The power of digital platforms is at the center of major ongoing policy and regulatory efforts. To advance existing debates, we designed and executed an experiment to measure the performative power of online search providers. Instantiated in our setting, performative power quantifies the ability of a search engine to steer web traffic by rearranging results. To operationalize this definition we developed a browser extension that performs unassuming randomized experiments in the background. These randomized experiments emulate updates to the search algorithm and identify the causal effect of different content arrangements on clicks. Analyzing tens of thousands of clicks, we discuss what our robust quantitative findings say about the power of online search engines, using the Google Shopping antitrust investigation as a case study. More broadly, we envision our work to serve as a blueprint for how the recent definition of performative power can help integrate quantitative insights from online experiments with future investigations into the economic power of digital platforms.

Dutch company employees work on a semiconductor lithography tool in Veldhoven, Netherlands, April 2019. Dutch company employees work on a semiconductor lithography tool in Veldhoven, Netherlands, April 2019. This is Europe's secret weapon against Trump: it could burst his AI bubble T he unthinkable has happened. The US is Europe's adversary. The stark, profound betrayal contained in the Trump administration's national security strategy should stop any further denial and dithering in Europe's capitals.

The power of digital platforms is at the center of major ongoing policy and regulatory efforts.

Elon Musk has stepped away from Doge with very little "efficiency" to show for it. While it may have been more of a showpiece than real policy, this brutal and short experiment in Silicon Valley governance reveals a long-simmering battle between digital utopians and the institutional infrastructures critical to functioning democracies. Doge's website dubiously claims 190bn in savings. The receipts show that they are less about efficiency than they are aimed at effective dissolution, a fate met by USAID, the federal agency responsible for distributing foreign assistance. These brash new reductions are not just your garden-variety small-government crusades or culture-war skirmishes.

T he emergence of A gentic Artificial I ntelligence ( A AI) systems capable of independently initiating digital interactions necessitates a new optimisation paradigm designed explicitly for seamless agent - platform interactions. This article introduces Agentic AI Optimisation ( AAIO) as an essential methodology for ensuring effective integration between websites and agentic AI systems. Like how Search Engine Optimisation (SEO) has shaped digital content discoverability, AAIO can define interactions between autonomous AI agents and online platforms. By examining the m utual interdependency between website optimisation and agentic AI success, the article highlights the virtuous cycle that AAIO can create. It further explores the governance, ethical, legal, and social implications (GELSI) of AAIO, emphasising the necessity of proactive regulatory frameworks to mitigate potential negative impacts. The article concludes by affirming AAIO's essential role as part of a fundamental digital infrastructure in the era of autonomous digital agents, advocating for equitable and incl usive access to its benefits. Keywords: Agentic Artificial Intelligence (AAI), Agentic AI Optimisation (AAIO), Digital Optimisation, AI Ethics, Digital Governance. 2 1. Introduction: From SEO to AAIO Over the past decades, Search Engine Optimisation (SEO) has significantly influenced digital content structure, discoverability, and consumption (Enge, Spencer, and Stricchiola 2023) .

News that GOG.com has delisted 29 games this month is a sobering reminder that at any moment the games you own could vanish from your PC game libraries at any time and there's not much you can do about it. Admittedly, GOG's games include titles that many gamers may not have heard about. But history has shown that this happens to well-known titles too and on platforms with millions of users like Steam and Origin. So how is it that something you've legitimately bought can be whipped away in a heartbeat? Don't we have consumer protection laws against that?

The advent of social media transformed interpersonal communication and information consumption processes. This digital landscape accommodates user intentions, also resulting in an increase of offensive language and harmful behavior. Concurrently, social media platforms collect vast datasets comprising user-generated content and behavioral information. These datasets are instrumental for platforms deploying machine learning and data-driven strategies, facilitating customer insights and countermeasures against social manipulation mechanisms like disinformation and offensive content. Nevertheless, the availability of such datasets, along with the application of various machine learning techniques, to researchers and practitioners, for specific social media platforms regarding particular events, is limited. In particular for TikTok, which offers unique tools for personalized content creation and sharing, the existing body of knowledge would benefit from having diverse comprehensive datasets and associated data analytics solutions on offensive content. While efforts from social media platforms, research, and practitioner communities are seen on this behalf, such content continues to proliferate. This translates to an essential need to make datasets publicly available and build corresponding intelligent solutions. On this behalf, this research undertakes the collection and analysis of TikTok data containing offensive content, building a series of machine learning and deep learning models for offensive content detection. This is done aiming at answering the following research question: "How to develop a series of computational models to detect offensive content on TikTok?". To this end, a Data Science methodological approach is considered, 120.423 TikTok comments are collected, and on a balanced, binary classification approach, F1 score performance results of 0.863 is obtained.

This paper critically analyses the "attention economy" within the framework of cognitive science and techno-political economics, as applied to both human and machine interactions. We explore how current business models, particularly in digital platform capitalism, harness user engagement by strategically shaping attentional patterns. These platforms utilize advanced AI and massive data analytics to enhance user engagement, creating a cycle of attention capture and data extraction. We review contemporary (neuro)cognitive theories of attention and platform engagement design techniques and criticize classical cognitivist and behaviourist theories for their inadequacies in addressing the potential harms of such engagement on user autonomy and wellbeing. 4E approaches to cognitive science, instead, emphasizing the embodied, extended, enactive, and ecological aspects of cognition, offer us an intrinsic normative standpoint and a more integrated understanding of how attentional patterns are actively constituted by adaptive digital environments. By examining the precarious nature of habit formation in digital contexts, we reveal the techno-economic underpinnings that threaten personal autonomy by disaggregating habits away from the individual, into an AI managed collection of behavioural patterns. Our current predicament suggests the necessity of a paradigm shift towards an ecology of attention. This shift aims to foster environments that respect and preserve human cognitive and social capacities, countering the exploitative tendencies of cognitive capitalism.

Before we start, I want to let you know that a human wrote this article. The same can't be said for many articles from News Corp, which is reportedly using generative AI to produce 3,000 Australian news stories per week. Media corporations around the world are increasingly using AI to generate content. By now, I hope it's common knowledge that large language models such as GPT-4 do not produce facts; rather, they predict language. We can think of ChatGPT as an "automated mansplaining machine" – often wrong, but always confident.