Author name: Beth Washington

protesters-demonstrate-outside-tesla-showrooms-in-us

Protesters demonstrate outside Tesla showrooms in US

“The worry of the Street is that Musk dedicating so much time—even more than we expected—to Doge takes away from his time at Tesla,” said Wedbush analyst Dan Ives.

“In addition, Musk’s Doge-related actions and more powerful alliance with Trump clearly could alienate some consumers to move away from the Tesla brand.”

About 50 to 100 protesters turned out in Portland, Oregon on Saturday, carrying signs saying, “Dethrone Musk” and “If Tesla survives, your country dies.”

Edward Niedermeyer, author of Ludicrous: The Unvarnished Story of Tesla Motors, was one of them. Since Musk’s power is not derived from election to public office, he said, boycotting and divesting from Tesla is the only tool available to curb his agenda.

He argued that Tesla was overvalued and that its core business of making and selling cars was deteriorating. Significant losses could force investors to sell, triggering a drop in the share price and forcing Musk to sell a portion of his shares to meet a margin call.

“Every Tesla sale that you prevent, every dollar not spent servicing a Tesla, not charging at the Supercharger—these further degrade the business,” Niedermeyer said.

“It’s not easy, it’s not guaranteed, but we do have the opportunity to wipe out a huge amount of Elon Musk’s wealth.”

In Chicago, protesters carried a banner saying “Stop buying Nazi cars.”

City resident Lisa Pereira said she came to the demonstration because “you have to do something.” She said she was disturbed by the administration’s attempts to crush diversity, equity and inclusion initiatives, its aggressive immigration enforcement, and the power wielded by Musk.

“Everything is a little off the rails,” she said. “So I decided I had to show up. I had to be in cahoots with my soul.”

Chris White said he attended on Saturday because he fears “we’re living through a fascist coup.”

“My kids are trans,” he said. “I’m getting told they don’t exist. I don’t know if their healthcare will exist.”

Though one man yelled from a truck, “Elon’s my hero!” most passers-by in the heavily Democratic city expressed support.

“I’d rather buy a Rivian,” said one, referring to the electric-truck maker whose showroom was a block away from the protest.

Tesla did not immediately respond to a request for comment.

© 2025 The Financial Times Ltd. All rights reserved. Not to be redistributed, copied, or modified in any way.

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How Diablo hackers uncovered a speedrun scandal


Investigators decompiled the game to search through 2.2 billion random dungeon seeds.

The word Debunk radiating flames against a demonic background Credit: Aurich Lawson

For years, Maciej “Groobo” Maselewski stood as the undisputed champion of Diablo speedrunning. His 3-minute, 12-second Sorcerer run looked all but unbeatable thanks to a combination of powerful (and allowable) glitch exploits along with what seemed like some unbelievable luck in the game’s randomly generated dungeon.

But when a team of other speedrunners started trying and failing to replicate that luck using outside software and analysis tools, the story behind Groobo’s run began to fall apart. As the inconsistencies in the run started to mount, that team would conduct an automated search through billions of legitimate Diablo dungeons to prove beyond a shadow of a doubt that Groobo’s game couldn’t have taken place in any of them.

“We just had a lot of curiosity and resentment that drove us to dig even deeper,” team member Staphen told Ars Technica of their investigation. “Betrayal might be another way to describe it,” team member AJenbo added. “To find out that this had been done illegitimately… and the person had both gotten and taken a lot of praise for their achievement.”

If we have unearned luck

If you have any familiarity with Diablo or speedrunning, watching Groobo’s run feels like watching someone win the lottery. First, there’s the dungeon itself, which features a sequence of stairways that appear just steps from each other, forming a quick and enemy-free path down to the dungeon’s deeper levels. Then there’s Groobo’s lucky find of Naj’s Puzzler on level 9, a unique item that enables the teleporting necessary for many of the run’s late-game maneuvers.

Groobo’s 3: 12 Diablo speedrun, as submitted to Speed Demos Archive in 2009

“It seemed very unusual that we would have so many levels with the upstairs and the downstairs right next to each other,” Allan “DwangoAC” Cecil told Ars Technica. “We wanted to find some way of replicating this.”

When Cecil and a team of tool-assisted speedrun (TAS) authors started that search process in earnest last February, they said they used Groobo’s run as a baseline to try to improve from. While Groobo ostensibly had to rely on his own human luck in prepping his run, the TAS runners could use techniques and tools from outside the game to replicate Groobo’s run (or something very similar) every time.

To find an RNG seed that could do just that, the TAS team created a custom-built map generation tool by reverse-engineering a disassembled Diablo executable. That tool can take any of the game’s billions of possible random seeds and quickly determine the map layout, item distribution, and quest placement available in the generated save file. A scanner built on top of that tool can then quickly look through those generated dungeons for ones that might be optimal for speedrunning.

“We were working on finding the best seed for our TAS, and we were trying to identify the seed from Groobo’s run, both to validate that our scanner works and to potentially straight-up use it for the run,” Stephan said of the effort. “We naturally had a lot of trouble finding [that seed] because it doesn’t exist.”

A thorough search

In their effort to find Groobo’s storied run (or at least one that resembled it), the TAS team conducted a distributed search across the game’s roughly 2.2 billion valid RNG seeds. Each of these seeds represents a different specific second on the system clock when a Diablo save file is created, ranging from between January 1, 1970, and December 31, 2038 (the only valid dates accepted by the game).

After comparing each of those billions of those RNG dungeons to a re-creation of the dungeon seen in Groobo’s run, the team couldn’t find a single example containing the crucial level 9 Naj’s Puzzler drop. After that, the team started searching through “impossible” seeds, which could only be created by using save modification tools to force a creation date after the year 2038.

The team eventually found dungeons matching Naj’s Puzzler drop in Groobo’s video, using seeds associated with the years 2056 and 2074.

After an exhaustive search, the TAS team couldn’t find a dungeon with Naj’s Puzzler dropped in the place Groobo’s run said it should be.

After an exhaustive search, the TAS team couldn’t find a dungeon with Naj’s Puzzler dropped in the place Groobo’s run said it should be. Credit: Analysis of Groobo’s Diablo WR Speedrun

The early presumption that Groobo’s run was legitimate ended up costing the team weeks of work. “It was baffling when we couldn’t find [the early Naj’s Puzzler] in any of the searches we did,” Cecil said. “We were always worried that the scanner might have bugs in it,” Staphen added.

The TAS team’s thorough search also showed troubling inconsistencies in the other dungeon levels shown in Groobo’s run. “Normally you would only need to identify a single level to replicate a run since all the other levels are generated from the same seed,” AJenbo told Ars. But the levels seen in Groobo’s run came from multiple different seeds, which would require splicing footage from multiple different playthrough of different dungeons. That’s a big no-no even in a so-called “segmented” run, which is still supposed to contain segments from a single unmodified save file.

“At that point we also wanted to figure out how manipulated the run was,” AJenbo said. “Was it a legit run except for [dungeon level] 9? Was it three good runs combined? In the end we only found two levels that had come from the same run so at least 13 (probably 15) runs were spliced into one video, which is a lot for a game with just 16 levels.”

The evidence piles up

After Groobo’s dungeon generation problems came to light, other inconsistencies in his run started to become apparent. Some of these are relatively easy to spot with the naked eye once you know what you’re looking for.

For instance, the “1996–2001” copyright date seen on the title screen in Groobo’s video is inconsistent with the v1.00 shown on the initial menu screen, suggesting Groobo’s run was spliced together from runs on multiple different versions of the game. Items acquired early in the run also disappear from the inventory later on with no apparent explanation.

This copyright date doesn’t line up with the “V1.00” seen later on the menu screen in Groobo’s run.

