
Intelligence on Earth Evolved Independently at Least Twice
May 11, 2025 7:00 AM Complex neural circuits likely arose independently in birds and mammals, suggesting that vertebrates evolved intelligence multiple times. Illustration: Samantha Mash for Quanta Magazine
The original version of this story appeared in Quanta Magazine.
Humans tend to put our own intelligence on a pedestal. Our brains can do math, employ logic, explore abstractions, and think critically. But we can't claim a monopoly on thought. Among a variety of nonhuman species known to display intelligent behavior, birds have been shown time and again to have advanced cognitive abilities. Ravens plan for the future, crows count and use tools, cockatoos open and pillage booby-trapped garbage cans, and chickadees keep track of tens of thousands of seeds cached across a landscape. Notably, birds achieve such feats with brains that look completely different from ours: They're smaller and lack the highly organized structures that scientists associate with mammalian intelligence.
'A bird with a 10-gram brain is doing pretty much the same as a chimp with a 400-gram brain,' said Onur Güntürkün, who studies brain structures at Ruhr University Bochum in Germany. 'How is it possible?'
Researchers have long debated about the relationship between avian and mammalian intelligences. One possibility is that intelligence in vertebrates—animals with backbones, including mammals and birds—evolved once. In that case, both groups would have inherited the complex neural pathways that support cognition from a common ancestor: a lizardlike creature that lived 320 million years ago, when Earth's continents were squished into one landmass. The other possibility is that the kinds of neural circuits that support vertebrate intelligence evolved independently in birds and mammals.
It's hard to track down which path evolution took, given that any trace of the ancient ancestor's actual brain vanished in a geological blink. So biologists have taken other approaches—such as comparing brain structures in adult and developing animals today—to piece together how this kind of neurobiological complexity might have emerged.
A series of studies published in Science in February 2025 provides the best evidence yet that birds and mammals did not inherit the neural pathways that generate intelligence from a common ancestor, but rather evolved them independently. This suggests that vertebrate intelligence arose not once, but multiple times. Still, their neural complexity didn't evolve in wildly different directions: Avian and mammalian brains display surprisingly similar circuits, the studies found.
'It's a milestone in the quest to understand and to integrate the different ideas about the evolution' of vertebrate intelligence, said Güntürkün, who was not involved in the new research.
When Fernando García-Moreno started his lab at the Achucarro Basque Center for Neuroscience, he knew he wanted to probe how the pallium region of the vertebrate brain evolved using a breadth of different methods. Photograph: Tatiana Gallego Flores
The findings emerge in a world enraptured by artificial forms of intelligence, and they could teach us something about how complex circuits in our own brains evolved. Perhaps most importantly, they could help us step 'away from the idea that we are the best creatures in the world,' said Niklas Kempynck, a graduate student at KU Leuven who led one of the studies. 'We are not this optimal solution to intelligence.'
Birds got there too, on their own. Pecking Disorder
For the first half of the 20th century, neuroanatomists assumed that birds were simply not that smart. The creatures lack anything resembling a neocortex—the highly ordered outermost structure in the brains of humans and other mammals where language, communication, and reasoning reside. The neocortex is organized into six layers of neurons, which receive sensory information from other parts of the brain, process it, and send it out to regions that determine our behavior and reactions.
In the 1960s, the neuroanatomist Harvey Karten's research into avian neural circuits changed how the field viewed bird intelligence.
'For the longest time, it was thought that this is the center of cognition, and you need this kind of anatomy to develop advanced cognitive abilities,' said Bastienne Zaremba, a postdoctoral researcher studying the evolution of the brain at Heidelberg University.
Rather than neat layers, birds have 'unspecified balls of neurons without landmarks or distinctions,' said Fernando García-Moreno, a neurobiologist at the Achucarro Basque Center for Neuroscience in Spain. These structures compelled neuroanatomists a century ago to suggest that much of bird behavior is reflexive, and not driven by learning and decision-making. This 'implies that what a mammal can learn easily, a bird will never learn,' Güntürkün said.
