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Three reasons Meta will struggle with community fact-checking

Earlier this month, Mark Zuckerberg announced that Meta will cut back on its content moderation efforts and eliminate fact-checking in the US in favor of the more “democratic” approach that X (formerly Twitter) calls Community Notes, rolling back protections that he claimed had been developed only in response to media and government pressure. The move is raising alarm bells, and rightly so. Meta has left a trail of moderation controversies in its wake, from overmoderating images of breastfeeding women to undermoderating hate speech in Myanmar, contributing to the genocide of Rohingya Muslims. Meanwhile, ending professional fact-checking creates the potential for misinformation and hate to spread unchecked. Enlisting volunteers is how moderation started on the Internet, long before social media giants realized that centralized efforts were necessary. And volunteer moderation can be successful, allowing for the development of bespoke regulations aligned with the needs of particular communities. But without significant commitment and oversight from Meta, such a system cannot contend with how much content is shared across the company’s platforms, and how fast. In fact, the jury is still out on how well it works at X, which is used by 21% of Americans (Meta’s are significantly more popular—Facebook alone is used by 70% of Americans, according to Pew).   Community Notes, which started in 2021 as Birdwatch, is a community-driven moderation system on X that allows users who sign up for the program to add context to posts. Having regular users provide public fact-checking is relatively new, and so far results are mixed. For example, researchers have found that participants are more likely to challenge content they disagree with politically and that flagging content as false does not reduce engagement, but they have also found that the notes are typically accurate and can help reduce the spread of misleading posts.  I’m a community moderator who researches community moderation. Here’s what I’ve learned about the limitations of relying on volunteers for moderation—and what Meta needs to do to succeed:  1. The system will miss falsehoods and could amplify hateful content There is a real risk under this style of moderation that only posts about things that a lot of people know about will get flagged in a timely manner—or at all. Consider how a post with a picture of a death cap mushroom and the caption “Tasty” might be handled under Community Notes–style moderation. If an expert in mycology doesn’t see the post, or sees it only after it’s been widely shared, it may not get flagged as “Poisonous, do not eat”—at least not until it’s too late. Topic areas that are more esoteric will be undermoderated. This could have serious impacts on both individuals (who may eat a poisonous mushroom) and society (if a falsehood spreads widely).  Crucially, X’s Community Notes aren’t visible to readers when they are first added. A note becomes visible to the wider user base only when enough contributors agree that it is accurate by voting for it. And not all votes count. If a note is rated only by people who tend to agree with each other, it won’t show up. X does not make a note visible until there’s agreement from people who have disagreed on previous ratings. This is an attempt to reduce bias, but it’s not foolproof. It still relies on people’s opinions about a note and not on actual facts. Often what’s needed is expertise.I moderate a community on Reddit called r/AskHistorians. It’s a public history site with over 2 million members and is very strictly moderated. We see people get facts wrong all the time. Sometimes these are straightforward errors. But sometimes there is hateful content that takes experts to recognize. One time a question containing a Holocaust-denial dog whistle escaped review for hours and ended up amassing hundreds of upvotes before it was caught by an expert on our team. Hundreds of people—probably with very different voting patterns and very different opinions on a lot of topics—not only missed the problematic nature of the content but chose to promote it through upvotes. This happens with answers to questions, too. People who aren’t experts in history will upvote outdated, truthy-sounding answers that aren’t actually correct. Conversely, they will downvote good answers if they reflect viewpoints that are tough to swallow.  r/AskHistorians works because most of its moderators are expert historians. If Meta wants its Community Notes–style program to work, it should  make sure that the people with the knowledge to make assessments see the posts and that expertise is accounted for in voting, especially when there’s a misalignment between common understanding and expert knowledge.  2. It won’t work without well-supported volunteers   Meta’s paid content moderators review the worst of the worst—including gore, sexual abuse and exploitation, and violence. As a result, many have suffered severe trauma, leading to lawsuits and unionization efforts. When Meta cuts resources from its centralized moderation efforts, it will be increasingly up to unpaid volunteers to keep the platform safe.  Community moderators don’t have an easy job. On top of exposure to horrific content, as identifiable members of their communities, they are also often subject to harassment and abuse—something we experience daily on r/AskHistorians. However, community moderators moderate only what they can handle. For example, while I routinely manage hate speech and violent language, as a moderator of a text-based community I am rarely exposed to violent imagery. Community moderators also work as a team. If I do get exposed to something I find upsetting or if someone is being abusive, my colleagues take over and provide emotional support. I also care deeply about the community I moderate. Care for community, supportive colleagues, and self-selection all help keep volunteer moderators’ morale high(ish).  It’s unclear how Meta’s new moderation system will be structured. If volunteers choose what content they flag, will that replicate X’s problem, where partisanship affects which posts are flagged and how? It’s also unclear what kind of support the platform will provide. If volunteers are exposed to content they find upsetting, will Meta—the company that is currently being sued for damaging the mental health of its paid content moderators—provide social and psychological aid? To be successful, the company will need to ensure that volunteers have access to such resources and are able to choose the type of content they moderate (while also ensuring that this self-selection doesn’t unduly influence the notes).     3. It can’t work without protections and guardrails  Online communities can thrive when they are run by people who deeply care about them. However, volunteers can’t do it all on their own. Moderation isn’t just about making decisions on what’s “true” or “false.” It’s also about identifying and responding to other kinds of harmful content. Zuckerberg’s decision is coupled with other changes to its community standards that weaken rules around hateful content in particular. Community moderation is part of a broader ecosystem, and it becomes significantly harder to do it when that ecosystem gets poisoned by toxic content.  I started moderating r/AskHistorians in 2020 as part of a research project to learn more about the behind-the-scenes experiences of volunteer moderators. While Reddit had started addressing some of the most extreme hate on its platform by occasionally banning entire communities, many communities promoting misogyny, racism, and all other forms of bigotry were permitted to thrive and grow. As a result, my early field notes are filled with examples of extreme hate speech, as well as harassment and abuse directed at moderators. It was hard to keep up with.  But halfway through 2020, something happened. After a milquetoast statement about racism from CEO Steve Huffman, moderators on the site shut down their communities in protest. And to its credit, the platform listened. Reddit updated its community standards to explicitly prohibit hate speech and began to enforce the policy more actively. While hate is still an issue on Reddit, I see far less now than I did in 2020 and 2021. Community moderation needs robust support because volunteers can’t do it all on their own. It’s only one tool in the box.  If Meta wants to ensure that its users are safe from scams, exploitation, and manipulation in addition to hate, it cannot rely solely on community fact-checking. But keeping the user base safe isn’t what this decision aims to do. It’s a political move to curry favor with the new administration. Meta could create the perfect community fact-checking program, but because this decision is coupled with weakening its wider moderation practices, things are going to get worse for its users rather than better.  Sarah Gilbert is research director for the Citizens and Technology Lab at Cornell University.

