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A major insurer on how it may replicate Medicare's chronic-care experiment
In this edition of STAT Health Tech: A California insurer on how it may replicate Medicare's tech-enabled chronic-care experiment.
Cosell: Jaguars WR Washington is New 'Bromance'
As the Jacksonville Jaguars continue to ascend, so does young wide receiver Parker Washington. Will he be able to replicate his success against the Denver Broncos in Week 2?
Goodwin wants repeat against other half of Old Firm
Jim Goodwin has called on Dundee United to replicate the level of performance they produced in their victory over Celtic as they bid to derail Rangers' Scottish Premiership title bid on Saturday. United defeated the other half of the Old Firm 2-0 at Tannadice in their last match before the international break to leave Martin O'Neill's side five points adrift of leaders Hearts. "We've got to take a lot of confidence out of that Celtic game," manager Goodwin said looking ahead to facing second-top Rangers, with whom they have drawn 2-2 home and away this season.
Making Our Own Luck
We are cross-posting a piece that former NIH official Kris Willis wrote for Macroscience. Kris is now the President and Founder of the Woodley Park Institute, for which I’m a board member. Picking winners or hedging bets? Federal science agencies have long wrestled with the question of how to ensure their billions of dollars of grants and contracts result in maximum benefits for Americans. On the surface, it might seem that a goal-oriented, interventionist style of management would be the best policy: prioritize our most pressing problems and distribute funding accordingly.This top-down strategy creates a unique hazard, though: bad choices can send resources in the wrong direction, resulting in slow progress that impedes the delivery of tangible benefits to citizens. Moreover, if decision makers pursue an applied advance when the underlying scientific principles remain poorly understood, they risk costly failures and a loss of support for the research enterprise at large. Examples include Nixon’s 1971 War on Cancer, undertaken on the premise that a cure could be achieved in less than a decade, the drive to build an operational commercial fusion plant by the mid-1990s, and the National Plan to Address Alzheimer’s Disease, which failed to produce the effective treatments and prevention strategies promised by 2025. The contrasting laissez-faire approach is to hedge your bets. If the path to an advance is impossible to predict, then the wisest choice might be to distribute funding to as widely as possible. The main risk of this strategy is that spreading funding thinly may limit the resources available to the most promising research, resulting in the same negative outcomes as the interventionist approach: limited support for the most promising topics, slow progress, and delayed returns. In reality, at science agencies like the National Science Foundation and National Institutes of Health, program managers navigate between these two extremes, setting some funds aside to target high-priority areas and using the rest to cover the widest possible range of meritorious proposals. This middle path seems like the most rational way to proceed, but it still requires making choices. How much should be set aside for high priority topics? What should those topics be? How should we define merit? Expert opinion can provide a helpful guide, but leading experts can have remarkably different opinions about what matters and what should be prioritized. Moreover, experts and administrators alike can be risk averse, giving an edge to high-profile, well-established concepts. What’s really needed is a data-driven framework to help guide decision-making, one that doesn’t simply repackage prestige or incumbency. Recognizing breakthroughs years in advance Many breakthroughs fail to attract funding or recognition at the earliest stages of development. Katalin Karikó, for example, spent years struggling to win grants for work on mRNA that would eventually win her and Drew Weissman a Nobel Prize. Similar stories can be told about Jim Allison’s groundbreaking studies leading to the development of cancer immunotherapy, Stan Pruisner’s demonstration that prions can self-replicate without DNA, or Robin Warren and Barry Marshall’s proof that Helicobacter pylori, not stress, causes ulcers. Yet at some point, the scientific community recognizes and responds to transformative breakthroughs like these. How does that change happen? And what if science funders could identify the areas that are on the cusp of a breakthrough? Those questions led my collaborators and me to study how scientists responded to past breakthroughs. Based on patterns in that data, we developed a means of predicting future breakthroughs, using biomedicine as a proof of concept. It’s worth taking a moment to describe how our approach works. Briefly, our prediction process begins by grouping the 18 million or so peer-reviewed papers in PubMed, the authoritative database of biomedical research, into roughly 50,000 unique topics. A small subset of these topics are so widely acclaimed as breakthroughs that they have been recognized with a major prize like a Nobel or a Lasker Award. We theorized that studying the development of these outliers over time, especially before they gained acclaim, was the key to identifying future instances of them. After defining topics, we chose 21 representative breakthroughs and asked what they looked like before they won prestigious prizes. To answer that question, we re-ran our topic-mapping algorithm repeatedly, winding back the clock one year each time, stringing together the years to follow the progress of each field as it grew and advanced. We found that in each case, the years leading up to a discovery followed a pattern: a burst of papers on a new, rapidly evolving topic, many of which quickly became influential. These characteristics—the percent of papers that are new, the percentage of papers that are brought in from other topics, and the influence of each individual paper—can all be measured separately for any given topic. Together, they act as a predictive signal that is detectable an average of five years before a transformative discovery is made, and up to thirty years before the discovery receives a major prize. Scanning the current research landscape for topics that display this signal allows us to predict what work will likely produce a future breakthrough. Each year we analyzed includes four or five signals, a number that appears to have remained constant across a twenty year time frame. In their classic work on the sociology of science, Bruno Latour and Steve Woolgar established that the desire to participate in a major discovery is an important motivator for scientists as they consider whether they should take up (or abandon) a research problem. Although further work is needed, the simplest explanation for our results is that this behavior is widespread enough to detect at scale. A foundational advance, or something like it, draws the attention of scientists and causes them to change the direction of their research. They vote with their feet, staking their own reputation and careers by publishing on and citing the new idea. That upswell of interest is the foundation of our breakthrough signal. What kind of research does our model identify as a breakthrough? After building a model based on the patterns in our 21 preselected breakthroughs, we tested it by identifying all early signals of discovery from 1994 through 1997. Of the 18 signals we found in that time period, 17 can be traced forward in time to a breakthrough. Examples include super-resolution fluorescence microscopy (see the image above), the directed evolution of proteins and enzymes (Chemistry Nobel, 2018), and sequencing the human genome (National Medal of Science, 2008). The discovery of the role of leptin signaling in obesity is a particularly interesting case study. Around 1950, scientists noticed the existence of a type of lab mouse that suffered from a unique inherited form of obesity. When given the freedom to choose their own diet, these mice ate more than normal and preferred food that was high in fat. Researchers had few clues as to why the animals overate; early experiments pointed to the existence of a soluble ob factor found circulating in the bloodstream that regulated appetite, but its identity was unknown. In 1994, Jeffrey Friedman and colleagues identified and sequenced the ob gene, noting that the protein it encoded appeared to fit the profile of a circulating factor. A year later, a trio of high-profile publications demonstrated that ob was a hormone that regulated body weight and fat deposition by regulating appetite. One of these was co-authored by Friedman, who christened it leptin from the ancient Greek word for thin. Using our model, the data from 1996 make it clear this advance would become a breakthrough. Scientists had flocked to study the biology of the new hormone, generating the flurry of new papers and citations required to produce a signal. One of these demonstrated that serum leptin concentrations reflected the amount of adipose tissue in the human body, providing clinical validation of the earlier mouse studies. Before the identification of leptin, scientists were unable to point to a specific molecule that controlled appetite and adiposity, so that it was impossible to rule out non-physiological causes of obesity; its characterization fundamentally changed our concept of weight gain. In 2010, Friedman and fellow pioneer Douglas Coleman were awarded a Lasker for the discovery. Our work also identifies breakthroughs in clinical practice and behavioral and social sciences, even though these areas are less likely to draw the attention of major prize committees. One example that falls into this category is the introduction of endovascular aneurysm repair, a minimally invasive surgical procedure for the treatment of abdominal aortic aneurysm, a leading cause of death among Americans over the age of 65. Relative to the previous standard of care, it reduces in-hospital mortality by almost four-fold. In 2017, Timothy Chuter received the American College of Surgeons’ prestigious Jacobsen Innovation award for his multiple refinements to the technique. Another under-recognized breakthrough identified by our model is the development of standardized survey instruments to assess the quality of life for HIV+ patients. The antiretroviral cocktails introduced in the mid-1990s reduced AIDS mortality, but treatment came with serious side effects. The new evaluations showed that patients whose disease progressed had worse physical functioning than Americans with other chronic diseases, while those who were asymptomatic maintained a health-related quality of life on par with the overall US population. This evidence made it possible for patients and activists to argue that even in the absence of a total cure, and in spite of the side effects, alleviation of symptoms was a worthwhile priority for clinicians. My colleagues and I have failed to identify any significant recognition for the physicians and health policy experts who pioneered this advance, although in 2016, researchers called for the World Health Organization to set targets for good health-related quality of life as part of its framework to end the AIDS pandemic. From forecasting to funding How should science funders use our predictions? Any serious discussion of what we should do with this new capability needs to begin with an understanding of its limitations. We can’t (and shouldn’t) invest all of our resources in the small number of topics predicted to produce breakthroughs. Innovative new fields are born from existing ones; they rarely arise de novo. If funders don’t maintain a diverse portfolio, the breakthroughs of tomorrow will have no antecedents. Think of this as eating the seed corn, or killing the goose that lays the golden egg. There are also multiple goals of funding beyond basic scientific discovery, including translating discoveries into clinical practice, developing new technologies, supporting economic development, and training new scientists. The investments needed to accomplish these goals are likely different from those required to support emerging breakthroughs. Finally, we must be aware that we may not capture every instance of a breakthrough. The concept is fuzzy, and not every significant discovery is recognized with a major award. Further, breakthroughs are rare, meaning that our dataset is, by necessity, small. This makes formal estimates of accuracy challenging. Although our overall success rate appears to be high, any one signal may turn out to be a false positive. We found one apparent example: the development of the third generation COX-2 inhibitors, Vioxx and Celebrex, which were much celebrated in the early 2000s as effective but non-addictive pain relievers, meets all the algorithmic criteria of a breakthrough. Five years after it received FDA approval, researchers demonstrated that Vioxx was associated with serious cardiovascular complications, and the manufacturer voluntarily pulled the drug from the market. Celebrex remains available, although like other NSAIDs, it carries an FDA warning for increased cardiovascular risk. Decision makers who were overly focused on the breakthrough potential of this research might have wasted substantial resources nurturing an area that ultimately proved to be a failure—but so would those who relied on expert opinion at the time. That said, the potential benefits of funding the next mRNA vaccine or cancer immunotherapy years earlier than we might have are large. When we can’t assume the outcome of an action, it’s worth conducting an experiment. Can targeted support for areas that are poised to produce a breakthrough increase the return on scientific investment without cannibalizing future advances? With the necessary administrative infrastructure in place, conducting this experiment would be straightforward. First, identify all the breakthrough signals between 2018 and 2023, select half at random, and commit to a decade of investment. For biomedicine, a six year window should yield about 30 topics with the potential to produce a breakthrough. Providing strong support to half of these might be accomplished for $300 million a year. That’s roughly half of the recent annual budget of the NIH Common Fund or around 20% of the fiscal year 2025 budget for ARPA-H. After five years, then again at ten, compare to see which group — the one that was actively managed, or the control — produced more breakthroughs, and on what timescale. Although we used biomedicine as an example, the same experiment could just as well be run for physics, materials science, or any other discipline. Any attempt to undertake such an experiment should keep three points top of mind. First, we can predict the topic of future breakthroughs, but the people and vision for how to move the work forward are still important. In practice, this means success requires active program managers with the expertise to develop the science and the authority to do so, including by recruiting investigators. Second, we would be wise to learn from the failures of past attempts to fund transformative research. High among these are funding modestly repackaged work, confusing the genuinely novel with the merely unfamiliar, and relying too much on incumbent investigators. All of these risks can be mitigated by thoroughly and intelligently integrating high quality data into portfolio management. Finally, we need to have patience. Even with strong funding support, producing a breakthrough takes time. If supporting breakthroughs is the best way to nurture the birth of new fields, we also need to accurately describe and properly manage the other two stages, so that we invest wisely throughout the productive lifetime of ideas and divest once they reach the point of diminishing returns. We would benefit from more study of all of these phases, especially the transition from one to another. More research could answer questions like: When does a breakthrough become an established field? How do the availability of funding, the size and characteristics of the available workforce, and the accumulation of evidence that disagrees with prevailing models affect a field’s decline? Exactly how do old ideas give birth to new ones, and can we speed up the process? The more we understand about the dynamics of scientific progress, the better we can maximize the odds that our curiosity leads to results. I’d call that finding a way to make our own luck.