The conventional thinking started to change in the 1960s when Harvey Karten, a young neuroanatomist at the Massachusetts Institute of Technology, mapped and compared brain circuits in mammals and pigeons, and later in owls, chickens, and other birds. What he found was a surprise: The brain regions thought to be involved only in reflexive movements were built from neural circuits—networks of interconnected neurons—that resembled those found in the mammalian neocortex. This region in the bird brain, the dorsal ventricular ridge (DVR), seemed to be comparable to a neocortex; it just didn't look like it.
In 1969, Karten wrote a 'very influential paper that completely changed the discussion in the field,' said Maria Tosches, who studies vertebrate brain development at Columbia University. 'His work was really revolutionary.' He concluded that because avian and mammalian circuits are similar, they were inherited from a common ancestor. That thinking dominated the field for decades, said Güntürkün, a former postdoc in Karten's lab. It 'sparked quite a lot of interest in the bird brain.'
'We are not this optimal solution to intelligence.'
A few decades later, Luis Puelles, an anatomist at the University of Murcia in Spain, drew the opposite conclusion. By comparing embryos at various stages of development, he found that the mammalian neocortex and the avian DVR developed from distinct areas of the embryo's pallium—a brain region shared by all vertebrates. He concluded that the structures must have evolved independently.
Karten and Puelles were 'giving completely different answers to this big question,' Tosches said. The debate continued for decades. During this time, biologists also began to appreciate bird intelligence, starting with their studies of Alex, an African gray parrot who could count and identify objects. They realized just how smart birds could be.
However, neither group seemed to want to resolve the discrepancy between their two theories of how vertebrate palliums evolved, according to García-Moreno. 'No, they kept working on their own method,' he said. One camp continued to compare the circuitry in adult vertebrate brains; the other focused on embryonic development.
In the new studies, he said, 'we tried to put everything together.' Same but Not the Same
Two new studies, which were conducted by independent teams of researchers, relied on the same powerful tool for identifying cell types, known as single-cell RNA sequencing. This technique lets researchers compare neuronal circuits, as Karten did, not only in adult brains but all the way through embryonic development, following Puelles. In this way, they could see where the cells started growing in the embryo and where they ended up in the mature animal—a developmental journey that can reveal evolutionary pathways.
For their study, García-Moreno and his team wanted to watch how brain circuitry develops. Using RNA sequencing and other techniques, they tracked cells in the palliums of chickens, mice, and geckos at various embryonic stages to time-stamp when different types of neurons were generated and where they matured.
They found that the mature circuits looked remarkably alike across animals, just as Karten and others had noted, but they were built differently, as Puelles had found. The circuits that composed the mammalian neocortex and the avian DVR developed at different times, in different orders, and in different regions of the brain. Illustration: Mark Belan/Quanta Magazine; source: Science 387, 732 (2025)
At the same time, García-Moreno was collaborating with Zaremba and her colleagues at Heidelberg University. Using RNA sequencing, they created 'the most comprehensive atlas of the bird pallium that we have,' said Tosches, who wrote a related perspective piece published in Science. By comparing the bird pallium to lizard and mouse palliums, they also found that the neocortex and DVR were built with similar circuitry—however, the neurons that composed those neural circuits were distinct.
'How we end up with similar circuitry was more flexible than I would have expected,' Zaremba said. 'You can build the same circuits from different cell types.'
Zaremba and her team also found that in the bird pallium, neurons that start development in different regions can mature into the same type of neuron in the adult. This pushed against previous views, which held that distinct regions of the embryo must generate different types of neurons.
There's limited degrees of freedom into which you can generate an intelligent brain, at least within vertebrates.
In mammals, brain development follows an intuitive path: The cells in the embryo's amygdala region at the start of development end up in the adult amygdala. The cells in the embryo's cortex region end up in the adult cortex. But in birds, 'there is a fantastic reorganization of the forebrain,' Güntürkün said, that is 'nothing that we had expected.'
Taken together, the studies provide the clearest evidence yet that birds and mammals independently evolved brain regions for complex cognition. They also echo previous research from Tosches' lab, which found that the mammalian neocortex evolved independently from the reptile DVR.
