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Invitation to the presentation of G5 Entertainment’s third quarter 2026
G5 Entertainment AB, a leading European casual mobile game developer and publisher will publish its financial results for the third quarter 2026 on Wednesday, November 4, 2026, at 07:00 a.m. (CET).At 08:00 a.m. (CET) on the same day, Vlad Suglobov (CEO) and Stefan Wikstrand (CFO) will host a presen…
Trump and Rubio plan to visit oil tanker carrying Venezuelan crude in Texas
The visit to Port Arthur, Texas, comes on the same day that Trump is campaigning in the state for Republican candidates in the midterms.
Clear your calendars, the Sports Equinox is here! How to watch every game free tonight
The 2026 Sports Equinox brings NFL, MLB, NBA and NHL action on the same day. See the schedule, TV channels and how to watch without cable.
Health Act Rally in Rochester
New Yorkers will rally on October 17 in a coordinated day of action calling on the State Legislature to pass the New York Health Act and guarantee healthcare for every New Yorker. Across hundreds of miles, New Yorkers will stand together on the same day, under the same banner, with one unified message for Albany: New York is facing a healthcare crisis.
Trump hit with second lawsuit over taxpayer-funded political propaganda ads
The lawsuit came the same day the Democratic National Committee (DNC) filed a separate challenge in Washington, D.C., seeking to stop the administration from using federal funds for the ad campaign.
Nebraska volleyball notches its 12th sweep of the season against Maryland
Over a thousand miles away from Lincoln, the No. 1 Husker volleyball team beat the Maryland Terrapins in their home arena in three sets (25-13, 25-20, 25-15) on Saturday, the same day as Nebraska’s football team beat Maryland 49-23. Sophomore setter Campbell Flynn started the game for senior Bergen Reilly for precautionary reasons, following an ankle sprain during the match against Penn State.
Kelly Clarkson recalls ‘beautiful’ moment with Dolly Parton 30 minutes after her divorce was finalized
Clarkson’s split from the late Brandon Blackstock occurred on the same day as the 2022 ACMs.
University of Pittsburgh launches health sciences startup incubator in Oakland
Pitt trustees approved the incubator on the same day the university announced a $3 billion fundraising campaign to support university initiatives further.
University of Pittsburgh launches health sciences startup incubator in Oakland
Pitt trustees approved the incubator on the same day the university announced a $3 billion fundraising campaign to support university initiatives further.
Parrot Island theft investigation ongoing, management company to end contract
On the same day Sebastian County Prosecuting Attorney Dan Shue said he is seeking a special prosecutor to investigate a Parrot Island Waterpark employee over theft allegations, it was learned...
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!
Dartmouth Health cuts 124 jobs, 300 open positions as costs rise
Dartmouth Health on Tuesday announced the elimination of hundreds of jobs, the same day that similar news came from the UVM Health Network.
1,000 baby born at UNC Health Chatham on milestone day
UNC Health Chatham recently celebrated two milestones on the same day.
Nurses to sympathy strike, picket on September 15 at five Prime Healthcare hospitals in four states
Registered nurses at three Prime Healthcare hospitals in Nevada and California, as well as nurses and health care workers at an additional hospital in California, will hold a sympathy strike for one day on September 15 in solidarity with nurses at Olympia Fields Hospital in Illinois who are striking on the same day to protest the suspension of six nurses at Olympia Fields Hospital.
Botpool Create Launches AI Website Builder Backed by Human SEO Experts for Small Businesses
The AI website builder lets small business owners describe their business in plain language, publish a live site the same day, and start capturi...
Apple Announces macOS Golden Gate Release Date
Apple this week announced that macOS 27, also known as macOS Golden Gate, will be released to all users with a compatible Mac on Monday, September 14. iOS 27, iPadOS 27, watchOS 27, tvOS 27, and visionOS 27 will be released on the same day. macOS Golden Gate features Siri AI, design improvements, performance enhancements, and more. A developer beta has been available since June, while a public beta followed in July, and now the update will be widely released on September 14.
Academy Sports (ASO) Rockets 14% on Solid Q2 Despite Consumer Spending Tests
Academy Sports and Outdoors Inc. (NASDAQ:ASO) saw its share price jump by 14.40 percent on Wednesday to close at $51.15 apiece after posting a strong earnings performance in the second quarter of the year despite weakness in consumer spending trends. In an earnings call on the same day, the sporting goods store chain said that […]
Microsoft (MSFT)’s Outlook and OpenAI’s ChatGPT Work Both Broke the Same Day
On August 31, 2026, CNBC reported that Microsoft Corporation (NASDAQ:MSFT) suffered a widespread Outlook and Exchange Online outage on August 31, with Downdetector reports peaking at 6,015 for Outlook and 2,044 for Microsoft 365. Microsoft said on its status page that “users may experience degraded functionality with various Exchange Online functionalities” while it reviewed service […]
Nurses announce sympathy strikes, pickets on Sept. 15 at five Prime Healthcare hospitals in four states
RNs at two Prime Healthcare hospitals in Nevada and California and nurses and health care workers at a Prime facility in California gave notice that they will hold a sympathy strike for one day on September 15 in solidarity with striking nurses at Olympia Fields Hospital in Chicago, Illinois. Nurses at another Chicago hospital and at a hospital in Lewiston, Maine, will hold informational pickets in solidarity with Olympia Fields RNs on the same day.
Top employees for data center company eyeing Yellowstone County made matching political donations before announcing plans
The Quantica employees gave the same amount to the same candidates and on the same days in 2024.
