Apple ratcheted up its lawsuit against OpenAI on Monday, telling a federal judge that the ChatGPT maker is destroying evidence while a former iPhone engineer used a confidential Apple circuit schematic in his new job.

In a court brief filed in support of expedited discovery, Apple said OpenAI is actively destroying crucial evidence in the Northern District of California case, which accuses the AI company, hardware chief Tang Yew Tan, former Apple engineer Chang Liu, and io Products of misappropriating hardware trade secrets. Apple also alleged that Liu “not only downloaded a confidential Apple circuit schematic but also used it in his work” at OpenAI.

The filing comes as Apple presses for early document production, forensic imaging of devices and accounts, and depositions of key witnesses. OpenAI has sought to dismiss the suit and has opposed Apple’s request for a fast-track discovery schedule and a preliminary injunction that would bar defendants from using or disclosing alleged Apple secrets while the case proceeds.

Apple sued in July, claiming a pattern of theft after OpenAI hired hundreds of former Apple employees and acquired Jony Ive’s hardware startup io. The complaint centers on Liu, an eight-year Apple senior systems electrical engineer who left in January 2026, and Tan, a 24-year Apple veteran who led iPhone and Watch product design and is now OpenAI’s chief hardware officer. Apple says Liu kept an Apple-issued laptop, exploited residual network access and an authentication bug, and downloaded dozens of confidential files—engineering presentations, specs for unreleased products, and manufacturing details for main logic boards—while already working for OpenAI. Tan, Apple alleges, used internal Apple project names in interviews and encouraged recruits to bring parts, prototypes, and supplier information.

OpenAI has denied the claims, calling the lawsuit careless and “oddly personal,” and saying it has no interest in Apple’s trade secrets. It has published messages it says show Apple employees contacting Liu after he left and asking for technical help, and it argues Apple delayed raising the core allegations for months.

As Bloomberg News’ Chris Welch and other reporters covering the Silicon Valley hardware race have noted, the case sits at the collision of two of tech’s most closely watched product efforts: Apple’s next devices under new CEO John Ternus and OpenAI’s push into consumer hardware after buying io. A hearing on Apple’s expedited-discovery motion is scheduled for Oct. 1 before Judge Edward Davila, though the court could rule sooner. Both companies declined additional comment beyond prior statements.

MacDailyNews Take: If you have nothing to hide, you don’t need a shredder. If your first device depends on Apple’s trade secrets, you definitely don’t want the schematics on your laptops.

Judge Davila gets this in October. Until then, OpenAI can keep insisting it’s all a misunderstanding. Apple is simply asking the court to look before the hard drives get “accidentally” wiped.

---

‎
Please help support MacDailyNews — and enjoy subscriber-only articles, comments, chat, and more — by subscribing to our Substack: macdailynews.substack.com. Thank you!

Support MacDailyNews at no extra cost to you by using this link to shop at Amazon.

The post Apple accuses OpenAI of destroying evidence as trade-secrets fight intensifies appeared first on MacDailyNews.

The panel examines a Bluetooth-related earbuds vulnerability, debating whether the issue lies in the protocol or Apple’s proprietary pairing approach, and why the implications feel unsettling. Chuck Joiner, David Ginsburg, Eric Bolden, Ben Roethig, Marty Jencius, Jim Rea, Jeff Gamet, and Guy Serle also touch on a useful tool born from a creator’s own workflow before turning to another LastPass incident and where it puts them on the trust spectrum. 

💾

From the Flip the Script event in San Francisco, Dmytro Melnyk, Chief Product Officer for MacPaw, discusses his role and the importance of the company’s careful approach to adding AI across products, from both the developer and customer viewpoints.. He explains why some users want frictionless AI while others resist it over privacy, cost, and complexity, and outlines Eney's goal of combining many Mac utility tasks into one assistant with a pay-as-you-go model. 

💾

A new research project at the New Jersey Institute of Technology is taking a closer look at a strange but important behavior in rubber-like materials: their properties can depend on what happened to them before. With a $125,000 New Directions grant from the American Chemical Society Petroleum Research Fund, chemical and materials engineering professor Gennady Gor and his students will investigate how petroleum-derived polymers absorb fluids, swell, contract and change their elasticity over time. The work could improve predictions of how elastomers perform in everything from industrial seals to oil and gas equipment.

Elastomers are polymers capable of undergoing large, reversible deformations. Silicone, neoprene and natural or synthetic rubber are familiar examples. Their molecular chains can stretch, rearrange and return toward their original configuration, giving these materials their characteristic flexibility. Yet exposure to liquids or vapors can dramatically alter that behavior. Hydrocarbon molecules may enter the polymer network, pushing molecular chains apart and causing the material to expand. At the same time, the absorbed fluid can change the polymer’s stiffness, strength and ability to recover its original shape.

