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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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Viewpoint: FP10 should remain open, excellent and secure
Europe is right to worry about research security. Foreign interference is real. So is the misuse of sensitive knowledge. Universities working in sensitive areas such as AI, quantum, semiconductors and advanced materials cannot afford to ignore the geopolitical environment around them.But Europe is in danger of drawing the wrong conclusion, one that would increase, not reduce, its strategic vulnerabilities.If the EU applies an “EU preference” logic to the next Framework Programme for research and innovation (FP10), it will weaken one of its greatest strategic assets: a research system built on openness, excellence and international collaboration. In the name of security, Europe could end up advancing science more slowly, innovating less effectively and making itself less attractive to global talent and partners in advanced science and technology.That would be a serious mistake.The EU preference included in Article 10 of the proposed European Competitiveness Fund may make sense in some industrial or deployment contexts, but is far harder to justify when applied to collaborative research in advanced science and technology, where excellence and global connectivity are crucial. Research is not procurement. It does not thrive when isolated. It advances through access to the best ideas, the best people, the best facilities and the best data, wherever they are found. This is why FP10 should not simply absorb industrial policy logic by default. Doing so would risk importing instruments that are ill-suited to research and that ultimately increase vulnerability rather than resilience.For universities of science and technology, the issue is especially acute. These institutions sit at the frontier of knowledge and technology creation in the very fields Europe sees as critical for its future competitiveness and resilience. They know better than most that security risks are real. But they also know that openness and security are not opposites. In practice, they have to be made to work together. Research security is not about closing doors. It is about building resilience through collective responsibility, trust, proportionate safeguards and better governance. That is a fundamentally different approach from default restriction.The risks of non-collaborationThe real danger in the current debate is that Europe talks too much about the risks of collaboration and not enough about the risks of non-collaboration. Those risks are strategic too. Cut researchers off from international networks and Europe loses access to knowledge, data, infrastructures and talent. Slow down global collaboration in critical technologies and Europe slows down its own progress on cutting-edge science. Make participation rules unpredictable and Europe weakens its attractiveness to researchers and investors alike.In other words, over-restricting collaboration will create strategic vulnerabilities, particularly in advanced science and technologies.This point is often underestimated in policy discussions that frame openness mainly as a value to be defended. Europe’s scientific strength, technological development and long-term competitiveness depend on remaining connected to the best research ecosystems in the world. For a continent that wants to accelerate deep tech, strengthen industrial capacity and reduce strategic dependencies, weakening the excellence base would be self-defeating. That does not mean ignoring risk. Quite the opposite. Universities are already strengthening due diligence, internal governance, awareness-raising, screening procedures and compliance support. Across Europe, institutions are working to manage risks linked to foreign interference, sanctions, export controls and the misuse of research. The right response is not complacency. It is better risk management. But that means targeted, evidence-based and proportionate measures, not blunt top-down restrictions with wide spillover effects. In the daily reality of universities, openness and restriction often coexist. The real challenge is how to apply safeguards without sliding into securitisation that damages academic freedom, open science and international cooperation. Restrict partners, not nationsMore restrictive treatment of collaborators from European Economic Area members Iceland, Liechtenstein and Norway, or from Switzerland and the UK, is strategically very difficult to justify. These are deeply embedded partners in Europe’s research and innovation ecosystem. Beyond that, Europe should still resist default exclusion and instead rely on case-by-case assessment. The relevant question is not nationality in the abstract. It is the actual risk profile of a collaboration: the institution, the affiliations, the field, the knowledge involved, the intended use and the safeguards available. Screening should focus on institutions and affiliations rather than nationality alone. Related articlesParliament ‘misreads’ link between Horizon Europe and ECF, Commission official saysFrustration grows at governance gap in next Horizon EuropeIf EU preference logic is imported into research without careful calibration, the likely result is a chilling effect. Researchers may step back from legitimate and beneficial collaboration, even where risks are manageable. International partners may see Europe as less predictable. And Europe may end up undermining the very excellence base it needs to compete, scale up and lead in strategic technologies, thereby increasing its dependence on others.FP10 should do the opposite. It should remain open by default, with restrictions used only exceptionally, and then on the basis of clear evidence. It should integrate research security into open science and research integrity frameworks, not set them against each other. And it should recognise that academic freedom, scientific excellence and responsible international collaboration are not obstacles to Europe’s security, they are part of it. Europe needs stronger tools to protect itself. But it also needs the discipline to avoid measures that would weaken it and increase its strategic vulnerabilities in the process. In FP10, that means staying open, excellent and secure.Louise Drogoul is senior advisor for innovation and sustainability at the Cesaer university association.
