(This appeared as an op-ed published Friday, Feb. 6 in the Daily Journal, a California legal newspaper.)

Section 230, “the 26 words that created the internet,” was enacted 30 years ago this week. It was no rush-job—rather, it was the result of wise legislative deliberation and foresight, and it remains the best bulwark to protect free expression online.

The internet lets people everywhere connect, share ideas and advocate for change without needing immense resources or technical expertise. Our unprecedented ability to communicate online—on blogs, social media platforms, and educational and cultural platforms like Wikipedia and the Internet Archive—is not an accident. In writing Section 230, Congress recognized that for free expression to thrive on the internet, it had to protect the services that power users’ speech. Section 230 does this by preventing most civil suits against online services that are based on what users say. The law also protects users who act like intermediaries when they, for example, forward an email, retweet another user or host a comment section on their blog.

The merits of immunity, both for internet users who rely on intermediaries—from ISPs to email providers to social media platforms, and for internet users who are intermediaries—are readily apparent when compared with the alternatives.

One alternative would be to provide no protection at all for intermediaries, leaving them liable for anything and everything anyone says using their service. This legal risk would essentially require every intermediary to review and legally assess every word, sound or image before it’s published—an impossibility at scale, and a death knell for real-time user-generated content.

Another option: giving protection to intermediaries only if they exercise a specified duty of care, such as where an intermediary would be liable if they fail to act reasonably in publishing a user’s post. But negligence and other objective standards are almost always insufficient to protect freedom of expression because they introduce significant uncertainty into the process and create real chilling effects for intermediaries. That is, intermediaries will choose not to publish anything remotely provocative—even if it’s clearly protected speech—for fear of having to defend themselves in court, even if they are likely to ultimately prevail. Many Section 230 critics bemoan the fact that it prevented courts from developing a common law duty of care for online intermediaries. But the criticism rarely acknowledges the experience of common law courts around the world, few of which adopted an objective standard, and many of which adopted immunity or something very close to it.

Congress’ purposeful choice of Section 230’s immunity is the best way to preserve the ability of millions of people in the U.S. to publish their thoughts, photos and jokes online, to blog and vlog, post, and send emails and messages.

Another alternative is a knowledge-based system in which an intermediary is liable only after being notified of the presence of harmful content and failing to remove it within a certain amount of time. This notice-and-takedown system invites tremendous abuse, as seen under the Digital Millennium Copyright Act’s approach: It’s too easy for someone to notify an intermediary that content is illegal or tortious simply to get something they dislike depublished. Rather than spending the time and money required to adequately review such claims, intermediaries would simply take the content down.

All these alternatives would lead to massive depublication in many, if not most, cases, not because the content deserves to be taken down, nor because the intermediaries want to do so, but because it’s not worth assessing the risk of liability or defending the user’s speech. No intermediary can be expected to champion someone else’s free speech at its own considerable expense.Nor is the United States the only government to eschew “upload filtering,” the requirement that someone must review content before publication. European Union rules avoid this also, recognizing how costly and burdensome it is. Free societies recognize that this kind of pre-publication review will lead risk-averse platforms to nix anything that anyone anywhere could deem controversial, leading us to the most vanilla, anodyne internet imaginable.

The advent of artificial intelligence doesn’t change this. Perhaps there’s a tool that can detect a specific word or image, but no AI can make legal determinations or be prompted to identify all defamation or harassment. Human expression is simply too contextual for AI to vet; even if a mechanism could flag things for human review, the scale is so massive that such human review would still be overwhelmingly burdensome.

Congress’ purposeful choice of Section 230’s immunity is the best way to preserve the ability of millions of people in the U.S. to publish their thoughts, photos and jokes online, to blog and vlog, post, and send emails and messages. Each of those acts requires numerous layers of online services, all of which face potential liability without immunity.

