When Apple sued OpenAI last week, the argument it made was that former employees had stolen Apple data and then used it to benefit OpenAI. 

The technical details — an employee used “a rare, previously unknown authentication bug to access Apple’s shared network folders” — are interesting. But the larger story is Apple’s ridiculous attempt to stop its people from using anything they learned at Apple in other jobs.

“Hiring managers don’t mind some files being brought into the org during onboarding, but suddenly take umbrage when that same employee exits with some files later on,” said Mike Wilkes, enterprise CISO at Aikido Security. “Legal should be equally concerned about both events.”

The lawsuit focused on Chang Liu, an employee who had been recruited to work at OpenAI after working at Apple for eight years as a Senior System Electrical Engineer. The full text of the filing depicts a comedy of errors by Apple, offering the perfect “do not do” list of handling employee resignations — especially when they’re going to a direct competitor. 

“When Apple contacted Mr. Liu to sign Apple’s confidentiality reminder, schedule an exit interview, and confirm that he had returned his devices and complied with other exit procedures, Mr. Liu did not respond.” And “after leaving Apple, Mr. Liu failed to return an Apple-issued work laptop that he had previously authenticated to Apple’s network.”

First, typical procedure for handling departures is to tie the return of all equipment and the signing of documents to any final payments. With Apple, that is likely to be a large amount of money. The lawsuit does not say whether Apple exerted any financial pressure on their employee for compliance. 

But in terms of equipment with high-level access, why weren’t all privileges revoked, both for the employee and any and all company-issued devices? Did they not maintain a remote-wipe capability for these devices? Although remote-wipe is usually used when devices are missing or stolen, it should work as well when a departing employee refuses to return company equipment. 

According to Apple, Liu apparently had help at Apple from Tang Yew Tan, who was supposedly also interviewing with OpenAI. “While employed by OpenAI, [Liu] accessed and used his former colleague’s Apple-issued work computer that was authenticated to Apple’s network, without Apple’s authorization.”

Apple tried to make much of this Liu’s fault. Legally, yes, there might be liability there; still, Apple made itself look as if it couldn’t protect its own data. “Upon discovering that he had this unauthorized access to Apple’s systems, [the former employee] did not report it, return his stolen Apple-issued work laptop or delete the program that allowed the access.” 

Really, Apple? Your data-protection plan relies on ex-employees to “delete the program that allowed the access”? I’m not so sure you didn’t bring some of this data-leakage on yourselves. 

This gets worse: “Over several weeks, while developing hardware for OpenAI, Mr. Liu surreptitiously accessed and downloaded dozens of Apple’s confidential hardware-related files, including voluminous, detailed information about unreleased products, engineering presentations, technical specifications, and proprietary project data.”

Setting aside the issue of privileges, access, and unreturned equipment that apparently had its own privileges, that statement points to massive data exfiltration from Apple systems. Even if it is coming from a current employee, why didn’t that raise any red flags? 

Let’s get back to the broader implications. When professionals move from one company to another, they — of course — are bringing their experience and knowledge with them. Can Apple reasonably tell them that they can’t do so? Isn’t that experience and knowledge exactly why another company would want to hire them?

Now, to be sure, stealing diagrams and product spec sheets is a clear violation. Let’s say Apple spent a lot of money on some hardware research projects. A member of that technical team would learn an awful lot, all on Apple’s dime. 

But is it fair and reasonable for Apple to say that the former employee can’t leverage that knowledge at his or her next job? 

This brings us back to the point Wilkes made: If Apple is going to try to prevent any former employee from leveraging on-the-job experience, then it should instruct all new employees not to use anything they learned in a previous job. 

That would be ridiculous. Companies pay for experienced talent because of that experience. Why pay for expertise if you insist employees not leverage any of it?

Then there’s the amorphous nature of knowledge. So, it’s wrong to take detailed diagrams and spec details and hand them over to a new employer. But what if that worker heading out the door memorizes the documents (photographic memory) a day before resigning? Is a person prohibited from using something from memory?

There’s also the fruit-of-the-poisonous tree legal argument. Even if a former employee doesn’t directly use stolen data, what if their knowledge leads to other money-saving insights for the new employer? 

Given that Apple hires as many specialists as it loses to rivals, wouldn’t it make sense to leverage everything your workforce knows and then let new employers do the same? But before you do that, Apple, tighten your exiting employee tech controls. 

