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Microsoft AI CEO: Vibe coding is making software easier to replace
Microsoft AI CEO says vibe coding is lowering the barrier to building apps, raising questions about how defensible software will be.
Buffalo Sabres’ Zach Benson’s U23 Ranking ‘Indefensible,’ Prospect Expert Says
Corey Pronman of The Athletic dropped an August bomb in the hockey world with the surprise of Zach Benson's U23 ranking. Benson landed behind players that he almost certainly should not have been ranked behind. The post Buffalo Sabres’ Zach Benson’s U23 Ranking ‘Indefensible,’ Prospect Expert Says appeared first on HEAVY.
Democratic lawmakers call for protections over third country deportations after ‘indefensible’ court ruling
Lawmakers say ‘Congress must step in’ after supreme court ruled to allow Trump officials to resume deporting people where their lives could be in danger
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??
Wildfire-Ready Coalition of Nevada County Urges Residents to Follow the Science in Adopting Zone 0 Rule
Nevada County’s Wildfire-Ready Coalition explains California’s new Zone 0 rule, urging homeowners to clear combustibles near homes.
“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.
Global Health & WASH: October 2026 Funding Opportunities (26 new opportunities)
41 opportunities on the Global Health & WASH page this cycle, 26 of them new. The health money on this page is mostly research money, and much of it is aimed at people early in their careers. Funder after funder, from the TANGO2 Research Foundation’s Research Pathways grants to the South African Medical Research Council’s Early Investigators Programme to the National Ataxia Foundation and Alex’s Lemonade Stand Foundation, is funding early-career investigators and the institutions that host them, with a clear tilt toward researchers based in or from the Global South, from AMBSO in Uganda to Kenya’s CONNECT-VAX network to Ersilia’s AI Incubator for African drug discovery. The money is organized by disease as much as by need: narrow foundations, the Parkinson’s Foundation, NTAP for neurofibromatosis, the RUNX1 Research Program for an inherited blood disorder, each run their own grant, so the question for an applicant is less which health problem you work on than which foundation owns your disease. The largest European cheques go to consortia only, with the European Innovation Council putting up to EUR €4 million behind a single healthy-ageing project and UNITE up to EUR €1 million behind each cross-border digital health team. Water sits apart, almost entirely in applied utility research: the Water Research Foundation’s cluster of USD $250,000 to $500,000 projects, each built around a utility problem from sewer laterals to reuse treatment. And the biggest single pot is public: HRSA is renewing community health center service areas with awards of up to USD $10 million each. This post is for paid subscribers. This helps support the time and effort it takes to curate and organize these opportunities. Subscribe To keep this accessible to everyone who needs it, we’re happy to offer pay what you can rates. You can find more details here. Open Opportunities 2027 Building the Field of Health Advocacy Grant, Georgia Health Initiative. *New!* *Closing soon!* Georgia Health Initiative, a nonprofit private foundation focused on health equity, is opening its 2027 Building the Field of Health Advocacy Grant to strengthen the organizations that push for healthier systems across the state. The Initiative treats advocacy as infrastructure, backing one-year projects that range from urgent responses to shifting federal policy to longer arc campaigns on housing, behavioral health, care access, and food security. It wants coalitions that elevate community voice, apply an equity lens, and arrive ready to deploy concrete strategies, and it deliberately seeks variety in issues, communities, and organizational size. The optional webinar and required lobbying training signal a funder investing in a durable field, not one grant cycle. The strongest fits are Georgia nonprofits and public agencies ready to turn advocacy readiness into systems change. Geographies: United States. Who can apply: Tax-exempt 501(c)(3) public charities, state or local government agencies, or coalitions/collaboratives using a 501(c)(3) fiscal sponsor, that work in Georgia on behalf of Georgians and align with the mission, vision, and values of Georgia Health Initiative. Only one application per organization, though an organization may be listed as a partner or subcontractor on multiple applications. Funding amount: Organizations can apply for up to USD $80,000 per project; the Initiative anticipates investing a total of USD $1.36 million. Targeted Sectors / SDGs: Governance, Policy & Advocacy · SDG3; SDG10. Deadline: October 2, 2026. Learn more and apply here. TDD Research Pathways Grant, TANGO2 Research Foundation. *New!