This law isn’t a shield for “big tech.” Its ultimate beneficiaries are all of us who want to post things online without having to code it ourselves, and so that we can read and watch content that others create. If Congress eliminated Section 230 immunity, for example, we would be asking email providers and messaging platforms to read and legally assess everything a user writes before agreeing to send it. 

For many critics of Section 230, the chilling effect is the point: They want a system that will discourage online services to publish protected speech that some find undesirable. They want platforms to publish less than what they would otherwise choose to publish, even when that speech is protected and nonactionable.

When Section 230 was passed in 1996, about 40 million people used the internet worldwide; by 2025, estimates ranged from five billion to north of six billion. In 1996, there were fewer than 300,000 websites; by last year, estimates ranged up to 1.3 billion. There is no workforce and no technology that can police the enormity of everything that everyone says.

Internet intermediaries—whether social media platforms, email providers or users themselves—are protected by Section 230 so that speech can flourish online.

This is amazing:

Opus 4.6 is notably better at finding high-severity vulnerabilities than previous models and a sign of how quickly things are moving. Security teams have been automating vulnerability discovery for years, investing heavily in fuzzing infrastructure and custom harnesses to find bugs at scale. But what stood out in early testing is how quickly Opus 4.6 found vulnerabilities out of the box without task-specific tooling, custom scaffolding, or specialized prompting. Even more interesting is how it found them. Fuzzers work by throwing massive amounts of random inputs at code to see what breaks. Opus 4.6 reads and reasons about code the way a human researcher would­—looking at past fixes to find similar bugs that weren’t addressed, spotting patterns that tend to cause problems, or understanding a piece of logic well enough to know exactly what input would break it. When we pointed Opus 4.6 at some of the most well-tested codebases (projects that have had fuzzers running against them for years, ...

Attorneys from the law firm Paul, Weiss are no longer representing the oil company in at least four lawsuits that ask the fossil fuel industry to pay for climate impacts.

Republican attorneys general argued the new chapter would put judges “firmly on one side” of climate lawsuits against the fossil fuel industry.

Legislators say they need it to help pay for wildfires. Vermont and New York already have passed climate superfund laws.

Energy affordability is expected to play a role in the midterm elections this year.

A new study confirms the H5N1 virus was responsible for at least 46 skua deaths on the Antarctic peninsula in 2024.

Much about these snails and insects is still a mystery, and researchers are trying to learn more about what’s fueling their spread.

One in 5 electric vehicles purchased in the past quarter had so-called vehicle-to-home capabilities.

For more than a decade, Koko Networks helped shift over 1.5 million Kenyan homes without access to public gas systems away from smoky charcoal stoves to bioethanol.

Under proposed rules, polluters can use the voluntary credits to offset up to 10 percent of their emissions.

A firm that wants to use a large language model (LLM) to summarize sales reports or triage customer inquiries can choose between hundreds of unique LLMs with dozens of model variations, each with slightly different performance.

To narrow down the choice, companies often rely on LLM ranking platforms, which gather user feedback on model interactions to rank the latest LLMs based on how they perform on certain tasks.

But MIT researchers found that a handful of user interactions can skew the results, leading someone to mistakenly believe one LLM is the ideal choice for a particular use case. Their study reveals that removing a tiny fraction of crowdsourced data can change which models are top-ranked.

They developed a fast method to test ranking platforms and determine whether they are susceptible to this problem. The evaluation technique identifies the individual votes most responsible for skewing the results so users can inspect these influential votes.

The researchers say this work underscores the need for more rigorous strategies to evaluate model rankings. While they didn’t focus on mitigation in this study, they provide suggestions that may improve the robustness of these platforms, such as gathering more detailed feedback to create the rankings.

The study also offers a word of warning to users who may rely on rankings when making decisions about LLMs that could have far-reaching and costly impacts on a business or organization.

