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‘The reality, for better or worse’: Columbia comp sci students and faculty grapple with AI’s disruption of the field
‘The reality, for better or worse’: Columbia comp sci students and faculty grapple with AI’s disruption of the field ‘The reality, for better or worse’: Columbia comp sci students and faculty grapple with AI’s disruption of the field A field that once seemed like a direct path to stable, lucrative work is becoming less certain under AI, students and faculty told Spectator. By Ria Vasishtha and Arjun Menon May 3, 2026 Shanying Liu / Deputy Illustrations Editor Asia Genawi, SEAS ’29, first encountered artificial intelligence during her sophomore year at her high school in Indiana. Back in 2023, policies on AI varied widely—even within the same district—and some schools had no guidance at all. As Genawi began to see the effects of AI trickle into her classroom, she turned to guidance from her home state’s Department of Education, which she said she found “outdated and inaccessible.” Genawi spent the rest of her high school career dedicated to the issue, including writing a 15-page memo exploring varying AI policies across Indiana schools, which she sent to every Indiana state senator and representative whose contact information she was able to find. She later helped draft a bill that would have required public high schools to establish and share their own AI policies with students in an accessible format. But with so many other bills already awaiting referral, hers never made it to committee. When Genawi arrived at Columbia to study computer science, she found that the gaps she had spent years trying to address in Indiana had followed her there. Many students and professors were also grappling with what AI meant for the field. Across Columbia’s undergraduate schools, computer science has become the most popular major—accounting for around 11 percent of degrees awarded in Columbia College, 32 percent in the School of Engineering and Applied Science, and 12 percent in the School of General Studies in 2024. While the number of computer science majors in SEAS grew from 166 to 173 during the 2024-25 academic year, the total number across the three schools fell around 4.5 percent from 393 to 375, driven by declines at Columbia College and General Studies. At Barnard, it has grown to become the second most popular major as of 2025, up from third the year prior. The number of degrees awarded have grown fivefold since 2016. Faculty members and students in the department told Spectator that the surge in the major’s popularity has been driven in large part by its apparent promise of a secure, well-paid career path. However, they noted that the recent rise of generative AI has compromised the sense of stability that once defined the field, even as interest in technical skills among students persists. According to data from the Federal Reserve Bank of New York in 2024, out of 74 majors tracked, computer science had the fifth highest unemployment rate among majors at 7 percent, while computer engineering had the second highest at 7.8 percent. “Anecdotally, people are moving away from CS as a major,” Daniel Bauer, a senior lecturer in the computer science department, said. “They’re still taking some of our classes, right, because they think they need that, but they’re instead majoring in other things.” Bauer described a field that had shifted from what he called a “nerdy outlier discipline” to one that “everyone needs,” a sign that technical skills have become a baseline expectation across industries with the emergence of AI. “A lot of people thought, ‘Oh, it’s a guaranteed path to a stable income. You get a six-figure job right out of your undergrad,’” Bauer said. Now, companies may see AI as a way to reduce demand for entry-level programmers, especially in what he calls “code monkey jobs,” roles where a large number of employees are “just writing out code nine-to-five.” Chris Murphy, a senior lecturer in the computer science department, described the current moment as the convergence of two forces: the downturn of the job market and generative AI coming onto the scene. “Things kind of started to really pick up in 2014, 16,” he said, recalling the years after the weaker 2010 market. Murphy added that this was also a period where “students were getting more experience outside of class than they were inside of class” through internships, jobs, and side projects. Murphy suggested that the earlier momentum helped make computer science feel like both an academic program and a reliable pathway into work. That feeling is harder to sustain now as the industry reconfigures, which is a shift that students are seeing firsthand. Rebecca Yu, SEAS ’27, said that during a recent internship in Big Tech, she saw that “every single tech company was racing to integrate” large language models. She added that her manager said her intern class was the first to complete their assigned projects, which she attributed to the increased efficiency that comes with AI. Frank Liu, SEAS ’29, said that with AI lowering the barrier to coding, companies now expect everyone to write code. “A lot of companies outright say if you’re not using coding in your job, you might get fired for not being productive enough,” Liu said. Yu similarly expressed that students increasingly have to accept AI being a part of industry workflow, adding that many also “make use of it to maximize their productivity.” Richard Li, CC ’28, described a “franticness” among his peers as the pace of change accelerates. He explained how “some new tool” is released regularly, noting that tools rise and fall in prominence within weeks. He has come to believe that he doesn’t need to “match that speed all the time” and keep up with every development. Li has learned that AI tools allow him to “take a breath” and “get back into the loop much easier than before” when he needs to. While some companies specifically target Columbia when hiring, sending representatives to campus career fairs and recruiting large numbers from Columbia, a computer science degree cannot be treated as a static credential, Yu described. “You can’t just get your degree and stop learning after that. You have to always be learning,” Yu said. “That is something you basically have to expect for your job.” AI has not only affected students’ decisions to major in computer science, but also reshaped how the subject is taught. Across departments, faculty members no longer treat work done outside the classroom as a reliable measure of understanding, shifting instead toward in-person exams, quizzes, and attendance checks. While homework once made up as much as 60 to 70 percent of a student’s grade in computer science classes, Murphy said that breakdown has since flipped, with exams and quizzes now accounting for the majority. “The assumption—especially among pessimists—is that they’re using it to do their homework,” Murphy said. “They take the homework assignment. They put it into generative AI. Out comes the solution. Submit solution. The end.” Murphy said that faculty members ranged in their responses from fully prohibiting AI use to actively teaching students how to effectively work with AI. He said he shifted his approach this year after his teaching assistants called his belief that students weren’t using the technology “naïve,” and now permits students to use AI on assignments. His surveys of students in his introductory classes have told a more complicated story than the one so-called pessimists offer: Around 60 to 70 percent of students who reported using AI said they used it to have concepts explained to them, to clarify assignment instructions, to generate test cases, or to create practice questions. “Those are all ways that you might have had a tutor in the past to do that, and now you have this AI essentially as your tutor,” he said. Bauer has taken a different approach, teaching students how to use AI productively without sacrificing the core principles of the discipline. “People who are trained to actually use AI tools productively, while being in charge of designing the overall project, will be very much in demand in the next couple of years,” he said. Over the past year, a working group of computer science faculty has been redesigning the introductory and intermediate programming sequence to integrate generative AI as both a subject and a tool, representatives from SEAS wrote in a statement to Spectator. The effort is “coordinated” across the department, “not course-by-course,” with a systematic assessment of how AI integration is affecting student outcomes underway to shape the next round of changes. The goal, the department explained, is for students to be able to “read, verify, and reason about code, whether they or a model wrote it.” Tony Dear, senior lecturer in the computer science department, is piloting individualized AI feedback in a discrete mathematics course, while a machine learning course is also testing a Socratic AI dialogue system designed to “guide inquiry rather than supply answers,” the statement reads. Beyond individual courses, SEAS funded seven cross-departmental faculty projects in generative AI this past academic year, spanning biomedical engineering, chemical engineering, industrial engineering, and computer science, according to the statement. This phenomenon extends beyond the computer science department. SEAS’ new AI minor, which it announced in November 2025, reflects the University’s attempt to adapt. The minor is specifically designed for noncomputer science majors and aims to provide “essential AI knowledge” while emphasizing practical and responsible use across disciplines, according to the SEAS website. At the center of the minor is AI in Context, a course taught by a team of faculty from the departments of computer science, applied mathematics, philosophy, music, literature, and the Writing Center, the representatives told Spectator. Bauer described it as targeted at students who want to bring AI into fields like economics or biology, rather than as a path into software engineering itself. At the graduate level, SEAS announced a new Master of Science in