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Meta to Launch “Community Notes” in the U.S. Using X’s Algorithm

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Meta will begin testing its new Community Notes feature in the U.S. starting March 18, utilizing technology from Elon Musk’s X, the company announced on Thursday. This move comes two months after Meta scrapped its fact-checking program under pressure from conservatives, signaling a shift from traditional fact-checking to a crowd-sourced model.

The feature will allow users to write and rate notes to flag false or misleading content across Instagram, Facebook, and Threads, effectively replacing the third-party fact-checkers that were previously responsible for content moderation. 200,000 U.S. users have already signed up as potential contributors to the new system.

Meta’s switch to the Community Notes program represents a significant overhaul in its approach to content management. The company has been keen to improve its relationship with the Trump administration, which has criticized social media platforms for silencing conservative voices. President Donald Trump praised Meta’s decision in January, acknowledging the shift toward a more inclusive and less biased content moderation process.

To power Community Notes, Meta will adopt X’s open-source algorithm, which was originally developed as part of X’s Birdwatch feature. The system, now known as Community Notes, allows users to contribute and vote on content’s accuracy. Meta’s version will limit notes to 500 characters and initially support six languages: English, Spanish, Chinese, Vietnamese, French, and Portuguese. Notes will remain anonymous and will be published only if users with differing viewpoints agree that the note provides helpful context.

Contributors must be over 18 and include a supporting link when posting notes. Meta has emphasized that this system will be less biased than the previous third-party fact-checking method. Once the new system is in place, third-party fact-check labels will no longer appear on U.S. content.

Meta, which boasts over 3 billion global users, continues to collaborate with nearly 100 certified fact-checking organizations across 60+ languages, according to the company.

Meta Secures Emergency Ruling to Halt Promotion of Former Employee’s Tell-All Book

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Meta Platforms has won an emergency arbitration ruling to temporarily halt the promotion of a tell-all book titled “Careless People” written by its former employee, Sarah Wynn-Williams. The ruling, issued by the American Arbitration Association, states that Wynn-Williams must cease promoting the book, which was released by Macmillan, and must take steps to stop its further publication, though the publisher is not required to take any action.

The book, which offers an unflattering portrayal of Meta and its leadership, including CEO Mark Zuckerberg, former COO Sheryl Sandberg, and Chief Global Affairs Officer Joel Kaplan, was described by the New York Times book review as “an ugly, detailed portrait” of the tech giant. Wynn-Williams, who was Meta’s former director of global public policy, claims in the book that the company’s executives were involved in unethical practices.

The ruling, issued after a hearing where Wynn-Williams did not appear, found that Meta would suffer “immediate and irreparable loss” without the emergency relief. Meta spokesperson Andy Stone commented on Threads, stating that the ruling confirmed that the book, which he characterized as “false and defamatory,” should not have been published.

Macmillan, the publisher of the book, argued that it was not bound by the arbitration agreement, which was part of Wynn-Williams’ severance agreement with Meta. Both Wynn-Williams and Macmillan have not yet responded to Reuters’ requests for comment on the arbitration decision.

Meta Tests Its First In-House AI Training Chip

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Meta, the parent company of Facebook, has initiated testing of its first in-house chip designed specifically for training artificial intelligence (AI) systems. This development marks a significant step in Meta’s plan to reduce its reliance on external chip suppliers like Nvidia and move toward producing its own custom silicon. Sources told Reuters that Meta has begun a small deployment of the chip and plans to expand production if the test proves successful.

Meta’s push to develop in-house chips is part of a broader strategy to reduce the high infrastructure costs associated with its AI projects. The company has forecast total 2025 expenses between $114 billion and $119 billion, including up to $65 billion in capital expenditure largely driven by investments in AI infrastructure.

The new chip is a dedicated accelerator, meaning it is built specifically for AI tasks, making it more power-efficient compared to graphics processing units (GPUs) typically used for AI workloads. Meta is collaborating with Taiwan-based TSMC to produce the chip. The initial design, known as the “tape-out,” has been completed, a crucial milestone in chip development. While tape-out is expensive, costing tens of millions of dollars, it is an essential part of the process to test the chip’s functionality.

Meta has experienced setbacks in its Meta Training and Inference Accelerator (MTIA) series in the past, even scrapping one chip after its initial tests failed. However, last year, Meta began using a MTIA inference chip for content recommendation systems on platforms like Facebook and Instagram. This progress has encouraged Meta to pursue further development of custom chips, aiming to use them for both training and inference of AI models, including generative AI products like Meta AI.

Meta plans to start using its own chips by 2026 for training purposes, aiming to reduce costs associated with AI model training. Chris Cox, Meta’s Chief Product Officer, discussed the company’s phased approach, noting that while progress has been slow, the success of the first-generation inference chip for recommendations has been a significant achievement. Despite the setbacks in developing custom chips, Meta continues to rely heavily on Nvidia’s GPUs for its AI needs, making it one of Nvidia’s largest customers.

The broader AI industry has raised questions about the effectiveness of scaling up large language models with ever more data and computing power. Chinese startup DeepSeek has introduced new, more efficient AI models that rely more heavily on inference rather than the computationally expensive training process. This has sparked concerns about the future value of GPUs like those from Nvidia, which have faced significant market volatility this year.