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The KIDS Act Would Require Age Checks To Get Online
Within the next week, Congress is preparing to vote on the KIDS Act, a sprawling package of legislation that seeks to control Americans’ web browsing and private messaging. The package includes a revised version of the Kids Online Safety Act, or KOSA, combined with a collection of other internet bills, study bills, reporting requirements, and new regulations. Instead of debating any of these proposals on their merits, lawmakers are attempting to move them all at once under an ultra-expedited process.
The package of cobbled-together bills is a mess, with different age-gating schemes for different services, using different standards. It’s a lot of complexity, and a lot of legal risk. Faced with that, many companies will conclude that the safest option is restrictive age-checking practices across their entire platforms.
Buried inside the KIDS Act are provisions that will push online services to verify all users’ ages, require government-directed moderation policies for online speech, and even create new rules about private and encrypted communications. While supporters continue to claim this bill protects minors online, its requirements come at the expense of privacy, free expression, and the ability of people of all ages to use the internet without revealing sensitive data.
Tell Congress to reject this age-gating bill
The KIDS Act Pressures Platforms to Check Everyone's AgeSupporters of KOSA have said the bill doesn’t require age verification. And technically, the KOSA section of the bill does say that KOSA shouldn’t be read to require age verification.
But if you read the rest of the bill, that disclaimer starts to look hollow.
Throughout the KOSA section of the legislation, special protections, controls, messaging settings, and parental tools are required whenever a website or app “knows or should have known” a user is a child (defined in the bill as anyone under 13) or a teen (defined as anyone between 13 and 16 years old).
The problem is a website operator doesn’t need actual knowledge that a user is a minor to get in legal trouble. It applies when a platform “knows or should have known” a user’s age—a low, negligence-style standard of knowledge. If an online service gets it wrong, it’s going to be up to courts and regulators to decide, after the fact, if an online service “should” have known a user was 16.
To try to avoid liability, services will have to determine which users are teenagers and which are not. Most won’t be able to simply trust their users. They’ll have to collect more information about age, before any lawsuit or government action arises. Some companies may respond by requesting driver's licenses or passports. Others will rely on age-estimation systems that attempt to guess users' ages by looking at existing activity or doing facial scans. Existing estimation systems make mistakes when estimating children’s ages correctly, which is a big problem when that is the population KOSA is trying to protect. And the systems fail more frequently for people of color, people with disabilities, and trans and nonbinary people.
The bill’s authors seem to know this is a problem. On the one hand, the new KOSA section says age verification is not required. On the other, it repeatedly imposes obligations that depend on knowing whether a user is under 17. But a disclaimer doesn’t magically eliminate legal risk, especially for smaller services and startups that can’t afford to defend lawsuits or fight regulators.
The "KIDS Act" Is an Age Surveillance Bill
KOSA is not the only part of this package that creates age-verification pressure. The SAFE BOTS Act, like KOSA, goes back to the standard that if a service “knows or should have known” that a user is a minor it can’t offer certain chatbot features.
The SCREEN Act requires services that host sexually explicit content to determine whether users are “more likely than not” under the relevant age limit, before allowing access to certain content.
The consequences of this liability will not be limited to minors. If websites and apps are expected to reliably identify teenagers, adults will be asked to prove they are adults. The result is a less private internet for everyone.
The KIDS Act Pressures Platforms To Police Lawful SpeechThe new version of KOSA removes the bill’s infamous "duty of care" provision, a significant change. The revised KOSA requires covered platforms to "establish, implement, maintain, and enforce" policies and procedures addressing several categories of content and conduct.
Some categories, such as true threats and sexual exploitation, involve unlawful activity. Others are much broader. The bill specifically requires policies addressing the "sale or use" of narcotic drugs, tobacco products, cannabis products, gambling, and alcohol. It also restricts discussions around financial fraud.
Sounds straightforward enough. Then you remember how people actually talk—online and off. Can teens discuss addiction and recovery? Can a 15-year-old post that she’s worried she has a friend who is drinking too much? Can they seek advice about a parent’s gambling problem, or get help if they or a family member have been scammed? Can they participate in harm-reduction communities or discuss substance abuse treatment? All of these young people would be engaging in lawful speech when discussing topics covered by KOSA’s enumerated harms.
