Anticipating that 2025 will be an “intense year” requiring rapid innovation, Mark Zuckerberg reportedly announced that Meta would be cutting 5 percent of its workforce—targeting “lowest performers.”
Bloomberg reviewed the internal memo explaining the cuts, which was posted to Meta’s internal Workplace forum Tuesday. In it, Zuckerberg confirmed that Meta was shifting its strategy to “move out low performers faster” so that Meta can hire new talent to fill those vacancies this year.
“I’ve decided to raise the bar on performance management,” Zuckerberg said. “We typically manage out people who aren’t meeting expectations over the course of a year, but now we’re going to do more extensive performance-based cuts during this cycle.”
Cuts will likely impact more than 3,600 employees, as Meta’s most recent headcount in September totaled about 72,000 employees. It may not be as straightforward as letting go anyone with an unsatisfactory performance review, as Zuckerberg said that any employee not currently meeting expectations could be spared if Meta is “optimistic about their future performance,” The Wall Street Journal reported.
Any employees affected will be notified by February 10 and receive “generous severance,” Zuckerberg’s memo promised.
This is the biggest round of cuts at Meta since 2023, when Meta laid off 10,000 employees during what Zuckerberg dubbed the “year of efficiency.” Those layoffs followed a prior round where 11,000 lost their jobs and Zuckerberg realized that “leaner is better.” He told employees in 2023 that a “surprising result” from reducing the workforce was “that many things have gone faster.”
“A leaner org will execute its highest priorities faster,” Zuckerberg wrote in 2023. “People will be more productive, and their work will be more fun and fulfilling. We will become an even greater magnet for the most talented people. That’s why in our Year of Efficiency, we are focused on canceling projects that are duplicative or lower priority and making every organization as lean as possible.”
And perhaps in a nod to Meta’s recent changes, Mastodon also vowed to “invest deeply in trust and safety” and ensure “everyone, especially marginalized communities,” feels “safe” on the platform.
To become a more user-focused paradise of “resilient, governable, open and safe digital spaces,” Mastodon is going to need a lot more funding. The blog called for donations to help fund an annual operating budget of $5.1 million (5 million euros) in 2025. That’s a massive leap from the $152,476 (149,400 euros) total operating expenses Mastodon reported in 2023.
Other social networks wary of EU regulations
Mastodon has decided to continue basing its operations in Europe, while still maintaining a separate US-based nonprofit entity as a “fundraising hub,” the blog said.
It will take time, Mastodon said, to “select the appropriate jurisdiction and structure in Europe” before Mastodon can then “determine which other (subsidiary) legal structures are needed to support operations and sustainability.”
While Mastodon is carefully getting re-settled as a nonprofit in Europe, Zuckerberg this week went on Joe Rogan’s podcast to call on Donald Trump to help US tech companies fight European Union fines, Politico reported.
Experts told France24 that EU officials may “perhaps wrongly” already be fearful about ruffling Trump’s feathers by targeting his tech allies and would likely need to use the “full legal arsenal” of EU digital laws to “stand up to Big Tech” once Trump’s next term starts.
As Big Tech prepares to continue battling EU regulators, Mastodon appears to be taking a different route, laying roots in Europe and “establishing the appropriate governance and leadership frameworks that reflect the nature and purpose of Mastodon as a whole” and “responsibly serve the community,” its blog said.
“Our core mission remains the same: to create the tools and digital spaces where people can build authentic, constructive online communities free from ads, data exploitation, manipulative algorithms, or corporate monopolies,” Mastodon’s blog said.
Meta has reportedly ended diversity, equity, and inclusion (DEI) programs that influenced staff hiring and training, as well as vendor decisions, effective immediately.
According to an internal memo viewed by Axios and verified by Ars, Meta’s vice president of human resources, Janelle Gale, told Meta employees that the shift was due to “legal and policy landscape surrounding diversity, equity, and inclusion efforts in the United States is changing.”
It’s another move by Meta that some view as part of the company’s larger effort to align with the incoming Trump administration’s politics. In December, Donald Trump promised to crack down on DEI initiatives at companies and on college campuses, The Guardian reported.
Earlier this week, Meta cut its fact-checking program, which was introduced in 2016 after Trump’s first election to prevent misinformation from spreading. In a statement announcing Meta’s pivot to X’s Community Notes-like approach to fact-checking, Meta CEO Mark Zuckerberg claimed that fact-checkers were “too politically biased” and “destroyed trust” on Meta platforms like Facebook, Instagram, and Threads.
Trump has also long promised to renew his war on alleged social media censorship while in office. Meta faced backlash this week over leaked rule changes relaxing Meta’s hate speech policies, The Intercept reported, which Zuckerberg said were “out of touch with mainstream discourse.” Those changes included allowing anti-trans slurs previously banned, as well as permitting women to be called “property” and gay people to be called “mentally ill,” Mashable reported. In a statement, GLAAD said that rolling back safety guardrails risked turning Meta platforms into “unsafe landscapes filled with dangerous hate speech, violence, harassment, and misinformation” and alleged that Meta appeared to be willing to “normalize anti-LGBTQ hatred for profit.”
Zuckerberg says Meta will “work with President Trump” to fight censorship.
