A vulnerability in Apple’s “Hide My Email” tool lets almost anyone discover a person’s real email address that is supposed to be hidden by the feature, and Apple has failed to fix it for more than a year, according to a security researcher and 404 Media’s own tests.
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“Apple Hide My Email is leaking email addresses that are supposed to be hidden. We reported the issue and replication instructions to Apple over a year ago. We don’t know why it hasn’t been fixed, but we don’t feel comfortable waiting any longer. Hide My Email users deserve to know that it may be possible for attackers to discover their hidden email addresses,” Tyler Murphy, the co-founder of EasyOptOuts, which discovered and reported the issue to Apple, told 404 Media.
“Free, publicly accessible people-search sites make it easy to link an email address to other personal details, so people relying on Hide My Email for safety may be at risk,” Murphy added.
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To test the issue I generated a new Hide My Email address and provided it to Murphy. Around five minutes later, he replied with my real email address linked to my Apple account which was supposed to be hidden.
Murphy said that he reported the issue to Apple in June of last year, and the company told him it was looking into it. Apple said it had been fixed in March of this year, but Murphy found that wasn’t the case. He again contacted Apple, with the company saying that it would appreciate him not revealing the existence of the flaw until it had been resolved.
Apple then said it planned to address the issue in June, but since it still hasn’t been fixed[…]
March 19, 2026: Using the reproduction instructions from our initial report, we determined that the vulnerabilities hadn’t been fixed.
May 22, 2026: We realized that the vulnerabilities may have greater severity and scope than we thought initially and reported this to Apple. Apple never acknowledged the report of increased severity.
June 30, 2026: Apple again reported that the vulnerabilities were fixed and asked us to verify. We determined that the vulnerabilities hadn’t been fixed.
Very few details are available right now. I have seen speculation that the original email address is revealed when someone replies using their hidden email address, but the impression I get from Cox’s reporting is that no user interaction is necessary[…]
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I am also unclear about how, as of May, the EasyOptOuts guys found the “vulnerability may have greater severity and scope” than initially reported. Ominous, though.
I don’t know whether I’ve accidentally stumbled upon that vulnerability, or perhaps in this case the Hide My Email user just messed up in some way, but I’ve definitely discovered a bizarre and troubling Hide My Email bug in Mail app (on macOS Sequoia 15.7.7).
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Bizarrely, however, Mail app displayed an alert that I’ve never seen before, “This message was forwarded to you by Hide My Email,” despite the fact that I have previously received many emails from people using Hide My Email.
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Why I am seeing two different @icloud.com email addresses, one of which appears to be the sender’s unhidden address?
Why are the To: and From: fields in my reply disabled?
Why is my reply not from my @underpassapp.com email address but rather displayed falsely as from the sender’s @icloud.com address and really from my unrelated Apple Account address?
Apple this week was sued over a reported “Hide My Email” flaw that could expose a user’s real email address. The proposed class action lawsuit alleges that Apple violated California’s false advertising law and other consumer protection statutes by knowingly offering a feature that does not work as advertised.
This is interesting in that the June 2025 bug report is the evidence that Apple “knew” the feature didn’t work, while “Apple continued to represent to consumers that Hide My Email
would actually hide users’ email.” How long are they supposed to get to fix a bug?
The misleading user interface elements in Mail app are a bug, but by themselves they’re not a vulnerability. The vulnerability is that when I send a reply to my maliciously crafted email, my Apple account email address is disclosed in the “From” field of the sent reply! In other words, the Mail app interface lies, fails to show the true sent value of the “From” field.
There are a couple of crazy aspects to this information disclosure vulnerability:
It does not require that the victim actually use Hide My Email! I never use it. As I mentioned in my previous blog post, I don’t even have an icloud.com email account.
It does not matter which of the victim’s email accounts receives the email from the attacker. If your Apple account email address is a@foo.com, but you receive a maliciously crafted email addressed to b@bar.com, Mail app will still send your reply from a@foo.com, thereby disclosing your Apple account address.
Apple addressed a vulnerability in Hide My Email that exposed a user's real email address, reports 404 Media. Apple told the site the issue was fully fixed in a patch released on July 3.
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While the bug has now been addressed, email logs that pre-date the fix could still expose user email addresses.
The Supreme Court on Monday ruled that when law enforcement officials used a “geofence warrant” – a warrant that instructed Google to provide location data for cellphone users who were near a particular place during a specific time period – to obtain evidence used to convict a Virginia man of a 2019 bank robbery, they conducted a “search” for purposes of the Fourth Amendment. By a vote of 6-3, the justices sent Okello Chatrie’s case back to the lower court for it to consider whether, as the Fourth Amendment requires, the search was “reasonable.”
Writing for the majority, Justice Elena Kagan emphasized that “[a]n individual has a reasonable expectation of privacy in records about his cell phone’s location, and police intrude on that constitutionally protected interest when they demand the information—even though for only a limited time, and from a third-party tech company.”
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For purposes of whether the government conducted a search, Kagan said, it does not matter that law enforcement officials “access[ed] only a short amount of cell-phone location information.” […] Nor does it matter, Kagan continued, that Chatrie gave Google permission to collect and use the location data.
The British government will introduce a ban on social media access for all users under 16 years of age, set to take effect in 2027.
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The plan goes further than a similar ban introduced in Australia. It will cover major platforms Snapchat, TikTok, YouTube, Instagram, Facebook, and X. An exhaustive list has not yet been released, but Starmer said the rules will apply to services “whose purpose is to enable social interaction and which allow users to post material.”
Messaging apps like WhatsApp and Signal are not covered by the ban, and most social media platforms already require children to be over 13 to create an account and use their services.
So-called AI ‘romantic companion’ chatbots – designed to simulate sexual relationships or roleplay with users – will have to enforce a minimum age of 18 and the government will also be looking in more detail at overnight curfews and breaks in infinite scrolling for under-18s, with more detail coming in July.
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The government said it will “learn the lessons” from Australia’s experience by introducing more highly effective age assurance measures to support compliance, making it far harder for children to bypass safeguards.