Then maybe you wont’t have to file lawsuits like this down the road.

OpenAI’s first device will be a screenless home AI system that can play music, control appliances, and respond to messages and questions, according to Bloomberg. I can’t help but ask what makes this device different from Apple’s HomePod with SiriAI?

The OpenAI product is intended to be the first of a family of solutions and is expected to use the recently-introduced GPT-Live large language model (LLM). The latter is an advanced model capable of processing information swiftly and of providing natural responses to conversations. 

A potential gold mine for hackers

This expertise might help it deliver more accurate responses to requests, though the information could also become a gold mine for data brokers, hackers, and advertisers if there turns out to be any way they can get their hands on it. It’s not yet known how — or even if — OpenAI proposes protecting user privacy within its systems. 

The device, which is still under development, is explained as being a home companion that also includes a built-in camera and sensors so it can gather contextual information about where you are, becoming an expert on you and your needs. It also features autonomous mechanical elements that physically shift on their own, intended to give the product a “personality,” rather than being a boring black box.

Can we live without this?

While OpenAI’s development is not yet complete and things could change before it reaches market, based on Bloomberg’s report I’m not terribly clear how unique it is going to be. After all, as LLM support is introduced in existing smart speaker systems from Apple, or even Amazon, what unique features does this device bring that consumers can’t live without? More particularly, what problems does it solve and why does it exist?

Manufacturing economics

What’s also unclear is how far along OpenAI is on the road to mass manufacturing the device. Apple’s recent lawsuit against OpenAI confirmed the challenger is speaking with Apple’s own manufacturing partners as well as hiring hundreds of Apple engineers. Despite the talent war, I consider it unlikely OpenAI will be able to lock in the kinds of manufacturing deals it needs to bring the product to market at an acceptable price. 

That suggests either that its inaugural “home companion” will seem incredibly expensive (as so many of the products Jony Ive has designed since leaving Apple seem to be), or that OpenAI will sell these things at a subsidy. 

Bloomberg suggests the systems will cost $200 to $300. That seems low given the current component market, expected design quality and the technology used if the plan is to make something good. And it leaves me wondering how deeply investors will underwrite hardware sales, given the eye-watering losses the company is already making.

Apple’s trade secrets case

Given ongoing speculation that Apple plans something similar in the form of a hybrid HomePod/iPad equipped with AI and limited mobility, the recent lawsuit strongly suggests Apple feels some of OpenAI’s plans cross the line into using proprietary technologies and ideas Cupertino has spent years pursuing. Apple’s lawsuit seems to bring much more meaningful evidence than just an argument concerning product design. 

Betting the farm on Ive

We also don’t know the extent to which consumers will be open to semi-sentient AI devices lurking in their lives. While Apple can lean into its loyal customer base and broaden its offering with rock-solid promises concerning user privacy, OpenAI has less to bring to the launch party.

That means it is attempting to pivot millions who use its services into investing in its hardware. It presumably hopes that it will be able to drive that transition by using the design involvement of acclaimed Apple designer Jony Ive as a form of magic talisman. 

The challenge is that while Ive is a big name in Apple history, Apple users are extremely loyal and may react against the involvement of their favorite designer. It’s like finding out someone you thought was on your team actually supported someone else. 

It will be different outside Apple, where less loyal cohorts might see the product introduction as a chance to put a design from Ive through its paces without signing up to a Mac, iPhone, iPad, or HomePod. 

For the rest of us, the question will be whether OpenAI’s Ive-designed product channels the successful design ethic of the iMac, or that of the far less successful hockey puck mouse. Like (timely World Cup klaxon) France against Spain, OpenAI’s investors have to hope the best version of Ive’s design principles show up, because their risked fortunes potentially depend on it. 

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Passkeys have been around for some time, but enterprise-wide adoption to this point has been slow for a number of reasons. But soon, many Microsoft customers won’t have a choice.

Starting September 1, Microsoft will roll out passkeys as the default authentication method in its cloud-based identity and access management (IAM) service Entra ID. And following a transition period, Microsoft-provided SMS and voice authentication will officially end on February 1, 2027.

With this move, Microsoft seems to be underlining the urgent need for a more secure authentication standard, as attackers up their game with AI.

This is an “important milestone,” because it moves passwordless authentication from an optional security enhancement to the expected standard, noted Ensar Seker, CISO at SOCRadar. “That shift is significant as attackers increasingly rely on AI to automate phishing campaigns, generate convincing login pages, and conduct large-scale credential theft.”

