Encyclopaedia Britannica and its dictionary subsidiary Merriam-Webster have filed a lawsuit against OpenAI in federal court in Manhattan, accusing the company of unlawfully using their reference materials to train its artificial intelligence models.

According to the complaint filed Friday, the publishers allege that OpenAI copied nearly 100,000 encyclopedia articles and dictionary entries from their online platforms to train its large language models, including the system behind ChatGPT.

Britannica claims that the AI-generated summaries produced by ChatGPT replicate its content and may reduce traffic to its websites by providing answers directly to users. The complaint argues that this practice “cannibalizes” Britannica’s audience and undermines its subscription-based information services.

The publishers also allege that ChatGPT can generate responses that closely resemble original Britannica entries, sometimes reproducing passages nearly verbatim. In addition, the lawsuit claims OpenAI improperly references Britannica in AI responses, which the company argues could mislead users into believing the chatbot has permission to reproduce its content.

Britannica is seeking unspecified financial damages and a court order preventing further use of its materials in AI training.

Part of Broader AI Copyright Disputes

The case is the latest in a series of legal battles between content owners and AI developers over the use of copyrighted material to train generative AI systems. Authors, publishers, and media companies have filed multiple lawsuits arguing that their work was used without permission or compensation.

Technology companies, including OpenAI, have defended their practices by arguing that training AI models on large datasets constitutes fair use because the models transform the information into new outputs rather than reproducing the original content.

Britannica previously filed a related lawsuit against AI startup Perplexity AI, which remains ongoing. The outcome of these cases could influence how courts interpret copyright law in the era of large-scale AI model training.

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Entrepreneur Sergey Solonin, widely known as the co-founder and longtime CEO of Qiwi, will lead Mercuryo AI Labs. He announced the move on LinkedIn, saying he will focus on developing infrastructure that enables AI agents to make payments to each other.

Solonin served as Qiwi’s CEO for nearly eight years before stepping down in January 2020. Since then, he has invested in multiple startups and launched an ambitious project in Bali called Nuanu, a creative city he began building in 2022. According to Solonin, around 5,000 people currently live there. Despite these projects, he has now decided to return to fintech.

Interestingly, Solonin didn’t start a new venture from scratch but instead joined Mercuryo, a cryptocurrency payments service. The company was founded in 2018 by Petr Kozyakov, Grigory Vaysman, and Alexander Vasiliev.

Public data shows two funding rounds for Mercuryo: €2.5 million in 2020 and $7.5 million in 2021, both led by venture fund Target Global. It is unclear whether Solonin participated as an investor in those rounds.

Solonin explains his renewed interest in fintech by pointing to the next evolution of payments:

“The next wave of payments won’t be between people. It will be between agents. Software will pay software. Autonomously. AI agents can already search, compare, negotiate and recommend. But the moment they actually need to pay for something, a human still has to step in. Every time. That’s the bottleneck — and it’s massive.”

He says Mercuryo AI Labs will focus on solving that bottleneck by building agentic payment infrastructure.

“So I’m going all in. Today I’m leading Mercuryo AI Labs — a company building infrastructure for agent payments. The idea is simple: give your AI agent a virtual card, set the spending rules, and let it transact. Settlements can happen in stablecoins or fiat. There’s a full audit trail. Humans set the policy — agents execute.”

AI & Machine Learning, News

Nebius Group has signed a long-term agreement with Meta to provide large-scale artificial intelligence infrastructure as part of the social media company’s expanding data center strategy. The contract has a total potential value of up to $27 billion over five years.

Under the agreement, Nebius will supply approximately $12 billion in dedicated AI computing capacity across multiple locations. The infrastructure will be built on one of the first large-scale deployments of Nvidia’s Vera Rubin computing platform.

Nebius expects to begin delivering the capacity starting in early 2027.

Meta has also committed to purchasing additional computing resources from Nebius clusters as they become available. The total value of these additional purchases could reach $15 billion during the five-year contract period.