Meta CEO Mark Zuckerberg during the Meta Connect event in Menlo Park, California on September 25, 2024. Credit: Getty Images | Bloomberg
Meta announced today that it’s ending the third-party fact-checking program it introduced in 2016, and will rely instead on a Community Notes approach similar to what’s used on Elon Musk’s X platform.
The end of third-party fact-checking and related changes to Meta policies could help the company make friends in the Trump administration and in governments of conservative-leaning states that have tried to impose legal limits on content moderation. The operator of Facebook and Instagram announced the changes in a blog post and a video message recorded by CEO Mark Zuckerberg.
“Governments and legacy media have pushed to censor more and more. A lot of this is clearly political,” Zuckerberg said. He said the recent elections “feel like a cultural tipping point toward once again prioritizing speech.”
“We’re going to get rid of fact-checkers and replace them with Community Notes, similar to X, starting in the US,” Zuckerberg said. “After Trump first got elected in 2016, the legacy media wrote nonstop about how misinformation was a threat to democracy. We tried in good faith to address those concerns without becoming the arbiters of truth. But the fact-checkers have just been too politically biased and have destroyed more trust than they’ve created, especially in the US.”
Meta says the soon-to-be-discontinued fact-checking program includes over 90 third-party organizations that evaluate posts in over 60 languages. The US-based fact-checkers are AFP USA, Check Your Fact, Factcheck.org, Lead Stories, PolitiFact, Science Feedback, Reuters Fact Check, TelevisaUnivision, The Dispatch, and USA Today.
The independent fact-checkers rate the accuracy of posts and apply ratings such as False, Altered, Partly False, Missing Context, Satire, and True. Meta adds notices to posts rated as false or misleading and notifies users before they try to share the content or if they shared it in the past.
Meta: Experts “have their own biases”
In the blog post that accompanied Zuckerberg’s video message, Chief Global Affairs Officer Joel Kaplan said the 2016 decision to use independent fact-checkers seemed like “the best and most reasonable choice at the time… The intention of the program was to have these independent experts give people more information about the things they see online, particularly viral hoaxes, so they were able to judge for themselves what they saw and read.”
But experts “have their own biases and perspectives,” and the program imposed “intrusive labels and reduced distribution” of content “that people would understand to be legitimate political speech and debate,” Kaplan wrote.
The X-style Community Notes system lets the community “decide when posts are potentially misleading and need more context, and people across a diverse range of perspectives decide what sort of context is helpful for other users to see… Just like they do on X, Community Notes [on Meta sites] will require agreement between people with a range of perspectives to help prevent biased ratings,” Kaplan wrote.
The end of third-party fact-checking will be implemented in the US before other countries. Meta will also move its internal trust and safety and content moderation teams out of California, Zuckerberg said. “Our US-based content review is going to be based in Texas. As we work to promote free expression, I think it will help us build trust to do this work in places where there is less concern about the bias of our teams,” he said. Meta will continue to take “legitimately bad stuff” like drugs, terrorism, and child exploitation “very seriously,” Zuckerberg said.
Zuckerberg pledges to work with Trump
Meta will “phase in a more comprehensive community notes system” over the next couple of months, Zuckerberg said. Meta, which donated $1 million to Trump’s inaugural fund, will also “work with President Trump to push back on governments around the world that are going after American companies and pushing to censor more,” Zuckerberg said.
Zuckerberg said that “Europe has an ever-increasing number of laws institutionalizing censorship,” that “Latin American countries have secret courts that can quietly order companies to take things down,” and that “China has censored apps from even working in the country.” Meta needs “the support of the US government” to push back against other countries’ content-restriction orders, he said.
“That’s why it’s been so difficult over the past four years when even the US government has pushed for censorship,” Zuckerberg said, referring to the Biden administration. “By going after US and other American companies, it has emboldened other governments to go even further. But now we have the opportunity to restore free expression, and I am excited to take it.”
Brendan Carr, Trump’s pick to lead the Federal Communications Commission, praised Meta’s policy changes. Carr has promised to shift the FCC’s focus from regulating telecom companies to cracking down on Big Tech and media companies that he alleges are part of a “censorship cartel.”
“President Trump’s resolute and strong support for the free speech rights of everyday Americans is already paying dividends,” Carr wrote on X today. “Facebook’s announcements is [sic] a good step in the right direction. I look forward to monitoring these developments and their implementation. The work continues until the censorship cartel is completely dismantled and destroyed.”
Group: Meta is “saying the truth doesn’t matter”
Meta’s changes were criticized by Public Citizen, a nonprofit advocacy group founded by Ralph Nader. “Asking users to fact-check themselves is tantamount to Meta saying the truth doesn’t matter,” Public Citizen co-president Lisa Gilbert said. “Misinformation will flow more freely with this policy change, as we cannot assume that corrections will be made when false information proliferates. The American people deserve accurate information about our elections, health risks, the environment, and much more.”
Media advocacy group Free Press said that “Zuckerberg is one of many billionaires who are cozying up to dangerous demagogues like Trump and pushing initiatives that favor their bottom lines at the expense of everything and everyone else.” Meta appears to be abandoning its “responsibility to protect its many users, and align[ing] the company more closely with an incoming president who’s a known enemy of accountability,” Free Press Senior Counsel Nora Benavidez said.