Microsoft’s six-month passkey roll-out

Passkeys require users to authenticate via a fingerprint, facial scan, or lock screen mechanism, rather than a password. They can be stored on physical USB keys (like YubiKey), or as digital credentials on computers, phones, or in cloud accounts.

This method, Microsoft contended, reduces reliance on phishable authentication tools like SMS and voice, and hardens protection against credential theft.

Passkeys “work better for users and worse for cyberattackers,” Nadim Abdo, Microsoft corporate VP for identity and network access engineering, wrote in a blog post.

Microsoft’s announced timeline for rolling out passkeys is relatively aggressive:

• September 1, 2026: All SMS or voice-enabled users will be “auto-enabled and nudged” to register a passkey upon multifactor authentication (MFA) sign-in.
• September 18, 2026: Pricing, commercial terms, and a list of supported telecom providers will be shared for scenarios that still require SMS or voice authentication due to regulation or technical or operational challenges.
• October 30, 2026: Enterprises still using SMS and voice must select and configure a supported telecom provider through the Microsoft Security Store. From then on, they will be responsible for any telecom-related costs.
• February 1, 2027: Microsoft-provided telecom delivery for SMS and voice authentication ends as a native Microsoft Entra capability.

After February 1, enterprises that require SMS or voice for MFA must register a passkey before sign-in. There will be no opt-out option.

It’s important to note that these dates apply to public cloud-hosted Entra ID. Support for other cloud environments will follow a separate timeline; additional guidance and dates are to come.

While SMS and voice have served their purpose well, Abdo said, bringing MFA to billions of users who otherwise would have had none, the threat environment has changed in “speed, scale, and sophistication,” necessitating this move to passkeys.

The benefits of passkeys

SOCRadar’s Seker pointed out that passkeys fundamentally change the attack surface because, unlike with passwords, there is no transmission of shared secrets that can be stolen by threat actors. Authentication requires possession of the user’s device, along with biometric verification or a PIN.

“Even highly convincing AI-generated phishing pages cannot simply trick users into handing over a passkey the way they can with passwords or one-time codes,” he said.

So why haven’t we seen widespread enterprise adoption? Identity ecosystems are “fragmented,” Seker noted, and many enterprises still rely on legacy applications that only support passwords. They also struggle with cross-platform compatibility, lifecycle management, recovery processes, shared accounts, and employee onboarding and offboarding.

Further, “until recently, many organizations viewed passkeys as a consumer technology rather than an enterprise identity strategy,” he said.

Microsoft’s move changes that equation, because Entra sits at the center of many organizations’ identity infrastructure, Seker noted. Default settings are typically the strongest drivers of security adoption, so when passwordless authentication becomes required rather than optional, organizations are far more likely to deploy it at scale.

Its biggest benefit would be a “dramatic reduction” in credential-based attacks, Seker said. He pointed out that most successful compromises still begin with stolen credentials obtained through phishing, infostealer malware, password reuse, or adversary-in-the-middle attacks. Passkeys “eliminate or significantly reduce” many of those attack paths, while reducing password fatigue and the help desk costs related to password resets.

In addition, rather than trying to continuously improve users’ ability to detect increasingly sophisticated phishing attempts, passkeys remove the credential from the equation altogether, Seker noted. “That represents a more sustainable long-term security strategy than relying solely on user awareness training.”

Still, passkeys are not a silver bullet, as they do not stop endpoint compromise, session token theft, malicious insiders, or attackers who already have control of a trusted device. Enterprises must complement passkeys with endpoint protection, continuous monitoring, conditional access policies, and identity threat detection, Seker advised.

How enterprises can prepare

To prepare for the shift to passkeys, Microsoft advised enterprises to review their authentication policy and identify the groups still using SMS or voice authentication. They should then select the best authentication method for user devices and workflows, and ensure all employees are given passkeys and security keys.

Entra ID supports both synced passkeys (those stored in platform credential managers like iCloud Keychain and Google Password Manager), and device-bound passkeys such as Microsoft Authenticator passkeys, Entra passkey on Windows, or FIDO2 security keys.

Seker advised enterprises to evaluate support for FIDO2 and passkeys across their identity infrastructure, and to develop clear enrollment and recovery procedures. They should also educate users on what’s changing, how passkeys work, and how they can complete registration. Further, Seker said, it’s important to establish secure device management practices and to continue enforcing least privilege, conditional access, and risk-based authentication policies throughout the transition.

