How the US is preparing for a potential bird flu pandemic

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How the US is preparing for a potential bird flu pandemic

This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here. This week marks a strange anniversary—it’s five years since most of us first heard about a virus causing a mysterious “pneumonia.” A virus that we later learned could cause a disease called covid-19. A virus that swept the globe and has since been reported to have been responsible for over 7 million deaths—and counting. I first covered the virus in an article published on January 7, 2020, which had the headline “Doctors scramble to identify mysterious illness emerging in China.” For that article, and many others that followed it, I spoke to people who were experts on viruses, infectious disease, and epidemiology. Frequently, their answers to my questions about the virus, how it might spread, and the risks of a pandemic were the same: “We don’t know.” We are facing the same uncertainty now with H5N1, the virus commonly known as bird flu. This virus has been decimating bird populations for years, and now a variant is rapidly spreading among dairy cattle in the US. We know it can cause severe disease in animals, and we know it can pass from animals to people who are in close contact with them. As of this Monday this week, we also know that it can cause severe disease in people—a 65-year-old man in Louisiana became the first person in the US to die from an H5N1 infection. Scientists are increasingly concerned about a potential bird flu pandemic. The question is, given all the enduring uncertainty around the virus, what should we be doing now to prepare for the possibility? Can stockpiled vaccines save us? And, importantly, have we learned any lessons from a covid pandemic that still hasn’t entirely fizzled out? Part of the challenge here is that it is impossible to predict how H5N1 will evolve. A variant of the virus caused disease in people in 1997, when there was a small but deadly outbreak in Hong Kong. Eighteen people had confirmed diagnoses, and six of them died. Since then, there have been sporadic cases around the world—but no large outbreaks. As far as H5N1 is concerned, we’ve been relatively lucky, says Ali Khan, dean of the college of public health at the University of Nebraska. “Influenza presents the greatest infectious-disease pandemic threat to humans, period,” says Khan. The 1918 flu pandemic was caused by a type of influenza virus called H1N1 that appears to have jumped from birds to people. It is thought to have infected a third of the world’s population, and to have been responsible for around 50 million deaths. Another H1N1 virus was responsible for the 2009 “swine flu” pandemic. That virus hit younger people hardest, as they were less likely to have been exposed to similar variants and thus had much less immunity. It was responsible for somewhere between 151,700 and 575,400 deaths that year. To cause a pandemic, the H5N1 variants currently circulating in birds and dairy cattle in the US would need to undergo genetic changes that allow them to spread more easily from animals to people, spread more easily between people, and become more deadly in people. Unfortunately, we know from experience that viruses need only a few such changes to become more easily transmissible. And with each and every infection, the risk that a virus will acquire these dangerous genetic changes increases. Once a virus infects a host, it can evolve and swap chunks of genetic code with any other viruses that might also be infecting that host, whether it’s a bird, a pig, a cow, or a person. “It’s a big gambling game,” says Marion Koopmans, a virologist at the Erasmus University Medical Center in Rotterdam, the Netherlands. “And the gambling is going on at too large a scale for comfort.” There are ways to improve our odds. For the best chance at preventing another pandemic, we need to get a handle on, and limit, the spread of the virus. Here, the US could have done a better job at limiting the spread in dairy cows, says Khan. “It should have been found a lot earlier,” he says. “There should have been more aggressive measures to prevent transmission, to recognize what disease looks like within our communities, and to protect workers.” States could also have done better at testing farm workers for infection, says Koopmans. “I’m surprised that I haven’t heard of an effort to eradicate it from cattle,” she adds. “A country like the US should be able to do that.” The good news is that there are already systems in place for tracking the general spread of flu in people. The World Health Organization’s Global Influenza Surveillance and Response System collects and analyzes samples of viruses collected from countries around the world. It allows the organization to make recommendations about seasonal flu vaccines and also helps scientists track the spread of various flu variants. That’s something we didn’t have for the covid-19 virus when it first took off. We are also better placed to make vaccines. Some countries, including the US, are already stockpiling vaccines that should be at least somewhat effective against H5N1 (although it is difficult to predict exactly how effective they will be against some future variant). The US Administration for Strategic Preparedness and Response plans to have “up to 10 million doses of prefilled syringes and multidose vials” prepared by the end of March, according to an email from a representative. We can’t predict how well these viruses will work, either. Flu viruses mutate all the time, and even seasonal flu vaccines are notoriously unpredictable in their efficacy. “I think we’ve become a little bit spoiled with the covid vaccines,” says Koopmans. “We were really, really lucky [to develop] vaccines with high efficacy.” One vaccine lesson we should have learned from the covid-19 pandemic is the importance of equitable access to vaccines around the world. Unfortunately, it’s unlikely that we have. “It is doubtful that low-income countries will have early access to [a pandemic influenza] vaccine unless the world takes action,” Nicole Lurie of the Coalition for Epidemic Preparedness Innovations (CEPI) said in a recent interview for Gavi, a public-private alliance for vaccine equity. And another is the impact of vaccine hesitancy. Making vaccines might not be a problem—but convincing people to take them might be, says Khan. “We have an incoming administration that has lots of vaccine hesitancy,” he points out. “So while we may end up having … vaccines available, it’s not very clear to me if we have the political and social will to actually implement good public health measures.” This is another outcome that is impossible to predict, and I won’t attempt to do so. But I am hoping that the relevant administrations will step up our defenses. And that this will be enough to prevent another devastating pandemic. Now read the rest of The Checkup Read more from MIT Technology Review’s archive Bird flu has been circulating in US dairy cows for months. Virologists are worried it could stick around on US farms forever. As the virus continues to spread, the risk of a pandemic continues to rise. We still don’t really know how the virus is spreading, but we do know that it is turning up in raw milk. (Please don’t drink raw milk.) mRNA vaccines helped us through the covid-19 pandemic. Now scientists are working on mRNA flu vaccines—including “universal” vaccines that could protect against multiple flu viruses. The next generation of mRNA vaccines is on the way. These vaccines are “self-amplifying” and essentially tell the body how to make more mRNA.