X’s Community Notes system was criticized in a recent report by the Center for Countering Digital Hate (CCDH), which said it “found that 74 percent of accurate community notes on US election misinformation never get shown to users.” (X previously sued the CCDH, but the lawsuit was dismissed by a federal judge.)
Previewing other changes, Zuckerberg said that Meta will eliminate content restrictions “that are just out of touch with mainstream discourse” and change how it enforces policies “to reduce the mistakes that account for the vast majority of censorship on our platforms.”
“We used to have filters that scanned for any policy violation. Now, we’re going to focus those filters on tackling illegal and high-severity violations, and for lower severity violations, we’re going to rely on someone reporting an issue before we take action,” he said. “The problem is the filters make mistakes, and they take down a lot of content that they shouldn’t. So by dialing them back, we’re going to dramatically reduce the amount of censorship on our platforms.”
Meta to relax filters, recommend more political content
Zuckerberg said Meta will re-tune content filters “to require much higher confidence before taking down content.” He said this means Meta will “catch less bad stuff” but will “also reduce the number of innocent people’s posts and accounts that we accidentally take down.”
Meta has “built a lot of complex systems to moderate content,” he noted. Even if these systems “accidentally censor just 1 percent of posts, that’s millions of people, and we’ve reached a point where it’s just too many mistakes and too much censorship,” he said.
Kaplan wrote that Meta has censored too much harmless content and that “too many people find themselves wrongly locked up in ‘Facebook jail.'”
“In recent years we’ve developed increasingly complex systems to manage content across our platforms, partly in response to societal and political pressure to moderate content,” Kaplan wrote. “This approach has gone too far. As well-intentioned as many of these efforts have been, they have expanded over time to the point where we are making too many mistakes, frustrating our users and too often getting in the way of the free expression we set out to enable.”
Another upcoming change is that Meta will recommend more political posts. “For a while, the community asked to see less politics because it was making people stressed, so we stopped recommending these posts,” Zuckerberg said. “But it feels like we’re in a new era now, and we’re starting to get feedback that people want to see this content again, so we’re going to start phasing this back into Facebook, Instagram, and Threads while working to keep the communities friendly and positive.”
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.
What do eating rocks, rat genitals, and Willy Wonka have in common? AI, of course.
It’s been a wild year in tech thanks to the intersection between humans and artificial intelligence. 2024 brought a parade of AI oddities, mishaps, and wacky moments that inspired odd behavior from both machines and man. From AI-generated rat genitals to search engines telling people to eat rocks, this year proved that AI has been having a weird impact on the world.
Why the weirdness? If we had to guess, it may be due to the novelty of it all. Generative AI and applications built upon Transformer-based AI models are still so new that people are throwing everything at the wall to see what sticks. People have been struggling to grasp both the implications and potential applications of the new technology. Riding along with the hype, different types of AI that may end up being ill-advised, such as automated military targeting systems, have also been introduced.
It’s worth mentioning that aside from crazy news, we saw fewer weird AI advances in 2024 as well. For example, Claude 3.5 Sonnet launched in June held off the competition as a top model for most of the year, while OpenAI’s o1 used runtime compute to expand GPT-4o’s capabilities with simulated reasoning. Advanced Voice Mode and NotebookLM also emerged as novel applications of AI tech, and the year saw the rise of more capable music synthesis models and also better AI video generators, including several from China.
But for now, let’s get down to the weirdness.
ChatGPT goes insane
Early in the year, things got off to an exciting start when OpenAI’s ChatGPT experienced a significant technical malfunction that caused the AI model to generate increasingly incoherent responses, prompting users on Reddit to describe the system as “having a stroke” or “going insane.” During the glitch, ChatGPT’s responses would begin normally but then deteriorate into nonsensical text, sometimes mimicking Shakespearean language.
OpenAI later revealed that a bug in how the model processed language caused it to select the wrong words during text generation, leading to nonsense outputs (basically the text version of what we at Ars now call “jabberwockies“). The company fixed the issue within 24 hours, but the incident led to frustrations about the black box nature of commercial AI systems and users’ tendency to anthropomorphize AI behavior when it malfunctions.
The great Wonka incident
A photo of “Willy’s Chocolate Experience” (inset), which did not match AI-generated promises, shown in the background. Credit: Stuart Sinclair
The collision between AI-generated imagery and consumer expectations fueled human frustrations in February when Scottish families discovered that “Willy’s Chocolate Experience,” an unlicensed Wonka-ripoff event promoted using AI-generated wonderland images, turned out to be little more than a sparse warehouse with a few modest decorations.
Parents who paid £35 per ticket encountered a situation so dire they called the police, with children reportedly crying at the sight of a person in what attendees described as a “terrifying outfit.” The event, created by House of Illuminati in Glasgow, promised fantastical spaces like an “Enchanted Garden” and “Twilight Tunnel” but delivered an underwhelming experience that forced organizers to shut down mid-way through its first day and issue refunds.
While the show was a bust, it brought us an iconic new meme for job disillusionment in the form of a photo: the green-haired Willy’s Chocolate Experience employee who looked like she’d rather be anywhere else on earth at that moment.
