Amazon Games Ends Support for 'New World: Aeternum' Amid Major Layoffs and Strategic Shift

International Business TimesWednesday, October 29, 2025 at 11:27:26 AM
Amazon Games has decided to stop updates for 'New World: Aeternum' following significant layoffs, marking a disappointing turn in their MMO strategy. This decision highlights the challenges the company faces in the gaming industry and raises questions about the future of their gaming projects. Fans of the game are left wondering what this means for the community and the potential for future content.
— Curated by the World Pulse Now AI Editorial System

Was this article worth reading? Share it

Recommended Readings
Amazon is winding down its still-popular New World MMO amid mass layoffs
NegativeArtificial Intelligence
Amazon's decision to wind down its popular MMO, New World, comes amid significant layoffs within the company, raising concerns about the future of its gaming division. This move is particularly impactful as New World had garnered a dedicated player base and was seen as a key player in the MMO market. The layoffs and the game's decline highlight the challenges Amazon faces in the competitive gaming industry, making it a critical moment for both the company and its community of gamers.
Latest from Artificial Intelligence
Not ready for the bench: LLM legal interpretation is unstable and out of step with human judgments
NegativeArtificial Intelligence
Recent discussions highlight the instability of large language models (LLMs) in legal interpretation, suggesting they may not align with human judgments. This matters because the legal field relies heavily on precise language and understanding, and introducing LLMs could lead to misinterpretations in critical legal disputes. As legal practitioners consider integrating these models into their work, it's essential to recognize the potential risks and limitations they bring to the table.
BioCoref: Benchmarking Biomedical Coreference Resolution with LLMs
PositiveArtificial Intelligence
A new study has been released that evaluates the performance of large language models (LLMs) in resolving coreferences in biomedical texts, which is crucial due to the complexity and ambiguity of the terminology used in this field. By using the CRAFT corpus as a benchmark, this research highlights the potential of LLMs to improve understanding and processing of biomedical literature, making it easier for researchers to navigate and utilize this information effectively.
Cross-Lingual Summarization as a Black-Box Watermark Removal Attack
NeutralArtificial Intelligence
A recent study introduces cross-lingual summarization attacks as a method to remove watermarks from AI-generated text. This technique involves translating the text into a pivot language, summarizing it, and potentially back-translating it. While watermarking is a useful tool for identifying AI-generated content, the study highlights that existing methods can be compromised, leading to concerns about text quality and detection. Understanding these vulnerabilities is crucial as AI-generated content becomes more prevalent.
Parrot: A Training Pipeline Enhances Both Program CoT and Natural Language CoT for Reasoning
PositiveArtificial Intelligence
A recent study highlights the development of a training pipeline that enhances both natural language chain-of-thought (N-CoT) and program chain-of-thought (P-CoT) for large language models. This innovative approach aims to leverage the strengths of both paradigms simultaneously, rather than enhancing one at the expense of the other. This advancement is significant as it could lead to improved reasoning capabilities in AI, making it more effective in solving complex mathematical problems and enhancing its overall performance.
POWSM: A Phonetic Open Whisper-Style Speech Foundation Model
PositiveArtificial Intelligence
The introduction of POWSM, a new phonetic open whisper-style speech foundation model, marks a significant advancement in spoken language processing. This model aims to unify various phonetic tasks like automatic speech recognition and grapheme-to-phoneme conversion, which have traditionally been studied separately. By integrating these tasks, POWSM could enhance the efficiency and accuracy of speech technologies, making it a noteworthy development in the field.
Monitoring Transformative Technological Convergence Through LLM-Extracted Semantic Entity Triple Graphs
PositiveArtificial Intelligence
A new study introduces a data-driven approach to track the emergence of transformative technologies, particularly in the fast-paced field of Information and Communication Technologies (ICTs). Traditional methods often fall short due to rapid innovation cycles and unclear terminology. This innovative pipeline aims to enhance our understanding of technological trends, making it easier for stakeholders to adapt and thrive in a constantly evolving landscape.