Nature Climate Change, Published online: 25 June 2026; doi:10.1038/s41558-026-02686-x

Changes in the ocean in response to climate change could impact its ability to function as a carbon sink. This study shows that under high emissions, circulation changes will reduce anthropogenic carbon uptake even as biological storage increases, whereas under low emissions, temperature is the key factor.

Sold to the public as a foreign surveillance tool, Section 702 is the law has let intelligence agencies spy on millions of Americans’ private conversations without a warrant. Despite years of revelations about this law's misuse, Congress has repeatedly reauthorized Section 702 without meaningful reform. Until this month, that is, when it finally lapsed in a major victory for privacy. In our latest EFFector newsletter, we're covering the expiration of Section 702 and what happens next.

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Prefer to listen in? EFFector is now available on all major podcast platforms. This time, we're chatting with EFF Senior Policy Analyst Matthew Guariglia on what the expiration of Section 702 means for warrantless domestic spying. You can find the episode and subscribe on your podcast platform of choice:

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When an animal’s environment changes faster than the animal can adapt, its chances of survival can flat-line. The same is true for populations, and even entire species. 

Now, scientists at MIT and the University of Leicester have found that this connection between evolutionary adaptation and the pace of environmental change holds up at the global scale as well — and can determine life’s susceptibility to mass extinction. The researchers developed a theoretical model of this phenomenon, which they present in a paper appearing today in Physical Review Letters.

The team compared the model with available data from past major mass extinctions, including how fast the global environment changed at the time of each event. The model successfully predicted the severity of most mass extinctions in Earth’s history, or the fraction of life that was unable to adapt, and therefore went extinct. 

Interestingly, the researchers found that the range of adaptation rates across animal groups is broadly similar to the range of rates at which the environment can change.

“What we’re beginning to see is a certain level of organization, and ways in which life behaves that are consistent with the ways in which the environment behaves,” says study author Daniel Rothman, professor of geophysics and co-director of the Lorenz Center at MIT. “It may be that life has evolved so that its range of adaptabilities matches the range of stresses that it meets.”

Rothman’s study co-author is Sergei Petrovskii, professor of applied mathematics at the University of Leicester in England.

A catastrophizing connection

The connection between extinction and environmental change is not new. In the late 18th century, the French naturalist Georges Cuvier, who is often referred to as the founding father of paleontology, was the first to propose the concept of “catastrophism.” He had discovered fossil bones near Paris that didn’t match any animal known to exist at the time. Cuvier concluded that the bones were from a group of giant mammals that existed at one time but was no longer around. He proposed, then, that an entire species could disappear, or go extinct, likely due to a widespread catastrophe. 

“That itself was a major idea, that a species could go extinct,” Rothman says. “And he had suggested it was an environmental catastrophe that had caused it.”

The concept of catastrophism later gave way to the view that Earth’s history was shaped mainly by slow, gradual processes. But in the mid-20th century the American geologist Norman Newell revisited the problem. In seeking the cause of extinctions, he proposed what Rothman and Petrovskii call the “rate-mismatch” hypothesis, the notion that extinction occurs when the rate of environmental change is higher than the rate at which a species can evolve to adapt. 

Biologists have since observed Newell’s hypothesis play out in many cases where changes in the environment have driven the extinction of individual species. Rothman and Petrovskii wondered: Could the hypothesis also apply at the global scale?

“We know that individual species go extinct when environmental change outpaces their ability to adapt,” Rothman notes. “But it hasn’t been clear whether this same idea applies at the scale of global extinction events.”

Finding a mismatch

For their new study, the researchers looked to test the rate mismatch hypothesis at the global scale. They wanted to see whether mass extinction events in history can be explained by a mismatch between the rate of global environmental change and the rate at which life around the world can adapt. 

To do so, at least in theory, they would have to compare two sources of data: the rates at which the global environment has changed over time and the rates at which different groups of organisms adapt to environmental change. The first can be found in geological records, which scientists have used extensively to infer how the Earth’s climate changed through history. The second, however, is almost impossible to record.

“We’re talking about the rates at which organisms adapt to major environmental change at effectively geologic timescales, from thousands to millions of years,” Rothman says. “And that doesn’t lend itself to direct observation.”

In place of actual data, the researchers aimed to construct a general mathematical theory to describe the range of adaptation rates across animal groups around the world. In this context, “adaptation” refers to any change within a species, over time periods that are much longer than a generation, that enable the species to persist as its environment changes. 