In practice that means anyone opening a new account will likely have to prove they’re over 16 by uploading an ID or passing a facial age scan, the same checks that adult sites serving UK visitors have implemented since July 2025 under the Online Safety Act.
Long-standing accounts are largely exempt, but signing up fresh now triggers verification, effectively ending anonymous account creation in the UK.
New VPN rules are set to be issued by the Labour Party government as part of the under-16 social media ban. The government has not revealed any plans to regulate them, but ministers have said details about action alongside the social media ban, including regarding VPN use, will come in July.
Young people will not simply be protected from being contacted by adults or endlessly scrolling—they’ll also lose access to educational videos on YouTube, local events on Facebook, and potentially cut off from distant friends and family.
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The provision also requires internet service providers to limit the time kids spend online, and has rules about who can contact them online. These extreme rules will take decisions about using technology away from families and put them in the hands of government regulators.
The problem is, there’s no such thing as “age verification” for the internet. What we call “age verification” is actually mass surveillance, so invasive and pervasive that it makes the ad-tech industry’s commercial surveillance look like some kind of cypherpunk darknet pirate utopia[…]
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Any attempt to save kids from online harms should start with saving kids from online surveillance, but that’s the opposite of what we’re doing today. After decades of failing to pass and enforce privacy controls for the internet, those same governments are breaking all land-speed records to pass “age verification” laws that make privacy illegal[…]
Lots of US states, European countries, and Australia have introduced “age verification” regulations. They present it as the classic “save the children” talking point, but it’s really just a precursor to attribution of speech, particularly attributing your words to your real identity.
Azdoufal is the security researcher who used Claude Code to help discover that every DJI Romo robot vacuum cleaner and a million baby monitors and security cameras were embarrassingly easy to hack. This time, he says he discovered over 985,000 photo IDs sitting on the public internet for any half-decent hacker to steal.
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Traditionally, you’d need to provide a photo ID every time you wanted to get into a club. But with the verification system, the receptionist can pull up your stored identity documents and check if your face matches. There’s also an optional app called PuffPal that lets clubs scan a QR code for faster entry.
But when Azdoufal decompiled that PuffPal app, he explains in his report, he discovered that Nefos had no meaningful level of security.
I find it incredible that the UK government is about to create a cliff edge where teens will simultaneously be able to access social media and vote, having had no experience of the latter and with scant understanding of how algorithms work.
There are almost no truly public spaces on the internet. Everywhere we congregate is privately owned, even if it operates as a public space. But we seem somehow content to accept them as fiefdoms in their own right, beyond the reach of government. Instead of demanding that they function as safe and pleasant public spaces for all of us, we are asking our governments just to ban teenagers from them.
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It is one thing to ban our children from one park when there are others nearby. It is quite another to ban them from every public space when there are no others on offer.
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I am skeptical as to whether social media is as dangerous as some of its critics say. I am even more skeptical as to whether it is particularly dangerous to teens and young people. […] The central facet of my argument in this post is that wherever you land on the matters of the harms of social media, a ban for teenagers is the wrong response. If the harms are overstated, the policy is unnecessary. If they are not, it is an abdication.
That show of respect for its customers may change with iOS/macOS 27. Reports suggest that, at least in the first beta, Apple Intelligence is mandatory[…]
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“So what’s the problem?”, you might ask. Apple’s AI takes up several gigabytes of storage and leaves less headroom for RAM.
Those are small inconveniences, however, compared to my biggest gripe with Siri AI: It’s completely ruined Spotlight.
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The new Siri-first interface that presumes that if you’re searching for anything but an app or file, you must want Siri to feed you a few links of Apple Intelligence’s choosing.
Getting to a web search from a Spotlight query now requires multiple taps: Type your query, tap “Show Results” (careful: hitting enter will trigger Siri to craft a response, eliminating the possibility of seeing any actual Spotlight content), tap on “Show More” next to the list of Siri-surfaced web results, scroll down until you see Search Google (or whatever engine you have set as your default), then tap that.
Apple Intelligence used to be opt-in, but now it seems that you can’t even opt out. I had previously mentioned the removal of the switch in the context of hypocrisy, but it seems there are enough issues here for it to warrant its own post. Also, I had previously written that the switch was combined with Siri, meaning that you could turn off Apple Intelligence if you turned off Siri, too. I now doubt that’s the case.
Apple today introduced the next generation of Apple Intelligence, powered by a bold new architecture that integrates the latest Apple Foundation Models deep into Apple’s platforms and is uniquely designed to protect users’ privacy.
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The next generation of Apple Intelligence also helps power Siri AI, an entirely new version of Siri.
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Image Playground offers new powerful ways for users to bring their imagination to life. They can create high-quality images in virtually any style, now including photorealistic, thanks to a new generative model that runs on Private Cloud Compute. This is a major transformation for image generation across platforms. And generated images will automatically include a hidden SynthID watermark to identify them as AI-generated.
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Now Messages offers one-tap suggestions based on the context of users’ conversations, making it easier than ever to get things done, such as creating a reminder or a note.
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Apple Intelligence powers even more enhancements across operating systems. With automatic proofreading, users receive improved suggestions for spelling and grammar as they type across the system.
What’s confusing about this Apple-Google partnership is that Google pretty much calls all things AI “Gemini”. The models are “Gemini”, the assistant is “Gemini”, and the feature integrations are “Gemini”. So Apple is taking pains to emphasize that they’re building atop the Gemini models, not the Gemini assistant.
Google creating the model, Apple figuring out how to integrate it and make it easy to use. Are we back to Apple and Google each focusing on what they do best?
Instead of bombarding you with a million new features that mostly feel half-assed, this feels like it’s actually catered to the user experience instead of to marketing checklists.
As I expected, Apple is going to punt the “on-device” story for Apple Intelligence and push towards the “private cloud compute” story for the models that you’ll actually want to use. I’m glad on-device isn’t going away, but it’s clear a hybrid approach is absolutely necessary
Apple today said it is expanding Private Cloud Compute (PCC) beyond its data centers, partnering with Google and NVIDIA to run Apple Intelligence workloads on Google Cloud.