Colorado Sues EarnIn For “Immoral, Unethical, Oppressive and Unscrupulous” Business Practices
Hey all, Jason here. When this hits your inbox, I should be en route to the airport — not to go anywhere myself, just to pick up my better half. I must admit, it’s been strange to be home alone (well, with two dogs) for two weeks! Really lets me “optimize” to spending more time on reading/writing/working, whether that’s healthy or not! Looking forward to getting back into a (somewhat) more balance routine. Subscribe or Support by Upgrading Compare Notes With the People Who Built Modern Fintech Partner content: Remitly CEO Sebastian Gunningham. Increase founder Darragh Buckley. Linda Du, Co-founder & President at Valon. They’ll unpack the decisions behind building and scaling major financial products: what they got right, what broke, what they would do differently, and how AI and new infrastructure are changing the operator playbook. Most events tell you where fintech is going. NerdCon puts the people who built the present in the room and asks them what comes next. Expect an operator-led conversation grounded in real products, real trade-offs and lessons earned the hard way. Fintech Business Weekly readers save 20% on Regular tickets with code FBW20 before standard pricing ends September 11. Meet the Builders Things To Know & Other Good Reads Advent and Stripe Abandon $50 Billion Pursuit of PayPal (Bloomberg) Trump Family’s New Crypto Bank Is Backed by Abu Dhabi Sheikh (Wall Street Journal) The multiplying risks of financing data centres (FT) Let the Bond Market Speak (Wall Street Journal) Did we waste a crisis? Modest proposals to reform deposit insurance. (Fintech Takes Banking) Untangling Guggenheim: How Private Credit Built Its Own Universe (Net Interest) The Hater’s Guide To Circular Financing — Part One (Ed Zitron) Fiat Ventures Rebrands to FGV Capital, Announces $35M Oversubscribed Fund II (BusinessWire) Socure Announces Strategic Growth Investment at $5.2B Valuation and Acquires Agentic Operations Platform Fravity (Socure) Listen: When Consumer Protection Disclosures Work Too Well (Consumer Finance Monitor Podcast) Colorado Sues EarnIn For “Immoral, Unethical, Oppressive and Unscrupulous” Business Practices When Andreessen Horowitz led EarnIn’s $39 million Series B in 2017, Alex Rampell, a partner at the storied venture capital firm, described EarnIn’s small-dollar advance product as “free,” relying on “entirely on voluntary contributions instead of fees.” Rampell favorably compared EarnIn’s approach to typical payday loans, writing, “Payday loans historically have had the potential to be a slippery slope for consumers into financial distress: opaque systems and steep fees that are hard to repay and set individuals back further than where they started. For many Americans living paycheck to paycheck, that kind of slide into debt can be extremely difficult to recover from.” But EarnIn’s product — which, the company says, isn’t a “loan,” legally speaking — ends up being anything but free for most users. While it is technically possible to take a no-fee advance from the company, most users incur “Lightning Speed” (instant funding) fees and/or ostensibly optional tips that combined can equate to annualized percent rates that can reach over 1,000%. With an average term of 9.77 days, even fees and tips that are small on an absolute dollar basis are equivalent to high APRs on an annualized basis. A lawsuit filed last week by Colorado Attorney General Phil Weiser against EarnIn describes the company and its product and business practices quite differently than Andreessen’s Rampell; “EarnIn’s business practices are unfair because they are immoral, unethical, oppressive and unscrupulous,” the complaint, filed in District Court in Denver, Colorado, says. EarnIn’s marquee product, Cash Out, isn’t a loan, the company says, as Cash Out is not “[f]orward [l]ooking” because “[t]he money is transferred based on earnings to date,” does not carry a mandatory fee to access funds, and is “non-recourse,” meaning EarnIn cannot pursue recovery from users who don’t repay advanced funds. EarnIn is sometimes referred to as an “Earned Wage Advance” service. Though, unlike some others in the broader category, EarnIn does not integrate with employers’ payroll or time and attendance systems. Rather, EarnIn attempts to verify a user’s income and employment, by examining direct deposit transaction data in their linked external bank account, by using GPS location and Bluetooth data to estimate a user’s time spent at work, if they work in a fixed location, and/or by having a user provided a work email address. While repayment is theoretically optional, as EarnIn would have no recourse, such as reporting non-payment to the credit bureaus or a collections agency, this is anything but clear to users of the EarnIn app, Colorado’s complaint argues. During the process of taking a Cash Out, users must agree to a preauthorized debit of the amount advanced, plus any expedited funding fee and/or tips. Though users technically can revoke this ACH authorization, the fact that they have this right isn’t presented to users during the Cash Out process, but rather is included in linked terms and conditions few users are likely to actually read. And users who do wish to revoke their ACH authorization must do so three or more days before the scheduled transaction date by emailing EarnIn’s customer support. Similarly, EarnIn’s marketing claims that it carries “no interest” and “no mandatory fees” is technically true, but is not the reality for many of the app’s users, the Colorado complaint argues. Further, EarnIn has extensively marketed to users the ability to “access your pay today,” to get “instant” funds, and to get funds “the same day you work” — when, in reality, this speed of access was only possible by paying the additional, undisclosed Lightning Speed fee. The Colorado complaint highlights this contradiction specifically, noting: EarnIn tells consumers that they can ‘Make any day payday with EarnIn’ and that there no ‘hidden’ or ‘mandatory fees.’ For a consumer to access their pay on the same day requested, they would have to pay the Lightning Speed fee. In the fine print of the advertisement EarnIn discloses that ‘[f]ees apply to use Lightning Speed,’ a direct contradiction to the larger text of the advertisement, and a fact that would not be immediately understood by a consumer as ‘Lightning Speed’ is not defined nor explained. Like other small-dollar lending services, rather than explicitly charge interest, EarnIn collects a markup on the optional Lighting Speed fee, which, given the nature of the transactions, many users opt for, as well as offering users the option to leave “tips.” EarnIn leverages either Real-Time Payments (RTP) rails or push-to-card via debit rails if a user opts for the optional Lightning Speed. But while these capabilities cost EarnIn, on average, $0.075 or $0.20 per transaction, respectively, EarnIn charges users significantly more. Initially, EarnIn charged as little as $1.99 for Cash Outs up to $24. But EarnIn has hiked these fees multiple times, and now charges $4.99 for Cash Outs up to $75 and $6.99 for those over $75. According to the Colorado complaint, EarnIn leverages “dark patterns” to manipulate users and to make it more difficult to avoid tipping. Examples of these “road blocks” highlighted in the complaint include: As of 2023, in the EarnIn app, a consumer attempting to get a loan under the default settings had