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Apple Lawsuit: OpenAI Seeks Dismissal of Trade Secrets Case

Apple lawsuit against OpenAI has reached its first major procedural challenge, with the ChatGPT developer asking a US federal judge to dismiss allegations that it acquired or used trade secrets taken by former Apple employees.

OpenAI submitted its 31-page dismissal motion on Wednesday in the US District Court for the Northern District of California. The filing argues that Apple has not adequately identified protectable trade secrets or alleged facts showing that OpenAI misappropriated confidential information.

Apple filed the case on July 10 against OpenAI, its io Products hardware operation, former Apple Vice President Tang Yew Tan, and former electrical engineer Chang Liu. The complaint accuses the defendants of obtaining information connected to Apple’s product development, manufacturing, components, testing, and supply chain operations.

The allegations have not been proven, and OpenAI denies possessing or seeking Apple’s trade secrets. US District Judge Edward Davila must now decide whether Apple’s complaint contains enough factual detail for the litigation to continue.

OpenAI Challenges the Apple Lawsuit

OpenAI’s motion focuses initially on how Apple described the information it claims was stolen. According to OpenAI, the complaint relies on generalized product-development categories without identifying the protected information with enough precision.

Trade secret plaintiffs face a difficult balance at the beginning of litigation. Apple must describe the protected material sufficiently to establish a plausible legal claim, but revealing the material publicly could expose the same information it is trying to protect.

Sensitive details can later be handled under seal or through a protective order. At the dismissal stage, however, Apple must still provide enough information for the court to distinguish genuine trade secrets from general engineering knowledge, employee experience, or skills acquired through years of work.

OpenAI argues that Apple has not crossed that threshold. Its motion says the complaint fails to establish ownership of a protectable trade secret and does not plausibly allege that OpenAI acquired, disclosed, or used such information.

The filing also disputes the commercial premise behind Apple’s accusations. OpenAI says it is developing products that are different from Apple’s hardware and has no need or desire to use the iPhone maker’s confidential material.

That position does not settle whether information was taken. A company can potentially misappropriate trade secrets even when its eventual product has a different design. The court’s immediate task is narrower: determining whether Apple has alleged facts that could support its claims if those facts are later proven.

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Apple Park | Image Source: Google

Disputed Access to Apple Information

Apple’s complaint centers partly on Chang Liu, who spent eight years working as a senior system electrical engineer at the company before joining OpenAI in January 2026.

Apple alleges that Liu retained an Apple-issued computer and accessed confidential technical information after leaving. It claims he exploited residual authentication access on multiple occasions to obtain internal documents while already working for OpenAI.

OpenAI presents a different explanation. It says Apple employees contacted Liu after his departure and asked him to help locate files needed for ongoing Apple work. The company published redacted messages that it says support this account.

OpenAI argues that Apple’s systems and policies allowed personal and corporate information to become intermingled through iCloud accounts. Former employees, it says, could retain access to Apple files without requesting it or realizing that access remained active.

The motion maintains that Apple cannot treat the foreseeable effects of its own access-management practices as proof that former employees intentionally stole data. Apple, in contrast, alleges that its records document repeated access to restricted information rather than an accidental discovery of lingering permissions.

These competing accounts are likely to become central if the case enters discovery. Authentication logs, downloaded files, device records, internal communications, and employment timelines could establish whether access was deliberate, what information was obtained, and whether it reached anyone working on OpenAI products.

A dismissal motion normally tests whether the complaint states a legally sufficient claim. It is not a trial, and the judge is not expected to resolve every factual disagreement using competing evidence at this point.

Tang Tan and OpenAI’s Hardware Team

Apple separately accuses Tang Tan, OpenAI’s chief hardware officer, of seeking confidential information from Apple employees during recruiting conversations.

Tan spent more than 24 years at Apple and worked on products including the iPhone, Apple Watch, AirPods, and iPod. He later helped establish io Products with former Apple design chief Jony Ive and other collaborators. OpenAI acquired the hardware company as part of its effort to create a family of devices built around artificial intelligence.

Apple alleges that Tan used confidential project names, retained internal materials, and encouraged job candidates to discuss unreleased components or bring physical parts to interviews. OpenAI rejects the claim that these interactions formed a coordinated effort to collect Apple secrets.