This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

This week marks a strange anniversary—it’s five years since most of us first heard about a virus causing a mysterious “pneumonia.” A virus that we later learned could cause a disease called covid-19. A virus that swept the globe and has since been reported to have been responsible for over 7 million deaths—and counting.

I first covered the virus in an article published on January 7, 2020, which had the headline “Doctors scramble to identify mysterious illness emerging in China.” For that article, and many others that followed it, I spoke to people who were experts on viruses, infectious disease, and epidemiology. Frequently, their answers to my questions about the virus, how it might spread, and the risks of a pandemic were the same: “We don’t know.”

We are facing the same uncertainty now with H5N1, the virus commonly known as bird flu. This virus has been decimating bird populations for years, and now a variant is rapidly spreading among dairy cattle in the US. We know it can cause severe disease in animals, and we know it can pass from animals to people who are in close contact with them. As of this Monday this week, we also know that it can cause severe disease in people—a 65-year-old man in Louisiana became the first person in the US to die from an H5N1 infection.

Scientists are increasingly concerned about a potential bird flu pandemic. The question is, given all the enduring uncertainty around the virus, what should we be doing now to prepare for the possibility? Can stockpiled vaccines save us? And, importantly, have we learned any lessons from a covid pandemic that still hasn’t entirely fizzled out?

Part of the challenge here is that it is impossible to predict how H5N1 will evolve.

A variant of the virus caused disease in people in 1997, when there was a small but deadly outbreak in Hong Kong. Eighteen people had confirmed diagnoses, and six of them died. Since then, there have been sporadic cases around the world—but no large outbreaks.

As far as H5N1 is concerned, we’ve been relatively lucky, says Ali Khan, dean of the college of public health at the University of Nebraska. “Influenza presents the greatest infectious-disease pandemic threat to humans, period,” says Khan. The 1918 flu pandemic was caused by a type of influenza virus called H1N1 that appears to have jumped from birds to people. It is thought to have infected a third of the world’s population, and to have been responsible for around 50 million deaths.

Another H1N1 virus was responsible for the 2009 “swine flu” pandemic. That virus hit younger people hardest, as they were less likely to have been exposed to similar variants and thus had much less immunity. It was responsible for somewhere between 151,700 and 575,400 deaths that year.

To cause a pandemic, the H5N1 variants currently circulating in birds and dairy cattle in the US would need to undergo genetic changes that allow them to spread more easily from animals to people, spread more easily between people, and become more deadly in people. Unfortunately, we know from experience that viruses need only a few such changes to become more easily transmissible.

And with each and every infection, the risk that a virus will acquire these dangerous genetic changes increases. Once a virus infects a host, it can evolve and swap chunks of genetic code with any other viruses that might also be infecting that host, whether it’s a bird, a pig, a cow, or a person. “It’s a big gambling game,” says Marion Koopmans, a virologist at the Erasmus University Medical Center in Rotterdam, the Netherlands. “And the gambling is going on at too large a scale for comfort.”

There are ways to improve our odds. For the best chance at preventing another pandemic, we need to get a handle on, and limit, the spread of the virus. Here, the US could have done a better job at limiting the spread in dairy cows, says Khan. “It should have been found a lot earlier,” he says. “There should have been more aggressive measures to prevent transmission, to recognize what disease looks like within our communities, and to protect workers.”

States could also have done better at testing farm workers for infection, says Koopmans. “I’m surprised that I haven’t heard of an effort to eradicate it from cattle,” she adds. “A country like the US should be able to do that.”

The good news is that there are already systems in place for tracking the general spread of flu in people. The World Health Organization’s Global Influenza Surveillance and Response System collects and analyzes samples of viruses collected from countries around the world. It allows the organization to make recommendations about seasonal flu vaccines and also helps scientists track the spread of various flu variants. That’s something we didn’t have for the covid-19 virus when it first took off.