to make at least eighteen separate taps to complete the transaction and reduce the tip to $0. There is an alternative method to leave a $0 tip, via the “Custom tip” feature, but EarnIn made this option difficult to access by visually deemphasizing it and positioning it near devices’ “home” button. Using language and imagery such as “Tip to pay it forward,” “Your generosity supports the service + helps,” “Your tips make a difference,” and “[t]he APR for this cash out is 0%. Your tips help support us.” While Lightning Speed and tipping are, in theory, both optional, in practice, users nearly always ended up paying something. According to the Colorado suit, users were charged either a tip or an expedite fee for more than 92% of transactions, with an average APR of nearly 388%. And, although the advances are “non-recourse,” EarnIn successfully collects on nearly all transactions — 99.18%, according to the complaint, which, Colorado argues, “demonstrat[es] that EarnIn’s advances function as high cost loans, not voluntary payments.” The share of EarnIn users incurring fees — despite the product being marketed as 0% APR — is hard to reconcile with Andreessen investor Rampell’s favorable description of EarnIn vs. “opaque systems and steep fees” associated with payday loans, which, Rampell says, can be “extremely difficult” for borrowers to extricate themselves from. According to Colorado’s suit, EarnIn’s Cash Out product “trapped many consumers in extreme cycles of high-cost reborrowing.” The suit gives an example of one consumer in the state, who took out a whopping 1,151 loans, paying a total of $4,038.50 in Lightning Speed expedited funding fees on loans that averaged the equivalent of 1,421% APR. Another Colorado user, the suit says, took out 1,033 loans, paying $8,561.22 in tips and fees at an average APR of 1,539% . These types of usage patterns demonstrate “the severe and repeated financial harm caused by EarnIn’s illegal lending model,” the Colorado complaint argues. In aggregate, from January 2023 through July 2025, EarnIn extended 3.1 million loans to 56,778 users in Colorado — an average of nearly 55 transactions per person during the time period. EarnIn lent more than $300 million and collected more than $16 million from Colorado users in Lightning Speed fees and in tips. Ultimately, the Colorado complaint argues that “EarnIn’s Cash Outs are loans under Colorado law, and EarnIn’s contention that its product is not a loan based on its disclaimer of any legal repayment obligation lacks any real-world significance given how the Cash Out transactions actually operate in practice.” Colorado further argues that “EarnIn was not working with companies here in providing consumers with funds but acted as a third-party lender and charged illegally high rates, used deceptive design strategies to extract some charges, and trapped consumers in repeat borrowing. Colorado will continue to stand up for consumers and hold companies accountable when they violate our credit laws or attempt to evade them through misleading practices.” Colorado’s suit alleges EarnIn assesses finance charge in excess of that permitted by Colorado law, that EarnIn failed to make legally required disclosures to Colorado borrowers, that EarnIn made supervised loans to consumers without obtaining the requisite license, that EarnIn violated the state’s Deferred Deposit Loan Act, that EarnIn engage in unfair and deceptive trade practices, and that EarnIn made false or misleading statements concerning price. The suit seeks a court order enjoining EarnIn from violating relevant Colorado laws, for EarnIn to refund amounts charged in excess of state law, fees, costs, and penalties, and other relief the court deems to be just. Fintech Business Weekly is made possible by the generous support of paying subscribers — bringing you independent analysis of banking, fintech, and crypto without fear or favor. You can support my work by becoming a paying subscriber if you aren’t already. Paying subscribers enjoy: access to the entire archive of nearly six years of newsletters extended versions of the weekly newsletter, with additional content and analysis and (for founding member tier) quarterly personal 1:1 fintech Q&A / strategy calls with me, tapping into my unparalleled knowledge of the intersection of banking and fintech and experience working in the sector, including helping to launch Goldman Sachs’ retail bank Marcus Support Fintech Business Weekly You can also support Fintech Business Weekly by sponsoring a newsletter or podcast, putting you in front of 93,000+ of the most influential decision makers in banking, fintech, and crypto. Learn more about sponsor opportunities or request a media kit by dropping me an email. Q2 Quarterly Banking Profile: Five Quick Takeaways The FDIC released its quarterly banking profile, which is always worth a read, for the second quarter. The profile provides an aggregate snapshot about the overall health of the 4,238 institutions whose deposits are insured by the FDIC, who, collectively, hold more than $26 trillion in assets and saw a total net income of more than $90 billion in the second quarter. Net income was up 12% vs. Q1 2026, with lower provisions and realized gains on securities helping to drive the improvement, somewhat offset by increases in non-interest expense and applicable income taxes. Overall net interest margin was basically flat quarter over quarter (up 1 basis point vs. Q1 2026), though community banks saw NIM grow by 10 basis points, to 3.81%. Unsurprisingly, given that interest rates haven’t changed, unrealized losses on securities changed little, edging up slightly to $326.7 billion vs. $325.1 billion in Q1 2026. Aggregate credit quality has remained benign, with asset quality actually improving slightly in Q2. Overall, institutions reported a 9 basis point decline in their past-due and non-accrual rate, to 1.44%. Net charge off rate also declined by 2 basis points, to 0.57%. And finally, the FDIC’s deposit insurance fund stands at $161.1 billion, making the DIF reserve ratio 1.48% — above the statutory minimum of 1.35%, but still below the designated target reserve ratio of 2%. [Paid Subscriber Exclusive] Nissan Withdraws ILC App, State Associations Plan BankChain Alliance, FDIC Defines “Unsafe or Unsound Practice” For First Time Automaker Nissan withdrew its application to charter a Utah industrial loan company and its corresponding application to the FDIC for deposit insurance last week, on August 19th. Nissan’s application had been outstanding for more than a year, as it originally applied in June 2025. Recent applications to the OCC to form national banks have been decisioned, one way or the other, substantially more quickly than has historically been the case.
Radiologists urge CMS to quash proposal that would slash pay for 2nd services delivered in same visit
Outlined in the 2027 proposed physician fee schedule, Medicare wants to pay 50% of full price in certain scenarios where patients visit an office and receive a second service on the same day.
Nintendo announces new Switch 2 bundles featuring Nintendo Switch Sports Resort, Mario Kart World
Nintendo just unveiled a pair of new bundles, which will feature Nintendo Switch Sprots Resort as well as Mario Kart World. They will release in late September and October 22 respectively. Europe will also be getting the Nintendo Switch Sports Resort bundle on the same day. Both will include the system, a copy of the game, and a 3-month subscription...