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Marathon has sold 1.2M copies across Steam, PS5, and Xbox
Alinea Analytics estimates show that Marathon has generated gross revenues of $55M via 1.2M copies sold on Steam, PS5, and Xbox: Looking at the split between Steam, PS5, and Xbox, Steam is clearly the main platform for Marathon, accounting for a little under 70% of the audience (800K copies sold). Meanwhile, PS5 takes about 19% (217K) and Xbox (including console, PC, and cloud) accounts for a bit over 11% or 133K. Marathon is technically a first-party Sony title, so seeing the home console struggle to break 20% of the volume is a notable data point for the ongoing platform-agnostic debate. PlayStation Studios online games will almost certainly continue being multiplatform (despite Sony reportedly pulling back on PC releases). Subscribe for free to get the most accurate games estimates in your inbox twice a week 🫶 Subscribe As for Marathon, the consensus among hardcore players who pushed through the awkward onboarding phase – myself included – is that it’s fantastic. The gunplay, aesthetic, and presentation are among the best in the shooter genre. A great game doesn’t always equal an immediate hit, though, as shown by our estimates. Things are looking lukewarm right now. But this might be one of those marathon-not-a-sprint situations. Had to, didn’t I? Why wasn’t Marathon a hit on the same level as Arc Raiders? One topic that came up in a lot of conversations at GDC this year was why Marathon hasn’t hit the same stratosphere as Arc Raiders. On paper, they’re both extraction shooters, and Marathon has the Bungie pedigree – the house that built the gold standard for gunplay in Halo and Destiny. Marathon’s neon-futuristic aesthetic also distinguishes itself from the gritty realism of Tarkov or the post-apocalyptic hope of ARC Raiders. But vibes can only get you so far for first impressions if the onboarding feels like a brick wall. Marathon is a sweaty PvP-focused experience with a first-time user experience and UI that I can only describe as dreadful. Players are dropped into a complex ecosystem where the UI, though visually striking, is functionally uninformative, forcing users to manually hover over nondescript, identical icons just to identify basic gear or weapon mods. This lack of clarity is compounded by punishing mechanics like a severe heat gauge for movement and a scarcity of vital resources, all without a clear tutorial or a low-stakes mode to learn the ropes. Most people – even top streamers and veteran shooter players – have no idea what’s going on in Marathon at first. Arc is on the complete other end of the spectrum. As we covered here and here, Arc Raiders offers a more collaborative, approachable loop where players often work together toward shared goals. And the objectives are instantly clear. To many players, this contrast only emphasised Marathon’s issues, especially during the testing period. Players understand the Arc Raiders loop within 30 minutes, while Marathon acts as a massive filter, chewing up newcomers and spitting them out before they can experience the depth of Bungie’s signature gunplay. What I’m getting at here is that the poor first-time user experience of Marathon’s Server Slam likely stifled launch sales, while the opposite was true for Arc. Let’s look at the pre-launch copies sold for each game during their respective Server Slams (launch-aligned) : Looking at the trajectory: Arc Raiders saw a massive 80% jump in copies sold during its three-day Server Slam (day -13 to -11). Players jumped in, realised the water was fine, and immediately opened their wallets. Marathon, meanwhile, saw a 49% increase in the four days following its Slam (Day -7 to -3). At the time of each game’s respective launch, Arc Raiders had nearly double the Steam footprint of Marathon – and word of mouth only continued to grow. Bungie could have had more time to fix the onboarding process had the beta happened earlier. But Marathon launched three days after the beta finished. These factors limited the launch sales quite a bit, I reckon. Marathon has found a niche audience, though Despite the friction-heavy start, the data suggests that those who survived Marathon’s onboarding are loving life. We’ve been tracking cross-platform DAUs, and while there’s the expected post-launch leakage, many players are sticking with it. After peaking at 478K total DAUs on its first Saturday, Marathon has settled into a respectable rhythm, holding 345K DAUs as of yesterday and averaging 380K DAUs across the weekend. Steam continues to do the heavy lifting here, consistently accounting for the lion’s share of the daily players. Players have been putting in the hours, too: On Steam, Marathon’s average playtime has climbed to 27.8 hours, significantly outpacing the console averages on PS5 (16.5h) and Xbox (17.3h). Even more telling than the averages: 22% of the Steam audience has surged past the 50-hour mark, and nearly 7% have already logged over 100 hours. Subscribe for free to get the most accurate games estimates in your inbox twice a week 🫶 Subscribe This data suggests that once the Bungie magic clicks, it hooks players deeper than the average extraction shooter. There’s also started to be a shift in the game’s discourse. Marathon launched as one of those games that’s cool to hate. But as Marathon’s 100-hour club grows, we’re seeing conversations