of the companies have not had independent evaluation of their HRV, such as Eight Sleep, but have published a low standard error on their website. There are some other published studies on the correlation between HRV and PRV, but they are all small and only in healthy adults. A scoping review emphasized the lack of study in underrepresented individuals, including the aged, people of color (which affects the PPG signal), and individuals who are underweight or obese. Add the typical adult age 60 plus with one or more chronic diseases. For example, one study in over 900 adults found poor correlation of HRV and PRV, non-uniformly underestimated across many chronic diseases (cardiovascular, endocrine, neurological, respiratory, and others), concluding PRV is “an invalid surrogate for HRV.” A recent systematic review of 43 studies comparing HRV and PRV found reasonable pooled absolute standardized error (HRV as gold standard) but only 10 of the studies provided quantitative synthesis in ideal conditions. Their main conclusion was similarly cautious: “PPG-derived HRV [PRV] should not be regarded as universally interchangeable with ECG-derived HRV across all devices, populations, and recording contexts.” Factors Affecting HRV and PRV That gets me to the long list of factors that affect HRV (and PRV) besides the device, the type of measurement (RMSSD, SDNN or others), the person’s signal, the sensor site, the duration of data capture, if weighting by sleep stage is used, how artifact is processed and corrected. And this list is not complete!: Oura puts out data from their community of users (who input data) on what affects their overnight HRV. The factors currently provided are: no alcohol (increase 8%), melatonin (increase 2%, float tank (increase 2%), wine (decrease 4%), and party (decrease 14%) in overnight HRV. Must be some big parties! Subscribe What is a PRV measurement good for? It has been falsely characterized as an index of “autonomic balance” and a specific indicator of stress. A 2018 review of the studies available for HRV and its relationship to stress, not using any of the current wearables, found that stress can lower HRV. But so can many other factors. The non-specificity of the signal, indexed to the table above, is striking. Evidence from a UK Biobank study of over 46,000 participants with actual HRV looked at genetically predicted HRV, a genetic risk score, that failed to show the expected HRV-mortality link, indicating that _HRV is likely not causa_l, but rather a reflection of person’s physiologic state. A review of consumer wearable HRV data from 5 longitudinal studies showed that nighttime PRV was not associated with perceived stress, and surprisingly higher HRV, in the largest cohort (N=717 participants), was correlated with higher stress. An Oura ring cohort of 525 first-year college students found a link between overnight PRV and perceived stress, but that was also seen with resting heart rate, sleep, and respiratory rate. Several very small studies have examined the relationship of HRV and athletic injuries or guiding training with mixed, and predominantly negative results. HRV biofeedback training with paced breathing had no significant effect on reducing stress or raising HRV, as demonstrated with sham controlled trials. When HRV for multiple days showed a decline in conjunction with body temperature, the Oura ring published data for prediction of Covid. The WHOOP company sponsored an observational study, published in 2026, of 30,000 users for 72 weeks, without a control group, that reported reduced alcohol intake (5.8 % points) by self-report. That doesn’t tell us much, and particularly about the merits of HRV for behavioral change. If you use the same device and conditions as longitudinal trends for multiple (at least 2-3) weeks that may be the one way to get something useful from the measurements. Data for overnight sleep with minimal motion and using RMSSD is the best proxy for real HRV. The reason to look at trends rather than any given night is that it more likely represents something, even though you won’t know with certainty what the “it” is. Keep in mind there are no data, no peer-reviewed evidence, to show that HRV fluctuation in-person has any correlation with health outcomes. Share Readiness Scores These are proprietary scores that integrate different metrics for each of the wearables: no algorithms have been disclosed. They are unvalidated against health outcomes. In a review of 14 composite health scores of readiness and recovery, HRV contributed 86% to the scores, followed by reading heart rate (79%), physical activity and sleep duration (both at 71%). That review noted the substantial variability in measurement protools and lack of standardization. Sleep staging is notoriously inconsistent and inaccurate by these sensors, which adds further to the HRV uncertainties for what the scores, which use sleep stage data, mean. Only resting heart rate has been shown consistently across devices to be extremely accurate. I’ve made a Table to summarize what we know about which metrics are included, the scores, any peer-reviewed studies that compared the readiness score with health outcomes, and the corresponding (if any, NA-not available) citation. You will note that some companies do not use the term “readiness," such as WHOOP for recovery, and Garmin, which has 2 different scores, one of which is Body Battery. Eight Sleep uses the term “Fitness Score.” They all include HRV; Apple includes a new metric they call “Recovery HRV” which among other components uses 7-days of sleep, but it is unclear what this means or how it is differentiated from other scores (there are clearly no data for outcomes). We have no knowledge of how the different components are weighted or whether any of these scores are better than resting heart rate, HRV alone, physical activity, or any other single metric. Since none of these are standardized, they are not interchangeable, so if you get a 90 for Oura that has no relationship to a 90 on a Google Fitbit Air. Notably, the company can update its algorithm for readiness score at any point without notification to device users. Without any useful evidence of actionability for these scores or established relationship with health outcomes, it is hard to make a case for their value. At the Apple recent announcement they showed their Readiness score (0-10) on the watch (Figure below) but there are no published data on this score, not even on their website. It’s available only on their new Watch Series 12 or Ultra 4 [of course, ;-)]. That exemplifies the problems with these scores, lack of data and evidence for being meaningful to promote health. Perhaps the best study (which isn’t saying much) is the WHOOP Recovery for golfer performance, because it did correlate with an objective outcome, even though there was no control group and the authors were all from the company. Among the 389 pro golfers, an absolute 10-per cent point increase in Recovery score was associated with about 0.5 fewer strokes per round. But that’s hardly a health outcome! WHOOP is also conducting a study in over 2,700 runners to see if their recovery score will be linked to less injuries and improved performance, but that is not yet published and has no control group or randomization. Putting This in Context For two decades I’ve been enthusiastic about the potential for digital health and particularly wearable biosensors. Over the years, we’ve seen some great progress for their ability to promote physical activity and accurately detect atrial fibrillation (the first FDA cleared deep learning AI for consumers). That work was the subject of rigorous research. But there are holes in the data and evidence for other metrics. One notable one is the “VO2 max” story that I wrote about earlier this year. At that time many subscribers asked me to cover heart rate variability, which I finally got to here. When I dived into the research and publication for HRV and readiness scores, I expected to find at least some that were of high quality and demonstrated their utility by linkage to health outcomes. To my surprise, I found none. The wearable sensor measurements for HRV (PRV) are, for the most part, accurate, but that validation work has only been done in small studies of healthy adults and does not take into account the long list of factors, from the device, software side, and the user side, that affect HRV measurements. Moreover, this metric chiefly relies on optical sensing and, as we have learned for heart rate PPG sensing, may be less accurate in people of color. Keep in mind that all of the health outcome association evidence comes from ECG-derived HRV; none are from wearable sensor data. I will repeat the key point: there's no peer-reviewed evidence to show that in-person HRV fluctuation—or efforts to raise your HRV— has any correlation with health outcomes. For those of you who look at your HRV on awakening, or even 2+ week trends of it being low, I hope this context helps to relieve any anxiety. Yes, low HRV (not PRV) has been shown to increase risk of some diseases as summarized above. But efforts to raise your HRV—a surrogate metric— has not been established for improving any health outcomes. HRV does not have any evidence of causality (the genetic evidence actually goes against this possibility). In this summary, I have not included data for other wearable sensors such as Polar, Samsung, Withings, Suunto, Amazfit, Coros, Ultrahuman, or additional mattress sensors. These are beyond my first-hand experience, and as far as I know from my in-depth review none have any peer-reviewed published data that differ from the 6 sensors I’ve reviewed here. From the points I’ve gone over above, we’re not ready for readiness scores. Besides being proprietary, they are predominantly based on metrics that have their own issues. It’s compounding the problem, like building a house without a solid foundation that has never undergone a rigorous inspection, and then selling it. Like I mentioned for PRV, you can look at trends over weeks rather than any single day, to get a handle, but even that may not be helpful. There’s simply no evidence that these scores meaningfully relate to health outcomes. I’d emphasize they might, but that requires doing prospective or randomized studies to prove it. None exist. There’s great promise for HRV/PRV utility_. For example**,**_ Prof Maiken Nedergaard, who discovered the brain glymphatics that are essential in eliminating metabolic waste products from the brain during sleep, has posited that HRV could be a non-invasive marker for neuromodulator oscillations, brain-body regulatory circuits, and brain clearance. That would be extremely useful, but like everything else on HRV and readiness scores it requires solid research and validation. The lay media isn’t helping much to get the story straight. Earlier this year The Economist published a piece entitled “The most useful indicator of your overall health” which ordained HRV as an “accumulated stress score.” That’s akin to the false assertion about VO2max: “V02 max is the singular most powerful marker for longevity.” As I’ve summarized here, that is not established. The fact is that so many things can lower HRV, including physical exercise (especially an intense workout), reduced sleep quality, stress, the list above, no less the device, signal, and software. Whatever fluctuations observed have not been correlated with any health outcome. Sadly, “datamaxxers” are widely using HRV and readiness scores that have never been validated to mean anything. We already know that for some people using the sensors for sleep metrics, it can induce “orthosomnia,” an obsession to get high sleep quality, with associated high levels of anxiety. In an experiment done by a company to promote sleep quality for its employees, “For those employees who did use the trackers, many reported feeling perfectly rested until their tracker told them they had had a terrible night. Others were told that they had slept like a baby when they had actually been lying awake worrying about the quality of their sleep. “ The same problem can result from preoccupation with HRV or readiness scores, with anxiety that would lead to further reduction in both. It you are using a wearable like >100 million American adults, it’s OK to look at these data, but contextualized with the major caveats reviewed here. If you are one to require evidence that HRV or readiness scores are linked to health outcomes, you may not even want to look. The companies make it hard to turn them off! Let me end with the companies that make and sell wearables. Apple’s doubling down on HRV (24-fold more reporting and heart rate very 5 seconds) and introduction of a Readiness score tells us that consumers have bought into these metrics and they are joining the club. However, all of this is occurring with a backdrop of tens millions of users, claims about the data that are not backed up by adequate evidence, marketing way out in front of whatever limited data exists, and not being transparent about their readiness score algorithms. The companies can well afford to do the research that is needed to connect these metrics with health outcomes show, once and for all, that increasing HRV or using readiness scores promotes our health. If they believed and invested in the products they are selling, we’d not be in this position of not knowing. That’s essentially where we are with HRV and readiness scores. Perhaps someday this will change and we’ll have good reason to embrace them. NB: I wrote this post. No AI. I have no conflicts of interest with any of its content. Loading... Ground Truths has 215,000 subscribers from every US state and 214 countries. There are over 300,000 followers of Ground Truths so more than 90,000 folks who can easily convert to be free subscribers. Your subscription to these free essays and podcasts makes my work in putting them together worthwhile. If you’re not a subscriber, please join! If you found this interesting PLEASE share it! Share Ground Truths The proceeds from all voluntary paid subscriptions go to support our summer internship program. It enabled us to accept and support a record number of 62 summer interns that joined us in 2026! These are high school, college and medical students selected from thousands of applicants. We couldn’t do this expanded program without the funds coming in through Ground Truths. Thank you!