Lucky Strike Entertainment to Report Fourth Quarter and Full Year 2026 Financial Results on August 27, 2026
RICHMOND, Va., August 20, 2026--Lucky Strike Entertainment (NYSE: LUCK), one of the world’s premier operators of location-based entertainment, will report financial results for the fourth quarter and full year fiscal 2026 on Thursday, August 27, 2026, before the U.S. stock market opens. Management will discuss the results via webcast at 9:00 AM ET on the same day.
How to watch several sports on the same day?
The 2026 Dakar Youth Olympic Games will take place from 31 October to 13 November 2026 in Dakar, Saly and Diamniadio, across eight different competition venues. Despite this, it will still be possible to attend several sporting events on the same day. Here’s how.
User-made maps from Steam top-seller Meccha Chameleon have been found to contain malware, just as the game's official Discord server got hacked
Meccha Chameleon's Discord was breached on the same day some of its Steam Workshop maps were discovered to contain malware.
'Apple Upgrade' Program Launching on Tuesday
In his Power On newsletter today, Bloomberg's Mark Gurman reiterated that a new "Apple Upgrade" program is set to launch in the U.S. on Tuesday, July 28. Apple will stop accepting iPhone Upgrade Program sign-ups on the same day, but he said customers who are already enrolled will be able to remain in the program "for the time being". Apple is partnering with buy-now, pay-later company Klarna on the program, he said.
Country star Waylon Wyatt says he nearly 'died' after sudden health crisis derailed his Grand Ole Opry debut
Waylon Wyatt shared a video from his hospital bed after announcing his sepsis diagnosis on the same day his debut album "Dustpiles" dropped.
The Morning Show heads off into the night.
Apple announced that one of its biggest early hits will be coming back for a fifth and final season in 2027. The Morning Show premiered alongside Apple TV as one of its prestige plays, with a notably star-studded cast. The news comes the same day Apple announced a new dating series. [Image: https://platform.theverge.com/wp-content/uploads/sites/2/2026/07/072226_The_Morning_Show_Fifth_Final_Season_Announcement_Big_Image_02.jpg?quality=90&strip=all]
Shaky political "science": breakdown of the peer review process
[This article is the latest in our “Shaky Political Science” series. In our 38 page report, “Shaky political ‘science’ misses mark on ranked choice voting,” released in December 2025, we examined over 40 studies focused on ranked choice voting (RCV). Many of these studies suffered from puzzling research methodologies, poorly constructed surveys, unrealistic simulated elections, cherry picked data and faulty analyses that often were contradicted by results from real-world elections (a link to our full paper is here). Here are links to the first four articles in our series: the first one summarizing the overall results of our report; the second one analyzed a study co-authored by University of Minnesota academic Larry Jacobs, which was one of the most error-prone of all the research we reviewed in our report; a third article showing an admirable example of two political scientists holding two other political scientists accountable for their flawed, sloppy research; and a fourth article that analyzed seven studies sponsored by New America’s political reform program that deployed flawed methodology based on “simulated” elections instead of real-world elections, which when combined with some basic misunderstandings of RCV, produced odd and misleading results]. In this fifth article in our “Shaky Political Science” series, we examine two flawed studies on the impact of RCV on voter turnout authored by San Francisco State University professor Jason McDaniel. McDaniel’s research in these two papers stands out for its puzzling methodology, inadequate and cherry-picked data, and faulty analyses that not only misunderstands ranked choice voting but reflects a basic misunderstanding of politics in general. The first of the McDaniel studies actually passed through a peer review process and then was subsequently published by the Journal of Urban Affairs, despite its obvious errors. It is perplexing that a jury of peer political scientists did not catch the errors and send it back to McDaniel for a rewrite or outright rejection. This calls into question the credibility of the peer review process itself. The peer review process is supposed to represent the intellectual pinnacle of the profession – a way for academics to certify the quality of each other’s work, and to act as a check against sloppy research. In the case of the McDaniel study, the peer review process failed. Yet his flawed study has been cited dozens of times by other political scientists in their own flawed works — an unvirtuous circle of academic research. Here is the first Jason McDaniel study that we analyzed: “Writing the Rules to Rank the Candidates: Examining the Impact of Instant-Runoff Voting on Racial Group Turnout in San Francisco Mayoral Elections,” by Jason McDaniel, 2016, Journal of Urban Affairs. This study covered San Francisco’s use of ranked choice voting (RCV) in mayoral elections, purporting to show that RCV reduces voter turnout overall, increases voter errors, and has a negative impact on “marginal populations,” i.e. racial minorities, mainly as the result of the alleged complexity of RCV asking voters to rank candidates and having to be familiar with multiple candidates and related dynamics. McDaniel reached these conclusions by examining five San Francisco mayoral elections taking place in odd years (i.e. where there are no federal or state races on the ballot) from 1995 to 2011, comparing three earlier non-RCV races (in 1995, 1999 and 2003) with the later two RCV races (in 2007 and 2011). Only two RCV races were studied, and as McDaniel acknowledged, only the contest in 2011 was “competitive” (that contest in 2007 was the first mayoral election using RCV, and incumbent mayor Gavin Newsom won easily in the first round of counting, earning 74% of first choices and far ahead of the second place candidate who had just 6.3% of first choices). Yet even the allegedly competitive contest in 2011was won by an incumbent with a landslide margin of nearly 20 points. No other elections were studied by McDaniel, even though San Francisco elects 17 other offices besides mayor with RCV, including the 11 seats on the Board of Supervisors (the name for the city council) in even years, and six other citywide offices in odd years. Previous academic research has found that competitive elections can stimulate greater voter involvement and higher turnout. For unexplained reasons, this study ignored at least 34 other RCV races between the years 2004 to 2011, many of which were very competitive and had high voter turnout. Instead, McDaniel’s analysis based its conclusions on data from only a single, marginally-competitive mayor’s race using RCV. These 2007 and 2011 mayoral elections were the only two RCV elections that McDaniel used as the sole basis for his small data set to determine the impact of RCV on voter turnout. Despite having no meaningful data to inform his study, McDaniel nonetheless concluded that RCV lowered turnout when compared to the previous three mayoral non-RCV elections using two-round runoffs. Then, using the same inadequate database of San Francisco elections, McDaniel also found that “Instant-runoff voting is associated with a significant decline in Black voter turnout and White voter turnout…On average, Black voter turnout decreases by 18 points and White voter turnout decreases by 16 points …” But how could he credibly arrive at that conclusion when his study was based