Key Takeaways Meta and Yandex have been found guilty of secretly listening to localhost ports and using them to transfer sensitive data from Android devices. The corporations use Meta Pixel and Yandex Metrica scripts to transfer cookies from browsers to local apps. Using incognito mode or a VPN can’t fully protect users against it. A Meta spokesperson has called this a ‘miscommunication,’ which seems to be an attempt to underplay the situation. Denmark’s Ministry of Digitalization has recently announced that it will leave the Microsoft ecosystem in favor of Linux and other open-source software. Minister Caroline Stage Olsen revealed this in an interview with Politiken, the country’s leading newspaper. According to Olsen, the Ministry plans to switch half of its employees to Linux and LibreOffice by summer, and the rest by fall. The announcement comes after Denmark’s largest cities – Copenhagen and Aarhus – made similar moves earlier this month. Why the Danish Ministry of Digitalization Switched to Open-Source Software The three main reasons Denmark is moving away from Microsoft are costs, politics, and security. In the case of Aarhus, the city was able to slash its annual costs from 800K kroner to just 225K by replacing Microsoft with a German service provider.  The same is a pain point for Copenhagen, which saw its costs on Microsoft balloon from 313M kroner in 2018 to 538M kroner in 2023. It’s also part of a broader move to increase its digital sovereignty. In her LinkedIn post, Olsen further explained that the strategy is not about isolation or digital nationalism, adding that they should not turn their backs completely on global tech companies like Microsoft.  Instead, it’s about avoiding being too dependent on these companies, which could prevent them from acting freely. Then there’s politics. Since his reelection earlier this year, US President Donald Trump has repeatedly threatened to take over Greenland, an autonomous territory of Denmark.  In May, the Danish Foreign Minister Lars Løkke Rasmussen summoned the US ambassador regarding news that US spy agencies have been told to focus on the territory. If the relationship between the two countries continues to erode, Trump can order Microsoft and other US tech companies to cut off Denmark from their services. After all, Microsoft and Facebook’s parent company Meta, have close ties to the US president after contributing $1M each for his inauguration in January. Denmark Isn’t Alone: Other EU Countries Are Making Similar Moves Denmark is only one of the growing number of European Union (EU) countries taking measures to become more digitally independent. Germany’s Federal Digital Minister Karsten Wildberger emphasized the need to be more independent of global tech companies during the re:publica internet conference in May. He added that IT companies in the EU have the opportunity to create tech that is based on the region’s values. Meanwhile, Bert Hubert, a technical advisor to the Dutch Electoral Council, wrote in February that ‘it is no longer safe to move our governments and societies to US clouds.’ He said that America is no longer a ‘reliable partner,’ making it risky to have the data of European governments and businesses at the mercy of US-based cloud providers. Earlier this month, the chief prosecutor of the International Criminal Court (ICC), Karim Khan, experienced a disconnection from his Microsoft-based email account, sparking uproar across the region.  Speculation quickly arose that the incident was linked to sanctions previously imposed on the ICC by the Trump administration, an assertion Microsoft has denied. Earlier this month, the chief prosecutor of the International Criminal Court (ICC), Karim Khan, disconnection from his Microsoft-based email account caused an uproar in the region. Some speculated that this was connected to sanctions imposed by Trump against the ICC, which Microsoft denied. Weaning the EU Away from US Tech is Possible, But Challenges Lie Ahead Change like this doesn’t happen overnight. Just finding, let alone developing, reliable alternatives to tools that have been part of daily workflows for decades, is a massive undertaking. It will also take time for users to adapt to these new tools, especially when transitioning to an entirely new ecosystem. In Aarhus, for example, municipal staff initially viewed the shift to open source as a step down from the familiarity and functionality of Microsoft products. Overall, these are only temporary hurdles. Momentum is building, with growing calls for digital independence from leaders like Ministers Olsen and Wildberger.  Initiatives such as the Digital Europe Programme, which seeks to reduce reliance on foreign systems and solutions, further accelerate this push. As a result, the EU’s transition could arrive sooner rather than later As technology continues to evolve—from the return of 'dumbphones' to faster and sleeker computers—seasoned tech journalist, Cedric Solidon, continues to dedicate himself to writing stories that inform, empower, and connect with readers across all levels of digital literacy. With 20 years of professional writing experience, this University of the Philippines Journalism graduate has carved out a niche as a trusted voice in tech media. Whether he's breaking down the latest advancements in cybersecurity or explaining how silicon-carbon batteries can extend your phone’s battery life, his writing remains rooted in clarity, curiosity, and utility. Long before he was writing for Techreport, HP, Citrix, SAP, Globe Telecom, CyberGhost VPN, and ExpressVPN, Cedric's love for technology began at home courtesy of a Nintendo Family Computer and a stack of tech magazines. Growing up, his days were often filled with sessions of Contra, Bomberman, Red Alert 2, and the criminally underrated Crusader: No Regret. But gaming wasn't his only gateway to tech.  He devoured every T3, PCMag, and PC Gamer issue he could get his hands on, often reading them cover to cover. It wasn’t long before he explored the early web in IRC chatrooms, online forums, and fledgling tech blogs, soaking in every byte of knowledge from the late '90s and early 2000s internet boom. That fascination with tech didn’t just stick. It evolved into a full-blown calling. After graduating with a degree in Journalism, he began his writing career at the dawn of Web 2.0. What started with small editorial roles and freelance gigs soon grew into a full-fledged career. He has since collaborated with global tech leaders, lending his voice to content that bridges technical expertise with everyday usability. He’s also written annual reports for Globe Telecom and consumer-friendly guides for VPN companies like CyberGhost and ExpressVPN, empowering readers to understand the importance of digital privacy. His versatility spans not just tech journalism but also technical writing. He once worked with a local tech company developing web and mobile apps for logistics firms, crafting documentation and communication materials that brought together user-friendliness with deep technical understanding. That experience sharpened his ability to break down dense, often jargon-heavy material into content that speaks clearly to both developers and decision-makers. At the heart of his work lies a simple belief: technology should feel empowering, not intimidating. Even if the likes of smartphones and AI are now commonplace, he understands that there's still a knowledge gap, especially when it comes to hardware or the real-world benefits of new tools. His writing hopes to help close that gap. Cedric’s writing style reflects that mission. It’s friendly without being fluffy and informative without being overwhelming. Whether writing for seasoned IT professionals or casual readers curious about the latest gadgets, he focuses on how a piece of technology can improve our lives, boost our productivity, or make our work more efficient. That human-first approach makes his content feel more like a conversation than a technical manual. As his writing career progresses, his passion for tech journalism remains as strong as ever. With the growing need for accessible, responsible tech communication, he sees his role not just as a journalist but as a guide who helps readers navigate a digital world that’s often as confusing as it is exciting. From reviewing the latest devices to unpacking global tech trends, Cedric isn’t just reporting on the future; he’s helping to write it. View all articles by Cedric Solidon Our editorial process The Tech Report editorial policy is centered on providing helpful, accurate content that offers real value to our readers. We only work with experienced writers who have specific knowledge in the topics they cover, including latest developments in technology, online privacy, cryptocurrencies, software, and more. Our editorial policy ensures that each topic is researched and curated by our in-house editors. We maintain rigorous journalistic standards, and every article is 100% written by real authors.