Ultimately, he pointed out, the move is crucial. “Over the next several years, organizations that continue relying primarily on passwords will likely face higher operational risk as AI continues to lower the cost and increase the effectiveness of credential-based attacks,” he said.

Apple has released the first public betas of its “27” series of operating systems, and feedback so far suggests they’re already very stable builds, even at this early end of the release cycle. 

For most intrepid public beta testers, the big attractions here are Siri AI and the heavily improved Apple Intelligence tools – though Siri AI is not yet available in Europe due to regulatory problems there. Overnight social media commentary has been highly positive, with Siri widely seen as delivering on what we always thought it should be rather than the limited product it became.

Caveat emptor

Once installed, the new operating system is fast and better performing on the iPhone, though there are some limitations anyone considering the beta should consider first:

• This is beta software; things can and sometimes do go wrong. So don’t install it on your primary device unless you know how to restore your device and its data.
• Some critical apps such as banking tools, VPNs and some smart home management software are reported to be unstable at times.
• Once the public beta is first installed, there’s a lengthy period during which your device will rebuild its database; this can take many hours and performance will be affected.
• As the OS beds in, users might experience sudden battery drain or the device might seem warmer than usual.
• You’ll need to join a lengthy Siri waitlist before you can install the updated Siri AI.
• Siri AI requires significant hardware capabilities and only runs on iPhone 15 Pro, iPhone 16 and iPhone 17 models. Alternatively, you must have an M1 or later Mac or an iPad running an M1 chip or later, or A17 Pro (iPad mini).

Siri AI is a big improvement

Siri AI is the big reward here. Joanna Stern called it “significantly better.” It will provide you with much better responses than its predecessor, and its contextual understanding is sophisticated and advanced. 

That’s because Siri can search across your messages, emails, photos and more to help you find what you’re looking for and has some understanding of where you are and what you are doing to help it make even more accurate decisions. It is faster than it’s ever been with a dedicated app (which includes logs of your interactions) and a new glowing design when activated. 

The assistant can now hold an ongoing conversation with you, understands what’s on screen, and take some actions in apps. One way that might be useful is if you are looking at a recipe online, you can ask Siri to write up a shopping list for the recipe ingredients and paste it in a Note. Siri has become much more knowledgeable than in the past thanks to its expanded and updated world knowledge database.

Apple Intelligence has been beefed up, too, with keyboard tools much improved on the last version. Siri can even reflect your personal tone and style based on the person you’re communicating with when sending a Mail or Messages post. 

Apple and the image

The Camera app now has a new Siri mode; it can do things like identify objects and people, or import event details from a leaflet. You can easily search or ask questions about what’s around you, and there are useful new actions you can take, such as getting nutritional insights about a plate of food.

Image Playground wasn’t terribly impressive when it first appeared, and a lot of people did little with it. It seems much better now, capable of generating photo-realistic images in virtually any style from natural language prompts or editing existing images. It’s a useful step up.

Another impressive feature is Spatial Reframing. This lets you shift the composition of a photo after you’ve taken it, using AI to create accurate renditions of what is outside the frame. A new Extend tool lets you expand images, which is useful for adjusting aspect ratios or creating Lock Screen wallpapers.

The future on your wrist

If you use an Apple Watch, you’ll be impressed, as the contextual AI extends to that device. So, you can have context-savvy conversations with your watch and ask it to do tasks on your behalf. It makes it feel like a bona fide computer on your wrist and bodes well for other future wearable products from the company. 

There are lots of other interesting features in the beta. Call Context can automatically surface the information you need, like a confirmation code or reservation number, when calling up a business. And a new Notify Me feature in Safari lets you know when a web page changes, so you can watch for stock availability or ticket sales.

How to install the beta

If you’re interested in installing the new OSes, Apple’s Beta Software Program website should be your first port of call. You’ll need to sign in to access the betas using your Apple Account. Once you’ve done that, open Settings and go to Software Update; there you can select Beta Updates and choose the 27 series Public beta. Tap Update Now and the installation will begin.

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Microsoft’s big lead over AI competitors like Google and others has vanished, and the company is now playing catch-up. As a result, Microsoft’s stock has tanked in the last year — down roughly 23% compared to a year ago, due mainly to its massive AI spending and an inability to monetize Copilot. 