on voter turnout in two RCV contests that were not even remotely competitive? Beyond that, McDaniel tacitly admits that the key determining factor driving Black voter turnout was not RCV vs non-RCV elections, but the presence of a viable Black candidate. McDaniel wrote, “Black voter turnout was about 17 percentage points higher for the two elections that featured an African American candidate compared to the three elections that did not” — but one of those three elections did not use RCV. The same dynamic was present for White voter turnout, in which higher turnout was not correlated with RCV vs non-RCV elections, but the presence of a viable White candidate. Beyond that, McDaniel’s voter turnout results, whether correlated with the race of the candidates or RCV vs non-RCV elections, showed no clear patterns. In his Figure 1, he shows low Asian voter turnout in the non-RCV elections in 1999 and 2003 when there was no viable Asian candidate running, but in the 2007 mayoral election using RCV, there is still no viable Asian candidate, yet Asian turnout increases dramatically. For Latinos, voter turnout is virtually the same in 1999 (no viable Latino candidate, non-RCV elections), 2003 (leading Latino candidate, non-RCV elections), 2007 (no viable Latino candidate, RCV elections) and 2011 (leading Latino candidate, RCV elections). Meanwhile White voter turnout increased dramatically from a low in the 2007 mayoral election, in which the leading candidate was a white incumbent, to the 2011 election in which there were no leading white candidates that finished any higher than sixth place with 5.6% of the vote. Clearly other factors were determining voter turnout than either the race of the candidates or RCV vs non-RCV elections. Despite these inconsistent findings, McDaniel boldly marches forth to render conclusions about the impact of RCV on different groups of racial voters, even though his conclusions are not remotely supported by his data. This looks like the worst kind of “data cherry picking” to cover up a puzzling conclusion based on a poor data set involving only two RCV elections, neither of which were remotely competitive. McDaniel’s cherry picking didn’t stop there. In calculating voter turnout for the three non-RCV mayoral elections in 1995, 1999 and 2003, he failed to mention that in each of these years there was a two-round runoff election, with a first round election in November in which no candidate garnered a winning majority of the vote, followed by the decisive runoff in December. So there were two non-RCV elections in each election year. But McDaniel only decided to include one of the two elections in his data set, apparently the December runoff in each year (which had higher turnout in two out of the three mayor’s races, and lower turnout in the third). So he ignored the November 1995 election, the November 1999 election when turnout was only 45 percent, and the November 2003 election when turnout was only 45.7 percent. This is flawed methodology based on blatant cherry picking, since McDaniel simply omitted three elections worth of data that did not fit within his frame. His study should never have made it through a peer review process, and it is an embarrassment to political science that not only was it approved for publication, but it has been uncritically cited by dozens of other political scientists. Share A “study” without wider context or credible data Lacking a data set that included even a single competitive RCV mayoral race, McDaniel declared that RCV lowered voter turnout in 2011, when turnout was 42.5 percent. But he also ignored -- or did not realize -- that voter turnout in mayoral elections during 2011 had sharply declined in all big cities, including far more steeply in nearby Los Angeles and other cities. In fact, San Francisco’s election in 2011 had the second highest turnout of any mayoral election in the nation’s 22 largest cities from 2008-2011. Was 2011 just a particularly low turnout year all across the country, perhaps because the country was still reeling from the aftermath of the housing market collapse in 2008-2010? McDaniel ignored this factor and did not search for alternative explanations beyond his flawed thesis. Furthermore, while McDaniel suggested that RCV leads to more “overvotes” – which is an invalidated ballot in which a voter, in either a non-RCV or RCV race, makes a mistake and selects two candidates for a single choice – he failed to address that in the June 2012 US Senate primary contest, which used the typical “one-choice” plurality method and did not use RCV, there was more than five times as many overvotes in San Francisco and Oakland than in the RCV mayoral elections in those cities. Additionally, turnout had been significantly higher and more representative in RCV elections for the San Francisco Board of Supervisors races -- that McDaniel didn’t even bother to study -- as compared to previous “delayed two-round runoffs” for those offices, often higher by as much as 40% in the RCV races. Perhaps the most surprising irony was that an earlier study co-authored by McDaniel (Overvoting and the Equality of Voice under Instant-Runoff Voting in San Francisco by Francis Neely and Jason McDaniel, 2015, California Journal of Politics and Policy) found that overvotes are often more common in lower-income precincts, but that the pattern of overvoting is similar in both RCV and non-RCV contests. Wrote Neely and McDaniel: “[Overvote] errors appear to be a function of complexity in general and not IRV per se…. the pattern of overvoting is similar in both IRV and non-IRV contests…Even among votes cast in a top-of-the-ticket race like U.S. Senate, overvoting occurs in an uneven fashion” (IRV, or ‘instant runoff voting,’ is another name for ranked choice voting in single-winner contests). So McDaniel not only contradicted his own conclusion in his earlier study with Neely, but the earlier study had been an important corrective for one of the most frequently made mistakes made by anti-RCV political scientists – a continual failure to provide crucial context by pairing a comparative analysis of real world RCV elections with an analysis of real world plurality or other electoral methods. Unfortunately McDaniel’s earlier study does not get cited much by other researchers, and McDaniel did not even cite it in his own flawed study analyzed above. Incredibly, McDaniel cherrypicked McDaniel, even as his flawed study has been cited dozens of times. Despite being a textbook example of poor political “science,” featuring both inadequate as well as cherrypicked data, the shelf life of McDaniel’s study has been extended endlessly by other political scientists and media outlets who have cited it uncritically. Equally troubling, the Journal of Urban Affairs put this paper through its peer review process and published it despite its numerous flaws. McDaniel gets it wrong, Part II Not content with producing one shoddy, confused paper, McDaniel produced another one a few years later, here is the link: Electoral Rules and Voter Turnout in Mayoral Elections: An Analysis of Ranked-Choice Voting by Jason McDaniel. San Francisco State Univ. (McDaniel, 2019). In this paper, McDaniel extended his previous flawed research focused on San Francisco, about voter turnout and voter impact, to multiple RCV cities through the 2018 elections. In his data set he included San Francisco, Minneapolis, St Paul, Oakland, Berkeley, San Leandro and Santa Fe. He then compared their turnout in mayoral elections to non-RCV cities, and to mayoral elections in those cities held before first RCV usage, and concluded “a significant decrease in voter turnout of approximately 3–5 percentage points in RCV cities after the