* *Closing soon!* The TANGO2 Research Foundation, working with a founding gift from Tango2UK, is opening its TDD Research Pathways Grant to fund early-career investigators anywhere in the world who study TANGO2 Deficiency Disorder. The foundation is using this money to seed pilot work and to build a research community around a rare disorder that few labs currently study. It wants to back emerging scientists, clinicians, and trainees who will stay with this field, not one-off projects, which is why it requires a committed mentor and real institutional support and asks each applicant for a plan for future contributions. The design rewards promise and persistence over track record. The strongest fits are students, residents, postdoctoral fellows, and new investigators ready to commit to TANGO2 research. Geographies: Global. Who can apply: Early-career investigators worldwide, including undergraduate, graduate, and medical trainees, postdoctoral fellows, residents, and clinical or research fellows. Early-career investigators must hold a PhD, MD, or equivalent doctoral degree and be within 10 years of that degree or within 5 years of initiating rare disease research, engaged in postdoctoral research at an academic institution, teaching hospital, or nonprofit research institute. Currently enrolled trainees and transitional PhD candidates beginning a postdoctoral appointment at a nonprofit institution by July 1, 2026 are also eligible. All applicants must identify a qualified mentor with relevant TDD or rare disease research experience and demonstrate institutional support. Funding amount: Up to USD $15,000. Targeted Sectors / SDGs: Innovation, Science & Technology. Deadline: October 2, 2026. Learn more and apply here. Call for Expression of Interest (EOI): AMBSO Impact Research Initiative, Africa Medical and Behavioral Sciences Organization (AMBSO). *Closing soon!* The Africa Medical and Behavioral Sciences Organization is inviting expressions of interest for its Impact Research Initiative, which funds early-stage pilot studies and proof of concept work on priority health challenges. AMBSO is a Ugandan research organization backing bold ideas that can grow into larger studies, and its tracks span antimicrobial resistance, epidemic preparedness, artificial intelligence for diagnostics, HIV prevention, climate and health, cancer, maternal and child health, and non-communicable diseases. The small award size and direct-cost-only rule point to a funder that wants evidence rather than overhead, and that favors projects able to attract follow-on money or use AMBSO’s own research infrastructure. Eligibility runs worldwide, welcoming researchers and institutions inside and outside Africa. The strongest fits are researchers with a feasible pilot that generates policy-ready evidence. Geographies: Global. Who can apply: Open worldwide to researchers, academic institutions, and research organizations whose work aligns with generating high-impact health evidence. Independent researchers are eligible if affiliated with an academic or research institution. Doctoral students are encouraged to apply as part of a research team led by a faculty Principal Investigator. Community-based organizations and other eligible entities may apply. Projects may be implemented inside or outside Uganda. Funding amount: Up to USD $10,000 per project, with no minimum funding threshold. Targeted Sectors / SDGs: SDG3. Deadline: October 2, 2026. Learn more and apply here. Entrepreneurs for Resilience 2027 (E4R27), Swiss Re Foundation. *Closing soon!* Swiss Re Foundation is opening the tenth edition of Entrepreneurs for Resilience, which backs healthcare social enterprises expanding affordable, quality primary care for low-income communities across low- and middle-income countries. The Foundation is using catalytic, blended funding plus multi-year advisory to take risk out of scaling, helping proven models reach more people sustainably rather than stay stuck at pilot size. It looks to back brick-and-click enterprises that pair physical care with digital tools and build affordability through savings, payments, or microinsurance, ideally integrated with public health systems. Requiring earned revenue, real customer scale, and gender-inclusive teams signals that this is growth capital for commercially serious ventures, not seed money. The