Cool Finds Archaeologists Stumble Onto Sprawling Ancient Roman Villa During Construction of a Road in France Located near Auxerre, the grand estate once possessed an exorbitant level of wealth, with thermal baths and heated floors Aerial view of the villa, with thermal baths at the bottom right, the garden and fountain in the center, and the agricultural fields expanding to the left Ch. Fouquin / INRAP In ancient times, all roads led to Rome—or so the saying goes. Nowadays, new roads can lead to Roman ruins. During construction on an alternative route to D606, a regional road just under two miles outside of Auxerre, in central France, salvage archaeologists unearthed a sprawling Roman villa complete with a stately garden, a fountain and an elaborate system of underfloor heating known as a hypocaust, according to a statement from the French National Institute for Preventive Archaeological Research (INRAP). While researchers have been aware of the ruins on the outskirts of the Gallo-Roman settlement of Autissiodorum (as Auxerre was once known) since the 19th century, previous excavations have been limited. The most recent dig, in 1966, found a 7,500-square-foot building with ten rooms and amenities that suggested its residents enjoyed great wealth and regional power. The site of Sainte-Nitasse, adjacent to a regional highway Ch. Fouquin / INRAP But until now, the true scale of the villa known as Sainte-Nitasse and its surrounding agricultural estates along the River Yonne was unclear. Archaeologists at INRAP have since discovered a 43,000-square-foot building thought to date to between the first and third centuries C.E. It suggests a previously unimagined level of grandeur. INRAP identifies the site as one of the “grand villas of Roman Gaul,” according to the statement. Grand villas are typified by their vast dimensions and sophisticated architectural style. They typically encompass both agricultural and residential portions, known in Latin as pars rustica and pars urbana, respectively. In the pars urbana, grand villas tend to feature stately construction materials like marble; extensive mosaics and frescoes; and amenities like private baths, fountains and gardens. So far, the excavations at Sainte-Nitasse have revealed all these features and more. The villa’s development is extensive. A 4,800-square-foot garden is enclosed by a fountain to the south and a water basin, or an ornamental pond, to the north. The hypocaust, an ancient system of central heating that circulated hot air beneath the floors of the house, signals a level of luxury atypical for rural estates in Roman Gaul. A section of the villa's hypocaust heating system, which circulated hot air beneath the floor Ch. Fouquin / INRAP “We can imagine it as an ‘aristocratic’ villa, belonging to someone with riches, responsibilities—perhaps municipal, given the proximity to Auxerre—a landowner who had staff on site,” Alexandre Burgevin, the archaeologist in charge of the excavations with INRAP, tells France Info’s Lisa Guyenne. Near the banks of the Yonne, a thermal bath site contains several pools where the landowner and his family bathed. On the other side of the garden, workers toiled in the fields of a massive agricultural estate. Aside from its size and amenities, the villa’s level of preservation also astounded archaeologists. “For a rural site, it’s quite exceptional,” Burgevin tells L’Yonne Républicaine’s Titouan Stücker. “You can walk on floors from the time period, circulate between rooms like the Gallo-Romans did.”Over time, Autissiodorum grew to become a major city along the Via Agrippa, eventually earning the honor of serving as a provincial Roman capital by the fourth century C.E. As Gaul began slipping away from the Roman Empire around the same time, the prominence of the city fluctuated. INRAP archaeologists speculate that the site was repurposed during medieval times, around the 13th century. Burgevin offers several explanations for why the site remained so well preserved in subsequent centuries. The humid conditions along the banks of the river might have prevented excess decay. Since this portion of the River Yonne wasn’t canalized until the 19th century, engineers may have already been aware of the presence of ruins. Or, perhaps the rubble of the villa created “bumpy,” intractable soil that was “not easy to pass over with a tractor,” he tells France Info. While the site will briefly open to the public on June 15 for European Archaeology Days, an annual event held at sites across the continent, excavations will continue until September, at which time construction on the road will resume. Much work is to be done, including filling in large gaps of the site’s chronology between the Roman and medieval eras. “We have well-built walls but few objects,” says Burgevin, per L’Yonne Républicaine. “It will be necessary to continue digging to understand better.” Get the latest stories in your inbox every weekday.

Transcript [MUSIC]      [BOOK PASSAGE]   PETER LEE: “In ‘The Little Black Bag,’ a classic science fiction story, a high-tech doctor’s kit of the future is accidentally transported back to the 1950s, into the shaky hands of a washed-up, alcoholic doctor. The ultimate medical tool, it redeems the doctor wielding it, allowing him to practice gratifyingly heroic medicine. … The tale ends badly for the doctor and his treacherous assistant, but it offered a picture of how advanced technology could transform medicine—powerful when it was written nearly 75 years ago and still so today. What would be the Al equivalent of that little black bag? At this moment when new capabilities are emerging, how do we imagine them into medicine?”   [END OF BOOK PASSAGE]     [THEME MUSIC]     This is The AI Revolution in Medicine, Revisited. I’m your host, Peter Lee.    Shortly after OpenAI’s GPT-4 was publicly released, Carey Goldberg, Dr. Zak Kohane, and I published The AI Revolution in Medicine to help educate the world of healthcare and medical research about the transformative impact this new generative AI technology could have. But because we wrote the book when GPT-4 was still a secret, we had to speculate. Now, two years later, what did we get right, and what did we get wrong?     In this series, we’ll talk to clinicians, patients, hospital administrators, and others to understand the reality of AI in the field and where we go from here.   [THEME MUSIC FADES] The book passage I read at the top is from “Chapter 10: The Big Black Bag.”  In imagining AI in medicine, Carey, Zak, and I included in our book two fictional accounts. In the first, a medical resident consults GPT-4 on her personal phone as the patient in front of her crashes. Within seconds, it offers an alternate response based on recent literature. In the second account, a 90-year-old woman with several chronic conditions is living independently and receiving near-constant medical support from an AI aide.    In our conversations with the guests we’ve spoken to so far, we’ve caught a glimpse of these predicted futures, seeing how clinicians and patients are actually using AI today and how developers are leveraging the technology in the healthcare products and services they’re creating. In fact, that first fictional account isn’t so fictional after all, as most of the doctors in the real world actually appear to be using AI at least occasionally—and sometimes much more than occasionally—to help in their daily clinical work. And as for the second fictional account, which is more of a science fiction account, it seems we are indeed on the verge of a new way of delivering and receiving healthcare, though the future is still very much open.  As we continue to examine the current state of AI in healthcare and its potential to transform the field, I’m pleased to welcome Bill Gates and Sébastien Bubeck.   Bill may be best known as the co-founder of Microsoft, having created the company with his childhood friend Paul Allen in 1975. He’s now the founder of Breakthrough Energy, which aims to advance clean energy innovation, and TerraPower, a company developing groundbreaking nuclear energy and science technologies. He also chairs the world’s largest philanthropic organization, the Gates Foundation, and focuses on solving a variety of health challenges around the globe and here at home.  Sébastien is a research lead at OpenAI. He was previously a distinguished scientist, vice president of AI, and a colleague of mine here at Microsoft, where his work included spearheading the development of the family of small language models known as Phi. While at Microsoft, he also coauthored the discussion-provoking 2023 paper “Sparks of Artificial General Intelligence,” which presented the results of early experiments with GPT-4 conducted by a small team from Microsoft Research.    [TRANSITION MUSIC]   Here’s my conversation with Bill Gates and Sébastien Bubeck.  LEE: Bill, welcome.  BILL GATES: Thank you.  LEE: Seb …  SÉBASTIEN BUBECK: Yeah. Hi, hi, Peter. Nice to be here.  LEE: You know, one of the things that I’ve been doing just to get the conversation warmed up is to talk about origin stories, and what I mean about origin stories is, you know, what was the first contact that you had with large language models or the concept of generative AI that convinced you or made you think that something really important was happening?  