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“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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In praise of popular science
As a kid, I was enthusiastically and unreservedly in favor of a manned space program. Our destiny lay in the stars, I figured. Get people out there. We need Star Trek here and now. As I got older, I became rather disenchanted with the idea. Humans are expensive, fragile, and inefficient as space cargo. We could flood the solar system with 100 robotic probes for the price of any single manned mission and get back just as useful data. Besides, it turns out space travel is dangerous. Tusk is a reader-supported publication. To support this work, please become a paid subscriber. Subscribe When it came to the recent Artemis II mission, my cynical side didn’t really see the point. Flying around the Moon and back is literally something we did before I was born, and I once owned a Members Only jacket. Apollo 8 circled the Moon ten times and returned to Earth safely the same month that “Chitty Chitty Bang Bang” was released. What was the point of repeating a feat that far braver people once did with tin foil and slide rules? Yet I’ve surprised myself with how moving I’ve found the Artemis II mission to be. The photos have been stunning. The crew has been professional and engaging. There have been modest but important milestones — the farthest humans have travelled from Earth; the first woman, person of color, and non-American to leave low-Earth orbit; innovations in communication, and more. One thing that really stood out is that this is really NASA at its finest. Not that this is the most important mission they ever pulled off, but they did this in a competent way that celebrated the achievements and kept the crew and the science at the forefront of the project. This wasn’t some billionaire throwing celebrities or cars into orbit as a vanity project — this was a collective effort to send experts in to do a job and come home safely. We don’t see that sort of thing much these days. I’m guessing few people would describe many government agencies as inspiring, but this one counts for me. We’re living through a profoundly anti-science era, in which pathbreaking, lifesaving discoveries like mRNA vaccines are routinely downplayed or even vilified. As I’ve noted previously, this era is something like that of the Red Scare in the 1940s and 50s, in which “egghead” professors were portrayed as out of touch or even hostile to regular Americans. One of the things that helped break that spell was Sputnik — the Soviet communications satellite that demonstrated that the United States was falling behind in crucial scientific research and applications. Its launch triggered a new appreciation for American science and goaded the federal government into supporting research universities and labs. I’m not saying that Artemis is the equivalent of Sputnik, but it potentially reminds people that there’s some value to scientific endeavors and the human faces attached to them. Despite my love for the show “For All Mankind,” I’m far from convinced that there’s any real value to putting colonies on the Moon or Mars that would justify the capital investment and the massive health risks to the astronauts. But sending scientific good will ambassadors on a trip into space once in a while? Bring it. Share
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MAGA Is Winning Its War Against U.S. Science
Source: NBER With all the other terrible news right now, you may not have noticed that Donald Trump is in the process of killing American science. OK, that’s an exaggeration — but not that much of an exaggeration. The Trump administration’s latest budget proposal calls for a gigantic increase in military spending combined with severe cuts to social programs. But as the chart above shows, it also calls for debilitating reductions in research funding. Furthermore, Trump appointees have already been strangling science by sharply reducing the rate at which research grants are approved. Here, for example, is the number of new grants approved by the National Science Foundation: Source: Nature Large numbers of existing grants have also been frozen or terminated, especially in the study of infectious diseases. Add to this a sharp drop in visas issued to foreign students, who often play a direct role in research and who help support academic departments that do research: Put all of this together, and much U.S. scientific research is set to come to a screeching halt — not a few years from now, but over the course of the next year or two. This new assault on U.S. science is taking place at a time when the role of American science in the world has already been greatly eroded. The chart below, based on research recently reported by the National Bureau of Economic Research, measures national strength in science by the share of publications in highly ranked journals. In the 1990s the United States had more such publications than the rest of the world combined. Since then we’ve dropped into third place — well behind China and slightly below the European Union. And this was before the Trump administration’s attack on science had time to take its full effect. Source Some erosion in U.S. scientific preeminence was inevitable given China’s growing sophistication and wealth. But we’ve also fallen behind Europe, even though everyone says that Europe is lagging economically and technologically. Claims about Europe’s underperformance are, in fact, dubious if one looks hard at the data. But it’s still striking to see America lagging. What’s going on? There are presumably multiple factors behind America’s scientific lag. But even before Trump II the growing hostility of the U.S. right to science surely had some negative effect. And since the rise of MAGA G.O.P. attitudes toward science in general have become overwhelmingly hostile. This is true even for the Republican rank and file: And while I haven’t been able to find good survey data, it’s obvious that the anti-science turn has been even more pronounced — and began earlier — among the Republican political elite. Chris Mooney published The Republican War on Science in 2005, and even then was describing a longstanding trend. Why have Republicans turned so anti-science? Part of the answer is that they believe that scientists don’t support them. And they’re right! A study of who scientists give money to shows that only a small percentage gave money to Republicans even 20 years ago, and that almost none of them donate to Republicans now: Social scientists have always been strongly pro-Democratic, while there used to be a significant number of physical, “hard” scientists supporting the GOP. But these days physicists are almost as uniformly Democratic, or at least non-Republican, as sociologists. Why are there almost no Republican scientists? It’s not a mystery. GOP political orthodoxy includes positions that