The company clearly needs to do something. And last week it did, though not what you might expect. It laid off 4,800 people, a little more than 2% of its worldwide workforce, with its Xbox division hit hardest. And it’s not reducing its massive spending on AI data centers or other AI-related costs.

The New York Times explained the cuts this way: “It is Microsoft’s latest employee culling as it plows tens of billions of dollars into the infrastructure for building artificial intelligence.”

Was cutting back on gaming (while still going all-in on AI) the right move for Microsoft? To answer that, let’s take a look at the details of the company’s July layoffs.

A year of layoffs

The recent cuts come in the wake of larger Microsoft workforce reductions over the last year or so. In May 2025, the company laid off 6,000 employees, about 3% of its workforce. Then a few months later, it laid off 9,000 more, about 4% of its workers. In both rounds of cuts, the company’s gaming division was hit — though it wasn’t the primary target.

This year, in April and May, the company rolled out its first voluntary retirement program for US employees. Approximately 3,000 people took the money and ran.

Then came last week, when Microsoft primarily targeted gaming. When the cuts take full effect over the next year, 2,850 gaming employees will be let go. In addition, Microsoft is cutting loose several of its gaming studio brands, which will become independent companies or be sold to buyers.

The layoffs hit the two remaining gaming studios, Activision Blizzard, which makes the big-selling games Call of Duty and Candy Crush, and ZeniMax Media, which publishes series including Fallout and The Elder Scrolls. Three years ago, in 2023, Microsoft bought Activision Blizzard for $69 billion. That followed its purchase of ZeniMax Media in 2020 for $7.5 billion. Both seemed like sizable acquisitions at the time. 

Compared to Microsoft’s AI spending now, they’re chump change.

Follow the money

A memo sent to employees about the July layoffs by Amy Coleman, Microsoft executive vice president and chief people officer, made clear the layoffs were more about AI than they were about gaming. 

Of the cuts, she wrote: “The “why” is this: our business is changing because the world around it is changing. The way technology is built, deployed, and used is transforming faster than at any point in my time here. Our customers’ needs are shifting, the business models that serve them are shifting, and that means the work itself — what we do, where we focus, and how we’re organized — has to transform, too.

“Our customers are navigating this same shift, and they’re counting on us to help them through it.”

That last sentence is an oblique reference to the early July launch of the Microsoft Frontier Company, which will embed 6,000 engineers inside customers’ businesses to help them more effectively deploy AI. The cost: $2.5 billion.

That sounds like a substantial amount of money. But it’s only a drop in the bucket of how much money the company plans to spend on AI. In April, Microsoft told investors it would spend $190 billion on data centers and other AI infrastructure this year, a 60% increase over what it spent last year. At the same time, Microsoft said it would shrink its workforce.

Its latest layoffs are clear-cut evidence of that. It’s also evidence that the company recognizes how badly Xbox has performed, and that it needed to do something about it. 

In early June, Microsoft sent a memo to everyone in its Xbox division entitled “Next 100 Days: XBOX Reset.” The memo laid out the problems with its ailing game business and pulled no punches. It noted that beyond the $69 billion the company spent three years ago to buy Activision, “Over the past five years, we have spent over $20 billion on ongoing investments in our content, platform, and hardware subsidy, but our annual revenue has declined nearly half a billion during that time. Going forward, this cannot continue.” 

The layoffs and spinoffs were the first steps. They won’t be the last.

There’s no doubt this is just the beginning of Microsoft’s disinvestment in gaming. The issue isn’t just that the company’s investments haven’t paid off. It’s that Microsoft’s AI ambitions are so large and expensive that it can no longer afford to seriously fund gaming.

Ultimately, it was the right thing to do, at least from a business perspective. The future is AI. It’s not in gaming.

So, for the foreseeable future at Microsoft, when it comes to AI — the sky’s the limit. But when it comes to gaming, things look much less rosy.

Except for the second user/refurbished smartphone markets, AI means the days of cheap phones are over, with huge price pressures putting low-end vendors out of business. 

Omdia data confirms that Apple and Samsung are undisputed kings of the hill, combining for 42% of the market even as smartphone sales overall have seen a 4% average decline. 

The two companies increased market share by 4% (Apple) and 2% (Samsung) compared with 2Q25. Meanwhile, the situation is becoming much worse for smaller vendors as memory prices spiral, leaving their businesses under immense strain. Data from Counterpoint Research tells a similar story, indicating Apple growth against a background of market decline. 