implementation of RCV.” But he replicated many of the same mistakes as his first study presented above. While in this study McDaniel attempted to distinguish between various factors, such as odd year vs. even year elections, competitive races vs. noncompetitive, contests with open seats and other factors, the analysis is superficial and inadequate. He attempts to incorporate so many often-conflicting variables that his conclusions are once again unsupported by the actual data, and ultimately unconvincing (this second study never underwent a peer review process). For example, McDaniel declared that for RCV cities “in elections that are more competitive than average (margin of victory less than 20%), there is no significant RCV effect on voter turnout.” Yet McDaniel is a professor in a city, San Francisco, in which the 2018 special election using RCV to fill a mayoral vacancy was exceedingly close, decided by a margin of only one percentage point, and resulted in the second highest turnout in San Francisco history for a mayoral election. Illustrating the complexity involved in assessing the critical factors in turnout, that special election was held on the same election day as federal and state offices, which tends to boost turnout. Yet more voters participated in that RCV mayoral election than voted on the same day in the non-RCV primary for governor in which former San Francisco mayor Gavin Newsom was running. Even more puzzling, in this study McDaniel inaccurately cited his own previous study from 2015 with Neely. McDaniel wrote, “Neely and McDaniel (2015) examine individual ballot image files within a natural experiment, and find higher rates of ballot errors in RCV elections compared to non-RCV elections” (emphasis added). However, we quoted from that 2015 study several paragraphs above, and in actual fact Neely and McDaniel actually concluded the opposite -- that the pattern of overvoting – which is the most common form of ballot error – is similar in both RCV and non-RCV contests. McDaniel also ignored the fact that in Bay Area RCV elections held in even years – at the same time as federal and state elections, when turnout is highest -- the “undervote” (that is, the dropoff in ballots cast for governor or mayor) has declined, which contradicts his thesis that the act of ranking ballots in RCV elections is too complex and presents a barrier for certain demographics of voters. To his credit, McDaniel actually acknowledged another contradiction in his own paper, which found a negative RCV turnout effect in some odd-year elections but not others, admitting that “may present a challenge to the theory that increased complexity [from RCV] will have a marginal negative effect on voter turnout.” But he tried to wallpaper over that contradiction by blaming it on an unlikely source -- bad election administration. McDaniels wrote, “This would suggest that the negative impact of RCV on voter participation may be alleviated by high quality election administration,” without presenting any data or even anecdotes to support such an unfounded theory. While competent election administration of any election, whether RCV or non-RCV, is crucially important, there is no credible research that supports the far-fetched notion that the quality of election administration has much of an impact on voter turnout in US elections, whether using RCV or otherwise. Upgrade to a $5 subscription The unvirtuous circle of academic research It is puzzling how McDaniel’s first study could have survived the peer review process for the Journal of Urban Affairs. The mistakes in McDaniel’s first study above should have been obvious to anyone with a basic understanding of ranked choice voting as well as elections in general, and elections in San Francisco specifically. It undermines faith in the credibility of the peer review process to think that a jury of peer political scientists did not catch the egregious errors and reject McDaniel’s sloppy research and paper. And it remains a black mark on political science that this thoroughly discredited study has been cited dozens of times by other researchers. Indeed, in our research paper it became apparent that many political scientists cite each other’s flawed research to pad their list of citations and to legitimize their own flawed research, creating an unvirtuous circle of political science. More broadly, McDaniel’s voter turnout studies, like other studies evaluating RCV’s impact on turnout, lacked a recognition that no credible opinion or research can claim that RCV increases voter turnout merely because so many voters are excited to vote for their favorite candidates instead of “lesser evil” candidates without worrying about spoilers. Instead, with respect to turnout, the advantage of RCV has always been that, in most places where it has been used, it has eliminated a pre-November primary or post-November runoff, which usually saw about half the turnout of the November general election. This is especially true during even years when there are state and federal elections on the ballot which substantially drive turnout more than local contests. Any study that does not adequately account for this non-November primary/runoff vs November general election dynamic, or odd year vs even year election factors, is simply not credible. Both of McDaniel’s studies largely failed on those grounds. Steven Hill and Paul Haughey Share Upgrade to a $5 subscription Thanks for reading DemocracySOS…your digital portal for the pro-democracy movement. Subscribe for only $5 per month to receive full benefits and to support our work. Subscribe
Windows 0-day drops the same day Microsoft releases record number of patches
HiveLegacy is a "powerful primitive" that's likely capable of other nefarious actions.
Minerals at the Negotiating Table: Resource Politics and Peace in Eastern DRC
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From The Sports Desk: Four World Cup games, four tightly contested draws on Monday
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A UAP Science Advisory Council to the U.S.
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Happy .500 de Mayo! Dbacks 9, Pirates 0
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Dodgers May Abandon Shohei Ohtani’s Pitching-Only Policy
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Florida legislature approves new congressional map intended to boost Republicans in midterms
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Trump wants to turn Fed into another 'political animal': Sen. Warren
The Senate Banking Committee will vote on Wednesday, April 29, over whether to confirm Kevin Warsh's nomination as the next chair of the Federal Reserve. This comes on the same day as Jerome Powell's last FOMC meeting as the head of the US central bank. Senator Elizabeth Warren (D-Ma.) — the ranking member of the Senate Banking Committee — speaks with Yahoo Finance Senior Fed Reporter Jennifer Schonberger about her questioning of Warsh, the likelihood that Powell will stay on the Fed's Board of Governors, and her perspective on President Trump's economic agenda.
What a long, strange weekend it's been for Boston sports fans
Alex Cora was fired as manager of the Red Sox on Saturday, the same day as Patriots head coach Mike Vrabel was absent from NFL draft following a scandal.
What a long, strange weekend it’s been for Boston sports fans
Alex Cora was fired as manager of the Red Sox on Saturday, the same day as Patriots head coach Mike Vrabel was absent from NFL draft following a scandal.
Xbox Game Pass Announces 3 Day One Games All Coming the Same Day
Xbox Game Pass has announced three day one games all coming on the same day, the biggest dump of day one games so far this year.