Earlier this month, Mark Zuckerberg announced that Meta will cut back on its content moderation efforts and eliminate fact-checking in the US in favor of the more “democratic” approach that X (formerly Twitter) calls Community Notes, rolling back protections that he claimed had been developed only in response to media and government pressure.

The move is raising alarm bells, and rightly so. Meta has left a trail of moderation controversies in its wake, from overmoderating images of breastfeeding women to undermoderating hate speech in Myanmar, contributing to the genocide of Rohingya Muslims. Meanwhile, ending professional fact-checking creates the potential for misinformation and hate to spread unchecked.

Enlisting volunteers is how moderation started on the Internet, long before social media giants realized that centralized efforts were necessary. And volunteer moderation can be successful, allowing for the development of bespoke regulations aligned with the needs of particular communities. But without significant commitment and oversight from Meta, such a system cannot contend with how much content is shared across the company’s platforms, and how fast. In fact, the jury is still out on how well it works at X, which is used by 21% of Americans (Meta’s are significantly more popular—Facebook alone is used by 70% of Americans, according to Pew).  

Community Notes, which started in 2021 as Birdwatch, is a community-driven moderation system on X that allows users who sign up for the program to add context to posts. Having regular users provide public fact-checking is relatively new, and so far results are mixed. For example, researchers have found that participants are more likely to challenge content they disagree with politically and that flagging content as false does not reduce engagement, but they have also found that the notes are typically accurate and can help reduce the spread of misleading posts

I’m a community moderator who researches community moderation. Here’s what I’ve learned about the limitations of relying on volunteers for moderation—and what Meta needs to do to succeed: 

1. The system will miss falsehoods and could amplify hateful content

There is a real risk under this style of moderation that only posts about things that a lot of people know about will get flagged in a timely manner—or at all. Consider how a post with a picture of a death cap mushroom and the caption “Tasty” might be handled under Community Notes–style moderation. If an expert in mycology doesn’t see the post, or sees it only after it’s been widely shared, it may not get flagged as “Poisonous, do not eat”—at least not until it’s too late. Topic areas that are more esoteric will be undermoderated. This could have serious impacts on both individuals (who may eat a poisonous mushroom) and society (if a falsehood spreads widely). 