It’s important to put the impact of raised RAM costs into perspective. While Apple and Samsung make products at the kind of scale that enables them to cut better deals, smaller makers don’t have the same advantage, leaving them far more exposed to memory price driven pressures.  

Memory prices are crushing the low end

And they really are exposed; not only are sales declining, but Omdia analyst Runar Bjorhovde notes that vendors at that end of the market are dealing with hugely destructive DRAM price hikes over just the past year — up to four or five times higher in some cases. That degree of increase is the kind of business-focused tsunami that drives people out of the market altogether and certainly leaves companies exposed to M&A activity.

Right now, memory and storage can account for more than 60% of the product cost, Bjorhovde said. And as costs continue to increase, what profitability that does exist in the low-cost, high competition lower end smartphone space is being utterly mauled. Omdia forecasts a 22% decline in the sub-$400 smartphone segment as a result.

“Samsung Electronics and Apple — the two market leaders — made huge market share gains…, whereas most players beyond went through steep volume declines,” Bjorhovde said.

From volume to value

Apple’s decision to expand its addressable market with the iPhone ‘e’ series just adds pressure, while Samsung’s enduring popularity helps make it difficult for smaller vendors to generate profit through market scale. “To adapt, vendors are shifting their strategies from volume to value by reoptimizing portfolios and adjusting retail pricing,” he said. 

“Although memory and storage costs are the biggest challenges for vendors, they are far from the only challenge,” Bjorhovde said. “New semiconductor bottlenecks, such as within foundries, are adding further cost pressures.”

With the cost of manufacturing set to continue to rise, it’s generally accepted that we’ll see the average selling price of smartphones climb in the coming 12 months, with Apple set to lead the market toward higher cost builds with the new Pro and Ultra iPhones this September.

Apple’s decision to hold smartphone prices so far has added another price pressure to low-end vendors; the longer it holds its prices down, the longer and more painful will smaller vendors hang onto their own low-price structure to compete.

Future shock: AI hardware

A further wild card is in Apple’s recent lawsuit against OpenAI, which accuses the ChatGPT maker of “illegal reliance on misappropriated trade secrets” in its hardware plans. OpenAI is thought to be planning an AI-driven iPhone competitor.

We’ve heard speculation about these plans before, of course. But what seems to be emerging in the wake of Apple’s litigation are hints OpenAI intends to introduce its first AI hardware product at some point in 2027.

Assuming that schedule remains on track, OpenAI will likely impose further component pricing pressure across the whole industry. After all, Apple’s customer loyalty leads the industry, and Samsung has built something similar. So, the companies with the most to lose to OpenAI will be the same set of smaller vendors who are already struggling with component price-driven market complexity.

OpenAI products will demand the same memory, similar processors, manufacturing, and other components as other devices, prompting further pricing pressure. That’s likely to put some small vendors out of business entirely, even as standard smartphone prices increase. 

Fragmentation will be next

Those outcomes won’t be universal, as the desire for sovereign data services and growing mistrust of US tech companies suggest OpenAI’s products might see limited adoption in most markets. But they could serve to accelerate divergence in smartphone purchasing patterns worldwide, while adding to market pressure.

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Meta pulled a new AI feature on Instagram just days after its launch because of criticism about how the tool handled publicly posted photos.

The feature was part of the new AI image generator, Muse Image, and allowed AI-generated images to be created using content from public Instagram accounts. By specifying a public account with an @mention, users could let the AI ​​model use the person’s images as a reference without informing the account holder.

The launch quickly prompted concerns about privacy and the risk of abuse, with critics saying the feature could be used to create manipulated or offensive images based on other people’s public content.

Meta said its the goal was to give users a creative tool where they could decide for themselves whether their public content could be used. But it removed the feature after feedback that the feature did not meet expectations.

Apple surprised the tech industry after financial markets closed Friday with news the company has sued OpenAI, alleging theft of trade secrets for ChatGPT hardware. The lawsuit particularly targets some senior ex-Apple employees now working at OpenAI.

Apple’s suit names two former employees — Chang Liu and Tang Tan, former vice president for product design, iPhone and Apple Watch — as well as OpenAI and that company’s recently-acquired firm, io Products, alleging “trade secret misappropriation and breach of contract.” 

