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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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Nomads at the Frontier: PTC 2026 Signals the Digital Infrastructure Industry’s Moment of Execution

Each January, the Pacific Telecommunications Council conference serves as a barometer for where digital infrastructure is headed next. And according to Nomad Futurist founders Nabeel Mahmood and Phillip Koblence, the message from PTC 2026 was unmistakable: The industry has moved beyond hype. The hard work has begun. In the latest episode of The DCF Show Podcast, part of our ongoing ‘Nomads at the Frontier’ series, Mahmood and Koblence joined Data Center Frontier to unpack the tone shift emerging across the AI and data center ecosystem. Attendance continues to grow year over year. Conversations remain energetic. But the character of those conversations has changed. As Mahmood put it: “The hype that the market started to see is actually resulting a bit more into actions now, and those conversations are resulting into some good progress.” The difference from prior years? Less speculation. More execution. From Data Center Cowboys to Real Deployments Koblence offered perhaps the sharpest contrast between PTC conversations in 2024 and those in 2026. Two years ago, many projects felt speculative. Today, developers are arriving with secured power, customers, and construction underway. “If 2024’s PTC was data center cowboys — sites that in someone’s mind could be a data center — this year was: show me the money, show me the power, give me accurate timelines.” In other words, the market is no longer rewarding hypothetical capacity. It is demanding delivered capacity. Operators now speak in terms of deployments already underway, not aspirational campuses still waiting on permits and power commitments. And behind nearly every conversation sits the same gating factor. Power. Power Has Become the Industry’s Defining Constraint Whether discussions centered on AI factories, investment capital, or campus expansion, Mahmood and Koblence noted that every conversation eventually returned to energy availability. “All of those questions are power,” Koblence said.

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Land and Expand: Early 2026 Megaprojects Reflect a Power-First Ethos

Vantage — Lighthouse (Port Washington, Wisconsin) Although the on-site ceremonial groundbreaking occurred in 2025, Vantage Data Centers’ Lighthouse campus in Port Washington, Wisconsin, remained one of the most closely watched AI infrastructure developments entering 2026, with updated local materials posted February 19 reinforcing the project’s scale and timeline. Announced in October 2025 in partnership with OpenAI and Oracle, Lighthouse is positioned as the Midwest anchor site within the companies’ broader Stargate expansion, which targets up to 4.5 gigawatts of additional AI capacity globally. Current plans call for four hyperscale data centers delivering nearly 902 MW of IT load on a site encompassing roughly 672 acres, with construction expected to run through 2028. From a Land and Expand perspective, the project exemplifies the new generation of AI campuses involving large-scale land banking paired with phased delivery designed to stay ahead of hyperscale demand curves. Just as notable is the project’s power and community framework. Vantage is working with WEC Energy Group’s We Energies on a dedicated rate structure under which the developer will underwrite 100% of the power infrastructure investment, a model explicitly designed to shield existing customers from rate increases. The utility partnership also includes plans to enable nearly 2 gigawatts of new zero-emission energy capacity, with approximately 70% allocated to the Lighthouse campus and the remainder supporting broader grid needs. Water and environmental positioning are also central to the project narrative. Lighthouse is designed around a closed-loop liquid cooling system intended to minimize water consumption, alongside local restoration investments aimed at achieving water positivity. Vantage has also committed to preserving significant portions of the site’s natural landscape while pursuing LEED certification for the campus. Economically, the development is expected to generate more than 4,000 primarily union construction jobs and over 1,000 long-term operational roles, while Vantage has pledged at

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7×24 Exchange’s Dennis Cronin on the Data Center Workforce Crisis: The Talent Cliff Is Already Here

The data center industry has spent the past two years obsessing over power constraints, AI density, and supply chain pressure. But according to longtime mission critical leader Dennis Cronin, the sector’s most consequential bottleneck may be far more human. In a recent episode of the Data Center Frontier Show Podcast, Cronin — a founding member of 7×24 Exchange International and board member of the Mission Critical Global Alliance (MCGA) — delivered a stark message: the workforce “talent cliff” the industry keeps discussing as a future risk is already impacting operations today. A Million-Job Gap Emerging Cronin’s assessment reframes the workforce conversation from a routine labor shortage to what he describes as a structural and demographic challenge. Based on recent analysis of open roles, he estimates the industry is currently short between 467,000 and 498,000 workers across core operational positions including facilities managers, operations engineers, electricians, generator technicians, and HVAC specialists. Layer in emerging roles tied to AI infrastructure, sustainability, and cyber-physical security, and the potential demand rises to roughly one million jobs. “The coming talent cliff is not coming,” Cronin said. “It’s here, here and now.” With data center capacity expanding at roughly 30% annually, the workforce pipeline is not keeping pace with physical buildout. The Five-Year Experience Trap One of the industry’s most persistent self-inflicted wounds, Cronin argues, is the widespread requirement for five years of experience in roles that are effectively entry level. The result is a closed-loop hiring dynamic: New workers can’t get hired without experience They can’t gain experience without being hired Operators end up poaching from each other Workers may benefit from the resulting 10–20% salary jumps, but the overall talent pool remains stagnant. “It’s not helping us grow the industry,” Cronin said. In a market defined by rapid expansion and increasing system complexity, that