The bill does not directly ban those conversations. But it places platforms under huge pressure to create and enforce moderation policies around broad categories of lawful speech. Faced with legal risk, many services will inevitably choose to remove that speech or restrict those discussions to spaces where they know only adults can participate. We’ve seen this movie before. When legal risk goes up, platforms will take down more speech.
The KIDS Act Regulates Private Messages, TooSeveral provisions of the bill create new rules around direct messages, disappearing or “ephemeral” messages, and AI chat services.
The bill includes language stating that certain KOSA requirements should not be construed to override strong encryption. But the protection is incomplete. The carve-out applies to certain features and messaging controls, but doesn’t apply to KOSA’s separate requirement that platforms "address" a list of harms to minors.
The KIDS Act never answers an obvious question: how exactly is a platform supposed to address those activities if they’re inside encrypted communications that it can’t read? That will create pressure for providers to weaken private communications or limit features on encrypted private services.
That approach is especially troubling when it comes to ephemeral messaging. Disappearing messages are not a “loophole” or a dangerous design trick. They are a useful privacy feature that allows online conversations to function more like ordinary real-world conversations, which are not preserved forever in a permanent database.
Like many other parts of the KIDS Act, these private messaging provisions also depend on websites and apps knowing who is a minor and who is not. The result is more age checks, more restrictions, and less privacy online.
How architecture influences political activity
Could the precise architectural form of your residence influence how much you participate in politics?
A new study by MIT scholars finds this to be exactly the case — at least in Accra, Ghana, where many people live in semi-communal structures known as “compound houses,” often sharing kitchens, bathrooms, and common living-room spaces, while having private bedrooms.
The detailed study of homes in Ghana’s capital finds that residents of compound houses are more likely to vote, attend rallies, and take part in political campaigns, compared to people with more private forms of housing.
“The overarching pattern we find is that if you compare people who live in compound houses to residents of other housing types, like single-family homes or self-contained apartments, there is a pretty big difference in political actions,” says Noah Nathan, an MIT political scientist and co-author of a newly published paper detailing the study’s results. “People seem to vote more, and there are more other types of political behavior, like going to rallies, participating in campaigns, and contacting politicians.”
While those differences could stem from factors other than housing, the highly granular study suggests the architecture itself really matters. The researchers examined the specific floor plans of compound houses and found variations in people’s political information and social connections — key factors that existing studies show predict political activity — that map to differences in where people live within compound houses.
“We show that those kinds of social relationships and exchanges of political information seem to vary systematically with people’s individual locations within the layouts of the buildings they live in,” says Nathan, an associate professor in MIT’s Department of Political Science. “That’s consistent with architectural design leading you to have different levels of political participation.”
The open-access paper, “Vernacular Architecture and Grassroots Urban Politics: How Politics Is Embedded in Residential Design,” appears in the American Political Science Review. Nathan’s co-author is Paige Bollen PhD ’23, an assistant professor of political science at Ohio State University.
Compound effects
Compound houses are a common form of residence in Ghana, much of West Africa, and some other parts of the world. They tend to house lower-income people who construct them out of inexpensive local materials. Trying to understand their effects is part of taking seriously the idea that place, and space, influence how people live.
“Rather than just thinking of cities as big agglomerations of people, we should evaluate cities through their actual built forms and designs,” Nathan says. “Space affects politics because people interact with each other in space. It’s not just that people are near each other, but the designs force them to interact or talk in ways that affect how information is exchanged and how social networks form, and that can aggregate up into politics in terms of action and cooperation.”
To conduct the study, Nathan and Bollen used three forms of data to draw out the effects of compound houses on politics. Through pre-existing administrative and electoral data, they first show that polling stations in neighborhoods with a high proportion of compound houses have better electoral turnout than neighborhoods with fewer compound houses. And from existing national survey data, the researchers determined that residents of compound houses actively participate in politics more often.
The researchers then conducted an original research survey of 1,272 residents in 391 compound houses in 30 neighborhoods of Accra, combined with mapping that showed the layout of those compound houses and where the survey participants lived within each one. In this way, they showed the effects of compound houses more precisely: Living in parts of them with especially high exposure to other people actually increases the amount of social network ties people report, as well as the amount of political information they obtain.
Quantitatively, changes in the centrality of people’s locations within compound houses seem to make a bigger difference in political engagement than other fundamental non-housing factors, such as changes in employment or measures of socioeconomic status.