Still, it seems likely there was some inheritance from a common ancestor. In a third study that used deep learning, Kempynck and his coauthor Nikolai Hecker found that mice, chickens, and humans share some stretches of DNA that influence the development of the neocortex or DVR, suggesting that similar genetic tools are at work in both types of animals. And as previous studies had suggested, the research groups found that inhibitory neurons, or those that silence and modulate neural signals, were conserved across birds and mammals.
The findings haven't completely resolved Karten and Puelles' debate, however. Whose ideas were closer to the truth? Tosches said that Puelles was right, while Güntürkün thought the findings better reflect Karten's ideas, though would partly please Puelles. García-Moreno split the difference: 'Both of them were right; none of them was wrong,' he said. How to Build Intelligence
Intelligence doesn't come with an instruction manual. It is hard to define, there are no ideal steps toward it, and it doesn't have an optimal design, Tosches said. Innovations can happen throughout an animal's biology, whether in new genes and their regulation, or in new neuron types, circuits, and brain regions. But similar innovations can evolve multiple times independently—a phenomenon known as convergent evolution—and this is seen across life.
'One of the reasons I kind of like these papers is that they really highlight a lot of differences,' said Bradley Colquitt, a molecular neuroscientist at UC Santa Cruz. 'It allows you to say: What are the different neural solutions that these organisms have come up with to solve similar problems of living in a complex world and being able to adapt in a rapidly changing terrestrial environment?'
Octopuses and squids, independently of mammals, evolved camera-like eyes. Birds, bats and insects all took to the skies on their own. Ancient people in Egypt and South America independently built pyramids—the most structurally efficient shape that will stand the test of time, García-Moreno said: 'If they make a tower, it will fall. If they make a wall, it won't work.'
Similarly, 'there's limited degrees of freedom into which you can generate an intelligent brain, at least within vertebrates,' Tosches said. Drift outside the realm of vertebrates, however, and you can generate an intelligent brain in much weirder ways—from our perspective, anyway. 'It's a Wild West,' she said. Octopuses, for example, 'evolved intelligence in a way that's completely independent.' Their cognitive structures look nothing like ours, except that they're built from the same broad type of cell: the neuron. Yet octopuses have been caught performing incredible feats such as escaping aquarium tanks, solving puzzles, unscrewing jar lids and carrying shells as shields.
It would be exciting to figure out how octopuses evolved intelligence using really divergent neural structures, Colquitt said. That way, it might be possible to pinpoint any absolute constraints on evolving intelligence across all animal species, not just vertebrates.
Such findings could eventually reveal shared features of various intelligences, Zaremba said. What are the building blocks of a brain that can think critically, use tools, or form abstract ideas? That understanding could help in the search for extraterrestrial intelligence—and help improve our artificial intelligence. For example, the way we currently think about using insights from evolution to improve AI is very anthropocentric. 'I would be really curious to see if we can build like artificial intelligence from a bird perspective,' Kempynck said. 'How does a bird think? Can we mimic that?'
Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences.