around its mechanical merit outweigh the negativity. Several high-profile creators, who were initially vocal critics, have spent the last week coming around on the mechanical depth of the endgame. It’s a subtle but positive signal; the hate train is losing steam, and a new narrative about the game is emerging. Bungie faithfuls unite Another interesting metric in Marathon’s first few weeks is who, exactly, is playing. Bungie leans heavily on its prestige as the “makers of Halo and Destiny,” in its marketing, and that message has clearly hit the mark. On Steam, the gravity (hammer) of Bungie’s previous work is unavoidable. A staggering 78.2% of Marathon players also played Destiny 2. The trend carries across other platforms, with 74.2% of PS5 players and 82.7% of Xbox players also having played Destiny 2. The Halo legacy lives on, it seems. More on that in a bit. While Destiny 2 went free to play, these numbers still represent a high-intent migration of a fanbase that trusts Bungie’s specific brand of kinetic gunplay and cryptic world-building. The Xbox data adds another layer of legacy. About 85.7% of Marathon players on Xbox have played Halo Infinite, while over half of the Steam audience has spent time in Halo Infinite and 36.1% in the Master Chief Collection (a paid collection of the old Bungie-made legacy games). Interestingly, we see a 55.9% overlap with ARC Raiders on Steam. Overlap is even higher there on PlayStation (60.5%) and Xbox (66.5%). And there’s also significant crossover with Helldivers 2 on all platforms, too. Subscribe for free to get the most accurate games estimates in your inbox twice a week 🫶 Subscribe Bungie has found some of its fans with Marathon, and the challenge now is keeping this engaged in a high-stakes PvP environment when they’ve spent the last decade primarily enjoying the power fantasies of PvE. On that note… Go the full 26 miles? Or cut and run? Time for some could’ve, should’ve, would’ve. These things would have improved Marathon’s performance at launch: A more robust onboarding process, perhaps in the form of a short single-player/PvE campaign. We know Bungie fans love this stuff. A traditional arena-based mode akin to Destiny’s Crucible could have been a bridge for the cautious players who’d have been otherwise deterred by the Server Slam. It’s also a great palate cleanser after getting merked. Introducing a dedicated PvE environment would have similarly diversified the conversation and lowered the barrier for the Bungie faithful who found the pure PvP extraction loop too abrasive. PvE is safer than PvP these days. There’s an argument that a thin PvE mode would have just felt like a budget Destiny, potentially hurting the brand more. But given how dire the FTUE is, I think it would have been a net positive. Any kind of tutorialised loop that demystifies the stakes, really. While there is merit in a studio sticking firmly to a singular creative vision, and going all in on the hardcore extraction loop. Less forgiving is the technical reality is that the user interface and first-time user experience (FTUE) were not at a commercial standard for launch. Subscribe for free to get the most accurate games estimates in your inbox twice a week 🫶 Subscribe In a landscape where player time is oversaturated and the most contested currency, a flawed FTUE is a critical failure point. But it’s not all doom and gloom, as Marathon has an incredible, unique gameplay loop, suggesting that financial success is still attainable if Bungie can pivot effectively. Bungie and PlayStation now face a difficult strategic crossroads, though – one within a challenging games market. They could: Double down on Marathon with a long-term Rainbow Six Siege- or No Man’s Sky-style recovery plan, which is something I heard Last Stand Media’s Chris Maldonado mention last week. While this could be a sunk-cost fallacy in action, it could eventually yield the audience the game’s mechanical depth deserves. Shift focus toward the inevitable Destiny 3 or another project, mitigating the escalating opportunity cost and cutting their losses, so to speak. Doing both is an expensive proposition, given the high overhead and burn rate of operating out of Bellevue, Washington. With Sony recently demonstrating a lower threshold for underperforming studios and projects, the margin for error has vanished. Whatever happens, the next six months will determine whether Marathon becomes a cornerstone of Sony’s live-service portfolio or a cautionary tale of vision exceeding accessibility. I’m hoping it’s the former, because Marathon rocks. As always, hit subscribe to get updates on Marathon and other relevant games. Subscribe Want to get your hands on our data for yourself – for free? We’re offering a free trial of our platform for games companies. Send us a message here, or reply to this email, and we’ll set you up. The last word Reply to this email – or reach out here – if you have any feedback for the newsletter – or want to request a game for us to cover. [Alinea Analytics boasts the most accurate PC and console estimates in the business. Game makers use our platform to understand their audience, keep an eye on the competition, monitor sales trends, and spot new opportunities. We equip game studios and financial institutions with accurate data and the confidence to make smarter, data-driven decisions. Want to talk about all things games market data? We’d love to chat!]