It is generally understood in evolutionary theory that a species can successfully adapt only when multiple conditions are met. For instance, there needs to be variation in the population, these variations must be heritable, some variations enable an organism to adapt better than others, and the organisms that adapt better should leave more offspring. If all these conditions are met, the entire species should be able to adapt to a given environmental change. However, if any one condition fails, the population will go extinct. 

Rothman and Petrovskii recognized that in this case, a species’ probability of successfully adapting multiplies with every condition that it meets. And it turns out that this pattern can be described mathematically as a very simple, bell-shaped curve. Such a curve essentially describes what fraction of the world’s animals can adapt at given rates, from the slowest to the fastest adapters, and how this fraction changes nonlinearly with the rate of adaptation. This curve generally shows that most animal groups can adapt at intermediate rates, while fewer animal groups adapt at the slowest and fastest rates. 

After they established this general pattern of adaptation rates, the researchers looked to see how this pattern compares to recorded rates of environmental change, and how these two rates match, or don’t match, at times of mass extinction. 

To do so, they considered paleontological and geochemical data from 27 episodes over the last 450 million years where the carbon cycle experienced significant change — a measure that is generally understood to reflect global environmental change. They then compared rates of environmental change with the fraction of animal groups that went extinct during each episode — numbers that were established previously in a well-regarded study by paleobiologist John Alroy. 

In the end, Rothman and Petrovskii observed that indeed, for almost every mass extinction event in the last 450 million years, there was a mismatch in the rates at which the environment changed and at which animals could adapt; mass extinctions occurred when a significant fraction of animals could not adapt fast enough to match the changing environment. Their results confirm that the rate mismatch hypothesis applies at the global scale.

What’s more, this mismatch in rates could predict the severity of extinction events, or the fraction of animal life that went extinct given the rate at which the environment changed. 

In the case of the end-Permian extinction, it’s likely that the rapid acidification of the ocean outpaced organisms’ ability to evolve adequate protections, leading to the extinction of over 80 percent of the world’s marine species. 

The team’s work focuses on applying the new model to past extinction events. But the work could also provide a framework for understanding modern extinction risk. 

“Carbon dioxide levels in the ocean are increasing today at a rate which, when appropriately re-scaled, is similar to rates of carbon-cycle change that are just lower than those associated with major extinction events in the past,” Rothman says. “It suggests that modern environmental change may be approaching rates beyond which adaptation becomes increasingly difficult.” 

This research is supported, in part, by Schmidt Sciences, LLC; the MIT Climate Grand Challenges; the U.S. National Science Foundation; the European Space Agency; and the London Mathematical Society.

At least one malware developer is adding text about nuclear and biological weapons to their spyware, in an effort to stop automatic AI analysis.

Details:

The _index.js payload begins with a large JavaScript block comment containing fake system instructions and policy-triggering content. Because it is inside a comment, it does not affect JavaScript execution. The runtime skips it. The real malware begins after the comment with a try{eval(…)} wrapper around a large character-code array and a ROT-style substitution function.

This header appears designed for AI-mediated analysis, not for Node, Bun, or Python. It attempts to derail scanners or analyst copilots that feed the beginning of a file to a language model without clearly isolating the content as untrusted data. In weak pipelines, this can cause refusal behavior, prompt confusion, context pollution, or premature classification before the scanner reaches the actual malware...

The measure passed by both chambers this week would send billions of dollars to lower-income communities reeling from catastrophes.

Gov. Kathy Hochul has more time to make difficult decisions about reducing emissions, but the state’s energy battles are far from settled.

The mayor ordered city offices to draft requirements for ensuring the safety of municipal workers in high temperatures.

The Golden State's top legal officer said the administration's deal would hinder the state's climate and energy goals.

Rainfall has helped the South Texas city delay a plan to impose new water restrictions on residences and refineries.

Climate change, poor infrastructure and distracted politicians all play a role.

Extreme heat has forced the cancellation of plans to discuss the impact of extreme heat.

The data for part of the equatorial Pacific shows it to be the largest warm deviation from the historical average for June since 1981.

POLITICO grilled multiple candidates on energy policy at an event Tuesday.

The European Commission’s original version of the grids package required grid operators to set aside 25 percent of unspent congestion revenues to finance cross-border projects to benefit broader connectivity.

In 2022, global production of construction materials accounted for more than 7 percent of total carbon emissions. But how many of those materials were truly necessary to build houses, buildings, and bridges?