And so, Bill, I think I’ve heard the story about, you know, the time when the OpenAI folks—Sam Altman, Greg Brockman, and others—showed you something, but could we hear from you what those early encounters were like and what was going through your mind?   GATES: Well, I’d been visiting OpenAI soon after it was created to see things like GPT-2 and to see the little arm they had that was trying to match human manipulation and, you know, looking at their games like Dota that they were trying to get as good as human play. And honestly, I didn’t think the language model stuff they were doing, even when they got to GPT-3, would show the ability to learn, you know, in the same sense that a human reads a biology book and is able to take that knowledge and access it not only to pass a test but also to create new medicines.  And so my challenge to them was that if their LLM could get a five on the advanced placement biology test, then I would say, OK, it took biologic knowledge and encoded it in an accessible way and that I didn’t expect them to do that very quickly but it would be profound.   And it was only about six months after I challenged them to do that, that an early version of GPT-4 they brought up to a dinner at my house, and in fact, it answered most of the questions that night very well. The one it got totally wrong, we were … because it was so good, we kept thinking, Oh, we must be wrong. It turned out it was a math weakness [LAUGHTER] that, you know, we later understood that that was an area of, weirdly, of incredible weakness of those early models. But, you know, that was when I realized, OK, the age of cheap intelligence was at its beginning.  LEE: Yeah. So I guess it seems like you had something similar to me in that my first encounters, I actually harbored some skepticism. Is it fair to say you were skeptical before that?  GATES: Well, the idea that we’ve figured out how to encode and access knowledge in this very deep sense without even understanding the nature of the encoding, …  LEE: Right.   GATES: … that is a bit weird.   LEE: Yeah.  GATES: We have an algorithm that creates the computation, but even say, OK, where is the president’s birthday stored in there? Where is this fact stored in there? The fact that even now when we’re playing around, getting a little bit more sense of it, it’s opaque to us what the semantic encoding is, it’s, kind of, amazing to me. I thought the invention of knowledge storage would be an explicit way of encoding knowledge, not an implicit statistical training.  LEE: Yeah, yeah. All right. So, Seb, you know, on this same topic, you know, I got—as we say at Microsoft—I got pulled into the tent. [LAUGHS]  BUBECK: Yes.   LEE: Because this was a very secret project. And then, um, I had the opportunity to select a small number of researchers in MSR [Microsoft Research] to join and start investigating this thing seriously. And the first person I pulled in was you.  BUBECK: Yeah.  LEE: And so what were your first encounters? Because I actually don’t remember what happened then.  BUBECK: Oh, I remember it very well. [LAUGHS] My first encounter with GPT-4 was in a meeting with the two of you, actually. But my kind of first contact, the first moment where I realized that something was happening with generative AI, was before that. And I agree with Bill that I also wasn’t too impressed by GPT-3.  I though that it was kind of, you know, very naturally mimicking the web, sort of parroting what was written there in a nice way. Still in a way which seemed very impressive. But it wasn’t really intelligent in any way. But shortly after GPT-3, there was a model before GPT-4 that really shocked me, and this was the first image generation model, DALL-E 1.  So that was in 2021. And I will forever remember the press release of OpenAI where they had this prompt of an avocado chair and then you had this image of the avocado chair. [LAUGHTER] And what really shocked me is that clearly the model kind of “understood” what is a chair, what is an avocado, and was able to merge those concepts.  So this was really, to me, the first moment where I saw some understanding in those models.   LEE: So this was, just to get the timing right, that was before I pulled you into the tent.  BUBECK: That was before. That was like a year before.  LEE: Right.   BUBECK: And now I will tell you how, you know, we went from that moment to the meeting with the two of you and GPT-4.  So once I saw this kind of understanding, I thought, OK, fine. It understands concept, but it’s still not able to reason. It cannot—as, you know, Bill was saying—it cannot learn from your document. It cannot reason.   So I set out to try to prove that. You know, this is what I was in the business of at the time, trying to prove things in mathematics. So I was trying to prove that basically autoregressive transformers could never reason. So I was trying to prove this. And after a year of work, I had something reasonable to show. And so I had the meeting with the two of you, and I had this example where I wanted to say, there is no way that an LLM is going to be able to do x.  And then as soon as I … I don’t know if you remember, Bill. But as soon as I said that, you said, oh, but wait a second. I had, you know, the OpenAI crew at my house recently, and they showed me a new model. Why don’t we ask this new model this question?   LEE: Yeah. BUBECK: And we did, and it solved it on the spot. And that really, honestly, just changed my life. Like, you know, I had been working for a year trying to say that this was impossible. And just right there, it was shown to be possible.   LEE: [LAUGHS] One of the very first things I got interested in—because I was really thinking a lot about healthcare—was healthcare and medicine.  And I don’t know if the two of you remember, but I ended up doing a lot of tests. I ran through, you know, step one and step two of the US Medical Licensing Exam. Did a whole bunch of other things. I wrote this big report. It was, you know, I can’t remember … a couple hundred pages.   And I needed to share this with someone. I didn’t … there weren’t too many people I could share it with. So I sent, I think, a copy to you, Bill. Sent a copy to you, Seb.   I hardly slept for about a week putting that report together. And, yeah, and I kept working on it. But I was far from alone. I think everyone who was in the tent, so to speak, in those early days was going through something pretty similar. All right. So I think … of course, a lot of what I put in the report also ended up being examples that made it into the book.  But the main purpose of this conversation isn’t to reminisce about [LAUGHS] or indulge in those reminiscences but to talk about what’s happening in healthcare and medicine. And, you know, as I said, we wrote this book. We did it very, very quickly. Seb, you helped. Bill, you know, you provided a review and some endorsements.  But, you know, honestly, we didn’t know what we were talking about because no one had access to this thing. And so we just made a bunch of guesses. So really, the whole thing I wanted to probe with the two of you is, now with two years of experience out in the world, what, you know, what do we think is happening today?  You know, is AI actually having an impact, positive or negative, on healthcare and medicine? And what do we now think is going to happen in the next two years, five years, or 10 years? And so I realize it’s a little bit too abstract to just ask it that way. So let me just try to narrow the discussion and guide us a little bit.   Um, the kind of administrative and clerical work, paperwork, around healthcare—and we made a lot of guesses about that—that appears to be going well, but, you know, Bill, I know we’ve discussed that sometimes that you think there ought to be a lot more going on. Do you have a viewpoint on how AI is actually finding its way into reducing paperwork?  GATES: Well, I’m stunned … I don’t think there should be a patient-doctor meeting where the AI is not sitting in and both transcribing, offering to help with the paperwork, and even making suggestions, although the doctor will be the one, you know, who makes the final decision about the diagnosis and whatever prescription gets done.   It’s so helpful. You know, when that patient goes home and their, you know, son who wants to understand what happened has some questions, that AI should be available to continue that conversation. And the way you can improve that experience and streamline things and, you know, involve the people who advise you. I don’t understand why that’s not more adopted, because there you still have the human in the loop making that final decision.  But even for, like, follow-up calls to make sure the patient did things, to understand if they have concerns and knowing when to escalate back to the doctor, the benefit is incredible. And, you know, that thing is ready for prime time. That paradigm is ready for prime time, in my view.  LEE: Yeah, there are some good products, but it seems like the number one use right now—and we kind of got this from some of the previous guests in previous episodes—is the use of AI just to respond to emails from patients. [LAUGHTER] Does that make sense to you?  