What Does the Science of Climate Change Really Look Like?
Editor’s Note: This is the second in a three-part series on how the Right should think about environmental and climate policy. Read Chris Barnard on Reclaiming Environmental Policy from the Left. More than a decade ago, the world’s governments negotiated and signed the Paris Climate Agreement, committing to hold global warming well below 2 degrees Celsius. In the following years, climate had immense cultural power. Greta Thunberg emerged. Fortune 500 companies established net-zero plans. The U.S. got the Inflation Reduction Act. Then the pendulum swung back. President Donald Trump returned to the Oval Office, speaking of green energy as a “scam” and carbon footprints as a “hoax” and on all matters of international policy emphasizing bilateral engagement and power over globalism and cooperation. The technology companies that had been at the forefront of net-zero pledges and green leadership discovered the importance of all-of-the-above power to their visions for artificial intelligence and began quietly postponing or removing their targets. On the left side of the political spectrum, rising concerns about inflation and cost pushed climate lower down in the stated priorities of voters and stump speeches of politicians. The moment is ripe for a reset on the politics of climate change and for conservatives especially to chart a course that acknowledges and addresses the real challenges in plausible ways. So what should we think about climate change as we enter a post-peak climate era? If it is neither an apocalypse nor a hoax, what will hold up as a durable view of climate change? And how does it project onto the concerns and priorities of the right-of-center’s emerging coalition? New to Commonplace_? Subscribe below to get the magazine in your inbox._ Subscribe A Solid Foundation As the politics and economics of climate careened over the past ten years, the physics carried on regardless. In 2015, the year the Paris Climate Agreement was adopted, the world emitted 41.2 gigatonnes of carbon dioxide. By 2025, emissions had risen only 2.4%, to 42.2 gigatonnes, because falling emissions from land use offset increasing emissions from fossil fuels. But slow growth in annual emissions is not rapid decline, let alone net-zero, so carbon dioxide kept accumulating in the atmosphere, going from 399 to 426 parts per million. Global temperature kept rising too, from about 1.1 degrees Celsius above the 1850–1900 average in 2015, to 1.4 degrees above it in 2025. None of this should surprise anyone. Even most prominent skeptics, like the authors of the 2025 report commissioned by the Trump administration’s Department of Energy, accept the basic physics. Carbon dioxide added to the atmosphere at industrial volumes accumulates and traps heat that would otherwise escape to space. That heat warms the upper ocean and increases temperatures at the surface. Increased ocean heat and melting of land ice cause sea levels to rise. Changes in the climate become noticeable to us as subtle shifts in temperature and precipitation, seasons arriving early or late, changes in the surrounding ecosystem, and weather extremes. The much more meaningful debate is not about whether these things are happening, but about what it all means for human societies. (While it is a proxy measure of overall risk, no one experiences global surface temperature.) This debate invokes at least three interesting questions: how much today’s warming is showing up in regional climate trends, how much more warming we should anticipate, and how extreme weather events can be understood in the context of climate change. Recent scientific advances give us new insights into each of these, but there is still much to learn. This is the context in which the next generation of conservative leaders will be the first to deal with significant climate change as a fact, not a forecast. It will be with them for their entire careers. Our evolving scientific picture of climate change will come from weather and climate records stretching further into the past, new kinds of observations in the present, better modeling tools for the future, and simply more time to observe the emergence of climate signals and their tangible effects on ecosystems and communities in the United States and around the world. The policy landscape will be shaped by not only the impacts of changes in the climate, but also by the ways in which societies respond and by the emergence of new technologies for both altering the trajectory of emissions and adapting to new climate realities. Global climate records now show the fingerprints of warming in different phenomena around much of the world. The IPCC, in its most recent assessment report, documents warming trends over all land regions, even as natural variability adds substantial variance to local trends. And the highest temperature extremes have become more intense and frequent, almost everywhere, since the 1950s. Scientists are highly confident that these changes are attributable to human influence. But confidence in detecting trends and attributing them to human influence degrades across heavy precipitation events; drought trends are heterogeneous and not attributable to human activity with great confidence. For hurricanes, tornadoes, and other severe storms there is even less confidence in trends or their relationship to climate change. In general, a fair summary of the evidence is that for well-observed phenomena, like surface temperature or heavy rainfall, with a clear relationship to warming, our multidecadal observations are consistent with human influence overcoming natural variability over long time periods and extremes increasing. For regions that are sampled more sparsely, or phenomena with a higher noise-to-signal ratio, we will need to observe them longer to understand the magnitude of the climate signal or improve dynamical understanding using models and observations together. But for Climate For those who can’t wait, we also have new techniques that attempt to understand the role climate change has in influencing particular extreme events. When the climate is changing and disaster strikes, it is natural to ask whether that disaster was somehow “caused” by climate change. For scientists and policymakers acting in good faith, answering that question helps develop a more accurate picture of the problem, how it may be getting worse, and what preparations we should be making to respond to it. It may, at some point, give some weight to how liability is assigned by courts or adaptation funding is distributed by society. But how we ask this question is extremely important. It is easy to get the analysis wrong by discounting the role that meteorology plays. As meteorologist Theodore Shepherd explained in a clear 2016 review of climate attribution methods, “if a weather or climate event is truly extreme in the present climate, then perforce it requires unusual meteorological conditions, which means that climate change is at most a contributing factor.” Share Scientists have two ways to probe climate as a contributing factor, both of which have entered media coverage of extreme weather events. One asks a probabilistic question: How much more likely is a particular event (e.g., a temperature record over a particular area) amid global warming? The answer involves using historical records and computer simulations to estimate the likelihood of such an event in both a changed and a preindustrial climate. The findings are less about the specific event, and more about events of that nature. The other question is: How has the changed climate affected the specific event in question? Here, scientists try to understand what a similar event would have looked like in the preindustrial climate. This is sometimes called the storyline approach, where the story is the specific meteorological details of the event. These methods offer ways to test the intuition of scientists about real weather extremes. In late June and early July of 2021, a persistent high-pressure ridge, or heat dome, set up over the Pacific Northwest and an extraordinary heat wave affected the area from Oregon to British Columbia. For six days, it shattered temperature records across the region and hundreds died from heat-related causes, in an area where such high, and persistently high, temperatures were well outside of experience and many live without air conditioning. Scientists have studied its connection to climate change using multiple approaches. This specific event was created by a rare combination of meteorological factors, a strong high-pressure ridge created the conditions for extreme heat, which occurred on top of higher average temperatures in the region from global warming. Probabilistic analyses showed that climate change increased the likelihood of such an event by at least 8-fold to more than 100-fold. One standout example found that such an event had effectively zero probability of occurring in the preindustrial climate. The enormous range in these assessments reflects how hard it is to estimate probabilities of events at the tail of the historical record. Estimates of the effect on the temperature of the event are more clustered. A recent review paper documents how multiple methodologies have found a positive influence, roughly 1-2 degrees Celsius of an anomaly that exceeded 15 degrees, of climate change on the temperature magnitude of the event. Multiple studies have investigated and found some positive influence of climate change in other extreme events. Attribution studies found climate likely increased the heavy rainfall that accompanied Hurricane Helene in North Carolina and surrounding regions in 2024. The extensive fires that struck Los Angeles in 2025 illustrate how the causal chain can become messy, though. Studies do detect a positive influence of climate on the event’s likelihood, but while climate change likely contributed to underlying aridity, it would not have played a role in the heavy winds or land practices that preceded disaster. These event attribution studies will become more common for extreme, or damaging, weather events. They can be produced quickly, and often are reported before peer review. As the Pacific Northwest heatwave example shows, when multiple methods converge on a positive attribution, the finding should probably carry some weight even if you have to be careful about accepting the results from a single study. I expect that as climate change proceeds and the climate thus departs further from a preindustrial counterfactual, the influence will become more detectable across a variety of extreme events and more easily identified. This will be used to cast blame, but can also be used to inform how communities and society adapt to ongoing change. We May Still Be Surprised One positive development, insofar as less climate change is better, has been that our central estimates for future climate change should probably be revised downward. Mostly, that is because the high-end warmings that scientists regularly analyzed about ten to 20 years ago, driven by high emissions throughout the twenty-first century, now appear to be somewhere between unlikely and impossible. At one time, the upper end of mainstream climate projections extended well into 4 to 6 degrees Celsius of warming by the end of this century, which, models suggest, would have wrought enormous real-world damage. Current energy and policy trends now point toward 2.5 or 3 degrees. Damages and risks are commonly modeled as increasing steeply with more warming, so this is already a better-than-previously-expected outcome for the climate (which is independent of the physical response to emissions). But we should maintain a wide range for plausible outcomes and prepare for the possibility of being surprised. Emissions trajectories are subject to deep uncertainty, the physical climate response is still developing, and whatever change occurs in the climate will then be mediated through unpredictable economic, social, and political institutions. Over the past decade, the pace of warming surprised some keen observers and appeared to accelerate, though not yet outside the range of expectation provided by climate models. The reasons for that apparent acceleration are being actively studied (as was the apparent pause in warming from 1998 to 2012), but no single driver has emerged. Some blame it on the El Niño, variability which would have no bearing on climate. Some think it is a result of factories in East Asia and global shipping fleets cutting aerosol emissions, which in the strongest version of the argument would indicate higher climate sensitivity to carbon-dioxide emissions. Others are more measured, not yet ready to draw solid conclusions as to whether climate projections require revision. We will have to see. Long-Term Thinking Climate change asks us to think over long time scales. But the pace of human-driven warming is compressing a large global change into a century. Changing weather patterns and extreme events are already causing adjustment costs and damages and will do more. Adaptation will help, but it is not free. For some communities, these costs will erode livelihoods and well-being, and may force migration. Are we prepared for large-scale managed retreats in the United States? Thankfully, American wealth, geographical diversity, and moderate climates may leave us better prepared than many around the world. But wealthy countries can still suffer serious disruption from narrowly concentrated costs, even if they appear entirely manageable in aggregate. Readers of Commonplace are familiar with the challenges of rapidly adapting to economic forces. The displacement that came from the China Shock is barely perceptible in aggregate GDP and employment data, but it affected millions of people and those people were far less mobile than economic models tended to assume. Deindustrialization rippled through the nation with enormous effects for not only our economic vitality, but also our national security. The forces surrounding climate change may not be so different; the world gets richer and must accept some diffuse costs. Those may be modest overall, but cause all manner of unpredictable effects with which policymakers must cope. In the case of the China Shock, our faith in a growing pie served us poorly. We’ll need to do better on climate. Leave a comment
Toronto Blue Jays Announce George Springer Decision Before Athletics Series
The Toronto Blue Jays fell 6-1 to the Kansas City Royals on Sunday but ultimately took two of three games in the series.Now, on Monday night, they’re in Sacramento to begin a three-game series against the Athletics as they continue their push for an American League playoff spot with exactly 18 regular-season games remaining.Ahead of […] The post Toronto Blue Jays Announce George Springer Decision Before Athletics Series appeared first on HEAVY.