strongest fits are established, revenue-generating healthcare enterprises ready to scale access for underserved populations. Geographies: Global. Who can apply: For-profit and hybrid healthcare social enterprises serving low-income and underserved populations in low- and middle-income countries, using combined physical and digital (brick-and-click) healthcare delivery models. Applicants must demonstrate measurable social impact, generate at least 50% of income through earned revenue, have been established no later than December 2023, already serve at least 50,000 people with potential to scale, and have at least 30% low-income customers. Preference for gender-inclusive enterprises with at least one female founder or at least 30% women in management. Funding amount: Pilot funding of up to USD $45,000 for the 6-8 shortlisted social enterprises; up to USD $1 million in blended funding (up to USD $600,000 unconditional plus up to USD $400,000 conditional) shared among 3-4 finalists over a two-year implementation period; total programme funding approximately USD $1.3 million. Targeted Sectors / SDGs: Innovation, Science & Technology · SDG3; SDG5. Deadline: October 4, 2026. Learn more and apply here. Horizon HealthTech 2026, IIITD Innovation & Incubation Center. *Closing soon!* IIITD Innovation and Incubation Center is opening Horizon HealthTech 2026, a nine-month MedTech incubation cohort run under the aegis of BIRAC EDGE for founders building medical devices, diagnostics, surgical and interventional tools, or remote monitoring. The center is backing teams that already hold a validated proof of concept and want to reach a tested prototype, pairing each founder with a named expert and clinical validation through a hospital partner. What they seek to back is Indian health technology that can survive real clinical scrutiny and regulatory review, not slideware. The structure, with lab access, CDSCO guidance, IP support, and investor connects, signals a program built to make startups ready for follow-on capital. The strongest fits are early-stage MedTech teams with defensible IP and a working path from bench to bedside. Geographies: India. Who can apply: Early-stage teams/startups with a validated idea/proof of concept (TRL 3 onwards) working on medical devices, diagnostics, surgical/interventional technologies, or remote monitoring, holding IP or with potential to generate IP. Funding amount: No cash award amount is stated on the official program page. Benefits are in-kind: prototyping lab and equipment access, clinical/hospital validation (Max Healthcare), expert mentorship, CDSCO regulatory guidance, IP filing support, workspace, cloud credits and tools, and investor connects. Run under the aegis of BIRAC EDGE and builds a path to follow-on Government/VC/Angel funding. Targeted Sectors / SDGs: Innovation, Science & Technology · SDG3; SDG9. Deadline: October 5, 2026. Learn more and apply here. OpenAI AI and Teen Development Research Grant Program, OpenAI. *New!* *Closing soon!* OpenAI is opening its AI and Teen Development Research Grant Program to fund independent studies of how generative AI shapes the lives of young people ages 13 to 17. OpenAI wants a stronger evidence base on how teens actually use these tools and what that use does to their social and emotional development, so that product teams, policymakers, and regulators can act on findings rather than assumptions. It seeks work on emotion regulation, relationships, belonging, and the safeguards that keep teens safer, welcoming interdisciplinary teams and any methodology. That it will fund research whose conclusions may not favor AI providers, and prioritizes studies touching ChatGPT, signals a genuine appetite for credible outside scrutiny. The strongest fits are research groups grounded in adolescent development, well-being, and AI safety. Geographies: Global. Who can apply: Researchers aged 18 or older who are affiliated with a research institution or organization, and/or have significant experience relevant to child or adolescent development, well-being, human-computer interaction, AI impacts, or a closely related field. Multiple institutions may collaborate. OpenAI is seeking to fund research rather than for-profit initiatives and will not prioritize for-profit organizations. Funding amount: Individual research grants up to USD $1 million, totaling up to USD $5 million; maximum 10% of each grant for overhead or indirect costs. Targeted Sectors / SDGs: Social Inclusion & Community Wellbeing; Innovation, Science & Technology. Deadline: October 6, 2026. Learn more and apply here.
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