BUBECK: Yeah. So maybe I want to second what Bill was saying but maybe take a step back first. You know, two years ago, like, the concept of clinical scribes, which is one of the things that we’re talking about right now, it would have sounded, in fact, it sounded two years ago, borderline dangerous. Because everybody was worried about hallucinations. What happened if you have this AI listening in and then it transcribes, you know, something wrong?  Now, two years later, I think it’s mostly working. And in fact, it is not yet, you know, fully adopted. You’re right. But it is in production. It is used, you know, in many, many places. So this rate of progress is astounding because it wasn’t obvious that we would be able to overcome those obstacles of hallucination. It’s not to say that hallucinations are fully solved. In the case of the closed system, they are.   Now, I think more generally what’s going on in the background is that there is something that we, that certainly I, underestimated, which is this management overhead. So I think the reason why this is not adopted everywhere is really a training and teaching aspect. People need to be taught, like, those systems, how to interact with them.  And one example that I really like, a study that recently appeared where they tried to use ChatGPT for diagnosis and they were comparing doctors without and with ChatGPT (opens in new tab). And the amazing thing … so this was a set of cases where the accuracy of the doctors alone was around 75%. ChatGPT alone was 90%. So that’s already kind of mind blowing. But then the kicker is that doctors with ChatGPT was 80%.   Intelligence alone is not enough. It’s also how it’s presented, how you interact with it. And ChatGPT, it’s an amazing tool. Obviously, I absolutely love it. But it’s not … you don’t want a doctor to have to type in, you know, prompts and use it that way.  It should be, as Bill was saying, kind of running continuously in the background, sending you notifications. And you have to be really careful of the rate at which those notifications are being sent. Because if they are too frequent, then the doctor will learn to ignore them. So you have to … all of those things matter, in fact, at least as much as the level of intelligence of the machine.  LEE: One of the things I think about, Bill, in that scenario that you described, doctors do some thinking about the patient when they write the note. So, you know, I’m always a little uncertain whether it’s actually … you know, you wouldn’t necessarily want to fully automate this, I don’t think. Or at least there needs to be some prompt to the doctor to make sure that the doctor puts some thought into what happened in the encounter with the patient. Does that make sense to you at all?  GATES: At this stage, you know, I’d still put the onus on the doctor to write the conclusions and the summary and not delegate that.  The tradeoffs you make a little bit are somewhat dependent on the situation you’re in. If you’re in Africa, So, yes, the doctor’s still going to have to do a lot of work, but just the quality of letting the patient and the people around them interact and ask questions and have things explained, that alone is such a quality improvement. It’s mind blowing.   LEE: So since you mentioned, you know, Africa—and, of course, this touches on the mission and some of the priorities of the Gates Foundation and this idea of democratization of access to expert medical care—what’s the most interesting stuff going on right now? Are there people and organizations or technologies that are impressing you or that you’re tracking?  GATES: Yeah. So the Gates Foundation has given out a lot of grants to people in Africa doing education, agriculture but more healthcare examples than anything. And the way these things start off, they often start out either being patient-centric in a narrow situation, like, OK, I’m a pregnant woman; talk to me. Or, I have infectious disease symptoms; talk to me. Or they’re connected to a health worker where they’re helping that worker get their job done. And we have lots of pilots out, you know, in both of those cases.   The dream would be eventually to have the thing the patient consults be so broad that it’s like having a doctor available who understands the local things.   LEE: Right.   GATES: We’re not there yet. But over the next two or three years, you know, particularly given the worsening financial constraints against African health systems, where the withdrawal of money has been dramatic, you know, figuring out how to take this—what I sometimes call “free intelligence”—and build a quality health system around that, we will have to be more radical in low-income countries than any rich country is ever going to be.   LEE: Also, there’s maybe a different regulatory environment, so some of those things maybe are easier? Because right now, I think the world hasn’t figured out how to and whether to regulate, let’s say, an AI that might give a medical diagnosis or write a prescription for a medication.  BUBECK: Yeah. I think one issue with this, and it’s also slowing down the deployment of AI in healthcare more generally, is a lack of proper benchmark. Because, you know, you were mentioning the USMLE [United States Medical Licensing Examination], for example. That’s a great test to test human beings and their knowledge of healthcare and medicine. But it’s not a great test to give to an AI.  It’s not asking the right questions. So finding what are the right questions to test whether an AI system is ready to give diagnosis in a constrained setting, that’s a very, very important direction, which to my surprise, is not yet accelerating at the rate that I was hoping for.  LEE: OK, so that gives me an excuse to get more now into the core AI tech because something I’ve discussed with both of you is this issue of what are the right tests. And you both know the very first test I give to any new spin of an LLM is I present a patient, the results—a mythical patient—the results of my physical exam, my mythical physical exam. Maybe some results of some initial labs. And then I present or propose a differential diagnosis. And if you’re not in medicine, a differential diagnosis you can just think of as a prioritized list of the possible diagnoses that fit with all that data. And in that proposed differential, I always intentionally make two mistakes.  I make a textbook technical error in one of the possible elements of the differential diagnosis, and I have an error of omission. And, you know, I just want to know, does the LLM understand what I’m talking about? And all the good ones out there do now. But then I want to know, can it spot the errors? And then most importantly, is it willing to tell me I’m wrong, that I’ve made a mistake?   That last piece seems really hard for AI today. And so let me ask you first, Seb, because at the time of this taping, of course, there was a new spin of GPT-4o last week that became overly sycophantic. In other words, it was actually prone in that test of mine not only to not tell me I’m wrong, but it actually praised me for the creativity of my differential. [LAUGHTER] What’s up with that?  BUBECK: Yeah, I guess it’s a testament to the fact that training those models is still more of an art than a science. So it’s a difficult job. Just to be clear with the audience, we have rolled back that [LAUGHS] version of GPT-4o, so now we don’t have the sycophant version out there.  Yeah, no, it’s a really difficult question. It has to do … as you said, it’s very technical. It has to do with the post-training and how, like, where do you nudge the model? So, you know, there is this very classical by now technique called RLHF [reinforcement learning from human feedback], where you push the model in the direction of a certain reward model. So the reward model is just telling the model, you know, what behavior is good, what behavior is bad.  But this reward model is itself an LLM, and, you know, Bill was saying at the very beginning of the conversation that we don’t really understand how those LLMs deal with concepts like, you know, where is the capital of France located? Things like that. It is the same thing for this reward model. We don’t know why it says that it prefers one output to another, and whether this is correlated with some sycophancy is, you know, something that we discovered basically just now. That if you push too hard in optimization on this reward model, you will get a sycophant model.  So it’s kind of … what I’m trying to say is we became too good at what we were doing, and we ended up, in fact, in a trap of the reward model.  LEE: I mean, you do want … it’s a difficult balance because you do want models to follow your desires and …  BUBECK: It’s a very difficult, very difficult balance.  LEE: So this brings up then the following question for me, which is the extent to which we think we’ll need to have specially trained models for things. So let me start with you, Bill. Do you have a point of view on whether we will need to, you know, quote-unquote take AI models to med school? Have them specially trained? Like, if you were going to deploy something to give medical care in underserved parts of the world, do we need to do something special to create those models?  GATES: We certainly need to teach them the African languages and the unique dialects so that the multimedia interactions are very high quality. We certainly need to teach them the disease prevalence and unique disease patterns like, you know, neglected tropical diseases and malaria. So we need to gather a set of facts that somebody trying to go for a US customer base, you know, wouldn’t necessarily have that in there.  