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WIRED
6 hours ago
- WIRED
How to Make AI Faster and Smarter—With a Little Help from Physics
Jun 1, 2025 7:00 AM Rose Yu has drawn on the principles of fluid dynamics to improve deep learning systems that predict traffic, model the climate, and stabilize drones during flight. Photograph: Peggy Peattie for Quanta Magazine The original version of this story appeared in Quanta Magazine. When she was 10 years old, Rose Yu got a birthday present that would change her life—and, potentially, the way we study physics. Her uncle got her a computer. That was a rare commodity in China 25 years ago, and the gift did not go unused. At first, Yu mainly played computer games, but in middle school she won an award for web design. It was the first of many computer-related honors. Yu went on to major in computer science at Zhejiang University, where she won a prize for innovative research. For her graduate studies, she chose the University of Southern California (USC), partly because the same uncle—who was the only person she knew in the United States—was then working at the Jet Propulsion Laboratory in nearby Pasadena. Yu earned her doctorate in 2017 with an award for best dissertation. Her most recent honor came in January, when President Joe Biden, in his last week in office, gave her a Presidential Early Career Award. Yu, now an associate professor at the University of California, San Diego (UCSD), is a leader in a field known as 'physics-guided deep learning,' having spent years incorporating our knowledge of physics into artificial neural networks. The work has not only introduced novel techniques for building and training these systems, but it's also allowed her to make progress on several real-world applications. She has drawn on principles of fluid dynamics to improve traffic predictions, sped up simulations of turbulence to enhance our understanding of hurricanes, and devised tools that helped predict the spread of Covid-19. This work has brought Yu closer to her grand dream—deploying a suite of digital lab assistants that she calls AI Scientist. She now envisions what she calls a 'partnership' between human researchers and AI tools, fully based on the tenets of physics and thus capable of yielding new scientific insights. Combining inputs from a team of such assistants, in her opinion, may be the best way to boost the discovery process. Quanta spoke with Yu about turbulence in its many guises, how to get more out of AI, and how it might get us out of urban gridlock. The interview has been condensed and edited for clarity. Yu on the UCSD campus, where she is an associate professor. Photograph: Peggy Peattie for Quanta Magazine When did you first try to combine physics with deep learning? Rose Yu: It started with traffic. I was a grad student at USC, and the campus is right near the intersection of I-10 and I-110. To get anywhere, you have to go through a lot of traffic, which can be very annoying. In 2016, I began to wonder whether I could do anything about this. Deep learning—which uses multilayered neural networks to elicit patterns from data—was getting really hot back then. There was already a lot of excitement about applications in image classification, but images are just static things. I wondered whether deep learning could help with problems where things are constantly changing. I wasn't the first person to consider this, but my colleagues and I did find a novel way of framing the problem. What was your new approach? First, we thought of traffic in terms of the physical process of diffusion. In our model, the flow of traffic over a network of roads is analogous to the flow of fluids over a surface—motions that are governed by the laws of fluid dynamics. But our main innovation was to think of traffic as a graph, from the mathematical field of graph theory. Sensors, which monitor traffic on highways and other roads, serve as the nodes of this graph. And the edges of the graph represent the roads (and distances) between those sensors. Yu's interest in computers began with a gift for her 10th birthday. Photograph: Peggy Peattie for Quanta Magazine A graph provides a snapshot of the entire road network at a given time, telling you the average velocity of cars at every point on the graph. When you put together a series of these snapshots, spaced every five minutes apart, you get a good picture of how traffic is evolving. From there, you can try to predict what will happen in the future. The big challenge in deep learning is that you need a lot of data to train the neural network. Fortunately, one of my advisers, Cyrus Shahabi, had worked for many years on the problem of traffic forecasting, and he'd accumulated a vast amount of LA traffic data that I had access to. So how good were your predictions? Prior to our work, people could only make traffic forecasts that were reliable for about 15 minutes. Our forecasts were valid for one hour—a big improvement. Our code was deployed by Google Maps in 2018. A bit later, Google invited me to become a visiting researcher. That's about when you began working on climate modeling, right? Yes, that started in 2018, when I gave a talk at the Lawrence Berkeley National Laboratory. Afterward, I spoke with scientists there, and we looked for a problem that would be a good testbed for physics-guided deep learning. We settled on predicting the evolution of