artificial intelligence in February, with the inaugural cohort starting in the fall. While recognizing how AI has helped them navigate coursework, students also described its rise as isolating, even among members of the Application Development Initiative, one of Columbia’s largest computer science clubs. “As people start to ask AI for questions that they would normally ask students and members, it starts to isolate people” Li said. Alexa Kafka, BC ’27, echoed Li’s sentiment, pointing out that computer science has always been a collaborative discipline with code reviews and pull requests, even as AI makes independent work easier. Despite this, Kafka noted that she hasn’t been to office hours “in at least a year.” Bauer added that faculty are exploring long-term solutions to the assessment problem. “One of the things that we’re trying to do at the department or school level is to maybe move towards computerized testing facilities,” Bauer said, referring to ideas in the department to revitalize computer labs as an alternative to pen-and-paper exams. “We have to accept that this is the reality, for better or worse.” But more exams, students say, does not mean better learning, nor does it reflect how computer science is actually done. “Code is so variable and can be approached in so many directions, there’s never just going to be one correct answer. There can be multiple optimal solutions,” Kafka said. Students also expressed concern that the shift toward exam-heavy grading pulls focus away from the applied, project-based work that better reflects both discipline and industry expectations. ADI has tried to respond more quickly than the curriculum can. Through a vibe coding workshop this past semester, club members said they wanted to help students understand not just that AI exists, but how to use it deliberately. “Vibe coding” refers to the practice of prompting AI to write code for a task or project, and has become widespread enough that ADI felt the gap in formal instruction was worth addressing, according to Sophia Huang, BC ’29. The workshop introduced students to the concept of context engineering, which Shaina Sahu, SEAS ’27, described as not just writing a prompt, but optimizing “the rest of the relevant information that you would feed into the model.” Without that, students said that AI-generated code can fail at scale or introduce difficult-to-detect bugs. Genawi said the shift toward AI has changed what it means to be good at computer science. “I think the shift has kind of gone towards what can you create and what can you do, instead of, what do you know,” she said. Indeed, during ADI DevFest, its annual hackathon, Li said more teams are trying to complete their projects than in years past. While sign-ups have historically outpaced submissions, Li said this is beginning to change because teams “have a tool that completes the vision for them.” While many students see AI as an equalizer that lowers the barrier to entry for coding and technical work, Murphy said it also creates new equity concerns inside the classroom around who can afford stronger tools and who already knows how to use them. “The equity issues I worry about are students who have access to paid versions of things versus free versions of things,” he said. Although popular AI models like ChatGPT and Claude have free versions available, both have paid models that can reach over $100 per month, which output different qualities of code. Murphy also pointed to differences in prior exposure to AI literacy itself. “Students who go to high schools or K-12 education, where they’re taught AI literacy, prompt engineering, context engineering, they understand how to do this, as opposed to students who go to schools who don’t teach that,” he said. “Then they come in, and then they’re somehow expected to know how to use these tools.” For Genawi, those disparities begin long before students arrive on campus. “It’s very interesting to consider how lucky you were geographically,” she said. “To be born where you were is influencing your ability to get access to these spaces.” Students like Yu, though broadly apprehensive about the future of computer science, are not without hope. “I’m optimistic, because I think if you’re truly passionate about CS, there will definitely be a place in the field to do something you want to do,” she said. Senior Staff Writer Ria Vasishtha can be contacted at ria.vasishtha@columbiaspectator.com. Follow Spectator on X @ColumbiaSpec. Staff Writer Arjun Menon can be contacted at arjun.menon@columbiaspectator.com. Follow Spectator on X @ColumbiaSpec. Want to keep up with breaking news? Subscribe to our email newsletter and like Spectator on Facebook. Produced with Spectate by Emily Ta
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Shaky political science: using "simulated" elections over real-world elections
[Dear DSOS readers – this article is the latest in our “Shaky Political Science” series. In our 38 page report, “Shaky political ‘science’ misses mark on ranked choice voting,” released in early December, we examined over 40 studies focused on ranked choice voting. We found that many of these studies suffered from puzzling research methodologies, poorly constructed surveys, unrealistic simulated elections, cherry picked data, and faulty analyses that often were contradicted by results from real-world elections (a link to our full paper is here). Here are links to the first three articles in our series: the first one summarizing the overall results of our report, the second one analyzing a study co-authored by University of Minnesota academic Larry Jacobs, which was one of the most error-prone of all the research we reviewed in our report, and a third article showing an admirable example of two political scientists holding two other political scientists accountable for their flawed, sloppy research]. In this fourth article in our “Shaky Political Science” series, we shine a spotlight on seven studies that were web-hosted and funded by New America’s Political Reform Program and its Electoral Reform Research Group (the ERRG is an ad hoc committee headed by New America’s Political Reform Program and joined by the American Enterprise Institute, the Unite America Institute and Stanford University’s Center on Democracy, Development, and the Rule of Law). This research consortium has been led by Lee Drutman, senior fellow with New America’s Political Reform Program. These seven studies are simply not credible from any objective standard about how political “science” is supposed to illuminate our understanding of either ranked choice voting or politics in general. The feature that six of these seven studies have in common is that they all used surveys – artificial, simulated elections -- to attempt to say something cogent or interesting about ranked choice voting (RCV) elections. None of them used real world data from actual RCV elections – either the 1000+ elections in the US or the many thousands of elections from places like Australia, Ireland and elsewhere where RCV has been used for 100 years. While a few of these studies conclude with some mixed positives about RCV, our analysis here is content neutral. The idea that, with a political reform like RCV, you can ask respondents in a mock, artificial election to vote with that method, with only the barest of explanation about how RCV works or its likely impacts (i.e. no spoiler candidates, majority winners, voters liberated to pick their favorite candidates, coalition-building), and then ask them their thoughts/reactions about voting in that system, with fake candidates and no real campaigning, no real exposure to an actual election or actual candidates, rests on some very puzzling assumptions. The findings of these seven studies actually are contradicted by the findings from many other studies based on real-world RCV elections. These New America studies also failed to list the real-world RCV studies in their own citations, instead including those studies that reinforced their conclusions. The seven studies featured in this article were chosen because they form a cohesive cohort which precisely illustrates one of the main points of our research paper, namely that using surveys, i.e. artificial elections, or mathematical/theoretical models, instead of real-world data, is in fact “shaky political science.” Below are summaries of our analysis of five of the seven New America studies. You can read our complete analysis of all seven studies at our paper, “Shaky political ‘science’ misses mark on ranked choice voting.” Choosing to “Vote As Usual” by Andre Blais, Carolina Plescia, and Semra Sevi. New America. (Blais et al., 2021). This study, web-hosted and funded by New America’s Political Reform Program and its Electoral Reform Research Group, used two surveys in which respondents completed ballots for simulated elections using plurality, approval, RCV and point (Score) voting. In both cases the authors provided the respondents with only minimal explanation of each method. For example, the entire explanation for RCV was “For this vote, rank the candidates from your first to your last choice (you do not need to rank them all). This is called the ranked vote.” That’s it. The study did not provide any context or explanation to survey respondents about why she/he might want to rank multiple candidates. Participants were not even told that ballots would be tabulated using an “instant” runoff, not to mention any description of RCVs ability to eliminate the spoiler effect or liberate voters to rank their favorite candidates without fear of helping the “greater evil” candidate to win. That’s like taking an iPhone back in time to 1970 and asking someone if they like it better than their rotary phone without explaining how it works. Their first survey targeted states that held 2020 Super Tuesday Democratic primaries and used the real candidates’ names in the survey; the second survey used fictitious candidates. In other words, they surveyed respondents who had only ever participated in plurality elections, had no history or background with using RCV or any of the other methods, had no clue about its likely impacts, and did not