Those two things are actually very straightforward because the additional training time is small. I’d say for the next few years, we’ll also need to do reinforcement learning about the context of being a doctor and how important certain behaviors are. Humans learn over the course of their life to some degree that, I’m in a different context and the way I behave in terms of being willing to criticize or be nice, you know, how important is it? Who’s here? What’s my relationship to them?   Right now, these machines don’t have that broad social experience. And so if you know it’s going to be used for health things, a lot of reinforcement learning of the very best humans in that context would still be valuable. Eventually, the models will, having read all the literature of the world about good doctors, bad doctors, it’ll understand as soon as you say, “I want you to be a doctor diagnosing somebody.” All of the implicit reinforcement that fits that situation, you know, will be there. LEE: Yeah. GATES: And so I hope three years from now, we don’t have to do that reinforcement learning. But today, for any medical context, you would want a lot of data to reinforce tone, willingness to say things when, you know, there might be something significant at stake.  LEE: Yeah. So, you know, something Bill said, kind of, reminds me of another thing that I think we missed, which is, the context also … and the specialization also pertains to different, I guess, what we still call “modes,” although I don’t know if the idea of multimodal is the same as it was two years ago. But, you know, what do you make of all of the hubbub around—in fact, within Microsoft Research, this is a big deal, but I think we’re far from alone—you know, medical images and vision, video, proteins and molecules, cell, you know, cellular data and so on.  BUBECK: Yeah. OK. So there is a lot to say to everything … to the last, you know, couple of minutes. Maybe on the specialization aspect, you know, I think there is, hiding behind this, a really fundamental scientific question of whether eventually we have a singular AGI [artificial general intelligence] that kind of knows everything and you can just put, you know, explain your own context and it will just get it and understand everything.  That’s one vision. I have to say, I don’t particularly believe in this vision. In fact, we humans are not like that at all. I think, hopefully, we are general intelligences, yet we have to specialize a lot. And, you know, I did myself a lot of RL, reinforcement learning, on mathematics. Like, that’s what I did, you know, spent a lot of time doing that. And I didn’t improve on other aspects. You know, in fact, I probably degraded in other aspects. [LAUGHTER] So it’s … I think it’s an important example to have in mind.  LEE: I think I might disagree with you on that, though, because, like, doesn’t a model have to see both good science and bad science in order to be able to gain the ability to discern between the two?  BUBECK: Yeah, no, that absolutely. I think there is value in seeing the generality, in having a very broad base. But then you, kind of, specialize on verticals. And this is where also, you know, open-weights model, which we haven’t talked about yet, are really important because they allow you to provide this broad base to everyone. And then you can specialize on top of it.  LEE: So we have about three hours of stuff to talk about, but our time is actually running low. BUBECK: Yes, yes, yes.   LEE: So I think I want … there’s a more provocative question. It’s almost a silly question, but I need to ask it of the two of you, which is, is there a future, you know, where AI replaces doctors or replaces, you know, medical specialties that we have today? So what does the world look like, say, five years from now?  GATES: Well, it’s important to distinguish healthcare discovery activity from healthcare delivery activity. We focused mostly on delivery. I think it’s very much within the realm of possibility that the AI is not only accelerating healthcare discovery but substituting for a lot of the roles of, you know, I’m an organic chemist, or I run various types of assays. I can see those, which are, you know, testable-output-type jobs but with still very high value, I can see, you know, some replacement in those areas before the doctor.   The doctor, still understanding the human condition and long-term dialogues, you know, they’ve had a lifetime of reinforcement of that, particularly when you get into areas like mental health. So I wouldn’t say in five years, either people will choose to adopt it, but it will be profound that there’ll be this nearly free intelligence that can do follow-up, that can help you, you know, make sure you went through different possibilities.  And so I’d say, yes, we’ll have doctors, but I’d say healthcare will be massively transformed in its quality and in efficiency by AI in that time period.  LEE: Is there a comparison, useful comparison, say, between doctors and, say, programmers, computer programmers, or doctors and, I don’t know, lawyers?  GATES: Programming is another one that has, kind of, a mathematical correctness to it, you know, and so the objective function that you’re trying to reinforce to, as soon as you can understand the state machines, you can have something that’s “checkable”; that’s correct. So I think programming, you know, which is weird to say, that the machine will beat us at most programming tasks before we let it take over roles that have deep empathy, you know, physical presence and social understanding in them.  LEE: Yeah. By the way, you know, I fully expect in five years that AI will produce mathematical proofs that are checkable for validity, easily checkable, because they’ll be written in a proof-checking language like Lean or something but will be so complex that no human mathematician can understand them. I expect that to happen.   I can imagine in some fields, like cellular biology, we could have the same situation in the future because the molecular pathways, the chemistry, biochemistry of human cells or living cells is as complex as any mathematics, and so it seems possible that we may be in a state where in wet lab, we see, Oh yeah, this actually works, but no one can understand why.  BUBECK: Yeah, absolutely. I mean, I think I really agree with Bill’s distinction of the discovery and the delivery, and indeed, the discovery’s when you can check things, and at the end, there is an artifact that you can verify. You know, you can run the protocol in the wet lab and see [if you have] produced what you wanted. So I absolutely agree with that.   And in fact, you know, we don’t have to talk five years from now. I don’t know if you know, but just recently, there was a paper that was published on a scientific discovery using o3- mini (opens in new tab). So this is really amazing. And, you know, just very quickly, just so people know, it was about this statistical physics model, the frustrated Potts model, which has to do with coloring, and basically, the case of three colors, like, more than two colors was open for a long time, and o3 was able to reduce the case of three colors to two colors.   LEE: Yeah.  BUBECK: Which is just, like, astounding. And this is not … this is now. This is happening right now. So this is something that I personally didn’t expect it would happen so quickly, and it’s due to those reasoning models.   Now, on the delivery side, I would add something more to it for the reason why doctors and, in fact, lawyers and coders will remain for a long time, and it’s because we still don’t understand how those models generalize. Like, at the end of the day, we are not able to tell you when they are confronted with a really new, novel situation, whether they will work or not.  Nobody is able to give you that guarantee. And I think until we understand this generalization better, we’re not going to be willing to just let the system in the wild without human supervision.  LEE: But don’t human doctors, human specialists … so, for example, a cardiologist sees a patient in a certain way that a nephrologist …  BUBECK: Yeah. LEE: … or an endocrinologist might not. BUBECK: That’s right. But another cardiologist will understand and, kind of, expect a certain level of generalization from their peer. And this, we just don’t have it with AI models. Now, of course, you’re exactly right. That generalization is also hard for humans. Like, if you have a human trained for one task and you put them into another task, then you don’t … you often don’t know. LEE: OK. You know, the podcast is focused on what’s happened over the last two years. But now, I’d like one provocative prediction about what you think the world of AI and medicine is going to be at some point in the future. You pick your timeframe. I don’t care if it’s two years or 20 years from now, but, you know, what do you think will be different about AI in medicine in that future than today?  BUBECK: Yeah, I think the deployment is going to accelerate soon. Like, we’re really not missing very much. There is this enormous capability overhang. Like, even if progress completely stopped, with current systems, we can do a lot more than what we’re doing right now. So I think this will … this has to be realized, you know, sooner rather than later.  