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All server components and software are part of a trusted computing base subject to verifiable transparency and no-privileged-access guarantees, plus Apple has a cryptographically verifiable ledger of all Google Cloud hardware that is part of the PCC fleet to mitigate the risk of supply chain attacks. PCC on Google Cloud also uses many of the same architectural security patterns as PCC on Apple silicon.
As we continue to refine our approach to image generation, the ImageCreator class is being discontinued and will no longer work in iOS 27, iPadOS 27, macOS 27, and visionOS 27 or later.
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Transition to presenting the Image Playground sheet, which provides a consistent, system-managed image generation experience. Alternatively, you can integrate another image generation service of your choice.
This week, Apple announced a series of discovery features that will personalize app recommendations based on users’ interests and behavior, providing a new way for developers to have their app discovered.
At Apple’s Worldwide Developers Conference (WWDC), the iPhone maker introduced Personalized Collections in the App Store, which will showcase recommendations tailored to the individual. These will also include new “App Notes” that explain why the specific apps were recommended to you. Starting this week, you’ll find these new personalized suggestions in various places in the App Store, including the Apps or Games tab or on the Search tab.
Now Apple is putting the extensive identifiable analytics they collect in the App Store in action. They record every tap and there’s no way to turn it off.
They can even calculate your typing speed.
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If you don’t like Apple Music privacy options, you can stream music from Spotify. But where else can you download apps on the iPhone?
The data is associated with your account and unencrypted.
The screenshot in the sub-post was taken from a CSV file you get when requesting your data from Apple. It’s called “App Store Click Activity.” Every event contains 110 attributes 🤯
In this PDF, Apple describes the data points collected in this category.
I requested my own data yesterday but am still waiting for Apple to prepare it.
There’s an ongoing class action lawsuit against Apple related to this. In iOS 26, Apple added an option to “reset” the identifier associated with “usage statistics,” but we haven’t seen any effect for this option. The analytics are still identifiable and linked to the user.
It is not only about recording your typing skills in the search field, the App Store app even reports how much time you spend on every part of an app description as you scroll, hence recording your reading skills 🙃.
Apple also forces iOS to open any App Store link in the App Store app, and that will immediately log a detailed event that user XYZ has viewed that particular app as well as the referrer.
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This data collection is not new. Apple has been doing it at least since iOS 14 when we discovered it. All data collected is NOT anonymous. The data is associated with your Apple ID. This means that given a court order, Apple has to hand this data to law enforcement.
Just for fun, I generated a 1000-character text and pasted it in the search field in the App Store. Well, the analytics captured the entire text, linked it to my ID and sent it to Apple, before even pushing “enter”. Now imagine you accidentally paste something private in there🙃
Update (2026-06-23): I received my own personal data from Apple and can confirm that it does include my timestamped App Store search queries.
Unfortunately, due to the Digital Markets Act (DMA), Apple will not be able to ship Siri AI in the European Union with the release of iOS 27 and iPadOS 27. Over the past several months, EU regulators did not accept any of Apple’s proposed solutions to bring Siri AI to the EU while safely supporting other virtual assistants.
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EU users will be able to access Siri AI on macOS 27 and visionOS 27. Developers located in the EU will not be able to test or use the new Siri AI features for their apps on iOS 27, iPadOS 27, and watchOS 27.
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According to EU regulators, the DMA requires Apple to give any AI system nearly unlimited access to a user’s device, as well as the ability to act on that access autonomously without a user’s ongoing visibility and control.
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Apple designed a solution called Trusted System Agent — an intermediary that would allow virtual assistants to safely access the same features and capabilities as Siri AI for devices in the EU. Apple also shared a plan to launch Siri AI in the EU while gradually rolling out this new solution over an 18-month period. The European Commission said no. In fact, the European Commission did not agree to any of Apple’s proposals.
This phrasing is not very clear, perhaps intentionally so. Apple is trying to give the impression that it did all this extra work to meet the DMA’s requirements, and yet the EC rejected its proposals, so it’s their fault Siri AI isn’t shipping in the EU. But it looks like the crux is that the Trusted System Agent is currently vaporware. What the EC rejected is that Apple wanted to be able to ship Siri AI now on the promise of future openness. The TSA would actually be built during this 18-month window of exclusivity. It’s not surprising that the EU would reject that proposal. The App Store doesn’t approve apps that flout the guidelines but promise to comply later. It doesn’t even entertain proposals of the form, “If I built this would it be approved?”
Apple said it detailed its plans for Siri AI to EU regulators six months ago, along with a technical proposal to allow secure third-party access to that data.
“In essence, a commission that’s asking us to conduct a very risky experiment on many, many, many tens of millions of users,” Greg Joswiak, Apple’s marketing chief, told reporters, “and we only want to ship these capabilities when we can do so safely.”
The EU said not to ship it without the TSA, yet Joswiak spins this as the EU asking Apple to be reckless? And if Apple wanted to ship them safely, why did it stop working on them?
Apple spoke of having spent hundreds of thousands of engineering hours on changes to comply with the EU. In the new report, though, Apple now says that it no longer knows what to work on.
So currently none of its engineers are working on adapting Siri AI to meet the EU’s demands.
It seems like Apple isn’t taking this very seriously. They’re more interested in trying to score PR points by blaming the delay on the EC rather than on building something compliant. So I guess the EC was prescient not to grant an exemption based on the promise that they’d follow through with the TSA.
However, do I think Apple should be made to open up that level of system access?
Honestly yes. They are worried about protecting my data. But it’s MINE. Me, the user.
Stick a big privacy prompt, maybe a system settings screen and reboot, but I want access to my data!
We can look to BrowserEngineKit to see how well it works out to introduce a trusted intermediate layer that Apple doesn’t itself rely on. And this seems like a much harder problem.
I keep seeing people (usually Americans) confused by where the EU is coming from with Apple and the DMA, eg “how can they care about privacy but also demand AI companies can access your data?”