communicate how the alternative methods would be counted, and then asked them: “How satisfied are you with using each system?” Predictably, the winner was plurality elections, the system used for president, Congress, gubernatorial and state legislative elections – in other words, the method they have been using, year after year, all their voting lives. With so little explanation of RCV and its impacts, this study is simply not credible in discovering anything useful about real-world RCV elections. It merely shows that a basic voter education campaign is needed when adopting a new election system. Additionally, the result contradicts many other post-election surveys following real RCV elections, asking opinions from voters who had just participated in a RCV election, that showed a great deal of satisfaction with RCV (see details on that in Studies 22-25 in our paper, as well as the four studies cited in the review for Study 28). Share Other studies on “satisfaction” and “understanding” make similar mistakes, in terms of basing their surveys on inadequate explanation to respondents and lack of education about context. How could any survey ever hope to measure accurately, in essence simulate a real-world election, without actual campaigning candidates, the media writing about those candidates, organizations endorsing candidates and voters being inundated with crucial information? More than anything, what these “studies” based on surveys show is that if you ask voters about something they don’t know very much about, and fail to adequately educate them, their responses will unsurprisingly reflect their confusion. Equally puzzling is that New America’s Electoral Reform Research Group, headed by political scientist Lee Drutman, found this to be a credible enough study that they not only published it but highlighted it in Drutman’s own discussion of research related to ranked choice voting. This pattern was repeated in study after study featured by New America’s Electoral Reform Research Group. For example in Ranked-Choice Voting and Political Expression: Voter Guides Narrow the Gap between Informed and Uninformed Citizens by Cheryl Boudreau, Jonathan Colner, and Scott MacKenzie, the researchers based their analysis on a survey which found that respondents who were asked to vote in a simulated RCV election, with minimal explanation of RCV or how it works, displayed less evidence of being informed about how to use RCV effectively, with the least informed voters ranking fewer candidates. As pointed out under the study above, in a real election voters are informed by a wide variety of methods, including public education by the election administration, by interested political groups, the media, and the candidates themselves who benefit from voters being aware of how to vote in the new system. In Support for Ranked-Choice Voting across Race and Partisanship by Joseph Anthony, David C. Kimball, Jack Santucci and Jamil Scott, the researchers used respondents from different parties and races voting in simulated plurality and RCV elections, who indicated their satisfaction with each voting method. Instead of using real world election results, the authors fell back on the easy, armchair and less reliable academic research methodology of making up mock elections. As with the other studies, little explanation was given of how RCV works or what its implications might be (electing majority winners or eliminating spoilers, for example). Unsurprisingly, this study found that respondents were not very satisfied with RCV. However, when the authors added an explanation that RCV has a tendency to elect more women and people of color, they found that this greater explanation increased the preference for RCV. The more voters were familiar with it, and the more they understood potential positive impacts – surprise! -- the more they were open to switching away from the status quo. Of note, the results from this study are thoroughly contradicted by a number of other studies whose data was obtained from real RCV elections with real voters. Those studies show a great deal of satisfaction with RCV. More “fake election” research An interesting wrinkle on this “research by fake elections” approach, which only served to illustrate how unreliable such “research” can become, was taken in Ranked-Choice Voting, Runoff, and Democracy: Insights from Maine and Other U.S. States by Joseph Cerrone and Cynthia McClintock_,_ also web-hosted and funded by New America’s ERRG. It gets high marks for creativity, but an eye-roll for thinking this could possibly add anything meaningful to our understanding of real world RCV elections. This study tried to determine whether RCV, plurality or two-round runoff elections provided greater voter satisfaction and acceptance of election results. To determine that, the authors conducted a survey asking if voters were satisfied with a fictional newspaper report of an election, with three different versions given to respondents – plurality, runoff and RCV, with variations of Republican/Democrat winners and variations of “come-from-behind” winners,” i.e. candidates not initially in first place but who win the “runoff.” Again, a minimal explanation of RCV or how it works was given, and the survey showed that respondents had the greatest unfamiliarity with RCV than the other methods. The unsurprising result? Respondents were much more satisfied with “come-from-behind” victories in a traditional two-round runoff election than in RCV, even though RCV is also a runoff system but with a single round of counting. Interestingly, the dissatisfaction with RCV disappeared for those who were more familiar with RCV. In fact, among those respondents who were “very familiar” with RCV, they were equally satisfied with RCV, runoff and plurality. Surprise, surprise. Again, what this study revealed, more than anything, is not only the inadequacy of the explanation for RCV given by the authors of this study, but the failure of the research format itself, thinking that such an artificial mock election could somehow simulate what actually happens in the real world in a real RCV election. Those four studies reviewed above are fitting examples of inadequate and shaky political “science.” Other studies reviewed in our paper from the New America/Electoral Reform Research Group cohort which also used mock elections included: Ranked-Choice Voting is an Acquired Taste by Joseph Anthony and David C. Kimball. New America. (Anthony et al., 2021). Ranked-Choice Voting is No Refuge for Extreme Candidates by Melissa Baker. New America. (Baker, 2021). Also included in this New America cohort is Does Ranked-Choice Voting Reduce Racial Polarization? by Yuki Atsusaka and Theodore Landsman, which actually did use real-world election results data but applied an odd and unconventional methodology to the data which relied on shaky assumptions and non-real-world scenarios that were too divorced from reality. To determine levels of “racially polarized voting,” the researchers imagined “ethnic parties,” by which they mean political parties for each ethnic group, which was especially odd since the election data they were using came from nonpartisan RCV races. But then they made an even odder assumption, namely that since race and ethnic characteristics of studied voters were not available, the authors tried to infer that information based on who voters selected and the racial make-up of voters in each district. This amounted to little more than vague and ambiguous guesswork dressed up with a bunch of mathematical equations used to give it a veneer of scientific appearance. Indeed, it seemed to assume racial polarization in order to prove racial polarization. Based on this questionable methodology, the authors concluded that RCV doesn’t reduce racial polarization. But the premise of this study missed an obvious reality: since RCV incentivizes more candidates to run and can result in more diverse fields of candidates, one would expect more racially-polarized voting in voters’ top rankings, not less, because more voters would have someone on that ballot who represents their racial or ethnic identity and more freedom to vote for their real first choice. By encouraging more diverse candidates and eliminating the spoiler effect, RCV facilitates voting for a favorite candidate of your own race and then selecting a second choice with the best likelihood of winning. The real measure is what voters do with their additional rankings – do they consider other candidates and vote “for the best of the rest”? This study didn’t tell us anything useful about that. But readily available data shows this is common in real RCV elections, and candidates act on this knowledge by engaging with voters outside their racial group. So the authors of this study failed to consider or account for what actually happens in real-world elections, and their conclusions are tainted by this failure. New America has contributed a great deal over the past 2.5 decades to our understanding of many policy areas, including politics and political reform (full disclosure: Steven Hill, one of the authors of this article and of the “Shaky Political Science” research paper and series, is a former senior fellow and former director of New America’s political reform program, 2005-2010). So it was especially disappointing that New America funded and published such a large batch of dubious studies about ranked choice voting. The stated praiseworthy goal of its Electoral Reform Research Group was to inspire new academic research about ranked choice voting that would shed light and help the public better understand the impacts of RCV. Instead, the ERRG research did just the opposite. It produced dubious research that created more confusion and misinformation. In so doing, New America’s political reform program contributed to “shaky political science.” Steven Hill and Paul Haughey Share Upgrade to $5 subscription Thanks for reading DemocracySOS, a reader-supported digital portal for the pro-democracy movement. Subscribe for only $5 per month to receive full benefits and to support our work. Subscribe
Soil that powers devices: Scientists create battery-free electricity from dirt