“We were surprised that these ranking platforms were so sensitive to this problem. If it turns out the top-ranked LLM depends on only two or three pieces of user feedback out of tens of thousands, then one can’t assume the top-ranked LLM is going to be consistently outperforming all the other LLMs when it is deployed,” says Tamara Broderick, an associate professor in MIT’s Department of Electrical Engineering and Computer Science (EECS); a member of the Laboratory for Information and Decision Systems (LIDS) and the Institute for Data, Systems, and Society; an affiliate of the Computer Science and Artificial Intelligence Laboratory (CSAIL); and senior author of this study.

She is joined on the paper by lead authors and EECS graduate students Jenny Huang and Yunyi Shen as well as Dennis Wei, a senior research scientist at IBM Research. The study will be presented at the International Conference on Learning Representations.

Dropping data

While there are many types of LLM ranking platforms, the most popular variations ask users to submit a query to two models and pick which LLM provides the better response.

The platforms aggregate the results of these matchups to produce rankings that show which LLM performed best on certain tasks, such as coding or visual understanding.

By choosing a top-performing LLM, a user likely expects that model’s top ranking to generalize, meaning it should outperform other models on their similar, but not identical, application with a set of new data.

The MIT researchers previously studied generalization in areas like statistics and economics. That work revealed certain cases where dropping a small percentage of data can change a model’s results, indicating that those studies’ conclusions might not hold beyond their narrow setting.

The researchers wanted to see if the same analysis could be applied to LLM ranking platforms.

“At the end of the day, a user wants to know whether they are choosing the best LLM. If only a few prompts are driving this ranking, that suggests the ranking might not be the end-all-be-all,” Broderick says.

But it would be impossible to test the data-dropping phenomenon manually. For instance, one ranking they evaluated had more than 57,000 votes. Testing a data drop of 0.1 percent means removing each subset of 57 votes out of the 57,000, (there are more than 10194 subsets), and then recalculating the ranking.

Instead, the researchers developed an efficient approximation method, based on their prior work, and adapted it to fit LLM ranking systems.

“While we have theory to prove the approximation works under certain assumptions, the user doesn’t need to trust that. Our method tells the user the problematic data points at the end, so they can just drop those data points, re-run the analysis, and check to see if they get a change in the rankings,” she says.

Surprisingly sensitive

When the researchers applied their technique to popular ranking platforms, they were surprised to see how few data points they needed to drop to cause significant changes in the top LLMs. In one instance, removing just two votes out of more than 57,000, which is 0.0035 percent, changed which model is top-ranked.

A different ranking platform, which uses expert annotators and higher quality prompts, was more robust. Here, removing 83 out of 2,575 evaluations (about 3 percent) flipped the top models.

Their examination revealed that many influential votes may have been a result of user error. In some cases, it appeared there was a clear answer as to which LLM performed better, but the user chose the other model instead, Broderick says.

“We can never know what was in the user’s mind at that time, but maybe they mis-clicked or weren’t paying attention, or they honestly didn’t know which one was better. The big takeaway here is that you don’t want noise, user error, or some outlier determining which is the top-ranked LLM,” she adds.

The researchers suggest that gathering additional feedback from users, such as confidence levels in each vote, would provide richer information that could help mitigate this problem. Ranking platforms could also use human mediators to assess crowdsourced responses.

For the researchers’ part, they want to continue exploring generalization in other contexts while also developing better approximation methods that can capture more examples of non-robustness.

“Broderick and her students’ work shows how you can get valid estimates of the influence of specific data on downstream processes, despite the intractability of exhaustive calculations given the size of modern machine-learning models and datasets,” says Jessica Hullman, the Ginni Rometty Professor of Computer Science at Northwestern University, who was not involved with this work.  “The recent work provides a glimpse into the strong data dependencies in routinely applied — but also very fragile — methods for aggregating human preferences and using them to update a model. Seeing how few preferences could really change the behavior of a fine-tuned model could inspire more thoughtful methods for collecting these data.”

This research is funded, in part, by the Office of Naval Research, the MIT-IBM Watson AI Lab, the National Science Foundation, Amazon, and a CSAIL seed award.