The core anti-Pangram constituency is phony writers
Welcome to The Closing Argument, our verdict on the news, plus everything The Argument published and appeared in this week. The Verdict, by Kelsey Piper A lot of people hate AI for drowning the internet in slop and displacing human creatives. You might think that these people would be thrilled by a program that can identify and flag AI-generated work. But many people who hate AI also hate Pangram, an impressively accurate AI detector that produces very few false positives, according to high-quality independent research. This loathing found its most recent expression in a bizarre Wired article that amplifies a wide range of false arguments from Pangram skeptics: that AI detectors are racist (they’re not) or ableist (they’re not)1 or don’t work (this one does)2. Rather than starting from the premise that it would be really valuable to have a working AI detector and scrutinizing Pangram to make sure it lives up to that promise, the article starts from the premise that Pangram obviously has bad vibes and sets out to discover why. (One answer: The CEO microwaved lunch during an interview!) Why aren’t writers who resent Pangram more eager to distinguish themselves from posters who are having ChatGPT ghostwrite all their work? There are a few different things at play, but I think one key dynamic is this: These are often the same people. A core constituency for arguments like Wired’s is “writers” who are using AI to write their posts but resent this fact being made public. They stand to lose from AI detectors because AI detectors call them out. Share When Substack announced a partnership with Pangram, a sizable fraction of the platform’s authors rose up in outrage. Some people had experience with other AI detectors that didn’t work, and some felt that AI writing ought to be encouraged — both of which are fair enough. But the fury was fueled by authors passionately insisting that Pangram kept wrongly accusing them on almost every post they published. Pangram can be wrong, especially on short passages. But in the overwhelming majority of cases where Pangram looked at a whole blog post rather than a single paragraph, the program correctly detected work as AI. Even if we can’t be 100% sure in any individual case, we can notice the trend in the aggregate. Wired quotes Sam Illingworth, a professor of critical AI literacy at Edinburgh Napier University, who said, “I don’t think [AI detectors] work. … I think that detectors are prejudiced against certain people.” This spring, he said that he used AI for research and editing; this summer he acknowledged that in addition to research and editing, he also uses it “now and then for a turn of phrase I keep.” Pangram informs us that most of his posts are 100% AI-written (some are mixed AI-and-human) and that 100% AI-written posts started appearing last year. The favorable interpretation is that, as someone whose recent work Pangram consistently mistakes for AI, Illingworth sees Pangram’s flaws firsthand. The unfavorable interpretation is that the tool’s credibility trades off against his own, because its account of who writes his posts is not consistent with his. Illingworth is not alone in insisting the detector does not work. “It’s a Monday night in late July, and I’m pasting my own Substack draft into an AI detector for the first time. Not because I’m worried. Because I’m curious. I wrote every word of it myself, the way I write everything, out loud in my head first, in a rhythm that still carries the accent of a language I learned to think in before I learned to write in this one,” begins one Substack post by an author indignant that Pangram flags her writing as AI. “That’s the part I had to sit with before I could write this.” If you, like me, have worked with AIs a lot, you won’t even need Pangram there. The author of that piece is AI. It is obviously AI! I checked 10 other posts on the blog, and they were also obviously AI! “I have spent 22 years as an award-winning author and essayist. And now this bullshit tool that learned from MY writing is going to accuse me of plagiarism,” popular Substack writer Carlyn Beccia wrote in an anti-Pangram post. “For the last and final time, AI DETECTORS DO NOT WORK.” Here’s what her writing sounded like in 2020: “Most success stories revolve around one person who believed enough in you to say — give it a try. We call these people muses, but they are so much more than that. They are the strong man giants whose shoulders we stand on so we can see the horizon. Throughout the editing process, I am sure my editor felt less like a giant of greatness and more like Atlas with the world on her shoulders.” Pangram says it was 100% human-written. (So were other articles of hers in 2020.) And here’s what it sounds like now: “And then there is the reshuffling, which sounds like paperwork until you remember that empires have always loved paperwork. The Trump regime recently moved special education oversight from the Education Department to HHS. That is not a minor bureaucratic transfer. It changes the frame.”3 Pangram says this is mostly AI. I am not convinced that Pangram is wrong. In my own experience, Pangram is very effective on long texts — if it marks a long essay as AI, the essay was either written by AI or deliberately written to sound like AI. On tweet-length or paragraph-length texts, it’s not perfectly reliable. It seems to work just as well for non-native English speakers as for native speakers. While I have seen dozens of claims of pre-2022 text returning a positive result on Pangram, I have tried repeatedly to corroborate those and have never once succeeded. To be clear, I’m absolutely not saying that complaining about Pangram suggests a writer is a cheat. There are plenty of other concerns. Most are mistakenly premised, such as the false claim that Pangram trains on work you put into it, but others are reasonable: It’s unreliable on sufficiently short text segments; it creates an internet witch-hunt mentality when there are defensible uses of AI; people who are open about their AI use are sometimes blamed, when the only people really doing something wrong are the ones who hide it; and it isn’t perfect, so a single positive result shouldn’t be taken as decisive proof of misconduct. I’m happy to concede that Pangram is imperfect and possible to over-rely on. If an author’s posts are mostly non-AI and one scans as partially AI, this could be a mistake. It’s also possible to defeat Pangram with intentional effort. If you think we need something more robust and more reliable, fine! But if you don’t like AI and don’t like Pangram, your attitude really ought to be that it’s a shame that our existing tools for AI detection are imperfect, and it’s important to invent better ones. If your stance is instead that AI is evil and so is looking for AI, then ultimately you’re contributing to the sloppification of the internet. You’re making it harder for people who write their own text to distinguish themselves, and providing cover for authors who are definitely not writing their own text. Subscribe Top stories this week, by Kobe Yank-Jacobs Commentators often worry that the decline of reading could mean the decline of liberal democracy — but deliberative governance actually predated widespread literacy. This week, Maibritt Henkel pointed out that many liberal cultures thrive on a culture of raucous, interactive debate, which can happen in-person as easily, if not more so, than in text. Read on, dear reader, to learn more. It may actually cheer you up: [ ](https://www.theargumentmag.com/p/reading-wont-save-liberalism) [ Reading won’t save liberalism ](https://www.theargumentmag.com/p/reading-wont-save-liberalism) Maibritt Henkel · Sep 1 [ Read full story ](https://www.theargumentmag.com/p/reading-wont-save-liberalism) It used to be bad to downplay climate change (if we mean denying its reality). Today, it’s actually good to downplay climate change (if we mean keeping the issue out of political fights so we can make real progress). This counterintuitive view is called “climate hushing,” and contributor Alex Trembath argued that it’s the key to enacting good policy going forward. Get up to speed on the latest in climate politics: [ ](https://www.theargumentmag.com/p/we-wont-stop-climate-change-by-talking) [ We won’t stop climate change by talking about it ](https://www.theargumentmag.com/p/we-wont-stop-climate-change-by-talking) Alex Trembath · Sep 2 [ Read full story ](https://www.theargumentmag.com/p/we-wont-stop-climate-change-by-talking) Canada’s Prime Minister Mark Carney is standing up for the forces of liberalism against Donald Trump’s assault on our closest ally. So, why is one prominent left-wing commenter attacking Carney? Columnist Matthew