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Powering AI When the Grid Can’t: Inside the New Behind-the-Meter Playbook

The AI infrastructure boom is forcing a hard reset in how the data center industry thinks about power. What was once a relatively straightforward utility procurement exercise is rapidly evolving into a complex, multi-disciplinary strategy problem spanning generation, fuel logistics, finance, and system architecture. That reality framed a recent special edition of The Data Center Frontier Show Podcast, which recast and updated one of the most consequential sessions from the DCF Trends Summit 2025: From Grid to Onsite Powering: Optimizing Energy Behind the Meter for Data Centers. Moderating the discussion was Fengrong Li, Senior Managing Director at FTI Consulting, whose questions and analytical framing shaped the conversation’s direction. With more than 20 years of experience across energy and infrastructure—including expert testimony before the Federal Energy Regulatory Commission (FERC), the Federal Communications Commission (FCC), and multiple state bodies—Li brought a systems-level perspective that pushed the panel well beyond a simple technology tour. Her premise was clear from the outset: the rise of AI is not just increasing data center demand. It is restructuring the entire power delivery paradigm. A Moderator Focused on the System-Level Shift Li’s role went well beyond traditional moderation. Drawing on a career that includes 13 years at Siemens focused on grid issues and eight years at Mitsui in commodity trading and infrastructure investment, she constructed the discussion around what she described as “one of the most urgent topics shaping digital infrastructure deployment.” “Onsite power and the rise of co-located, integrated power and AI campuses,” Li told the panel, “are accelerating data centers beyond traditional hubs and changing how they interact with the grid.” Throughout the session, Li repeatedly pushed panelists to connect near-term deployment realities with longer-term structural implications particularly around redundancy, financing, and regulatory exposure. The result was a grounded look at an industry that is

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Data Center Jobs: Engineering, Construction, Commissioning, Sales, Field Service and Facility Tech Jobs Available in Major Data Center Hotspots

Each month Data Center Frontier, in partnership with Pkaza, posts some of the hottest data center career opportunities in the market. Here’s a look at some of the latest data center jobs posted on the Data Center Frontier jobs board, powered by Pkaza Critical Facilities Recruiting. Looking for Data Center Candidates? Check out Pkaza’s Active Candidate / Featured Candidate Hotlist Electrical Applications Engineer Pittsburgh, PA This position is also available in: Denver, CO and Andrews, SC Our client is a leading provider and manufacturer of industrial electrical power equipment used in industrial applications for mission critical operations. They help their customers save money by reducing energy and operating costs and provide solutions for modernizing their customer’s existing electrical infrastructure. This company provides cooling solutions to many of the world’s largest organizations and government facilities and enterprise clients, colocation providers and hyperscale companies. This career-growth minded opportunity offers exciting projects with leading-edge technology and innovation as well as competitive salaries and benefits. Electrical Commissioning Engineer Dallas TXThis traveling position is also available in: New York, NY; White Plains, NY;  Ashburn, VA; Richmond, VA; Montvale, NJ; Charlotte, NC; Atlanta, GA; Hampton, GA; New Albany, OH; Cedar Rapids, IA; Phoenix, AZ; Salt Lake City, UT; Kansas City, MO; Omaha, NE; Chesterton, IN or Chicago, IL. *** ALSO looking for a LEAD EE and ME CxA Agents and CxA PMs and a Director of CxA Colos in Dallas, TX *** Our client is an engineering design and commissioning company that has a national footprint and specializes in MEP critical facilities design. They provide design, commissioning, consulting and management expertise in the critical facilities space. They have a mindset to provide reliability, energy efficiency, sustainable design and LEED expertise when providing these consulting services for enterprise, colocation and hyperscale companies. This career-growth minded opportunity offers exciting projects with leading-edge

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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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