Today, MIT plays a key role in maintaining U.S. competitiveness, technological leadership, and national defense — and much of the Institute’s work to support the nation’s standing in these areas can be traced back to 1953.

Two months after he took office that year, U.S. President Dwight Eisenhower received a startling report from the military: The USSR had successfully exploded a nuclear bomb nine months sooner than intelligence sources had predicted. The rising Communist power had also detonated a hydrogen bomb using development technology more sophisticated than that of the U.S. And lastly, there was evidence of a new Soviet bomber that rivaled the B-52 in size and range — and the aircraft was of an entirely original design from within the USSR. There was, the report concluded, a significant chance of a surprise nuclear attack on the United States.

Eisenhower’s understanding of national security was vast (he had led the Allies to victory in World War II and served as the first supreme commander of NATO), but the connections he’d made during his two-year stint as president of Columbia University would prove critical to navigating the emerging challenges of the Cold War. He sent his advisors in search of a plan for managing this threat, and he suggested they start with James Killian, then president of MIT.

Killian had an unlikely path to the presidency of MIT. “He was neither a scientist nor an engineer,” says David Mindell, the Dibner Professor of the History of Engineering and Manufacturing and a professor of aeronautics and astronautics at MIT. “But Killian turned out to be a truly gifted administrator.”

While he was serving as editor of MIT Technology Review (where he founded what became the MIT Press), Killian was tapped by then-president Karl Compton to join his staff. As the war effort ramped up on the MIT campus in the 1940s, Compton deputized Killian to lead the RadLab — a 4,000-person effort to develop and deploy the radar systems that proved decisive in the Allied victory.

Killian was named MIT’s 10th president in 1948. In 1951, he launched MIT Lincoln Laboratory, a federally funded research center where MIT and U.S. Air Force scientists and engineers collaborated on new air defense technologies to protect the nation against a nuclear attack.

Two years later, within weeks of Eisenhower’s 1953 request, Killian convened a group of leading scientists at MIT. The group proposed a three-part study: The U.S. needed to reassess its offensive capabilities, its continental defense, and its intelligence operations. Eisenhower agreed.

Killian mobilized 42 engineers and scientists from across the country into three panels matching the committee’s charge. Between September 1954 and February 1955, the panels held 307 meetings with every major defense and intelligence organization in the U.S. government. They had unrestricted access to every project, plan, and program involving national defense. The result, a 190-page report titled “Meeting the Threat of a Surprise Attack,” was delivered to Eisenhower’s desk on Feb. 14, 1955.

The Killian Report, as it came to be known, would go on to play a dramatic role in defining the frontiers of military technology, intelligence gathering, national security policy, and global affairs over the next several decades. Killian’s input would also have dramatic impacts on Eisenhower’s presidency and the relationship between the federal government and higher education.

Foreseeing an evolving competition

The Killian Report opens by anticipating four projected “periods” in the shifting balance of power between the U.S. and the Soviet Union.

In 1955, the U.S. had a decided offensive advantage over the USSR, but it was overly vulnerable to surprise attack. In 1956 and 1957, the U.S. would have an even larger offensive advantage and be only somewhat less vulnerable to surprise. By 1960, the U.S.’ offensive advantage would be narrower, but it would be in a better position to anticipate an attack. Within a decade, the report stated, the two nations would enter “Period IV” — during which “an attack by either side would result in mutual destruction … [a period] so fraught with danger to the U.S. that we should push all promising technological development so that we may stay in Periods II and III as long as possible.”

The report went on to make extensive, detailed recommendations — accelerated development of intercontinental ballistic missiles and high-energy aircraft fuels, expansion and increased ground security for “delivery system” facilities, increased cooperation with Canada and more studies about establishing monitoring stations on polar pack ice, and “studies directed toward better understanding of the radiological hazards that may result from the detonation of large numbers of nuclear weapons,” among others.