And I think it’s probably dependent on these benchmarks and proper evaluation and tying this with regulation. So these are things that take time in human society and for good reason. But now we already are at two years; you know, give it another two years and it should be really …   LEE: Will AI prescribe your medicines? Write your prescriptions?  BUBECK: I think yes. I think yes.  LEE: OK. Bill?  GATES: Well, I think the next two years, we’ll have massive pilots, and so the amount of use of the AI, still in a copilot-type mode, you know, we should get millions of patient visits, you know, both in general medicine and in the mental health side, as well. And I think that’s going to build up both the data and the confidence to give the AI some additional autonomy. You know, are you going to let it talk to you at night when you’re panicked about your mental health with some ability to escalate? And, you know, I’ve gone so far as to tell politicians with national health systems that if they deploy AI appropriately, that the quality of care, the overload of the doctors, the improvement in the economics will be enough that their voters will be stunned because they just don’t expect this, and, you know, they could be reelected [LAUGHTER] just on this one thing of fixing what is a very overloaded and economically challenged health system in these rich countries.  You know, my personal role is going to be to make sure that in the poorer countries, there isn’t some lag; in fact, in many cases, that we’ll be more aggressive because, you know, we’re comparing to having no access to doctors at all. And, you know, so I think whether it’s India or Africa, there’ll be lessons that are globally valuable because we need medical intelligence. And, you know, thank god AI is going to provide a lot of that.  LEE: Well, on that optimistic note, I think that’s a good way to end. Bill, Seb, really appreciate all of this.   I think the most fundamental prediction we made in the book is that AI would actually find its way into the practice of medicine, and I think that that at least has come true, maybe in different ways than we expected, but it’s come true, and I think it’ll only accelerate from here. So thanks again, both of you.  [TRANSITION MUSIC]  GATES: Yeah. Thanks, you guys.  BUBECK: Thank you, Peter. Thanks, Bill.  LEE: I just always feel such a sense of privilege to have a chance to interact and actually work with people like Bill and Sébastien.    With Bill, I’m always amazed at how practically minded he is. He’s really thinking about the nuts and bolts of what AI might be able to do for people, and his thoughts about underserved parts of the world, the idea that we might actually be able to empower people with access to expert medical knowledge, I think is both inspiring and amazing.   And then, Seb, Sébastien Bubeck, he’s just absolutely a brilliant mind. He has a really firm grip on the deep mathematics of artificial intelligence and brings that to bear in his research and development work. And where that mathematics takes him isn’t just into the nuts and bolts of algorithms but into philosophical questions about the nature of intelligence.   One of the things that Sébastien brought up was the state of evaluation of AI systems. And indeed, he was fairly critical in our conversation. But of course, the world of AI research and development is just moving so fast, and indeed, since we recorded our conversation, OpenAI, in fact, released a new evaluation metric that is directly relevant to medical applications, and that is something called HealthBench. And Microsoft Research also released a new evaluation approach or process called ADeLe.   HealthBench and ADeLe are examples of new approaches to evaluating AI models that are less about testing their knowledge and ability to pass multiple-choice exams and instead are evaluation approaches designed to assess how well AI models are able to complete tasks that actually arise every day in typical healthcare or biomedical research settings. These are examples of really important good work that speak to how well AI models work in the real world of healthcare and biomedical research and how well they can collaborate with human beings in those settings.  You know, I asked Bill and Seb to make some predictions about the future. You know, my own answer, I expect that we’re going to be able to use AI to change how we diagnose patients, change how we decide treatment options.   If you’re a doctor or a nurse and you encounter a patient, you’ll ask questions, do a physical exam, you know, call out for labs just like you do today, but then you’ll be able to engage with AI based on all of that data and just ask, you know, based on all the other people who have gone through the same experience, who have similar data, how were they diagnosed? How were they treated? What were their outcomes? And what does that mean for the patient I have right now? Some people call it the “patients like me” paradigm. And I think that’s going to become real because of AI within our lifetimes. That idea of really grounding the delivery in healthcare and medical practice through data and intelligence, I actually now don’t see any barriers to that future becoming real.  [THEME MUSIC]  I’d like to extend another big thank you to Bill and Sébastien for their time. And to our listeners, as always, it’s a pleasure to have you along for the ride. I hope you’ll join us for our remaining conversations, as well as a second coauthor roundtable with Carey and Zak.   Until next time.   [MUSIC FADES]

It’s fairly easy to block the constant, incessant advertising that appears on YouTube. Google would prefer that you don’t, or pay up (quite a lot) to make them go away. Last weekend, the company started its latest campaign to try and badger ad-block users into disabling their extensions. Since then, it looks like YouTube has escalated things and is now intentionally slowing down videos. Posters on Reddit and the Brave browser forum have observed videos being blacked out on first load, approximately for the length of pre-roll ads, with a pop-up link that directs users to the ad-blocking section of this technical support page. “Check whether your browser extensions that block ads are affecting video playback,” suggests Google. “As another option, try opening YouTube in an incognito window with all extensions disabled and check if the issue continues.” PCWorld staff has seen this in action, using uBlock Origin Lite. Google Ad-block extension developers quickly got around the pop-up issue earlier this week, with one AdGuard representative calling the process “a classic cat-and-mouse game.” But if Google wanted to instigate a more serious crackdown on users blocking ads without paying up, it could do so easily—and we’ve seen it pull this same move before. Posters on the latest issue speculate that the slowdowns might be tagged to specific Google or YouTube user accounts that were detected blocking ads previously, which would bypass any kind of interaction with a specific browser or extension. I can’t independently confirm that’s happening, but it wouldn’t surprise me. It also wouldn’t shock me if Google is seeing a larger percentage of YouTube users blocking advertising, as is the case all across the web, as the quantity of advertising rises while quality takes a nosedive. YouTube video creators are having to get, well, creative to seek alternate revenue beyond basic AdSense accounts, as sponsored videos are now constant across the platform and more channels put new videos behind paywalls on YouTube itself or via other platforms like Patreon. YouTube is attacking the issue from other angles as well. Tech-focused creators that show how to use third-party tools to block ads or download videos from the site (again, without paying the steep fees for YouTube Premium) are getting their videos taken down and their accounts flagged, for violation of the extremely vague policy around “harmful and dangerous content.” If I may editorialize a bit: Google, if you want more people to subscribe to YouTube Premium and remove advertising, you need to make it cheaper. Charging $14 per month just to get rid of ads is the same cost of a premium subscription from other sources where users can watch full movies and series. YouTube as a platform is a much lower bar and just doesn’t compete at that level. I’m not going to pay that much to get rid of ads, not when it doesn’t actually get rid of all the ads—those sponsored and subscriber-only videos are still all over the place—and the site is filling up with AI slop. “Premium Lite,” which neuters the offerings for mobile and music-focused users, doesn’t make the cut either. And to be clear, I have no problem paying for the stuff I watch. I already pay more than $15 a month to support the individual YouTube channels I enjoy, like Second Wind, Drawfee, and several tech podcasts. But I do it via Patreon because sending that money through YouTube feels gross. If Google wants people to pay up, it needs to lower the price enough so that it’s no longer worth the hassle of blocking them. It’s a lesson that the music, movie, and game industries learned a long time ago as they fought the initial wave of internet piracy… and now seem to be forgetting again.

