OpenAI has signed a new deal valued at $38bn with Amazon that will allow the artificial intelligence giant to run AI workloads across Amazon Web Services (AWS) cloud infrastructure. The seven-year deal announced on Monday is the first big AI push for the e-commerce giant after a restructuring last week. Experts say this does not mean that it will allow OpenAI to train its model on websites hosted by AWS - which includes the websites of The New York Times, Reddit and United Airlines. "Running OpenAI training inside AWS doesn't change their ability to scrape content from AWS-hosted websites [which they could already do for anything publicly readable]. This is strictly speaking about the economics of rent vs buy for GPU [graphics processing unit] capacity," Joshua McKenty, CEO of the AI detection company PolyguardAI, told Al Jazeera. The deal is also a major vote of confidence for the e-commerce giant's cloud unit, AWS, which some investors feared had fallen behind rivals Microsoft and Google in the artificial intelligence (AI) race.

OpenAI said the deal would give it access to hundreds of thousands of Nvidia graphics processors to train and run its AI models. OpenAI said the deal would give it access to hundreds of thousands of Nvidia graphics processors to train and run its AI models. Agreement to use AWS datacentres, and Nvidia chips inside them, part of $1.4tn spending spree on AI infrastructure Mon 3 Nov 2025 13.09 ESTLast modified on Mon 3 Nov 2025 15.16 EST OpenAI has signed a $38bn (£29bn) deal to use Amazon infrastructure to operate its artificial intelligence products, as part of a more than $1tn spending spree on computing power. The agreement with Amazon Web Services means OpenAI will be able to use AWS datacentres, and the Nvidia chips inside them, immediately. Last week, OpenAIâ s chief executive, Sam Altman, said his company had committed to spending $1.4tn on AI infrastructure, amid concerns over the sustainability of the boom in using and building datacentres.

OpenAI has signed a $38bn (£29bn) contract with Amazon to access its cloud computing infrastructure, as the start-up continues its run of major partnerships to secure computing power . In 2025, the ChatGPT maker has signed deals worth more than $1tn with Oracle, Broadcom, AMD and chip-making giant Nvidia. Its latest deal reduces its reliance on Microsoft. As part of the seven-year agreement, OpenAI will gain access to Nvidia graphics processors to train its artificial intelligence models. The deal follows a sweeping restructure of OpenAI last week which saw it convert away from being a non-profit and changed its relationship with Microsoft to give OpenAI more operational and financial freedom.

Red teaming has evolved from its origins in military applications to become a widely adopted methodology in cybersecurity and AI. In this paper, we take a critical look at the practice of AI red teaming. We argue that despite its current popularity in AI governance, there exists a significant gap between red teaming's original intent as a critical thinking exercise and its narrow focus on discovering model-level flaws in the context of generative AI. Current AI red teaming efforts focus predominantly on individual model vulnerabilities while overlooking the broader sociotechnical systems and emergent behaviors that arise from complex interactions between models, users, and environments. To address this deficiency, we propose a comprehensive framework operationalizing red teaming in AI systems at two levels: macro-level system red teaming spanning the entire AI development lifecycle, and micro-level model red teaming. Drawing on cybersecurity experience and systems theory, we further propose a set of six recommendations. In these, we emphasize that effective AI red teaming requires multifunctional teams that examine emergent risks, systemic vulnerabilities, and the interplay between technical and social factors.

We quantify the impact of Generative Artificial Intelligence (GenAI) on firm productivity through a series of large-scale randomized field experiments involving millions of users and products at a leading cross-border online retail platform. Over six months in 2023-2024, GenAI-based enhancements were integrated into seven consumer-facing business workflows. We find that GenAI adoption significantly increases sales, with treatment effects ranging from $0\%$ to $16.3\%$, depending on GenAI's marginal contribution relative to existing firm practices. Because inputs and prices were held constant across experimental arms, these gains map directly into total factor productivity improvements. Across the four GenAI applications with positive effects, the implied annual incremental value is approximately $\$ 5$ per consumer-an economically meaningful impact given the retailer's scale and the early stage of GenAI adoption. The primary mechanism operates through higher conversion rates, consistent with GenAI reducing frictions in the marketplace and improving consumer experience. We also document substantial heterogeneity: smaller and newer sellers, as well as less experienced consumers, exhibit disproportionately larger gains. Our findings provide novel, large-scale causal evidence on the productivity effects of GenAI in online retail, highlighting both its immediate value and broader potential.