“Eisenhower really wanted to draw the perspectives of scientists and engineers into his decision-making,” says Mindell. “Generals and admirals tend to ask for more arms and more boots on the ground. The president didn’t want to be held captive by these views — and Killian’s report really delivered this for him.”

On the day it arrived, President Eisenhower circulated the Killian Report to the head of every department and agency in the federal government and asked them to comment on its recommendations. The Cold War arms race was on — and it would be between scientists and engineers in the United States and those in the Soviet Union.

An odd couple

The Killian Report made many recommendations based on “the correctness of the current national intelligence estimates” — even though “Eisenhower was frustrated with his whole intelligence apparatus,” says Will Hitchcock, the James Madison Professor of History at the University of Virginia and author of “The Age of Eisenhower.” “He felt it was still too much World War II ‘exploding-cigar’ stuff. There wasn’t enough work on advance warning, on seeing what’s over the hill. But that’s what Eisenhower really wanted to know.” The surprise attack on Pearl Harbor still lingered in the minds of many Americans, Hitchcock notes, and “that needed to be avoided.”

Killian needed an aggressive, innovative thinker to assess U.S. intelligence, so he turned to Edwin Land. The cofounder of Polaroid, Land was an astonishingly bold engineer and inventor. He also had military experience, having developed new ordnance targeting systems, aerial photography devices, and other photographic and visual surveillance technologies during World War II. Killian approached Land knowing their methods and work style were quite different. (When the offer to lead the intelligence panel was made, Land was in Hollywood advising filmmakers on the development of 3D movies; Land told Killian he had a personal rule that any committee he served on “must fit into a taxicab.”)

In fall 1954, Land and his five-person panel quickly confirmed Killian and Eisenhower’s suspicions: “We would go in and interview generals and admirals in charge of intelligence and come away worried,” Land reported to Killian later. “We were [young scientists] asking questions — and they couldn’t answer them.” Killian and Land realized this would set their report and its recommendations on a complicated path: While they needed to acknowledge and address the challenges of broadly upgrading intelligence activities, they also needed to make rapid progress on responding to the Soviet threat.

As work on the report progressed, Land and Killian held briefings with Eisenhower. They used these meetings to make two additional proposals — neither of which, President Eisenhower decided, would be spelled out in the final report for security reasons. The first was the development of missile-firing submarines, a long-term prospect that would take a decade to complete. (The technology developed for Polaris-class submarines, Mindell notes, transferred directly to the rockets that powered the Apollo program to the moon.)

The second proposal — to fast-track development of the U-2, a new high-altitude spy plane —could be accomplished within a year, Land told Eisenhower. The president agreed to both ideas, but he put a condition on the U-2 program. As Killian later wrote: “The president asked that it should be handled in an unconventional way so that it would not become entangled in the bureaucracy of the Defense Department or troubled by rivalries among the services.”

Powered by Land’s revolutionary imaging devices, the U-2 would become a critical tool in the U.S.’ ability to assess and understand the Soviet Union’s nuclear capacity. But the spy plane would also go on to have disastrous consequences for the peace process and for Eisenhower.

The aftermath(s)

The Killian Report has a very complex legacy, says Christopher Capozzola, the Elting Morison Professor of History. “There is a series of ironies about the whole undertaking,” he says. “For example, Eisenhower was trying to tamp down interservice rivalries by getting scientists to decide things. But within a couple of years those rivalries have all gotten worse.” Similarly, Capozzola notes, Eisenhower — who famously coined the phrase “military-industrial complex” and warned against it — amplified the militarization of scientific research “more than anyone else.”

Another especially painful irony emerged on May 1, 1960. Two weeks before a meeting between Eisenhower and Khrushchev in Paris to discuss how the U.S. and USSR could ease Cold War tensions and slow the arms race, a U-2 was shot down in Soviet airspace. After a public denial by the U.S. that the aircraft was being used for espionage, the Soviets produced the plane’s wreckage, cameras, and pilot — who admitted he was working for the CIA. The peace process, which had become the centerpiece of Eisenhower’s intended legacy, collapsed.