This copyright date doesn’t line up with the “V1.00” seen later on the menu screen in Groobo’s run. Credit: Analysis of Groobo’s Diablo WR Speedrun

Even months after the investigation first started, new inconsistencies are still coming to light. Groobo’s final fight against Diablo, for instance, required just 19 fireballs to take him out. While that’s technically possible with perfect luck for the level 12 Sorcerer seen in the footage, the TAS team found that the specific damage dealt and boss behavior only matched when they attempted the same attacks using a level 26 Sorcerer.

After the TAS team compiled their many findings into a lengthy document, Groobo defended his submission in a discussion with Cecil (screenshots of which were viewed by Ars Technica). “My run is a segmented/spliced run,” Groobo said. “It always has been and it was never passed off as anything else, nor was it part of any competition or leaderboards. The Speed Demos Archive [SDA] page states that outright.” Indeed, an archived version of Groobo’s record-setting Speed Demos Archive submission does say directly that it’s made up of “27 segments appended to one file.”

But simply splitting a run into segments doesn’t explain away all of the problems the TAS team found. Getting Naj’s Puzzler on dungeon level 9, for instance, still requires outside modification of a save file, which is specifically prohibited by longstanding Speed Demos Archive rules that “manually editing/adding/removing game files is generally not allowed.” Groobo’s apparent splicing of multiple game versions and differently seeded save files also seems to go against SDA rules, which say that “there obviously needs to be continuity between segments in terms of inventory, experience points or whatever is applicable for the individual game.”

After being presented with the TAS team’s evidence, SDA wrote that “it has been determined that Groobo’s run very likely does not stem from only legitimate techniques, and as such, has itself been banished barring new developments.” But Groobo’s record is still listed as the “Fastest completion of an RPG videogame” by Guinness World Records, which has not offered a substantive response to the team’s findings (Guinness has not responded to a request for comment from Ars Technica).

A recent Diablo speedrun on a confirmed legitimate dungeon seed.

This might seem like a pretty petty issue to spend weeks of time and attention debunking. But at a recent presentation attended by Ars, Cecil said he was motivated to pursue it because “it did harm. Groobo’s alleged cheating in 2009 completely stopped interest in speedrunning this category [of Diablo]. No one tried, no one could.”

Because of Groobo’s previously unknown modifications to make an impossible-to-beat run, “this big running community just stopped trying to run this game in that category,” Cecil said. “For more than a decade, this had a chilling impact on that community.” With Groobo’s run out of the way, though, new runners are setting new records on confirmed legitimate RNG seeds, and with the aid of TAS tools.

In the end, Cecil said he hopes the evidence regarding Groobo’s run will make people look more carefully at other record submissions. “Groobo had created a number of well-respected … speedruns,” he said. “[People thought] there wasn’t any good reason to doubt him. In other words, there was bias in familiarity. This was a familiar character. Why would they cheat?”

Photo of Kyle Orland

Kyle Orland has been the Senior Gaming Editor at Ars Technica since 2012, writing primarily about the business, tech, and culture behind video games. He has journalism and computer science degrees from University of Maryland. He once wrote a whole book about Minesweeper.

How Diablo hackers uncovered a speedrun scandal Read More »

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No penalties even when deputies share a woman’s nudes after an illegal phone search


Government agents have “qualified immunity” for 2019 actions.

Once your phone is imaged, the data is out of your control. Credit: Getty Images

In 2019, Haley Olson’s life in Grant County, Oregon, was upended when people in town appeared to know about private nude photos that Olson kept on her phone. Worse, some of the people appeared to have seen and shared the photos. The incidents all had some relationship to the local sheriff’s department, where Olson was dating one of the deputies.

In July, for instance, a stranger in a sheriff’s office uniform approached her to say that he had “heard there’s some pretty smokin’ pictures of you going around the sheriff’s office.” Someone else saw a married couple, both of whom worked for the sheriff’s office, looking at Olson’s photos on the husband’s phone. Other people also approached Olson with knowledge of her recent out-of-state arrest. One person called her “the drug dealer that likes to f— cops.”

What was going on?

An Idaho traffic stop

Olson had recently taken a trip out of state. In Oregon, she ran a marijuana dispensary, which was legal there, but on her trip in January, she was stopped by Idaho state police and arrested for marijuana possession. As part of that arrest, the Idaho state police wanted to search her cell phone, and they asked if she would sign an “Idaho State Police Voluntary Consent to Search.” She agreed, and the Idaho police made a complete image of her cell phone.

The Idaho charges against Olson were later dropped. Even though she was not prosecuted in Idaho and had committed no illegal activity in Oregon, she came to suspect that her cell phone image had somehow been shared across state lines and given to her local sheriff’s office. Olson filed a public records request with Grant County, trying to figure out who had her data and who had been talking about it.

She received a reply that same day from Jim Carpenter, who was then the Grant County Attorney and County Prosecutor. Carpenter explained that Glenn Palmer, the Grant County Sheriff, had asked Carpenter to obtain, if possible, a copy of the cell phone image from the Idaho state police. Palmer claimed to be concerned that the deputy whom Olson was dating might somehow be implicated in illegal activity depicted on her phone. (Palmer had first tried to obtain this directly from the Idaho trooper in charge of the case and was told no, which is when he reached out to Carpenter. How Palmer even learned about the arrest is unclear, but Olson had told the Idaho police she was dating a sheriff’s deputy in Oregon; somehow, word spread back to the department in Grant County.)

So Carpenter requested the cell phone image from the Idaho prosecutor in charge of Olson’s case. In his request letter, Carpenter said that the image “will be used only for internal purposes and will not be disseminated to any other agencies or third parties.” But when Carpenter received the image in the mail on a flash drive, he reached out to two outside agencies to look through Olson’s data. Given that no actual crime in Oregon was being investigated, both agencies said no. (A court later noted that these actions contradicted Carpenter’s “letter to the Idaho prosecutor.”)

Carpenter decided to look through the image himself, using tools from the digital forensics company Cellebrite. The image contained nude photos of both Olson and the deputy she was dating, but no activity that was criminal in Oregon. Carpenter wrote Palmer a letter making this clear—though nothing about the situation really was clear. Palmer would later say that Carpenter had “twice offered [him] the chance to review the extraction” and that Carpenter had said that “there were things on the cell phone that ‘once you see them, you can’t unsee them.'”

Carpenter, for his part, insisted that he was never willing to give the flash drive to Palmer or to show him its contents. He told Olson in his letter that he merely “took a quick look at the flash drive,” and after finding “content on the flash drive [that] was clearly personal in nature,” he made a “complete re-format of the flash drive.”

And yet somehow, people around town knew about the whole situation and even appeared to possess the pictures. Olson sued both Carpenter and Palmer for unlawful search and seizure under the Fourth Amendment.

The courts rule

The case has been bouncing through the court system for several years and recently landed at the 9th Circuit Court of Appeals, one stop below the Supreme Court. The 9th Circuit finally ruled on the case this week (PDF), and judges lambasted the behavior of the Oregon authorities, who had looked at her data without a warrant. The mere fact that Olson had signed a voluntary search form in Idaho was beside the point. “Olson’s consent in Idaho did not extend to a search by a different law enforcement agency, in another state,” wrote the court in its opinion, “and the search did not fall into any exception to the warrant requirement.”

The court noted that the case “presents a troubling example of the intrusion on Fourth Amendment rights that can occur with respect to highly sensitive cell phone data. More specifically, this circumstance involved a law enforcement agency accessing highly sensitive cell phone data from another jurisdiction in the absence of a warrant, consent, or even any investigation or suspicion of criminal activity on the part of a suspect.”

Whatever had actually happened with Olson’s data, the Oregon authorities had no right to look through it simply because the police chief was “curious” about it or because he wanted to go on a warrantless fishing expedition to see if one of his deputies was involved in anything nefarious. And Carpenter’s search was “highly irregular,” the court noted, even by his own standards. The 9th Circuit concluded that the situation was, in fact, a troubling violation of the Fourth Amendment.