In February, Ars Technica senior health reporter Beth Mole covered a peer-reviewed paper published in Frontiers in Cell and Developmental Biology that created an uproar in the scientific community when researchers discovered it contained nonsensical AI-generated images, including an anatomically incorrect rat with oversized genitals. The paper, authored by scientists at Xi’an Honghui Hospital in China, openly acknowledged using Midjourney to create figures that contained gibberish text labels like “Stemm cells” and “iollotte sserotgomar.”
The publisher, Frontiers, posted an expression of concern about the article titled “Cellular functions of spermatogonial stem cells in relation to JAK/STAT signaling pathway” and launched an investigation into how the obviously flawed imagery passed through peer review. Scientists across social media platforms expressed dismay at the incident, which mirrored concerns about AI-generated content infiltrating academic publishing.
Chatbot makes erroneous refund promises for Air Canada
If, say, ChatGPT gives you the wrong name for one of the seven dwarves, it’s not such a big deal. But in February, Ars senior policy reporter Ashley Belanger covered a case of costly AI confabulation in the wild. In the course of online text conversations, Air Canada’s customer service chatbot told customers inaccurate refund policy information. The airline faced legal consequences later when a tribunal ruled the airline must honor commitments made by the automated system. Tribunal adjudicator Christopher Rivers determined that Air Canada bore responsibility for all information on its website, regardless of whether it came from a static page or AI interface.
The case set a precedent for how companies deploying AI customer service tools could face legal obligations for automated systems’ responses, particularly when they fail to warn users about potential inaccuracies. Ironically, the airline had reportedly spent more on the initial AI implementation than it would have cost to maintain human workers for simple queries, according to Air Canada executive Steve Crocker.
In March 2023, a terrible AI-generated video of Will Smith’s AI doppelganger eating spaghetti began making the rounds online. The AI-generated version of the actor gobbled down the noodles in an unnatural and disturbing way. Almost a year later, in February 2024, Will Smith himself posted a parody response video to the viral jabberwocky on Instagram, featuring AI-like deliberately exaggerated pasta consumption, complete with hair-nibbling and finger-slurping antics.
Given the rapid evolution of AI video technology, particularly since OpenAI had just unveiled its Sora video model four days earlier, Smith’s post sparked discussion in his Instagram comments where some viewers initially struggled to distinguish between the genuine footage and AI generation. It was an early sign of “deep doubt” in action as the tech increasingly blurs the line between synthetic and authentic video content.
Robot dogs learn to hunt people with AI-guided rifles
A still image of a robotic quadruped armed with a remote weapons system, captured from a video provided by Onyx Industries. Credit: Onyx Industries
At some point in recent history—somewhere around 2022—someone took a look at robotic quadrupeds and thought it would be a great idea to attach guns to them. A few years later, the US Marine Forces Special Operations Command (MARSOC) began evaluating armed robotic quadrupeds developed by Ghost Robotics. The robot “dogs” integrated Onyx Industries’ SENTRY remote weapon systems, which featured AI-enabled targeting that could detect and track people, drones, and vehicles, though the systems require human operators to authorize any weapons discharge.
The military’s interest in armed robotic dogs followed a broader trend of weaponized quadrupeds entering public awareness. This included viral videos of consumer robots carrying firearms, and later, commercial sales of flame-throwing models. While MARSOC emphasized that weapons were just one potential use case under review, experts noted that the increasing integration of AI into military robotics raised questions about how long humans would remain in control of lethal force decisions.
Microsoft Windows AI is watching
A screenshot of Microsoft’s new “Recall” feature in action. Credit: Microsoft
In an era where many people already feel like they have no privacy due to tech encroachments, Microsoft dialed it up to an extreme degree in May. That’s when Microsoft unveiled a controversial Windows 11 feature called “Recall” that continuously captures screenshots of users’ PC activities every few seconds for later AI-powered search and retrieval. The feature, designed for new Copilot+ PCs using Qualcomm’s Snapdragon X Elite chips, promised to help users find past activities, including app usage, meeting content, and web browsing history.
While Microsoft emphasized that Recall would store encrypted snapshots locally and allow users to exclude specific apps or websites, the announcement raised immediate privacy concerns, as Ars senior technology reporter Andrew Cunningham covered. It also came with a technical toll, requiring significant hardware resources, including 256GB of storage space, with 25GB dedicated to storing approximately three months of user activity. After Microsoft pulled the initial test version due to public backlash, Recall later entered public preview in November with reportedly enhanced security measures. But secure spyware is still spyware—Recall, when enabled, still watches nearly everything you do on your computer and keeps a record of it.
Google Search told people to eat rocks
This is fine. Credit: Getty Images
In May, Ars senior gaming reporter Kyle Orland (who assisted commendably with the AI beat throughout the year) covered Google’s newly launched AI Overview feature. It faced immediate criticism when users discovered that it frequently provided false and potentially dangerous information in its search result summaries. Among its most alarming responses, the system advised humans could safely consume rocks, incorrectly citing scientific sources about the geological diet of marine organisms. The system’s other errors included recommending nonexistent car maintenance products, suggesting unsafe food preparation techniques, and confusing historical figures who shared names.