A technique called topology optimization can design structures that reduce the amount of material used, in some cases by as much as 90 percent, which would represent a multi-gigaton reduction in building emissions. Unfortunately, topology optimization is mostly used by researchers for applications like 3D printing rather than by engineers designing at the scale of buildings and bridges.

That’s because topology optimization doesn’t create structures that can easily be built on time and budget, which are the things builders really care about.

Now MIT researchers have created a way to make topology optimization designs more buildable. Their framework, described in a new paper in Automation in Construction today, allows users to apply constraints to algorithmically generated structures to limit their complexity. For instance, the approach allows users to limit how many components meet at each point of their design and how small they want their smallest parts. It also builds on previous work by designing structures with multiple materials and taking into account materials’ properties to distribute load and specify part connections.

“There’s an interplay between the materials you’re using, the constructability of designs, and the optimization of the structure,” says senior author Josephine Carstensen, MIT’s Gilbert W. Winslow (1937) Career Development Professor in Civil Engineering. “You need to be able to address all three at the same time. That’s what we tried to do here.”

The researchers used their approach to design steel, wood, and multimaterial truss structures that support loads in buildings and bridges, showing the carbon emissions associated with materials changed significantly when different constraints were applied. They hope their framework will move topology optimization closer to being used in real-world construction.

“In the literature, there’s sometimes been a disconnect between the carbon savings you can achieve on a computer and the realistic carbon savings you can achieve for built structures — especially when it comes to design technologies like topology optimization,” Carstensen says. “The problem lies in the lack of constructability of designs. These designs have been perceived as too difficult to make with conventional methods, so they are never even attempted. That’s what is exciting about our approach: We can add constraints so that you will never be in a situation where the design that comes out is too hard to make.”

Joining Carstensen on the paper is first author and civil and environmental engineering PhD student Zane Schemmer.

More buildable designs

Computer-based topology optimization has been around for decades. It uses computer programs to optimally distribute material in a given space, for instance creating the strongest possible structures at the lowest weight. The resulting designs are often complex, spider web-like structures that would be a challenge for even the most capable engineers to build.

“A big question Josephine and I were asking is why isn’t industry using it?” Schemmer recalls. “What are the obstacles that prevent industry from designing things more efficiently, and how can we fill the gaps between research and real life?”

In recent years, several researchers have developed ways to make topology optimization easier to use. For their study, Schemmer and Carstensen wanted to bring those approaches together and add new capabilities, like creating designs that use multiple materials, which has been another challenge in the field.

“A big aspect of sustainability going forward will be not only using less material, but also implementing materials efficiently based on considerations like where you are in the world, your access to materials, and each of their associated carbon costs,” Schemmer says.

To build their framework, they used a class of equations called mixed integer algorithms that help make binary decisions about things like materials and connections.

“You can’t have a part that’s 72 percent timber and 28 percent steel,” Schemmer says. “Instead, it says, ‘This truss or cable is going to be made out of this,’ and then based on that decision, how do we make sure all of these connections meet their strength standards?”

The system’s decisions also take into account material properties. For instance, steel struts can withstand compressive loads, but steel cables cannot. The model also has more realistic modeling of how parts connect than previous approaches.

“In 3D printing, the way things come together is easy,” Carstensen says. “In construction, that’s not the case. If you’re building with timber there’s a certain rule set, versus steel has a different rule set.”

Users can also decide how complex they want their design to be by specifying the maximum number of connections at each joint and the minimum angle between connected components. The model also creates minimum size limits for parts, further improving its constructability.

“It’s tough to give a contractor these complex, intricate designs because it’s going to be super difficult to build,” Schemmer says. “A lot of times contractors won’t pick up a project like that to begin with.”

The researchers compared structures designed with their approach to structures designed with conventional topology optimization, showing dramatic differences in final designs that transformed how the structures would be built. Using the Lockport “Upside-Down Bridge” near Buffalo, New York, as an example, they applied individual constraints, like a minimum angle on part connections or minimum part sizes, to the bridge’s truss design, to better understand how each constraint impacted final designs.

Finally, they made truss designs that used wood only, steel only, and combined wood and steel, showing how different projects offered tradeoffs with respect to environmental impact and constructability.

“We saw how the system knew that you could design a bridge of pure steel, but that might not be best from a carbon standpoint,” Schemmer says. “Or you could design a bridge out of purely timber, but that might not be the strongest. But these materials can work together, so you use timber for the carbon savings and steel where you need extra strength, and there’s a balance you can find in these structures.”