turbulent flow, which is a key factor in climate models, as well as an area of major uncertainty. Familiar examples of turbulence are the swirling patterns you see after pouring milk into a cup of coffee and giving it a stir. In the oceans, swirls like this can span thousands of miles. Predictions of turbulent behavior that are based on solving the Navier-Stokes equation, which describes the flow of fluids, are considered the gold standard in this field. But the required calculations are very slow, which is why we don't have good models for predicting hurricanes and tropical cyclones. The heavy congestion of Los Angeles first inspired Yu to model highway traffic as the flow of fluids. Photograph: Peggy Peattie for Quanta Magazine And deep learning can help? The basic idea is that deep neural networks that are trained on our best numerical simulations can learn to imitate—or as we say, 'emulate'—those simulations. They do that by recognizing properties and patterns buried within the data. They don't have to go through time-consuming, brute-force calculations to find approximate solutions. Our models sped up predictions by a factor of 20 in two-dimensional settings and by a factor of 1,000 in three-dimensional settings. Something like our turbulence prediction module might someday be inserted into bigger climate models that can do better at predicting things like hurricanes. Where else does turbulence show up? It's pretty much everywhere. Turbulence in blood flow, for instance, can lead to strokes or heart attacks. And when I was a postdoc at Caltech, I coauthored a paper that looked into stabilizing drones. Propellor-generated airflows interact with the ground to create turbulence. That, in turn, can cause the drone to wobble. We used a neural network to model the turbulence, and that led to better control of drones during takeoffs and landings. I'm currently working with scientists at UCSD and General Atomics on fusion power. One of the keys to success is learning how to control the plasma, which is a hot, ionized phase of matter. At temperatures of about 100 million degrees, different kinds of turbulence arise within the plasma, and physics-based numerical models that characterize that behavior are very slow. We're developing a deep learning model that should be able to predict the plasma's behavior in a split second, but this is still a work in progress. Yu and doctoral student Jianke Yang in her office at UCSD. Photograph: Peggy Peattie for Quanta Magazine Where did your AI Scientist idea come from? In the past couple of years, my group has developed AI algorithms that can automatically discover symmetry principles from data. For example, our algorithm identified the Lorentz symmetry, which has to do with the constancy of the speed of light. Our algorithm also identified rotational symmetry—the fact, for example, that a sphere doesn't look any different regardless of how you rotate it—which is something it was not specifically trained to know about. While these are well-known properties, our tools also have the capability to discover new symmetries presently unknown to physics, which would constitute a huge breakthrough. It then occurred to me that if our tools can discover symmetries from raw data, why don't we try to generalize this? These tools could also generate research ideas or new hypotheses in science. That was the genesis of AI Scientist. What exactly is AI Scientist—just a fancy kind of neural net? It's not a single neural network, but rather an ensemble of computer programs that can help scientists make new discoveries. My group has already developed algorithms that can help with individual tasks, such as weather forecasting, identifying the drivers of global temperature rise, or trying to discover causal relationships like the effects of vaccination policies on disease transmission. We're now building a broader 'foundation' model that's versatile enough to handle multiple tasks. Scientists gather data from all types of instruments, and we want our model to include a variety of data types—numbers, text, images, and videos. We have an early prototype, but we want to make our model more comprehensive, more intelligent and better trained before we release it. That could happen within a couple of years. What do you imagine it could do? AI can assist in practically every step of the scientific discovery process. When I say 'AI Scientist,' I really mean an AI scientific assistant. The literature survey stage in an experiment, for example, typically requires a massive data-gathering and organization effort. But now, a large language model can read and summarize thousands of books during a single lunch break. What AI is not good at is judging scientific validity. In this case, it can't compete with an experienced researcher. While AI could help with hypothesis generation, the design of experiments and data analysis, it still cannot carry out sophisticated experiments. How far would you like to see the concept go? As I picture it, an AI Scientist could relieve researchers of some of the drudgery while letting people handle the creative aspects of science. That's something we're particularly good at. Rest assured, the goal is not to replace human scientists. I don't envision—nor would I ever want to see—a machine substituting for, or interfering with, human creativity. Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences.