Yglesias explains: [ ](https://www.theargumentmag.com/p/when-economic-liberty-means-central) [ When "economic liberty" means central planning ](https://www.theargumentmag.com/p/when-economic-liberty-means-central) Matthew Yglesias · Aug 31 [ Read full story ](https://www.theargumentmag.com/p/when-economic-liberty-means-central) 🌟Abundance Wins of the Week🌟 Shout out to my home state of California, whose legislature passed two housing bills this week. One requires a housing density minimum in transit-oriented areas of the state’s most transit-rich cities (Los Angeles, Sacramento, San Francisco, Oakland, San Jose, San Diego, and Long Beach). Another bill closes two common loopholes used by third parties to delay housing projects, including an opportunity to challenge maps of a given lot created by the developer after a project has been approved. Shout out to our big-state rival, Texas, where pro-housing SB 785 took effect this week. The new state law requires municipalities to have at least one district permitted for manufactured housing without a special permit. This will promote zoning changes in 44% of municipalities. The company Fervo Energy signed a deal with Google for what is set to be the world’s largest enhanced geothermal facility, proving a technology can work at a scale that could be crucial to a decarbonized, energy-abundant economy. Share Worth watching… This week, forecasting legend Nate Silver joined Jerusalem, Matt, and Jeremiah on a crowded episode of the pod. It was a party. And speaking of parties, our round table debated the factional lines within the Democratic Party: Is it possible to be center-left and also anti-establishment? Watch or listen to find out: The Rio Grande Valley in Texas is one of the areas that swung hardest toward Trump in 2024, but musician Bobby Pulido is running to flip it back. Right now, The Argument/Split Ticket’s model has him running 1.1 points behind his Republican opponent in a district Trump won by 18 points. Tune in to find out how he differs from national Democrats and what he thinks he can to do win this seat. On Thursday, Bernie Sanders was among the first politicians to call for a ban on superintelligence. My guest this week, Andrea Miotti, has been lobbying to ban superintelligence for several years, most recently in the U.K., U.S., and Canada. I asked him how he gets legislators to take speculative risks seriously, and we dug into what plans might look like to actually pass a ban. Listen to learn more: What’s News with The Argument The Argument recommends, by Kobe Yank-Jacobs If we ever lose track of Justin Zuckerman, I’ll have the team split up and check all the movie theaters in the city. I’m shocked (and a little jealous) at how often he gets out to see good cinema. This week, he brings us It Ends, a “horror-ish movie,” which he described as a “Gen Z Waiting for Godot.” I was intrigued before he added that it was “funny, dark and existential” — at which point I was fully locked in. I guess I also had existential themes on the mind this week. One morning, a single line from a poem I read long ago just came to me: “Is death a stretch of time in which a life is just a flash?” The poem is called “From 20,000 Feet” by Heather McHugh. I suggest the version in The Vintage Book of Contemporary American Poetry, which, by tradition, my best friend and I always bring backpacking, extra weight be damned. We’ll stay thematically dark for just one more rec from senior editor John Robert Thomason, whose upcoming trip to Baltimore reminded him to suggest Michael Clune’s memoir White Out (about being addicted to heroin in Baltimore in the 1990s). Ben Lerner’s blurb called it _“_Disturbing, brilliant, hilarious—it’s as if Proust had written Jesus’ Son.” John told me this “seems accurate, with the caveat that I’ve never read Proust.” I, personally, have also shelved Proust until retirement. At this point, I was a little concerned for the team since all of us tossed out such psychologically grim material — maybe we’re all down about the end of summer — but thankfully, at least a few of us were into more upbeat selections. Milan Singh recommended Made In’s 5-quart stainless clad saucier, suggesting he had some fun in the kitchen. Jeremiah Johnson asked if he could recommend yet more Carly Rae Jepsen. Once denied, he offered up Game Changer instead_,_ calling it “the most wildly creative game show, maybe in the entire history of the genre.” Eli Richman mentioned _Lucky, “_a B-tier show with an A-tier cast and an S-tier theme song.” I suppose he was recommending the song, Horns of a Bull. Still, despite these cheerier recs, I couldn’t help but be concerned that we were subconsciously worried about racing toward summer’s end — the end of swim trunks and splashing around. Then a message landed reminding me of the three-day weekend to come. A whole extra day for sunshine. One more swim in the lake. I was grateful this called to mind yet another line from a poem, one by Mary Oliver, the patron saint of every gorgeous summer day (and about whom a documentary is now out). Speaking to God in the poem “Thirst,” she wrote: grant me, in your mercy,a little more time. Love for the earthand love for you are having such a longconversation in my heart. We have merch! We have quarter-zips, keychains, hats, and stickers. Each one is a great conversation starter in its own way. Buy them here. Subscribe More to read: [ The Opening Argument ](https://www.theargumentmag.com/p/seeing-like-an-elite)[ Seeing like an elite ](https://www.theargumentmag.com/p/seeing-like-an-elite) Kobe Yank-Jacobs · Sep 4 What do Americans hear when they watch Sen. Bernie Sanders or Vice President JD Vance rail against the elite? Are they thinking of right-wing tech billionaires? Imperious college professors? Liberal magazine writers? [ Read full story ](https://www.theargumentmag.com/p/seeing-like-an-elite) [ The Weekly Scroll ](https://www.theargumentmag.com/p/god-is-back-on-the-timeline)[ God is back on the timeline ](https://www.theargumentmag.com/p/god-is-back-on-the-timeline) Jeremiah Johnson · Sep 5 This week, we’re talking about Substack’s new religious debates, the continuing MAGA influencer scandal, how to retire from YouTube, and aura battles. [ Read full story ](https://www.theargumentmag.com/p/god-is-back-on-the-timeline) 1 This was a legitimate thing to worry about, but doesn’t seem to be a problem given the actual technology: Marijke Van Vlasselaer, Filip Van Droogenbroeck, and Bram Spruyt’s 2026 paper looks at whether Pangram is less accurate when evaluating the work of non-native speakers of English. It isn’t. That’s because Pangram isn’t really looking at how “unusual” a piece of writing is but whether it has the characteristic patterns of LLMs. The Wired article makes an odd gesture at a different Pangram-is-racist argument. There were several controversies this summer during which upcoming books were canceled after being discovered to be AI-written: “The three novels mentioned earlier — Shy Girl, Daggermouth, and Call Me, easily publishing’s biggest AI-detection scandals — were all written by writers of color.” But come on! As Zvi Mowshowitz put it: “They were not primarily written by people of color. They were primarily written by AIs.” That’s the whole problem! And as my colleague Jeremiah Johnson recently observed with respect to Cambridge’s Arday scandal, a community being unwilling to call out fraud if it’s perpetuated by people of color is not an anti-racist tendency; it is ultimately quite racist. 2 Many non-Pangram detectors don’t work, but many possible airplane designs also don’t work, and no one considers this a general argument against aviation; the thing that matters is whether any detection works. 3 The phrase “empires have always loved” mysteriously became all the rage in the last few years. See here, here, here, here and here. Before 2025, the only use of that phrase I have been able to find is a single Tweet. The logic of the sentence, too, is incoherent in an AI-characteristic way. “...sounds like paperwork, until you remember that empires have always loved paperwork”? Does remembering that make it sound less like paperwork? It’s a craft-ful sentence, but what is it actually saying??
Bold predictions for Cincinnati Bearcats vs. Boston College
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The Toronto Blue Jays fell 3-1 to the New York Yankees in their series opener Friday night.Now, as the Blue Jays welcome Vladimir Guerrero Jr. back into the lineup after he spent time on the seven-day concussion IL, manager John Schneider made some notable changes ahead of Saturday’s game. The matchup will feature Toronto ace […] The post Toronto Blue Jays Announce George Springer Decision During Yankees Series appeared first on HEAVY.