Doing press for his latest movie, Chris Evans was flat-out asked by a journalist: Are you returning for Marvel’s Avengers: Doomsday? That rumor has floated around the internet for months, no doubt buoyed by the fact that Evans made a surprise cameo in last summer’s Deadpool vs. Wolverine, despite the fact that he was supposed to be “retired” from the Marvel Cinematic Universe after the last Avengers movie, Endgame.Evans claimed he wasn’t involved. But he wouldn’t be the first Marvel star to lie about a role in an MCU movie — and he wouldn’t be the first “retired” Marvel hero returning for Doomsday either.Avengers: Doomsday video we look at the facts and speculate about whether Evans might or might not appear in the film (or, for that matter, its sequel, Avengers: Secret Wars)Watch our full discussion on Chris Evans and Doomsday below:READ MORE: The Weirdest Marvel Comics Ever PublishedIf you liked that video on whether Chris Evans is secretly in Avengers: Doomsday, check out more of our videos below, including one on the original plan for Madame Web and why it was so much better than what Sony actually made, one on the connection between Wanda and Doctor Doom, and one on the canceled X-Men vs. Fantastic Four film we never got to see. Plus, there’s tons more videos over at ScreenCrush’s YouTube channel. Be sure to subscribe to catch all our future episodes. Avengers: Doomsday is scheduled to open in theaters on December 18, 2026.Sign up for Disney+ here.Get our free mobile appEvery Marvel Cinematic Universe Movie, Ranked From Worst to BestIt started with Iron Man and it’s continued and expanded ever since. It’s the Marvel Cinematic Universe, with 36 movies and counting. But what’s the best and the worst? We ranked them all.

Something to look forward to: This year's Summer Game Fest presentation ended with a reveal trailer for Resident Evil Requiem, which Capcom confirmed is the ninth mainline title in the long-running survival horror game series. Details on the upcoming title are scant, but it is set to launch on PC and current-generation consoles in a few months. Capcom has not yet revealed gameplay details for Resident Evil Requiem, as the initial trailer focuses on the story, characters, and locations. The game's scenario appears to draw heavily from the franchise's history, likely to celebrate the 30th anniversary of the original Resident Evil's 1996 release. Much of the trailer highlights the ruins of Raccoon City, suggesting that players will revisit the setting of the series' first three entries. Brief shots clearly show the decayed remains of the city's police station – where much of Resident Evil 2 and 3 took place – with layouts that appear nearly identical to those in the 2019 and 2020 remakes. Another shot depicts the city's deserted landscape, featuring a crater at its center left by the missile that destroyed the town following the events of RE3. Additionally, the game's protagonist is FBI agent Grace Ashcroft, the daughter of one of the main characters from Resident Evil Outbreak, an online multiplayer spin-off released for the PlayStation 2 in 2003. The game's website mentions technological advancements, suggesting it will showcase the next evolution of Capcom's RE Engine. This graphics engine debuted in 2017 with Resident Evil 7, which was known for its impressive level of realism and surprisingly fast performance. However, more recent titles using the engine, such as Dragon's Dogma II and the enormously successful Monster Hunter Wilds, are far more demanding, in part due to their massive open-world environments. Capcom's shift toward open-world games has led some to speculate that the next Resident Evil title might adopt a similar gameplay structure, representing a stark contrast to the franchise's traditional preference for isolated locations. A ruined city would provide a fitting backdrop for such a radical change, but it's difficult to say what Capcom has planned. // Related Stories Other games revealed this week include Atomic Heart II, Game of Thrones: War for Westeros, Dying Light: The Beast, Lego Voyagers, Killer Inn, Felt That Boxing, Nioh 3, 007 First Light, Lumines Arise, Marvel Tōkon, Thief VR, Mortal Kombat Legacy Kollection, and more. More new titles are expected to debut this weekend during the Xbox Games Showcase 2025 on Sunday, June 8, at 1 pm ET. Resident Evil Requiem launches on February 27 on Steam, PlayStation 5, and Xbox Series consoles.