Crucially, X’s Community Notes aren’t visible to readers when they are first added. A note becomes visible to the wider user base only when enough contributors agree that it is accurate by voting for it. And not all votes count. If a note is rated only by people who tend to agree with each other, it won’t show up. X does not make a note visible until there’s agreement from people who have disagreed on previous ratings. This is an attempt to reduce bias, but it’s not foolproof. It still relies on people’s opinions about a note and not on actual facts. Often what’s needed is expertise.

I moderate a community on Reddit called r/AskHistorians. It’s a public history site with over 2 million members and is very strictly moderated. We see people get facts wrong all the time. Sometimes these are straightforward errors. But sometimes there is hateful content that takes experts to recognize. One time a question containing a Holocaust-denial dog whistle escaped review for hours and ended up amassing hundreds of upvotes before it was caught by an expert on our team. Hundreds of people—probably with very different voting patterns and very different opinions on a lot of topics—not only missed the problematic nature of the content but chose to promote it through upvotes. This happens with answers to questions, too. People who aren’t experts in history will upvote outdated, truthy-sounding answers that aren’t actually correct. Conversely, they will downvote good answers if they reflect viewpoints that are tough to swallow. 

r/AskHistorians works because most of its moderators are expert historians. If Meta wants its Community Notes–style program to work, it should  make sure that the people with the knowledge to make assessments see the posts and that expertise is accounted for in voting, especially when there’s a misalignment between common understanding and expert knowledge. 

2. It won’t work without well-supported volunteers  

Meta’s paid content moderators review the worst of the worst—including gore, sexual abuse and exploitation, and violence. As a result, many have suffered severe trauma, leading to lawsuits and unionization efforts. When Meta cuts resources from its centralized moderation efforts, it will be increasingly up to unpaid volunteers to keep the platform safe. 

Community moderators don’t have an easy job. On top of exposure to horrific content, as identifiable members of their communities, they are also often subject to harassment and abuse—something we experience daily on r/AskHistorians. However, community moderators moderate only what they can handle. For example, while I routinely manage hate speech and violent language, as a moderator of a text-based community I am rarely exposed to violent imagery. Community moderators also work as a team. If I do get exposed to something I find upsetting or if someone is being abusive, my colleagues take over and provide emotional support. I also care deeply about the community I moderate. Care for community, supportive colleagues, and self-selection all help keep volunteer moderators’ morale high(ish). 

It’s unclear how Meta’s new moderation system will be structured. If volunteers choose what content they flag, will that replicate X’s problem, where partisanship affects which posts are flagged and how? It’s also unclear what kind of support the platform will provide. If volunteers are exposed to content they find upsetting, will Meta—the company that is currently being sued for damaging the mental health of its paid content moderators—provide social and psychological aid? To be successful, the company will need to ensure that volunteers have access to such resources and are able to choose the type of content they moderate (while also ensuring that this self-selection doesn’t unduly influence the notes).    

3. It can’t work without protections and guardrails 

Online communities can thrive when they are run by people who deeply care about them. However, volunteers can’t do it all on their own. Moderation isn’t just about making decisions on what’s “true” or “false.” It’s also about identifying and responding to other kinds of harmful content. Zuckerberg’s decision is coupled with other changes to its community standards that weaken rules around hateful content in particular. Community moderation is part of a broader ecosystem, and it becomes significantly harder to do it when that ecosystem gets poisoned by toxic content. 

I started moderating r/AskHistorians in 2020 as part of a research project to learn more about the behind-the-scenes experiences of volunteer moderators. While Reddit had started addressing some of the most extreme hate on its platform by occasionally banning entire communities, many communities promoting misogyny, racism, and all other forms of bigotry were permitted to thrive and grow. As a result, my early field notes are filled with examples of extreme hate speech, as well as harassment and abuse directed at moderators. It was hard to keep up with. 