Gor’s project focuses on a phenomenon known as hysteresis, in which a material’s current state depends not only on its present conditions but also on the path it took to reach them. For an elastomer, that means the material may respond differently when it is swelling than when it is shrinking, even if its size and surrounding environment are identical. The difference can arise from molecular rearrangements, delayed relaxation or changes in the interactions between polymer chains and absorbed molecules. Such path-dependent behavior is central to understanding why materials sometimes fail to return fully to their previous mechanical state.

The question is especially relevant for petroleum-related technologies. Elastomers are widely used as O-rings, gaskets and seals because they can deform to fill microscopic gaps and maintain a hermetic barrier. In oil and gas environments, however, these components may encounter complex mixtures of hydrocarbons under pressure and at elevated temperatures. If a seal swells excessively, softens or loses its ability to recover after repeated exposure, it can become unreliable. A better understanding of hysteresis could help engineers select materials and operating conditions that reduce leakage and extend component lifetimes.

The new study represents a shift for Gor’s Laboratory for Materials Interfaces. His previous research has largely examined porous adsorbent materials, which collect molecules on their internal surfaces. Elastomers are different: they do not need pores to take up substantial amounts of fluid. Instead, molecules diffuse into the polymer matrix and interact with its flexible chains. The resulting process is more like a sponge absorbing liquid, although the molecular mechanisms are governed by diffusion, thermodynamics and the elasticity of the polymer network.

Günel Nabiyeva, a fourth-year doctoral student, will lead much of the experimental effort with undergraduate chemical engineering student Elitsa Mileva and second-year doctoral student Supun Rangana. The team has already begun collecting data on how rubber deforms in the presence of liquids and vapors. For Mileva, who has spent considerable time reviewing the scientific literature, the grant will provide an opportunity to move deeper into laboratory work. Nabiyeva describes the project as a new system for the research group because the laboratory has not previously studied elastomers in this way.

The researchers will expose polymer samples to controlled concentrations of hydrocarbon vapors and track how the materials respond. Their measurements will combine swelling, fluid uptake and elasticity rather than treating these properties separately. This approach is essential because a polymer can gain mass and volume while simultaneously becoming softer or, under some conditions, displaying a more complicated mechanical response. Different hydrocarbons may produce different results because their molecular size, chemical structure and compatibility with the polymer influence how quickly they enter the material and how strongly they interact with its chains.

Rangana will contribute expertise in ultrasonic sensing, which can measure subtle changes without physically contacting or damaging the sample. Ultrasonic waves travel through a material in ways that depend on its density, structure and mechanical properties. By analyzing changes in wave transmission or reflection, scientists can detect variations in stiffness and internal condition with high precision. Coupling these measurements to vapor exposure could reveal how quickly elasticity changes as molecules enter the polymer and whether the material follows the same path during desorption as it did during absorption.

The team hopes its results will fill a gap in the fundamental understanding of polymer hysteresis. Although hysteresis is well known in many areas of physics and engineering, relatively few studies have examined how history-dependent elasticity develops in swollen elastomers. The new measurements may help distinguish reversible molecular motions from more persistent structural changes and improve models used to predict polymer performance. Ultimately, the researchers aim to make elastomer behavior less mysterious and more reliable, helping engineers design seals and flexible components that remain dependable across repeated cycles of chemical exposure, swelling and recovery.

Subject of Research: Elastomers, petroleum-derived polymers, polymer swelling, elasticity and hysteresis

Article Title: Ultrasonic Experiments Aim to Reveal Why Rubber Remembers Its History

Web References: Gennady Gor’s NJIT profile; NJIT Laboratory for Materials Interfaces

Image Credits: Courtesy of New Jersey Institute of Technology

Keywords

Elastomers, polymers, hysteresis, rubber, polymer swelling, ultrasonic sensors, materials science, chemical engineering, petroleum research, NJIT

As Waymo deploys more driverless cars to 15 U.S. cities and counting, its vehicles keep encountering new and unexpected situations that they have no script to handle.

Clean, free power from the sun is easier and more affordable to capture than ever with the best portable solar panels.

A new study published in the British Journal of Cancer is challenging the way lung-cancer risk is discussed among people who do not smoke conventional cigarettes. Researchers led by S. Vaja, J.L. Dickson and C. Horst examined the effects of cigar, cigarillo and pipe smoking, focusing on two questions with immediate public-health consequences: how these products influence the likelihood of developing lung cancer and whether their users are being identified as eligible for screening.

Lung-cancer prevention strategies have traditionally been built around cigarette consumption, especially the number of cigarette packs smoked per year. This measure, known as pack-years, combines smoking intensity and duration. One pack-year generally represents smoking 20 cigarettes a day for one year. Because cigars, cigarillos and pipes are used differently from cigarettes, their tobacco exposure is harder to convert into the same standardized measurement. That difference can leave regular users outside the systems designed to identify people at high risk.

The study addresses a persistent misconception: that alternative combustible tobacco products are inherently safer because they are not smoked in the same way as cigarettes. Cigar and pipe users may inhale less deeply or less frequently, but the smoke still contains carcinogenic compounds produced by burning tobacco. These include polycyclic aromatic hydrocarbons, tobacco-specific nitrosamines, volatile organic compounds and fine particulate matter. The precise dose entering the lungs can vary substantially, yet repeated exposure can still damage airway cells and contribute to mutations associated with cancer development.