Jony Ive, who sold io Products to OpenAI, is not named in the lawsuit, though it seems relevant that Tan was one of the senior ex-Apple executives who founded that company.

For its part, OpenAI issued a brief statement in response to the litigation. “We have no interest in other companies’ trade secrets,” the company said. “We remain focused on building innovative technology that empowers people everywhere.”

The allegations against Tan

Some of the claims and allegations included in Apple’s lawsuit include:

• That in the months before leaving Apple, Tan met with OpenAI or its collaborators and discussed meetings with a key Apple supplier.
• He emailed himself information about suppliers and internal summaries.
• When interviewing former Apple staffers for jobs, he used confidential information, such as internal project code names, to gain even more knowledge.
• He asked candidates to bring actual parts from Apple to interviews to discuss — and a then-Apple employee screenshotted and downloaded files concerning a highly confidential Apple project before attending an OpenAI recruitment session.
• Tan asked Apple employees to bring CAD/design artifacts to their interviews.
• Tan allegedly instructed new hires on how to avoid scrutiny when leaving Apple, such as instructing them not to tell the company they had taken jobs at OpenAI.

The ‘so funny’ laptop bug

The lawsuit also claimed that after quitting Apple for OpenAI in January 2026, Chang Liu managed to keep or “otherwise acquire” an Apple-issued notebook which he used to access confidential data on the company’s private network while at OpenAI. “LOL, I found out I can access the [server], so funny,” Liu texted a friend still working at Apple.

The suit alleges that he made no effort to report the situation, which was a bug in the system he had uncovered. Apple eventually discovered the exfiltration was taking place and took steps to prevent it, but Liu allegedly downloaded more than 1,000 pages of data, including “confidential technical presentations, spreadsheets, PDFs, and written work product,” Apple said.

“Only OpenAI and Mr. Liu know all the ways they have been exploiting the trove of Apple confidential information he stole, and to the extent they have not concealed or destroyed the evidence of these misappropriations, it will be investigated thoroughly in discovery.” 

Apple’s lawsuit also alleges Liu was simultaneously coaching a current Apple employee named Alyssa Peng on how to copy files from Apple workstations without triggering the security team, asking her to get specific confidential information and using Apple’s stolen data to help her get ready for an eventual OpenAI interview.

Why this could get bigger

There’s much in the litigation that it will garner serious international attention as it unfolds. Apple’s argues that a competitor with access to so much of its own proprietary information could “bypass years of independent research and development, skip the capital expenditure required to build genuine expertise, and bring products to market faster and at lower cost, harming the value of Apple’s investments.”

It’s not just the secrets behind actively-used processes Apple is protecting; the company is also asserting its rights to regain control of information it has assembled over time concerning processes and manufacturing attempts that have failed. That’s understandable – you can invest a lot of money in finding out what doesn’t work and knowing that is a trade secret in itself. 

“OpenAI coaches candidates to prepare for their interviews by studying Apple’s confidential engineering documentation, internal presentations, and proprietary technical materials,” the litigation claims. “OpenAI then uses its insider Apple information to ask detailed questions to extract more: about Apple’s proprietary tools, vendor management processes, engineering methodologies, manufacturing workflows, and supplier relationships, for example.

“OpenAI has turned to trade secret misappropriation to free-ride off Apple’s decades of innovation,” Apple said. “This is the tip of the iceberg.” 

The lawsuit also confirms that Apple often designs and customizes the specialized machinery used in its suppliers’ factories, and that trade secrets concerning those efforts have been grabbed. The suit notes that OpenAI works with established Apple suppliers Foxconn, Luxshare, and Goertek on its own hardware.

If true, these allegations go right to the top of OpenAI’s hardware development plans. Tan is now OpenAI’s Chief Hardware Officer.

The lawsuit points out that OpenAI now employs more than 400 Apple engineers and executives (including the company’s former Vision Pro Vice President), suggesting its entire approach to hardware recruitment is based on extracting Apple’s proprietary knowledge from potential hires. 

“Apple lacks visibility into what’s been happening behind closed doors at OpenAI, where such misconduct is normalized and exemplified by leadership,” the lawsuit argues. “This much is clear, however: at every level, from members of its Technical Staff to its Chief Hardware Officer, and in coordination with business partners, OpenAI has been stealing Apple’s trade secrets and confidential information. As a natural result, OpenAI’s nascent hardware business now rests on the shakiest of foundations, rotten to its core by its illegal reliance on misappropriated trade secrets.”