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Ikea and Samsung promise glitch-free SmartThings integration
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New Microsoft Pro and Surface Laptop release delay rumoured
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Advancing epidemic and pandemic preparedness through research readiness: SEAR Unity Studies Network Sites chart the way forward in 2026
On 25 March 2026, WHO Regional Office for the South‑East Asia Region (SEARO) convened the first meeting of the SEAR Chapter of the Unity Studies Network Meeting in 2026 to take stock of progress and plan next steps for strengthening epidemic and pandemic preparedness across the Region. The meeting focused on reviewing the ongoing adaptation of selected, standard Unity Study methodologies, sharing field implementation experiences from 2025, and identifying key priorities to guide collaborative work in 2026.Held virtually, the meeting brought together representatives from six Unity Studies network sites across five countries (Bangladesh, India, Nepal, Sri Lanka, and Thailand) as well as participants from all three levels of WHO.The meeting took place against the backdrop of ongoing regional efforts in South‑East Asia to strengthen early investigations and studies for epidemic and pandemic preparedness, with a focus on pandemic influenza and other emerging respiratory pathogens with epidemic and pandemic potential. In this context, and as part of preparedness for the rapid generation of evidence, the WHO Health Emergencies Programme in SEAR (WHE/SEAR) encourages Unity Studies sites to adapt prioritized, standardized Unity Study protocols to their national contexts. This approach supports the generation of timely and reliable evidence to inform public health decision‑making during epidemics and pandemics.These efforts are supported through a combination of experience sharing, participation in the global Unity Studies network webinars, capacity‑building of the national public health workforce, and regional collaboration that promotes inter‑country, inter‑agency, and inter‑ministerial action. In parallel, sites are encouraged to explore diversified funding sources to help sustain research preparedness over the long term.Discussions highlighted the importance of WHO’s catalytic financial support through the Pandemic Influenza Preparedness (PIP) Partnership Contributions (PIP‑PC). When combined with national and institutional resources, this support enables countries and sites to maintain continuity of activities at a core level and remain prepared to respond rapidly and effectively to future epidemics and pandemics.Regional and Global Updates: Opening the session, Dr Pushpa Ranjan Wijesinghe, Programme Area Manager, Pandemic and Epidemic Management (PEM) Unit, WHO Health Emergencies Programme, highlighted the progress made in operationalizing the Unity Studies Network across the Region and acknowledged the collaborative efforts of WHO at all three levels, together with country unity studies sites. He emphasized the availability of limited but catalytic funding through the Pandemic Influenza Preparedness (PIP) Partnership Contribution (PIP‑PC) workplan at SEARO to sustain network activities in 2026, while encouraging sites to leverage additional funding sources to expand implementation.Representing SEARO, Dr Ashok Basnet, consultant, Pandemic Influenza Preparedness reported that, at the regional level, the Unity Studies Network currently includes six sites across five countries: Bangladesh, India, Nepal, Sri Lanka, and Thailand. He noted that in 2025, catalytic funding supported protocol adaptation, training, and pilot implementation. In addition, a regional Unity Studies symposium held during the Global Public Health Summit in Sri Lanka strengthened and advocated collaboration between the Unity Studies site, national decision‑making forum, relevant technical units of the ministry of health, WHO and other partners.The symposium also highlighted the role of early investigations and studies as a key component of a multi‑source surveillance system needed at country level to generate a broad range of evidence. It underscored the critical role of country Unity Studies sites and similar research entities in generating evidence to support public health decision‑making during epidemics and pandemics.Participants of the WHO GISRS Unity Studies Sites Network- SEAR Chapter Meeting 2026 (Photo credit- PEM/WHE/SEARO). SEARO also updated participants on ongoing efforts to develop a regional Unity Studies webpage and to promote participation in global knowledge‑sharing platforms, including webinars and the WHO Community of Practice. Priorities for 2026 include supporting the implementation of Unity protocols, strengthening collaboration and experience sharing across sites, documenting and publishing findings, integrating Unity Studies activities as a component of multi-source national surveillance systems, and building workforce capacity.Dr Nickle Boddington of the Global Influenza Programme (GIP) at WHO Headquarters, which coordinates the Unity Studies Network globally, provided updates on global Unity Studies activities, noting that the network has expanded to 16 sites worldwide. New and updated protocols, including rapid clinical severity assessment tools and standardized data platforms, are being developed to support implementation. The global Community of Practice has also been launched to facilitate technical exchange, and continued support will be provided to sites for protocol adaptation, ethical approvals, and readiness for early investigation during outbreaks. Updates from Unity Studies sites in SEAR in 2025Unity Studies sites shared progress and experiences in implementing protocols across diverse country contexts. Sites initiated activities in 2025 using catalytic funding from SEARO, complemented by the mobilization of internal national resources. The Institute of Epidemiology, Disease Control and Research (IEDCR), Bangladesh, reported the adoption and piloting of investigation protocols on non‑seasonal influenza and emerging respiratory diseases during recent avian influenza outbreaks, demonstrating a multi‑source funding approach involving government, WHO and One Health partners.The Hamdard Institute of Medical Sciences and Research (HIMSR), New Delhi, India, highlighted the piloting of household transmission investigation studies, the expansion of the national Unity Studies network, and the contribution of study findings to global evidence through published outputs. The All-India Institute of Medical Sciences (AIIMS), New Delhi, India, shared experiences from large‑scale seroprevalence studies conducted during the COVID‑19 pandemic, recent efforts to strengthen national Unity network sites through training activities, and plans to initiate multi‑centre studies on respiratory viruses.Similarly, Prince of Songkla University, Thailand, highlighted its experience in developing integrated surveillance and diagnostic protocols for multiple respiratory pathogens, enhancing readiness for targeted investigations of emerging threats. The University of Sri Jayewardenepura, Sri Lanka, reported ongoing engagement with the Ministry of Health and exploration of additional funding sources to initiate studies. Meanwhile, the National Public Health Laboratory, Nepal, provided updates on continued coordination with national stakeholders to initiate implementation, despite delays related to the country context.Across sites, shared experiences underscored the importance of protocol adaptation, multisectoral collaboration, and sustainable funding to operationalize Unity Studies and generate evidence for public health action.Discussions and Way ForwardThe meeting reinforced key priorities for 2026, including strengthening unity studies protocol implementation, enhancing collaboration, knowledge sharing, integrating research as a component of national multi-source surveillance systems, and ensuring that generated evidence informs public health decision‑making. Participants also emphasized the importance of documentation, visibility, and leveraging multiple funding sources, and agreed on the following action points.Unity Studies Sites and WHO Country Offices:Advance adoption and implementation of at least one Unity protocol, continuing activities initiated in 2025 with catalytic funding, supplemented with other sources of funding where feasible.Document site-specific experiences, outcomes, and lessons learned; participate in regional and global knowledge-sharing platforms including WHO webinars and the Community of Practice.Work closely with ministries of health to establish unified national platforms for translating generated evidence into national public health decision-making. WHO Country Offices may facilitate technical support and complementary, synergistic funding opportunities where feasible. WHO Regional OfficeCoordinate with WHO Country Offices to support implementation of core unity studies activities by network sites through catalytic funding and provide technical guidance and virtual engagement opportunities throughout 2026.Facilitate the participation of all Unity Studies sites in the regional meeting on Unity Studies implementation in October 2026, to be held back‑to‑back with the Annual SEAR Regional PIP‑PC Meeting; develop and host a SEAR Unity Studies website; and mobilize support from academic institutions within the SEAR Unity Studies Network to support the development of regional manuscripts that showcase Unity Studies activities to scientific audiences and promote knowledge‑sharing.Explore complementary funding opportunities and partnerships to sustain and expand network activities across the Region.WHO HeadquartersProvide global technical guidance, updated protocols, and toolkits to support the implementation of Unity Studies in SEAR.Facilitate engagement of SEAR sites in global webinars, the Community of Practice, and opportunities to showcase their work.Support joint publications, inclusion of SEAR experiences in global outputs, and identification of additional funding and collaborative opportunities to strengthen Unity Studies activities.