“AI polls” are fake polls
A few weeks after Donald Trump’s second presidential win, I took the train up from London (where I was living at the time) to Oxford to attend a conference on polls and forecasts of the 2024 election. Most of the attendees were pollsters or academics, but I also watched presentations from Aaru and Electric Twin, two companies that do what is interchangeably called synthetic sampling, silicon sampling, or synthetic audiences. Stripped of startup jargon, that means they use large language models (LLMs) to simulate responses to public opinion polls by having AI agents take on the role of survey respondents. I had already heard of Aaru thanks to some articles with eye-catching headlines like “No people, no problem: AI chatbots predict elections better than humans” in the months leading up to Election Day. The founders were making some big, some might even say far-fetched claims, such as: “within two years, we will simulate the entire globe — from the way crops are grown in Ukraine to how that impacts production of oil in Iraq, trade through the strait of Malacca, and elections for the mayor of Baltimore.” When Semafor asked Aaru’s cofounders — Cameron Fink and Ned Koh — about my boss, they said “we respect all those who came before us.” Nate (as he so often does) shared his thoughts on Twitter: Fink and Koh were relatively good-natured about this back-and-forth when we spoke at Oxford. They even offered to mail me one of the t-shirts featuring Nate’s quote they apparently had made. I never took them up on the offer, which I now somewhat regret. These synthetic sampling companies fell off my radar for a while, but they do still exist. In fact, Aaru recently received a $1 billion valuation. Is what they’re doing anywhere close to the most important frontier in AI development? Not by a longshot, especially when Anthropic just developed a model so adept at exploiting software vulnerabilities that it’s only being released to 40 companies. Still, silicon sampling is increasingly finding its way into public polling. Axios reported in March that “a majority of people trust their own doctors and nurses” based on findings from Aaru — without mentioning that the “people” in that sentence were actually LLMs. Around the same time, the Public Sentiment Institute “boosted” their online sample of 373 real survey respondents with 114 AI agents.1 (Spoiler alert: even the co-founder of Electric Twin doesn’t think that’s a particularly defensible approach.) Polling companies like Qualtrics and Ipsos are also developing synthetic data panels. So, what should we make of these … “polls”? Let’s get one thing out of the way: whatever they are, they’re not polls in the way that term is usually defined. Subscribe You can’t replace polls with AI On one hand, using LLMs to essentially make up fake survey respondents sort of sounds like the dumbest idea ever, one that will at best imperfectly replicate real polls while introducing all sorts of biases. On the other hand, with LLMs improving at a remarkable, perhaps even alarming rate, maybe that means I’m a dinosaur at the ripe old age of 24 because I still want to rely on polls that talk to actual people. I’m not going to argue that synthetic samples are completely useless. In fact, as I’ll return to later, there is evidence that some techniques can replicate topline survey results quickly and cheaply. But the marketing from certain companies can be slightly optimistic. “No traditional poll will exist by the time the next general election occurs,” said Fink in 2024. We’re just 206 days away from the midterms, and based on the fact that I still have to collect a bunch of polls every day, I’d say he should have run that prediction by a sample of AI agents before the interview.2 To see why synthetic samples can’t replace polls, here’s a quick primer on how they work. The simplest version of these models involves taking a LLM (like ChatGPT or Claude), giving it a demographic profile (e.g., a white, college-educated woman who lives in Utah and makes $70k a year), and then asking it to respond to a survey question. You repeat that process a few thousand times using different demographic profiles and end up with a sample of synthetic survey responses. The actual models used by private companies are more sophisticated than this, usually because they incorporate more hypothesized demographic characteristics for each agent and provide them with extra information. Aaru, for example, feeds agents a diet of news and information they’d be likely to consume, while Electric Twin incorporates their customers’ proprietary data about the audience they’re trying to replicate. The way Ben Warner, the co-founder of Electric Twin, explained it to me was “we have a large amount of data on […] for instance, 5,000 people. Can we make an accurate prediction of how they would respond to another question?” Still, it should be obvious why synthetic samples can’t replace polls. Polling is fundamentally a data collection process. We might use surveys to make predictions by feeding them into election forecasts, but the main purpose of a poll isn’t prediction, it’s gathering new data about what people think and how they feel. Silicon sampling, on the other hand, produces no new data. It’s simply a model: you input LLM training data, demographic prompts, and a bunch of other information, and it spits out a prediction for what a poll would say. We love models here, but models aren’t polls. That difference is an important philosophical sticking point for most pollsters I talk to. “I think politics should stay away from [synthetic sampling], because we’re trying to […] represent the voice of the people,” said Natalie Jackson, a vice president at GQR Insights. Democratic pollster John Hagner told me: “I think I’m just incredibly skeptical of this idea. I don’t think it’s research. At that point, you’re asking the machine to tell you what you already believe.” Hagner has seen some presentations of early synthetic sampling experiments, but so far, “if it’s being used in a campaign, people are keeping it incredibly quiet.”3 But Eli, I hear you saying, aren’t polls themselves increasingly governed by modeling decisions? Indeed they are: pollsters’ choices on which sampling method to use, how to define their likely voter models, and how to weight their samples can and do lead to dramatic differences in the results they publish. Aaru even referenced these limitations in the methodology statement included with that maternal mortality “poll” — although I’m using the term “methodology statement” loosely here, because it doesn’t really explain how the model works at all. We can ignore the (frankly preposterous) implication that synthetic sampling isn’t subject to a separate set of biases. The important point is that there’s still a meaningful difference between using weighting and other statistical techniques on actual polling data and using a model to predict what a poll would say. The latter is far closer to election forecasts or techniques like MRP — potentially useful models, but not a replacement for polls.4 To be fair, other synthetic sampling companies are perfectly happy with the distinction between polls and models. Warner compared polling and synthetic sampling to different tools in a toolbox. “The mistake I think we make is we think that these new tools should either work in exactly the same way or somehow replace these old tools,” he said. “Rather than thinking of it as, okay, so we’ve always had the hammer, we’ve always had the screwdriver, now we’ve got a saw. But don’t use a saw to try [to] do the job of a hammer.” A quick comment from Nate Eli didn’t ask me for a comment — rather rude of him, don’t you think? But since I’m editing this story, I figured I’d add a few quick thoughts rather than putting words in his mouth. Beyond the