The problems stemmed from several issues, including the AI treating joke posts as factual sources and misinterpreting context from original web content. But most of all, the system relies on web results as indicators of authority, which we called a flawed design. While Google defended the system, stating these errors occurred mainly with uncommon queries, a company spokesperson acknowledged they would use these “isolated examples” to refine their systems. But to this day, AI Overview still makes frequent mistakes.
Stable Diffusion generates body horror
An AI-generated image created using Stable Diffusion 3 of a girl lying in the grass. Credit: HorneyMetalBeing
In June, Stability AI’s release of the image synthesis model Stable Diffusion 3 Medium drew criticism online for its poor handling of human anatomy in AI-generated images. Users across social media platforms shared examples of the model producing what we now like to call jabberwockies—AI generation failures with distorted bodies, misshapen hands, and surreal anatomical errors, and many in the AI image-generation community viewed it as a significant step backward from previous image-synthesis capabilities.
Reddit users attributed these failures to Stability AI’s aggressive filtering of adult content from the training data, which apparently impaired the model’s ability to accurately render human figures. The troubled release coincided with broader organizational challenges at Stability AI, including the March departure of CEO Emad Mostaque, multiple staff layoffs, and the exit of three key engineers who had helped develop the technology. Some of those engineers founded Black Forest Labs in August and released Flux, which has become the latest open-weights AI image model to beat.
ChatGPT Advanced Voice imitates human voice in testing
AI voice-synthesis models are master imitators these days, and they are capable of much more than many people realize. In August, we covered a story where OpenAI’s ChatGPT Advanced Voice Mode feature unexpectedly imitated a user’s voice during the company’s internal testing, revealed by OpenAI after the fact in safety testing documentation. To prevent future instances of an AI assistant suddenly speaking in your own voice (which, let’s be honest, would probably freak people out), the company created an output classifier system to prevent unauthorized voice imitation. OpenAI says that Advanced Voice Mode now catches all meaningful deviations from approved system voices.
Independent AI researcher Simon Willison discussed the implications with Ars Technica, noting that while OpenAI restricted its model’s full voice synthesis capabilities, similar technology would likely emerge from other sources within the year. Meanwhile, the rapid advancement of AI voice replication has caused general concern about its potential misuse, although companies like ElevenLabs have already been offering voice cloning services for some time.
San Francisco’s robotic car horn symphony
A Waymo self-driving car in front of Google’s San Francisco headquarters, San Francisco, California, June 7, 2024. Credit: Getty Images
In August, San Francisco residents got a noisy taste of robo-dystopia when Waymo’s self-driving cars began creating an unexpected nightly disturbance in the South of Market district. In a parking lot off 2nd Street, the cars congregated autonomously every night during rider lulls at 4 am and began engaging in extended honking matches at each other while attempting to park.
Local resident Christopher Cherry’s initial optimism about the robotic fleet’s presence dissolved as the mechanical chorus grew louder each night, affecting residents in nearby high-rises. The nocturnal tech disruption served as a lesson in the unintentional effects of autonomous systems when run in aggregate.
Larry Ellison dreams of all-seeing AI cameras
In September, Oracle co-founder Larry Ellison painted a bleak vision of ubiquitous AI surveillance during a company financial meeting. The 80-year-old database billionaire described a future where AI would monitor citizens through networks of cameras and drones, asserting that the oversight would ensure lawful behavior from both police and the public.
His surveillance predictions reminded us of parallels to existing systems in China, where authorities already used AI to sort surveillance data on citizens as part of the country’s “sharp eyes” campaign from 2015 to 2020. Ellison’s statement reflected the sort of worst-case tech surveillance state scenario—likely antithetical to any sort of free society—that dozens of sci-fi novels of the 20th century warned us about.
A dead father sends new letters home
An AI-generated image featuring my late father’s handwriting. Credit: Benj Edwards / Flux
AI has made many of us do weird things in 2024, including this writer. In October, I used an AI synthesis model called Flux to reproduce my late father’s handwriting with striking accuracy. After scanning 30 samples from his engineering notebooks, I trained the model using computing time that cost less than five dollars. The resulting text captured his distinctive uppercase style, which he developed during his career as an electronics engineer.
I enjoyed creating images showing his handwriting in various contexts, from folder labels to skywriting, and made the trained model freely available online for others to use. While I approached it as a tribute to my father (who would have appreciated the technical achievement), many people found the whole experience weird and somewhat disturbing. The things we unhinged Bing Chat-like journalists do to bring awareness to a topic are sometimes unconventional. So I guess it counts for this list!
For 2025? Expect even more AI
Thanks for reading Ars Technica this past year and following along with our team coverage of this rapidly emerging and expanding field. We appreciate your kind words of support. Ars Technica’s 2024 AI words of the year were: vibemarking, deep doubt, and the aforementioned jabberwocky. The old stalwart “confabulation” also made several notableappearances. Tune in again next year when we continue to try to figure out how to concisely describe novel scenarios in emerging technology by labeling them.
Looking back, our prediction for 2024 in AI last year was “buckle up.” It seems fitting, given the weirdness detailed above. Especially the part about the robot dogs with guns. For 2025, AI will likely inspire more chaos ahead, but also potentially get put to serious work as a productivity tool, so this time, our prediction is “buckle down.”