Nebius plans to offer the majority of the new infrastructure capacity to its broader AI cloud customer base. Any remaining unused capacity will be reserved for Meta under the agreement.

Expanding AI Cloud Infrastructure

The deal reflects increasing demand for large-scale computing infrastructure as technology companies train and deploy more advanced AI systems. Large models and AI agents require enormous amounts of computing power, driving companies to secure long-term infrastructure agreements.

Nebius has positioned itself as a dedicated AI cloud provider, focusing on high-performance computing environments optimized for machine learning workloads. The company operates specialized infrastructure designed for training and inference tasks used by AI developers and enterprises.

Arkady Volozh, founder and chief executive of Nebius, said the agreement strengthens the company’s collaboration with Meta while supporting the expansion of its AI cloud business.

“We are pleased to expand our significant partnership with Meta as part of securing more large, long-term capacity contracts to accelerate the build-out and growth of our core AI cloud business,” Volozh said.

The partnership also supports Nebius’s broader strategy of building large-scale AI data centers based on Nvidia’s next-generation accelerated computing platforms.

Nebius said its financial guidance for 2026 remains unchanged following the announcement.

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A technology entrepreneur in Sydney has helped develop an experimental personalized cancer vaccine for his dog using artificial intelligence tools and genomic analysis.

Paul Conyngham turned to AI after his dog Rosie was diagnosed with cancer in 2024. Initial treatments, including chemotherapy and surgery, failed to stop the tumors from progressing.

Conyngham, an electrical and computing engineer and co-founder of Core Intelligence Technologies, began researching alternative options. Using AI tools, he developed a strategy for identifying potential immunotherapy targets.

ChatGPT helped suggest immunotherapy approaches and pointed him toward researchers at the University of New South Wales Ramaciotti Centre for Genomics. Conyngham later worked with scientists at the university to sequence Rosie’s genome and analyze the mutations driving the cancer.

He also used AlphaFold, an AI system developed by Google DeepMind, to analyze mutated proteins and identify potential targets for treatment.

Development of a Personalized mRNA Vaccine

After analyzing the genomic data, Conyngham collaborated with researchers at UNSW’s RNA Institute to design a custom mRNA cancer vaccine tailored to Rosie’s specific tumor mutations.

The vaccine was developed in less than two months by Pall Thordarson, director of the institute and a specialist in nanomedicine and RNA technologies.

According to Thordarson, the case represents one of the first known examples of a personalized mRNA cancer vaccine created specifically for a dog.

“This is still at the frontier of where cancer immunotherapeutics are,” he said. “What Rosie is teaching us is that personalized medicine can be very effective, and done in a time-sensitive manner, with mRNA technology.”

Rosie received her first injection of the vaccine in December and a booster dose in February. Researchers reported that many of the tumors have since shrunk significantly, although some have not responded to the treatment.

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Proud with @UNSWRNA to have been involved & making the mRNA-LNP for Rosie. There are nuances here that the thread below misses but nevertheless, the intersection of RNA technology, genomic & AI poses an opportunity to change the way do medicine and make access more equitable 1/8 https://t.co/lRsBtSNgld

— Palli Thordarson (@PalliThordarson) March 15, 2026

Implications for AI in Medicine

While Rosie’s condition has not been fully cured, her health has improved considerably. According to Conyngham, the dog regained energy within weeks of receiving the treatment.

Researchers caution that the experimental therapy is still at an early stage and may not represent a universal solution for cancer treatment. However, the case demonstrates how AI tools can help accelerate the design of personalized therapies by analyzing genomic data and identifying potential biological targets.

The project also highlights the growing role of AI in biomedical research. Technologies such as protein-structure prediction and large language models are increasingly being used by scientists to analyze complex datasets and propose potential treatment strategies.

Some technology leaders say stories like Rosie’s illustrate how AI could transform the pace of medical discovery. As AI-assisted research tools become more widely available, researchers expect personalized medicine approaches to become faster and more accessible in both veterinary and human healthcare.

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