Michael Pollan explains why AI will never replicate human consciousness
Michael Pollan tells Scientific American why the science of consciousness may ultimately be too subject to our own conscious minds to crack
Steven Fulop on Fighting for Business Interests in Zohran Mamdani's New York
Steven Fulop, the new President and CEO of the Partnership for New York City, the city’s leading big business advocacy group, spoke with The New York Editorial Board on the morning of March 19, 2026. Fulop recently began in the role after 12 years as mayor of Jersey City and a 2025 run for governor of New Jersey. He replaced longtime Partnership President/CEO Kathryn Wylde. (photo by Liena Žagare) Participating journalists: Nicole Gelinas, Josh Greenman, Alyssa Katz, Ben Max, Akash Mehta, Harry Siegel, Ben Smith, Liena Žagare Full Transcript **Ben Max**Good morning. Thanks for being here. So start us off: The people who hired you for this role, what did they make clear were their top priorities for you coming into it, and for the Partnership, and what did you sort-of pitch as the direction you thought things could go? Give us a little of that bifurcation of where the minds met in terms of the priorities and the leadership for the new era. **Steven Fulop**Let me explain to you how I ended up in the job, OK? That’s probably a good starting point, because I don’t think anybody initially thought that I was the pick that they coveted or thought that would be an ideal fit for this position. I’d run for [New Jersey] governor, as you guys know, and I was very deliberate in the lane that I was taking as a candidate for governor. The same candidate when I ran for mayor and city council — it was outside the establishment. It was building on issues, single-issue voters — very, very concentrated on detailed policy. And I thought that if I could build an organization, outside of the typical political organization, of single-issue voters, you could become viable because they’re passionate people generally, OK? And one is mayor, outside the establishment doing that. You won two races in the country that Obama actually engaged in as president. Rahm Emanuel and Jeremiah Healy, my predecessor, was a ten-year incumbent. So we won based on this structure. Run for governor, kind of in that same model. And I thought at the time that 550,000 people would turn out in the election. That was based on history in a similar election, when Chris Christie left and it was Trump’s first-year primary, you know, changeover in governor. There was five hundred or four eighty-seven [thousand]. So I thought five-fifty was a bump up. Same numbers Sherrill had, Ras Baraka had. I thought five hundred and fifty thousand people in the election, six candidates running that are viable — two congresspeople, two mayors, senate president, and the teachers’ union president — that if I got to a hundred and thirty thousand votes, single-issue passionate voters, I would get in through the primary with twenty, twenty-five percent, and then put it back together even though I was kind of the outside establishment guy. I think I did everything perfect during that election, OK? With the exception of winning. Which is the big deal. But I miscalculated one thing. And I don’t know in hindsight how I could have changed anything. But I miscalculated that the turnout ended up spiking to eight-fifty, OK? So I said five-fifty. We all said five-fifty. Going into Election Day on Tuesday, and I’m a pretty pragmatic person. Going into Election Day on Tuesday, if you said, Steve, are you gonna win? I would have told you, we caught them and we’re gonna win, OK? We have more volunteers. I mean, to give you perspective on it: Uber, which is a data company, like, the week before, they wrote a million-dollar check to my super PAC, five hundred [thousand] to Sherrill, five hundred [thousand] to Ciattarelli. Election Day, the New York Times embedded one reporter, photographer for most of the day with only one of us, me. CNN, the day before, me, right? The trend was there. I miscalculated that there was an additional 300,000 people that came out. I saw, similar to here [in New York City], the voter numbers surging early, and I just thought it was a displaced Election-Day voter. I thought you were so excited to vote that you were voting early. I never imagined that there were 300,000 additional people voting. And if you tuned in late, then you would have ultimately– I had the wrong message. My message was, Trenton was broken, these are the things we would fix. And Sherrill, for example, was: I’m a helicopter pilot to fight Trump, right? Her message was better for that. It was over in five minutes. So why do I tell you that? I was shell-shocked, didn’t really know what happened, and the way this business works is when you lose, your phone goes dead. Nobody calls. It’s just, that’s it. Silence. And so, I’m trying to think about what happened and two weeks after the fact, I get a phone call by this guy. His name is Jim Simpson. He was Christie’s DOT chair, OK? Commissioner. And he worked on the widening of the Pulaski Skyway with me as the mayor of Jersey City. And Christie went after me with Bridgegate, and I was a government witness during Bridgegate. But even when nobody in the Christie administration could talk to me, Jim Simpson was always like, I have this major project going through Hudson County, predominantly Jersey City. I’m gonna deal with the guy. And he would tell you that Steve was a professional, good guy to deal with. I think that’s what Jim Simpson would say. I didn’t talk to Jim Simpson for 12 years. I lose the election. Two weeks after, I get a phone call. He leaves a voicemail. Hey, Steve, it’s Jim Simpson. Can you give me a call back? And I was kind of like fuck the world at that point. I didn’t want to call anybody back. And I called Jim Simpson back, and I was like, Hey, Jim. How’s it going? And he says to me, he says, Look, I’m very thankful you remember who I am, blah, blah, blah, blah. He said, Look, I had an issue in a warehouse I own in Jersey City. Didn’t know really how to navigate the building department. I figured it out, right? But thanks for calling me back. That was the conversation, OK? And I’m like, OK, great. You know? Whatever. So the conversation ended. He says, You know, I moved to Pennsylvania, but I’m sometimes up in New York. If I’m up there again, why don’t we just get together? I said Sure. I got nothing but time, right? And so he calls again about a week-and-a-half later, says, I’m gonna be in New York tomorrow. Wanna grab breakfast? Sure. So we meet for breakfast, and he knew nothing about me. When I got there, he was googling me, OK? Like, he knew nothing about my deeds. He’s like, Oh, you were in the military. That’s amazing. Like, it was a weird conversation. And, you know, towards the end of the conversation, he says, What are you gonna do next? And I said, I honestly don’t know. You know, I think maybe this, maybe that. And he says, You know who you should call? I said, No idea. Who, Jim? And he says, I don’t get along with her, he said. I used to be a Rockefeller fellow. I do not get along with her. We do not see eye to eye, but you should call this woman named Kathy Wylde. And I said, I met Kathy, ten years ago once. I don’t know if I would call her, and I don’t…That’s a weird conversation, just to call a stranger and ask for help with a job, right? And he says, No, you should figure out how to call her. We’re gonna figure out how to get you in touch with her again. I gotta think about it. I said, OK. We left. I never thought anything of it. He calls me four days later, and he says, I was giving it some thought. I was googling and I saw she’s leaving [as President and CEO of the Partnership for New York City]. You should apply for the job. And I said, I don’t know anybody. It’s a weird dynamic. And he said, We gotta figure it out. You know? Good guy. A couple weeks later, I was going to a conference in Montana, and three years earlier, I had met Rob Speyer at the conference. Spoke to Rob probably twice in my life since then, OK? But, we had a night we sat next to each other at a dinner, had a nice conversation. So I texted him. I said, Are you going to this conference by chance, in a couple weeks? He said, I am going, right? I said, Oh, can I grab you when you’re there for a couple minutes? He said, Sure. I go with my wife. He comes over, says hello. He knew my wife from a couple years ago, pleasantries. I said, Hey, Rob. you know, I texted you, if I could speak to you. He said, Yeah, yeah. What’s up? And I was, like, nervous at this point. I’m, like, sweating. Like, I’m gonna give a pitch. I said, I see you’re very involved with the Partnership. I think I’d be killer at that job. And he says, Why? And I gave him a pitch, which I’ll tell you in a second. And he said, Honestly, he said, Steve, there’s 12 people in the search committee. He says, I couldn’t even help you if I wanted to, nor would I. But what I can do when people ask me is just get in touch with Heidrick & Struggles and, go through the process. I said, That’s all I could ask. It’s fine. So I send him a text two days later with my resume, and I said I was serious about this. Can you connect me to the person? He says, Sure. Heidrick & Struggles would tell you that they did the interview as a courtesy. And that they thought that it was going nowhere, largely because when the search committee, which is a who’s who of business, outlined who they wanted, they said they wanted somebody from New York City, and they wanted somebody who was not in elected office. So here I am. I would go through these interviews, and I would outline what I thought the Partnership was doing wrong, and how politics has changed, and where I thought the potential was. Largely that the model of shuttle diplomacy, of relationships in the way that it worked 20, 30 years ago, doesn’t work anymore. And today you see, both with Mamdani and Trump— The assets that they [Partnership member companies] have are that they have 800,000 employees. They have the ability to communicate at scale, and they have a general centrist message, and none of those things are leveraged in any meaningful way, is what I would outline. And I really believe, and I continue to believe, that if the Partnership is perceived as a voice of very wealthy people and the CEOs, it will no longer exist in five or ten years. And the reason I say that is because one of the assets that people value is this convening component. And it’s a convening component because you have everybody from, you know, the CEO of JP Morgan and Goldman Sachs towards mid-level to low-level CEOs, and they want to be in the room with the next person in that food chain, so to speak. And when people view it as not good use of their time, because it’s not effective, you start seeing deterioration and you no longer get that involvement. And I would explain this to them, and I would say, This is how we’re going to organize employees, and I think is the most effective way to do it. It’s something that I did as a [mayor] or a candidate when I was running these campaigns, so it’s something I know. And they would ask a lot of questions, and we would go through this model in detail, and they would ask, and they would just move me along in the campaign in, in this situation. And then at the end, probably ten interviews in, the woman from, you know, Heidrick says, You know, I think, you know, as crazy it is, you might land this plane. And I said I knew I was going to land the plane. But— **Josh Greenman**Can I ask? So you mentioned— **Steven Fulop**Sorry to take so much time on that, but— **Josh Greenman**Three hundred thousand, or I forget the number. **Steven Fulop**Eight hundred thousand. **Josh Greenman**Eight hundred thousand employees. And correct me if any of the assumptions embedded in the question are wrong. But you’re a membership organization, you’re supposed to be speaking for the large employers. **Steven Fulop**Yes. Large employees. Employers and employees. **Josh Greenman**Employers and employees. But you don’t canvas them every time an issue comes up. You described it as centrist. OK. An issue comes up. First of all, how do you, as the head of the Partnership, decide whether to get engaged and involved in that particular issue? And second of all, how do you decide what the posture of the Partnership is, if it’s not predefined? How much of it is your instinct? How much of it is, Let me take the temperature of the employees and the employers first? **Steven Fulop**The honest answer is I think it’s a hybrid. At the last board meeting, I spoke about being more narrow in the things that we’re going to be involved in. I think it’s very important we’re very deliberate and specific on issues that we care about and being less involved in kind of broad things that don’t necessarily touch 250 of the different companies. **Josh Greenman**Even though you made a statement about antisemitism. **Steven Fulop**I made a statement on antisemitism because it touches a lot of our companies, if not all of them, because they have Jewish employees, and they’re all very invested here. **Ben Smith**What’s something that you think the Partnership should get out of? **Steven Fulop**We had a lot of time last year in the fashion industry. I think the fashion industry is important. I don’t know who reads that report ultimately or how you move the needle ultimately on the fashion industry. We did some work very publicly on short-term rentals. Not really sure that that’s the best place to be spending a lot of time, because I don’t think it pertains to every single person here. I think that there are things that do pertain, quality-of-life issues that touch everybody. How do those things come to light? So I spoke broadly at the executive board meeting about things that I thought were important. They had lots of questions, Why this, that, and I outlined things both in a framework of government and politics: This is what I think. This is where the opportunity is. This is where the vulnerability is, et cetera, and we went through some of that stuff. When we’re working, for example, now, I gotta review it. We’ve been going back and forth on a blueprint of things to change for New York City that I think, or our team thinks, are meaningful, practical, and tangible things that you could change that would impact the budget in a meaningful way. And a normal person would say, You’re probably right about that. And the politics could be tough for those. So put a litany of, let’s say, 330 of them together. A lot of it’s our team’s ideas. Some of them are a little bit maybe venturing into a place that the Partnership historically has felt maybe uncomfortable. Those are broader conversations because I don’t yet have 20 years of trust with those people. So I say, Here’s what I’m looking to do. Here’s the reasons why. And we have those conversations with the executive board. **Liena Žagare**Does universal childcare fit into that? **Steven Fulop**Universal childcare is a part of it ‘cause— universal childcare is a part of it. It’s definitely in there. And I’ll tell you this. We polled our employee base because we were curious. It’s something again that touches, I believe, the affordability crisis. I think Mamdani and Hochul are right on this. It touches everybody who lives here in some way or another. Roughly two-thirds of our employers provide some sort of childcare today. Some is on-site, some is off-site, some is subsidies for low-income, some is subsidies for everybody, some is using a tax credit. And I’ve said this to the mayor before, that our knowledge of this space is vast because we have experience, practical experience on this sort of thing that others don’t have. They may have abstract experience or be ideological in a certain way. We experience it. We can tell you what works and what doesn’t work in the Building Department, what the challenges are in the bureaucracy, what the issues are in accomplishing this in a different way, because a lot of us have tried to provide that, right? So how do we communicate that publicly in a way that makes sense from our objective, which is improving New York, and at the same time in a way that’s value-added to the mayor or the administration, is kind of where we are. But it’s a hundred-percent part of that blueprint. There is a section on this. **Akash Mehta**It’s interesting to hear you emphasize that you represent the employees, not just the employers. Are there formal structures that put that into practice? Are there elected board positions from the employee base, or anything like that? **Steven Fulop**What we’re starting to do now— So it’s a shift, OK? And what we’re doing now, the short answer is no. What we’re doing now is starting a lot of these town hall-type conversations with the CEOs and me. It is an April, May, June, two, three-year initiative, where we’re talking about civic involvement, the importance of New York, and people can opt to sign up for information from us. So we’re communicating in that way. People will opt in, and then we’ll have the ability to better communicate with the employee base with regards to what we’re doing. You know, there’s no secrets to what I’m saying. I mean— **Ben Smith**But you think the opportunity is to mobilize employees? **Steven Fulop**The opportunity is to better communicate because I think there’s a disconnect between a lot of the things that happen, and how and when we communicate to employees things that may impact them. Because you have all sorts of legalities, and general counsels, obviously, that put obstacles in place on this stuff. And we need to figure out a way, because people are invested here in a meaningful way, so they should have knowledge of this. I don’t believe– Of course, our employee base is different than working for the Hotel Trade Council or Carpenters [union] or whatever, right? They’re monolithic, told to do something, they’re gonna do it. Employee bases aren’t that. I think that’s an asset. Having a generally college-educated employee base, if you lay out an issue in a reasonable way, they could make an informed decision whether that works for them or doesn’t work for them. The— **Akash Mehta**Wait, the carpenters can’t make an informed decision? **Steven Fulop**I didn’t say they can’t make an informed decision. I’m gonna say they’re more monolithic in what they do, and it’s driven by the decision of a leader, specifically in the Carpenters. The Carpenters would say from their executive board, maybe in Washington, maybe in New York, maybe in New Jersey, say, Directionally, we are supporting Ben. And then we support Ben. We don’t have that luxury. That’s not the way we’re structured. **Akash Mehta**What does that have to do with them being college-educated? **Steven Fulop**Well, it doesn’t have to. I mean, I think that you don’t have to necessarily be college-educated, it’s a fair question, to weigh something. But I think generally somebody that’s gone through some sort of rigorous education and training on pragmatic thinking has a better chance because they’ve been educated to think that way. I don’t think it’s a referendum on, you only could understand an issue if you’re college-educated. **Alyssa Katz**But I want to dig in a little more to that when you’re talking about pragmatic thinking, because it’s not an elephant exactly, but the sort-of power in the room is the mayor, who’s elected with actually a lot of college-educated support. And also based on a sort of different way of looking at pretty much everything in some ways, right? Like around– OK, we’re gonna start with principles, we’ll figure out the practicalities later. And also around mobilizing large numbers of people who have interests potentially at odds with capitalism, right? Tenants, people who generally think that money and politics and billionaires are the core problem, or a core problem to address, and on and on. So I’m interested in your efforts to mobilize people in that light. I mean, are you looking to build a counterforce? How does that factor in? **Steven Fulop**I don’t use a “counter force.” I don’t think people are generally sympathetic or upset with capitalism or sympathetic to socialism. I view it more as a couple of things were true in the November election. A, you had an alternative to Mamdani that was tremendously flawed. So they had a ceiling. It was an exact situation that happened in Jersey City, where you had a former governor who resigned