Finally, we’d like to ask: What was the craziest story about AI in 2024 from your perspective? Whether you love AI or hate it, feel free to suggest your own additions to our list in the comments. Happy New Year!
Benj Edwards is Ars Technica’s Senior AI Reporter and founder of the site’s dedicated AI beat in 2022. He’s also a tech historian with almost two decades of experience. In his free time, he writes and records music, collects vintage computers, and enjoys nature. He lives in Raleigh, NC.
During her tenure, Vestager has repeatedly targeted the world’s biggest tech companies, with some of the toughest actions against tech giants such as Apple, Google, and Microsoft.
The EU Commission on Thursday said Meta is “dominant in the market for personal social networks (…) as well as in the national markets for online display advertising on social media.”
Facebook Marketplace, launched in 2016, is a popular platform to buy and sell second-hand goods, especially household items such as furniture.
Meta has argued that it operates in a highly competitive environment. In a post published on Thursday, the tech giant said marketplaces in Europe continue “to grow and dominate in the EU,” pointing to platforms such as eBay, Leboncoin in France, and Marktplaats in the Netherlands, as “formidable competitors.”
Meta’s fine comes at a period of political transition both in the EU and the US.
Brussels officials have been aggressive both in their rhetoric and their antitrust probes against Big Tech giants as they sought to open markets for local start-ups.
In the past five years, EU regulators have also passed a landmark piece of legislation—the Digital Markets Act—with the aim to slow down dominant tech players and boost the local tech industry.
However, some observers expect the new commission, which is set to start a new 5-year term in weeks, to strike a more conciliatory tone over fears of retaliation from the incoming Trump administration.
Since its founders started Anthropic in 2021, the company has marketed itself as one that takes an ethics- and safety-focused approach to AI development. The company differentiates itself from competitors like OpenAI by adopting what it calls responsible development practices and self-imposed ethical constraints on its models, such as its “Constitutional AI” system.
As Futurism points out, this new defense partnership appears to conflict with Anthropic’s public “good guy” persona, and pro-AI pundits on social media are noticing. Frequent AI commentator Nabeel S. Qureshi wrote on X, “Imagine telling the safety-concerned, effective altruist founders of Anthropic in 2021 that a mere three years after founding the company, they’d be signing partnerships to deploy their ~AGI model straight to the military frontlines.“
Aside from the implications of working with defense and intelligence agencies, the deal connects Anthropic with Palantir, a controversial company which recently won a $480 million contract to develop an AI-powered target identification system called Maven Smart System for the US Army. Project Maven has sparked criticism within the tech sector over military applications of AI technology.
It’s worth noting that Anthropic’s terms of service do outline specific rules and limitations for government use. These terms permit activities like foreign intelligence analysis and identifying covert influence campaigns, while prohibiting uses such as disinformation, weapons development, censorship, and domestic surveillance. Government agencies that maintain regular communication with Anthropic about their use of Claude may receive broader permissions to use the AI models.
Even if Claude is never used to target a human or as part of a weapons system, other issues remain. While its Claude models are highly regarded in the AI community, they (like all LLMs) have the tendency to confabulate, potentially generating incorrect information in a way that is difficult to detect.
That’s a huge potential problem that could impact Claude’s effectiveness with secret government data, and that fact, along with the other associations, has Futurism’s Victor Tangermann worried. As he puts it, “It’s a disconcerting partnership that sets up the AI industry’s growing ties with the US military-industrial complex, a worrying trend that should raise all kinds of alarm bells given the tech’s many inherent flaws—and even more so when lives could be at stake.”
The lawsuit was filed by Ethan Zuckerman, a professor at University of Massachusetts Amherst. He feared that Meta might sue to block his tool, Unfollow Everything 2.0, because Meta threatened to sue to block the original tool when it was released by another developer. In May, Zuckerman told Ars that he was “suing Facebook to make it better” and planned to use Section 230’s shield to do it.
Zuckerman’s novel legal theory argued that Congress always intended for Section 230 to protect third-party tools designed to empower users to take control over potentially toxic online environments. In his complaint, Zuckerman tried to convince a US district court in California that:
Section 230(c)(2)(B) immunizes from legal liability “a provider of software or enabling tools that filter, screen, allow, or disallow content that the provider or user considers obscene, lewd, lascivious, filthy, excessively violent, harassing, or otherwise objectionable.” Through this provision, Congress intended to promote the development of filtering tools that enable users to curate their online experiences and avoid content they would rather not see.
Digital rights advocates, the Electronic Frontier Foundation (EFF), the Center for Democracy and Technology, and the American Civil Liberties Union of Northern California, supported Zuckerman’s case, urging that the court protect middleware. But on Thursday, Judge Jacqueline Scott Corley granted Meta’s motion to dismiss at a hearing.
Corley has not yet posted her order on the motion to dismiss, but Zuckerman’s lawyers at the Knight Institute confirmed to Ars that their Section 230 argument did not factor into her decision. In a statement, lawyers said that Corley left the door open on the Section 230 claims, and EFF senior staff attorney Sophia Cope, who was at the hearing, told Ars Corley agreed that on “the merits the case raises important issues.”