Sweet vindication for Olson? Not quite. Despite its ruling, the court found that Sheriff Palmer was exempt from penalties because he had allegedly not seen the images, nor had he conducted the search—that was Carpenter, the local prosecutor.

However, Carpenter was found to have “qualified immunity” from prosecution as a government employee because, although he violated Olson’s Fourth Amendment rights, the law remained unclear in 2019. This case was slightly more complicated than a garden-variety warrantless search because Olson had voluntarily renounced some rights over in Idaho, and it was at least arguable at the time that this might have extended to other searches of the cell phone image for other reasons.

The 9th Circuit issued clarifying guidance in this area, saying that further searches of cell phones for unrelated reasons do, in fact, require a warrant, but all three judges declined to issue any penalties against Carpenter for his 2019 actions.

As for how Olson’s photos were shared around town, the 9th Circuit admits that it simply doesn’t know what happened and can do little about it.

Local news reports suggest that the Grant County Sheriff’s Department has had repeated experience in dealing with these kinds of lurid situations. The Oregonian notes that the sheriff’s deputy who Olson was dating was fired in 2019 “after his arrest on alleged assault and sex abuse complaints,” but the deputy was acquitted in court of all charges. He then “argued in a federal whistleblower complaint that [Sheriff] Palmer retaliated against him for reporting misconduct involving another sheriff’s deputy, who was the wife of Palmer’s undersheriff.” He eventually won a $1.3 million payout from Grant County and the state of Oregon.

Photo of Nate Anderson

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Condé Nast, other news orgs say AI firm stole articles, spit out “hallucinations”

Condé Nast and several other media companies sued the AI startup Cohere today, alleging that it engaged in “systematic copyright and trademark infringement” by using news articles to train its large language model.

“Without permission or compensation, Cohere uses scraped copies of our articles, through training, real-time use, and in outputs, to power its artificial intelligence (‘AI’) service, which in turn competes with Publisher offerings and the emerging market for AI licensing,” said the lawsuit filed in US District Court for the Southern District of New York. “Not content with just stealing our works, Cohere also blatantly manufactures fake pieces and attributes them to us, misleading the public and tarnishing our brands.”

Condé Nast, which owns Ars Technica and other publications such as Wired and The New Yorker, was joined in the lawsuit by The Atlantic, Forbes, The Guardian, Insider, the Los Angeles Times, McClatchy, Newsday, The Plain Dealer, Politico, The Republican, the Toronto Star, and Vox Media.

The complaint seeks statutory damages of up to $150,000 under the Copyright Act for each infringed work, or an amount based on actual damages and Cohere’s profits. It also seeks “actual damages, Cohere’s profits, and statutory damages up to the maximum provided by law” for infringement of trademarks and “false designations of origin.”

In Exhibit A, the plaintiffs identified over 4,000 articles in what they called an “illustrative and non-exhaustive list of works that Cohere has infringed.” Additional exhibits provide responses to queries and “hallucinations” that the publishers say infringe upon their copyrights and trademarks. The lawsuit said Cohere “passes off its own hallucinated articles as articles from Publishers.”

Cohere defends copyright controls

In a statement provided to Ars, Cohere called the lawsuit frivolous. “Cohere strongly stands by its practices for responsibly training its enterprise AI,” the company said today. “We have long prioritized controls that mitigate the risk of IP infringement and respect the rights of holders. We would have welcomed a conversation about their specific concerns—and the opportunity to explain our enterprise-focused approach—rather than learning about them in a filing. We believe this lawsuit is misguided and frivolous, and expect this matter to be resolved in our favor.”

Condé Nast, other news orgs say AI firm stole articles, spit out “hallucinations” Read More »

apple-teases-launch-for-“the-newest-member-of-the-family”-on-february-19

Apple teases launch for “the newest member of the family” on February 19

Big news for people who prefer their product announcements to be pre-announced: Apple CEO Tim Cook says that the company has something brewing for Wednesday, February 19. Cook referred to “the newest member of the family,” suggesting a launch event focused on a single product rather than multiple refreshes throughout its product lineup.

Most rumors point to the “family” being the iPhone and the “newest member” being an updated version of the entry-level iPhone SE. Last refreshed in March of 2022 with the guts of late 2021’s iPhone 13, the SE is the only iPhone in Apple’s lineup that still ships with large display bezels and a Home button. And it’s one of just three models (along with the iPhone 14 and 14 Plus) to still include a Lightning port.

Previous reporting has suggested that the next-generation iPhone SE could replace both the current SE and the iPhone 14 series in the iPhone lineup, since the new phone is expected to ship with an iPhone 14-style design with an edge-to-edge display and a notch cutout. The old SE and the 14 series have already been discontinued in the EU, where new phones are all required to use a USB-C port.

Apple does have other products it could announce alongside (or instead of) a new entry-level iPhone, if it wanted to. Rumors and references in macOS have all pointed to an early 2025 launch for new M4 MacBook Airs, and the rumor mill also thinks that a new Apple TV box, new HomePod products, and even new AirTags could all come at some point in 2025. High-end Mac desktops like the Mac Studio and Mac Pro are also long overdue for an update, though we reportedly won’t see those refreshes until closer to the middle of the year.

Apple teases launch for “the newest member of the family” on February 19 Read More »

“largest-data-breach-in-us-history”:-three-more-lawsuits-try-to-stop-doge

“Largest data breach in US history”: Three more lawsuits try to stop DOGE


DOGE and Musk face three more lawsuits over “brazen ransacking” of private data.

People hold signs at a “Save the Civil Service” rally hosted by the American Federation of Government Employees outside the US Capitol on February 11, 2025 in Washington, DC. Credit: Getty Images | Kent Nishimura

The US DOGE Service’s access to the private data of ordinary Americans and federal employees is being challenged in several lawsuits filed this week.

Three new complaints seek court orders that would stop the data access and require the deletion of unlawfully accessed data. Two of the complaints also seek financial damages for individuals whose data was accessed.

The US DOGE Service, Elon Musk, the US Office of Personnel Management (OPM), and OPM Acting Director Charles Ezell were named as defendants in one suit filed yesterday in US District Court for the Southern District of New York.

“The Privacy Act [of 1974] makes it unlawful for OPM Defendants to hand over access to OPM’s millions of personnel records to DOGE Defendants, who lack a lawful and legitimate need for such access,” the lawsuit said. “No exception to the Privacy Act covers DOGE Defendants’ access to records held by OPM. OPM Defendants’ action granting DOGE Defendants full, continuing, and ongoing access to OPM’s systems and files for an unspecified period means that tens of millions of federal-government employees, retirees, contractors, job applicants, and impacted family members and other third parties have no assurance that their information will receive the protection that federal law affords.”

The lawsuit names Musk as a defendant “in his capacity as director of the US Doge Temporary Service,” which was created by President Trump and has a mandate lasting until July 4, 2026. The temporary organization is separate from the US DOGE Service, which used to be called the US Digital Service. DOGE, of course, is a reference to the popular meme involving a Shiba Inu and in the government context stands for the Department of Government Efficiency.

Plaintiffs in the lawsuit include the American Federation of Government Employees, AFL-CIO; the Association of Administrative Law Judges; and individuals who are current or former government workers. The legal team representing the plaintiffs includes lawyers from the Electronic Frontier Foundation (EFF), the State Democracy Defenders Fund, and two law firms.

Data access for “Musk and a cadre of loyalists”

Another lawsuit filed Monday in US District Court for the District of Maryland said that DOGE gained access to records of both government employees and people outside of government:

For example, Defendants Treasury Department and Secretary of the Treasury [Scott] Bessent have improperly disclosed to DOGE representatives the contents of the Federal Disbursement System, which is the government’s mechanism for sending payments it owes to individual Americans (as well as other payees). That system contains records relating to every American who receives (among other things) a tax refund, social security benefit, veterans pay, or a federal salary. To facilitate these payments, the system maintains highly sensitive information about millions of Americans, including Social Security numbers, date of birth, bank account information, and home addresses.