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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 the summer of 2014, a California groundskeeper named Dewayne Johnson started noticing patches of raw, weeping skin spreading across his body. He had spent years applying Roundup, Monsanto’s glyphosate-based herbicide, for a school district, sometimes drenched in it when equipment malfunctioned. By 2014, he had a diagnosis: non-Hodgkin’s lymphoma. In 2018, a jury awarded him $289 million in damages, later reduced on appeal. Tens of thousands of lawsuits followed. The story hasn’t quieted down. It’s all over the internet, swinging between “this will kill you, buy organic” and “relax, you’re fear-mongering.” Some of that recent chatter stems from an Executive Order protecting glyphosate and its production, which set off intense infighting within the MAHA movement. Some of it reflects a deeper truth: Americans genuinely care about harmful chemicals in their environment (see poll below). And the conversation isn’t going anywhere, with a Supreme Court case involving Monsanto set for this April. Glyphosate (the active ingredient in Roundup) is one of the most widely used herbicides in the world. But does glyphosate cause cancer? And how much exposure is actually risky? With so much mistrust in institutions and corporations (and some genuine conflicts of interest), making sense of the actual scientific evidence is genuinely hard. Here is a translation of what the scientific evidence actually says. Does glyphosate cause harm? Glyphosate is an herbicide that kills most plants it contacts rather than targeting specific species. It works by blocking an enzyme pathway (EPSPS) that plants and many microorganisms need to produce essential amino acids. Without those amino acids, the plant can’t grow and dies. Glyphosate was first developed by Monsanto in the 1970s and became ubiquitous after the company introduced “Roundup Ready” crops in the 1990s. In 2024, Bayer removed glyphosate from residential Roundup-branded products due to litigation pressure. But today it is still the most widely used herbicide in the world. Farmers also discovered they could spray it directly on crops like oats, lentils, beans, and peas just before harvest to dry them out faster and make harvesting easier. Because of its widespread use, it’s obviously really important to understand if it’s harmful to humans. The key principle: The dose makes the poison The most important concept in toxicology is that “the dose makes the poison.” Sixteenth-century physician Paracelsus articulated what remains the foundational principle of toxicology: sola dosis facit venenum. Water will kill you if you drink enough of it. Aspirin saves lives at 81 milligrams and causes internal bleeding at higher chronic doses. So, at what dose is glyphosate poisonous? The LD50 in rats (the dose that kills half the test animals) is around 5,600 mg/kg body weight. Table salt is roughly 3,000 mg/kg. By that classic measure, glyphosate is less acutely toxic than salt. But some people are exposed to a lot of glyphosate through their work. And among the general public, there are many questions about low-dose, cumulative exposure from multiple sources over long periods. Does glyphosate cause health problems? Cancer Correlation maps are striking, with one analysis finding that 60% of counties that spray the most glyphosate have non-Hodgkin’s lymphoma rates above the national average. But correlation alone doesn’t establish cause, which is why animal and human studies matter. In animals: Rat studies are typically what most fear is rooted in, but most people don’t know the study details on the dosage given to rats. A pooled analysis of the 13 most rigorous rat studies found evidence that very high doses, around 55,000 times greater than typical food residue exposure, can cause certain cancers. In humans: One of the strongest studies followed 55,000 herbicide users over 20 years and found no overall increased cancer risk. There was one signal: among those with the highest use, rates of acute myeloid leukemia (AML) appeared elevated. A broader meta-analysis of 18 human studies also found no overall cancer risk, though a separate meta-analysis focused specifically on non-Hodgkin lymphoma found that people with high cumulative glyphosate exposure faced a 41% higher relative risk compared to those with low or no exposure. These human studies are observational studies (randomized controlled trials would be unethical), and meta-analyses are only as reliable as the studies they pool. But the story seems consistent: at very high exposure levels, there may be some cancer risk. But that’s about as far as the science currently takes us. Emerging research: gut and reproductive health Gut: Because glyphosate targets a pathway that exists in some gut bacteria, scientists have questioned whether it could affect the microbiome. In mice, a 2023 University of Iowa study found that glyphosate altered gut bacterial composition and raised markers of intestinal inflammation. A 2024 systematic review found similar patterns: disrupted gut bacteria and problems with the mucus layer that protects the intestinal wall in mice. Whether this translates to humans is an open question. The human gut is highly variable and resistant, and we don't yet have strong studies confirming the same effects in people. Reproductive health: Animal studies have found that glyphosate-based herbicides can interfere with reproductive hormones, including disrupting egg cell development in mice and reducing the number of eggs available in sheep’s ovaries. However, the doses used in these experiments were high, so it’s not yet clear whether the same effects would occur in humans. On the human side, a 2021 pilot study led by Mount Sinai researchers tested urine samples from pregnant women across the U.S. and found glyphosate in 95% of them. The study also found a possible connection between higher glyphosate exposure during pregnancy and a subtle physical difference measured in female newborns. That said, some connections weren’t statistically reliable and this was a small study. The researchers themselves said more and larger studies are needed before drawing firm conclusions. What do regulators say about glyphosate safety? In 2015, the International Agency for Research on Cancer (IARC), part of the World Health Organization, classified glyphosate as "probably carcinogenic to humans" (Group 2A). The classification is worth taking seriously. But, there’s an important distinction: IARC classifies hazard, meaning the potential to cause harm under some conditions. It does not assess risk, which accounts for actual exposure levels and the real-world probability of harm. The U.S. EPA, the European Food Safety Authority, Health Canada, and regulatory agencies in Australia and Japan have all concluded that glyphosate is “unlikely to be carcinogenic to humans” at realistic exposure levels. They’re asking a different question: given how much of this people actually encounter, what is the probability of harm? Your actual exposure is what determines your actual risk. What does glyphosate mean for your daily life? At the grocery store: Food residues are small. The EPA’s acceptable daily intake is 1.75 mg/kg of body weight per day. To hit that threshold from oatmeal alone, a 150-pound adult would need to eat roughly 50 pounds of oats every day. But this calculation addresses only one pathway (food), and we don’t yet have robust tools to measure people’s actual total exposure. If reducing exposure matters to you, organic certification prohibits synthetic herbicides, and organic oat products generally test at lower or undetectable levels. Either way, a diet rich in whole grains, conventional or organic, is far better for your health than one that omits them out of fear of residues. On the farm: Occupational exposure is orders of magnitude higher than consumer exposure. Farmers and farm workers who mix, load, and apply glyphosate-based herbicides, especially without protective equipment, face meaningfully different risk profiles. Proper PPE isn’t optional. Integrated weed management strategies that rotate chemical modes of action are also worth taking seriously, both for health and for long-term effectiveness. If you live near a farm: Spray drift is real. Concentrations near field edges can spike during and after application, particularly on windy days. This is an area where I think public health could really step up and empower rural communities with buffer zones, rather than placing the onus on individuals. But for now, knowing your local agricultural calendar and keeping windows closed during application are the first steps. Bottom line Dewayne Johnson was exposed to glyphosate at levels most people will never encounter, and a jury decided he deserved compensation. That outcome is not evidence that the oatmeal in your pantry is dangerous. But it is a reminder that the dose, the context, and the person doing the exposing all matter enormously. Glyphosate is a chemical tool with benefits, trade-offs, and genuine open scientific questions. Learning to hold that complexity without collapsing into panic or dismissal is, honestly, one of the most useful and hardest things to do in navigating modern health science. Love, YLE Your Local Epidemiologist (YLE) is founded and operated by Dr. Katelyn Jetelina, MPH PhD—an epidemiologist, wife, and mom of two little girls. Hannah Totte, MPH, is an epidemiologist and YLE Community Manager. YLE reaches more than 425,000 people in over 132 countries with one goal: “Translate” the ever-evolving public health science so that people will be well-equipped to make evidence-based decisions. This newsletter is free to everyone, thanks to the generous support of fellow YLE community members. To support the effort, subscribe or upgrade below: Subscribe
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PRET for impact: Advancing pandemic preparedness in the Democratic People’s Republic of Korea