CNN
7 hours ago
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The US has long been a research powerhouse. After Trump's cuts, other countries are stepping in
Growing up in Brazil, neuroscientist Danielle Beckman always dreamed of moving to the US for work. So, in 2017, when Beckman got the opportunity to work at the California National Primate Research Center at UC Davis, she jumped on it. 'I was so excited,' she recalled. 'Coming to the US was always the dream. It was always the place to be, where there's the biggest investment in science.' But months into President Donald Trump's second term, as his administration wages an unprecedented war on the country's top universities and research institutions, Beckman no longer sees the US as a welcome home for her or her research, which focuses on how viral infections like Covid-19 affect the brain. She told CNN she now plans to move and is looking at opportunities in Germany and France. Beckman is part of a growing wave of academics, scientists and researchers leaving the US in what many are warning could be the biggest brain drain the country has seen in decades. But America's loss could be the rest of the world's gain. As the Trump administration freezes and slashes billions of dollars in research funding, meddles with curricula, and threatens international students' ability to study in the US, governments, universities and research institutions in Canada, Europe and Asia are racing to attract fleeing talent. The European Union pledged €500 million ($562 million) over the next three years 'to make Europe a magnet for researchers.' A university in Marseille, France, is wooing persecuted academics under a new program called a 'Safe Place for Science.' Canada's largest health research organization is investing 30 million Canadian dollars ($21.8 million) to attract 100 scientists early in their careers from the US and elsewhere. The Research Council of Norway launched a 100 million kroner ($9.8 million) fund to lure new researchers. The president of Singapore's Nanyang Technological University recently told a crowd at a higher education summit the school is identifying 'superstar' US researchers and making them offers as soon as the next day. The Australian Academy of Science also launched a new talent program to recruit disillusioned US-based scientists and lure Australians back home. 'We know these individuals are highly trained, talented, and have much to offer,' said Anna-Maria Arabia, chief executive of the academy, noting the program has received 'encouraging interest' so far. Arabia told CNN the flood of institutions rushing to fill the void left by US funding cuts reflects a 'global hunger' for science and technology professionals. 'It's vitally important that science can continue without ideological interference,' Arabia said. The US has long been a powerhouse when it comes to research and development, attracting talent from far afield with its big budgets, high salaries and swanky labs. Since the 1960s, US government expenditure in research and development (R&D) has more than doubled from $58 billion in 1961 to almost $160 billion in 2024 (in inflation-adjusted dollars), according to federal data. When incorporating R&D funding from the private sector, that number balloons to more than an estimated $900 billion in 2023. The US's enormous investment in R&D has led to an outsized influence on the world stage. The US has racked up more than 400 Nobel Prizes, more than double the amount of the next country, the United Kingdom. More than a third of the US's prizes were won by immigrants. 'We have been respected worldwide for decades because we have trained succeeding generations of researchers who are pushing into new territories,' said Kenneth Wong, a professor of education policy at Brown University. But Trump's second term has upended the relationship between higher education and the federal government. Trump's gutting of federal health and science agencies has led to sweeping job losses and funding cuts, including at the National Institutes of Health, which funds nearly $50 billion in medical research each year at universities, hospitals and scientific institutions. Between the end of February and the beginning of April, the administration cancelled almost 700 NIH grants totaling $1.8 billion, according to an analysis in the Journal of the American Medical Association. The Trump administration has proposed reducing the NIH's budget in 2026 by 40%. The National Science Foundation has also slashed nearly $1.4 billion worth of grants. On Wednesday, 16 US states sued the Trump administration over the NSF cuts, which they argue will impede 'groundbreaking scientific research' and '(jeopardize) national security, the economy and public health.' Trump has also targeted elite universities and is in the middle of a legal battle with Harvard University over its refusal to bow to his administration's directives to eliminate diversity, equity and inclusion programs, resulting in billions in frozen federal funding. That battle significantly escalated this month when Trump banned Harvard's ability to enroll international students – a decision swiftly halted by a federal judge hours after Harvard filed suit. This week, the White House directed federal agencies to cancel all remaining contracts with Harvard. 'The president is more interested in giving that taxpayer money to trade schools and programs and state schools where they are promoting American values, but most importantly, educating the next generation based on skills that we need in our economy and our society: apprenticeships, electricians, plumbers,' White House Press Secretary Karoline Leavitt said on Fox News this week. 