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AI needs science’s search history
“Actually, that’s where the gold is,” said Alasdair Russell, my graduate school friend who leads a pre-clinical genome editing group at Cambridge. He was talking about the winding road of science that is omitted from published papers. “When you’re discussing how to do the experiment, and why this way is better than another way, and what does the data really mean? I know what it shows, but what does it mean?” At RAAIS 2026, he described how his group has begun logging the twisting path of discovery as it happens, recording the verbal and written exchanges that normally disappear. Ideas become nodes in a living graph: they branch when a meeting produces two plausible experiments, merge when separate lines of evidence converge, and go dark when someone quietly stops pursuing them. In his implementation, one agent scores novelty, while another tries to learn “how scientists think and how they navigate through a complex world of data,” so that high-potential nodes can trigger deeper investigation. As frontier AI labs deploy agents toward scientific discovery, giving AI authentic scientific taste remains a trillion-dollar dilemma. The bottleneck is that the record we have kept for centuries of science might be insufficient to get us there. The experiments that never make the paper Peter Medawar, 1960 Nobel laureate and “father of transplantation,” asked in 1963 whether the scientific paper was a fraud, and answered yes: the form of the paper misrepresents the thinking that produced it. Sixty years on, the diagnosis is unchanged. A paper presents a clean progression from hypothesis to result to conclusion. Lost along the way are the unconventional theories, the abandoned or unaffordable methods, and the underwhelming and inconclusive data. It reads like a browser history with every dead end deleted: the ten open tabs, the four rephrased queries, the wrong turn down a subforum. All scrubbed, leaving one clean path from question to answer as the canonical path. What has changed since then is that the discarded material is now worth something. No high-impact journal wants these artifacts, but raw trial and error is the most likely source of the training data required to develop scientific intuition, or what researchers call taste. Earlier attempts to capture the discards were motivated by scientific integrity. The Journal of Negative Results in Biomedicine launched in 2002 to publish rigorous studies that disputed established models or exposed ineffective treatments. Its archive preserved negative conclusions after they had become papers, rather than the live alternatives and arguments that produced them. BioMed Central closed it in 2017, saying the mission had been served now that other journals publish null results. A less generous reading is that in fifteen years it published around 200 papers because almost nobody wants to read a negative result, let alone write one up when it will not count toward academic tenure. AI models are different. Even an uninteresting negative result can be useful, provided it is labeled. Earlier this year, Anthropic put a version of this to the test. The company pulled 129 decision points from real Claude Code sessions between January and March 2026, showed models the work up to a human detour, and asked what should happen next. Claude Mythos Preview beat the human choice 64% of the time, while Opus 4.5 managed 51%. The comparison was tilted toward the models because Anthropic deliberately chose moments where the human decision had room for improvement. On 127 further scenarios where the human action was already strong, the models improved on it only about 20% of the time. The study was possible because Anthropic’s researchers work inside a tool that logs by default. The reasoning, detours and outcome are produced in the same working environment. Biology has no equivalent. Its reasoning happens in hallways, at benches, in Slack threads and on whiteboards, while the outcome arrives weeks later somewhere else. The record has to emerge from the work itself. Ask scientists to reconstruct it afterwards and we’ll create another polished account. Subscribe A log is not a label Before any of this becomes training data, a negative result has to say what failed. The first paper published in the Journal of Negative Results in Biomedicine examined 234 negative studies across five leading medical journals. Only 30% discussed statistical power, and half clearly defined a primary outcome. Tell a model an experiment failed, without telling it whether the assay was underpowered, a reagent had degraded, or the hypothesis was simply wrong, and it will learn noise with confidence. Decision histories have a second missing label too. When a lab considers five experiments and runs one, only the chosen branch returns an outcome. The other four are experimental counterfactuals. Researchers call this the selective labels problem: the data reveal results only for the actions someone chose to take. Such a record can teach a model to imitate a lab’s taste, but it cannot establish that the taste was good. It also smuggles local constraints into general lessons. A lab that never ran cryo-EM because it did not own a microscope teaches a model that cryo-EM is rarely the right call. Alasdair’s nodes preserve the candidate set, which is the necessary first step. To be useful, each decision record needs six fields: The evidence available at that moment, and nothing that arrived later. The candidates considered, including the ones dismissed in a sentence. For each candidate: the expected result, the confidence attached to it, and the cost in time and money. The route chosen, and the reason. What happened next. How the evidence changed the scientist’s view. These must be timestamped before the outcome, because hindsight turns uncertainty into inevitability. Disagreement also has to survive. Averaging three scientists into one clean rationale reproduces exactly the information loss we are trying to fix. But there is an obvious failure mode here. Once decisions are logged and scored, people log for the record post-facto. Anyone who has watched an electronic lab notebook fill up with retrospective tidying knows how quickly a research tool becomes a compliance exercise. A record that costs a scientist ten minutes of honest reflection per decision is worth far more than one that costs an hour of performance. A publication initiative called Registered Reports offers a useful starting point. Here, researchers submit their rationale, methods and analysis plan for peer review before the data exist, and the journal commits in principle to publish if they follow the approved plan. Nature has now expanded the format across every field it covers. This approach to paper writing timestamps intent before the outcome, but it freezes one plan. A useful search history must also preserve how the plan changed, which alternatives were rejected, when, and on what evidence. Subscribe Run the runner-up Autonomous labs show what happens when decisions and outcomes are connected in a loop. Liverpool’s mobile robotic chemist ran 688 experiments over eight days in a ten-variable formulation space. A batched Bayesian search used each result to choose the next experiments and found photocatalyst mixtures six times more active than the starting formulations. Every completed experiment changed what the system did next. The robot optimized within a goal and search space that humans had already chosen. It did not decide which scientific question mattered. So what this work demonstrates is narrower, but still useful: a search history pays off when the options return standardized outcomes quickly. Open-ended biology is harder because branches can take weeks and the discarded alternatives may never be run. Some exploration therefore has to be bought. Where two branches are plausible and the stakes justify the cost, a lab should sometimes run the runner-up. Otherwise the record captures what today’s scientists usually chose and stays silent on what they systematically overlooked. A funder could ring-fence a small fraction of a grant for the branch not taken, provided that the decision record and the outcome are both deposited. That costs money, but so does having every lab rediscover the same abandoned path. Test whether taste transfers When it comes time to evaluate if our new AI system exhibits taste, the comparison should run across laboratories and fields. At selected decision points in a live research campaign, we should freeze the six fields: the evidence, the candidates, expected results, confidence, costs and the choice made. Experts then rank the options before the outcomes are known, and those outcomes are checked independently later. We could give models one of two diets - papers alone, or papers plus decision histories - and ask them to rank the next experiment on unfamiliar projects. Then, we score information gained per dollar and week, calibration, and how quickly weak branches are abandoned. If histories improve choices only inside the lab that produced them, they amount to useful organizational memory. If they improve choices on unfamiliar problems elsewhere, Alasdair’s group will have captured something science has never managed to write down: a transferable record of taste. Until that happens, a scientific search history is a promising record, not yet a training set.
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If Not AOC, Then Who?
What do we do if Alexandria Ocasio-Cortez decides not to run for president in 2028? It makes sense that many leftists are currently focused on calling on her to take the plunge. But the absence of a Plan B, or even much discussion about one, is worrisome. The US Left can’t afford to sit out the 2028 Democratic primary. Doing so in 2024 was a disaster: our abstention self-marginalized anti-billionaire politics from the national conversation and thereby gave space for Harris to pivot to the “center” (i.e. the policy preferences of corporate donors, establishment hacks, and AIPAC). Abstaining again in 2028 would be especially counterproductive since democratic socialists now have so much wind in our sails after our big electoral wins in NYC and across the country. It’s time to deepen our momentum, organization, and profile — not to retreat to the margins during America’s single most important political battle. Even more urgently, keeping Trump’s heir out of the White House may depend on whether leftists and the party’s unprecedentedly disenchanted base can force the Democrats in 2028 to take meaningful steps towards a very angry electorate and away from the corporate-consultant blob that has helped get us into this mess. If AOC (or Abdul El-Sayed) decides to run, we should enthusiastically go all in — DSA, fighting unions, progressive organizations, anybody who wants to see a better country and world. But, as far as I can tell from the outside, there’s a real chance that she doesn’t run. And we can’t just wait and see where she lands before discussing alternatives, since AOC’s decision might not come for quite a while, giving us little time to scramble at the last minute for an alternative. So we should start working now on getting a Plan B into motion, and recruiting a good candidate for the role. Here’s one idea. A Worker Candidate — Healthcare Not Warfare 2028 Run a union worker Leftists don’t need to always run experienced, well-known socialist politicians for high-profile contests. Lula, the current president of Brazil, began his political career as a metalworker militant who ran for governor of São Paulo in 1982. Especially in an era of institutional distrust, it’s possible to lean hard into making a virtue out of a working-class comrade’s distance from traditional politics. Experience abroad shows this clearly. Rachel Kéké — an immigrant hotel housekeeper — ran as the candidate of the leftist La France Insoumise for the National Assembly in 2022, defeating a former minister in the Macron government. The Belgian Workers’ Party in 2024 won its first Flemish seat in the European Parliament by running Rudi Kennes, a three-decade veteran of the Opel Antwerp auto factory. And the South Korean left in 2024 elected auto factory militant Yoon Jong-oh to the National Assembly. If a tiny French Trotskyist organization could make such a big splash in 2002 and 2007 by running a well-spoken postal worker cadre for office — the LCR’s Olivier Besancenot received about 1.5 million first-round votes — it’s not far-fetched to expect that a much larger organization like DSA, together with a broader coalition of the willing, could run this playbook to much greater effect. This is especially the case now that almost everything DSA does immediately becomes a national news story. We don’t need someone famous. We just need a charismatic, well-vetted comrade who works in a relatable working-class occupation — e.g. a nurse, or a worker in auto or construction, or at Amazon. Ideally, they have a compelling personal story or have been part of a significant strike or organizing campaign, but even those criteria don’t necessarily have to be deal-breakers. The main lesson of the Graham Platner fiasco isn’t that we should never run outsiders for office (the momentum he generated proves the exact opposite), but that they need proper vetting and roots in democratic membership organizations like DSA or a union. In any case, there are plenty of ways to signal working-class authenticity without