My read is, the EU’s starting point is that a gatekeeper cannot treat itself as inherently more trustworthy than any other company. So if anyone’s AI can access your data, including Apple’s, it’s not up to the gatekeeper to say “we trust ourselves but don’t trust other companies” and force that on users.
The users should get to choose who they trust. And I think Apple is trying to draw a different line than it says. It worries that AI could be hijacked to steal your passwords and photos. But you can already install apps that manage and access that data. You can also install apps that access your calendar and mail data, and these apps can incorporate AI. Such apps actually get more access than what the EC wants because they must communicate with your servers directly. I would rather let an AI search my downloaded e-mails than read/write to my IMAP server, but Apple is blocking that option. The actual line it’s drawing isn’t that AI can’t access your data but that (aside from iMessage) it can only access it via additional engineering work and additional hoops for users to enter credentials. It seems like what Apple really wants is to make it hard for anyone to compete with Siri.
I’m skeptical that Siri AI is even capable of being meaningfully private or secure. As far as I know there is no mitigation for prompt injection, as Matthew Green describes here.
If it has access to private information, is exposed to external input and can access the Internet then how can they meaningfully claim it’s private or secure?
There’s a lot to unpack here, including more background information — and on-the-record statements — from a briefing Apple held Tuesday that I was invited to at Apple Park.
It’s frustrating that Apple has said so little on the record. They want to communicate that their “offers for compromise have been rejected,” but what exactly did they offer? Personally, I don’t think the TSA would be a very good solution because it would lock in both the limitations of Apple’s technology and their role as a gatekeeper. But Apple did not even say that the EC rejected the idea of the TSA, only that it rejected their proposal to violate the law for 18 months while building it.
But the bottom line is that Apple’s public statements regarding the DMA and the European Commission have never been this strident before.
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Now, they’re a bit more on war footing. There’s a massive gulf between what Apple is willing to do with Siri AI in the EU and what the Commission is demanding from Apple for DMA compliance.
The European Commission isn’t demanding anything, the law is demanding it. The laws have been in place for several years now, Apple is currently in default of the law on several fronts, and is fighting its existing fines.
When the Chinese government wanted access to user data, Apple complied, but when the EU parliament voted for their citizens to be able to choose how to share their own data, that put Apple on a “war footing.”
There is some kind of irony in how Apple isn’t allowing Europe access to the new AI stuff; and meanwhile for the US, Apple is removing the setting for us to opt-out of it, effectively forcing it on us.
That’s how it is these days, huh? Either a government or a giant corporation makes our technology choices for us. We don’t get to choose for ourselves.
What exactly is the motivation for Apple and Google to engineer entirely separate systems for the EU to bring new features into compliance with the Commission’s broad interpretation of the DMA? Because if Apple doesn’t engineer a DMA-compliant version of Siri AI, iOS users in the EU will ... switch to Android, whose system-level AI was deemed noncompliant by the Commission a few months ago?
I briefly hoped that the DMA would lead to a sort of California effect for software, where products are designed for the stricter (more open, in this case) jurisdiction, and everyone else reaps the benefits, too, because that’s easier for companies than making separate versions of their products. But it turns out that Apple finds it well worth it to feature flag away the EU features it doesn’t want us to have, as well as the ones it doesn’t want to adapt for the DMA.
This doesn’t directly hurt Apple. It doesn’t force Apple to design, engineer, and ship a compliant EU-exclusive version of Siri AI that supports plug-and-play LLM back ends. It only hurts iPhone users who live in the EU, who are stuck with the old dumb version of Siri for the foreseeable future.
This demonstrates a market failure or legislative failure, depending on how you look at it. Both halves of the duopoly are in conflict with the law of the second largest economy in the word. Users will get a worse Siri, but this doesn’t bother Apple enough to do anything about it. Maybe the customers in the EU can be used as a bargaining chip. Neither Apple nor Google is worried that a competitor will come along with a compliant phone to challenge their dominance.
Gotta admit that as an EU citizen, I feel handicapped when it comes to brainstorming ideas to utilize Foundation Models.
Like, if I were to build a new app around them, it’s difficult to gauge if this is something Siri AI does out of the box when it isn’t available to me.
And unlike Apple, I intend to build software for the entire world.
Apple is acting out of greed and spite (see their hairbrained fee structures for alternative marketplaces as an example), but also yes, the DMA and enforcement thereof seems like a particularly difficult and capricious regulatory dynamic.
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Those arguing that Apple is acting purely out of spite seem to suggest there is some obvious solution for iPhone Mirroring that would satisfy the interoperability mandate, but they never go into what that obvious solution is because they don’t know. They don’t know because I would guess no one does, not even Apple and certainly not the European Commission.
As far as I can tell, the European Commission doesn’t see it as their responsibility to help gatekeepers design features ahead of time in a way that should be compliant with the DMA. What they want instead is to decide whether or not a given feature runs afoul of the DMA after its released in the EU and presumably after they’ve gotten feedback from third parties.
If a developer emails Apple and asks if an app idea they have will be approved, they will get told to build it, submit it to the App Store, and see. The EU is the App Store here, and Siri AI is the app. I guess Apple don’t like their own policies.
I sympathize, even though Apple is in a better position than the developer, because it has more resources, because it would only potentially be blocked from shipping in one market, and because it can still sell its product in the EU, just with certain features disabled. Building something that might not be accepted is risky, but if you don’t submit anything that you think would be complaint with the rules, you don’t seem very serious. It’s still unclear to me exactly what Apple’s plan is. Its press release says it “will continue working” on bringing the features to the EU, but its SVP of marketing said in an interview that “none of its engineers are currently working on solutions.” Perhaps this implies that it’s banking on a legal or political solution rather than a technical one.
While the relationship between the European Commission and Google tells us nothing about playing ball, it tells us exactly how inconsequential support for alternative marketplaces is when it comes to regulatory scrutiny of integrated features. Apple hasn’t played ball with alternative marketplaces and now its new AI-based features are being withheld from the EU lest they get scrutinized for fines by the European Commission. Google hasn’t had to play ball with alternative marketplaces and now its new AI-based features are either being scrutinized for fines by the European Commission or being withheld lest they also get scrutinized. The European Commission is, rightly so in my opinion, regulating both operating systems equally and regardless of their support for alternative marketplaces.