OpenAI says Tan repeatedly instructed employees that confidential information from previous employers could not be brought into its organization. It also offers an alternative explanation for an Apple offboarding document found in his possession, arguing that Tan retained it to help incoming employees comply with Apple’s departure procedures.

Apple’s position is that the document and recruiting communications must be examined alongside the other alleged conduct. It describes the events as part of a connected campaign rather than isolated employment or administrative issues.

The dispute comes during intense competition for engineers capable of developing consumer AI hardware. Apple alleges that more than 400 former employees have moved to OpenAI, although hiring people from a competitor does not by itself establish trade secret misappropriation.

A company must generally connect recruitment to the acquisition, disclosure, or use of protected information. OpenAI is relying heavily on that distinction, presenting Apple’s lawsuit as an attempt to respond to employee departures and its difficulties competing for AI talent.

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Image Credit: Getty Images

Apple Seeks Immediate Restrictions

OpenAI’s dismissal request arrives shortly after Apple asked the court for a preliminary injunction. Apple wants restrictions imposed while the lawsuit proceeds, arguing that it could suffer harm if its confidential information is used during OpenAI’s hardware development.

The requested relief would prevent OpenAI and the individual defendants from acquiring, accessing, using, or disclosing the alleged trade secrets. Apple has also requested expedited discovery and forensic examination of relevant devices and accounts.

OpenAI says those restrictions are unnecessary because it does not possess or want Apple’s protected information. It has described Apple’s request as based on false information and has offered to cooperate in addressing specific concerns.

The two companies also disagree over their communications before the lawsuit. Apple initially said its concerns had been raised with OpenAI months earlier. OpenAI responded that Apple’s outside lawyers contacted the wrong person after confusing names and incorrectly described a conversation with OpenAI’s general counsel.

OpenAI says Apple did not present the specific accusations before filing the case and that the companies had previously discussed resolving any access issues. Apple has since provided additional information in support of its injunction request, including allegations involving other former employees.

These disputes have moved beyond ordinary court filings. OpenAI published messages and emails on its website to support its version of events, calling Apple’s lawsuit aggressive and poorly investigated. Apple has primarily presented its account through formal legal submissions.

Judge Davila could dismiss some or all of Apple’s claims, deny the motion, or allow Apple to amend its complaint with more specific allegations. Even a dismissal would not necessarily end the case if permission to revise the complaint is granted.

The next scheduled public test is an October 1 hearing covering Apple’s preliminary injunction and request for early discovery. That proceeding may determine whether OpenAI faces immediate limits on its hardware work before the underlying trade secret claims receive a full evidentiary review.

The post Apple Lawsuit: OpenAI Seeks Dismissal of Trade Secrets Case appeared first on AppleMagazine.

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Turning Quantum Potential into Reality

In a groundbreaking stride toward the future of quantum technology, researchers have unlocked a new mechanism to control quantum emitters embedded in hexagonal boron nitride (hBN), a layered two-dimensional material. This breakthrough could serve as a critical leap forward in integrating quantum emitters into practical devices, bringing quantum computing, secure quantum communications, and ultra-sensitive quantum sensors closer to reality. This promising development emerged from meticulous experimental studies demonstrating the ability to reversibly tune the color and wavelength of light emitted by quantum defects in hBN simply by twisting its atomic layers.

Quantum emitters are nanoscopic sources of light that emit photons with quantum characteristics, pivotal for next-generation technologies reliant on quantum information processing. However, a formidable challenge in utilizing these emitters has been the difficulty in precisely controlling their optical properties post-fabrication. Dr. Angus Gale, the lead scientist behind this research, elucidated how their work provides an innovative “lever” by exploiting the intrinsic layered nature of hBN to adjust the quantum emission properties dynamically. Unlike traditional solid-state hosts such as diamond or silicon carbide, whose structures are rigid and three-dimensional, hBN’s atomically thin layers can be mechanically manipulated, lending itself to an unprecedented degree of tunability.

The team’s experiments demonstrated that by picking up, stacking, twisting, and restacking these ultra-thin hBN slices at varied angles, they could modulate the quantum emitters’ light emission dramatically. These shifts in wavelength were not minor perturbations but substantial changes far exceeding what has been typically achievable in similar solid-state quantum systems. This finding challenges the traditional paradigm which often seeks to stabilize quantum defect behavior within rigid host materials. Instead, the researchers embraced the inherent flexibility and twistability of hBN to realize a new class of quantum control.