in disgrace running for mayor, my old seat, same exact outcome. So it wasn’t, it wasn’t a reflection of, everybody’s sympathetic to socialism. I think elections are choices ultimately, and these are the four choices that you ultimately had. Do I think that people are frustrated and more fiscally liberal than they’ve ever been before? Yeah, absolutely. I think that when people say that I, people say, a twenty-year model ago would say, I am, you know, fiscally conservative and socially liberal, that model has flipped really. People are fiscally liberal because what’s going on today isn’t working for them. They’re not necessarily sympathetic to socialism, but they’re open to trying things because there’s an acknowledgement that what’s happening isn’t working. And we see that too. We’re not blind to that. So do I think that you can organize a counterweight, was the question? Is that what it is? I don’t know if I would frame it as a counterweight. I think that you need to organize something that has a voice that is relevant in the political atmosphere, definitely. **Josh Greenman**Can you just explain both the number and percentage of people the Partnership represents of employees, versus the employment base of the city as a whole? **Steven Fulop**Yeah, I don’t know the— **Josh Greenman**What’s happened to that over time? I’m curious, like, is it— **Steven Fulop**I can get that to you. I don’t know. I don’t know that— **Josh Greenman**And then the other is inside the Partnership. What are the constituent companies and groups, and…Are there a couple of companies that have a highly disproportionate share of Partnership members? **Steven Fulop**No. **Josh Greenman**So how broad-based is it? Can you just describe it? **Steven Fulop**I mean, there’s 350 companies. They range in size from– I mean, they would be significant companies, maybe 50 employees, OK, to several thousand employees. But on the executive board, you can see from the site and the broader board, they vary in size and background. Most of the companies you would have some familiarity with. But, they vary in size. **Josh Greenman**How is it determined whether you’re part of the Partnership as a company in New York City? **Steven Fulop**As part of the Partnership, is generally we go through a process of meetings. I meet with them initially, provide them some information. They express that they’re interested, I express that we’re interested. I take it to the board to make sure that we’re moving in that direction, and then the board validates that. This year we’ve had, I mean, a bunch more have joined, and we’ve restructured some things so some have left intentionally. It’s not intentionally, but I guess– **Nicole Gelinas**You’re running pretty hard against the two income-tax hike proposals that the mayor ran for office and won on. Do you see the employee base as part of the battle against those tax increases? Would Partnership company employees go up to Albany to make their point known against that? **Steven Fulop**Yeah. But I don’t think you’re gonna have that same sort of advocacy group that the DSA does. It’s a totally different type of person that would engage, number one. Do I think that a lot of the employees would be opposed to an increased tax structure in New York? I think without clear parameters on what that tax structure looks like, I think the answer is yes. And it’s not only the income taxes that we’re concerned about. So we’ll go into that, too. The reality on this is, if you look at it from a reasonable point of view, if you added the income tax, the payroll, the PTET tax, which is one that we care a lot about and we’re concerned about, the corporate tax, the estate tax, and the property taxes: You put all of those together, right? And you said, OK, we’re gonna do them all. And without some structural changes to the budget, you will be at the trough again in three years. And I think a reasonable person who’s even supportive of, maybe, the highest earners paying more would say, Does it make sense to do this every two or three years, and at what point is there a tripwire? So I think, yeah, the people would be sympathetic. It depends how and what information you provide and how those taxes are. But we don’t have that structure in place yet. I just got started. So we’re just building that stuff out, which I’ve told you, but it’s new days on this. **Nicole Gelinas**So there’s, there’s a reasonable person, and then there’s Albany. Now that the budget is— **Steven Fulop**No, I gotta be careful ‘cause I gotta work with people. So I can’t say they’re unreasonable. **Nicole Gelinas**Now that the budget is just a week-and-a-half away, what do you see as your prospects for stopping those tax hikes? And if you don’t stop them, does that hurt your power early on? **Steven Fulop**No. I don’t think any reasonable person would say that there’s a structure in place today, after two months of me being there, that is gonna push back against everything in Albany from a political standpoint. I don’t think that’s a fair thing to say. I don’t think the Partnership leadership think of it that way, and I think that most people wouldn’t think of it that way. I think the governor has been very strong [against tax increases], so that’s good. That pass-through tax that was put in place when the SALT reduction was eliminated, and now there’s a proposal to eliminate that, that would be an income tax increase on not only the wealthiest people, it would be an income tax increase on business owners throughout the city. And that is one that we’re keeping track of right now that I think doesn’t get the scrutiny that it deserves. Everybody focuses on the income tax and the corporate tax, which the governor is fairly clear, at least now, that she’s not supportive of, but we’ll see. **Josh Greenman**On the corporate tax, Mamdani has repeatedly said it shouldn’t harm the climate here in New York City because it applies to anyone who does business in the state, not if you’re headquartered here, so it shouldn’t lead to corporate flight of any kind. Is that how you see it? **Steven Fulop**No. So first of all, it’s challenging when it’s depicted as corporate flight, because then it becomes a conversation around, Well, you didn’t leave. You didn’t leave. So I know what I did is OK because you didn’t leave. The reality of how this works is that it’s attrition over time, and decisions on where to scale outside of New York. That’s generally how this works. And because the immediacy isn’t there, people don’t view it as, Well, I made this decision, and tomorrow you left. And that’s a challenge for us, because the argument we lose, even though what we’re saying happens to be true, that people constantly are making decisions to scale [businesses] elsewhere. You see it. You see it. So I think that his argument is flawed, although it’s a political argument ultimately is what he’s making, and I understand that. But if you do change the tax rate as he proposes, you will be one hundred percent higher than New Jersey, right, which is at 11 percent, and you would be at 22 percent, according to the CBC, when you layer in all these other things. So at that point, you start saying, Well, I have the same talent pool that I’m pulling from here in the New York system. I’m no longer making the decision to go to Texas or Florida or Tennessee, where it’s a geographic total distance, but at a hundred percent premium to be in New York City, is that worth it, and should I be elsewhere? Your competition is elsewhere at that point in proximity. That’s the dangerous thing to what he’s saying. **Ben Smith**Do you think even more broadly, there is just this sense among— ’cause I think you’re right. You talk to business leaders, and most of the really big businesses in New York don’t care about New York anymore. They’re not New York businesses. They’re global businesses that happen to be here, and New York taxes are a diminishing share of the thing they worry about. Because they’re so global now, they don’t have the kind of commitment they did when David Rockefeller was running the Partnership. And I think a lot of them and a lot of people just think, long-term, New York is cooked. There’s a flight not to Jersey, but to the Sun Belt — to Houston, to Dallas, to Atlanta, to Miami — that’s kind of unstoppable at this point. And you see just the growth of those cities demographically. You see it in terms of their businesses. You know, maybe that’s not this year or next, but I think if you just literally look at the lines on paper of New York, Philadelphia, Boston…these northeastern cities, versus the Sun Belt. Just feels like an irreversible trend: these places are in decline, these places are rising. This is probably beyond our professional careers even, we’ll be fine. But do you think long-term that it’s true, that New York is just sort of irreversibly declining? **Steven Fulop**I think it’s fragile. There’s a lot of indicators and pressure to push people away, and there isn’t an easy solution to it. First, let’s just talk about last week. Regardless of what you say about the corporate tax conversation, we could disagree or agree, regardless. You know, your job isn’t to agree or disagree. But using the estate tax that was proposed last week, OK, or two weeks ago, nobody above the age of 60 would live in New York City if that happened to be approved. Period. End of story. You would see flight, because no sane person would do that? Because the tax rates, that you’re not talking about the one percent, you’re talking about somebody who has a $750,000 home or apartment, OK? You know who that is. So you have pressure because of the dialogue that’s happening there for sure. You definitely have gradual attrition and people moving jobs elsewhere because they’re more portable. There’s no question about that. You have pressure at the bottom because it is very hard for entry-level people to find jobs, and at the same time to find housing. It’s a true thing here in New York, the pressure. So you have a lot of difficult situations going on and absence of a tough political solution, meaning willing to make decisions that are not popular, you won’t be able to change the trajectory. And that is only one that the mayor can actually drive, OK? So the property tax reform stuff that de Blasio and Adams both tried to do, it’s necessary. It’s really necessary, but it’s gotta be driven by the mayor leaning into it, making that a number one priority. Use things like FHEPS, OK? You guys have all written about that. **Ben Smith**I don’t know what that is, sorry. **Steven Fulop**It’s the rental assistance program, OK? It’s growing at four percent per month. It’s a billion-dollar-plus program. Four percent per month! It’s not sustainable. And if the mayor was sitting here, he would say, Well, I didn’t approve the expansion of the program. That’s what he would say. Because I had a conversation with one of his people last night on this, and I said, Yeah, but the expansion would have made it worse. The existing program is four percent per month, right? We need to solve for that. How do you solve for that? That program is a byproduct of the de Blasio administration when Cuomo got rid of the rental assistance program at the state, and so the city inherited that. There isn’t another city in the country that has a similar entitlement program. It’s not sustainable. That program belongs at the state or at the federal government, but it is a politically tough thing to do. So you gotta be able to make decisions like the property tax reform, that. Those are not easy things to do. But outside of that, your trajectory for the city is very, very dangerous. It’s a fact, you know? [Note: After this interview occurred but before it was published, the Mamdani administration continued the appeal of a court ruling requiring the city to expand the FHEPS program as dictated in a law passed by the City Council, while the two sides of City Hall negotiate a potential settlement.] **Akash Mehta**Do you broadly agree with Mamdani’s analysis that Cuomo shafted New York City — that Albany shafted New York City under Cuomo? **Steven Fulop**Honestly, it predates me, so it’s hard for me to kind of give you a fair assessment on that. I would tell you that I was mayor for 12 years in Jersey City, economic backbone for New Jersey. We drive jobs, we drive the statewide economy, we drive every number. It’s disproportionately housing growth. Everything is Jersey City. And I used to say, We don’t get what we put in. We don’t get that back. It’s true, OK. I know New Jersey and New York both say to the federal government, you know, they both pay much more to the federal government than what they get back. I mean, this is a narrative that people say. It’s probably true. Does it get corrected? I don’t know. You know, it’s on the governor’s situation. I have no idea, is the short answer, ‘cause I’m not in the weeds on that. **Akash Mehta**One quick question on the tax battle. The Partnership has a [501] C4 that was seeded with $10 million. What are your plans for the group? Are you planning to spend that money on this battle? **Steven Fulop**Um…unclear. And, conversations this week on that. You know— **Josh Greenman**Do you run the C4? **Steven Fulop**We hired an executive director this week. There is a willingness, we had a call this week and there is a willingness to spend real money to make sure that New York City stays competitive. So this was literally this week. There was a call. I would tell you that there is a serious appetite there. It’s literally a ripe conversation that’s happening in the last 24 hours. Good question, though. **Nicole Gelinas**Large donors proved last year that they’re very good at spending a lot of money — but it didn’t work. In fact, it may have worked against them. Are you confident that beyond the money, you understand local and state politics well enough that some of these people are actually listening? I mean, is [State Assembly Speaker] Carl Heastie, is [State Senate Majority Leader] Andrea Stewart-Cousins listening to these arguments and saying, We didn’t realize that the potential for attrition in the corporate side was this great. Because the money hasn’t worked before. **Steven Fulop**The short answer is, I’ve been there two months. I doubt at this stage, we don’t have that relationship. I mean, I’ve met everybody. I doubt that I have the standing or credibility with them to make, or the structure in place to make that argument today more compelling than it was made six months ago. It’s just the reality of the situation. I think that you have two types of people that exist in any political sphere. You have ideologues and opportunistic people, OK, that just think about their next election. Those are the two types only. And absent somebody that has an alternative, the opportunistic person will only always be sympathetic to the ideologue, because they view that as their best route to get reelected because that’s where the gravity of energy is. **Josh Greenman**What were you? **Steven Fulop**It’s a good question. I was a hybrid. **Josh Greenman**Oh, the only one. **Steven Fulop**[Laughing] That’s right. I was unique. I think that when you look at Albany, you have a growing DSA base for sure. The question that I would have as I get into it is how much of those— the rest of the people that aren’t pure ideologues are there because there’s not an alternative that they see, can be supportive, and helpful to a pragmatic view. So I don’t know what they’ll listen to — Carl and Andrea — in the next week-and-a-half. I mean, who knows? **Josh Greenman**Should we charge more for street parking? **Steven Fulop**It’s a good question. I happen to be a supporter of some of those proposals on street safety and better streets. I don’t think it’s a broader position that the Partnership has taken, and I don’t think it’s something, going back to what I said earlier, narrowing view and going back to your question earlier on how we approve things. I’ll tell you that, as I wrote out this idea of a blueprint of things that, pragmatically, that are politically difficult to change that I believe are the right things to do, I included it in there. And going back to what you said and questioned earlier in how we get these things approved, because the nature of what we wrote, you know, once we get it to a place, I will circulate that a little bit more to the executive board and get some more feedback on whether we wanna go down this road at this point or not, or not at this time. So there’s a process on this stuff. **Ben Max**What are a few other examples? So property tax reform, some streetscape stuff, universal childcare, what else? **Steven Fulop**If you can wait till next week, it’ll be better. OK. Like– **Josh Greenman**Are you fast enough for the political world? What I’m saying is, if you’ve got to go through that process, and these conversations are happening, the budget’s gonna be cooked in ten days, are you gonna be behind the curve? **Steven Fulop**I am obsessed with the political world. I mean, I, I lived in this— Ben MaxHe means the Partnership. **Josh Greenman**The Partnership. Is the Partnership behind the curve if it’s got to deliberate on all this? **Steven Fulop**Look, I think the answer is yes. The short answer is, I don’t know, right? Like, I don’t know. I’ve not been in long enough to have a gauge on all of this. But my gut tells me yes, because the leadership there is hyper-engaged in New York City and the Partnership, it’s not something where they just kind of parachute in once every three months and think about, kind of the Partnership and what’s happening in Albany and happening in New York City. They happen to be very engaged here on a regular basis. So I think the short answer is yes. They will be able to turn stuff around relatively quickly, and I feel pretty good about that. It’s not where I think about obstacles. **Ben Max**So big picture, I’m still trying to figure out right now: What does the Partnership care about, big picture. I know you’re working on a blueprint. I know you’re rethinking some of this, but is it first and foremost, the business climate and economic growth? **Steven Fulop**Right now, I wouldn’t say this is in perpetuity. I mean, obviously, in this moment in time, it’s competitiveness and the business climate. But when the C4 was launched initially last year, they spent money on one initiative, OK? Spent a couple million dollars on the discovery bill. It had nothing to do with finances, taxes. It had to do with public safety and what they viewed as in the best interest of quality of life. So their interests aren’t necessarily only about taxes. We happen just to be in this fire drill at this moment that I got thrown into, where there’s a new tax proposal every other day. So trying to deal with that is where we are right now. I think broadly speaking, the place of the Partnership is really wanting to have a relationship where we’re kind of helping bridge the gap between the public and private sector, and helping find solutions. We have a venture fund, a $200 million venture fund, which I don’t think gets the recognition it deserves. It’s been involved in creating 37,000 jobs, seeding very early-stage New York companies that do a lot of good here. We have a tech lab that partners with DOT, with the Port Authority, with New Jersey Transit, experimenting on technologies to make government more efficient. It is a collaboration that has happened for 15 or 20 years. A lot of companies are a part of that, new companies helping on that on-ramp. It is a unique and special thing. We actually, I think, at the end of this month, have kind of that shark tank at our office where everybody comes in and pitches, and the board of that is like a who’s who. So, I mean, there’s a lot of things beyond what you guys see on the C4 and the advocacy and the lobbying sort-of side. **Nicole Gelinas**So on the Transit Tech Lab, some of the ideas that have come out of there have been very good ideas. But more broadly, for the MTA or the state and local government to actually save enough money so that we don’t have what you described with them coming back for new revenue every couple years, you have to confront the unions on some big issues, whether it’s work rules on the construction side, whether it’s two people on every single subway train, whether it’s Department of Education rules that mean they can’t be as flexible as charter schools. Do you see the Partnership taking on the