From research to industry

The researchers say their approach is more computationally intensive than some others, but they were able to use a MacBook Pro to run the programs in their experiment, and they believe it’s practical for most civil engineering firms.

“It’s computationally a little tougher to solve, but there’s a lot of tools coming out nowadays that make these problems a lot more feasible,” Schemmer says. “This approach has been avoided by industry in the past, but now we think it’s a practical way to solve problems dealing with variable constraints.”

If users have more computational resources, the researchers say their approach could work with a long list of materials and far bigger structures than homes, small buildings, and bridges.

Moving forward, Carstensen says the team plans to build scaled-down structures designed by the model to further validate its predictions. They also want to add constraints to their model to make it even more seamless for civil engineers to use when designing the world’s infrastructure.

“As a structural engineer by training, I was never taught how to design for low-carbon,” Schemmer says. “To tackle a problem as big as climate change, addressing the built environment is a great place to start. One of the most tangible things we can do is work at the layer of construction, at the design stage, because that’s a fundamental step that we can control. There’s a lot of decisions we make early on that lead us to use extra material we don’t need.”

The work was funded by the MIT Morningside Academy for Design.

At the recent AI and Society Forum at MIT, experts from across the Institute discussed the potential benefits and dangers of technological innovation on labor, the nature of work, civil discourse, election administration, and other topics.

The event featured individual research presentations and panel discussions, as well as a musical performance exploring the use of generative artificial intelligence in the arts.

The forum was co-organized by the School of Humanities, Arts, and Social Sciences (SHASS) and the Social and Ethical Responsibilities of Computing (SERC). It was presented in collaboration with two of MIT’s strategic initiatives: the MIT Generative AI Impact Consortium (MGAIC) and the MIT Human Insight Collaborative (MITHIC).

Agustín Rayo, the Kenan Sahin Dean of SHASS, and Dan Huttenlocher, dean of the MIT Schwarzman College of Computing, provided opening remarks.

Rayo said bringing scholars from across MIT together was intentional because understanding AI’s impact requires expertise from disciplines throughout the Institute.

“Paying attention to the societal consequences of AI is not a departure from MIT’s mission; it’s a way of ensuring that our technical leadership has maximum impact,” Rayo said.

Huttenlocher added that computing and AI’s rapid growth makes it critical to support interdisciplinary conversations and research.

“Understanding where AI excels and where it falls short is essential not only to unlocking its benefits, but also to avoiding critical errors, overreliance, and unintended consequences,” Huttenlocher said.

Jobs and AI 

Held in the Tull Concert Hall in MIT’s Linde Music Building, the May 12 forum opened with a keynote presentation from economist David Autor, the Daniel (1972) and Gail Rubinfeld Professor in the MIT Department of Economics. Autor challenged the common narrative that AI will simply eliminate jobs by proposing instead that technology's impact depends on how it affects the scarcity and value of human expertise. 

“When I think about how technology interacts with the value of labor, I think about it in terms of how it changes the scarcity of expertise, whether it makes it more valuable or whether it makes it more of a commodity,” he said.

Autor said that what matters is whether automation removes routine supporting tasks or removes expert tasks. He argued that AI will likely create new specialized work, requiring proactive policies around worker training, wage insurance, and broader capital ownership.

A panel discussion followed, moderated by Rob Loughlin, a partner at McKinsey & Company, featuring experts from MIT discussing how work is changing and what it means for society. 

Daniela Rus, the MIT Panasonic Professor of Computer Science and director of the Computer Science and Artificial Intelligence Laboratory (CSAIL), described excitement around ways AI could enhance the workplace.

“I’d like to imagine the robot as your friend and assistant, as someone who watches you and figures out how to help you as someone you can task at a high level,” she said. 

Still, Rus said, human judgment remains critical in decision-making.

“We could really think about co-work with the AI tools, but the role of the human as the decider, as the person with good judgment, as the person deciding the next step, whatever that is, remains super important,” she said.

David Mindell, professor of Aeronautics and Astronautics and the Dibner Professor of the History of Engineering and Manufacturing in the Program in Science, Technology, and Society, says the nature of work has constantly changed over the years, but “what matters is the new work.” 

“We need to be supporting individuals, the economy, professions, to constantly be creating the new work,” he said. “It’s absolutely imperative that we give the tools to the young people and let them do what they find creative and show us what the new work is going to be.”