Professor Igor Aharonovich, the supervising author, emphasized the broader implications of this twist-controlled modulation approach. He noted that coupling two-dimensional materials at specific twist angles opens up an entirely fresh set of physical phenomena that were previously inaccessible. By assembling layers with precision angular misalignment, new quantum states can emerge, potentially transforming the landscape of material engineering for quantum applications. This strategic layering and twisting, known as “twistronics,” has already inspired revolutionary advances in graphene-based systems, yet this study extends the concept into the realm of quantum emitters embedded in hBN.

One key advantage of using hBN is that its layered crystal structure allows researchers to systematically alter the interaction between layers, effectively tuning the electronic environment surrounding the defects responsible for quantum light emission. As Dr. Gale metaphorically illustrated, far from being a solid block, hBN behaves more like a stack of cheese slices. Just as peeling and recombining slices of cheese can change how flavors intermingle, twisting hBN slices changes the interaction between atomic planes, which in turn alters quantum emitter characteristics. This mechanical engineering at the nanoscale provides a versatile platform for on-demand tuning of quantum light sources.

This technology breakthrough is fundamentally experimental but holds widespread implications. Adjustable quantum emitters could accelerate the development of quantum computing hardware, where precise photon control is essential for encoding and manipulating qubits. Similarly, secure quantum communication protocols rely on tailored quantum light sources to guarantee the generation and distribution of entangled photons immune to interception. Moreover, quantum sensing that exploits the extreme sensitivity of quantum states to environmental changes stands to benefit significantly from tunable quantum emitters, pushing the boundaries of detection limits in fields ranging from medicine to navigation.

Another remarkable facet of the research is the ability to repeatedly pick up, twist, and restack hBN layers without degrading the quantum emitter properties. This reversibility enables iterative fine-tuning of device characteristics and might yield adaptable quantum systems reconfigurable post-production, a feature highly desirable from a technological deployment perspective. This contrasts sharply with permanent structural modifications used in conventional quantum emitter fabrication, which restrict adjustable control and device flexibility.

The team’s methodology involved sophisticated nanofabrication techniques and high-resolution optical spectroscopy to probe the quantum emitters’ response to twist-induced modifications. By correlating the angular misalignment with spectral shifts in emitted light, they constructed a detailed understanding of the underlying physics governing emission control. These insights provide a blueprint for engineering tailored quantum emitters with bespoke characteristics suitable for specific applications.

The research findings have been detailed in a paper published in the eminent journal Science Advances, signaling the scientific community’s enthusiasm for the potential unlocked by twistable quantum systems. Importantly, the authors have declared no competing interests, affirming the independent and foundational nature of this inquiry. Funded by the Australian Research Council and the Air Force Office of Scientific Research, the work highlights the vibrant collaboration between academic institutions and funding bodies committed to propelling quantum technologies.

This discovery heralds a new era in quantum material science, where layered two-dimensional crystals are no longer passive hosts but active tunable platforms. By harnessing the mechanical and electronic versatility of materials like hBN, researchers are gradually unlocking complex quantum phenomena with precision control previously deemed unachievable. The implications extend well beyond fundamental research, marking exciting progress toward quantum devices that could redefine computational power, information security, and sensory precision globally.

In summary, the twist-controlled modulation of quantum emitters in hexagonal boron nitride exemplifies the convergence of cutting-edge experimental physics and materials science innovation. By capitalizing on the unique layered architecture of hBN and the emerging discipline of twistronics, this research pioneers functional tunability in quantum light sources. As the scientific community continues to develop and refine these approaches, we edge closer to the practical implementation of quantum technologies that promise profound societal and technological transformation.


Subject of Research: Not applicable

Article Title: Twist-controlled modulation of quantum emitters in hexagonal boron nitride

News Publication Date: 19-Jun-2026

Web References: 10.1126/sciadv.aec0101

References:

  • Gale, A., Aharonovich, I., et al. Twist-controlled modulation of quantum emitters in hexagonal boron nitride. Science Advances. 19 June 2026.

Keywords:
Quantum mechanics, Hexagonal boron nitride, Two-dimensional materials, Quantum emitters, Twistronics, Quantum light sources, Quantum computing, Quantum communication, Quantum sensing, Layered materials, Nanofabrication, Photonic modulation

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