There were also some brighter outcomes of the Killian Report, Capozzola says. It marked a dramatic reset of the national government’s relationship with academic scientists and engineers — and with MIT specifically. “The report really greased the wheels between MIT scientists and Washington,” he notes. “Perhaps more than the report itself, the deep structures and relationships that Killian set up had implications for MIT and other research universities. They started to orient their missions toward the national interest,” he adds.

The report also cemented Eisenhower’s relationship with Killian. After the launch of Sputnik, which induced a broad public panic in the U.S. about Soviet scientific capabilities, the president called on Killian to guide the national response. Eisenhower later named Killian the first special assistant to the president for science and technology. In the years that followed, Killian would go on to help launch NASA, and MIT engineers would play a critical role in the Apollo mission that landed the first person on the moon. To this day, researchers at MIT and Lincoln Laboratory uphold this legacy of service, advancing knowledge in areas vital to national security, economic competitiveness, and quality of life for all Americans.

As Eisenhower’s special assistant, Killian met with him almost daily and became one of his most trusted advisors. “Killian could talk to the president, and Eisenhower really took his advice,” says Capozzola. “Not very many people can do that. The fact that Killian had that and used it was different.”

A key to their relationship, Capozzola notes, was Killian’s approach to his work. “He exemplified the notion that if you want to get something done, don’t take the credit. At no point did Killian think he was setting science policy. He was advising people on their best options, including decision-makers who would have to make very difficult decisions. That’s it.”

In 1977, after many tours of duty in Washington and his retirement from MIT, Killian summarized his experience working for Eisenhower in his memoir, “Sputnik, Scientists, and Eisenhower.” Killian said of his colleagues: “They were held together in close harmony not only by the challenge of the scientific and technical work they were asked to undertake but by their abiding sense of the opportunity they had to serve a president they admired and the country they loved. They entered the corridors of power in a moment of crisis and served there with a sense of privilege and of admiration for the integrity and high purpose of the White House.”

Nature Climate Change, Published online: 09 February 2026; doi:10.1038/s41558-025-02549-x

Mountains, with their sharp climatic contrasts, are emblematic of climate-driven species movement and, ultimately, loss. Here, we argue that these same contrasts make mountains powerful natural laboratories for discovering the mechanisms that underlie biological change.

Nature Climate Change, Published online: 09 February 2026; doi:10.1038/s41558-026-02572-6

Disappearing glaciers and missing snow in mountain regions are some of the most immediate signs of global change today. In this issue, we focus on the broader changes in mountains and how they affect people living both within and far away from their peaks and valleys.

Nature Climate Change, Published online: 09 February 2026; doi:10.1038/s41558-025-02544-2

Visitors are increasingly drawn to disappearing glacier landscapes for their beauty and scientific value. This Comment examines the paradoxes reshaping relationships among glaciers, people and communities, and highlights research needed to avoid maladaptation harming local communities.

Nature Climate Change, Published online: 09 February 2026; doi:10.1038/s41558-025-02551-3

Mountains and their ecosystems have been important to religious beliefs in many regions around the world. In this Viewpoint, researchers describe how climate change in mountain regions is interpreted by local communities and how they transform their spiritual practice in response to it.

Nature Climate Change, Published online: 09 February 2026; doi:10.1038/s41558-025-02552-2

Mountains are hotspots of climate change, with melting glaciers, changing water flows and moving ecosystems. Here the authors discuss how these different changes in mountain regions affect downstream regions.

This is a video of advice for squid fishing in Puget Sound.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Blog moderation policy.

Once. Someone named “Vincenzo lozzo” wrote to Epstein in email, in 2016: “I wouldn’t pay too much attention to this, Schneier has a long tradition of dramatizing and misunderstanding things.” The topic of the email is DDoS attacks, and it is unclear what I am dramatizing and misunderstanding.

Rabbi Schneier is also mentioned, also incidentally, also once. As far as either of us know, we are not related.