IN THE SPOTLIGHT PRET for impact: Advancing pandemic preparedness in the Democratic People’s Republic of KoreaThe Democratic People’s Republic of Korea (DPR Korea) is making significant progress in strengthening pandemic preparedness, with dedicated support from the WHO South‑East Asia Regional Office (SEARO) and the Pandemic Influenza Preparedness (PIP) Partnership Contribution (PC).Aligned with the WHO Global Influenza Strategy and the Pandemic Influenza Preparedness (PIP) Framework Partnership Contribution High‑Level Implementation Plan III (2024–2030), the country is strengthening its capacity to prepare for and respond to influenza and other respiratory threats. In early 2025, a core team from the Ministry of Public Health of the Democratic People’s Republic of Korea (DPRK) participated in a two‑day virtual workshop, which included a mini‑simulation exercise. The workshop was organized by WHO SEARO in collaboration with the Preparedness and Resilience for Emerging Threats (PRET) Secretariat at WHO headquarters and the WHO Country Office for the DPRK. Read more A REGIONAL CONSULTATION GUIDES WHO SEARO IN DEVELOPING A ROADMAP FOR IMPLEMENTING THE PREPPAREDNESS AND RESILIENCE FOR EMERGING THREATS (PRET) INITIATIVE BASED RESPIRATORY PATHOGEN PANDEMIC PREPAREDNESSInfluenza and other respiratory pathogens with pandemic potential continue to pose major public health risks globally. To strengthen regional preparedness and response, Member States of the WHO South‑East Asia Region (SEAR) convened in Colombo, Sri Lanka, to review and provide input on the draft Roadmap and Action Framework for Broad Respiratory Pathogen Pandemic Planning (2026–2031). Supported through the Pandemic Influenza Preparedness (PIP) Partnership Contribution (PC) funds, the proposed roadmap aims to advance robust, integrated preparedness and response capacities for influenza and other respiratory threats across SEAR. This work is aligned with the Preparedness and Resilience for Emerging Threats (PRET) initiative and the SEAR Strategic Roadmap on Health Security and Health System Resilience for Emergencies Read moreSRI LANKA’S USE OF ANTIVIRALS TO REDUCE THE SEASONAL INFLUENZA BURDEN Sri Lanka is strengthening its influenza response by placing greater emphasis on rapid testing, timely treatment, and integration with maternal health services. With support from WHO, the country is building a stronger evidence base on the burden of influenza and the value of vaccination, advancing a holistic approach to protect vulnerable populations and enhance pandemic preparedness. To reduce hospitalizations and deaths from seasonal influenza, Sri Lanka is taking proactive steps to expand access to rapid diagnostic testing and ensure prompt initiation of treatment. National clinical guidelines recommend the use of oseltamivir for individuals at high risk of complications, helping prevent severe illness and shorten the duration of infection. This strategy not only saves lives but also supports local pharmaceutical production, contributing to a reliable supply of antivirals for both seasonal outbreaks and future pandemics.Read moreBHUTAN BOOSTS PANDEMIC INFLUENZA READINESS WITH PANDEMIC INFLUENZA PREPAREDNESS FRAMEWORK PARTNERSHIP CONTRIBUTION SUPPORTED TRAINING AND SIMULATIONSOriginally developed in 2012, the National Influenza Pandemic Preparedness and Response Plan (NIPPRP) has long served as Bhutan’s central framework for managing influenza threats. In 2024, the plan underwent a comprehensive update to reflect evolving global and regional guidance, lessons learned from the COVID‑19 pandemic, and new capacities developed under the PIP Framework. The revised NIPPRP is now more clearly aligned with Bhutan’s broader emergency and disaster risk management structures, ensuring a coordinated, whole‑of‑government approach to pandemic response. As part of efforts to strengthen national pandemic preparedness, Bhutan conducted three national‑level training sessions followed by simulation exercises on influenza pandemic preparedness and response at five sentinel hospitals between April and May 2025, with support from the PIP Partnership Contribution (PC). These activities brought together more than 140 health professionals and key partners from across the country, with a focus on operationalizing the updated NIPPRP and enhancing readiness at key frontline facilities. Read moreSTRENGTHENING INFLUENZA SURVEILLANCE: WHO BRINGS TOGETHER INFLUENZA EXPERTS FROM SOUTH-EAST ASIA AND WESTERN PACIFIC REGIONSThe World Health Organization (WHO) convened the 18th Bi‑regional Meeting of National Influenza Centres (NICs) and Influenza Surveillance for the WHO South‑East Asia and Western Pacific Regions from 9–11 September 2025. Hosted by the WHO South‑East Asia Regional Office (SEARO), the meeting brought together representatives from Member States, Territories, and Areas across both regions, alongside experts from the WHO Collaborating Centres for Reference and Research on Influenza in Australia and Japan, the WHO Collaborating Centre for Studies on the Ecology of Influenza in Animals in the United States, quadripartite partners (WOAH and FAO), and other technical counterparts. The consultation provided a platform to review progress, share experiences, and outline priorities for strengthening influenza and respiratory pathogen surveillance across the two WHO regions.Read moreNETWORKS AND TECHNICAL PARTNERSHIPSSTRENGTHENING PANDEMIC READINES: SOUTH-EAST ASIA ADVANCES UNITY STUDIES UNDER THE PANDEMIC INFLUENZA PREPAREDNESS INITIATIVEThe WHO Global Influenza Surveillance and Response System (GISRS) Investigations & Studies initiative, also known as Unity Studies, provides a standardized, globally coordinated framework to support these efforts. Originally developed for influenza and adapted during COVID-19, Unity Studies enable countries to conduct rapid, nationally led investigations and studies to assess transmissibility, severity, susceptibility, and intervention effectiveness. To better prepare for future influenza pandemics, WHO has recently launched a network of sites which will be ready to conduct these investigations and studies rapidly in the event of a pandemic, or epidemic of an emerging or re-emerging respiratory pathogen. The WHO South-East Asia Region now hosts six sites of WHO GISRS Unity Studies across the five countries including Sri Lanka, strengthening regional capacity for early pandemic investigations. From 13–16 July 2025 in Colombo, the Global Public Health Summit offered an opportunity to advance this agenda. Read moreSTRENGTHENING PANDEMIC PREPAREDNESS THROUGH UNITY STUDIES: REAL-WORLD APPLICATIONS FROM INDIA IN WHO’S SOUTH-EAST ASIA REGION As countries continue to strengthen their pandemic preparedness through investments in resilient health systems during the interpandemic period, the World Health Organization’s (WHO) Global Influenza Surveillance and Response System (GISRS) Investigations & Studies Global Initiative, Global Initiative, commonly known as Unity Studies, remains a vital framework for respiratory pathogen pandemic preparedness. These standardized epidemiological protocols enable countries to rapidly generate high‑quality data on transmissibility, infection severity, population immunity, and the effectiveness of public health interventions. To highlight how Unity Studies protocols are being adapted and applied in national settings, WHO Headquarters, together with WHO Regional Offices, including the WHO South‑East Asia Regional Office (SEARO), hosted a global webinar. The session showcased case studies from India and the United Kingdom (UK), demonstrating innovative approaches to respiratory virus surveillance and pandemic readiness. Read moreSTRENGTHENING REGIONAL INFLUENZA SURVEILLANCE AND PREPAREDNESS: WHO SEARO HOSTS HIGH-LEVEL WEBINAR AMID RISING H5 THREATS In response to the increasing detection of avian influenza A(H5) cases in humans globally and within the WHO South‑East Asia Region, the WHO Regional Office for South‑East Asia (SEARO) convened a regional webinar on 25 July 2025 to review preparedness mechanisms, strengthen surveillance pathways, and promote timely reporting of unusual respiratory events. The virtual session brought together more than 50 participants from Ministries of Health, National Influenza Centres (NICs), National IHR Focal Points (NFPs), WHO Headquarters, WHO Country Offices across the Region, and WHO Collaborating Centres (CCs) for influenza. The webinar provided an opportunity to share situational updates, discuss surveillance gaps, and reinforce coordination mechanisms critical for early detection and rapid response to avian influenza threats. Read moreOther Key PIP Updates (July-December 2025)MEMBER STATES:Bangladesh drafted and conducted a series of Core Group Meetings in 2025 to review and update the National Pandemic Preparedness and Response Plan for Respiratory Pathogens. In addition, the country carried out Rapid Response Team (RRT) trainings on seasonal and avian influenza and other re‑emerging infectious diseases. Bangladesh also updated its epidemiological data‑training programme for physicians at sentinel surveillance sites, using a revised epidemiological data‑collection tool.Bhutan revised and updated its National Influenza Pandemic Preparedness and Response Plan (NIPPRP) in 2025 and developed a national Standard Operating Procedure (SOP) for influenza outbreak management. DPR Korea conducted a nationwide simulation exercise in July 2025 to support the update of its National Influenza Pandemic Preparedness Plan (2019). This followed a PRET‑based virtual orientation and mini‑simulation exercise held in May 2025 by SEARO and the WHO PRET Secretariat. The comprehensive exercise, conducted across central and provincial levels, assessed strengths and gaps in pandemic influenza preparedness and response. The feedback gathered has been incorporated into the