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Apple faces a $2.7 billion class action lawsuit: accused of unfair application tracking rules against third-party developers, gaining improper advantages in its own advertising ecosystem

According to a report by Reuters, Apple Inc. is facing a class-action lawsuit in London, with claims amounting to £2 billion (approximately $2.7 billion). The lawsuit was filed today in the London Competition Appeal Tribunal by Ann Pope, a former senior official of the UK's Competition and Markets Authority, representing app developers.The core allegation is that Apple's "App Tracking Transparency" (ATT) feature, launched in 2021, imposes stricter restrictions on third-party developers than on its own services, giving Apple's own advertising ecosystem an unfair competitive advantage. Ann Pope stated that Apple's policies "have caused very significant harm to businesses that rely on Apple as a gatekeeper."Since its launch, the ATT feature has been a focal point of concern for global regulators for several years. Apple's official stance is that the feature is designed to allow users to control whether to permit apps to track their activities across other companies and websites.However, the plaintiffs argue that the actual enforcement of this rule has a double standard—tracking requests from third-party apps require strict pop-up authorization, while Apple's own personalized ads and services can bypass the same restrictions. This lawsuit represents the latest legal challenge Apple faces regarding its ATT policy and is the first large-scale private antitrust lawsuit initiated in the UK market against Apple's app ecosystem rules following scrutiny from regulators in the EU, the US, and several other countries.

first_img Ondo urges the U.S. SEC and CFTC to regulate U.S. stock perpetual contract business

Ondo Finance is urging U.S. regulators to bring the business of perpetual contracts anchored to individual stocks under regulation, asserting that this product can operate in compliance under the existing securities and futures framework without new rules. In three comment letters sent to the U.S. Securities and Exchange Commission (SEC) and the Commodity Futures Trading Commission (CFTC) on August 24, Ondo stated that existing rules can accommodate stock perpetual futures while considering modern margin practices and on-chain listing data. Ondo mentioned that its affiliated company in Panama has been offering stablecoin-settled perpetual contracts anchored to U.S. stocks overseas, with a cumulative trading volume reaching $8 billion as of August 14 since its launch in June.Ondo believes that regular funding rate payments can keep perpetual contracts aligned with the underlying stock prices, functioning similarly to the expiration settlement of traditional futures. In the letter, Ondo stated that there are no provisions in the statutory definition of securities futures products that require a fixed expiration date to be set. The company also pointed out that many offshore perpetual contracts have underlying stocks primarily traded on U.S. exchanges, and bringing the related business back to the U.S. should be a direction actively promoted by both agencies. According to RWA.xyz data, Ondo is one of the largest managers in the tokenized real-world assets (RWA) space, with an allocated value of approximately $2.6 billion as of Wednesday, ranking fourth.Ondo's proposal comes as U.S. regulators are re-examining the existing market rules for on-chain products, including perpetual contracts and tokenized securities. In March of this year, the SEC and CFTC signed a memorandum of understanding to coordinate regulation in overlapping areas of jurisdiction.

first_img Google claims that the cost of AI server memory has exceeded 75%, promoting a dual-track strategy for software and hardware

The SEMICON Taiwan 2026 Memory Summit took place on the 1st, where Nikhil Cherian, Senior Director of Supply Chain Infrastructure at Google Cloud under Alphabet, pointed out that with the popularity of multimodal and mixed expert architectures, AI computation has shifted from being power-limited to memory-limited, with high-performance memory accounting for over 75% of the cost of AI server hardware bill of materials. In the face of capacity, bandwidth, and power consumption bottlenecks, Google is breaking through the AI memory bottleneck through a dual-track strategy of hardware offloading for inference and training, and lossless quantization software algorithms.Google adopts an offloading strategy in hardware architecture, launching TPU 8i for low-latency inference and TPU 8t specialized for large-scale training. The TPU 8i is equipped with 288 GB of high-bandwidth memory, with SRAM capacity on the chip increased threefold to 384 MiB, placing dynamic conversation states and key-value caches on the chip itself to achieve zero chip-off latency. The TPU 8t forms a super-large computing cluster with 9600 chips, achieving a shared pool of HBM at a scale of 2 PB, eliminating chip-off data transfer bottlenecks, along with TPU Direct Storage technology.Google has developed the training-free TurboQuant lossless quantization algorithm, compressing the key-value cache of large models from 32 bits to 3 bits, reducing memory usage by six times without loss of accuracy, resulting in an eightfold acceleration in attention computation, and integrating old-generation DRAM technology to extend the lifecycle of components.
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