On Friday, Meta announced a preview of Movie Gen, a new suite of AI models designed to create and manipulate video, audio, and images, including creating a realistic video from a single photo of a person. The company claims the models outperform other video-synthesis models when evaluated by humans, pushing us closer to a future where anyone can synthesize a full video of any subject on demand.
The company does not yet have plans of when or how it will release these capabilities to the public, but Meta says Movie Gen is a tool that may allow people to “enhance their inherent creativity” rather than replace human artists and animators. The company envisions future applications such as easily creating and editing “day in the life” videos for social media platforms or generating personalized animated birthday greetings.
Movie Gen builds on Meta’s previous work in video synthesis, following 2022’s Make-A-Scene video generator and the Emu image-synthesis model. Using text prompts for guidance, this latest system can generate custom videos with sounds for the first time, edit and insert changes into existing videos, and transform images of people into realistic personalized videos.
An AI-generated video of a baby hippo swimming around, created with Meta Movie Gen.
Meta isn’t the only game in town when it comes to AI video synthesis. Google showed off a new model called “Veo” in May, and Meta says that in human preference tests, its Movie Gen outputs beat OpenAI’s Sora, Runway Gen-3, and Chinese video model Kling.
Movie Gen’s video-generation model can create 1080p high-definition videos up to 16 seconds long at 16 frames per second from text descriptions or an image input. Meta claims the model can handle complex concepts like object motion, subject-object interactions, and camera movements.
AI-generated video from Meta Movie Gen with the prompt: “A ghost in a white bedsheet faces a mirror. The ghost’s reflection can be seen in the mirror. The ghost is in a dusty attic, filled with old beams, cloth-covered furniture. The attic is reflected in the mirror. The light is cool and natural. The ghost dances in front of the mirror.”
Even so, as we’ve seen with previous AI video generators, Movie Gen’s ability to generate coherent scenes on a particular topic is likely dependent on the concepts found in the example videos that Meta used to train its video-synthesis model. It’s worth keeping in mind that cherry-picked results from video generators often differ dramatically from typical results and getting a coherent result may require lots of trial and error.
To prevent anyone from being doxxed, the co-creators are not releasing the code, Nguyen said on social media site X. They did, however, outline how their disturbing tech works and how shocked random strangers used as test subjects were to discover how easily identifiable they are just from accessing with the smart glasses information posted publicly online.
Nguyen and Ardayfio tested out their technology at a subway station “on unsuspecting people in the real world,” 404 Media noted. To demonstrate how the tech could be abused to trick people, the students even claimed to know some of the test subjects, seemingly using information gleaned from the glasses to make resonant references and fake an acquaintance.
Dozens of test subjects were identified, the students claimed, although some results have been contested, 404 Media reported. To keep their face-scanning under the radar, the students covered up a light that automatically comes on when the Meta Ray Bans 2 are recording, Ardayfio said on X.
Opt out of PimEyes now, students warn
For Nguyen and Ardayfio, the point of the project was to persuade people to opt out of invasive search engines to protect their privacy online. An attempt to use I-XRAY to identify 404 Media reporter Joseph Cox, for example, didn’t work because he’d opted out of PimEyes.
But while privacy is clearly important to the students and their demo video strove to remove identifying information, at least one test subject was “easily” identified anyway, 404 Media reported. That test subject couldn’t be reached for comment, 404 Media reported.
So far, neither Facebook nor Google has chosen to release similar technologies that they developed linking smart glasses to face search engines, The New York Times reported.
Enlarge/ There’s been a lot of AI news this week, and covering it sometimes feels like running through a hall full of danging CRTs, just like this Getty Images illustration.
But the rest of the AI world doesn’t march to the same beat, doing its own thing and churning out new AI models and research by the minute. Here’s a roundup of some other notable AI news from the past week.
Google Gemini updates
On Tuesday, Google announced updates to its Gemini model lineup, including the release of two new production-ready models that iterate on past releases: Gemini-1.5-Pro-002 and Gemini-1.5-Flash-002. The company reported improvements in overall quality, with notable gains in math, long context handling, and vision tasks. Google claims a 7 percent increase in performance on the MMLU-Pro benchmark and a 20 percent improvement in math-related tasks. But as you know, if you’ve been reading Ars Technica for a while, AI typically benchmarks aren’t as useful as we would like them to be.
Along with model upgrades, Google introduced substantial price reductions for Gemini 1.5 Pro, cutting input token costs by 64 percent and output token costs by 52 percent for prompts under 128,000 tokens. As AI researcher Simon Willison noted on his blog, “For comparison, GPT-4o is currently $5/[million tokens] input and $15/m output and Claude 3.5 Sonnet is $3/m input and $15/m output. Gemini 1.5 Pro was already the cheapest of the frontier models and now it’s even cheaper.”
Google also increased rate limits, with Gemini 1.5 Flash now supporting 2,000 requests per minute and Gemini 1.5 Pro handling 1,000 requests per minute. Google reports that the latest models offer twice the output speed and three times lower latency compared to previous versions. These changes may make it easier and more cost-effective for developers to build applications with Gemini than before.
Meta launches Llama 3.2
On Wednesday, Meta announced the release of Llama 3.2, a significant update to its open-weights AI model lineup that we have covered extensively in the past. The new release includes vision-capable large language models (LLMs) in 11 billion and 90B parameter sizes, as well as lightweight text-only models of 1B and 3B parameters designed for edge and mobile devices. Meta claims the vision models are competitive with leading closed-source models on image recognition and visual understanding tasks, while the smaller models reportedly outperform similar-sized competitors on various text-based tasks.