revised draft plan to address identified weaknesses and strengthen overall readiness.India conducted a targeted nationwide online re‑orientation training on review and updates to the influenza portal for all State Surveillance Officers (SSOs) and relevant officials using the Integrated Health Information Platform (IHIP). The training emphasized correct marking and reporting of influenza samples as “unsubtypable” or “not subtyped” where applicable, to ensure accurate national surveillance.Maldives conducted simulation exercises in three atolls based on its existing National Pandemic Preparedness and Response Plan (NPPRP). The country plans to conduct similar exercises in three additional atolls after updating the NPPRP in 2026. In addition, Maldives conducted training on FluID and FluMart as part of a broader data‑management training package for Ministry of Health and Health Protection Agency officials, aimed at improving routine reporting to WHO platforms and the DHIS2 system.In Myanmar, technical support was provided to the National Influenza Centre (NIC) through dedicated human‑resource support (January–December 2025) to sustain data management and laboratory detection capacity for influenza and other emerging and re‑emerging respiratory viruses. This support also included strengthening genomic sequencing capacity.Nepal conducted a consultative workshop on the National Pandemic Preparedness and Response Plan (NPPRP) in July 2025. The workshop engaged key stakeholders from multiple sectors to review lessons from past pandemic responses, assess current mechanisms, and gather critical inputs for strengthening national preparedness. Key recommendations included revising the 2019 NPPRP and developing an updated Influenza Pandemic Preparedness Plan aligned with emerging risks, national priorities, and relevant international frameworks. Nepal also held a two day workshop dedicated to advancing the National Action Plan for Genomic Surveillance, aimed at reinforcing the country’s capacities for early detection and monitoring of emerging respiratory pathogens.Sri Lanka received the final report from WHO on the feasibility assessment for conducting an Influenza Burden of Disease (BoD) study in the country. The assessment, conducted from 28 April to 9 May 2025, included a desk review of existing data sources, document review, visits to sentinel surveillance sites, and key informant interviews. The study identified critical data sources required for a future BoD assessment, as well as their availability and quality. Officials from the Epidemiology Unit, the National Influenza Centre (NIC), and SARI/ILI sentinel hospitals across the country participated in the assessment.Timor Leste conducted on‑the‑job training and monitoring activities at two sentinel surveillance sites to strengthen surveillance and laboratory capacity for the detection of SARI, ILI, ARI, and other respiratory infections. REGIONAL OFFICE:SEARO conducted a Member States Consultation on the first draft of the Regional Roadmap and Strategic Action Framework for implementing WHO’s PRET‑based broad respiratory pathogen pandemic planning for 2026–2031. The consultation took place on 29–30 October 2025 during the final two days of the Regional PIP PC Annual Review and PRET Meeting in Colombo, Sri Lanka. Participants included representatives from Member States, WHO Country Offices, WHO SEARO, WHO Headquarters, partner agencies, and invited multidisciplinary technical experts. They reviewed the draft and provided detailed feedback, which has since been incorporated into a revised version. The consultation helped shape a shared regional vision for robust, PRET‑aligned pandemic preparedness, building on country‑level progress, addressing existing gaps and challenges, and aligning with global and regional health security frameworks. The publication is planned for release during the SEAR regional committee in 2026.SEARO documented the evolution of the National Influenza Pandemic Preparedness Planning process in the Region, tracing its development from its origins as a planning tool for responding to avian influenza outbreaks in 2006, through the experiences of the 2009 and 2019 pandemics, to the current status. The document outlines the way forward for respiratory pathogen pandemic planning across all 10 SEAR countries, aligning future national plans with WHO’s PRET Framework for broad respiratory pathogen pandemic preparedness.The Infectious Hazard Management (IHM) Unit has drafted the WHO South‑East Asia Regional Guidance on Influenza Prevention and Control, aligned with the Global Influenza Strategy and other regional initiatives. The draft is currently under review. Although this technical product was developed in response to a request from the Ministry of Public Health of the Democratic People’s Republic of Korea (MoPH‑DPRK), SEARO is finalizing it for use not only in DPRK but also across all Member States in the Region.The Immunization and Vaccine Development (IVD) Unit, in collaboration with WHE/SEARO, completed a regional landscape analysis on key barriers, challenges, and opportunities for the introduction of seasonal influenza vaccination in WHO South‑East Asia Regional (SEAR) countries.Additionally, the Immunization and Vaccine Development (IVD) Unit has completed a regional landscape analysis of influenza vaccine procurement systems across the WHO South‑East Asia Region (SEAR).KEY MEETINGS AND PUBLICATIONSMEETINGS:Six-month progress review of the implementation of the PIP-PC workplans in SEAR, focusing on Outputs 1 and 2 for the period January to June 2025, and held virtually across three sessions on 4, 8, and 10 July 2025. Read moreAnnual Regional Meeting (Hybrid) on Pandemic Influenza Preparedness (PIP) Partnership Contribution (PC) and WHO’s Preparedness and Resilience for Emerging Threats (PRET) Implementation in SEAR in Colombo, Sri Lanka, 27 to 30 October 2025. Read more18th Bi-regional Meeting (Virtual) of National Influenza Centres (NICs) and influenza surveillance in WHO South-East Asia and Western Pacific Regions from 9 to 11 September 2025. Read morePUBLICATIONSImplications of influenza A virus surveillance in Southeast Asian Region countries: a scoping review of approaches for the surveillance of swine influenza viruses at human-swine interfaces. Read moreAnnual South-East Asia regional meeting on operational planning for implementation of global influenza strategy and preparedness for respiratory viruses in 2025 Report on annual review meeting of implementation of pandemic influenza preparedness (PIP) partnership Contribution (PC) Fund in South-East Asia Region and the simulation exercise on PRET Status of influenza in who south-East Asia Region
Five Berkeley Lab Scientists Receive DOE Early Career Research Awards
Five scientists from Lawrence Berkeley National Laboratory (Berkeley Lab) have received Early Career Research Program (ECRP) awards from the Department of Energy. The ECRP program, now in its 16th year, was established to bolster the nation’s scientific workforce by supporting exceptional researchers at the outset of their careers, when many scientists do their most formative work. Recipients use the funding to pursue projects that have the potential to solve scientific challenges and advance the country’s STEM capabilities. To be eligible for the program, a researcher must be an untenured, tenure-track assistant or associate professor at a U.S. academic institution, or a full-time employee at a DOE national laboratory or Office of Science user facility, who is within 10 years of having earned their doctorate. Awards to an institution of higher education will be approximately $875,000 over five years, and awards to a DOE national laboratory or Office of Science user facility will be approximately $2,750,000 over five years. This year’s Berkeley Lab awardees and their projects are listed below: Timon Heim: Better detectors for more powerful particle colliders Timon Heim is a staff scientist in the Physics Division. His ECRP project will develop a new type of detector that can track the particles generated by collisions in particle colliders with high spatial and temporal resolution. Current detectors in use at high-energy colliders, like the ATLAS detector at the Large Hadron Collider (LHC) at CERN, can track the trajectory and measure momentum of particles, but they can’t track a particle’s time of arrival with sufficient precision to discern from which of the many particle interactions they originate. Heim’s proposed technology — called a 4D tracking detector, because it captures data about particles’ positions in space and time — aims to solve this challenge, allowing researchers to glean more precise data from collision experiments to uncover new insights into fundamental forces and potentially discover new particles. The enhanced capabilities of 4D detectors will be a necessity for experiments at future colliders, such as the Muon Collider or Future Circular Collider. The project will develop custom microelectronics that process the signal of a pixelated silicon sensor that can register charged particles passing through it in an extreme radiation environment that these colliders produce during operation. The goal is to measure individual particles’ position with 50-micro-meter spatial and 50 pico-second (one trillionth of a second) temporal precision, for an incoming flux of around 1 billion particles per second per square centimeter. Harrison Lisabeth: Studying underground processes to unleash abundant energy Harrison Lisabeth is a research scientist in the Energy Geoscience Division focused on combining real-world experiments and AI to guide development of geothermal energy systems. His project will explore how deep underground rock formations – where geothermal wells are dug – are stressed by chemical reactions of the minerals and mechanical strain from movements in the Earth. A better understanding of subsurface stresses is essential