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Apnimed Announces Strategic Monetization of Shionogi-Apnimed Sleep Science (SASS) Joint Venture Stake for $150M and Royalties, Strengthening Focus on its Wholly-Owned OSA Program, AD109
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Forum on harnessing Artificial Intelligence for health equity
Register to this forumOver the past decade, artificial intelligence (AI) has significantly evolved in complexity, performance, and utility. Its integration across a wide range of industrial sectors has accelerated, with healthcare emerging as one of the most transformative sectors. In this sector, AI has enabled high-impact applications – from enhancing the speed and accuracy of disease diagnosis and supporting clinical decision-making, to strengthening public health initiatives such as disease surveillance and workforce optimization. Moreover, AI holds immense potential to empower individuals, enabling them to take greater control of their personal health and well-being. Yet the central question remains: Who benefits, who is at risk, and do these innovations close or widen health inequities? While the potential of AI-enabled applications is substantial, many countries have approached their adoption with caution. This hesitancy stems from concerns about risks and benefits, including model bias, data breaches, privacy violations, and the erosion of professional skills due to over-reliance on AI. Additional barriers include inadequate data infrastructure and a lack of robust evidence on the most effective forms and interfaces of AI to enhance service delivery and system performance. Moreover, the rapid rise of Generative AI has introduced a heightened urgency for the development of comprehensive governance and ethical frameworks. The Asian Development Bank (ADB) Health Practice Team and Digital Sector, in collaboration with the World Health Organization (WHO) Regional Office for the Western Pacific, are convening the Forum on Harnessing AI for Health Equity on 25–26 March at ADB Headquarters. Senior health officials from 16 developing member countries (DMCs), alongside leading researchers, innovators, and policymakers from across Asia and the Pacific, will engage in discussions on how AI can be governed, financed, and scaled to deliver healthier lives for all. The forum is funded through the High-Level Technology Fund with support from the Government of Japan. Objectives The two-day forum aims to:Identify high-impact, evidence-based health AI use cases suitable for resource-constrained settings and map out a pipeline for potential supportIntroduce and validate the WHO's policy guidance on minimum necessary ecosystem for countries and institutions to self-assess readiness for AI adoptionObtain initial commitment from participating stakeholders to join a pilot regional initiative focused on data/model sharing for a priority health challengeEndorse a prioritized set of actionable policy recommendations focusing on governance, regulation, and sustainable financing necessary to scale high-impact health AI use cases in resource-constrained settings Target participants The forum will convene participants from 16 ADB DMCs including Bangladesh, Bhutan, Cambodia, Cook Islands, Fiji, Indonesia, Lao People's Democratic Republic, Mongolia, Nepal, Palau, Papua New Guinea, Philippines, Sri Lanka, Thailand, Uzbekistan, and Viet Nam. In addition, health system leaders and practitioners, private sector technologists, academic experts, representatives from civil society organizations, development partners, and philanthropic institutions engaged in AI for Health from Australia, the People’s Republic of China, Japan, Malaysia, New Zealand, Singapore, and the Republic of Korea will also be invited. SessionsDay 1 (25 March)Session 1: AI Use Cases for the Care Continuum: Improving Access and QualitySession 2: Adopting a risk-based approach for AI use case prioritizationSession 3: Establishing the Minimum Ecosystem for Implementation Risk ManagementSession 4: Future-Ready Health Workforce: Building Competency for Human-AI CollaborationDay 2 (26 March)Session 5: The Political Economy of AI in Health: Understanding Stakeholder Interests and their Impact on EquitySession 6: AI and Health Financing: Supporting Fair Coverage and ProtectionSession 7: AI Regulation and Oversight: Pathways for Different RealitiesSession 8: The Way Forward – Building Alliances and Regional Partnership How to registerThe forum will be held in a hybrid format. In-person attendance is by invitation only, while livestreaming of the forum will be available to the public. To register, please complete this form on or before 24 March 2026.Event organizers / partnersAsian Development Bank (ADB)WHO Regional Office for the Western Pacific High-Level Technology Fund (HLTF)ContactAkihito WatabeHealth Specialist, SD3-HSDawatabe@adb.org Jae Kyoun KimHealth Specialist, SD3-HSDjkkim@adb.org
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Strengthening One Health Field Epidemiology Capacity in the WHO South-East Asia Region