We are also better placed to make vaccines. Some countries, including the US, are already stockpiling vaccines that should be at least somewhat effective against H5N1 (although it is difficult to predict exactly how effective they will be against some future variant). The US Administration for Strategic Preparedness and Response plans to have “up to 10 million doses of prefilled syringes and multidose vials” prepared by the end of March, according to an email from a representative.

We can’t predict how well these viruses will work, either. Flu viruses mutate all the time, and even seasonal flu vaccines are notoriously unpredictable in their efficacy. “I think we’ve become a little bit spoiled with the covid vaccines,” says Koopmans. “We were really, really lucky [to develop] vaccines with high efficacy.”

One vaccine lesson we should have learned from the covid-19 pandemic is the importance of equitable access to vaccines around the world. Unfortunately, it’s unlikely that we have. “It is doubtful that low-income countries will have early access to [a pandemic influenza] vaccine unless the world takes action,” Nicole Lurie of the Coalition for Epidemic Preparedness Innovations (CEPI) said in a recent interview for Gavi, a public-private alliance for vaccine equity.

And another is the impact of vaccine hesitancy. Making vaccines might not be a problem—but convincing people to take them might be, says Khan. “We have an incoming administration that has lots of vaccine hesitancy,” he points out. “So while we may end up having … vaccines available, it’s not very clear to me if we have the political and social will to actually implement good public health measures.”

This is another outcome that is impossible to predict, and I won’t attempt to do so. But I am hoping that the relevant administrations will step up our defenses. And that this will be enough to prevent another devastating pandemic.

Now read the rest of The Checkup

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The joint reference design appears in Trane’s Continuum Rubin DSX and Eaton’s Beam Rubin DSX platforms. The goal is a pre-coordinated architecture stretching from grid power to the chip rather than requiring developers and engineering teams to independently assemble electrical and mechanical systems for each project. The systems are also intended to exchange operating data. Rather than cooling and electrical systems responding independently, the systems can instead exchange leading indicators and respond more dynamically to changing operating requirements. This is an approach that closely mirrors NVIDIA’s larger DSX philosophy. Trane and Eaton are also designing the architecture to accommodate future liquid-cooling and direct-current power-distribution technologies. That future-proofing matters as rack power densities continue to rise. An electrical and cooling plant optimized for one GPU generation may otherwise become a constraint several hardware generations later. The Broader DSX Buildout The Lancium, Cloverleaf and Trane/Eaton agreements are part of a considerably wider expansion of the DSX ecosystem. Earlier deals show NVIDIA moving into many of the same infrastructure layers through partnerships spanning powered land, electrical design, digital twins and even project financing. In May, NVIDIA and IREN announced plans to support as much as 5 GW of DSX-aligned AI infrastructure across IREN’s global development pipeline, with the companies identifying IREN’s 2 GW Sweetwater campus in Texas as an expected flagship DSX deployment. NVIDIA also received a five-year right to purchase up to 30 million IREN shares at $70 each, representing a potential investment of as much as $2.1 billion. The infrastructure ecosystem has widened as well. Siemens, NVIDIA and Fluence, incorporating nVent design considerations, have developed a DSX Vera Rubin-aligned electrical, power and controls architecture extending from the utility connection to the rack. ABB is integrating digital models of medium-voltage switchgear, power-distribution equipment and UPS systems into the Omniverse DSX Blueprint, while Vertiv

ERCOT Puts Texas AI Megawatts to the Test

Texas has no shortage of proposed data center megawatts. The harder question is how many of them are real. That distinction is becoming central to the Electric Reliability Council of Texas (ERCOT) as the state works through an unprecedented wave of AI, hyperscale and other large-load requests. In June, ERCOT said it was tracking more than 438 GW of proposed large loads, nearly 89% associated with data centers. By Aug. 3, Gov. Greg Abbott said ERCOT was considering approximately 474 GW of connection requests, roughly 90% from data centers and more than five times the system’s record peak demand. Neither figure represents a forecast of what will actually get built. And that is increasingly the point. ERCOT’s new Batch Zero process is beginning to put harder boundaries around Texas’ enormous development pipeline, asking which projects have enough maturity, technical information and commitment to warrant space in the transmission plan. At the same time, new requirements surrounding voltage ride-through and dynamic modeling are forcing another realization on the AI infrastructure industry: at hundreds of megawatts, a data center is no longer simply a customer at the edge of the grid. Its behavior can affect the grid itself. For developers, utilities and investors, Texas is becoming a large-scale test of what separates an announced AI campus from executable infrastructure. The Queue Is Not the Grid The sheer scale of ERCOT’s large-load queue can obscure how early many projects remain. ERCOT’s April 2026 monthly report offered a revealing snapshot. Large-load applications totaled 445.8 GW through 2033, but 321 GW had no studies submitted to ERCOT. Another 93.7 GW was under ERCOT review, while 22 GW had met the applicable Section 9.5 requirements. Against that enormous development funnel, ERCOT reported just 5.9 GW of observed energized large loads, with another 3.2 GW approved to

Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs). In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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