resorting to Platner’s or Fetterman’s performative macho white dude affect. Better to have someone of any gender or race who is actually working class, with firm socialist politics, who can speak from experience about the day-to-day realities of trying to make it in America. There are surely at least a dozen such worker leaders in our movement nationwide; we just need to sit down and convince one that their personal sacrifice for the greater good (i.e. running for president) will be worth it. If we don’t have a frontrunner with a clear path to victory like AOC (or El-Sayed, were he to win Michigan), it’s better to embrace someone whose decision to run in itself tells an important story about US politics: workers have been iced out of our political system and are now fighting hard to break back in. The novelty factor, in itself, would generate press. And it would help democratic socialists signal to the American public that our movement’s defining characteristic is that we represent workers vs. bosses, not that we’re “the furthest left” force in American politics — a framework that suggests we’re extremists and that helps our opponents isolate us from non-college-educated voters by highlighting extremely minoritarian (and misguided) stances included in DSA’s platform like prison abolition. We don’t get many opportunities to speak to tens of millions of Americans; we need to use these opportunities to broaden our appeal beyond those already in our orbit. Run to polarize the 2028 primary and general election around “Medicare for All” and “End All Aid to Israel” Why should democratic socialists use our scarce time and volunteer energy to run in a race we’re unlikely to win? Running in the 2028 primary will not only recruit us scores of members (imagine having a DSA member intervening in eleven nationally televised primary debates!), but — more importantly — it has the potential to be a decisive leap forward towards an America that finally starts meeting the needs of working people instead of HMO and AIPAC billionaire donors. Polarizing litmus tests are good, actually — as long as they’re fought for around popular demands in a thoughtful and strategic way. Democratic socialists have an opportunity and responsibility to make the 2028 primary about two of the most widely and deeply felt issues of our moment: domestically, America’s unaffordable, family-breaking private healthcare system; internationally, America’s bankrolling of the genocidal Israeli government. A candidacy relentlessly demanding Medicare for All and No Aid to Israel would pose a sharp question to the American public, the Democratic Party, and other 2028 contenders: Why can’t working-class Americans afford their healthcare bills at the same time that our government is spending at least $3.8 billion yearly to fund an Israeli regime eager to keep dragging us and the world into more forever wars? Our candidate can agitate around a clear message: I’m running to give voice to all those working-class Americans who are struggling to get by. No matter what state you live in, who you voted for in 2024, or your background, it’s just plain common sense that we should make healthcare more affordable instead of spending billions to enable the Israeli government’s crimes against humanity. Every American who agrees with me when I say ‘Medicare for All — Not a Dime for Israel’ should make their voice heard through our campaign. Racking up a high number of votes for this agenda is a crucial way to show the Democratic Party and the country as a whole that we can’t keep supporting a life-destroying status quo in our healthcare system and in the Middle East. If we want to defeat Trump’s handpicked crony this coming November, we need to speak to ordinary people’s frustrations with the status quo — we can’t keep running the same tired establishment playbook and expecting a different result. For those highly engaged voters (or skeptical media pundits) who raise concerns about wasting votes, the candidate can take the time to explain why this isn’t the case: It's true I'm a long shot. But Zohran showed anything can happen — and voting for me is a pragmatic decision, not a wasted vote. In a heavily divided primary with a large number of candidates and a very discredited establishment, my campaign and the democratic socialist movement I’m part of are positioned to be the balance of power if we can get a large enough number of voters to loudly demand Healthcare Not Warfare. Since there’s a high chance no candidate will get to 50% in the 2028 primaries, the Democrats could very well be heading towards a brokered convention, where a disciplined, principled current committed to breaking from corporate donors can play an outsized role in making sure whoever ends up with the nomination raises the banner of single-payer healthcare and cutting aid to Israel. How exactly could running this sort of primary campaign be translated into actual leverage? The candidate replies: I’m aiming to win the nomination, but if I don’t achieve that goal, my delegates and I will pledge our support in the primary only for a candidate who agrees in the general election to call for ending US aid to Israel and establishing Medicare for All at home. These are both winning planks for November against the Republicans, and the only reason I can see that other candidates haven’t yet raised these is that they’re still more scared of the donor class than they are of the people. That needs to change. If Democratic leaders want young people, workers, and those sick of the status quo to show up in big numbers this November, they need to meet us halfway. In this way, whether or not we win the nomination, democratic socialists could still play a decisive role in 2028. The approach outlined here is similar to what radical left parties periodically do in parliamentary democracies like Denmark or Sweden when they find themselves as a junior partner electorally: they dictate to center-left allies what their terms would be for enabling, from the outside, the formation of a new government. Running a Healthcare Not Warfare candidate will make it significantly more likely that the 2028 Democratic candidate stands with ordinary people instead of donors on these two pivotal planks — a shift that will, in turn, make it easier to achieve the existentially important task of defeating MAGA’s heir in November. Look at how much popular traction the Uncommitted movement got with a similar strategy in 2024 with so little on-the-ground organization — surely we can go much further with a working-class candidate, a much bigger DSA, and a Democratic Party base that has turned against Israel, billionaires, and the hacks in the party who prop them up. Objections and Responses There are quite a few objections to this proposal, many of which are very reasonable. Let me briefly address each here. Socialists shouldn’t waste their scarce time and resources on a candidate unlikely to win. This is a good rule in general, but there are important exceptions, and presidential elections are the main one. There’s no other political contest that gets anything like this level of attention and engagement in the US, so if you want to be a serious force in nationwide politics, you basically can’t afford not to have a candidate. Far from being an overall drain on our resources, running a dynamic 2028 campaign would likely result in a major net increase in members and capacity. In that spirit, the very pragmatic Milwaukee sewer socialists — who mostly ran to win at home in Wisconsin — supported Eugene Debs’s propagandistic presidential campaigns, which they correctly saw as a crucial means of recruiting to the Socialist Party. And now that the (post-1972) Democratic Party gives more weight to voters than party operatives in determining its presidential nomination, it’s become feasible to run as a socialist in primaries without risking a split in the vote against the Republicans in November. We should just support Ro Khanna, who also supports M4A and ending aid to Israel. I like Ro Khanna and would enthusiastically vote and canvass for him for president were he to get the nomination. But there’s a real chance that Khanna’s bid for office doesn’t catch on; he lacks the grassroots organizational apparatus of DSA, and, because he’s not really of the Left or labor (despite his current very good positions), he’s unlikely to generate the type of buy-in and enthusiasm that a DSA candidate would. Moreover, Khanna would be running as an individual, not as someone emerging from and building an organized movement — his campaign would not likely focus on building sustained power from below, nor would it likely polarize the 2028 primary around M4A and Israel aid. Also, and not inconsequentially, the poor electoral results for Tom Steyer and Saikat Chakrabarti this spring suggest that there’s perhaps not much of a popular appetite in the Democrats’ base to support multi-millionaire class traitors. Much better, then, to run a democratic socialist worker. If by Super Tuesday it’s clear that our campaign has captured the main left lane in the primary, Khanna should drop out and endorse us; conversely, if he’s managed to capture this lane, DSA could at that point transform its Healthcare Not Warfare campaign into an independent campaign to elect Ro Khanna focused on winning (and holding him accountable to) those two critical planks. An openness to consolidating an anti-corporate candidate by Super Tuesday is a necessary component of the strategy I’m proposing, because otherwise we risk splitting the primary votes (per DNC rules, you need at least 15% to get any delegates to convention). We should convince Shawn Fain or Sara Nelson to run for president instead. I agree it’d be great if either ran for president, but my impression is that there’s basically no chance that will happen in 2028, because both are firmly focused on their union leadership duties (and, in the case of Fain, beating back a federal witch-hunt). This plan could have DSA end up endorsing a non-socialist, non-DSAer for president. That’s against our principles. It’s a myth, and bad politics, that socialists can’t ever support candidates who aren’t members of their organization and/or who don’t call themselves socialists. Bernie, after all, was not a DSAer. And socialists abroad regularly make tactical decisions about if and when supporting a non-socialist candidate makes sense from the standpoint of promoting working-class interests, defending democracy, and building a socialist current in the process. If your “principles” prevent you and DSA from helping achieve the momentous stride forward of having a Democratic candidate in 2028 run on Medicare for All and ending aid to Israel, those “principles” are not very good. In fact, they actively undermine the causes they’re aiming to support. We shouldn’t confuse principles (working-class independence) with tactics (the best way to advance this in a given context). In my recent article in Catalyst on Milwaukee sewer socialism (I’ve uploaded the full text here), I have a section showing how similarly rigid rules about endorsements had to be eventually discarded in the old Socialist Party nationwide after WWI because they tended to lead socialists towards a sectarian abstention from broader anti-corporate electoral insurgencies beyond their party’s control. For what it’s worth, Lenin’s current under Tsarism frequently backed bourgeois candidates in the second round of elections and Lenin himself advocated that British Communists support Labour Party candidates; for his part, Kautsky in 1912 defended the German Social Democracy’s electoral pact with the Progressives in the runoffs. If our predecessors could be that tactically flexible in an era before the limits of their underlying strategic assumptions about intransigent class politics in capitalist democracies were made clear, then there’s no need to insist on being more tactically rigid today, after a century’s worth of accumulated experience. As long as you maintain your own political profile and organization while engaging in broader coalitional efforts, there’s zero inherent contradiction with the principle of political independence. More specifically: DSA and allied organizations should enthusiastically back Michigan’s Abdul El-Sayed if AOC declines to run and he throws his hat in the presidential ring. (I have no idea whether he’s even considering this path, but there’ll surely be chatter about it if he wins his Senate race this November.) Though he doesn’t call himself a socialist, El-Sayed is a principled Berniecrat, a consistent fighter for Palestine, and would have a clear path to victory. Using the “s” word is far less important than whether a campaign fights for the interests of working people at home and abroad — and whether actively participating in it can grow the organized working class and the Left. And the same holds true if a less anti-corporate candidate were, under pressure, to adopt our two Healthcare Not Warfare planks. Why focus only on M4A and ending US aid to Israel? There are so many other important issues to fight for! Obviously there are many pivotal issues beyond healthcare and Israel, and our candidate — and especially our organizations involved in the campaign — should be prepared to speak on all of these. But one of the crucial lessons of the Zohran campaign is that you have to relentlessly stay on message if you want to cut through the noise. CNN and Fox News will want to talk about DSA’s views on Venezuela and