Recent public health emergencies—including the COVID-19 pandemic, recurrent zoonotic disease outbreaks, and the high level of antimicrobial resistance—have underscored the urgent need for strong, multisectoral field epidemiology capacities. This reinforces the importance of a workforce equipped with One Health field epidemiology competencies. To address this need, WHO works with the Food and Agriculture Organization of the United Nations (FAO), the United Nations Environment Programme (UNEP), and the World Organisation for Animal Health (WOAH), as part of Quadripartite Alliance for One Health, to strengthen field epidemiology training programmes using a One Health approach.These efforts are aligned with WHO SEARO’s Strategic Framework for Action for Strengthening Surveillance, Risk Assessment and Field Epidemiology and the Regional Roadmap to Advance Field Epidemiology Capacities 2025–2029 emphasize workforce development, multisectoral collaboration, and enhanced preparedness for health emergencies. With funding from the Pandemic Fund, The Competency for One Health Field Epidemiology (COHFE) integration within Field Epidemiology Training Programmes (FETPs) or similar initiatives has been initiated in Bangladesh and India and is planned in Sri Lanka.BangladeshIn Bangladesh, COHFE is being adapted to strengthen the Field Epidemiology Training Programme, Bangladesh (FETP-B) Advanced programme. Initiated in July 2025 and led by the Institute of Epidemiology, Disease Control and Research (IEDCR), the process brought together stakeholders from human, animal, and environmental health sectors, academia, and laboratories.A Multisectoral Working Group (MWG) was established to guide implementation, conduct readiness assessments, and identify opportunities to institutionalize One Health collaboration. Through national multisectoral consultations and a competency prioritization workshop held in Dhaka in November 2025, stakeholders identified priority technical competencies for surveillance, outbreak investigation, data analysis, and risk assessment, alongside functional competencies related to leadership, coordination, and the laboratory–epidemiology interface. The process also surfaced systemic barriers—such as challenges in data sharing and limited joint investigations—enabling targeted recommendations. These outcomes are guiding curriculum revision, the introduction of One Health simulations and joint field exercises, and the development of a national multisectoral mentorship pool, with updated training materials expected to be implemented in the upcoming cohort.Figure 1. Competency prioritization involving multisectoral stakeholders“COHFE represents a significant step forward in operationalizing the One Health approach. The collaboration between national institutions and the Quadripartite partners is critical for building resilient systems to address complex health challenges in the region.”, Prof. (Dr) Tahmina Sirin, Director, Institute of Epidemiology, Disease Control and Research (IEDCR), Bangladesh.Aligning India’s One Health Training with COHFEIn India, the National Centre for Disease Control (NCDC) has strengthened One Health workforce development through its SectorConnect initiative. WHO supported a comprehensive competency mapping and curriculum review to align the Field Epidemiology Programme in One Health (FEP-OH) with all 14 COHFE domains. The review identified strong foundations while generating actionable recommendations to strengthen One Health learning outcomes, expand joint field-based learning, and enhance competency-based assessment. These improvements are informing programme refinement and phased scale-up across additional states.Figure 2. Strengthening learning outcomes for the SectorConnect FEP OH CurriculumThrough these efforts, WHO SEARO will continue to support countries to move from separate sector-based training toward an integrated, impact-driven One Health field epidemiology workforce capable of addressing complex health threats at human-animal-environment interface.ReferencesFood and Agriculture Organization of the United Nations, World Health Organization, & World Organisation for Animal Health. (2024). Competencies for One Health field epidemiology (COHFE) framework. https://www.who.int/publications/i/item/9789240080058World Health Organization. (2023). Strategic framework for action for strengthening surveillance, risk assessment and field epidemiology for health security threats in the WHO South-East Asia Region. WHO Regional Office for South-East Asia. https://www.who.int/publications/i/item/9789290210030World Health Organization. (2024). Regional roadmap to advance field epidemiology capacities in the WHO South-East Asia Region 2025–2029. WHO Regional Office for South-East Asia. https://www.who.int/publications/i/item/9789290229544
Emotional Abuse in College Sports
March Madness is here. The high-stakes, sudden-death college basketball tournament is a beloved tradition in American sports. For the players, it’s a chance to showcase the skills they’ve developed through years of hard training of the body and mind. In many cases, that push produces incredible feats of athletic performance. But an investigation by reporters Julia Haney and Elizabeth Santos has found instances in which athletes allege that the push from coaches goes too far. Emotional abuse by coaches, some athletes maintain, can cause lasting, even irreparable damage. On this episode of The Sunday Story, we hear from athletes who fought back.*A warning that today’s story includes mentions of sexual and emotional abuse, suicide, bullying and includes explicit language.
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