unions in any sense? **Steven Fulop**Going back to the pragmatic that you said, I would use the word pragmatic and then you asked me about kind of a little bit more about that. You brought up the conductors on the trains and the proposals on that. It is very expensive. There isn’t a city in the country that has that structure. If you outline that to an employee of XYZ Corporation, and you said to them, Here’s the landscape, OK, We need help with XYZ, I would venture to say that they would be sympathetic to our point of view. That’s what I was getting at, just to give that to you earlier. You know, the New York Post called me the day that Mamdani entered into the— he said at the end of a press conference, I wasn’t there, obviously, but he mentioned that he wasn’t gonna do the sweeps of the homeless encampment. And it was probably my second or third day on the job. And the Post called and said, Do you want to comment on this? And I said, Yeah. And I said something critical of him at the time. Prior to doing that, because it was like my second or third day, I did say to some of the people on the team, going back to how nimble the organization is, I said, Hey, I would like to comment on this, OK? Because I think it’s wrong, his approach, and I think he’s gonna have to move off of where he is, ultimately. And some people said, Well, is that really the hill you wanna climb? Is that really that important to us? And I said, It absolutely is. Because, A, he’s wrong on the issue, and B, it’s a quality-of-life issue. Going back to the fact that it ended up being right, he ended up moving on, and you could see it from, like, far away, just based on being a legislator versus an executive and accountability. But I think our job is to call balls and strikes and just be fair about it. So do I think– Let me say it this way better. If you are gonna grow housing, OK, ‘cause he says it’s a priority. During his election, when he was a candidate, he would tell you if he was sitting here, he would tell you, you look at his CBS, the debates, he would talk about Jersey City and Tokyo, Jersey City and Tokyo as housing growth models. He would tell you that if he was sitting here — he said it on TV multiple times. Now, he followed that about what we did in Jersey City ‘cause we grew a lot. How are we able to grow? OK, A, you had layers of government here that we do not have there, so you need to clean that up. We upzoned, of course, more aggressively than the City of Yes around transit centers, which you have to do here for sure. And then the third part about it is that if you mandate affordable housing, community givebacks, the union labor, et cetera, et cetera, the projects financially cannot handle that. They don’t pencil out. So what we would generally do is that, on the union labor front, because the people that get squeezed the most were the ones that pressure the building trades better, because their jobs are usually the ones that get–are most competitive, are the less skilled parts of the building trades. So when you build a high-rise building, the electricians, the plumbers, the operating engineers, the ironworkers are generally gonna be union, because it’s a more complicated thing. Carpenters, laborers, painters generally are the ones that have competition from non-union, and that’s really where it is ultimately. And we would say in Jersey City, on the projects that are important to us, if they were coming to the city for some sort of subsidies or big zoning changes, and it was an important project, we would work with the developer to give the unions a last look to be competitive with the open-shop bid, and some sort of grace between five and ten percent to be competitive. Now, absent that, there is no way to get it built in union labor. So if the mayor continues to be strident that everything is gonna be a hundred percent building trades, you’re never gonna meet the objective of whatever he has a hundred, two hundred thousand units. So I think that our voice is gonna be that you need some practical reforms to these incentives, and practical reforms means that you cannot pay a 30 percent premium for the carpenters. **Nicole Gelinas**So something very simple on that, though. Two people on a train. Would you see yourself making a statement similar to the encampments that, you know, Hey, intuitively, it doesn’t make a lot of sense to leave people in encampments. Intuitively, it doesn’t make a lot of sense to mandate two people on a train. Would you just call up the Post and say that, or that’s a different— **Steven Fulop**I don’t think that— I think if you called me and you said to me, What do you think? I’d probably just answer you, that I think it’s not something that any other city does, and in a financial crisis, you probably gotta be reasonable about that. **Ben Max**This is what you got hired in part for, right? To be out there more and to be a voice pushing back. **Steven Fulop**I think that it’s just my personality. I don’t know if it’s necessarily, it’s just like this is— You’d ask me, and I would say it. I wouldn’t go call up the Post and say, Let me-- Carl [Campanile], let me comment on this, right? I wouldn’t do that. Like, that’s not how I would think about it, right? I’d be going with my day, and then Carl would say, Do you care to comment on this? Or he would call, and I would just be like, Yeah, I think so and so. And he would say, Is that on the record? And generally, I’m not gonna say, This is off the record, on anything. I generally just talk. OK? So— **Josh Greenman**You talked about incentives, government incentives. I want to talk about literal government incentives, like, Amazon, right? Remember how many years ago, half of Amazon’s HQ2 was gonna be here. **Steven Fulop**Yeah, I remember. We tried to pitch it too. **Josh Greenman**It didn’t happen. You tried to pitch it, too. What do you think of the state’s current corporate incentives? Do they make sense as currently structured? To try to attract businesses to New York State, to try and attract businesses to New York City, there are various programs. There’s been a lot of talk about restructuring those programs to make them more rational, and there’s a larger question of, should we have this environment in which cities chase corporations by throwing money at them or tax abatements at them, or should there be some kind of compact where cities agree that they don’t do this to each other? **Steven Fulop**Yeah. I generally think — putting on my mayor’s hat — that the structure exists today is a race to zero, and it’s not healthy from the public side. But you cannot unilaterally disarm, is the unfortunate part of the conversation. And so absent governors getting together and having some sort of détente where they’re no longer doing that, I do not see how a state doesn’t participate in this. That said, I think that the competitiveness conversation in New York and New York City is more complicated than just incentives. I think that, you think about other places in the country, where people have mobility to get to, and there’s opportunity, there’s more of a deliberate effort to attract beyond just the incentives. Texas reordered their entire court system to replicate Delaware, to make it more friendly for corporations. They’re having a Texas stock exchange to have better access to capital markets. It’s more of a deliberate strategy than just saying, Hey, we’re gonna give incentives. Now, it is not helpful– you know, people, absent certainty on communication, they try to interpret a lot of what somebody means or what they say. So, governor or mayor says X, Y, Z in a statement, they try to interpret, what does that ultimately mean? How should I envision that to manifest itself for what I’m trying to do? And the thing that we are trying to calibrate for, and I say this publicly and privately, is that a lot of the rhetoric out of City Hall, which is consistent tax, tax, tax on any front that’s possible, whether you make $500,000, whether you have a house that’s $750,000, whether it’s a pass-through and you have a small business, it creates a very volatile environment. And that’s the problem ultimately. So I think you need certainty on that. **Josh Greenman**I have one other topic that we haven’t talked about that I just wanna make sure we discuss, which is AI. You represent a lot of employees. And AI is going to be increasingly a large employer, in theory in the city, but it’s also coming for some of those employees’ jobs, maybe many of those employees’ jobs. **Ben Smith**And the employers are very excited about it. **Josh Greenman**And some of the employers, the CEOs, are excited about being able to save dollars and innovate in various ways. **Steven Fulop**Well, they don’t communicate that with me, but just go ahead. **Josh Greenman**So how do you, how do you balance those tensions, which are very, very real, among employees who might be excited and employers who might be excited about AI, and those who might say, Holy shit, my job is gonna be gone in 10 years, or five? **Steven Fulop**I think that that is— I said this, maybe I touched on it a little bit earlier, I think that young people not being able to find jobs and the housing crisis was pressure from the bottom that is one of the most at-risk situations for New York. AI is part of that conversation, potentially — so you have macro environment, why people aren’t hiring, and then AI might be part of that too. You know, though they don’t clarify that with me. They don’t say, Here’s the reason I’m doing this or doing that. I do think that one of the biggest risks over the next 12 or 18 months is the AI impact on jobs. I agree. And I don’t think anybody has a good solution, both in the Partnership or in City Hall, yet on that. And in order to kind of figure out how to do that and solve for that, I think that you need to have some sort of broader conversation on implications. I will tell you that one of the things that we outlined in kind of what we were talking about on this blueprint was that, I do think that part of the procurement process for city government services needs to factor in who is training and who is factoring in the AI conversation, and those scores should be appropriately weighted, beneficial for companies that are thinking about training or have a training program as part of it. So we’re trying to think of ways to kinda think about training people and getting people into another space and using the leverage of City Hall to do that. I don’t think there’s a broad enough plan yet to solve for it. It is the biggest risk for New York, because New York City relies on white-collar jobs disproportionately for its budget. And where AI is coming is directly at the white-collar jobs as opposed to the blue-collar jobs. And so New York City is poised to get hit in a different sort-of way than anybody else. Short answer is, I don’t think anybody has a clear-eyed solution on that. Some of that’s gotta be set by the mayor, you know, that’s the truth too. I said earlier in the conversation, we had a roundtable on childcare in December with the mayor. And the mayor, at the end of it, I said to his staff, I said, You know, we outlined some of the inefficiencies and bureaucracies to getting these childcare centers open, which we’re familiar with. I told you earlier. We can help you with that from a technology standpoint, okay? And this goes back to making tough decisions. You’ve seen employee headcount since COVID skyrocket at City Hall. Whole, another conversation. I mean, that’s a fair thing to say. We said we’d help you with some efficiencies on that from a technology standpoint. And they were nice in their response but there was really no follow-up on that. And, and my point on that is we can only do so much from the Partnership. You need a dance partner in order to dance, right? Like, that’s really the reality of this. So, I do not know the solution, and I push back. People aren’t— They don’t, at least they don’t express to me they’re happy about, you know, not hiring jobs. You know, maybe to you, Ben. But, and AI displacement, I think people are very concerned about it in the Partnership too. **Harry Siegel**You said maybe without some changes, the Partnership won’t be there in five to ten years. We just did have a big turnout election, that led to an avowedly socialist mayor. Can you just step back and say— **Steven Fulop**No, I think the Partnership— I think I clarified that on the, that current trajectory of how it positions itself needed to change. So I think that’s part of the reason we’re here, to make sure it’s relevant for the next ten years, but— **Harry Siegel**But the business community seems increasingly less relevant to the political direction of the city. The Partnership seems less relevant. This is a bit of an inside conversation. For people who maybe vaguely know what it is or just spot its name in headlines or whatever, what is the point of the Partnership? And frankly, what is the good and utility that the business community brings to the city, if you just want to explain that? **Steven Fulop**What does the Partnership add to the city? **Harry Siegel**Sure. **Steven Fulop**Or the politics shifting left. **Harry Siegel**However you want to take it. **Steven Fulop**I think, forget about the Partnership as an organization for a second. I think the big corporations add a disproportionate amount of philanthropy, revenue, budget help, employee help to the ecosystem here in New York City. If those larger corporations migrate out, you would have a massive budget problem, you would have significant more unemployment, and the amount of philanthropy, which is tens of millions of dollars a year across, which sometimes is underreported or undershared, is a good thing ultimately, helps private institutions or public institutions, culture and arts institutions disproportionately, hospitals disproportionately. So I think that the business community here has a huge impact. The Partnership acts as a conduit from them to the public sector, and has multiple layers to it as I touched on earlier, where we help incubate early companies, facilitate connection to New York City, help advocate for policy change, et cetera, et cetera. **Harry Siegel**Outside of real estate, the business community, as you and Ben [Smith] were talking about, is less and less rooted here, and the problem is sort of a slow trickle to other regions, as opposed to people just pushing out and moving. Lots more people are voting. They seem less interested in the priorities of the business community. You’re talking about employees as a sort of base for the organization, 800,000 of them. I’m just trying to understand how business makes itself more relevant. **Steven Fulop**That’s a little bit of a process that I touched on earlier, a little bit of the transition that I touched on earlier on why we’re here. I think that people, as you saw in this last election, are struggling, an affordability crisis is real. There’s a lot of pressure, whether it’s jobs or housing. I think that’s all real. And I think that people have become, as I said earlier, fiscally more liberal because what’s working for them today and not working today has shifted from 10 years ago. So in order to be relevant, we need to be cognizant of that. **Alyssa Katz**People mentioned earlier that antisemitism is a priority for you, fighting it, and you know, I know you— **Steven Fulop**It’s a priority for us, yeah. **Alyssa Katz**Yeah, and we had a couple incidents that may have coincided with right when you arrived at the Partnership, right? The most visible one was, you know, this Israeli drone company. **Steven Fulop**Oh, the Israeli drone company, Easy Aerial? Yeah. OK. **Alyssa Katz**Yeah. What’s the other one? **Steven Fulop**It was the Park East Synagogue situation. **Alyssa Katz**Oh, right. Well, listen, but I’m talking purely on the business side. We can talk about it more broadly. But you know, there’s a company that was in a city-owned property. The city says it was a lease issue but it had been the target of a campaign saying, Well, you’re providing drones to the Israeli military, and we need you out of here. And then sure enough, that’s exactly what happened. And I’m just kind of curious about how you are approaching that kind of pressure on Israeli individuals and their businesses to get out of town, basically. How do you address that? **Steven Fulop**I think it’s a new conversation, obviously. We do think that antisemitism is rising, and it’s a significant concern. The BDS conversation and singling out one country to different standards than other countries that the U.S. or the public agrees with or disagrees with, or that has human rights violations or doesn’t have human rights violations, is a totally different conversation that we don’t agree with — singling out a single country. And the reality is that that BDS conversation, building on that, is not acceptable to us. And as you see elected officials more vocal in this space, unfortunately, if that ends up being, I think that is a place you’d definitely see us spend money. **Liena Žagare**Do you think Mayor Mamdani is doing a good job, and where would you— **Steven Fulop**Doing a good job? **Liena Žagare**Yeah. Doing a good job in the city and— **Steven Fulop**I think— Yeah, look, I think— **Liena Žagare**Where should he be more careful? **Steven Fulop**I think, I think there is— The short answer is, it’s been two-and-a-half months, so it’s hard to tell. I think that there’s a lot of mixed signals between things he says sometimes in smaller meetings versus what he says publicly. I think that, there’s been some missteps along the way, like for example the real estate tax proposal I thought was a misstep. I think that there’s been some really encouraging things. How he’s handled the president has been terrific. And I think that the city is in a better place than anybody would have thought post-election with regards to the president as a result of his communication there. I think that he has shown some willingness to move away from an extreme left position that he came in. That transition from a legislator to an executive is a transition, and from being a backbench Assemblyperson to a prominent front and center mayor, that transition is always complicated. Homeless encampments, the library funding, the FHEPS situation, he’s moderated some of those views, which are good. And at the same time, I think sometimes going back to how we interpret things that he puts out there, sometimes he’ll put something out that we’ll interpret in a way that I – ‘cause you have no choice but to interpret this stuff – you’re concerned about. You know, like, and I’ve said to them, Look, when American Express announced their headquarters, right? It’s a big deal. You know, a big American brand establishing a significant presence again here, recommitting. It’s good for his narrative ultimately, right? You know, that was in the works for some time. Or the Bank of America situation. If you look at the kind of statements that came out as a result of that, they never acknowledged the corporate side. They talked about the building trades. And that signals to us, A, questions about the housing conversation, right? And B, whether you understand the ecosystem that American Express employs thousands of people here, and they’re hugely involved philanthropically and committed to the city, and what that means. So I would say it’s mixed. **Nicole Gelinas**One thing that’s been missing from the ecosystem is a person like Dick Ravitch, who passed a...
Investigating the replicability of the social and behavioural sciences
Nature - A large-scale study on the replicability of claims from social and behavioural science journals reports that about half of the results replicate in the same patterns as the original study.
New, massive study finds only half of social science findings replicate
The University of Virginia led the work, highlighting the ongoing challenges in the reliability and reproducibility of research.
Only 50% of social science research can be replicated, study finds
But researcher says psychology publishing still improving.
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The Carolina Panthers need to replicate the recipe they used to beat the Rams a few weeks ago.
Denver Broncos' Second-Half Schedule Breakdown, Analysis On Roundtable's Podcast
The Denver Broncos will be looking to replicate the success of the 2025 season, but the road ahead will be tough.