to engineer safe and reliable geothermal wells that access the heat of deep rocks to produce electricity. It is estimated that the U.S. could generate ten times the amount of energy needed to power the grid using geothermal sources. Lisabeth plans to simulate how minerals grow, dissolve, and influence the behavior of surrounding rock in geothermal reservoirs by tracking chemical changes in rock samples under pressure in the lab. The project will also develop machine learning models to predict geologic strain based on field measurements and refine existing models that describe how common rock types deform under stress. Combining these approaches, Lisabeth hopes to develop a framework to predict stress to improve stability and longevity of geothermal wells. Daniel Carney: Pushing the quantum boundaries of particle measurement Daniel Carney is a staff scientist in the Physics Division who uses quantum information science to study particle physics and gravity. At very small scales, “quantum noise” — random fluctuations from the act of measurement — can drown out faint signals from rare processes. Carney’s ECRP project, “Fundamental Physics at the Quantum Limits of Measurement,” aims to build a new mechanical quantum sensor that can measure extremely small particle interactions below the limits of that quantum noise. The approach uses lasers to hold a tiny bead containing radioactive atoms; when one decays, the system measures the bead’s motion and the energy of any particles emitted. The experiment’s initial goal is to search for heavy sterile neutrinos, hypothetical particles predicted by some extensions of the Standard Model. The system could also help measure the mass of the neutrino, study the way particles interact through the electromagnetic and weak forces, and hunt for low-mass dark matter — all unsolved mysteries in particle physics. Callum Wilkinson: Using machine learning to demystify neutrinos Callum Wilkinson is a staff scientist in the Physics Division and part of the international Deep Underground Neutrino Experiment (DUNE) hosted by Fermi National Accelerator Laboratory. DUNE will create an intense beam of neutrinos that interact in detectors in Illinois and, after an 800-mile journey through the earth, in massive detectors in South Dakota. By precisely measuring neutrinos and how they change, researchers will get a better understanding of their fundamental properties and whether they are responsible for why there is so much more matter than antimatter in our universe. Wilkinson’s ECRP project will apply two machine learning techniques to improve the analysis of DUNE’s data and enhance the experiment’s precision. One approach will look for hidden patterns in how neutrinos interact and produce low-energy particles, while the other will help correct for distortions in what the detectors record. The methods will be tested on one of DUNE’s prototype detectors and could be used in other neutrino experiments, improving their measurements and helping scientists build better models. Aditi Krishnapriyan: Machine learning methods that scale as datasets grow Aditi Krishnapriyan is a faculty scientist in Berkeley Lab’s Applied Mathematics and Computational Research Division (AMCR) and assistant professor at UC Berkeley. Her project, awarded through UC Berkeley, will develop machine learning methods that scale with dataset size for modeling complex, multi-scale phenomena. The resulting tools can be applied in a wide range of research, including the study of new electrolytes for batteries or the examination of molecular interactions. ### Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to groundbreaking research focused on discovery science and solutions for abundant and reliable energy supplies. The lab’s expertise spans materials, chemistry, physics, biology, earth and environmental science, mathematics, and computing. Researchers from around the world rely on the lab’s world-class scientific facilities for their own pioneering research. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 17 Nobel Prizes. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy’s Office of Science. DOE’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.
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After rebooting the Pebble smartwatch, founder Eric Migicovsky is expanding his company's device lineup with a new smart wearable: an AI-powered smart ring known as Index 01. Named for the finger where the ring is meant to be worn, the new $75 ring is not meant to be a competitor to the always-on, always-listening AI devices, like the AI pendant Friend, but instead offers a way to record quick notes and reminders with a press of a button on the ring's side. AI only comes into play via the open source, speech-to-text, and AI models that run locally on your smartphone, via the open source Pebble mobile app. That is, if the Ring's button is not being pressed, it's not recording. (And this is a press-and-hold gesture, too, which means you can't start the ring's recording and then let go to surreptitiously record a conversation.) You can wear the stainless steel ring while in the shower, washing hands, doing dishes, or in the rain, but you would have to take it off for other water-related activities, like swimming. At launch, it's water-resistant to 1 meter. The ring is also not a fitness tracker or sleep monitor. It doesn't record details about your heart rate or health. And it's not there to be your AI friend. "I'm not trying to build some AI assistant thing," Migicovsky told TechCrunch in an interview. "I build things that solve one main problem, and they solve it really well," he explains. "I think of as external memory for my brain...that's what this is. It's always with you." Plus, the ring has been designed to be highly reliable and privacy-preserving, he says, as all your thoughts are stored on your phone, not in the cloud. There is no subscription. Migicovsky has been wearing the ring for three months now and says he cannot imagine going back to a world where he doesn't always have a memory device with him. "The problem is that, during the day, I get ideas or I remember something, and if I don't write it down that second, I forget it," he says. The ring solves this problem, he adds, without becoming another device you need to charge. "The battery lasts for years," Migicovsky claims. Technically, the ring is said to support roughly 12 to 14 hours of recording. On average, the founder says he uses it 10-20 times per day to record 3-6 second thoughts. At that rate, he'll get about two years of usage. When the ring's battery dies, you can ship it back to the company for recycling. When using Index, you can record up to five minutes of audio, which can be saved to the ring and synced to your phone later. This makes sense for recording briefer, personal thoughts and notes, even when you don't have your phone handy, but it wouldn't work for recording a longer chat, like a presentation, meeting, or in-person interview of some kind. The ring also supports 99+ languages and has a bit of on-device memory, in case you're not in Bluetooth range of your device, where the recording is ultimately saved and transcribed. (The raw audio is retained, too, in case the speech-to-text is garbled due to loud background noise). If you own a Pebble smartwatch or one from another brand, your recorded thought can even appear on the watch's screen so you can verify it's correct. The ring works with Pebble's mobile app, which offers notes and reminders, but can optionally integrate with your phone's calendaring system, too, or other apps, like Notion. And the ring's software is open source, which makes it hackable by the community, the founder points out. Because of its open nature, the ring's button is already programmable. In addition to the press-and-hold gesture, you can program the ring to do other things with a single or double press, like play or pause your music or control the shutter on your phone's camera. You could use it to send a message through the universal chat app Beeper, which Migicovsky also created, or you could add your own voice actions via MCP. A new approach to hardware Migicovsky acknowledges that hardware can be difficult to get right -- as the previous exit of Pebble to Fitbit showed. (Fitbit, too, was later acquired by Google in 2021). "I didn't earn any money during Pebble -- we exited, but it was not a great exit," Migicovsky admits. This year, however, he decided to reboot the Pebble project after Google open sourced the PebbleOS, which opened up the door to new hardware. With his new company, Core Devices, Migicovsky plans to do things differently. Still, the founder doesn't regret his previous choices, he clarifies. "I wouldn't have gone back and changed anything. I loved what we built. I loved what we did. I love the company that we built, but it's not the only way to build a company," he told TechCrunch. " And, speaking as an ex-YC partner, there are -- there's a time and a place for building a venture-backed startup. Some companies are phenomenal when they raise money and build a big team, and I tried that...I think what I'm doing now is trying an alternative path, which is start from profitability," he says. The new company is a small team of five, self-funded, and focused on sustainability. So far, Core Devices has shipped the Pebble 2 Duo smartwatch with a black-and-white display. Its first run sold out, and the company is now preparing to ship the upgraded version, the Pebble Time 2. The newer device, which has seen 25,000 pre-orders, is a stainless steel watch with a larger, color e-ink screen. As for the Index 01, the ring's pre-order offer ends in March 2026. After that, the price increases to $99. It currently comes in silver, polished gold, and matte black and works with iOS and Android devices. Customers can select from eight ring sizes and three colors.
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