“We leverage that variation to show that even within compound houses, the people with more exposures to neighbors have different social network ties and different forms of information than neighbors who live in more private locations,” Nathan notes.
Encouraging participation
As the scholars discuss in the paper, the effects of architecture on civic involvement are hardly immutable, but likely depend very much on the type of political state in question.
“We think under different conditions, this kind of architecture could have different effects,” Nathan says. “If you live in an authoritarian regime with an active police state, inhabiting an architecture in which you’re constantly on display to your neighbors is probably going to have the exact opposite implications from what we find in the study.”
However, he adds, since Ghana has a generally healthy democracy and is not a repressive state, “In this context, where there are not such high costs to participating in politics, we think these effects are going to break in the direction of more political participation.”
The study itself is an outgrowth of long-running, overlapping research interests on the part of Nathan and Bollen. Nathan is currently developing a book project about urban form, architecture, and politics both in Ghana, where he has conducted research for many years, and in other cities across the African continent. Bollen conducted her PhD research at MIT on public spaces, interactions, and political dynamics in Ghana and South Africa; her advisor was MIT Professor Evan Lieberman.
Sociologists, management experts, architects, and planners have all studied the effects of building design on human behavior, but have often focused on issues such as workplace productivity. Some political scientists, including MIT Associate Professor Bernardo Zacka, have also highlighted the salience of architecture to politics. But few political scientists have undertaken quantitative empirical studies of the subject. If they do, Nathan thinks, the results might surprise some people.
“There’s a famous idea that cities can be anonymizing,” Nathan says. “I think that’s actually not true. When you go to urban Ghana, people know each other, and there is a great deal of social capital and social connections. And I think part of the reason is that many people live in architectures that are not anonymizing.”
Improving the speed and energy-efficiency of AI agents
Agentic workflows are artificial intelligence-powered software systems that chain together multiple models and external tools to tackle complicated tasks, like analyzing a video and answering questions about it.
But the way these highly fragmented systems are designed and deployed often causes inefficiencies that can lead to wasted computation, energy, and cost.
To improve efficiency, researchers from MIT and Microsoft developed an intelligent system that streamlines the process of designing agentic workflows and automatically optimizes how those workflows are implemented.
With this new method, a developer can describe what they want the agentic workflow to do in plain language, without needing to specify all the details of their application in advance.
The system automatically figures out the best models and tools to use, as well as the ideal hardware configuration and computational resource allocation when the workflow is executed by a cloud provider.
It adjusts those configurations on the fly based on each user’s priorities, such as minimizing costs or maximizing speed.
When tested on several agentic workloads, this new system reduced the number of computational units needed for deployment, significantly cutting energy requirements and costs compared to traditional approaches without hampering performance.
“Agentic workflows are getting very complicated and quickly becoming the backbone of what cloud providers are doing. Energy usage is a huge concern, so we need to be very careful about how efficient these workflows are. It is very easy to over-allocate resources, wasting energy and money. Enabling a cloud provider to intelligently make these workflows more resource-optimal is a win for everyone involved,” says Gohar Chaudhry, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on this system.
He is joined on the paper by Adam Belay, an associate professor of EECS and a member of the MIT Computer Science and Artificial Intelligence Laboratory; senior author Ricardo Bianchini, technical fellow and corporate vice president at Microsoft Azure; and others at Microsoft Azure. The paper will be presented at the USENIX Symposium on Operating Systems Design and Implementation.
A configuration conundrum
An agentic workflow is a system composed of several autonomous AI agents that collaboratively use various models and tools, like databases or Python programs, to dynamically complete a multi-step task, such data processing or code generation.
These workflows can serve as behind-the-scenes processes that power user-facing applications.
Typically, developers must hard-code all technical choices upfront. They need to define which AI agents, models, and tools to use, and the order in which to use them. They also must specify the hardware that runs the workflow and how to balance tradeoffs like speed versus cost.
This is especially challenging because agentic workflows bring together multiple black-box models and diverse tools, each with their own configuration options, which may be offered by different companies.
If a new AI model is released that would improve the application’s accuracy or efficiency, the developer would need to start from scratch to implement it.
“Even if you wanted to do all this manually, it is unlikely that you’ll be able to configure the workflow optimally because the space of possible configurations is so large,” Chaudhry says.
In addition, the cloud data center that deploys the application for customers can’t see inside the workflow to allocate its hardware resources in the most efficient manner at the time of the user’s request.