Intuit is betting its 40 years of small business data can outlast the SaaSpocalypse
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We can create the future of science right now
Paul Litvak is the founder and Executive Director of the Robyn Dawes Institute and a Visiting Scholar at UC Berkeley. He has a PhD in Behavioral Decision Research from Carnegie Mellon where he studied emotions and the sunk cost bias. Over 15 years in industry, he solved a wide range of challenging technical problems. At Meta he created models optimizing the ad review process. At Google he ran experiments to measure social influence. At Airbnb, he was a product manager leading machine learning teams optimizing search ranking and price suggestions. He also co-founded and led product at Rhythmic Health, a venture-backed biosensing startup, creating an accurate low cost system using a color changing strip and a smart phone to measure salivary lactate. The bottleneck At this point, it is uncontroversial to say that science needs to stop using the PDF article as the unit of knowledge and currency. The unbearable slowness of scientific publishing, the profit motive and margins: I’m not saying anything new. The PDF also sucks because it’s hard to extract structured information, which makes it hard to do evidence synthesis. As a result, we do much less evidence synthesis than is needed. And evidence synthesis ultimately undergirds most policy and medical decision-making. I can see second-by-second real-time odds for any sporting or newsworthy event, but a school board can’t see the best evidence on whether their 8th graders should be taught algebra. As a society, we don’t treat this as an important problem. Again, not controversial. Not only is the problem well understood, but the solution has already been laid out. What we need is AI-assisted living evidence synthesis - (1) an open knowledge graph of atomic claims, (2) claims linked to evidence, (3) assessment and synthesis of each piece of evidence, and (4) continuous updating with new data. What few realize (yet) is that the technical capacity to build this vision for a significant portion of science already exists. Not only that, scientists and startup teams are already building many of these components. I know this because I’ve been surveying the space and talking to many of the builders. There are some missing pieces: for example, evaluations of how well some of the components work. But at this point, most of what’s missing is a fully end-to-end working integration of all of these parts. In the rest of this essay, I’m going to lay out all the parts of a working living evidence layer for science and who is working on them, and propose concrete next steps for building this system. What’s now possible The diagram above outlines the components of a living evidence synthesis platform, including some of the teams working on each component1. Scientific PDFs are processed into claims with associated evidence. The evidence is subjected to a forensic audit, methodological evaluation and robustness and reproducibility checks. Finally it’s given a weight in a continuously updating synthesis. What follows is a description and status of each component and a few of the teams working on them. Document understanding The first thing you need to be able to do is turn an article into structured data. Mostly that means parsing PDFs. There are often multicolumn layouts that confuse non-specialized PDF to text processing libraries. For scientific papers, there is the added complexity of parsing formulas and tables and figures. This is a really hot area - there are startups offering APIs, and it seems like a new open source package gets posted to Github every few weeks. What follows isn’t exhaustive. A package called GROBID was the state of the art for a while, they didn’t update their package for nearly two years until very recently. In the meantime reducto.ai released an AI powered PDF extraction API, PaddleOCR became popular, IBM released a model called Docling, and both Mistral and Gemini created models and libraries. I also know of at least one other well-funded psychology research group working on a paper parser. By contrast, there are few open evals in this space, with no extensive evals for complex table comprehension in particular. Nonetheless, I’m confident this will be a solved problem soon, given the combination of LLM advances and developer interest. Hypothesis level extraction There has been increasing interest in comprehending the extracted text of papers and linking information to evidence for each hypothesis. A lot of work has already been done. Trialstreamer (Marshall et al. 2020) and RobotReviewer LIVE (Marshall et al. 2023) demonstrated automated extraction of trial population, intervention, and outcome at scale on clinical RCTs. PaperQA2 (Skarlinski et al. 2024) and Ai2 ScholarQA (2024) extended this to retrieval-augmented question answering with citation grounding. Elicit, Consensus, and SciSpace operationalized claim-level extraction for end users. OpenEval (Booeshaghi et al. 2026) is the most recent and most ambitious: 1.96 million atomic claims extracted from 16,087 eLife manuscripts using Claude Sonnet 4.5, grouped into ~299,000 results, with LLM evaluations showing 81% agreement with human peer review on a 2,487-paper subset. None of these solutions link claims to test-statistics, as you would need to evaluate randomized controlled trials. This is why I built the evidence.guide API - to extract hypotheses and associated test statistics from behavioral science papers. The best public eval of this kind of extraction I’m aware of comes from the recent SCORE project - they had humans code thousands of psychology papers to extract their claims by hand. It would be extremely helpful to the world if all scientific PDFs were available as structured open data. I’ve been working to make this happen, both directly at Berkeley and through coordination with large entities I can’t yet speak of; as hard as it is to do, I think it’s possible2. Forensic audit A lot of work has been done on forensic audit, but some gaps remain. Of course, for biology papers that rely on images for evidence, there are a variety of tools (notably Proofig and ImageTwin) to spot anomalies. These are still well short of what sleuths like Elizabeth Bik can do on her own, but these tools are constant companions among fraud analysts. There’s someone working on auditing Excel files for anomalies, and a number of teams are automating numerical checks like GRIM and SPRITE, including Scrutiny project, the INSPECT-SR team as well as statcheck. The regcheck team is building a way to use AI to compare preregistrations to analyses in papers, to ensure there aren’t significant deviations. Nonetheless, there are many other kinds of anomalies to screen for, both public and less publicly known. And there are no formal evals for anomaly detection that I’m aware of. Still, there’s a lot to draw from in this space and I’m pretty certain we will be able to scan papers for most kinds of obvious anomalies in the near future. Methodological review This area has been white hot, though I fear for many of the startups in this space, because this capability may become commoditized. There are at least six different AI peer review companies, including Refine.ink, Reviewer3, ReviewerZero.ai, Q.E.D. Science, Paper Wizard, and Isitcredible. Coarse (a pun on refine) was also recently created as an open source alternative. These systems provide qualitative feedback on the content of papers, spotting methodological weaknesses and mathematical errors. They seem to work pretty well, and many academics report bitterly that they exceed the average quality of typical peer reviewers. But there are few evals here either. What evals exist so far involve using LLM-as-judge (circularity problems abound) or comparing against human reviews of questionable quality. What you’d ideally want is an eval that measures capturing known errors in papers3. Reproducibility and robustness Another active area has been using AI agents to automate computational reproducibility4 and robustness5 checks in papers that report numerical results. For more recent papers where data and code are available, AI agents can see whether they can re-run the analyses and produce the numbers reported in the published paper. In addition to a handful of individual academics who have been experimenting using Claude Code for this, the Institute for Replication is a leading group working on building an end to end system. The evals related to this problem are the most mature, with CORE-Bench (Siegel, Kapoor, Narayanan 2024) and PaperBench (OpenAI 2025) available to benchmark agents on this task. There is also work on getting AI agents to test alternative ways of analyzing the data to ensure the results are robust to small analytic design choices. Synthesis This is the most underdeveloped area where significant investment is required. Although some automated evidence synthesis systems exist — for example, otto-sr is building an AI agent to write systematic reviews — none of these incorporate the full range of paper level signals to weight evidence appropriately. Nor is there anything like an eval or a gold standard for a good systematic review. Arguably Cochrane reviews are the closest we have to gold standard human systematic reviews, though I’ve heard academics in the know complain about their uneven quality. A key question for a synthesis platform is how to weight anomalies and methodological issues in assessing the quality of a piece of evidence. This is an unsolved problem and one I’m very keen to work on. Continuous updating There are many pieces of basic infrastructure available for monitoring for new research and initiating updates. OpenAlex is the current open citation graph. Retraction Watch integrated into Crossref in October 2023. Scite tracks how citations support, contrast, or mention prior claims. The Living Evidence Network demonstrated continuous-update workflows in clinical guidelines. Engineering this is a relatively straightforward task. When you look over this technical architecture and all the progress being made, it’s hard not to be optimistic that a living guide to scientific evidence will be built. The stakeholders are ready The social infrastructure for this is starting to coalesce — it’s not just a pie in the sky academic exercise to imagine this coming into existence. Institutions like the Center for Open Science, the Institute for Replication, the INSPECT-SR, the Living Evidence Network and more are all working on scaling work to improve research quality. Funders are also aligned. The Sloan Foundation has funded living evidence work through COS. Coefficient Giving supports the Institute for Replication and COS. The Astera Institute and the Institute for Progress have shown interest in this space. NIH has established an Office for Replication and Reproducibility. Although there are (very unfortunately) serious headwinds in science funding generally, there is an active group of funders interested in metascience. A brief word about what I’ve been doing at RDI. First, as a Visiting Scholar at Berkeley I’ve been actively figuring out how a non-profit and a public university can conduct and make public the results of large-scale academic article data mining. With some of the money I raised from donors, I commissioned a legal analysis of recent case law and publisher text data mining (TDM) agreements in order to understand whether a massive open data mining of academic articles is possible (with caveats, it is). I’ve also been working to bring together stakeholders in this space, and identify gaps. I’ve also been doing some software development in this space, with more to come. A pilot proposal The assumption undergirding all of this is that an AI, given all this information, would make the right judgment about a scientific claim with lots of conflicting evidence, weighing all the factors appropriately. That’s the hypothesis we need to test. Randomized control trial research is the best place to focus on first. RCTs are used to make many of the important decisions in society - from medical trials to public policy changes. And they use a relatively uniform set of inferential statistics with lots of known and available diagnostics. Behavioral science experiments, within the broader realm of RCTs, should be first used as a testbed whose results can be generalized. Because behavioral science is at the vanguard of open science practices, replications abound (there are thousands of them) to serve as ground truth training data. Key Hypothesis Therefore the pilot would test, in behavioral RCTs that have been replicated, whether the quality of evidence for a claim can be used to accurately predict whether that claim will replicate. Secondary Hypotheses Compared to claims that replicated, non-replicated claims demonstrate a greater share of forensic anomalies in their source literatures. Hypothesis level claim and statistic extraction is accurate enough to scale living evidence without onerous human review costs. Replication prediction is more accurate than prediction markets6 or journal prestige. If all the different quality signals we gather do accurately predict which studies will replicate, then we can use that model to score evidence to power the living evidence layer. Why this is informative regardless of outcome If the pilot succeeds, the architecture extends to medical RCTs (where Living Evidence already operates and integration is mostly about claim representation), then to slices of basic biology with stable replication structure. If it fails, the field learns which quality signals are load-bearing and which ones metascience has oversold. Either result is a contribution to knowing what the literature supports. Implications for funders Because this burgeoning ecosystem of builders already exists, a well-informed philanthropic or government funder could play a crucial catalyzing role in bringing this future about. They could play at least three roles: creating open structured datasets, publishing open benchmarks and incentivizing the solving of key technical challenges. First, open archives of papers that the government maintains, like PubMed, could be turned into structured data amenable to large scale metascience and claim aggregation. I know the US government already has an interest in doing this, though some key open questions remain unanswered. How do you determine the best models and systems for accurately extracting information from papers? How can you establish a robust way to allow researchers to flag errors and correct them? And finally, how do you create a legal regime in working with publishers to maximize the scope of available papers? For the latter, a university or a private philanthropy may be better positioned to make structured data publicly available under journal subscription terms or fair use. Philanthropists or government funders could also coordinate to create or commission benchmarks that evaluate whether important problems have been solved. For example, an open benchmark for claim extraction from a range of different scientific article types would be extremely helpful. Ensuring the underlying data are accurate is vital for creating these evaluations. I’ve discussed opportunistically using various human-created datasets for this purpose, but a consistent problem is that errors in human data make it difficult for them to serve as a gold standard. Finally, with structured data and benchmarks available, the government or private philanthropy could use them to incentivize groups to develop machine learning systems that meet these benchmarks. Prizes are one potentially valuable tool for this. For example, you could establish a prize for a system that accurately updates a living meta-analysis for a small set of claims. Prizes are particularly useful as signals of problem importance, and can help create vibrant ecosystems of public and private research—see, for example the role the government played in kickstarting the current work on nuclear fusion. Conclusion The drawbacks of the current scientific publishing system are known. Scientists agree, metascientists agree, philanthropists agree: the published PDF plus citation graph isn’t the right substrate for maintaining a representation of the evidence base in science. The pieces needed to build the alternative either already exist or are rapidly taking shape. The community is forming around exactly this problem, with concrete partnerships and shared infrastructure. A pilot should start on behavioral science RCTs because that’s the slice of empirical science most amenable to legibility, where replication ground truth is richest, and where the failure modes are best documented. What’s been missing is the galvanizing mission to assemble these pieces into something that works. That’s what I’m proposing to build. *** 1 I have a broader field map that I’ll release publicly soon. This is me, building in public! 2 If this is something that you are excited about, please reach out and talk to me. 3 More on this very soon too! 4 This tests whether, given the code and the data, you can get the same statistics as reported in the published paper. 5 This tests whether the results are the same as a paper’s given alternative analytical decisions in conducting the analysis (like outlier omission). Closely related is the idea of a “multiverse” where you come up with many different ways of answering the same underlying research question with the same data, and test whether the results hold in all those alternative methods. There’s been work on the latter as well. 6 Some of the replications, e.g. those from the SCORE project, had paired the experiments with forecasts from prediction markets. So we get to look at this for free.
Shaky political "science": breakdown of the peer review process
[This article is the latest in our “Shaky Political Science” series. In our 38 page report, “Shaky political ‘science’ misses mark on ranked choice voting,” released in December 2025, we examined over 40 studies focused on ranked choice voting (RCV). Many of these studies suffered from puzzling research methodologies, poorly constructed surveys, unrealistic simulated elections, cherry picked data and faulty analyses that often were contradicted by results from real-world elections (a link to our full paper is here). Here are links to the first four articles in our series: the first one summarizing the overall results of our report; the second one analyzed a study co-authored by University of Minnesota academic Larry Jacobs, which was one of the most error-prone of all the research we reviewed in our report; a third article showing an admirable example of two political scientists holding two other political scientists accountable for their flawed, sloppy research; and a fourth article that analyzed seven studies sponsored by New America’s political reform program that deployed flawed methodology based on “simulated” elections instead of real-world elections, which when combined with some basic misunderstandings of RCV, produced odd and misleading results]. In this fifth article in our “Shaky Political Science” series, we examine two flawed studies on the impact of RCV on voter turnout authored by San Francisco State University professor Jason McDaniel. McDaniel’s research in these two papers stands out for its puzzling methodology, inadequate and cherry-picked data, and faulty analyses that not only misunderstands ranked choice voting but reflects a basic misunderstanding of politics in general. The first of the McDaniel studies actually passed through a peer review process and then was subsequently published by the Journal of Urban Affairs, despite its obvious errors. It is perplexing that a jury of peer political scientists did not catch the errors and send it back to McDaniel for a rewrite or outright rejection. This calls into question the credibility of the peer review process itself. The peer review process is supposed to represent the intellectual pinnacle of the profession – a way for academics to certify the quality of each other’s work, and to act as a check against sloppy research. In the case of the McDaniel study, the peer review process failed. Yet his flawed study has been cited dozens of times by other political scientists in their own flawed works — an unvirtuous circle of academic research. Here is the first Jason McDaniel study that we analyzed: “Writing the Rules to Rank the Candidates: Examining the Impact of Instant-Runoff Voting on Racial Group Turnout in San Francisco Mayoral Elections,” by Jason McDaniel, 2016, Journal of Urban Affairs. This study covered San Francisco’s use of ranked choice voting (RCV) in mayoral elections, purporting to show that RCV reduces voter turnout overall, increases voter errors, and has a negative impact on “marginal populations,” i.e. racial minorities, mainly as the result of the alleged complexity of RCV asking voters to rank candidates and having to be familiar with multiple candidates and related dynamics. McDaniel reached these conclusions by examining five San Francisco mayoral elections taking place in odd years (i.e. where there are no federal or state races on the ballot) from 1995 to 2011, comparing three earlier non-RCV races (in 1995, 1999 and 2003) with the later two RCV races (in 2007 and 2011). Only two RCV races were studied, and as McDaniel acknowledged, only the contest in 2011 was “competitive” (that contest in 2007 was the first mayoral election using RCV, and incumbent mayor Gavin Newsom won easily in the first round of counting, earning 74% of first choices and far ahead of the second place candidate who had just 6.3% of first choices). Yet even the allegedly competitive contest in 2011was won by an incumbent with a landslide margin of nearly 20 points. No other elections were studied by McDaniel, even though San Francisco elects 17 other offices besides mayor with RCV, including the 11 seats on the Board of Supervisors (the name for the city council) in even years, and six other citywide offices in odd years. Previous academic research has found that competitive elections can stimulate greater voter involvement and higher turnout. For unexplained reasons, this study ignored at least 34 other RCV races between the years 2004 to 2011, many of which were very competitive and had high voter turnout. Instead, McDaniel’s analysis based its conclusions on data from only a single, marginally-competitive mayor’s race using RCV. These 2007 and 2011 mayoral elections were the only two RCV elections that McDaniel used as the sole basis for his small data set to determine the impact of RCV on voter turnout. Despite having no meaningful data to inform his study, McDaniel nonetheless concluded that RCV lowered turnout when compared to the previous three mayoral non-RCV elections using two-round runoffs. Then, using the same inadequate database of San Francisco elections, McDaniel also found that “Instant-runoff voting is associated with a significant decline in Black voter turnout and White voter turnout…On average, Black voter turnout decreases by 18 points and White voter turnout decreases by 16 points …” But how could he credibly arrive at that conclusion when his study was based on voter turnout in two RCV contests that were not even remotely competitive? Beyond that, McDaniel tacitly admits that the key determining factor driving Black voter turnout was not RCV vs non-RCV elections, but the presence of a viable Black candidate. McDaniel wrote, “Black voter turnout was about 17 percentage points higher for the two elections that featured an African American candidate compared to the three elections that did not” — but one of those three elections did not use RCV. The same dynamic was present for White voter turnout, in which higher turnout was not correlated with RCV vs non-RCV elections, but the presence of a viable White candidate. Beyond that, McDaniel’s voter turnout results, whether correlated with the race of the candidates or RCV vs non-RCV elections, showed no clear patterns. In his Figure 1, he shows low Asian voter turnout in the non-RCV elections in 1999 and 2003 when there was no viable Asian candidate running, but in the 2007 mayoral election using RCV, there is still no viable Asian candidate, yet Asian turnout increases dramatically. For Latinos, voter turnout is virtually the same in 1999 (no viable Latino candidate, non-RCV elections), 2003 (leading Latino candidate, non-RCV elections), 2007 (no viable Latino candidate, RCV elections) and 2011 (leading Latino candidate, RCV elections). Meanwhile White voter turnout increased dramatically from a low in the 2007 mayoral election, in which the leading candidate was a white incumbent, to the 2011 election in which there were no leading white candidates that finished any higher than sixth place with 5.6% of the vote. Clearly other factors were determining voter turnout than either the race of the candidates or RCV vs non-RCV elections. Despite these inconsistent findings, McDaniel boldly marches forth to render conclusions about the impact of RCV on different groups of racial voters, even though his conclusions are not remotely supported by his data. This looks like the worst kind of “data cherry picking” to cover up a puzzling conclusion based