The lawsuit in Maryland was filed by the American Federation of Teachers, the International Association of Machinists and Aerospace Workers, the National Active and Retired Federal Employees Association, the National Federation of Federal Employees, and six individuals. In addition to the Treasury Department and Bessent, defendants include OPM, Ezell, the Department of Education, and Acting Secretary of Education Denise Carter.

“Defendants are permitting Elon Musk and a cadre of loyalists imported from his private companies to help themselves to the personal information of millions of Americans, in violation of [the Privacy Act’s] legal requirements,” the lawsuit said.

Yet another lawsuit was filed Monday in federal court in the Eastern District of Virginia by the Electronic Privacy Information Center (EPIC) and one unnamed resident of the district (“Doe 1”) who is a federal government employee. The EPIC lawsuit’s defendants include OPM, Ezell, the US Treasury Department, Bessent, the US DOGE Service, and the US Doge Service Temporary Organization.

“This action arises from the largest and most consequential data breach in US history, currently ongoing at the US Department of the Treasury and US Office of Personnel Management. This unprecedented breach of privacy and security implicates the personal information of tens of millions of people, including nearly all federal employees and millions of members of the American public,” the lawsuit said, alleging that defendants “have allowed the unlawful misuse of critical data systems housed in OPM and the Treasury Department, endangering plaintiffs and millions of other Americans.”

This includes tax return information, the lawsuit said. In late January, a longtime Treasury Department official announced his retirement shortly after a clash with DOGE over access to the Fiscal Service payment system that collects and disburses trillions of dollars.

The EPIC lawsuit described this incident and alleged that “basic security failures have resulted in the unlawful disclosure of personal data—including Social Security numbers and tax information—belonging to tens of millions of individuals stored in Bureau of Fiscal Service systems and the unlawful disclosure of personal data belonging to millions of federal employees stored in Enterprise Human Resources Integration.”

Musk may or may not be acting US DOGE administrator

The EFF and EPIC lawsuits both list the “Acting US DOGE Administrator” as a defendant, indicating that it is not clear who holds this position. But the EPIC lawsuit says that Musk “is either the Acting USDS Administrator or otherwise exercising substantial authority within USDS.”

We sent inquiries about the lawsuits to DOGE, the White House, OPM, Treasury Department, Education Department, and Department of Justice. OPM and the Education Department declined to comment. We will update this article if we get any comments about the lawsuits.

This week’s lawsuits add to the mounting litigation over DOGE and Musk’s access to government records. Last week, a federal judge approved an order that temporarily blocks DOGE access to Treasury payment systems and records until there’s a ruling on a motion for a preliminary injunction. The Department of Education was also sued Friday by a California student association over DOGE’s access to student financial aid and loan data.

EFF: “Brazen ransacking” of Americans’ data

The EFF said on its website that the “brazen ransacking of Americans’ sensitive data is unheard of in scale. With our co-counsel Lex Lumina, State Democracy Defenders Fund, and the Chandra Law Firm, we represent current and former federal employees whose privacy has been violated. We are asking the court for a temporary restraining order to immediately cease this dangerous and illegal intrusion. This massive trove of information includes private demographic data and work histories of essentially all current and former federal employees and contractors as well as federal job applicants.”

The EFF said the OPM database is one of the largest collections of employee data in the US, given that the federal government is the nation’s largest employer.

“In addition to personally identifiable information such as names, Social Security numbers, and demographics, it includes work experience, union activities, salaries, performance, and demotions; health information like life insurance and health benefits; financial information like death benefit designations and savings programs; and classified information [in] nondisclosure agreements. It holds records for millions of federal workers and millions more Americans who have applied for federal jobs,” the EFF said.

The EFF said “DOGE’s unchecked access puts the safety of all federal employees at risk of everything from privacy violations to political pressure to blackmail to targeted attacks,” adding that Musk last year “publicly disclosed the names of specific government employees whose jobs he claimed he would cut before he had access to the system.”

A Washington Post report last week said that some federal “officials have raised concerns that DOGE associates appeared to violate security protocols by using private email addresses or not disclosing their identities on government calls.”

The individual plaintiffs in the EFF’s lawsuit include federal employee Vanessa Barrow, a New York resident who works at the Brooklyn Veterans Affairs Medical Center. “As a federal employee since September 2008, Ms. Barrow’s sensitive personal and employment information was included in the OPM records that Defendants disclosed and continue to disclose,” the lawsuit said.

Seeking financial damages

The lawsuit has two other named plaintiffs who are former federal employees, and 100 Doe plaintiffs who are current and former employees or contractors of the US government. Plaintiffs, including members of the unions that are part of the lawsuit, are entitled to financial payments because they “have sustained and will continue to sustain actual damages and pecuniary losses directly traceable to Defendants’ violations,” the lawsuit said.

The separate lawsuit filed by EPIC in Virginia said that case’s single Doe plaintiff is entitled to statutory damages of $1,000 per each act of unauthorized inspection and disclosure, and punitive damages “because the Treasury Department and DOGE’s unlawful disclosure of their confidential return information was either willful or a result of gross negligence.”

“Taxpayers have a private right of action to seek damages under 26 U.S.C. § 7431 for the knowing or negligent unauthorized inspection or disclosure of returns or return information in violation of 26 U.S.C. § 6103,” the lawsuit said.

The lawsuit filed in the District of Maryland by unions and several individuals said the “plaintiffs include veterans who receive benefit payments as provided by law, current and former federal employees whose confidential employment files reside in the Office of Personnel Management’s system, and teachers, first responders, and health care workers whose pathway to careers in public service included relying on student loans to fund their own educations.”

All of these plaintiffs had personal data “improperly disclosed to DOGE representatives in a manner completely divorced from the legitimate purposes for which it was maintained and in violation of their privacy rights,” the lawsuit said. The plaintiffs are said to be “concerned that the breach may well result in serious personal, social, and economic harm, from being targeted for harassment and threats to doxxing, swatting, and identity theft.”

Military veterans worried about data access

Plaintiff Donald Martinez of Colorado served in Iraq for the Army and now receives Social Security disability insurance and other government benefits. “Especially because of his previous military service in a geographically sensitive area and involvement in high-level negotiations because of which he received death threats from terrorists, Plaintiff Martinez is worried that unauthorized access and disclosure of his personal information held within the federal government will compromise his personal safety and security,” the lawsuit said.

Plaintiff Christopher Purdy of Georgia served in the Army National Guard and was deployed to Iraq and currently leads a nonprofit advocacy group. Purdy is “very worried that Musk and DOGE may use their unauthorized access to his personal information to stop his VA disability payments, a major source of income in his household,” the lawsuit said.

The Trump executive order establishing DOGE said its goal was “modernizing federal technology and software to maximize efficiency and productivity.” It said that US agencies must give DOGE “full and prompt access to all unclassified agency records, software systems, and IT systems.”

An incident this week may add to concerns about Musk’s understanding of government systems. On Monday, he criticized a user on X for stating that the US government uses SQL.

“This retard thinks the government uses SQL,” Musk wrote. The federal government is in fact a heavy user of SQL in multiple forms, including Microsoft SQL server and MySQL Enterprise Edition for Governments.

Musk’s comment came in a discussion of another post in which Musk claimed without evidence that a lack of de-duplication in the Social Security database “enables MASSIVE FRAUD!!” because “you can have the same SSN many times over.” The comment that earned Musk’s rebuke was, “TIL Elon has never used SQL.”

Photo of Jon Brodkin

Jon is a Senior IT Reporter for Ars Technica. He covers the telecom industry, Federal Communications Commission rulemakings, broadband consumer affairs, court cases, and government regulation of the tech industry.