frequently misleading marketing, what bothers me about the AI “poll” hype is that as AI tools make statistical inference cheaper and/or better (note that these are not synonyms) that actually increases the comparative value of collecting original data. You might be able to train a model to make a reasonable estimate of what some hard-to-reach poll respondent would say — say, a young Black man who voted for Trump. (Such a person checks a number of boxes for a voter who is usually hard to reach in surveys.) Indeed, this is closely related to what models like the Silver Bulletin forecast already do. They essentially smooth out the kinks in noisy survey data by making inferences based on past voting patterns or national polls or surveys of other states. But you don’t actually know what these voters think unless you’re reaching them directly. If there’s a shift in opinion among this subgroup, you’re not going to detect it. So if I were running a campaign, I’d invest more in going the extra mile to find a representative sample of those voters. And then I’d hire some smart quants — or Claude? — to figure out the implications for campaign strategy based on proprietary data that my competitors didn’t have access to. -Nate Silver Are these models any good? If synthetic surveys are just a new type of model, the next obvious question is whether the models are at least accurate. The answer very much depends on who you ask. On one end of the spectrum, you have the maximalist argument that synthetic sampling is more accurate than actual polls. “It’s an incredibly challenging problem to go to someone and say ‘hey, we’re going to be more accurate at predicting human behavior than you, even when you talk to your customers directly’,” Koh recently told CNBC. In his view, synthetic sampling isn’t a saw to polling’s hammer, it’s “magic.” There’s certainly evidence that synthetic samples can replicate certain survey toplines. But if Aaru does have any examples of their approach outperforming the polls, they’re keeping those to themselves.5 Aaru’s 2024 election model, for example, had Kamala Harris leading in Michigan, Nevada, Pennsylvania, and Wisconsin on November 4th. And although they’ve since taken down their forecast page, they gave Harris a 50.5 percent chance of winning the race on November 2nd.6 After the election, Fink told Semafor he was happy enough with those results because they were “within margin of error,” a term that is completely meaningless when applied to a “sample” of AI agents. And of course, Aaru says their models have improved since 2024, so supposedly now they’d be more accurate than the polls? Still, their stronger argument is on cost: “We are significantly faster and cheaper than traditional polling, and still more accurate,” said Fink. The first two claims are undeniably true, but the third brings us to the opposite end of the spectrum. Both Jackson and Hagner are skeptical that these models are reliable for anything beyond replicating common survey toplines. “I just […] don’t think the machines are what we want when we’re looking for nuanced views. My example on this is people in Arizona and Nevada in 2024 who voted for Trump and voted for expanding abortion in their states on ballot initiatives,” said Jackson. Hagner identified another issue. Maybe the synthetic respondents, like sycophantic LLMs, are inhuman in one important way: they’re too nice. “The reports that have come through at the meetings that I’ve been at are that the early experiments on this, they cannot get respondents to be as racist or sexist or, frankly, as negative as human respondents,” he said. Academic research mostly agrees on this point. While there are some papers that show promising results when using LLMs to replicate polling data, most show that LLMs suffer from various quirks like producing too few “don’t know” responses and can seriously overpredict the favorability of politicians like Donald Trump and Kamala Harris. They also seem to struggle with too little variation between demographic subgroups, so the difference in predicted opinion between Democrats and Republicans, for example, is too small. When I asked Warner about these studies, his response to these papers was that just because academics can’t get synthetic sampling to work doesn’t mean that the technique doesn’t work in general. “Actually, the argument is, okay, yours does not [work]. That does not mean […] for this complex set of machinery, which uses a lot of investment, a lot of time, a lot of money, you can’t get it to work.” Cards on the table, I’m somewhat sympathetic to this argument because academics aren’t exactly great at making election forecasts. Usually, the people with skin in the game are the most accurate. Warner’s argument is that the approach Electric Twin takes — which includes, for example, making multiple predictions for each synthetic respondent using different models and prompts and subsequently averaging those to get a final prediction in a sort of ensemble forecast — produces better results than the simpler academic models. Warner shared a comparison between his method and the method from a recent academic paper with me, and Electric Twin was indeed able to get more accurate replication. But even still, he acknowledged that synthetic sampling “is not a crystal ball.” “If you asked me, do I think using other data sources will be more accurate than asking somebody who they will vote for, I would probably say no. But if you asked me ‘would your system be useful for our turnout modeling today?’ I would say yes.” For better or worse, it looks like the method is already getting more popular in the market research world. Most of the clients Aaru touts these days are businesses like EY and McDonald’s. And AI will probably begin to pop up in other parts of the political polling process. Pollsters are already using it to code open-ended survey responses, and some firms, like YouGov, are testing using LLMs to ask survey respondents questions. More worryingly, one danger to actual polls is that AI agents can be used to infiltrate online surveys. Most online polls use various checks to prevent that from happening, but there’s conflicting evidence on how effective those filters are and how prevalent AI agents currently are in online panels. If those agents ever become impossible to detect, it might spell the end of online polling, but the solution isn’t to replace all of your respondents with ChatGPT. Silver Bulletin is a reader-supported publication. To receive new posts and support our work, consider becoming a subscriber. Subscribe 1 That particular poll obviously doesn’t meet Silver Bulletin standards for aggregation. But we exclude all Public Sentiment Institute polls from our averages because we classify them as an amateur polling firm. 2 You could argue that Fink meant the next presidential election, but (a) I’m also confident we’ll still have real polls in 2028 and (b) in that case he should have asked an LLM to define “general election.” 3 Quick caveat: that’s reporting from a Democratic pollster. It’s possible that Republicans are more willing to use AI in political campaigns. 4 Indeed, Silver Bulletin does not include “polls” produced by MRP in our forecasts or averages, and we think it’s extremely misleading when their practitioners describe them in a way that suggests original data had been collected among a large number of states or Congressional districts. 5 A recent report from Aaru and EY did show two examples of a synthetic estimate being closer than a survey to a real-world benchmark — but I’d take those findings with a grain of salt because the report reads more like an ad and didn’t involve any sort of prediction being made ahead of time. 6 For comparison, our odds for Harris on the same day were 48.2 percent.
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