With this new system, called Murakkab (an Urdu word that means a composition of things), the researchers sought to optimize the entire agentic workflow process.
Dynamic decision-making
First, Murakkab enables developers to create an agentic workflow by describing their intent for the application in high-level terms, rather than detailing how the many components of that workflow should be combined.
For instance, a developer might describe a video Q&A application that extracts key frames, generates a transcript, and then answers user queries about the video.
“There are many ways to do this, and all these different models and tools have implications on how fast the application can finish the task,” he says.
Murakkab takes the developer’s straightforward specifications and automatically identifies the best existing models and tools to put together into the workflow.
It also determines which components need to run sequentially and which can be run in parallel to boost performance.
“The platform makes configuration decisions dynamically over time, so if a new model or GPU accelerator comes out tomorrow, the developer doesn’t need to worry about that,” he says.
When the cloud provider deploys that application for a customer, Murakkab optimizes the workflow by configuring its components to meet the user’s constraints, such as prioritizing accuracy while meeting a latency requirement.
It adaptively identifies ideal hardware allocations and deployment schedules to maximize efficiency in real time, then generates a workflow that is ready for the cloud provider to execute.
“Our system also gives cloud providers visibility into multiple workloads, so the provider can share computational resources in the most efficient manner while satisfying the constraints of users,” he says.
When tested on diverse agentic workflows for video Q&A and code generation, Murakkab met user requirements while using only about 35 percent of the computation required by other methods. It consumed only about 27 percent as much energy for less than 25 percent of the cost.
The dynamic nature of Murakkab also enables users to balance tradeoffs. In one instance, the system lowered energy consumption of an agentic workflow by more than an order of magnitude with only about a 2 percent drop in accuracy for the customer.
The system was also able to identify an unexpectedly ideal configuration for a model that selects video frames, optimizing performance for a video Q&A task. This type of optimization would be nearly impossible for a developer to do manually, Chaudhry says.
Next, the researchers plan to expand their system to more complex workflows and larger computing clusters while exploring opportunities to optimize new agentic applications.
“There is a lot of potential to make these workflows more resource-optimal so they consume far less energy, but we need to be thinking about this at the scale of major cloud platforms,” says Chaudhry.
This research was supported, in part, by the Semiconductor Research Corporation and the U.S. Defense Advanced Research Projects Agency.
Multi-channel analysis suggests the UK faces large climate-related losses
Nature Climate Change, Published online: 25 June 2026; doi:10.1038/s41558-026-02664-3
A methodology that incorporates climate risks to health, productivity and agriculture and other sector-specific channels, as well as catastrophic, cross-boundary and missing risks, has been applied to assess climate impacts in the UK. The results show a 2% reduction in welfare, in gross domestic product-equivalent terms, up to 2030 and losses of more than 10% by 2100, under a baseline scenario.Climate change is causing more local extinction of temperate species than tropical species
Nature Climate Change, Published online: 25 June 2026; doi:10.1038/s41558-026-02671-4
Climate change is driving loss of biodiversity worldwide, yet whether tropical or temperate species are more vulnerable to warming remains debated. Large-scale analyses of more than 5,100 plant and animal species show that climate change has caused more frequent local extinctions of temperate species than tropical species, overturning decades of previous studies.Declining tropical sea surface temperature variability under post-2050s greenhouse warming
Nature Climate Change, Published online: 25 June 2026; doi:10.1038/s41558-026-02684-z
Tropical climate, including sea surface temperatures (SSTs), varies with natural cycles such as the El Niño–Southern Oscillation, Indian Ocean Dipole and Atlantic Niño/Niña variability. This work shows that the SST variability weakens under future greenhouse warming, with implications for predictability.Multi-centennial response of marine carbon pumps to global warming
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.🦅 Domestic Spying Takes an L | EFFector 38.12
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.
For over 35 years, EFFector has been your guide to understanding the intersection of technology, civil liberties, and the law. This issue covers a disastrous plan to overhaul the U.S. Copyright Office, why the UK's social media ban will cause more harm than it prevents, and a new Senate bill taking aim at government pressure to silence lawful speech online.
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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What happens when environmental change outpaces life’s ability to adapt?
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.
Embedding Forbidden Text in Spyware to Discourage AI Analysis
At least one malware developer is adding text about nuclear and biological weapons to their spyware, in an effort to stop automatic AI analysis.
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...