on a poor data set involving only two RCV elections, neither of which were remotely competitive. McDaniel’s cherry picking didn’t stop there. In calculating voter turnout for the three non-RCV mayoral elections in 1995, 1999 and 2003, he failed to mention that in each of these years there was a two-round runoff election, with a first round election in November in which no candidate garnered a winning majority of the vote, followed by the decisive runoff in December. So there were two non-RCV elections in each election year. But McDaniel only decided to include one of the two elections in his data set, apparently the December runoff in each year (which had higher turnout in two out of the three mayor’s races, and lower turnout in the third). So he ignored the November 1995 election, the November 1999 election when turnout was only 45 percent, and the November 2003 election when turnout was only 45.7 percent. This is flawed methodology based on blatant cherry picking, since McDaniel simply omitted three elections worth of data that did not fit within his frame. His study should never have made it through a peer review process, and it is an embarrassment to political science that not only was it approved for publication, but it has been uncritically cited by dozens of other political scientists. Share A “study” without wider context or credible data Lacking a data set that included even a single competitive RCV mayoral race, McDaniel declared that RCV lowered voter turnout in 2011, when turnout was 42.5 percent. But he also ignored -- or did not realize -- that voter turnout in mayoral elections during 2011 had sharply declined in all big cities, including far more steeply in nearby Los Angeles and other cities. In fact, San Francisco’s election in 2011 had the second highest turnout of any mayoral election in the nation’s 22 largest cities from 2008-2011. Was 2011 just a particularly low turnout year all across the country, perhaps because the country was still reeling from the aftermath of the housing market collapse in 2008-2010? McDaniel ignored this factor and did not search for alternative explanations beyond his flawed thesis. Furthermore, while McDaniel suggested that RCV leads to more “overvotes” – which is an invalidated ballot in which a voter, in either a non-RCV or RCV race, makes a mistake and selects two candidates for a single choice – he failed to address that in the June 2012 US Senate primary contest, which used the typical “one-choice” plurality method and did not use RCV, there was more than five times as many overvotes in San Francisco and Oakland than in the RCV mayoral elections in those cities. Additionally, turnout had been significantly higher and more representative in RCV elections for the San Francisco Board of Supervisors races -- that McDaniel didn’t even bother to study -- as compared to previous “delayed two-round runoffs” for those offices, often higher by as much as 40% in the RCV races. Perhaps the most surprising irony was that an earlier study co-authored by McDaniel (Overvoting and the Equality of Voice under Instant-Runoff Voting in San Francisco by Francis Neely and Jason McDaniel, 2015, California Journal of Politics and Policy) found that overvotes are often more common in lower-income precincts, but that the pattern of overvoting is similar in both RCV and non-RCV contests. Wrote Neely and McDaniel: “[Overvote] errors appear to be a function of complexity in general and not IRV per se…. the pattern of overvoting is similar in both IRV and non-IRV contests…Even among votes cast in a top-of-the-ticket race like U.S. Senate, overvoting occurs in an uneven fashion” (IRV, or ‘instant runoff voting,’ is another name for ranked choice voting in single-winner contests). So McDaniel not only contradicted his own conclusion in his earlier study with Neely, but the earlier study had been an important corrective for one of the most frequently made mistakes made by anti-RCV political scientists – a continual failure to provide crucial context by pairing a comparative analysis of real world RCV elections with an analysis of real world plurality or other electoral methods. Unfortunately McDaniel’s earlier study does not get cited much by other researchers, and McDaniel did not even cite it in his own flawed study analyzed above. Incredibly, McDaniel cherrypicked McDaniel, even as his flawed study has been cited dozens of times. Despite being a textbook example of poor political “science,” featuring both inadequate as well as cherrypicked data, the shelf life of McDaniel’s study has been extended endlessly by other political scientists and media outlets who have cited it uncritically. Equally troubling, the Journal of Urban Affairs put this paper through its peer review process and published it despite its numerous flaws. McDaniel gets it wrong, Part II Not content with producing one shoddy, confused paper, McDaniel produced another one a few years later, here is the link: Electoral Rules and Voter Turnout in Mayoral Elections: An Analysis of Ranked-Choice Voting by Jason McDaniel. San Francisco State Univ. (McDaniel, 2019). In this paper, McDaniel extended his previous flawed research focused on San Francisco, about voter turnout and voter impact, to multiple RCV cities through the 2018 elections. In his data set he included San Francisco, Minneapolis, St Paul, Oakland, Berkeley, San Leandro and Santa Fe. He then compared their turnout in mayoral elections to non-RCV cities, and to mayoral elections in those cities held before first RCV usage, and concluded “a significant decrease in voter turnout of approximately 3–5 percentage points in RCV cities after the implementation of RCV.” But he replicated many of the same mistakes as his first study presented above. While in this study McDaniel attempted to distinguish between various factors, such as odd year vs. even year elections, competitive races vs. noncompetitive, contests with open seats and other factors, the analysis is superficial and inadequate. He attempts to incorporate so many often-conflicting variables that his conclusions are once again unsupported by the actual data, and ultimately unconvincing (this second study never underwent a peer review process). For example, McDaniel declared that for RCV cities “in elections that are more competitive than average (margin of victory less than 20%), there is no significant RCV effect on voter turnout.” Yet McDaniel is a professor in a city, San Francisco, in which the 2018 special election using RCV to fill a mayoral vacancy was exceedingly close, decided by a margin of only one percentage point, and resulted in the second highest turnout in San Francisco history for a mayoral election. Illustrating the complexity involved in assessing the critical factors in turnout, that special election was held on the same election day as federal and state offices, which tends to boost turnout. Yet more voters participated in that RCV mayoral election than voted on the same day in the non-RCV primary for governor in which former San Francisco mayor Gavin Newsom was running. Even more puzzling, in this study McDaniel inaccurately cited his own previous study from 2015 with Neely. McDaniel wrote, “Neely and McDaniel (2015) examine individual ballot image files within a natural experiment, and find higher rates of ballot errors in RCV elections compared to non-RCV elections” (emphasis added). However, we quoted from that 2015 study several paragraphs above, and in actual fact Neely and McDaniel actually concluded the opposite -- that the pattern of overvoting – which is the most common form of ballot error – is similar in both RCV and non-RCV contests. McDaniel also ignored the fact that in Bay Area RCV elections held in even years – at the same time as federal and state elections, when turnout is highest -- the “undervote” (that is, the dropoff in ballots cast for governor or mayor) has declined, which contradicts his thesis that the act of ranking ballots in RCV elections is too complex and presents a barrier for certain demographics of voters. To his credit, McDaniel actually acknowledged another contradiction in his own paper, which found a negative RCV turnout effect in some odd-year elections but not others, admitting that “may present a challenge to the theory that increased complexity [from RCV] will have a marginal negative effect on voter turnout.” But he tried to wallpaper over that contradiction by blaming it on an unlikely source -- bad election administration. McDaniels wrote, “This would suggest that the negative impact of RCV on voter participation may be alleviated by high quality election administration,” without presenting any data or even anecdotes to support such an unfounded theory. While competent election administration of any election, whether RCV or non-RCV, is crucially important, there is no credible research that supports the far-fetched notion that the quality of election administration has much of an impact on voter turnout in US elections, whether using RCV or otherwise. Upgrade to a $5 subscription The unvirtuous circle of academic research It is puzzling how McDaniel’s first study could have survived the peer review process for the Journal of Urban Affairs. The mistakes in McDaniel’s first study above should have been obvious to anyone with a basic understanding of ranked choice voting as well as elections in general, and elections in San Francisco specifically. It undermines faith in the credibility of the peer review process to think that a jury of peer political scientists did not catch the egregious errors and reject McDaniel’s sloppy research and paper. And it remains a black mark on political science that this thoroughly discredited study has been cited dozens of times by other researchers. Indeed, in our research paper it became apparent that many political scientists cite each other’s flawed research to pad their list of citations and to legitimize their own flawed research, creating an unvirtuous circle of political science. More broadly, McDaniel’s voter turnout studies, like other studies evaluating RCV’s impact on turnout, lacked a recognition that no credible opinion or research can claim that RCV increases voter turnout merely because so many voters are excited to vote for their favorite candidates instead of “lesser evil” candidates without worrying about spoilers. Instead, with respect to turnout, the advantage of RCV has always been that, in most places where it has been used, it has eliminated a pre-November primary or post-November runoff, which usually saw about half the turnout of the November general election. This is especially true during even years when there are state and federal elections on the ballot which substantially drive turnout more than local contests. Any study that does not adequately account for this non-November primary/runoff vs November general election dynamic, or odd year vs even year election factors, is simply not credible. Both of McDaniel’s studies largely failed on those grounds. Steven Hill and Paul Haughey Share Upgrade to a $5 subscription Thanks for reading DemocracySOS…your digital portal for the pro-democracy movement. Subscribe for only $5 per month to receive full benefits and to support our work. Subscribe
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WHO recognizes healthy, innovative cities across the Western Pacific Region
Nine Healthy City champions from across the World Health Organization (WHO) Western Pacific Region received special recognition at an awarding ceremony during the 11th Global Conference of the Alliance for Healthy Cities in Sydney, Australia.The WHO Regional Office for the Western Pacific has presented the Healthy Cities Recognition Awards every two years since 2004 to celebrate innovative and impactful city-led initiatives that improve the health and well-being of urban populations.Eight cities received awards for their best practices, and one National Forum received the WHO Regional Director’s Special Award in recognition of its long-standing leadership in advancing the Healthy Cities movement.Awardees were selected through a rigorous evaluation process and recognized for demonstrating measurable, innovative and sustainable approaches to health challenges faced by cities.Remarking on the significance of the awards, Dr Huong Tran, Director of the Division of Health Promotion, Disease Prevention and Control at the Regional Office, said that, “There is no single blueprint for a healthy city. What works in one context may need to be adapted to another. But by sharing our experiences, including what worked, what was challenging, and what we learnt along the way, we can help one another move faster and further.”Award-winning cities and initiativesThis year's awards recognize outstanding city-led initiatives that have demonstrated measurable impact, innovation and sustainability in advancing health initiatives for urban populations.Baguio City (Philippines) – Data for equity: informing action, improving healthDr Donnabel Tubera-Panes, City Epidemiologist – Medical Officer IV of the Health Services Office accepted the award on behalf of Baguio City. Photo credit: Maria BoyadgisBaguio City was recognized for developing a pioneering Livability Index that provides village-level data across all 128 barangays (villages), enabling evidence-based planning and more equitable allocation of resources. By integrating community participation and institutionalizing the index within city planning and budgeting processes, the city has demonstrated how data can drive action to reduce health and social inequities.Eunpyeong-gu (Seoul, Republic of Korea) – Communities living in vulnerable situations: advancing health equity through community engagementVice Mayor Honourable Ahn Hyoung Jun accepted the award on behalf of Eunpyeong-gu. Photo credit: Maria BoyadgisEunpyeong-gu was recognized for its inclusive health equity programme supporting vulnerable populations, including isolated older adults and LGBTQI+ communities. Through peer-led community networks, social cooperatives and inclusive service design, the district has strengthened access to care, promoted social inclusion and advanced community ownership of health initiatives.Madiun City (Indonesia) – Adapting and mitigating climate-related crises: building heat resilience and preventing flood-related health risksMs Yuni Dwi Purwani, Secretary General of the National Forum for Indonesian Healthy Cities/Regencies, accepted the award on behalf of Madiun City. Photo credit: Maria BoyadgisMadiun City was recognized for its SUPERMIE (Sustainable Public Environmental Management Initiative) – a community-based environmental governance model that addresses urban flooding, climate resilience and environmental sustainability. The initiative contributed to reductions in flood-prone areas, greenhouse gas emissions, diarrhoeal disease and dengue fever, while at the same time improving food security through neighbourhood food gardens. The programme has already been replicated in 15 cities and regencies across Indonesia.Macao Special Administrative Region (China) – Protecting communities from tobacco and alcohol-related harms: tackling commercial drivers of riskDr LO Iek Long, Director of the Health Bureau, Macao SAR, China accepted the award on behalf of Macao. Photo credit: Maria BoyadgisMacao SAR was recognized for implementing a comprehensive and integrated approach to tobacco and alcohol control. Through legislation, monitoring systems, enforcement, public awareness campaigns and multisectoral partnerships, the programme has strengthened protection against major risk factors for noncommunicable diseases and established a model that is highly transferable to other urban settings.Phnom Penh Capital City (Cambodia) – Safe, active and health-enabling transport: advancing health and reducing inequities in urban mobilityH.E. Khoung Sreng, Governor of Phnom Penh Capital City accepted the award on behalf of Phnom Penh Capital City. Photo credit: Maria BoyadgisPhnom Penh received the award for its Safe School Zone Project, which combines infrastructure improvements, speed management, enforcement, education and stakeholder engagement to protect children and communities. The initiative has demonstrated measurable reductions in crashes and vehicle speeds while providing a scalable model for safer urban mobility across the country.Quezon City (Philippines) – Transforming food environments: addressing policy, market and urban design drivers of health and nutritionMs Jirah Asa M. Sideco, Chief of the Nutrition Section of the Quezon City Health Department accepted the award on behalf of Quezon City. Photo credit: Maria BoyadgisQuezon City was recognized for its public food procurement policy that promotes healthier diets through government-run institutions and feeding programmes. Supported by strong policy frameworks and monitoring systems, the initiative improves access to nutritious food for vulnerable populations while serving as a benchmark for other local governments.Samarinda City (Indonesia) – Preventing violence and promoting gender equality: transforming urban systems, norms and spaces Mrs Rinda Wahyuni, Chairperson of Samarinda Healthy City Forum accepted the award on behalf of Samarinda City. Photo credit: Maria BoyadgisSamarinda City was recognized for its multisectoral approach to preventing violence and advancing gender equality. Responding to local evidence on violence against women and children, the city has positioned violence prevention as a public health priority, bringing together health services, schools, social welfare institutions, law enforcement, civil society and community leaders to strengthen prevention, reporting, survivor-centred support and legal protection. Through inclusive policies, community engagement and sustainable city financing, Samarinda has developed a locally owned and scalable model for creating safer, more equitable and inclusive urban communities.Singapore – Age-friendly cities: transforming systems to bridge generations for a healthier tomorrowDr Tan Weng Mooi, Director, Integrated Health Promotion, accepted the award on behalf of Singapore. Photo credit: Maria BoyadgisSingapore received recognition for its Healthy Precinct Framework, which translates national healthy ageing priorities into neighbourhood-level action. The initiative integrates preventive care, social support, service coordination and age-friendly environments while actively engaging older persons, caregivers and community organizations. Its expansion across multiple precincts demonstrates its scalability and long-term sustainability.WHO Regional Director’s Special AwardNational Forum for Indonesian Healthy Cities/Regencies (Indonesia)Ms Yuni Dwi Purwani, Secretary General of the National Forum for Indonesian Healthy Cities/Regencies, accepted the award on behalf of the National Forum. Photo credit: Maria BoyadgisThe WHO Regional Director's Special Award was presented to the National Forum for Indonesian Healthy Cities/Regencies (FKKSI) in recognition of its long-standing leadership in advancing the Healthy Cities movement across Indonesia. As a national platform bringing together local governments, communities, academia and civil society, FKKSI has played a critical role in promoting collaboration, capacity-building, knowledge exchange and advocacy. Its efforts have strengthened and institutionalized healthy urban development nationwide and established a model for multisectoral engagement in the Region. Healthy Cities for allThe 2026 Healthy Cities Recognition Awards highlight how cities can lead transformative action on health by addressing climate change, inequity, ageing, nutrition, mobility, social inclusion, violence prevention and the commercial determinants of health. The recognized initiatives demonstrate that local governments, communities and partners can work together to create healthier, safer and more resilient urban environments for everyone. As urban populations continue to grow across the Western Pacific Region, WHO remains committed to supporting cities in advancing health, equity and sustainable development through the Healthy Cities approach and the Alliance for Healthy Cities. ###Note to editorsSince 2004, the WHO Healthy Cities Recognition Awards have provided a platform for cities to share innovative and effective approaches to improving health and well-being. The awards recognize achievements in areas including climate resilience, healthy ageing, health equity, gender equality, tobacco and alcohol control, urban mobility, nutrition and data-driven governance. In 2026, more than 150 submissions were received from cities and local governments across the Western Pacific Region. The titles and affiliations of awardees are presented as they were at the time the photo was taken.______________For more information, or to arrange media interviews, contact: WHO Regional Office for the Western Pacific: wprocom@who.int
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Global webinar on core public health capacities to conduct ship inspections and issue ship sanitation certificates
Ships cross borders every day, connecting countries and serving as a backbone of the global economy. However, outbreaks onboard can disrupt maritime traffic, leading to significant economic and operational negative consequences. Without strong public health capacities to detect, prevent, and respond to public health risks on board ships, maritime travel can also become a pathway for the spread of infectious diseases. To support countries in strengthening these capacities, the World Health Organization (WHO) convened a two-day webinar on 11–12 February, bringing together more than 700 ship inspectors, port health officers, and public health authorities, primarily from the African, Eastern Mediterranean, and European Regions. The webinar came at a pivotal time. With the amended International Health Regulations entering into force on 19 September 2025, the need to focus on public health preparedness has never been greater. Strengthening public health capacities at borders, including onboard conveyances, is a cornerstone of global health security and central to the amended IHR.“The high level of attendance and active participation throughout the two days of the webinar highlighted the growing demand for technical guidance and capacity-building support in ship inspection and issuance of ship sanitation certificates”, said Dr Ninglan Wang, Head of the Borders, Mass Gatherings and Health Protection Measures (BMM) Unit at the Health Emergency Preparedness Department at WHO headquarters. “The dynamic interactions among experts and countries underscored the value of continued cross-border collaboration in the maritime sector and knowledge exchange in this area”. Conducting regular public health inspections on board ships for the issuance of ship sanitation certificates (SSCs) in accordance with the IHR requirements is crucial for ensuring that ships meet specific health standards, thereby minimizing the risk of the international spread of diseases via maritime traffic. These certificates, mandated by the IHR, require ports to have trained personnel who can identify and control public health risks on board vessels and apply recommended control measures. This standardized global procedure not only ensures compliance with the IHR but also promotes consistency in health practices across ports worldwide. SSCs are the globally-recognized tool used to prevent and control known public health risks on ships, providing a common way to register and communicate public health events and measures taken on board between all stakeholders involved in this process, including public health authorities in ports and ship operators. Only SSCs issued by ports authorized by national authorities to issue such certificates are considered valid. As per the IHR, all States Parties are required to send to WHO their list of authorized ports (available here) and to keep it updated. However, many Member States continue to face practical challenges, such as limited resources, high staff turnover, and competing priorities. These realities underscore the value of platforms such as this webinar, which foster collective learning, peer-to-peer exchange, and practical solutions to help countries build and sustain the capacities required under the IHR.Over the two days, participants engaged in presentations and discussions covering a wide range of technical and operational topics, such as the overarching international legal framework governing ship sanitation; considerations for pre-inspection planning; risk assessment factors by type of vessel; the completion and issuance of SSCs and evidence report forms; or practical insights on the inspection of various technical areas, such as food safety, medical facilities, water systems, or vector control, among others. In addition, public health authorities from the Netherlands, Morocco, and Tanzania shared case studies to showcase their practical application of these IHR requirements in their respective countries. Given its success, WHO plans to replicate similar sessions in the future to reach additional time zones and regions and cover other pressing border health-related topics, ensuring that Member States can continue benefiting from these opportunities to share knowledge and expertise.