It struck me today what a contrast this is with the rest of the AI world, increasingly connected via MCP. Imagine if Gmail was only accessible to AI on Android phones. If Teams was only accessible from a Windows Surface laptop. That is the kind of environment Apple’s policies create, with data locked into silos and monopolies free to leverage their existing strengths into dominance of new technologies too.
No thanks. Just as I should be able to sideload an app onto my own phone, I should be able to choose whether ChatGPT can access my iMessage history. The world is not going to come crashing down by giving users control of their own data.
Apple CEO Tim Cook held “constructive” talks with EU tech chief Henna Virkkunen on Tuesday about releasing Siri AI in the bloc while complying with the bloc’s digital rules, reports the Financial Times ($).
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As for Apple’s proposed Trusted System Agent, a Commission official told the publication its contact with Apple on the idea was limited, and that it lacked a concrete proposal or details on how such an agent would work beyond the general concept. The official claimed Apple “focused on obtaining a green light to delay compliance.”
The DMA has been in effect for nearly four years and there is no reason why either party should be having so much difficulty with this proposal stage. Either the Commission is mischaracterizing Apple’s engagement, or Apple’s representatives need to be far better prepared.
It seems like Apple’s strategy is to encourage its customers to pressure the EU into changing the rules.
Siri AI is an entirely new version of Siri deeply integrated into iPhone, iPad, Mac, Apple Watch, and Apple Vision Pro. It can draw on personal context understanding to search across messages, emails, photos, and more, and get things done across apps with even more systemwide app actions. Additionally, Siri AI can answer questions related to the content on a user’s screen or go out to the web to get up-to-date information using broad world knowledge and generate a helpful answer. A dedicated Siri app allows users to revisit a past conversation or kick off a new one — all in one place — and uses iCloud to privately sync conversational history across a user’s products.
Powered by the next generation of Apple Intelligence, Siri AI is a completely reimagined version of Siri that is more helpful, more capable, and more intelligent. With detailed, engaging responses and natural back-and-forth conversation, Siri AI helps users get more done than ever.
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Siri has been rebuilt from the ground up with powerful AI at its core. It takes full advantage of the bold new architecture for Apple Intelligence, including the next generation of Apple Foundation Models that run on device and on servers using Private Cloud Compute. When Private Cloud Compute is handling users’ requests, their personal data is not stored nor made accessible to Apple or anyone else. Outside experts can continue to verify this privacy promise at any time. Additionally, Siri AI uses the system orchestrator to tap into core capabilities like the Spotlight index and App Toolbox, which work entirely on device to keep users in control of their data.
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For products that support Apple’s most advanced on-device model ever, Siri AI offers even more expressive voices, as well as a major boost in accuracy with systemwide dictation. Users have the ability to customize the expressiveness and pace of Siri’s voice so it’s just right for them. Dictation now captures what users say as polished text with greater precision, automatically handling capitalization, punctuation, and formatting as they speak. With improved speech understanding, users can speak naturally and trust that their words will appear clearly, accurately, and as intended.
I remain uninterested in these more advanced Siri features. They matter in that Apple needs to show that they’re not “behind” and that they can actually deliver on what they announce and advertise. But I don’t find the use cases compelling and wouldn’t trust the results.
If you really care about a particular concert, would you believe Siri about how the tickets work and that it will auto-create a reminder at the right time? Why should I believe that it can figure out whether a particular irregularly shaped object can fit inside an irregularly shaped container when said shapes are probably not even published on the Web? Can it really dig into my e-mail to find the right flight info? An answer without being able to see the sources is not useful. And I certainly don’t want it to pick for itself which vacation photos to send out.
I just want the basic features—from a decade or so ago, like music controls, reminders, and access to my purchases—to work quickly, reliably, and offline to the extent possible. I did not hear anything to suggest that this was a focus. Instead, everyone is celebrating how slow the demos were because that proves they weren’t fake.
The other interesting thing to note from the keynote: watching the demos, there is ZERO doubt these are real, recorded demonstrations on live devices. There will be no confusion about what is real or fake like there was at WWDC 2024 and the initial attempt at the new Siri.
And, if your standards are the state of the art in AI circa June 2024, when Apple took their first crack at answering the question, they did quite well.
[…]
What’s fascinating about this specific demo is that it also showed just how far behind Apple is.
[…]
Still, the bubble was strong enough to convince OpenAI to make the exact same mistake Dropbox did: the company somehow convinced itself that it could make enough money selling subscriptions to consumers; Anthropic, meanwhile, realized that it was enterprises who were willing to pay for AI’s massive productivity benefits, even as OpenAI failed to capitalize on their consumer market penetration by refusing to build an advertising product.
This is a long-winded way of saying that I don’t think that Apple’s agentic shortcomings are a big deal, at least for now. Agents help you do work and be more productive, and consumers don’t want to work or care about being productive. What they do want to do is watch short-form video, and an iPhone is simply much better at that than any other device ever will be; in that context, Siri being good enough is enough, and it appears that Apple crossed that bar.
They need to execute, they need to prove this can scale, and most of all, they need to get third-party apps on board with App Intents and App Schemas. But it seems like they’re doing all of that. This is not a done deal but it is very realistic.
Siri doesn’t have access to the filesystem or tool-calling, so it’s completely unable to do the kinds of tasks you might easily do with Codex or Claude. It’s a good start, but the state of the art has already dramatically shifted over the past year. The question is will Siri move in this direction, or stick to providing answers from the web?
Will Apple introduce its own distinct Codex-style agentic programming model that can do all this, and work with Xcode? You would hope so…
The revamped version of Siri that is officially named “Siri AI” will be available to test in the iOS 27, iPadOS 27, macOS 27, and visionOS 27 developer betas starting today, and it is coming to the Apple Watch in a future watchOS 27 beta. “Siri AI” will also be included in the public betas of each platform that are launching in July.