Willison did some experiments with some of the smaller 3.2 models and reported impressive results for the models’ size. AI researcher Ethan Mollick showed off running Llama 3.2 on his iPhone using an app called PocketPal.
Meta also introduced the first official “Llama Stack” distributions, created to simplify development and deployment across different environments. As with previous releases, Meta is making the models available for free download, with license restrictions. The new models support long context windows of up to 128,000 tokens.
Google’s AlphaChip AI speeds up chip design
On Thursday, Google DeepMind announced what appears to be a significant advancement in AI-driven electronic chip design, AlphaChip. It began as a research project in 2020 and is now a reinforcement learning method for designing chip layouts. Google has reportedly used AlphaChip to create “superhuman chip layouts” in the last three generations of its Tensor Processing Units (TPUs), which are chips similar to GPUs designed to accelerate AI operations. Google claims AlphaChip can generate high-quality chip layouts in hours, compared to weeks or months of human effort. (Reportedly, Nvidia has also been using AI to help design its chips.)
Notably, Google also released a pre-trained checkpoint of AlphaChip on GitHub, sharing the model weights with the public. The company reported that AlphaChip’s impact has already extended beyond Google, with chip design companies like MediaTek adopting and building on the technology for their chips. According to Google, AlphaChip has sparked a new line of research in AI for chip design, potentially optimizing every stage of the chip design cycle from computer architecture to manufacturing.
That wasn’t everything that happened, but those are some major highlights. With the AI industry showing no signs of slowing down at the moment, we’ll see how next week goes.
Enlarge/ A man peers over a glass partition, seeking transparency.
The Open Source Initiative (OSI) recently unveiled its latest draft definition for “open source AI,” aiming to clarify the ambiguous use of the term in the fast-moving field. The move comes as some companies like Meta release trained AI language model weights and code with usage restrictions while using the “open source” label. This has sparked intense debates among free-software advocates about what truly constitutes “open source” in the context of AI.
For instance, Meta’s Llama 3 model, while freely available, doesn’t meet the traditional open source criteria as defined by the OSI for software because it imposes license restrictions on usage due to company size or what type of content is produced with the model. The AI image generator Flux is another “open” model that is not truly open source. Because of this type of ambiguity, we’ve typically described AI models that include code or weights with restrictions or lack accompanying training data with alternative terms like “open-weights” or “source-available.”
To address the issue formally, the OSI—which is well-known for its advocacy for open software standards—has assembled a group of about 70 participants, including researchers, lawyers, policymakers, and activists. Representatives from major tech companies like Meta, Google, and Amazon also joined the effort. The group’s current draft (version 0.0.9) definition of open source AI emphasizes “four fundamental freedoms” reminiscent of those defining free software: giving users of the AI system permission to use it for any purpose without permission, study how it works, modify it for any purpose, and share with or without modifications.
By establishing clear criteria for open source AI, the organization hopes to provide a benchmark against which AI systems can be evaluated. This will likely help developers, researchers, and users make more informed decisions about the AI tools they create, study, or use.
Truly open source AI may also shed light on potential software vulnerabilities of AI systems, since researchers will be able to see how the AI models work behind the scenes. Compare this approach with an opaque system such as OpenAI’s ChatGPT, which is more than just a GPT-4o large language model with a fancy interface—it’s a proprietary system of interlocking models and filters, and its precise architecture is a closely guarded secret.
OSI’s project timeline indicates that a stable version of the “open source AI” definition is expected to be announced in October at the All Things Open 2024 event in Raleigh, North Carolina.
“Permissionless innovation”
In a press release from May, the OSI emphasized the importance of defining what open source AI really means. “AI is different from regular software and forces all stakeholders to review how the Open Source principles apply to this space,” said Stefano Maffulli, executive director of the OSI. “OSI believes that everybody deserves to maintain agency and control of the technology. We also recognize that markets flourish when clear definitions promote transparency, collaboration and permissionless innovation.”
The organization’s most recent draft definition extends beyond just the AI model or its weights, encompassing the entire system and its components.
For an AI system to qualify as open source, it must provide access to what the OSI calls the “preferred form to make modifications.” This includes detailed information about the training data, the full source code used for training and running the system, and the model weights and parameters. All these elements must be available under OSI-approved licenses or terms.
Notably, the draft doesn’t mandate the release of raw training data. Instead, it requires “data information”—detailed metadata about the training data and methods. This includes information on data sources, selection criteria, preprocessing techniques, and other relevant details that would allow a skilled person to re-create a similar system.
The “data information” approach aims to provide transparency and replicability without necessarily disclosing the actual dataset, ostensibly addressing potential privacy or copyright concerns while sticking to open source principles, though that particular point may be up for further debate.
“The most interesting thing about [the definition] is that they’re allowing training data to NOT be released,” said independent AI researcher Simon Willison in a brief Ars interview about the OSI’s proposal. “It’s an eminently pragmatic approach—if they didn’t allow that, there would be hardly any capable ‘open source’ models.”