Panelists also talked about the need to maintain safety standards, while also exploring ways to find efficiencies. Mindell used an example of cargo flights that require six pilots due to the length of the flight.

“We don’t know how to take that six number down to five yet, much less two, one, or zero. There's a lot of money behind solving that problem, but there's also a very rich system that has evolved to make those systems safe,” he said.

Sendhil Mullainathan, the Peter de Florez Professor with dual appointments in the MIT departments of Economics and Electrical Engineering and Computer Science (EECS), described a vision of AI’s utility and growth that offers productivity improvements, but also cautioned, “I think it's very much worth differentiating productivity gains from things that actually drive long-term growth.”

Either way, Mullainathan said, it’s clear we’re entering a time of high variance with regard to AI’s impact on the workforce.

“If you said, ‘exactly how will organizations restructure?’ I don’t know. But is there going to be a lot of restructuring? It’s hard to believe there isn’t going to be a lot of restructuring. And in some sense, if we know that what we’re entering is a period of high variance, that itself is incredibly informative,” he said.

Democracy and AI

The day’s second session focused on AI technology and its impact on democracy. 

Chara Podimata, the Class of 1942 Career Development Assistant Professor and assistant professor of operations research and statistics in the MIT Sloan School of Management, presented her research on auditing large language models for bias in election information.

“Algorithms decide a lot of things about our lives right now,” she said. “With regard to chatbots and election information, if I take two people and they interact with the same chatbot … how will the chatbot respond? How will it personalize the information it gives to these people?”

A longitudinal study of 12 major models during the 2024 U.S. presidential election season found responses varied dramatically based on stated demographics and political leanings. Her research team is now working on a new audit of the 2026 U.S. midterm elections, using a redesigned survey with input from political science experts.

During a panel discussion moderated by Songyee Yoon, founder and managing partner at Principal Venture Partners and member of the MIT Corporation, experts raised concern about the potential for AI to erode democratic norms and processes, but also explored potential positive outcomes.

Bailey Flanigan, the Theodore T. Miller (1922) Career Development Professor in the Department of Political Science, who holds an MIT Schwarzman College of Computing shared position with EECS, said she’s skeptical of how some are applying AI as a tool that can get people to reach decisions or consensus more quickly.

“And there is a reason to think that this is nice because it is more efficient. It's easier. But it loses a lot of these procedural elements of democracy that are the rituals of how we come together and make decisions,” she said. “And I think it’s a mistake to forget about that when we start thinking about automation.”

Charles Stewart III, the Kenan Sahin (1963) Distinguished Professor of Political Science and founding director of the MIT Election Data and Science Lab, said one challenge is that governmental structures do not evolve at the same rate as technology.

Stewart said his biggest concern is the potential for AI to lead to chaos during and after elections.

“If and when things go wrong, they can go really bad, and really wrong. If an election is called into question, that can lead to violence,” Stewart said.

“We’ve already seen in the low-tech eras election results being manipulated. What worries me is what I’m going to observe this coming Election Day, and the Wednesday after, and if AI has helped to create irreversible disruptions to the election system,” he added.

Lily Tsai, the Ford Professor of Political Science and director and founder of the MIT Governance Lab (MIT GOV/LAB), said in many ways, AI runs against the democratic norms and commitments necessary for a healthy democracy.

“It is really important not just in terms of design principles, but the commitments of designers to be familiar with the values and principles that characterize what democracy is based on: agency, political equality, mutual respect, inclusion, and autonomy,” Tsai said.

Tsai also noted her research has shown some people are more comfortable interacting with machines. She described a “Socratic dialogue chatbot” her team designed that asks people to articulate the thinking behind their beliefs and positions.

“And that actually, interestingly, seems to moderate their policy position in the process,” Tsai said. “So there are absolutely examples of ways in which AI can have positive impacts on democracy. But it really is about designing with the right principles and evaluating them rigorously.”

Fable 5 is the supposed safe version of Anthropic’s Mythos Preview, with guardrails to ensure that it can’t be used to create cyberattacks.

Well, that restriction was bypassed within days.

The voluntary initiative was not presented to the industry ahead of time.

In declining to hear the case, the justices sidestepped a broader fight over limits on the power of Congress.

Every Democrat running for attorney general supports a lawsuit that Boulder has filed against the oil and gas industry. Republicans say it could threaten the state’s energy industry.