“Siri AI” requires a device that is compatible with Apple Intelligence.
Apple senior vice president of software engineering Craig Federighi has explained why the company launched a standalone Siri app in iOS 27, after previously characterizing a dedicated chatbot as contrary to its Apple Intelligence strategy.
[…]
“We see Siri not as a separate chatbot, just an unintegrated place you go and chit-chat, but rather as an integral, conversational tool that you use in the moment, deeply integrated into your experience.”
…And playlists, contacts, settings and preferences. Peel off the agentic slop bandwagon and focus on core performance, smarter caching, and resilience under adverse network conditions.
Though this is a test conducted on the very first version of Siri A.I. from a fixed point in Calgary, it seems to work quite well. So far, it is better than any version of Siri Apple has released yet and, as you can see above, it is almost as good as the original 2010 demo, before Apple acquired the company.
On the one hand, if Siri has indeed become this reliable at understanding queries like those demoed so far, it’s indeed impressive. On the other hand, I can’t help but ask myself, once again, whether people at Apple even know how regular people actually use their phones and devices.
Apple executives downplay and obfuscate the role of Google in Siri AI. The Siri AI announcement didn’t even mention Google or Gemini once! Does anyone actually believe Apple’s BS? The argument seems to be that since Google made a bespoke implementation for Apple, Google is not really involved, and it’s all Apple. Ridiculous.
It’s really odd to me that Apple is getting so much credit for Google’s work. Siri now “works” to the extent it does because Siri is to a very large extent Gemini, and Apple has wrapped that in a bow.
Apple didn’t “nail” anything. Apple failed at its own internal efforts and was forced to outsource.
This is the advantage of being a platform: you can fail and still win.
Apple’s Siri team, led by Craig Federighi, held a post-WWDC keynote tech talk with members of the press this afternoon to talk through iOS 27 and the new Siri AI.
During the talk, Federighi shared more details about Apple’s collaboration with Google.
Apple looks like it will use some combination of Google Gemini models, combined with Google’s Confidential Inference and Apple’s own Private Cloud Compute for private hosting. These systems will process both your queries and evaluate private data from your devices.
[…]
Apple has since “expanded” PCC to encompass Google’s hardware as well. I will confess that I find the details of the new “expanded” PCC just a bit vague. It sounds a lot like Apple is primarily going to rely on Google’s existing confidential compute (running in Google datacenters) to process this data, but they’re bolting on a new layer of technical security to control which models are actually running. In any case: security experts can argue about whether this is good enough to keep Cozy Bear away from your data. What I will grant is that it’s probably good enough to keep Google and Apple from accessing your stuff, which is what most people are worried about in the first place.
So why am I so nervous?
[…]
So far we’ve discussed two adversaries: the misaligned designers of private agents (such as search operators), and the possibility of remote prompt injection attacks. But of course, in any discussion of technical privacy systems we need to talk about the last elephant in the room: your government.
Meta, who owns and operates FaceBook and Instagram, has enabled a setting automatically that allows anyone to use your public Instagram photos, profile picture, and/or reels for AI purposes. This includes everything from your posts, stories, stickers, reels, and even your profile photo. Some users may not like this, whether they’re opposed to AI in ... Read More
Apple Intelligence on-device processing remains Apple’s most convincing argument in artificial intelligence. Rivals may lead in public model rankings, chatbot adoption and the speed of releasing experimental features, but Apple is building AI inside devices people already use for their most personal work.
That distinction changes the definition of winning.
Apple does not need every iPhone owner to open a separate chatbot app. It can place intelligence inside Messages, Mail, Photos, Notes, Shortcuts, Siri and third-party apps. Local models can operate on private information without automatically transferring that information to an external server.
The strategy appeared slow while the AI market measured progress through chatbot launches and model size. It looks more competitive as the industry confronts cloud costs, privacy concerns, unreliable connectivity and the difficulty of turning general-purpose AI into useful daily actions.
In his video “Apple Lost the AI Race,” Marques Brownlee captures the contradiction in one concise line:
“Apple lost the AI race. Actually, Apple won the AI race.”
The apparent reversal describes Apple’s current position well. Apple may have lost the first race for public perception. It is increasingly prepared for the longer contest over where AI runs, how it reaches users and who controls the personal data required to make it useful.
Apple Intelligence On-Device Changes the AI Relationship
Most popular generative AI products began as cloud services. A user sends a request to a remote model, the provider processes it in a data center and the answer returns through an app or browser.
That structure supports enormous models but creates dependence on internet access, server capacity and continuing infrastructure expense. It also means personal prompts leave the device, even when the provider uses strong security and privacy policies.
Apple Intelligence on-device begins from the opposite direction. The iPhone, iPad or Mac first attempts to handle the request locally using Apple Foundation Models optimized for Apple silicon.
That local model does not need to know everything available on the internet. It needs to understand the user’s request, work with relevant personal context and complete common tasks efficiently.
The difference is similar to owning a capable computer rather than renting every calculation from a distant server. Cloud systems remain useful for difficult workloads, but they no longer need to participate in every interaction.
For Apple, local processing is also a product advantage. The company controls the chip, operating system, model architecture, memory system and apps. Each layer can be designed around the others rather than assembled from unrelated services.
Image Credit: Apple Inc.
Privacy Becomes a Technical Feature
Technology companies frequently describe privacy as a policy. Apple is trying to make it part of the AI architecture.
When a request runs entirely on an iPhone, the associated data does not need to leave the device. A model can work with messages, appointments, photos or app content without uploading that personal context to a conventional cloud account.
That structure is especially relevant to Siri AI. A truly personal assistant needs access to information that users may never place willingly inside an ordinary chatbot: private conversations, travel plans, health-related details, work documents and relationships between people.
An assistant becomes more useful as it gains context. It also becomes more invasive when the same context is stored, analyzed or used for model training outside the device.
Apple Intelligence on-device allows Apple to expand personal context while maintaining a stronger boundary around it. The assistant can understand information locally, perform an action and discard temporary processing data without building a permanent advertising or training profile.