'We need more of those in our country, and less LGBTQ graduate majors from Harvard University.' Foreign institutions have already jumped on the chance to welcome Harvard students now caught in legal limbo. On Monday, Hong Kong University of Science and Technology said it will accept any Harvard students that wish to transfer, as well as prospective students with a current offer from Harvard. 'I see this as the most significant crisis that universities are facing since World War Two,' Wong said. 'We are seeing a complete reset of this collaborative relationship between the federal government and leading research institutions.' Once the beacon of scientific research, the US has now become an increasingly hostile place to study, teach, and do research. Three quarters of US scientists surveyed by the journal Nature in March said they were considering leaving because of the Trump administration's policies. Some have already jumped ship. Yale professors Jason Stanley, Marci Shore and Timothy Snyder, preeminent fascism scholars, announced in March they were leaving for the University of Toronto across the border in Canada because of Trump's affronts to academic freedom. Beckman, the Brazilian neuroscientist, said her lab has seen $2.5 million in grant funding cancelled in recent months. On top of these funding woes, Beckman said the Trump administration's crackdown on immigrants, and shifting attitudes towards foreigners in the US, has also pushed her to look for work elsewhere. 'It's the first time since I moved here that I don't feel so welcome anymore,' she said. As the US research ecosystem responds to shrinking budgets and intrusions on academic freedom, early-career scientists are going to be hardest hit, Wong said. But younger researchers are also more mobile, and institutions around the world are welcoming them with open arms. 'What we are losing is this whole cadre of highly productive, young, energetic, well-trained, knowledgeable, advanced researchers who are primed to take off,' Wong said. Other countries have long deprioritized investment in scientific research as the US absorbed the R&D needs of the world, Wong said. But that trend is shifting. R&D spending in China has surged in recent decades, and the country is close to narrowing the gap with the US. China spent more than $780 billion on R&D in 2023, according to OECD data. The European Union is also spending more on R&D. R&D investment in the bloc has increased from about $336 billion in 2007 to $504 billion in 2023, according to the OECD. For a couple of months, Beckman said she considered stepping away from her Covid-19 research, which has become increasingly politicized under the Trump administration. But then she started getting interviews at institutions in other countries. 'There is interest in virology everywhere in the world except the US right now.'


CNN
7 hours ago
- CNN
The US has long been a research powerhouse. After Trump's cuts, other countries are stepping in
Growing up in Brazil, neuroscientist Danielle Beckman always dreamed of moving to the US for work. So, in 2017, when Beckman got the opportunity to work at the California National Primate Research Center at UC Davis, she jumped on it. 'I was so excited,' she recalled. 'Coming to the US was always the dream. It was always the place to be, where there's the biggest investment in science.' But months into President Donald Trump's second term, as his administration wages an unprecedented war on the country's top universities and research institutions, Beckman no longer sees the US as a welcome home for her or her research, which focuses on how viral infections like Covid-19 affect the brain. She told CNN she now plans to move and is looking at opportunities in Germany and France. Beckman is part of a growing wave of academics, scientists and researchers leaving the US in what many are warning could be the biggest brain drain the country has seen in decades. But America's loss could be the rest of the world's gain. As the Trump administration freezes and slashes billions of dollars in research funding, meddles with curricula, and threatens international students' ability to study in the US, governments, universities and research institutions in Canada, Europe and Asia are racing to attract fleeing talent. The European Union pledged €500 million ($562 million) over the next three years 'to make Europe a magnet for researchers.' A university in Marseille, France, is wooing persecuted academics under a new program called a 'Safe Place for Science.' Canada's largest health research organization is investing 30 million Canadian dollars ($21.8 million) to attract 100 scientists early in their careers from the US and elsewhere. The Research Council of Norway launched a 100 million kroner ($9.8 million) fund to lure new researchers. The president of Singapore's Nanyang Technological University recently told a crowd at a higher education summit the school is identifying 'superstar' US researchers and making them offers as soon as the next day. The Australian Academy of Science also launched a new talent program to recruit disillusioned US-based scientists and lure Australians back home. 'We know these individuals are highly trained, talented, and have much to offer,' said Anna-Maria Arabia, chief executive of the academy, noting the program has received 'encouraging interest' so far. Arabia told CNN the flood of institutions rushing to fill the void left by US funding cuts reflects a 'global hunger' for science and technology professionals. 