are at odds with the scientific consensus on multiple issues, ranging from the validity of the theory of evolution, to the reality of climate change, to the efficacy and safety of vaccines. In each case the scientific consensus is solidly grounded in evidence. But even before the rise of MAGA the U.S. right was increasingly hostile to evidence-based policymaking — especially, of course, where the evidence is unfavorable to fossil fuel interests or quack medicine, both financial mainstays of right-wing politics. So scientists don’t support Republicans, and the feeling is mutual. Today’s Republican Party doesn’t like science or scientists. It doesn’t like having its preconceived views challenged by appeals to evidence. It knows that very few scientists are on its side electorally. In general, it sees scientific research as a threat to its grasp on political power. Add in MAGA’s combination of rabid anti-intellectualism and allergy to any hint of criticism, and one has the makings of a drastic anti-science turn in policy. “Ignorance is strength” might was well be an official MAGA motto. And as I said, we aren’t talking about something that will happen over the course of multiple years: The U.S. scientific enterprise is threatened with severe damage, even collapse, over just the next year. There are many reasons to find this prospect horrifying: Think of all the beneficial advances, affecting almost every part of life, that won’t happen because U.S. science — still crucial to the world — has been eviscerated. But think, also, of America’s international standing. Can a nation that has forfeited its role as a leader, or even a contender, in global science, still be a Great Power? No. MUSICAL CODA
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A million dollars in cash, split and handed straight to his mother. That’s the first place BigX goes on “From the Bottom.” He’d probably still be in the streets if he didn’t have his son and daughter. He only started rapping to make life better for Carter. Couldn’t start his car back then, now people get starstruck at Starbucks, and he throws the whole field away in one sentence: “How you think we competin’? You on the same bag, I just made me some millions off country.” Goyard bags off a Nashville payday, and he wants you to know exactly how far he’s come from everybody else. “Safe to Say” does something similar but looser. It’s a straight Ice Cube “It Was a Good Day” flip, and BigX nails it. Wakes up, checks both phones, nobody’s locked up, kid accounts look good. Gets pulled over, only catches a warning. Pulls up on the squad to liquor, ladies, and haze. Rosama takes the second verse and matches him. Wakes up to his girl cleaning his cuticles, twins playing peekaboo, pays off his car, calls a cousin, sees the opps and they don’t trip. Lawyer calls, he beat his case. Rosama is the strongest voice in the 600 camp after BigX, and “Life of a Gangster” is why. Third person, riding low, blacked-out windows, snub-nose revolver. A character who chose sticking people up over selling dope. “Quick eraser like a number-two pencil.” He never breaks character, and his delivery drops lower and slower than anything he does on the group cuts, like he’s telling you something at a cookout that he probably shouldn’t. Then on “Long Live Fre$h” he’s a completely different rapper. “Short-bus diamonds, these bitches retarded,” wide-body Durango he can’t even park. That kind of range matters on a group record. MurdaGang PB’s best verse is on the title track. Feds at his door at 6 AM, cameras busted, flushing dope, ram truck putting everybody on the floor. Yung Hood on “The Hottest” just lays out four reasons to keep hustling. Who wants to be broke, women cost money, you need bail, and home has to be straight. It’s funnier and more honest than most rappers get when they’re trying to explain why they sell drugs. KevanGotBandz on “Ain’t Never Slowin’ Down” talks about his father getting caught on the dope while he’s sitting on $300K somewhere in the hills, which is a specific kind of guilt that doesn’t usually make it onto crew tapes. KaineMusic, the newest signee, takes the back half of “Who Dunnit” and introduces herself in third person—“Took flight just to show these hoes how to perform”—but it’s too short to know if she can hold a full song. The flex tracks fill the middle of the LP and mostly run together. “I Go” is Rosama and PB trading brands, guns, and food punchlines. Rosama’s “anemic, my iron tucked low in this shirt” is a slick gun bar, PB fires back with “.223 with a clip ‘bout as big as a toddler” and “I go Burger King, stay with that whopper.” Quick, funny sometimes, but it’s all shopping lists. “Dopeman” is a chant. “6ixer Party” brings Snoop Dogg, who does exactly what you’d expect Snoop Dogg to do. BigX contributes the night’s wildest overshare: “She say she brought a friend too, well, I’m tryna put sausage on her dental.” These songs move fast and don’t overstay, but three or four of them could trade verses and nobody would catch it. Midway through, The D.O.C. shows up for a spoken-word interlude. Tracy Curry. West Dallas native, former N.W.A affiliate, co-writer on a lot of the records getting sampled on this very album. He thanks BigX for the tribute and then gets direct: “This your time, this DFW time. You need to do it like we do it down here.” Leave bread crumbs so everybody behind you can eat. Keep God in your life. It’s fatherly and generous, and it does raise the question of whether 6WA follows that advice. The album borrows heavy. The cover art, the Eazy-E samples, “Amerikkka’s Most Wanted” opening with a direct “Boyz-N-The-Hood” lift, Yung Hood rapping “Flag on my left side, Crip until I die.” But the stuff that actually stays with you is Dallas. BigX talking about Pleasant Grove, Rosama calling himself a country boy, PB recounting a raid that could’ve been last Tuesday. The record smells most like itself when it smells like Ferris, Texas, and least like Compton. BigX on “The Hottest” says his father was the first person he knew who had chickens. Not the way rappers usually wave at the drug trade. He means his actual father, his actual family. On “600 Degrees,” Rosama compares his chopper’s stutter to a speech disorder, PB claims he still has the block on his back even after leaving the city. BigX closes that one saying he used to spin and smack, and now the plaques are platinum. Thirty-two minutes was right for this. Nobody overstays, Tony Coles and Rance and Charley Cooks keep the production punchy, and BigX gives his signees real room instead of crowding every song. 6WA doesn’t reinvent anything. Five rappers, good beats, short clock. Most of what they say you’ve heard before. Some of it—the million to his mother, the cuticles and peekaboo, the four reasons to hustle—you won’t forget. Solid (★★★½☆) Favorite Track(s): “Safe to Say,” “From the Bottom,” “The Hottest” Subscribe
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