“Is that concrete all around, or is it in my head?” asked Ian Hunter in “All the Young Dudes,” the song David Bowie wrote for Mott the Hoople in 1972. Concrete is all around us, and we haven’t quite wrapped our heads around it. It’s one of the indispensable materials of modernity; as we try to decarbonize the built environment, it’s part of the problem, and innovations in its composition may become part of the solution. Understanding its history more clearly, the Skyscraper Museum’s new exhibition in Manhattan implies, just might help us employ it better. Concrete is “the second most used substance in the world, after water,” the museum’s founder/director/curator Carol Willis told AN during a recent visit. For plasticity, versatility, and compressive strength, reinforced concrete is hard to beat, though its performance is more problematic when assessed by the metric of embodied and operational carbon, a consideration the exhibition acknowledges up front. In tall construction, concrete has become nearly hegemonic, yet its central role, contend Willis and co-curator Thomas Leslie, formerly of Foster + Partners and now a professor at the University of Illinois, Urbana-Champaign, is underrecognized by the public and by mainstream architectural history. The current exhibition aims to change that perception. The Skyscraper Museum in Lower Manhattan features an exhibition, The Modern Concrete Skyscraper, which examines the history of material choices in building tall towers. (Courtesy the Skyscraper Museum) The Modern Concrete Skyscraper examines the history of tall towers’ structural material choices, describing a transition from the early dominance of steel frames to the contemporary condition, in which most large buildings rely on concrete. This change did not happen instantly or for any single reason but through a combination of technical and economic factors, including innovations by various specialists, well-recognized and otherwise; the availability of high-quality limestone deposits near Chicago; and the differential development of materials industries in nations whose architecture grew prominent in recent decades. As supertalls reach ever higher—in the global race for official height rankings by the Council on Tall Buildings and Urban Habitat (CTBUH) and national, corporate, or professional bragging rights—concrete’s dominance may not be permanent in that sector, given the challenge of pumping the material beyond a certain height. (The 2,717-foot Burj Khalifa, formerly Burj Dubai, uses concrete up to 1,987 and steel above that point; Willis quotes SOM’s William Baker describing it as “the tallest steel building with a concrete foundation of 156 stories.”) For the moment, however, concrete is ahead of its chief competitors, steel and (on a smaller scale) timber. Regardless of possible promotional inferences, Willis said, “we did not work with the industry in any way for this exhibition.” “The invention of steel and the grid of steel and the skeleton frame is only the first chapter of the history of the skyscraper,” Willis explained. “The second chapter, and the one that we’re in now, is concrete. Surprisingly, no one had ever told that story of the skyscraper today with a continuous narrative.” The exhibition traces the use of concrete back to the ancient Roman combination of aggregate and pozzolana—the chemical formula for which was “largely lost with the fall of the Roman Empire,” though some Byzantine and medieval structures approximated it. From there, the show explores comparable materials’ revival in 18th-century England, the patenting of Portland cement by Leeds builder Joseph Aspdin in 1824, the proof-of-concept concrete house by François Coignet in 1856, and the pivotal development of rebar in the mid-19th century, with overdue attention to Ernest Ransome’s 1903 Ingalls Building in Cincinnati, then the world’s tallest concrete building at 15 stories and arguably the first concrete skyscraper. The exhibition includes a timeline that depicts concrete’s origins in Rome to its contemporary use in skyscraper construction. (Courtesy the Skyscraper Museum) Baker’s lectures, Willis reported, sometimes pose a deceptively simple question: “‘What is a skyscraper?’ In 1974, when the World Trade Center and Sears Tower are just finished, you would say it’s a very tall building that is built of steel, an office building in North America. But if you ask that same question today, the answer is: It’s a building that is mixed-use, constructed of concrete, and [located] in Asia or the Middle East.” The exhibition organizes the history of concrete towers by eras of engineering innovation, devoting special attention to the 19th- and early-20th-century “patent era” of Claude Allen Porter Turner (pioneer in flat-slab flooring and mushroom columns) and Henry Chandlee Turner (founder of Turner Construction), Ransome (who patented twisted-iron rebar), and François Hennebique (known for the re-inforced concrete system exemplified by Liverpool’s Royal Liver Building, the world’s tallest concrete office building when completed in 1911). In the postwar era, “concrete comes out onto the surface [as] both a structural material and aesthetic.” Brutalism, perhaps to some observers’ surprise, “does not figure very large in high-rise design,” Willis said, except for Paul Rudolph’s Tracey Towers in the Bronx. The exhibition, however, devotes considerable attention to the work of Pier Luigi Nervi, Bertrand Goldberg (particularly Marina City), and SOM’s Fazlur Khan, pioneer of the structural tube system in the 1960s and 1970s—followed by the postmodernist 1980s, when concrete could express either engineering values or ornamentation. The exhibition highlights a number of concrete towers, including Paul Rudolph’s Tracey Towers in the Bronx. (Courtesy the Skyscraper Museum) “In the ’90s, there were material advances in engineering analysis and computerization that helped to predict performance, and so buildings can get taller and taller,” Willis said. The current era, if one looks to CTBUH rankings, is dominated by the supertalls seen in Dubai, Shanghai, and Kuala Lumpur, after the Petronas Towers (1998) “took the title of world’s tallest building from North America for the first time and traumatized everybody about that.” The previous record holder, Chicago’s Sears (now Willis) Tower, comprised steel structural tubes on concrete caissons; with Petronas, headquarters of Malaysia’s national petroleum company of that name, a strong concrete industry was represented but a strong national steel industry was lacking, and as Willis frequently says, form follows finances. In any event, by the ’90s concrete was already becoming the standard material for supertalls, particularly on soft-soiled sites like Shanghai, where its water resistance and compressive strength are well suited to foundation construction. Its plasticity is also well suited to complex forms like the triangular Burj, Kuala Lumpur’s Merdeka 118, and (if eventually completed) the even taller Jeddah Tower, designed to “confuse the wind,” shed vortices, and manage wind forces. Posing the same question Louis Kahn asked about the intentions of a brick, Willis said, with concrete “the answer is: anything you want.” The exhibition is front-loaded with scholarly material, presenting eight succinct yet informative wall texts on the timeline of concrete construction. The explanatory material is accompanied by ample photographs as well as structural models on loan from SOM, Pelli Clarke & Partners, and other firms. Some materials are repurposed from the museum’s previous shows, particularly Supertall! (2011–12) and Sky High and the Logic of Luxury (2013–14). The models allow close examination of the Burj Khalifa, Petronas Towers, Jin Mao Tower, Merdeka 118, and others, including two unbuilt Chicago projects that would have exceeded 2,000 feet: the Miglin-Beitler Skyneedle (Cesar Pelli/Thornton Tomasetti) and 7 South Dearborn (SOM). The Burj, Willis noted, was all structure and no facade for a time: When its curtain-wall manufacturer, Schmidlin, went bankrupt in 2006, it “ended up going to 100 stories without having a stitch of glass on it,” temporarily becoming a “1:1 scale model of the structural system up to 100 stories.” Its prominence justifies its appearance here in two models, including one from RWDI’s wind-tunnel studies. Eero Saarinen’s only skyscraper, built for CBS in 1965 and also known as “Black Rock,” under construction in New York City. (Courtesy Eero Saarinen Collection, Manuscripts, and Archives, Yale University Library) The exhibition opened in March, with plans to stay up at least through October (Willis prefers to keep the date flexible), with accompanying lectures and panels to be announced on the museum’s website (skyscraper.org). Though the exhibition’s full textual and graphic content is available online, the physical models alone are worth a trip to the Battery Park City headquarters. Intriguing questions arise from the exhibition without easy answers, setting the table for lively discussion and debate. One is whether the patenting of innovations like Ransome bar and the Système Hennebique incentivized technological progress or hindered useful technology transfer. Willis speculated, “Did the fact that there were inventions and patents mean that competition was discouraged, that the competition was only in the realm of business, rather than advancing the material?” A critical question is whether research into the chemistry of concrete, including MIT’s 2023 report on the self-healing properties of Roman pozzolana and proliferating claims about “green concrete” using alternatives to Portland cement, can lead to new types of the material with improved durability and lower emissions footprints. This exhibition provides a firm foundation in concrete’s fascinating history, opening space for informed speculation about its future. Bill Millard is a regular contributor to AN.