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The Beauty Of Settled Science
Commenters convinced me I made a subtle mis-step in my framing of Psychology Research Is Mostly Fine. My framing emphasized that social priming studies had failed to replicate, but that many other subfields of psychology hadn’t. This naturally led commenters to point to failed replications in other subfields. Although none of these were as bad as social priming, few or none were entirely problem-free. Instead of emphasizing the difference between social priming and other subfields, I should have emphasized the difference between settled science and novel research. Here are some of my favorite psychology findings: The brain is sculpted by reinforcement learning, and a key driver of this learning is a prediction-error-based algorithm located in the nucleus accumbens and implemented through dopamine. Psychedelic drugs exert their effects through 5-HT2A receptors, pointing to a role for these receptors in some sort of construct like the balance between priors and new evidence. Certain cognitive functions are localized to certain brain areas (for example, impulse control to the frontal lobes; linguistic structure to Broca’s area, linguistic meaning to Wernicke’s area. But injure one of these areas, or render a certain function useless (eg a blind person having no more use for their visual cortex), and these mappings can gradually shift - suggesting that neurons are general-purpose compute whose specialization is a contingent fact about convenience and efficiency rather than a necessity. Most personality traits and general capabilities are about 50% genetic and 50% random, with little role for family environment or childhood history, with a few notable exceptions like partisan preference. Recent research complicates this story, but the further you go from this basic picture, the gnarlier the chains of indirect causation you require. Neurons fire together in “brain waves”, whose exact frequency corresponds with the character of consciousness; These divide neurons into natural subpopulations and linked graphs. The gamma rhythm corresponds surprisingly well to items in conscious awareness. GABA receptors seem to be involved in something like relaxation. By hitting different GABA receptors in different ways, you can modulate the exact type of relaxation you produce. Hitting GABA-A directly is too powerful and can cause dangerous levels of sedation, but allosteric modulation of GABA is an incredibly fruitful strategy that underlies drugs from benzos to barbituates to alcohol. If you know your GABA-A subunits, you can target specific subtypes of relaxation; for example, Ambien hits GABA-A1 and induces sleep. Memory divides somewhat fuzzily into bins of immediate-sensory, short-term, and long-term, with separate retrieval and storage functions, and separate systems for declarative vs. procedural learning. These can be harmed selectively, usually by hippocampal injury, so that some patients can remember their life stories but not form new long-term memories, or learn new skills like bike-riding but not new facts. The exact substrate of memory is unclear but seems to involve new synaptic connections between neurons in at least some way. The ability of modern psychology and psychiatry to treat mental illness is real but limited. SSRIs work for half or slightly fewer than half of patients; antipsychotics work with extreme side effects; benzodiazpines work but produce addiction and tolerance in many (not all) people. Most supplements are stuck in Poor Quality Study Replication Crisis Hell, but melatonin and l-theanine stand out as especially promising. Psychotherapy works with moderate effect size, but (with a few exceptions) seems to be more about the relationship with the provider than the details of the therapy itself. Mental hospitalization can sometimes defuse immediate crises but work less well than mental hospitals would like, with some studies showing overall negative effects. Schizophrenia is mostly genetic, but risk also increases with prenatal infection (especially rubella) and obstetric complications; there is suggestive evidence for roles of social stress and drug use (with psychedelics and marijuana making things worse, and nicotine if anything making things better). Bipolar and schizophrenia are so closely linked on a pathophysiological level that it seems like they might be the same disease; on the clinical level, they do seem to follow two separate courses/prognoses but can sometimes surprise clinicians by eg someone who seemed condemned to permanent schizophrenia unexpectedly recovering. The brain’s native reinforcement learning algorithms cash out in the phenomena of classical and operant conditioning; different reinforcement schedules reinforce at different levels, which can be exploited by bad actors to make especially compelling products (eg slot machines). None of these are immune to doubt. A few are speculative; one or two are genuinely unorthodox. But they’re all a step above one p = 0.049 paper that might not replicate. If they get overturned, they’ll be overturned by the same sort of genuine scientific shift that replaced Ptolemy with Copernicus or Lamarck with Darwin, not some sort of sordid discovery about someone entering random numbers into a spreadsheet. In 2008, Eliezer Yudkowsky wrote about The Beauty Of Settled Science: If you only care about scientific issues that are controversial, you will end up with a head stuffed full of garbage. The media thinks that only the cutting edge of science is worth reporting on. How often do you see headlines like “General Relativity still governing planetary orbits” or “Phlogiston theory remains false”? So, by the time anything is solid science, it is no longer a breaking headline. “Newsworthy” science is often based on the thinnest of evidence and wrong half the time—if it were not on the uttermost fringes of the scientific frontier, it would not be breaking news. Scientific controversies are problems so difficult that even people who’ve spent years mastering the field can still fool themselves. That’s what makes for the heated arguments that attract all the media attention. Worse, if you aren’t in the field and part of the game, controversies aren’t even fun. Don’t be embarrassed to read elementary science textbooks, either. If you want to pretend to be sophisticated, go find a play to sneer at. If you just want to have fun, remember that simplicity is at the core of scientific beauty [...] A steady diet of science news is bad for you: You are what you eat, and if you eat only science reporting on fluid situations, without a solid textbook now and then, your brain will turn to liquid. Psychology isn’t centrally the new paper in Annals Of Recently-Breaking News saying that an n=5 study showing that large portion sizes increase support for terrorism. Psychology is centrally ideas like brain waves, GABA receptors, reinforcement learning, twin studies, and connectionism. People who think differently are worrying too much about BREAKING NEWS!!!, and should stop. The key distinction isn’t “recent research in social psychology” vs. “recent research in cognitive/bio/behavioral psychology”, as my essay last week unfortunately implied. It’s recent research in general vs. the deposit of settled science, battle-tested hypotheses, and carefully-earmarked speculation. And part of the reason social priming in particular was such a disaster was that it defied that heuristic, building itself up over hundreds of studies until it approached or even entered the pantheon of settled science - before collapsing (5-HTTLPR was also sort of like this). This isn’t a claim that settled science is always right - Freud was “settled science” for a long time - just that there’s an intuitive distinction between the level of credence you should have in the existence of power posing vs. the existence of Broca’s Area. (I compounded this error by calling the last post “Psychology Research Is Mostly Fine”, which suggested I was talking about the new research rather than the settled science. I actually meant to distinguish between research-psychology-the-field vs. clinical-psychology-aka-therapy, but this framing unintentionally pushed against the point I wanted to make) Some commenters on the previous article understood this point, but challenged its weight. It’s very cool, they said, to know how many neurons are in which brain lobe, but the main way that Psychology affected their lives was through bad clickbait studies used to buttress dumb policies; some p = 0.049 study says that exposure to microaggressions makes minorities’ brains shrivel into raisins, and now their company has to implement punitive DEI or be sued for hazardous work conditions. I won’t dispute this as an accurate description of how the sausage gets made, but it’s worth examining the trains of thought that connect this observation to a campaign against psychology in general: The dumb woke psychology was wrong and bad, and I don’t have good heuristics for distinguishing dumb woke psychology from other psychology, so I’m going to assume it’s all wrong and bad. The dumb woke psychology was wrong and bad; it seems useful to punish/discredit psychology as a whole to assist my campaign against it, so for propaganda purposes I will try to discredit psychology as a whole. To people in the first camp, my response is: come on, your heuristics aren’t that bad. “Microaggressions makes minorities’ brains shrivel up like raisins” is in the woke category; “the hippocampal theta rhythm seems to be associated with both memory and pathfinding, suggesting an unexpected yet surprisingly compelling relationship between the two” is in the not-woke category, don’t play dumb here. To people in the second camp, my response is: deliberately exaggerated propaganda campaigns are bad and this is a confession, not an excuse. Inability to view science through anything but a political lens was what got us into this problem; doubling down on it is a temporary solution but a recipe for permanent disaster. If you pronounce entire fields of science to be acceptable collateral damage in your campaign against bad political actors, you will alienate not just scientists but patients, clinicians, and general lovers of truth. Instead, you can say the thing you mean - that woke partisans shouldn’t try to occupy and exploit the otherwise-noble science of psychology for their own political gain - and wage the same campaign with a halo rather than a comic-book-villain goatee. From the font of oracular wisdom that is Weird Sun Twitter: [ Vessel Of Spirit@VesselOfSpirit i love you all conditionally but i love the good part of you unconditionally. even if it becomes a very small part 2:11 AM · Jan 22, 2015 1 Reply · 5 Reposts · 29 Likes ](https://x.com/VesselOfSpirit/status/558084551772471298) This is how I feel about psychology1, too, but with a touch more optimism. The good part of psychology is enormous, a hoard of endless treasures. You just have to look past the headlines! 1 To play devil’s advocate - should one always be so magnamious? Astrology? Phrenology? These seem different in that their entire orientation was flawed from the start; they didn’t simply fail to find the correct constellation-personality connections, the whole project of searching for connections between constellations and personality was doomed from the start. More difficult might be asking the same question about education research, which is notoriously bad. I don’t know enough about education research to know if there’s some settled science of correct and enlightening results lurking in the background beyond the day-to-day froth of poorly-conducted and politically-motivated studies. I think if it’s 90% bad, 10% good, it’s correct to call it bad as a compact way of communicating a useful truth; I think psychology is better than that, but admit it’s a tough judgment call.
“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.
Creating almost-life in the lab
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Scientists say they have built a cell from scratch for the first time
Scientists say they have built a cell from scratch for the first time that feeds, grows and replicates like a natural cell, a breakthrough in synthetic biology.
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Thunder Must Replace Pressure, Not Just Production
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AbCellera women’s health data top ‘most aggressive upside scenario,’ putting Astellas and Bayer on notice
AbCellera has reported phase 2 results “beyond even the most aggressive upside scenario” that its CEO dared to consider, boosting the biotech’s chances of challenging Astellas and Bayer for a women’s health market. The phase 2 clinical trial compared ABCL635, an antibody targeting NK3R, to placebo in patients with hot flashes caused by menopause. Investigators enrolled 92 postmenopausal women experiencing a mean of about 10 moderate or severe hot flashes per day. By Week 4, the number of moderate or severe hot flashes had fallen by a mean of 8.8 on ABCL635 and by 3.5 on placebo. That statistically significant, placebo-adjusted reduction of 5.3 hit the trial’s primary endpoint. The level of efficacy also cleared the bar set by existing drugs. While cross-trial comparisons can be misleading, AbCellera CEO Carl Hansen, Ph.D., had seen enough to start hyping the program. “We now view ABCL635 as a potential best-in-class non-hormonal therapy with strong differentiation against approved NK3R competitors across all important dimensions, including efficacy, safety, dosing and convenience,” Hansen said on a Monday morning conference call with investors to discuss the data. “We believe ABCL635 is now a highly de-risked program.” Last week, Hansen used AbCellera’s earnings call to set expectations for readout, with the CEO defining success as a clean safety profile and efficacy comparable to the two approved small molecules. The data vaulted over that bar. Astellas linked (PDF) one of the small molecules, Veozah, to placebo-adjusted reductions in hot flash frequency of 2.1 and 2.6 at Week 4 of its two phase 3 trials. Bayer reported (PDF) placebo-adjusted reductions of 3.04 and 3.29 at Week 4 of pivotal studies for its small molecule, Lynkuet. At 5.3, the placebo-adjusted reduction in AbCellera’s trial comfortably cleared the standard set by the incumbents. Veozah and Lynkuet came to market based on evidence of their ability to reduce the frequency and severity of hot flashes. AbCellera’s phase 2 study covered both endpoints, with the Canadian biotech reporting a mean reduction in severity of 1.4 points at Week 4 on ABCL635, compared to 0.3 points for placebo. Again, the placebo-adjusted result compares favorably to data on Veozah and Lynkuet. Analysts shared Hansen’s enthusiasm for the data. The phase 2 results “firmly validate ABCL635 as a potentially best-in-class treatment,” BMO Capital Markets analysts said in a note to investors Monday. ABCL635’s apparent efficacy edge “is likely attributable to the higher dosing/target engagement the asset is able to reach vs. the oral agents due to its wider therapeutic index,” the analysts added. The analysts, who model $2.3 billion in unadjusted peak sales for ABCL635, flagged the safety profile as another positive of the phase 2 readout. While Veozah carries a boxed warning for liver damage, nobody on ABCL635 in the phase 2 trial had elevated liver enzymes or bilirubin values. If replicated in phase 3, the lack of a liver signal could allow approval without the monitoring requirements imposed on Veozah. Investors appeared equally enthused, sending AbCellera's stock up 32% to $9.21 in premarket trading Monday from a Friday closing price of $6.93. Next, AbCellera will collect the Week 12 data and talk to its advisors about further development. The results open up “a wide variety of possibilities of how we might take this forward into phase 3 studies to truly maximize the value of the asset and make sure that ABCL635 is able to demonstrate its differentiation potential,” Sarah Noonberg, M.D., Ph.D., AbCellera’s chief medical officer, told investors.