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Consumer electronics prices are shooting up. Energy prices are increasing fast. Even water bills are climbing. For a technology that promises “efficiency,” the ongoing AI gold rush seems to be taking things away, much like the proverbial gift that keeps on grabbing.

With hundreds of billions in AI investment already racked up for 2026, it’s important to remember the entire industry is currently built on a mountain of debt — and much of this borrowed money is being spent on data center capacity. That’s true, even though consumers would probably rather have a cheap Mac than spend money on an AI subscription service. 

All this debt is being amassed because a small number of people at a very small number of firms have decided to make huge investments in the tech, which at present requires huge quantities of energy, memory and data center capacity to run. 

But it won’t always be this way.

A mountain of debt, but we’re short of memory

Look, the industry as it is now just doesn’t seem sustainable. Trillions are being spent and memory vendors are shifting capacity to make the high-value, high-bandwidth memory these server farms require — at the expense of traditional consumer electronic suppliers. 

The rapid rollout just creates AI tech will need to be replaced, likely at greater cost, in a few years’ time. In a nutshell, the industry is spending trillions to make billions; Sequoia’s David Cahn estimates the AI revenue gap between infrastructure expenditure and the revenue to justify it has already fallen $600 billion a year short. 

At some point, the VC money will run dry, after which it is inevitable deployment will slow and demand for all the components — including memory used in these large language model (LLM) data centers will fall. Some analysts think capex growth in the sector could halt by mid-2027.

At that point, memory vendors will have expensive production facilities and extensive defaults on their order books. If the 2027 prediction is true, those vendors will feel this impact in the form of reduced forward orders by the end of 2026.

The problem is that the investments have become so vast that any slowdown will have consequential effects across all sections of the economy. 

After the gold rush

Almost certainly, the technology will continue to improve, and the problems we’re looking to solve today might no longer be challenges once fresh innovation strikes. So, what happens next? 

Let’s think about memory, the biggest pain point at the moment and where we will (hopefully) find future innovation. At present, some of the largest LLMs sit inside data centers supported by vast quantities of memory. These machines are built to handle really complex tasks, but most of the time are used to search the web, deliver writing assistance and summarize documents. Those frequently-transacted tasks barely stretch the capabilities of these services and Apple, and others have already figured out how to run such tasks on device.

That’s the first obvious space in which to innovate – to invest in 1-bit data LLM systems to miniaturize and distill models so they actually run on the device you’re using, rather than relying on all those remote servers. 

The Apple shopping list

Apple’s interest in 1-bit data LLM pioneer PrismML speaks volumes about where the iPhone maker sees LLM development going, as did its acquisitions of Kuzu Inc., WhyLabs Inc, Pointable Inc., and Datakalab Inc. in recent years. 

The beauty of PrismML’s tech is what it can do. It was recently used to compress Alibaba’s huge 27-billion-parameter Qwen 3.6 model from 54GB down to under 4GB, running with all 27 billion parameters active simultaneously — all without sacrificing benchmark performance. 

The kicker? It managed to run that advanced, sophisticated AI model on an iPhone 17 Pro. My take? Just as music used to be captured on reel-to-reel tape and is now digitized and in the air, AI will move from the data center to the device, possibly faster than people expect. 

Apple has three pillars for AI: On-device for most of what you need, on Private Cloud Compute servers for most of the rest, or via third-party server-based systems for the most demanding tasks. That’s a blueprint for how the industry will evolve as technologies represented by PrismML tend toward bringing more of that intelligence to the device. Over time, those local tasks will become more sophisticated, eroding the available market for today’s heavily-indebted AI incumbents. 

Emerging priorities such as the need for privacy, data sovereignty, and trusted cloud will also spur the emergence of a multipolar AI future in which no one vendor dominates, further complicating their journey to profitability. It’s a model that favors the kind of service-agnostic, edgeAI approach Apple has taken.

EdgeAI for the rest of us

In the end, I don’t think there will be a need for much of the AI data center capacity now being built, because Apple and others will figure out how to use data minimization to transact sophisticated AI tasks on the device. For the most part, EdgeAI will deliver the consumer AI experience, while data centers cater to more sophisticated use. One day, after this gold rush has run its course, we’ll peer outside of our basements to see which of today’s AI firms actually are the chosen ones.

They may not be the ones you expect.

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