'It's vitally important that science can continue without ideological interference,' Arabia said. The US has long been a powerhouse when it comes to research and development, attracting talent from far afield with its big budgets, high salaries and swanky labs. Since the 1960s, US government expenditure in research and development (R&D) has more than doubled from $58 billion in 1961 to almost $160 billion in 2024 (in inflation-adjusted dollars), according to federal data. When incorporating R&D funding from the private sector, that number balloons to more than an estimated $900 billion in 2023. The US's enormous investment in R&D has led to an outsized influence on the world stage. The US has racked up more than 400 Nobel Prizes, more than double the amount of the next country, the United Kingdom. More than a third of the US's prizes were won by immigrants. 'We have been respected worldwide for decades because we have trained succeeding generations of researchers who are pushing into new territories,' said Kenneth Wong, a professor of education policy at Brown University. But Trump's second term has upended the relationship between higher education and the federal government. Trump's gutting of federal health and science agencies has led to sweeping job losses and funding cuts, including at the National Institutes of Health, which funds nearly $50 billion in medical research each year at universities, hospitals and scientific institutions. Between the end of February and the beginning of April, the administration cancelled almost 700 NIH grants totaling $1.8 billion, according to an analysis in the Journal of the American Medical Association. The Trump administration has proposed reducing the NIH's budget in 2026 by 40%. The National Science Foundation has also slashed nearly $1.4 billion worth of grants. On Wednesday, 16 US states sued the Trump administration over the NSF cuts, which they argue will impede 'groundbreaking scientific research' and '(jeopardize) national security, the economy and public health.' Trump has also targeted elite universities and is in the middle of a legal battle with Harvard University over its refusal to bow to his administration's directives to eliminate diversity, equity and inclusion programs, resulting in billions in frozen federal funding. That battle significantly escalated this month when Trump banned Harvard's ability to enroll international students – a decision swiftly halted by a federal judge hours after Harvard filed suit. This week, the White House directed federal agencies to cancel all remaining contracts with Harvard. 'The president is more interested in giving that taxpayer money to trade schools and programs and state schools where they are promoting American values, but most importantly, educating the next generation based on skills that we need in our economy and our society: apprenticeships, electricians, plumbers,' White House Press Secretary Karoline Leavitt said on Fox News this week. 'We need more of those in our country, and less LGBTQ graduate majors from Harvard University.' Foreign institutions have already jumped on the chance to welcome Harvard students now caught in legal limbo. On Monday, Hong Kong University of Science and Technology said it will accept any Harvard students that wish to transfer, as well as prospective students with a current offer from Harvard. 'I see this as the most significant crisis that universities are facing since World War Two,' Wong said. 'We are seeing a complete reset of this collaborative relationship between the federal government and leading research institutions.' Once the beacon of scientific research, the US has now become an increasingly hostile place to study, teach, and do research. Three quarters of US scientists surveyed by the journal Nature in March said they were considering leaving because of the Trump administration's policies. Some have already jumped ship. Yale professors Jason Stanley, Marci Shore and Timothy Snyder, preeminent fascism scholars, announced in March they were leaving for the University of Toronto across the border in Canada because of Trump's affronts to academic freedom. Beckman, the Brazilian neuroscientist, said her lab has seen $2.5 million in grant funding cancelled in recent months. On top of these funding woes, Beckman said the Trump administration's crackdown on immigrants, and shifting attitudes towards foreigners in the US, has also pushed her to look for work elsewhere. 'It's the first time since I moved here that I don't feel so welcome anymore,' she said. As the US research ecosystem responds to shrinking budgets and intrusions on academic freedom, early-career scientists are going to be hardest hit, Wong said. But younger researchers are also more mobile, and institutions around the world are welcoming them with open arms. 'What we are losing is this whole cadre of highly productive, young, energetic, well-trained, knowledgeable, advanced researchers who are primed to take off,' Wong said. Other countries have long deprioritized investment in scientific research as the US absorbed the R&D needs of the world, Wong said. But that trend is shifting. R&D spending in China has surged in recent decades, and the country is close to narrowing the gap with the US. China spent more than $780 billion on R&D in 2023, according to OECD data. The European Union is also spending more on R&D. R&D investment in the bloc has increased from about $336 billion in 2007 to $504 billion in 2023, according to the OECD. For a couple of months, Beckman said she considered stepping away from her Covid-19 research, which has become increasingly politicized under the Trump administration. But then she started getting interviews at institutions in other countries. 'There is interest in virology everywhere in the world except the US right now.'