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PROFILE: Ben Sio
New President of CenterState CEO Brings Nearly Two Decades of Experience to Drive CNY Growth By Stefan Yablonski Ben Sio serves as president of the CenterState Corporation for Economic Opportunity — or CenterState CEO. The independent Syracuse-based nonprofit is committed to creating inclusive, equitable and sustainable economic growth in Central New York. “I started in this position in March, so it has been six months. I’m in my 19th year with CenterState,” he said in August. As president, he is responsible for overseeing the organization’s administration and governance, while leading and executing its vision to be an effective advocate, exceptional employer and a force for positive outcomes for the community. He previously served as chief of staff and senior vice president of strategy, policy and planning, overseeing the organization’s long-term strategic planning, consulting, corporate governance, fundraising and development, land-use planning and government relations. In his years with the organization, Sio has led or supported many high-profile strategic initiatives including the creation of the CNY Rising Plan, the launch of the NUAIR Alliance and the region’s UAS strategy, the creation of the Micron-enabled Community Priorities Document and the recently awarded NY Smart I Corridor Tech Hub. “When we decided to split Rob’s [Simpson] role into two positions, he has the CEO title and I got the president title. Rob remains focused on forward looking vision strategy for the organization and I’m more focused on the day-to-day operations,” he explained. “I’m also responsible for building consensus around strategic needs and priorities and managing the change necessary to drive execution. It’s an eventful job. “The experiment that we call CenterState is always evolving and shifting and addressing where we see market demand and market failure. “If we see something in our economy that isn’t going the way we think it should go, then whether it is housing development or workforce development or something else, we are always figuring out how to step into these situations and address those challenges.” “We are the largest ever in terms of staff that we have ever been. We are no longer the 20-person organization we were when we started out many, many years ago,” he said. “There are more day-to-day things to take care of. But also there is just so much happening in the community right now that is pulling CenterState and our expertise in so many directions. I tell people if we were bored and not busy, then I’d be really concerned about what we are doing. But we are not bored. We’ve certainly got our hands full — and that is a good thing.” CenterState has now approximately 63 employees on its staff, according to recent nonprofit database records. It’s involved in myriad programs, ranging from Genius NY, a business accelerator competition for uncrewed systems, IoT and robotics startups, to CenterState Up Start, a collaborative business development program that connects local, service-based businesses and aspiring entrepreneurs to the tools and networks that help them thrive. CNY Guy Through and Through A Central New York guy through and through, Sio was born and raised in Skaneateles. “My mom was a teacher, an art teacher. My father worked for many, many years with National Grid. He was there through the transition from Niagara Mohawk to National Grid,” he said. “Neither of my parents were touched by cutbacks and job loss. But I grew up going through that transition and being concerned that there were rounds of layoffs. My childhood connection to the economy here in Central New York … years ago it was on the decline. There were people that lived here that were concerned about losing their job. They were thinking they lived in a place that didn’t show much promise.” That narrative translated to his generation, he added. “The kids that grew up in the ‘90s in Central New York were very much told, directly or indirectly, by their parents, that they had to go somewhere else to find economic opportunity. That is what I grew up thinking,” he said. “I only went a few miles down Route 20 for college — in Geneva, New York; I graduated from Hobart and William Smith College with a bachelor’s degree in public policy and political science. “I got real interested in regional economies; because what makes regional economy is what makes the economy successful. And when I graduated, I had an opportunity to move to Washington, DC and work down there at a nonprofit doing this work.” However, he decided to stay in Central New York. “I got hired two weeks after I graduated. I got hired by the Greater Syracuse Chamber of Commerce. At the time it was one of two entities doing economic development in Central New York — [the Greater Syracuse Chamber of Commerce and the Metropolitan Development Association] — and it eventually became CenterState CEO,” he said, referring to 2010 merger between the two organizations. During his time at CenterState, Sio has also split time at the Brookings Institution in Washington, D.C., where he supported communities developing export and foreign direct investment (FDI), a long-term cross-border investment where an individual or company from one country buys a lasting stake or control in a business located in another country. The program — known as Global Cities Initiative — was designed to help leaders in major U.S. and international metropolitan areas reorient their local economies toward global markets. “Early on in my tenure at CenterState, we had done a lot of work with the Brookings Institution, just regional planning stuff. We worked with them on developing a regional export plan, to export more products out of Central New York and a larger business plan for the region. They hired me on to help replicate those plans in different parts of the country,”Sio explained. “So, I worked there for three or four years. I worked for CenterState, but I was technically loaned to Brookings for a period of time to help them do this work elsewhere.” Family ties “My wife is Carly. We have been married 14 years. We have two kids, a 3-year-old and a 7-year-old. “With a 3-year-old and a 7-year-old, fun is a relative term these days. We do a lot of hiking. The kids this summer got very interested in waterfalls. We’ve hit every waterfall we could in a short hike — we’ve done Skaneateles and some others.” “I do woodworking and wood turning — it’s a branch of woodworking; you use a wood lathe. My big thing there is making wood bowls. I do that in most of my free time, which is not much,” he quipped. “I like to get into my woodshop and make a bowl and decompress and turn off the rest of the world for a little bit. I usually do it late at night when the house is quiet. It is definitely my escape from the day to day. I make a lot of bowls; all different sizes, all different species of wood. It’s a fun thing to do for me.” He gives away many of his creations. “They are mostly gifts — I don’t really sell them. I really don’t have time to go set up a booth at a fair or craft show. I usually give them away. I donate them to different things. If you are in my periphery, you’ve likely received a salad bowl from me as a gift. I just need to find something to do with all the stuff that I create,” he said. Lifelines Name: Benjamin Sio Position: President, CenterState CEO Birth Date: Not provided Birth place: Syracuse Residence: Westvale Education: Hobart and William Smith Colleges in the Finger Lakes Affiliations: Member of the boards of Skaneateles Festival, Greccio Foundation, Interfaith Works, CNY Community Foundation Personal/family: Married with two kids Hobbies: Woodworking and woodturning
Engrams Embedding Entendre: Codesign for Efficient DRAM/SSD Offloading
Engram extends standard token embeddings with learned multi-token lookups. Recurring local patterns retrieve vectors directly, reducing the need to reconstruct them through attention and feed-forward layers. With Engram model architecture optimization, it allows for lower HBM capacity to be needed for models at the same quality. This does not mean there won’t be an insane demand for HBM but it just means that model architecture will continue to innovate around constraints. This model architecture design is naturally codesigned for parameter offloading: each token accesses a few embedding rows whose addresses depend on token IDs, not hidden states. The runtime can prefetch those rows from host DRAM while earlier layers compute, keeping the table outside HBM without transferring entire weight matrices. Our Memory model contains our latest estimates of quarter by quarter HBM, DRAM, & NAND supply and demand. Offloading frees HBM for model weights and KV cache, potentially supporting larger batches or more concurrent sessions. When DRAM becomes the next constraint, NVMe offers another tier. Recommendation systems already cache frequently or recently accessed embedding rows in faster memory while backing colder rows with SSDs. After NVIDIA roadmap had to change due to massively despec’ing Rubin Ultra from 1024GB to now ~200GB of HBM per chip, model architecture optimizations like emgram maybe helpful. Our DeepSeek-V4.1-Flash configuration uses roughly 189 GiB of memory for Engram. We replace it with a memory-mapped (mmap) file and measure serving performance with offload to SSD. Later on in our report, we will show our Engram offloading experiments along with the official InferenceX agentic inference serving results on Engram models like DeepSeekv4.1 Flash across all 6 NVIDIA GPU SKUs along with MI355X. Unsurprisingly, the CUDA Moat is still mogging MI355X on the ultra popular DeepSeekV4.1 Flash model. We also show how even on high capacity HBM SKUs, offloading emgrams to DRAM could result in even better performance for most of the pareto than keeping the emgram in HBM. Our benchmark has been widely reproduced, validated and/or supported by almost every major buyer of compute from Google Cloud to Microsoft Azure to Oracle, to Meta and many more. Furthermore, it has the support of the ML community including from vLLM, LMCache, SGLang, PyTorch, Huggingface and the support of major labs like OpenAI, MiniMax, ZAI, Qwen, Moonshot Kimi, etc. Star the InferenceX GitHub repository if you find the open-source benchmark and data useful!. InferenceX is the only inference benchmark in the world to have TPUv7, Jalapeño, Nvidia Rubin NVL72, AMD, and soon, SambaNova and Trainium. Due to how realistic AgentX scenario is to real world agentic inference workloads, AMD has committed to collaborating on MI455X UALoE72 too. DeepSeek did not release the original paper’s two trained Engram models. We replicated its setup on fineweb-edu using the released code and training hyperparameters, at an estimated 6E18 FLOPs per run. We observed the same U-shape scaling: Engram improved performance over pure MoE baselines. We also reproduced DeepSeek’s results, where earlier-layer representations with Engram resembled those of later layers. Like the original Engram paper, it is possible to probe Engram’s gate scores to see what n-grams DeepSeek-V4.1-Flash makes the most use of. Our gate scan finds names, code fragments, relational phrasing, and boilerplate. These examples prioritize interesting-ness over gate strength. One unexpected result was Wright : Ace Attorney. These examples suggest learned memory optimizes the training objective, not a judgment of which facts deserve storage. Licenses, bibliography fragments, API scaffolding, and website furniture can provide prediction shortcuts, so the value of additional Engram capacity may depend on what survives data preparation. This does not show that table capacity is “wasted”: the evaluation-corpus scan establishes neither training exposure nor the capacity occupied by each category. For offloading, strong gates do not identify cache-hot rows. Low gates do not automatically save reads either: computing the gate requires the retrieved key and undoes the performance gain of a fused kernel. Skipping reads would require a separate usefulness predictor before retrieval. In the original paper’s inference-time ablation, factual-knowledge benchmarks retained just 29–44% of their original performance, while reading comprehension retained 81–93%. This is due to the training–inference mismatch. The resulting degradation therefore measures this trained model’s dependence on Engram, not the performance difference between models trained with and without it. In our ablations, suppressing Engram worsens token likelihood across all evaluated domains, especially encyclopedia text and several code corpora. Surprisingly, GSM8K accuracy stays within measured run-to-run variation and removing Engram has no effect. Engram is not a detachable dictionary beside an unchanged MoE. Removing it changes downstream features and expert selection. We tested whether rerouting hurts or compensates by holding tokens fixed in a teacher-forced experiment on CRUXEval, a code-reasoning benchmark of small Python functions where the model predicts a function’s output from its code and an input, and scoring the reference answer. Removing Engram raised answer loss from 0.2848 to 0.3093 bits/token. Forcing the ablated model to use the original Engram-on expert choices made it worse still, at 0.3375 bits/token. Rerouting partially compensates for the missing memory. Memory features and expert selection work together, rather than following a clean “memory stores facts; experts reason” division. On the same CRUXeval, removing Engram during either phase reduced accuracy and increased generated tokens; removing it throughout produced the largest changes. Keeping Engram for prefill leads to more correct answers than keeping for decode likely due to semantically richer KV cache transferred to decode workers, allowing it to mitigate some of the performance loss. Engram’s table is large, but each lookup is small. DeepSeek-V4.1-Flash requests 24 rows at each of two Engram layers, about 12.4 KiB per processed token position across the model, or 3.1 KiB per GPU when split across four GPUs. Currently as of Day 7 since Model Release, MI355X is still 2-4x worse performance per dollar compared to B200 even when normalized by Mi355X’s lower TCO. Our full total cost of ownership breakdown comes from our AI Cloud TCO Model along with monthly market surveys of over 100+ gpu clouds & gpu cloud customers. On the Day 0 release of DeepSeekv4.1 Flash, NVIDIA vLLM works out of the box with zero issues across all 6 SKUs: H100, H200, B200, B300, GB200, GB300! This was thanks to the amazing work by the NVIDIA & Interact teams! In comparison, AMD vLLM did not work on day 0 for DeepSeekv4.1 Flash.Source: SemiAnalysis InferenceX AMD’s vLLM documentation points to using vllm/vllm-openai-rocm:deepseekv41-flash-0909, but from hour 0 of the model release to hour 23, AMD has not publicly released the image. AMD claims, “SPEED IS THE MOAT,” yet it has still not released it by the 23rd hour. We wish that, going forward, the AMD team has a better process for hour 0 model releases. Eventually when they did publicly release for “day 0” image support, performance-wise, it is currently up to 14.8x worse perf per dollar than H200 and up to 42x worse perf per dollar than B200/B300. The power of the CUDA MOAT is NVIDIA’s collaboration with its massive 6 million-developer community ecosystem including most of the vLLM & SGLang & Tokenspeed maintainers which means that CUDA is optimized on day 0. Overall, AMD did make significant improvements but the performance per dollar is still currently 2-4x worse than B200. AgentX Engram DRAM Offloading Improving Performance The HBM and DRAM offload use the same GPU kernel to select and dequantize rows. With HBM, it reads GPU memory; with Unified Virtual Addressing (UVA), it reads pinned host memory directly. Both support full decode graphs. Moving the table into HBM accelerates only the sparse lookup, leaving decoder computation and communication unchanged, resulting in little overall benefit while consuming memory otherwise available to KV cache. Another benefit of Engram offloaded to DRAM which means you can reduce the communication overhead by using less HBM GPUs per replica. For example, when enabling Engram offloading on B300, we are able to switch from TP4 to now TP2 which improves the pareto curve by up to 1.6x. When iso-model quality, less HBM is required as the engrams could be offloaded to host DRAM. Thus HBM bandwidth matters way more than HBM capacity. For inference workloads where memory bandwidth matters the most, 4-hi HBM provides the best $/bandwidth and therefore lowest cost per token. If China continues to make more and more revolutionary model architecture innovations, soon it could potentially 0Hi HBM stacks. [ ](https://newsletter.semianalysis.com/p/long-live-the-short-king-why-4-hi) [ Long Live the Short King: Why 4-hi HBM Wins ](https://newsletter.semianalysis.com/p/long-live-the-short-king-why-4-hi) Myron Xie, Bryan Shan, and 3 others · Sep 13 [ Read full story ](https://newsletter.semianalysis.com/p/long-live-the-short-king-why-4-hi) Moreover, on B300 and week-0 stack, moving the Engram table back to HBM did not improve results and stay within run-to-run variance. This is a result of the work optimizing DRAM offload, such as async, and overlap. SSD Offloading On B200, we created a unoptimized vLLM fork and stored the Engram tables in memory-mapped files on local SSDs. File backing lets the OS reclaim table pages when other applications need RAM. Pages already cached in memory can be served without reading the SSD again. Furthermore, note that we were unable to turn on GDS The unoptimized SSD implementation changes how rows reach the GPU. It copies row IDs to the CPU, deduplicates them, gathers the requested rows into pinned buffers, copies those rows back to the GPU and dequantizes them. This work runs between segments of the GPU execution graph. Native UVA performs row selection and dequantization directly on the GPU, avoiding the CPU round trip. A warm filesystem cache removes physical SSD reads, but leaves the coordination, row gathering and transfers. This is why a file already cached in RAM can still perform worse than a pinned DRAM table. The comparison measures the whole serving path; it does not separate the time spent on each of these operations. B200 DRAM dominates both measured SSD serving curves in total tokens per dollar and P90 interactivity. Near 125 tokens/s/user, DRAM delivers 121 million total tokens per dollar versus 52 million for SSD. For production serving, SSD offloading is likely not worth the tradeoff. On the B200 configurations we measured, SSD offloading loses on both measures: every observed SSD point has a DRAM alternative that delivers higher P90 interactivity and more total tokens per dollar. The only points where Cheaper storage does not automatically produce a cheaper inference service. Moving Engram to SSD leaves the same four expensive GPUs and the rest of the server in place. Reclaiming RAM only creates an economic benefit if it enables a cheaper server configuration or additional useful capacity. The current unoptimized path provides neither benefit, and the filesystem cache still consumes RAM when table pages are resident. Next we will look at the mechanisms and specific implementation of ngrams from DeepSeekv4.1 Flash, LongCat, Qwen3.8 Flash Next.
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