It's no secret that Spotify is the dominant player in the music streaming market, with 293 million paid subscribers and 761 million monthly active users across both paid and free tiers. Along with massive market dominance has come a fair bit of controversy in recent years. As the biggest music-streaming platform, Spotify is usually the first mentioned in the discussion about whether artists are fairly compensated for their work. The math around that question is obvious -- buying a digital album outright typically costs between 10 and 15 depending on where you buy it, while a Spotify Premium subscription costs 13 a month in the US. Fair pay is far from the only drama surrounding Spotify, though.

Taiwan's tech titan Foxconn has reported a forecast-busting second-quarter net profit, as exploding demand for artificial intelligence servers needed in data centres powers the company's growth. The world's largest contract electronics maker said on Wednesday that net income rose 35 percent to $59.97 billion New Taiwan dollars ($1.86bn) in the April-June period. "AI infrastructure is driving growth," Foxconn said in its earnings announcement. In an earnings release, Foxconn, Nvidia's biggest server maker and Apple's top iPhone assembler, stuck to its previous forecast of "strong" growth for revenue this year. Governments and tech giants are pouring huge sums into building data centres that can train and run AI tools such as chatbots, image generators and agents that can execute tasks.

Is the war entering a new phase? 'Long-range sanctions': Ukraine hopes strikes cause Russia fear and losses Then come indistinguishable shouts of male voices, followed by a crescendo - a loud sound like scissors tearing through wrapping paper. "Like an orchestra, every part has to work together in order for us to achieve effective strikes," says Colonel Ray, commander of the Kairos Battalion of the Magyars Birds Brigade. The drone, roughly the length of a compact SUV with a wingspan of nearly 6 metres (19.7 feet), climbs into the night. Where exactly it is headed is known only to him.

Look Up Say More Versus Creator Hub Switch Off Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Trending Now Safety Net In My Bag VidCon with Mashable Back to School Furtastic All Series X retires revenue sharing for new'Original Content Rewards' program Chance Townsend is the General Assignments Editor at Mashable, covering tech, video games, dating apps, digital culture, and whatever else comes his way. He has a Master's in Journalism from the University of North Texas and is a proud orange cat father. His writing has also appeared in PC Mag and . X is overhauling how it pays creators, phasing out its long-running Revenue Sharing program in favor of a new system called the Original Content Rewards Program. The platform's Creators account announced the change in a post, saying the program is meant to reward creators who bring original ideas, expertise, reporting, creativity, and commentary to X.

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We examine hypothesis testing within a principal-agent framework, where a strategic agent, holding private beliefs about the effectiveness of a product, submits data to a principal who decides on approval. The principal employs a hypothesis testing rule, aiming to pick a p-value threshold that balances false positives and false negatives while anticipating the agent's incentive to maximize expected profitability. Building on prior work, we develop a game-theoretic model that captures how the agent's participation and reporting behavior respond to the principal's statistical decision rule. Despite the complexity of the interaction, we show that the principal's errors exhibit clear monotonic behavior when segmented by an efficiently computable critical p-value threshold, leading to an interpretable characterization of their optimal p-value threshold.

The next frontier of online advertising is revenue generation from LLM-generated content. We consider a setting where advertisers aim to influence the responses of an LLM, while platforms seek to maximize advertiser value and ensure user satisfaction. The challenge is that advertisers' preferences generally conflict with those of the user, and advertisers may misreport their preferences. To address this, we introduce MOSAIC, an auction mechanism that ensures that truthful reporting is a dominant strategy for advertisers and that aligns the utility of each advertiser with their contribution to social welfare. Importantly, the mechanism operates without LLM fine-tuning or access to model weights and provably converges to the output of the optimally fine-tuned LLM as computational resources increase. Additionally, it can incorporate contextual information about advertisers, which significantly improves social welfare. Via experiments with publicly available LLMs, we show that MOSAIC leads to high advertiser value and platform revenue with low computational costs. While our motivating application is online advertising, our mechanism can be applied in any setting with monetary transfers, making it a general-purpose solution for truthfully aggregating the preferences of selfinterested agents over LLM-generated replies.

We study revenue maximization for agents with additive preferences, subject to downward-closed constraints on the set of feasible allocations. In seminal work, Alaei [Ala14] introduced a powerful multi-to-single agent reduction based on an ex-ante relaxation of the multi-agent problem. This reduction employs a rounding procedure which is an online contention resolution scheme (OCRS) in disguise, a now widely-used method for rounding fractional solutions in online Bayesian and stochastic optimization problems. In this paper, we leverage our vantage point, 10 years after the work of Alaei, with a rich OCRS toolkit and modern approaches to analyzing multi-agent mechanisms; we introduce a general framework for designing non-sequential and sequential multi-agent, revenue-maximizing mechanisms, capturing a wide variety of problems Alaei's framework could not address. Our framework uses an interim relaxation, that is rounded to a feasible mechanism using what we call a two-level OCRS, which allows for some structured dependence between the activation of its input elements. For a wide family of constraints, we can construct such schemes using existing OCRSs as a black box; for other constraints, such as knapsack, we construct such schemes from scratch. We demonstrate numerous applications of our framework, including a sequential mechanism that guarantees a 2ee 1 3.16 approximation to the optimal revenue for the case of additive agents subject to matroid feasibility constraints. The simplicity of our developed two-level CRSs and OCRSs highlights the strength of our framework: even with a simple analysis, it yields state-of-the-art approximation guarantees across a wide range of settings. Finally, we show how it naturally extends to multi-parameter procurement auctions.

We initiate the study of contextual dynamic pricing with a heterogeneous population of buyers, where a seller repeatedly posts prices (over T rounds) that depend on the observable d-dimensional context and receives binary purchase feedback. Unlike prior work assuming homogeneous buyer types, in our setting the buyer's valuation type is drawn from an unknown distribution with finite support size K . We develop a contextual pricing algorithm based on optimistic posterior sampling with regret eO(K dT), which we prove to be tight in dand T up to logarithmic terms. Finally, we refine our analysis for the non-contextual pricing case, proposing a variance-aware zooming algorithm that achieves the optimal dependence on K .