abolishing the police; we need to polarize the debate on the widely and deeply felt issues that are most favorable for growing the Left, isolating our opponents, and winning urgently needed changes. One recent poll found 63% of Americans support M4A, even after respondents were told it would raise taxes and eliminate most private insurance. Another found that 74% of Democrats opposed “providing additional economic and military support to Israel.” And focusing on two demands sets the campaign up to be able to leverage its mandate to make clear demands upon other candidates (which we can’t do if we run with a laundry list of demands). Talking about a Plan B is a distraction from the more urgent work of building a groundswell of calls on AOC to run. It’s good for DSAers, fightback unionists, and progressive organizations to try to convince AOC to run. This proposal is meant as an addendum to pushes to get AOC to run and to line DSA up for that eventuality, not as a substitute for those efforts. But I’m worried that, like in 2024, we might eventually find ourselves with no time to find a viable alternative unless we start now. DSA, with its abundant deliberative processes, moves slowly. The approach you’re suggesting seems risky. If a DSA candidate runs but doesn’t receive a lot of votes, it’ll undercut our current national momentum. This is a reasonable worry. All big initiatives always carry risks, and the path I’m advocating is certainly not free of these. That said, if a Healthcare Not Warfare campaign doesn’t catch on like we envisioned, it’s a pretty simple pivot in March 2028 to explain that in the future we really need our standard-bearers like AOC, or our growing bench of national electeds, to step up in presidential primaries. Moreover, it’s worth noting that the risk of my Plan B proposal is on the whole significantly less than the risk entailed by an AOC run. We should all back her if she runs, since she’d fire up the base, provide huge openings for organizing, have a real shot at defeating a Republican (especially given MAGA’s unpopularity), be a great standard-bearer for our issues, particularly M4A and ending aid to Israel, and be less willing than mainstream candidates to capitulate to any anti-democratic machinations from Trump and co. But it’s also true that AOC could lose, which would be a devastating setback to the country and the Left. Or she could win and find herself unable to pass much, if any, of her (and our) program, a demoralizing prospect. So if we’re willing to take these risks with AOC (and we should), it follows that we should be able to take lower-level risks if she doesn’t throw her hat in the ring. What’s Your Plan B? The US Left is facing its biggest opening for growth and influence since the 1930s. Initiatives that would have been impossible a few months ago are now potentially feasible. A President AOC is now easier to imagine, but if she ultimately assesses that the risks aren’t worth the potential reward in 2028, we can’t be left flat-footed. So even if you’re not convinced by the specifics of my Plan B, I hope you can at least agree that it’d be a disaster for us not to have one — and not to start getting a Plan B into motion. Hopefully other organizers and political currents will put forward their responses and alternative ideas ASAP. The stakes are too high to just wait and see. More As always, please share this article widely. This newsletter depends on all you lovely people to get the word out. Thank you! I uploaded the full text of my Catalyst article on Milwaukee sewer socialism here (it’s updated and 3x as long as the version published earlier on this substack). If you haven’t yet, please make sure to subscribe to Catalyst here; I read every issue front to back, highly recommend. 🌹🎵 Ivan Lins (arranged by Arthur Verocai) — “General da Banda” 🌹🎵
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Making Our Own Luck
We are cross-posting a piece that former NIH official Kris Willis wrote for Macroscience. Kris is now the President and Founder of the Woodley Park Institute, for which I’m a board member. Picking winners or hedging bets? Federal science agencies have long wrestled with the question of how to ensure their billions of dollars of grants and contracts result in maximum benefits for Americans. On the surface, it might seem that a goal-oriented, interventionist style of management would be the best policy: prioritize our most pressing problems and distribute funding accordingly.This top-down strategy creates a unique hazard, though: bad choices can send resources in the wrong direction, resulting in slow progress that impedes the delivery of tangible benefits to citizens. Moreover, if decision makers pursue an applied advance when the underlying scientific principles remain poorly understood, they risk costly failures and a loss of support for the research enterprise at large. Examples include Nixon’s 1971 War on Cancer, undertaken on the premise that a cure could be achieved in less than a decade, the drive to build an operational commercial fusion plant by the mid-1990s, and the National Plan to Address Alzheimer’s Disease, which failed to produce the effective treatments and prevention strategies promised by 2025. The contrasting laissez-faire approach is to hedge your bets. If the path to an advance is impossible to predict, then the wisest choice might be to distribute funding to as widely as possible. The main risk of this strategy is that spreading funding thinly may limit the resources available to the most promising research, resulting in the same negative outcomes as the interventionist approach: limited support for the most promising topics, slow progress, and delayed returns. In reality, at science agencies like the National Science Foundation and National Institutes of Health, program managers navigate between these two extremes, setting some funds aside to target high-priority areas and using the rest to cover the widest possible range of meritorious proposals. This middle path seems like the most rational way to proceed, but it still requires making choices. How much should be set aside for high priority topics? What should those topics be? How should we define merit? Expert opinion can provide a helpful guide, but leading experts can have remarkably different opinions about what matters and what should be prioritized. Moreover, experts and administrators alike can be risk averse, giving an edge to high-profile, well-established concepts. What’s really needed is a data-driven framework to help guide decision-making, one that doesn’t simply repackage prestige or incumbency. Recognizing breakthroughs years in advance Many breakthroughs fail to attract funding or recognition at the earliest stages of development. Katalin Karikó, for example, spent years struggling to win grants for work on mRNA that would eventually win her and Drew Weissman a Nobel Prize. Similar stories can be told about Jim Allison’s groundbreaking studies leading to the development of cancer immunotherapy, Stan Pruisner’s demonstration that prions can self-replicate without DNA, or Robin Warren and Barry Marshall’s proof that Helicobacter pylori, not stress, causes ulcers. Yet at some point, the scientific community recognizes and responds to transformative breakthroughs like these. How does that change happen? And what if science funders could identify the areas that are on the cusp of a breakthrough? Those questions led my collaborators and me to study how scientists responded to past breakthroughs. Based on patterns in that data, we developed a means of predicting future breakthroughs, using biomedicine as a proof of concept. It’s worth taking a moment to describe how our approach works. Briefly, our prediction process begins by grouping the 18 million or so peer-reviewed papers in PubMed, the authoritative database of biomedical research, into roughly 50,000 unique topics. A small subset of these topics are so widely acclaimed as breakthroughs that they have been recognized with a major prize like a Nobel or a Lasker Award. We theorized that studying the development of these outliers over time, especially before they gained acclaim, was the key to identifying future instances of them. After defining topics, we chose 21 representative breakthroughs and asked what they looked like before they won prestigious prizes. To answer that question, we re-ran our topic-mapping algorithm repeatedly, winding back the clock one year each time, stringing together the years to follow the progress of each field as it grew and advanced. We found that in each case, the years leading up to a discovery followed a pattern: a burst of papers on a new, rapidly evolving topic, many of which quickly became influential. These characteristics—the percent of papers that are new, the percentage of papers that are brought in from other topics, and the influence of each individual paper—can all be measured separately for any given topic. Together, they act as a predictive signal that is detectable an average of five years before a transformative discovery is made, and up to thirty years before the discovery receives a major prize. Scanning the current research landscape for topics that display this signal allows us to predict what work will likely produce a future breakthrough. Each year we analyzed includes four or five signals, a number that appears to have remained constant across a twenty year time frame. In their classic work on the sociology of science, Bruno Latour and Steve Woolgar established that the desire to participate in a major discovery is an important motivator for scientists as they consider whether they should take up (or abandon) a research problem. Although further work is needed, the simplest explanation for our results is that this behavior is widespread enough to detect at scale. A foundational advance, or something like it, draws the attention of scientists and causes them to change the direction of their research. They vote with their feet, staking their own reputation and careers by publishing on and citing the new idea. That upswell of interest is the foundation of our breakthrough signal. What kind of research does our model identify as a breakthrough? After building a model based on the patterns in our 21 preselected breakthroughs, we tested it by identifying all early signals of discovery from 1994 through 1997. Of the 18 signals we found in that time period, 17 can be traced forward in time to a breakthrough. Examples include super-resolution fluorescence microscopy (see the image above), the directed evolution of proteins and enzymes (Chemistry Nobel, 2018), and sequencing the human genome (National Medal of Science, 2008). The discovery of the role of leptin signaling in obesity is a particularly interesting case study. Around 1950, scientists noticed the existence of a type of lab mouse that suffered from a unique inherited form of obesity. When given the freedom to choose their own diet, these mice ate more than normal and preferred food that was high in fat. Researchers had few clues as to why the animals overate; early experiments pointed to the existence of a soluble ob factor found circulating in the bloodstream that regulated appetite, but its identity was unknown. In 1994, Jeffrey Friedman and colleagues identified and sequenced the ob gene, noting that the protein it encoded appeared to fit the profile of a circulating factor. A year later, a trio of high-profile publications demonstrated that ob was a hormone that regulated body weight and fat deposition by regulating appetite. One of these was co-authored by Friedman, who christened it leptin from the ancient Greek word for thin. Using our model, the data from 1996 make it clear this advance would become a breakthrough. Scientists had flocked to study the biology of the new hormone, generating the flurry of new papers and citations required to produce a signal. One of these demonstrated that serum leptin concentrations reflected the amount of adipose tissue in the human body, providing clinical validation of the earlier mouse studies. Before the identification of leptin, scientists were unable to point to a specific molecule that controlled appetite and adiposity, so that it was impossible to rule out non-physiological causes of obesity; its characterization fundamentally changed our concept of weight gain. In 2010, Friedman and fellow pioneer Douglas Coleman were awarded a Lasker for the discovery. Our work also identifies breakthroughs in clinical practice and behavioral and social sciences, even though these areas are less likely to draw the attention of major prize committees. One example that falls into this category is the introduction of endovascular aneurysm repair, a minimally invasive surgical procedure for the treatment of abdominal aortic aneurysm, a leading cause of death among Americans over the age of 65. Relative to the previous standard of care, it reduces in-hospital mortality by almost four-fold. In 2017, Timothy Chuter received the American College of Surgeons’ prestigious Jacobsen Innovation award for his multiple refinements to the technique. Another under-recognized breakthrough identified by our model is the development of standardized survey instruments to assess the quality of life for HIV+ patients. The antiretroviral cocktails introduced in the mid-1990s reduced AIDS mortality, but treatment came with serious side effects. The new evaluations showed that patients whose disease progressed had worse physical functioning than