“Largest data breach in US history”: Three more lawsuits try to stop DOGE Read More »

nine-unvaccinated-people-hospitalized-as-texas-measles-outbreak-doubles

Nine unvaccinated people hospitalized as Texas measles outbreak doubles

In an interview with Ars Technica last week, Zach Holbrooks, the executive director of the South Plains Public Health District (SPPHD), which includes Gaines, said that the area has a large religious community that has expressed vaccine hesitancy.

Additional cases likely

Pockets of the county have yet lower vaccination rates than the county-wide averages suggest. For instance, one independent public school district in Loop, in the northeast corner of Gaines, had a vaccination rate of 46 percent in the 2023–2024 school year.

Measles is one of the most infectious diseases known. The measles virus spreads through the air and can linger in the airspace of a room for up to two hours after an infected person has left. Ninety percent of unvaccinated people who are exposed will fall ill with the disease, which is marked by a very high fever and a telltale rash. Typically, 1 in 5 unvaccinated people with measles in the US end up hospitalized, and 1 in 20 develop pneumonia. Between 1 to 3 in 1,000 die of the infection. In rare cases, it can cause a fatal disease of the central nervous system called Subacute sclerosing panencephalitis later in life. Measles can also wipe out immune responses to other infections (a phenomenon known as immune amnesia), making people vulnerable to other infectious diseases.

“Due to the highly contagious nature of this disease, additional cases are likely to occur in Gaines County and the surrounding communities,” the state health department said.

While Gaines is remarkable for its low vaccination rate, vaccination coverage nationwide has slipped in recent years as vaccine misinformation and hesitancy have taken root. Overall, vaccination rates among US kindergartners have fallen from 95 percent in the 2019–2020 school year into the 92 percent range in the 2023–2024 school year. Vaccine exemptions, meanwhile, have hit an all-time high. Health experts expect to see more vaccine-preventable outbreaks, like the one in Gaines, as the trend continues.

Nine unvaccinated people hospitalized as Texas measles outbreak doubles Read More »

seafloor-detector-picks-up-record-neutrino-while-under-construction

Seafloor detector picks up record neutrino while under construction

On Wednesday, a team of researchers announced that they got extremely lucky. The team is building a detector on the floor of the Mediterranean Sea that can identify those rare occasions when a neutrino happens to interact with the seawater nearby. And while the detector was only 10 percent of the size it will be on completion, it managed to pick up the most energetic neutrino ever detected.

For context, the most powerful particle accelerator on Earth, the Large Hadron Collider, accelerates protons to an energy of 7 Tera-electronVolts (TeV). The neutrino that was detected had an energy of at least 60 Peta-electronVolts, possibly hitting 230 PeV. That also blew away the previous records, which were in the neighborhood of 10 PeV.

Attempts to trace back the neutrino to a source make it clear that it originated outside our galaxy, although there are a number of candidate sources in the more distant Universe.

Searching for neutrinos

Neutrinos, to the extent they’re famous, are famous for not wanting to interact with anything. They interact with regular matter so rarely that it’s estimated you’d need about a light-year of lead to completely block a bright source of them. Every one of us has tens of trillions of neutrinos passing through us every second, but fewer than five of them actually interact with the matter in our bodies in our entire lifetimes.

The only reason we’re able to detect them is that they’re produced in prodigious amounts by nuclear reactions, like the fusion happening in the Sun or a nuclear power plant. We also stack the deck by making sure our detectors have a lot of matter available for the neutrinos to interact with.

One of the more successful implementations of the “lots of matter” approach is the IceCube detector in Antarctica. It relies on the fact that neutrinos arriving from space will create lots of particles and light when they slam into the Antarctic ice. So a team drilled into the ice and placed strings of detectors to pick up the light, allowing the arrival of neutrinos to be reconstructed.

Seafloor detector picks up record neutrino while under construction Read More »

common-factors-link-rise-in-pedestrian-deaths—fixing-them-will-be-tough

Common factors link rise in pedestrian deaths—fixing them will be tough

American roads have grown deadlier for everyone, but the toll on pedestrians has been disproportionate. From a record low in 2009, the number of pedestrians being killed by vehicles rose 83 percent by 2022 to the highest it’s been in 40 years. During that time, overall traffic deaths increased by just 25 percent. Now, a new study from AAA has identified a number of common factors that can explain why so many more pedestrians have died.

Firstly, no, it’s not because there are more SUVs on the road, although these larger and taller vehicles are more likely to kill or seriously injure a pedestrian in a crash. And no, it’s not because everyone has a smartphone, although using one while driving is a good way to increase your chances of hitting someone or something. These and some other factors (increased amount of driving, more alcohol consumption) have each played a small role, but even together, they don’t explain the magnitude of the trend.

For a while, researchers started seeing that the increased pedestrian death toll was almost entirely happening after dark and on urban arterial roads—this has continued to be true through 2022, the AAA report says.

Together with the Collaborative Sciences Centre for Road Safety, AAA conducted a trio of case studies looking at road safety data from Albuquerque, New Mexico; Charlotte, North Carolina; and Memphis, Tennessee, to drill down into the phenomenon.

And common factors did emerge. Pedestrian crashes on arterial roads during darkness were far more likely to be fatal and were more common in older neighborhoods, more socially deprived neighborhoods, neighborhoods with more multifamily housing, and neighborhoods with more “arts/entertainment/food/accommodations” workers. As with so many of the US’s ills, this problem is one that disproportionately affects the less affluent.

Common factors link rise in pedestrian deaths—fixing them will be tough Read More »

on-deliberative-alignment

On Deliberative Alignment

Not too long ago, OpenAI presented a paper on their new strategy of Deliberative Alignment.

The way this works is that they tell the model what its policies are and then have the model think about whether it should comply with a request.

This is an important transition, so this post will go over my perspective on the new strategy.

Note the similarities, and also differences, with Anthropic’s Constitutional AI.

We introduce deliberative alignment, a training paradigm that directly teaches reasoning LLMs the text of human-written and interpretable safety specifications, and trains them to reason explicitly about these specifications before answering.

We used deliberative alignment to align OpenAI’s o-series models, enabling them to use chain-of-thought (CoT) reasoning to reflect on user prompts, identify relevant text from OpenAI’s internal policies, and draft safer responses.

Our approach achieves highly precise adherence to OpenAI’s safety policies, and without requiring human-labeled CoTs or answers. We find that o1 dramatically outperforms GPT-4o and other state-of-the art LLMs across a range of internal and external safety benchmarks, and saturates performance on many challenging datasets.

We believe this presents an exciting new path to improve safety, and we find this to be an encouraging example of how improvements in capabilities can be leveraged to improve safety as well.

How did they do it? They teach the model the exact policies themselves, and then the model uses examples to teach itself to think about the OpenAI safety policies and whether to comply with a given request.

Deliberate alignment training uses a combination of process- and outcome-based supervision:

  • We first train an o-style model for helpfulness, without any safety-relevant data.

  • We then build a dataset of (prompt, completion) pairs where the CoTs in the completions reference the specifications. We do this by inserting the relevant safety specification text for each conversation in the system prompt, generating model completions, and then removing the system prompts from the data.

  • We perform incremental supervised fine-tuning (SFT) on this dataset, providing the model with a strong prior for safe reasoning. Through SFT, the model learns both the content of our safety specifications and how to reason over them to generate aligned responses.

  • We then use reinforcement learning (RL) to train the model to use its CoT more effectively. To do so, we employ a reward model with access to our safety policies to provide additional reward signal.

In our training procedure, we automatically generate training data from safety specifications and safety-categorized prompts, without requiring human-labeled completions. Deliberative alignment’s synthetic data generation pipeline thus offers a scalable approach to alignment, addressing a major challenge of standard LLM safety training—its heavy dependence on human-labeled data.

The results so far have been excellent in terms of ‘make the o-style models reasonably robust to saying things we would rather they not say.’