This is difficult for competitors whose businesses depend on cloud services, large-scale data collection or engagement inside separate AI products. Apple’s hardware business gives it different incentives.
Private Cloud Compute Extends the Model
Not every AI task can fit within the power and memory limits of an iPhone. Apple uses Private Cloud Compute when a request requires a larger model.
Private Cloud Compute is designed to extend the privacy architecture of Apple devices into the cloud. User information is processed only for the request, is not stored and is not made available to Apple for unrelated purposes.
Independent researchers can inspect software images used by the system, creating an unusual level of outside visibility into the cloud environment. Apple has also expanded the architecture while maintaining control over the software and privacy protections used for processing.
This creates a layered system rather than a simple choice between local and cloud AI.
Apple Intelligence first determines whether the device can handle the request. More complex work can move to Private Cloud Compute. External models may be offered separately when the user chooses to involve them.
The user does not need to understand the technical path behind every answer. The system chooses the smallest and most private environment capable of completing the task.
Apple Silicon Was an AI Investment
Apple’s control of its processors has become one of its largest advantages in the AI era.
The Neural Engine first appeared years before generative AI became a consumer obsession. It supported Face ID, photography, speech recognition and other machine-learning tasks. Apple continued expanding that hardware across the iPhone, iPad and Mac.
Those investments now provide a large installed base of devices designed to run machine-learning workloads locally. Recent Apple chips combine CPU, GPU, Neural Engine and unified memory in one architecture that can move information efficiently between different types of processing.
The same design helps foundation models run without requiring the power consumption or separate memory systems associated with conventional data-center hardware.
Apple also controls the operating systems that schedule those workloads. A model can be optimized for the exact processors, memory limits and energy behavior of supported devices.
Competitors can build excellent models, but they often need to support hardware from several manufacturers with inconsistent capabilities. Apple can decide that an AI feature requires a specific generation of silicon and design the complete experience around it.
Developers Gain a Local Model
The Foundation Models framework extends Apple Intelligence on-device beyond Apple’s own apps. Developers can use the system model through native Swift tools rather than shipping a separate large model or paying for every cloud request.
An app can generate structured content, interpret natural language or use tool calling while keeping supported processing on the device. Apple handles model delivery and system integration.
This can reduce operating costs for developers. A cloud-based AI feature creates an ongoing expense each time a user submits a request. Local inference uses hardware the customer already owns.
It can also improve reliability. Features may remain available without an internet connection and respond without waiting for a remote service.
The framework encourages a quieter form of AI adoption. Apps do not need to add a prominent chatbot tab or redesign themselves around a conversational interface. Intelligence can appear inside existing workflows as a useful capability.
That is where Apple’s strategy may reach scale. Millions of apps can incorporate small AI functions that users experience as normal software behavior rather than as visits to an AI destination.
Image Credit: Apple Inc.
The Installed Base Is Apple’s Distribution Advantage
The AI race is often discussed as a contest between models. Consumer adoption also depends on distribution.
Apple can deliver new intelligence features through operating-system updates across the iPhone, iPad, Mac and Vision Pro. Users do not need to create another account, choose a provider or transfer their personal information into a new service.
Siri already occupies the system interface. Apple Intelligence can work across first-party apps. Developers can connect their own features through Apple frameworks.
The scale is difficult to reproduce. A model provider may have stronger benchmark scores but still need users to download an app and consciously open it. Apple can introduce AI during actions people already perform: replying to a message, locating a photo, organizing a note or completing a shortcut.
This does not guarantee success. Apple must make Siri AI reliable, keep feature availability consistent and prove that local models can deliver enough capability to satisfy users accustomed to powerful cloud assistants.
The distribution advantage only becomes meaningful when the experience works.
Winning Without Building the Loudest Chatbot
Apple has made mistakes during its AI transition. Siri improvements arrived later than promised, regional regulations delayed features and competitors established powerful consumer brands before Apple completed its new system.
Those setbacks explain why the claim that Apple lost the AI race became persuasive.
They do not settle the outcome.
The first stage rewarded companies that released chatbots quickly and demonstrated increasingly large models. The next stage may reward systems that can integrate AI into ordinary life without demanding constant cloud access, high subscription costs or broad collection of personal information.
Apple Intelligence on-device is built for that stage. It uses hardware already in the user’s hand, protects more personal context locally and gives developers access to intelligence without requiring every interaction to become a server transaction.
Apple does not need to win every benchmark or release the most talkative assistant. It needs AI to become a reliable part of the devices people already trust.
Brownlee’s title works because both statements can be true. Apple lost the visible sprint that defined the early generative AI market. By concentrating on local processing, silicon, privacy and operating-system integration, it may already be positioning itself to win the race that follows.
EU tech regulation, Digital Markets Act, Apple Europe sales, Apple regulation, European Union, Siri AI, iPhone features, device interoperability, App Store changes, alternative marketplaces, iPhone Mirroring, AirPods translation, Apple privacy, iPhone security, app sideloading, platform competition, DMA compliance, Apple Intelligence, European technology, government control
EU tech regulation is no longer limited to App Store payment rules or competition disputes hidden from ordinary consumers. It is beginning to determine which features reach European devices, how quickly they arrive and how deeply third-party companies can interact with the iPhone, iPad and Mac.
The Digital Markets Act was designed to reduce the control of large technology platforms over software distribution, payments, browsers, digital assistants and connected hardware. For Apple, compliance has already produced alternative app marketplaces, web distribution, third-party payment options, additional default-app controls and expanded access to iPhone technologies.
The next phase reaches much deeper into the operating system. European regulators want competing accessories, cloud services and AI assistants to receive access to capabilities traditionally reserved for Apple’s own products.
Apple argues that some of those demands could weaken privacy and security. The European Commission says the company is responsible for creating compliant systems that preserve both competition and user protection.
Between those positions sits a large and commercially significant group of customers. Europe generated $28.06 billion in Apple sales during the quarter ending March 28, 2026, up from $24.45 billion one year earlier. Six-month European sales reached $66.20 billion, compared with $58.32 billion during the same period of fiscal 2025.