Americans with other chronic diseases, while those who were asymptomatic maintained a health-related quality of life on par with the overall US population. This evidence made it possible for patients and activists to argue that even in the absence of a total cure, and in spite of the side effects, alleviation of symptoms was a worthwhile priority for clinicians. My colleagues and I have failed to identify any significant recognition for the physicians and health policy experts who pioneered this advance, although in 2016, researchers called for the World Health Organization to set targets for good health-related quality of life as part of its framework to end the AIDS pandemic. From forecasting to funding How should science funders use our predictions? Any serious discussion of what we should do with this new capability needs to begin with an understanding of its limitations. We can’t (and shouldn’t) invest all of our resources in the small number of topics predicted to produce breakthroughs. Innovative new fields are born from existing ones; they rarely arise de novo. If funders don’t maintain a diverse portfolio, the breakthroughs of tomorrow will have no antecedents. Think of this as eating the seed corn, or killing the goose that lays the golden egg. There are also multiple goals of funding beyond basic scientific discovery, including translating discoveries into clinical practice, developing new technologies, supporting economic development, and training new scientists. The investments needed to accomplish these goals are likely different from those required to support emerging breakthroughs. Finally, we must be aware that we may not capture every instance of a breakthrough. The concept is fuzzy, and not every significant discovery is recognized with a major award. Further, breakthroughs are rare, meaning that our dataset is, by necessity, small. This makes formal estimates of accuracy challenging. Although our overall success rate appears to be high, any one signal may turn out to be a false positive. We found one apparent example: the development of the third generation COX-2 inhibitors, Vioxx and Celebrex, which were much celebrated in the early 2000s as effective but non-addictive pain relievers, meets all the algorithmic criteria of a breakthrough. Five years after it received FDA approval, researchers demonstrated that Vioxx was associated with serious cardiovascular complications, and the manufacturer voluntarily pulled the drug from the market. Celebrex remains available, although like other NSAIDs, it carries an FDA warning for increased cardiovascular risk. Decision makers who were overly focused on the breakthrough potential of this research might have wasted substantial resources nurturing an area that ultimately proved to be a failure—but so would those who relied on expert opinion at the time. That said, the potential benefits of funding the next mRNA vaccine or cancer immunotherapy years earlier than we might have are large. When we can’t assume the outcome of an action, it’s worth conducting an experiment. Can targeted support for areas that are poised to produce a breakthrough increase the return on scientific investment without cannibalizing future advances? With the necessary administrative infrastructure in place, conducting this experiment would be straightforward. First, identify all the breakthrough signals between 2018 and 2023, select half at random, and commit to a decade of investment. For biomedicine, a six year window should yield about 30 topics with the potential to produce a breakthrough. Providing strong support to half of these might be accomplished for $300 million a year. That’s roughly half of the recent annual budget of the NIH Common Fund or around 20% of the fiscal year 2025 budget for ARPA-H. After five years, then again at ten, compare to see which group — the one that was actively managed, or the control — produced more breakthroughs, and on what timescale. Although we used biomedicine as an example, the same experiment could just as well be run for physics, materials science, or any other discipline. Any attempt to undertake such an experiment should keep three points top of mind. First, we can predict the topic of future breakthroughs, but the people and vision for how to move the work forward are still important. In practice, this means success requires active program managers with the expertise to develop the science and the authority to do so, including by recruiting investigators. Second, we would be wise to learn from the failures of past attempts to fund transformative research. High among these are funding modestly repackaged work, confusing the genuinely novel with the merely unfamiliar, and relying too much on incumbent investigators. All of these risks can be mitigated by thoroughly and intelligently integrating high quality data into portfolio management. Finally, we need to have patience. Even with strong funding support, producing a breakthrough takes time. If supporting breakthroughs is the best way to nurture the birth of new fields, we also need to accurately describe and properly manage the other two stages, so that we invest wisely throughout the productive lifetime of ideas and divest once they reach the point of diminishing returns. We would benefit from more study of all of these phases, especially the transition from one to another. More research could answer questions like: When does a breakthrough become an established field? How do the availability of funding, the size and characteristics of the available workforce, and the accumulation of evidence that disagrees with prevailing models affect a field’s decline? Exactly how do old ideas give birth to new ones, and can we speed up the process? The more we understand about the dynamics of scientific progress, the better we can maximize the odds that our curiosity leads to results. I’d call that finding a way to make our own luck.
Multi-wavelength spin dynamics of defects in hexagonal boron nitride
Light: Science & Applications - Different excitation wavelengths strongly influence the spin dynamics and photophysics of single spin defects in hexagonal boron nitride, enabling a threefold...
Patriots Coach Jeremy Springer Announces Engagement To NBC Sports Host Kayla Burton
NBC Sports host Kayla Burton is engaged to an NFL coach. The popular Boston-based sports media personality announced her engagement to New England Patriots special teams coordinator Jeremy Springer. The two announced their big news in a joint Instagram post Monday, sharing photos from the NFL ...
Circulating metabolites at the crossroads of lifestyle and brain health
Nature Aging - Circulating metabolites might provide early insights into brain aging before clinical symptoms appear. Ahmad and colleagues show that specific blood metabolites in midlife are...
Fermented food microbiome: influence on oral and gut microbiota, and human health
Nature Reviews Microbiology - The bioactive compounds and microbial consortia contained in plant-based fermented foods support oral and gut microbiota, host immune function and metabolic...
Multi-omics insights into floral–fruity aroma formation during Pu’er tea fermentation inoculated with a synthetic fungal community
npj Science of Food - Multi-omics insights into floral–fruity aroma formation during Pu’er tea fermentation inoculated with a synthetic fungal community
The growing concentration of national influence in global science
Nature Human Behaviour - This analysis of nearly 240 million scientific papers reveals growing concentration of discursive influence in core, resource-wealthy countries, indicating that a few...
A cloud-based miniscope for neurosurveillance of brain health and disease in freely behaving animals
Nature Methods - A miniscope with cloud-based operation and multicontrast capabilities enables continuous recording of neuronal activity, hemodynamics or cellular dynamics over multiple days. This...
Long-term health risks of e-cigarettes after smoking cessation
Nature Medicine - Real‑world evidence from a nationwide cohort suggests that using electronic cigarettes after smoking cessation may attenuate the benefits of quitting in reducing lung cancer...
Modelling the odor profile of fungal Solid-State Fermentation
npj Science of Food - Modelling the odor profile of fungal Solid-State Fermentation
Smartphones passively monitor heart health
Nature Reviews Electrical Engineering - A study in Nature uses deep learning to passively monitor resting heart rates via front-facing smartphone cameras, yielding highly accurate cardiovascular...
Chronotherapy considerations in lithium treatment for bipolar disorder
Nature Mental Health - In this Perspective, Ray and colleagues review 50 years of studies on the effectiveness and toxicity of lithium and argue that its effects depend on dosing time. They propose...
Testing longitudinal gene–environment interactions
Nature Computational Science - A gene–environment interaction test (SAGELD) is developed to speed up large-scale genome-wide analyses using longitudinal data, facilitating novel discoveries...
A competency framework for digital public health in Europe: an updated scoping review
npj Digital Public Health - A competency framework for digital public health in Europe: an updated scoping review
Team careers in science: formation, composition and success of persistent collaborations
npj Complexity - Team careers in science: formation, composition and success of persistent collaborations
Global multipolarity through the lens of network science and game theory
npj Complexity - The “Theory of Competing Networks”, which integrates network science and game theory, reveals that cooperation is more stable than hierarchy, offering insights into...
A novel belief-degree-based uncertain Tchebycheff norm DEA model for the case study of risks prioritizing in e-business projects
Scientific Reports - A novel belief-degree-based uncertain Tchebycheff norm DEA model for the case study of risks prioritizing in e-business projects
A career of taking biological species for a spin
Nature Reviews Chemistry - Ahead of his 70th birthday, Adriaan (Ad) Bax, Distinguished Investigator at the National Institutes of Health (NIH), spoke about his career in science.
Environmental roots of neonatal health: how social and structural exposures shape early-life outcomes
Pediatric Research - Environmental roots of neonatal health: how social and structural exposures shape early-life outcomes
Facilitators of, barriers to and strategies for health-system guidance implementation: a critical interpretive synthesis
npj Health Systems - Facilitators of, barriers to and strategies for health-system guidance implementation: a critical interpretive synthesis
Science for kids: a melody that mends
Pediatric Research - Science for kids: a melody that mends
A reporting checklist for large language models in behavioural science
Nature Human Behaviour - Large language models offer new opportunities for behavioural science, but their rapid evolution poses challenges for research rigour. We introduce a consensus-based...
Jays Crushed By Orioles
Orioles A Lot Blue. Jays A little. That was less than fun. Everything that mattered happened in the bottom of the fifth and the top of the sixth. The Jays scored two in the top of the fifth. With two out George Springer reached on catcher interference, yet again. They are going to name catcher […]
Sulfonamide resistance as a global one health challenge
The Journal of Antibiotics - Sulfonamide resistance as a global one health challenge
A century of innovative spirit and an optical scientist’s pursuit
Light: Science & Applications - A century of innovative spirit and an optical scientist’s pursuit
Life’s essential 8 and longevity: the sustained impact of cardiovascular health on mortality from middle age to centenarians
npj Aging - Life’s essential 8 and longevity: the sustained impact of cardiovascular health on mortality from middle age to centenarians
Mapping the avoid-ome: a systematic open-science approach to predictive ADMET
Nature Communications - Mapping the avoid-ome: a systematic open-science approach to predictive ADMET
Acute coronary syndromes across the lifespan of women
npj Cardiovascular Health - Acute coronary syndromes across the lifespan of women
A scoping review of studies on secure messaging through patient portals: persistent challenges and potential solutions
npj Health Systems - A scoping review of studies on secure messaging through patient portals: persistent challenges and potential solutions
Preserving the future of vaccine science in the USA
Nature Microbiology - Vaccines have saved an estimated 154 million lives in 50 years, yet that infrastructure is under threat. We highlight the global impact of vaccine scientists who trained in...
Glycerol is a new ally for heat production and bone health
Nature Metabolism - The mechanisms controlling activation of the futile creatine cycle are unknown, calling into question its physiological relevance. In work recently published in Nature, Hussain,...
A framework for longitudinal health AI agents
Nature Health - This Perspective outlines the main components needed to design AI models that can assist individuals in the long-term management of their health, formalizing also the shortcomings...
Axel Springer Revenue Rises 2.6% Following Business Split
The media company said it had a strong start to the year after posting higher revenue—in its first results since splitting into two businesses in April of last year.