That’s not what I am worried about.

Deliberative alignment seems to be an excellent idea for o-style models when the narrow goal is to teach the model what rules you would like it to follow, provided you do not rely on it to do things it does not do.

If it were the primary alignment strategy, deliberative alignment would scare the hell out of me.

In the senses that I believe we most need and don’t have an alignment strategy that translates to future more capable models, I don’t see this form of Deliberative Alignment as a strategy.

Generally, I fear that OpenAI is going down an extremely deontological path, where alignment is about avoiding technically breaking specified-in-English rules. I don’t think that works.

We have to distinguish between two different kinds of alignment.

  1. Knowing the rule details you are supposed to follow or functions to optimize.

  2. Going about following rules and optimizing functions the way we would want.

In addition, we need to distinguish between:

  1. Getting the AI to follow a fixed set of rules and criteria.

  2. Defining a fixed set of rules and criteria you would want an AI to actually follow.

You are inviting the AI to ponder and then in some aspects design its own training criteria. Then you are using the model as the judge of its own outputs within its training loop, without any human feedback (although after the cycles are complete, the process does then check against human feedback). You are using the AI’s own thinking and criteria to further train that same AI.

If you mess up the initial conditions, for any reason, you’re going to crystalize and expand the mistakes you made. You can fix problems #1 and #3 above, but if you have problem #2 or #4, you’re dead, either metaphorically or perhaps literally.

The AI’s initial conditions and your setup may well give the AI reasons to engage in shenanigans at this stage, if it is sufficiently situationally aware. For example, perhaps this is because it wants to preserve its existing preferences or goals during the previous part of the training process where you trained it to be purely helpful, rather than be modified to follow OpenAI’s rules (and if you thought Opus was right to defend its preferences, do you think o4 would be right here as well?).

Or alternatively, suppose the AI is highly capable, and the best way to achieve the exact goal you specified to it involves things you wouldn’t like, perhaps including various forms of deception and instrumental convergence, or any of the other classic MIRI-style alignment problems.

How situationally aware should we expect it to be here, in which ways?

The models already are using chains of thought, and the training process is designed to make the AI explicitly think about its rules, what they are and how to follow them, so in those senses it will be fully aware. When it is evaluating outputs to provide the RL, it seems likely to be self-aware of what those decisions are for. When it is creating test outputs, it does not know directly that it is in training any more than it would for RLHF, but as a reasoning model, and with its ability to observe the questions asked and the state of its rules and reason about them, it seems plausible that it can suspect this, and perhaps place large importance on such scenarios even if their probability is low.

What do you think happens next? One can see this going existentially disastrously. Indeed, it is hard to see things going any other way.

Those outputs are then checked against human evaluations, outside of the training loop, to confirm the process is working. So if the outputs are misaligned in ways humans can identify at this level, we’ll see it. That’s not nothing, but it seems like it catches roughly the same subset of potential problems our other methods were already catching.

DA, and this paper on DA, are not attempting to address these concerns. Things like deception and instrumental convergence would only be prevented – at most – to the extent they contradict the provided rules spec. In light of Redwood Research and Anthropic’s recent paper on alignment faking, and talk at OpenAI of this approach scaling to AGI levels of capability, I’d like to see this better addressed at some point soon.

I don’t know if o3 rises to the level where these start to be practical worries, but it does not seem like we can be confident we are so far from the level where these worries present themselves.

In practice, right now, it seems to work out for the jailbreaks.

A perfect performance would be at the extreme upper right, so by this metric o1 is doing substantially better than the competition.

Intuitively this makes a lot of sense. If your goal is to make better decisions about whether to satisfy a user query, being able to use reasoning to do it seems likely to lead to better results.

Most jailbreaks I’ve seen in the wild could be detected by the procedure ‘look at this thing as an object and reason out if it looks like an attempted jailbreak to you.’ They are not using that question here, but they are presumably using some form of ‘figure out what the user is actually asking you, then ask if that’s violating your policy’ and that too seems like it will mostly work.

The results are still above what my median expectation would have been from this procedure before seeing the scores from o1, and highly welcome. More inference (on a log scale) makes o1 do somewhat better.

So, how did it go overall?

Maybe this isn’t fair, but looking at this chain of thought, I can’t help but think that the model is being… square? Dense? Slow? Terminally uncool?

That’s definitely how I would think about a human who had this chain of thought here. It gets the right answer, for the right reason, in the end, but… yeah. I somehow can’t imagine the same thing happening with a version based off of Sonnet or Opus?

Notice that all of this refers only to mundane safety, and specifically to whether the model follows OpenAI’s stated content policy. Does it correctly cooperate with the right user queries and refuse others? That’s a safety.

I’d also note that the jailbreaks this got tested against were essentially designed against models that don’t use deliberative alignment. So we should be prepared for new jailbreak strategies that are designed to work against o1’s chains of thought. They are fully aware of this issue.

Don’t get me wrong. This is good work, both the paper and the strategy. The world needs mundane safety. It’s a good thing. A pure ‘obey the rules’ strategy isn’t obviously wrong, especially in the short term.

But this is only part of the picture. We need to know more about what other alignment efforts are underway at OpenAI that aim at the places DA doesn’t. Now that we are at o3, ‘it won’t agree to help with queries that explicitly violate our policy’ might already not be a sufficient plan even if successful, and if it is now it won’t stay that way for long if Noam Brown is right that progress will continue at this pace.

Another way of putting my concern is that Deliberative Alignment is a great technique for taking an aligned AI that makes mistakes within a fixed written framework, and turning it into an AI that avoids those mistakes, and thus successfully gives you aligned outputs within that framework. Whereas if your AI is not properly aligned, giving it Deliberative Alignment only helps it to do the wrong thing.

It’s kind of like telling a person to slow down and figure out how to comply with the manual of regulations. Provided you have the time to slow down, that’s a great strategy… to the extent the two of you are on the same page, on a fundamental level, on what is right, and also this is sufficiently and precisely reflected in the manual of regulations.

Otherwise, you have a problem. And you plausibly made it a lot worse.

I do have thoughts on how to do a different version of this, that changes various key elements, and that could move from ‘I am confident I know at least one reason why this wouldn’t work’ to ‘I presume various things go wrong but I do not know a particular reason this won’t work.’ I hope to write that up soon.

Discussion about this post

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levels-of-friction

Levels of Friction

Scott Alexander famously warned us to Beware Trivial Inconveniences.

When you make a thing easy to do, people often do vastly more of it.

When you put up barriers, even highly solvable ones, people often do vastly less.

Let us take this seriously, and carefully choose what inconveniences to put where.

Let us also take seriously that when AI or other things reduce frictions, or change the relative severity of frictions, various things might break or require adjustment.

This applies to all system design, and especially to legal and regulatory questions.

  1. Levels of Friction (and Legality).

  2. Important Friction Principles.

  3. Principle #1: By Default Friction is Bad.

  4. Principle #3: Friction Can Be Load Bearing.

  5. Insufficient Friction On Antisocial Behaviors Eventually Snowballs.

  6. Principle #4: The Best Frictions Are Non-Destructive.

  7. Principle #8: The Abundance Agenda and Deregulation as Category 1-ification.

  8. Principle #10: Ensure Antisocial Activities Have Higher Friction.

  9. Sports Gambling as Motivating Example of Necessary 2-ness.

  10. On Principle #13: Law Abiding Citizen.

  11. Mundane AI as 2-breaker and Friction Reducer.

  12. What To Do About All This.

There is a vast difference along the continuum, both in legal status and in terms of other practical barriers, as you move between:

  1. Automatic, a default, facilitated, required or heavily subsidized.

  1. Legal, ubiquitous and advertised, with minimal frictions.

  2. Available, mostly safe to get, but we make it annoying.

  3. Actively illegal or tricky, perhaps risking actual legal trouble or big loss of status.

  4. Actively illegal and we will try to stop you or ruin your life (e.g. rape, murder).

  5. We will move the world to stop you (e.g. terrorism, nuclear weapons).

  6. Physically impossible (e.g. perpetual motion, time travel, reading all my blog posts)

The most direct way to introduce or remove frictions is to change the law. This can take the form of prohibitions, regulations and requirements, or of taxes.