But halfway through 2020, something happened. After a milquetoast statement about racism from CEO Steve Huffman, moderators on the site shut down their communities in protest. And to its credit, the platform listened. Reddit updated its community standards to explicitly prohibit hate speech and began to enforce the policy more actively. While hate is still an issue on Reddit, I see far less now than I did in 2020 and 2021. Community moderation needs robust support because volunteers can’t do it all on their own. It’s only one tool in the box. 

If Meta wants to ensure that its users are safe from scams, exploitation, and manipulation in addition to hate, it cannot rely solely on community fact-checking. But keeping the user base safe isn’t what this decision aims to do. It’s a political move to curry favor with the new administration. Meta could create the perfect community fact-checking program, but because this decision is coupled with weakening its wider moderation practices, things are going to get worse for its users rather than better. 

Sarah Gilbert is research director for the Citizens and Technology Lab at Cornell University.

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Infrastructure Maturity Defines the Next Phase of AI Deployment

The State of Data Infrastructure Global Report 2025 from Hitachi Vantara arrives at a moment when the data center industry is undergoing one of the most profound structural shifts in its history. The transition from enterprise IT to AI-first infrastructure has moved from aspiration to inevitability, forcing operators, developers, and investors to confront uncomfortable truths about readiness, resilience, and risk. Although framed around “AI readiness,” the report ultimately tells an infrastructure story: one that maps directly onto how data centers are designed, operated, secured, and justified economically. Drawing on a global survey of more than 1,200 IT leaders, the report introduces a proprietary maturity model that evaluates organizations across six dimensions: scalability, reliability, security, governance, sovereignty, and sustainability. Respondents are then grouped into three categories—Emerging, Defined, and Optimized—revealing a stark conclusion: most organizations are not constrained by access to AI models or capital, but by the fragility of the infrastructure supporting their data pipelines. For the data center industry, the implications are immediate, shaping everything from availability design and automation strategies to sustainability planning and evolving customer expectations. In short, extracting value from AI now depends less on experimentation and more on the strength and resilience of the underlying infrastructure. The Focus of the Survey: Infrastructure, Not Algorithms Although the report is positioned as a study of AI readiness, its primary focus is not models, training approaches, or application development, but rather the infrastructure foundations required to operate AI reliably at scale. Drawing on responses from more than 1,200 organizations, Hitachi Vantara evaluates how enterprises are positioned to support production AI workloads across six dimensions as stated above: scalability, reliability, security, governance, sovereignty, and sustainability. These factors closely reflect the operational realities shaping modern data center design and management. The survey’s central argument is that AI success is no longer

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AI’s New Land Grab: Meta’s Indiana Megaproject and the Rise of Europe’s Neocloud Challengers

While Meta’s Indiana campus anchors hyperscale expansion in the United States, Europe recorded its own major infrastructure milestone this week as Amsterdam-based AI infrastructure provider Nebius unveiled plans for a 240-megawatt data center campus in Béthune, France, near Lille in the country’s northern industrial corridor. When completed, the campus will rank among Europe’s largest AI-focused data center facilities and positions northern France as a growing node in the continent’s expanding AI infrastructure map. The development repurposes a former Bridgestone tire manufacturing site, reflecting a broader trend across Europe in which legacy industrial properties, already equipped with heavy power access, transport links, and industrial zoning, are being converted into large-scale digital infrastructure hubs. Located within reach of connectivity and enterprise corridors linking Paris, Brussels, London, and Amsterdam, the site allows Nebius to serve major European markets while avoiding the congestion and power constraints increasingly shaping Tier 1 data center hubs. Industrial Infrastructure Becomes Digital Infrastructure Developers increasingly view former industrial sites as ideal for AI campuses because they often provide: • Existing grid interconnection capacity built for heavy industry• Transport and logistics infrastructure already in place• Industrial zoning that reduces permitting friction• Large contiguous parcels suited to phased campus expansion For regions like Hauts-de-France, redevelopment projects also offer economic transition opportunities, replacing legacy manufacturing capacity with next-generation digital infrastructure investment. Local officials have positioned the project as part of broader efforts to reposition northern France as a logistics and technology hub within Europe. The Neocloud Model Gains Ground Beyond the site itself, Nebius’ expansion illustrates the rapid emergence of neocloud infrastructure providers, companies building GPU-intensive AI capacity without operating full hyperscale cloud ecosystems. These firms increasingly occupy a strategic middle ground: supplying AI compute capacity to enterprises, startups, and even hyperscalers facing short-term infrastructure constraints. Nebius’ rise over the past year

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Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs).  In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

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John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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