Cigarillos occupy a particularly complicated position because they range from small, cigarette-like products to larger cigars that may be smoked over a longer period. Their nicotine content and smoke volume can also differ widely. Some users inhale cigarillo smoke deeply, while others do not. Pipes introduce another set of variables, including the duration of a smoking session, the amount of tobacco burned and the frequency with which the pipe is used. These differences make simple comparisons difficult, but they do not eliminate the biological hazards of combustion.

According to the research, the central concern is not only whether cigar, cigarillo and pipe smoking increases lung-cancer risk, but also whether current screening rules recognize that risk. Lung-cancer screening commonly relies on age and a history of cigarette smoking measured in pack-years. People who have smoked other combustible tobacco products may therefore be classified as having insufficient exposure, even when their cumulative tobacco use has been substantial. If risk is underestimated at the eligibility stage, some people could miss the opportunity to detect a tumour before symptoms appear.

Screening works by identifying disease at an earlier and more treatable stage, often using low-dose computed tomography, or LDCT. Unlike a conventional CT scan, LDCT uses a lower radiation dose to create detailed images of the lungs. Its effectiveness depends on selecting people whose probability of developing lung cancer is high enough to justify the benefits and potential harms of testing. Those harms include false-positive findings, unnecessary follow-up procedures, anxiety and cumulative radiation exposure. The challenge is to expand access without encouraging indiscriminate scanning.

The researchers’ analysis highlights why tobacco history needs to be recorded in greater detail. A questionnaire that asks only whether someone smokes cigarettes can overlook the frequency, duration and type of cigar, cigarillo or pipe use. It may also fail to capture former users whose exposure occurred years earlier. More comprehensive risk models could incorporate the number of products used, smoking intensity, years since quitting, inhalation patterns and additional factors such as age, occupational exposure, air pollution and family history.

The findings also carry a message for people who use these products socially or intermittently. Risk is not determined solely by whether smoking occurs every day. The biological impact depends on cumulative exposure, the composition of the product and how smoke is inhaled. Occasional use may carry less risk than heavy, long-term use, but “less” does not mean “none.” Combustible tobacco remains fundamentally different from non-combustible nicotine products because burning creates the toxic mixture responsible for much of the damage to the respiratory system.

The study does not suggest that every cigar or pipe smoker should automatically undergo a CT scan. Instead, it points to a gap between real-world tobacco exposure and the simplified categories used in many screening policies. The authors’ work could encourage clinicians and policymakers to assess alternative tobacco use more systematically and to investigate whether existing eligibility thresholds should be adapted. Any change would require careful validation, because screening recommendations must balance earlier diagnosis against the risks of testing people who are unlikely to benefit.

The broader lesson is that lung-cancer risk cannot be reduced to a single cigarette-based number. As smoking patterns evolve and tobacco products diversify, prevention programmes must keep pace with the ways people actually consume tobacco. The study by Vaja and colleagues adds evidence that cigar, cigarillo and pipe smoking deserve a more visible place in lung-health research and clinical risk assessment. For now, the clearest preventive message remains unchanged: avoiding all combustible tobacco products offers the strongest protection, while anyone with a history of substantial use should discuss their individual risk with a healthcare professional.

Subject of Research: The effects of cigar, cigarillo and pipe smoking on lung-cancer risk and eligibility for lung-cancer screening.

Article Title: The effect of cigar, cigarillo and pipe smoking on lung cancer risk and screening eligibility.

Article References: Vaja, S., Dickson, J.L., Horst, C. et al. “The effect of cigar, cigarillo and pipe smoking on lung cancer risk and screening eligibility.” British Journal of Cancer (2026). https://doi.org/10.1038/s41416-026-03578-9

Image Credits: AI Generated

DOI: 10.1038/s41416-026-03578-9

Keywords: lung cancer, cigar smoking, cigarillo smoking, pipe smoking, tobacco exposure, cancer screening, low-dose CT, smoking-related cancer, public health, epidemiology

My favorite budget laptop, the HP OmniBook 5, has an OLED screen and gets great battery life, despite being just $750 right now.

My favorite budget laptop, the HP OmniBook 5, has an OLED screen and gets great battery life, despite being just $750 right now.

You don’t want any old gaming laptop. Here’s my take on which to get, based on hundreds of hours of testing.

You don’t want any old gaming laptop. Here’s my take on which to get, based on hundreds of hours of testing.

I tested the best webcams across various prices to find the top option. Here’s what I learned.

These powerful, high-capacity portable batteries are the best ones when laptop charging is a priority. All have at least 20,000 mAh, enough to charge most laptops twice.

As gaming laptop prices continue to rise, it’s increasingly difficult to find affordable options that aren’t terrible. Here are your best options based on performance and cost.

The penalty, imposed by a state judge, is on top of a $375 million fine ordered by a jury that found Meta misled users about the safety of its platforms.

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.

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.

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.