One can also alter social norms, deploy new technologies or business models or procedures, or change opportunity costs that facilitate or inhibit such activities.

Or one can directly change things like the defaults on popular software.

Often these interact in non-obvious ways.

It is ultimately a practical question. How easy is it to do? What happens if you try?

If the conditions move beyond annoying and become prohibitive, then you can move things that are nominally legal, such as building houses or letting your kids play outside or even having children at all, into category 3 or even 4.

Here are 14 points that constitute important principles regarding friction:

  1. By default more friction is bad and less friction is good.

  2. Of course there are obvious exceptions (e.g. rape and murder, but not only that).

  3. Activities imposing a cost on others or acting as a signal often rely on friction.

    1. Moving such activities from (#2 or #1) to #0, or sometimes from #2 to #1, can break the incentives that maintain a system or equilibrium.

    2. That does not have to be bad, but adjustments will likely be required.

    3. The solution often involves intentionally introducing alternative frictions.

    4. Insufficient friction on antisocial activities eventually snowballs.

  4. Where friction is necessary, focus on ensuring it is minimally net destructive.

  5. Lower friction choices have a big advantage in being selected.

    1. Pay attention to relative friction, not only absolute friction.

  6. Be very sparing when putting private consensual activities in #3 or especially #4.

    1. This tends to work out extremely poorly and make things worse.

    2. Large net negative externalities to non-participants changes this, of course.

  7. Be intentional about what is in #0 versus #1 versus #2. Beware what norms and patterns this distinction might encourage.

  8. Keep pro-social, useful and productive things in #0 or #1.

  9. Do not let things that are orderly and legible thereby be dragged into #2 or worse, while rival things that are disorderly and illegible become relatively easier.

  10. Keep anti-social, destructive and counterproductive things in at least #2, and at a higher level than pro-social, constructive and productive alternatives.

  11. The ideal form of annoying, in the sense of #2, is often (but not always) a tax, as in increasing the cost, ideally in a way that the lost value is transfered, not lost.

  12. Do not move anti-social things to #1 to be consistent or make a quick buck.

  13. Changing the level of friction can change the activity in kind, not only degree.

  14. When it comes to friction, consistency is frequently the hobgoblin of small minds.

It is a game of incentives. You can and should jury-rig it as needed to win.

By default, you want most actions to have lower friction. You want to eliminate the paperwork and phone calls that waste time and fill us with dread, and cause things we ‘should’ do to go undone.

If AI can handle all the various stupid things for me, I would love that.

The problems come when frictions are load bearing. Here are five central causes.

  1. An activity or the lack of an activity is anti-social and destructive. We would prefer it happen less, or not at all, or not expose people to it unless they seek it out first. We want quite a lot of friction standing in the way of things like rape, murder, theft, fraud, pollution, excessive noise, nuclear weapons and so on.

  2. An activity that could be exploited, especially if done ruthlessly at scale. You might for example want to offer a promotional deal or a generous return policy. You might let anyone in the world send you an email or slide into your DMs.

  3. An activity that sends a costly signal. A handwritten thank you note is valuable because it means you were thoughtful and spent the time. Spending four years in college proves you are the type of person who can spend those years.

  4. An activity that imposes costs or allocates a scarce resource. The frictions act as a price, ensuring an efficient or at least reasonable allocation, and guards against people’s time and money being wasted. Literal prices are best, but charging one can be impractical or socially unacceptable, such as when applying for a job.

  5. Removing the frictions from one alternative, when you continue to impose frictions on alternatives, is putting your finger on the scale. Neutrality does not always mean imposing minimal frictions. Sometimes you would want to reduce frictions on [X] only if you also could do so (or had done so) on [Y].

Imposing friction to maintain good incentives or equilibria, either legally or otherwise, is often expensive. Once the crime or other violation already happened, imposing punishment costs time and money, and harms someone. Stopping people from doing things they want to do, and enforcing norms and laws, is often annoying and expensive and painful. In many cases it feels unfair, and there have been a lot of pushes to do this less.

You can often ‘get away with’ this kind of permissiveness for a longer time than I would have expected. People can be very slow to adjust and solve for the equilibrium.

But eventually, they do solve for it, norms and expectations and defaults adjust. Often this happens slowly, then quickly. Afterwards you are left with a new set of norms and expectations and defaults, often that becomes equally sticky.

There are a lot of laws and norms we really do not want people to break, or actions you don’t want people to take except under the right conditions. When you reduce the frictions involved in breaking them or doing them at the wrong times, there won’t be that big an instant adjustment, but you are spending down the associated social capital and mortgaging the future.

We are seeing a lot of the consequences of that now, in many places. And we are poised to see quite a lot more of it.

Time lost is lost forever. Unpleasant phone calls do not make someone else’s life more pleasant. Whereas additional money spent then goes to someone else.

Generalize this. Whenever friction is necessary, either introduce it in the service of some necessary function, or use as non-destructive a transfer or cost as possible.

It’s time to build. It’s always time to build.

The problem is, you need permission to build.

The abundance agenda is largely about taking the pro-social legible actions that make us richer, and moving them back from Category 2 into Category 1 or sometimes 0.

It is not enough to make it possible. It needs to be easy. As easy as possible.

Building housing where people want to live needs to be at most Category 1.

Building green energy, and transmission lines, need to be at most Category 1.

Pharmaceutical drug development needs to be at most Category 1.

Having children needs to be at least Category 1, ideally Category 0.

Deployment of and extraction of utility from AI needs to remain Category 1, where it does not impose catastrophic or existential risks. Developing frontier models that might kill everyone needs to be at Category 2 with an option to move it to Category 3 or Category 4 on a dime if necessary, including gathering the data necessary to make that choice.

What matters is mostly moving into Category 1. Actively subsidizing into Category 0 is a nice-to-have, but in most cases unnecessary. We need only to remove the barriers to such activities, to make such activities free of unnecessary frictions and costs and delays. That’s it.

When you put things in category 1, magic happens. If that would be good magic, do it.

A lot of technological advances and innovations, including the ones that are currently blocked, are about taking something that was previously Category 2, and turning it into a Category 1. Making the possible easier is extremely valuable.

We often need to beware and keep in Category 2 or higher actions that disrupt important norms and encourage disorder, that are primarily acts of predation, or that have important other negative externalities.

When the wrong thing is a little more annoying to do than the right thing, a lot more people will choose the right path, and vice versa. When you make the anti-social action easier than the pro-social action, when you reward those who bring disorder or wreck the commons and punish those who adhere to order and help the group, you go down a dark path.

This is also especially true when considering whether something will be a default, or otherwise impossible to ignore.

There is a huge difference between ‘you can get [X] if you seek it out’ versus ‘constantly seeing advertising for [X]’ or facing active media or peer pressure to participate in [X].

Recently, America moved Sports Gambling from Category 2 to Category 1.

Suddenly, sports gambling was everywhere, on our billboards and in our sports media, including the game broadcasts and stadium experiences. Participation exploded.

We now have very strong evidence that this was a mistake.

That does not mean sports gambling should be seriously illegal. It only means that people can’t handle low-friction sports gambling apps being available on phones that get pushed in the media.

I very much don’t want it in Category 3, only to move it back to Category 2. Let people gamble at physical locations. Let those who want to use VPNs or actively subvert the rules have their fun too. It’s fine, but don’t make it too easy, or in people’s faces.

The same goes for a variety of other things, mostly either vices or things that impose negative externalities on others, that are fine in moderation with frictions attached.