We review the historical development and current trends of artificially intelligent agents (agentic AI) in the social and behavioral sciences: from the first programmable computers, and social simulations soon thereafter, to today's experiments with large language models. This overview emphasizes the role of AI in the scientific process and the changes brought about, both through technological advancements and the broader evolution of science from around 1950 to the present. Some of the specific points we cover include: the challenges of presenting the first social simulation studies to a world unaware of computers, the rise of social systems science, intelligent game theoretic agents, the age of big data and the epistemic upheaval in its wake, and the current enthusiasm around applications of generative AI, and many other topics. A pervasive theme is how deeply entwined we are with the technologies we use to understand ourselves.

ChatGPT on App Store displayed on a phone screen on 07 June 2025. ChatGPT on App Store displayed on a phone screen on 07 June 2025. Has OpenAI really made ChatGPT better for users with mental health problems? Prompts indicating suicidal ideation got alarming replies, which experts say shows'how easy it is to break the model' A n OpenAI statement released this week claimed the company had made its popular service ChatGPT better at supporting users experiencing mental health problems like suicidal ideation or delusions, but experts tell the Guardian they need to do more to truly ensure users are protected. The Guardian tested several prompts indicating suicidal ideation with the ChatGPT GPT-5 updated model, which is now the default, and got alarming responses from the large language model (LLM) chatbot.

Several new sites such as this are in the pipeline in the UK. Several new sites such as this are in the pipeline in the UK. 'A lot of this is speculative': faith and fear mix amid $3tn global datacentre boom The global investment spree in artificial intelligence is producing some remarkable numbers and a projected $3tn (£2.3tn) spend on datacentres is one of them. These vast warehouses are the central nervous system of AI tools such as OpenAI's ChatGPT and Google's Veo 3, underpinning the training and operation of a technology into which investors have poured vast sums of money. Despite concerns that the AI boom could be a bubble waiting to burst, there are few signs of it at the moment.

The increasing capabilities of Large Language Models (LLMs) have raised concerns about their misuse in AI-generated plagiarism and social engineering. While various AI-generated text detectors have been proposed to mitigate these risks, many remain vulnerable to simple evasion techniques such as paraphrasing. However, recent detectors have shown greater robustness against such basic attacks. In this work, we introduce Adversarial Paraphrasing, a training-free attack framework that universally humanizes any AI-generated text to evade detection more effectively. Our approach leverages an off-the-shelf instruction-following LLM to paraphrase AI-generated content under the guidance of an AI text detector, producing adversarial examples that are specifically optimized to bypass detection. Extensive experiments show that our attack is both broadly effective and highly transferable across several detection systems. For instance, compared to simple paraphrasing attack--which, ironically, increases the true positive at 1% false positive (T@1%F) by 8.57% on RADAR and 15.03% on Fast-DetectGPT--adversarial paraphrasing, guided by OpenAI-RoBERTa-Large, reduces T@1%F by 64.49% on RADAR and a striking 98.96% on Fast-DetectGPT. Across a diverse set of detectors--including neural network-based, watermark-based, and zero-shot approaches--our attack achieves an average T@1%F reduction of 87.88% under the guidance of OpenAI-RoBERTa-Large. We also analyze the tradeoff between text quality and attack success to find that our method can significantly reduce detection rates, with mostly a slight degradation in text quality. Our adversarial setup highlights the need for more robust and resilient detection strategies in the light of increasingly sophisticated evasion techniques.

Generative artificial intelligence (Gen AI) systems represent a critical technology with far-reaching implications across multiple domains of society. However, their deployment entails a range of risks and challenges that require careful evaluation. To date, there has been a lack of comprehensive, interdisciplinary studies offering a systematic comparison between open-source and proprietary (closed) generative AI systems, particularly regarding their respective advantages and drawbacks. This study aims to: i) critically evaluate and compare the characteristics, opportunities, and challenges of open and closed generative AI models; and ii) propose foundational elements for the development of an Open, Public, and Safe Gen AI framework. As a methodology, we adopted a combined approach that integrates three methods: literature review, critical analysis, and comparative analysis. The proposed framework outlines key dimensions, openness, public governance, and security, as essential pillars for shaping the future of trustworthy and inclusive Gen AI. Our findings reveal that open models offer greater transparency, auditability, and flexibility, enabling independent scrutiny and bias mitigation. In contrast, closed systems often provide better technical support and ease of implementation, but at the cost of unequal access, accountability, and ethical oversight. The research also highlights the importance of multi-stakeholder governance, environmental sustainability, and regulatory frameworks in ensuring responsible development.