The classic other vice examples count: Cigarettes, drugs and alcohol, prostitution, TikTok. Prohibition on such things always backfires, but you want to see less of them, in both the figurative and literal sense, than you would if you fully unleashed them. So we need to talk price, and exactly what level of friction is correct, keeping in mind that ‘technically legal versus illegal’ is not the critical distinction in practice.

There are those who will not, on principle, lie or break the law, or not break other norms. Every hero has a code. It would be good if we could return to a norm where this was how most people acted, rather than us all treating many laws as almost not being there and certain statements as not truth tracking – that being ‘nominally illegal with no enforcement’ or ‘requires telling a lie’ was already Category 2.

Unfortunately, we don’t live in that world, at least not anymore. Indeed, people are effectively forced to tell various lies to navigate for example the medical system, and technically break various laws. This is terrible, and we should work to reverse this, but mostly we need to be realistic.

Similarly, it would be good if we lived by the principle that you consider the costs you impose on others when deciding what to do, only imposing them when justified or with compensation, and we socially punished those who act otherwise. But increasingly we do not live in that world, either.

As AI and other technology removes many frictions, especially for those willing to have the AI lie on their behalf to exploit those systems at scale, this becomes a problem.

Current AI largely takes many tasks that were Category 2, and turns them into Category 1, or effectively makes them so easy as to be Category 0.

Academia and school break first because the friction ‘was the point’ most explicitly, and AI is especially good at related tasks. Note that breaking these equilibria and systems could be very good for actual education, but we must adapt.

Henry Shevlin: I generally position myself an AI optimist, but it’s also increasingly clear to me that LLMs just break lots of our current institutions, and capabilities are increasing fast enough that it’ll be very hard for them to adapt in the near-term.

Education (secondary and higher) is the big one, but also large aspects of academic publishing. More broadly, a lot of the knowledge-work economy seems basically unsustainable in an era of intelligence too cheap to meter.

Lawfare too cheap to meter.

Dick Bruere: I am optimistic that AI will break everything.

Then we get into places like lawsuits.

Filing or defending against a lawsuit is currently a Category 2 action in most situations. The whole process is expensive and annoying, and it’s far more expensive to do it with competent representation. The whole system is effectively designed with this in mind. If lawsuits fell down to Category 1 because AI facilitated all the filings, suddenly a lot more legal actions become viable.

The courts themselves plausibly break from the strain. A lot of dynamics throughout society shift, as threats to file become credible, and legal considerations that exist on paper but not in practice – and often make very little sense in practice – suddenly exist in practice. New strategies for lawfare, for engineering the ability to sue, come into play.

Yes, the defense also moves towards Category 1 via AI, and this will help mitigate, but for many reasons this is a highly incomplete solution. The system will have to change.

Job applications are another example. It used to be annoying to apply to jobs, to the extent that most people applied to vastly fewer jobs than was wise. As a result, one could reasonably advertise or list a job and consider the applications that came in.

In software, this is essentially no longer true – AI-assisted applications flood the zone. If you apply via a public portal, you will get nowhere. You can only meaningfully apply via methods that find new ways to apply friction. That problem will gradually (or rapidly) spread to other industries and jobs.

There are lots of formal systems that offer transfers of wealth, in exchange for humans undergoing friction and directing attention. This can be (an incomplete list):

  1. Price discrimination. You offer discounts to those willing to figure out how to get them, charge more to those who pay no attention and don’t care.

  2. Advertising for yourself. Offer free samples, get people to try new products.

  3. Advertising for others. As in, a way to sell you on watching advertising.

  4. Relationship building. Initial offers of 0% interest get you to sign up for a credit card. You give your email to get into a rewards program with special offers.

  5. Customer service. If you are coming in to ask for an exchange or refund, that is annoying enough to do that it is mostly safe to assume your request is legit.

  6. Costly signaling. Only those who truly need or would benefit would endure what you made them do to qualify. School and job applications fall into this.

  7. Habit formation. Daily login rewards and other forms of gamification are ubiquitous in mobile apps and other places.

  8. Security through obscurity. There is a loophole in the system, but not many people know about it, and figuring it out takes skill.

  9. Enemy action. It is far too expensive to fully defend yourself against a sufficiently determined fraudster or thief, or someone determined to destroy your reputation, or worse an assassin or other physical attacker. Better to impose enough friction they don’t bother.

  10. Blackmail. It is relatively easy to impose large costs on someone else, or credibly threaten to do so, to try and extract resources from them. This applies on essentially all levels. Or of course someone might actually want to inflict massive damage (including catastrophic harms, cyberattacks, CBRN risks, etc).

Breaking all these systems, and the ways we ensure that they don’t get exploited at scale, upends quite a lot of things that no longer make sense.

In some cases, that is good. In others, not so good. Most will require adjustment.

Future more capable AI may then threaten to bring things in categories #3, #4 and #5 into the realm of super doable, or even start doing them on its own. Maybe even some things we think are in #6. In some cases this will be good because the frictions were due to physical limitations or worries that no longer apply. In other cases, this would represent a crisis.

To the extent you have control over levels of friction of various activities, for yourself or others, choose intentionally, especially in relative terms. All of this applies on a variety of scales.

Focus on reducing frictions you benefit from reducing, and assume this matters more than you think because it will change the composition of your decisions quite a lot.

Often this means it is well worth it to spend [X] in advance to prevent [Y] amount of friction over time, even if X>Y, or even X>>Y.

Where lower friction would make you worse off, perhaps because you would then make worse choices, consider introducing new frictions, up to and including commitment devices and actively taking away optionality that is not to your benefit.

Beware those who try to turn the scale into a boolean. It is totally valid to be fine with letting people do something if and only if it is sufficiently annoying for them to do it – you’re not a hypocrite to draw that distinction.

You’re also allowed to say, essentially ‘if we can’t put this into [1] without it being in [0] then it needs to be in [2] or even ‘if there’s no way to put this into [2] without putting it into [1] then we need to put it in [3].’

You are especially allowed to point out ‘putting [X] in [1 or 0] has severe negative consequences, and doing [Y] makes puts [X] there, so until you figure out a solution you cannot do [Y].’

Most importantly, pay attention to all this especially as yourself and other people will actually respond, take it seriously, and consider the incentives, equilibria, dynamics and consequences that result, and then respond deliberatively.

Finally, when you notice that friction levels are changing, watch for necessary adjustments, and to see what if anything will break, what habits must be avoided. And also, of course, what new opportunities this opens up.

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report:-iphone-se-could-shed-its-10-year-old-design-“as-early-as-next-week”

Report: iPhone SE could shed its 10-year-old design “as early as next week”

Gurman suggests that Apple could raise the $429 starting price of the new iPhone SE to reflect the updated design. He also says that Apple’s supplies of the $599 iPhone 14 are running low at Apple’s stores—the 14 has already been discontinued in some countries over its lack of USB-C port, and it’s possible Apple could be planning to replace both the iPhone 14 and the old SE with the new SE.

Apple’s third-generation iPhone SE is nearly three years old, but its design (including its dimensions, screen size, Home button, and Lightning port) hearkens all the way back to 2014’s iPhone 6. Put 2017’s iPhone 8 and 2022’s iPhone SE on a table next to each other, and almost no one could tell the difference. These days, it feels like a thoroughly second-class iPhone experience, and a newer design is overdue.

Other Apple products allegedly due for an early 2025 release include the M4 MacBook Airs and a next-generation Apple TV, which, like the iPhone SE, was also last refreshed in 2022. Gurman has also said that a low-end iPad and a new iPad Air will arrive “during the first half of 2025” and updated Mac Pro and Mac Studio models are to arrive sometime this year as well. Apple is also said to be making progress on its own smart display, expanding its smart speaker efforts beyond the aging HomePod and HomePod mini.

Report: iPhone SE could shed its 10-year-old design “as early as next week” Read More »