Europe therefore represented more than one-quarter of Apple’s latest quarterly revenue. It is too valuable to leave behind, but increasingly difficult to serve with the same software experience available elsewhere.
EU Tech Regulation Is Splitting Feature Availability
Regional differences once centered mainly on language support, licensing agreements or local services. EU tech regulation is creating a more structural divide.
Apple has blamed Digital Markets Act requirements for delaying Siri AI across the European Union. The company says regulators expect competing AI assistants to receive extensive access to personal information, apps and device actions before Apple can reserve those capabilities for its own assistant.
The disagreement concerns more than answering questions. Siri AI is designed to read personal context, understand onscreen information and complete actions across apps. That may include finding messages, accessing files, making purchases or sending information on the user’s behalf.
Opening those capabilities to outside AI systems requires new permission structures, technical interfaces and safeguards. Apple says it cannot release its assistant first and add competing access later without risking regulatory action. The Commission says Apple had sufficient time to design an interoperable solution.
Other delayed European features have included iPhone Mirroring on Mac, Live Translation through AirPods and certain location-based Maps capabilities. Each feature touches a different part of the ecosystem, but all depend on privileged communication between Apple devices, software and services.
The consequence is becoming visible at purchase. Two customers can buy the same iPhone model at similar prices and receive different capabilities because one lives inside the European Union.
Image Credit: Apple Inc.
Feature Adoption Becomes Slower and Less Predictable
Delayed availability can weaken the adoption of new hardware features even when the devices themselves continue selling well.
Siri AI is intended to become a central part of newer iPhones, iPads and Macs. Buyers who cannot access it lose part of the justification for newer chips, additional memory and on-device AI processing. Developers also face a smaller addressable audience for apps built around the new assistant.
AirPods Live Translation presents a similar problem. A feature designed to demonstrate the combination of iPhone processing, microphones and wearable audio becomes unavailable in a region built around dozens of languages and frequent cross-border travel.
iPhone Mirroring affects the relationship between iPhone and Mac. Users outside the EU can interact with the phone from macOS, while European users may receive notifications without gaining the complete experience.
These differences do not necessarily stop hardware sales. Apple’s newest results show strong European demand despite years of regulatory conflict. They can, however, reduce the speed at which customers use the ecosystem’s newest capabilities.
The effect may grow as software becomes a larger part of hardware differentiation. A faster processor has limited emotional value when the feature designed to demonstrate it is unavailable.
Apple Sales Show Why Europe Cannot Be Ignored
Apple does not report European Union revenue separately. Its Europe segment also includes countries outside the bloc, along with India, the Middle East and Africa. The official segment remains the best regularly published measure of the region’s commercial weight.
European sales rose approximately 15% year over year in Apple’s latest reported quarter. During the first six months of fiscal 2026, the segment expanded by roughly 14%.
Those figures weaken the argument that regulation is already driving customers away from Apple hardware. Demand remains strong, supported by the iPhone 17 family, Mac sales, services and the company’s installed base.
The financial risk is more gradual. Regional software fragmentation can raise engineering costs, delay launches and make European devices less competitive against products able to offer complete feature sets.
Apple must maintain separate distribution rules, payment systems, browser options and eligibility checks. Developers must decide whether alternative stores, external payments or EU-specific business terms justify additional work. Support teams must explain why features differ between countries using the same operating-system version.
A market responsible for more than $28 billion in one quarter can absorb considerable compliance expense. It can also influence Apple’s global product decisions when regulators demand interfaces that cannot be restricted to Europe easily.
A Slow Transfer of Ecosystem Control
The escalating regulatory environment also creates a more fundamental concern: control over complex technology ecosystems can gradually move away from the companies that designed them and toward government committees, regulatory bodies and administrative processes.
That transfer rarely happens through one dramatic order. It develops through repeated obligations covering app distribution, payment systems, browser engines, digital assistants, hardware access, data portability and interoperability. Each requirement may appear narrow when considered alone. Together, they can redefine who determines how the ecosystem operates.
Apple built its platform around tight integration between hardware, software, services and security. Regulators increasingly treat parts of that integration as market power that must be opened to competitors. The danger is that an ecosystem designed as one coordinated environment becomes subject to decisions made by committees that do not carry direct responsibility for product quality, reliability or long-term maintenance.
This can become a slow trap. Once a platform is required to expose one layer, regulators may use that access as the basis for demanding another. A payment exception can be followed by alternative app stores. Alternative stores can lead to broader installation rights. Device interoperability can expand into access for AI assistants, personal data and system actions.
The boundaries of the platform are then set less by engineering judgment and more by regulatory negotiation.
Government agencies have legitimate authority to address competition and consumer harm. The concern appears when regulation shifts from preventing abusive conduct to directing product architecture. At that point, public authorities begin influencing feature design, release timing, software permissions and the relationship between first-party and third-party services.
That creates consequences beyond Apple. Any company considering a tightly integrated product may decide that deep integration invites intervention. Businesses could avoid building ambitious ecosystems if successful coordination later becomes grounds for forced access.
Innovation then becomes shaped by what regulators are likely to permit rather than by what engineers believe will produce the safest or most coherent experience.
Image Credit: Ink Drop / Alamy Stock Photo
Competition Gains Come With Real Consumer Choices
EU policy has produced benefits that Apple rarely emphasizes. Users can install apps through alternative marketplaces or directly from authorized developers. Developers can offer external payment methods, promote different prices and avoid relying entirely on Apple’s distribution system.
Alternative browser engines can compete with WebKit, while users have more control over default apps. Contactless payment access has expanded beyond Apple Pay, allowing competing wallet providers to use the iPhone’s NFC technology under approved conditions.
Interoperability requirements could also improve third-party accessories. Smartwatches, headphones and other devices may gain better notification access, background connectivity and proximity pairing with the iPhone.
These changes can increase competition and reduce the advantage Apple gives its own services and accessories. A third-party smartwatch should not become artificially less useful simply because it was not made in Cupertino.
The difficulty is separating legitimate platform access from unlimited access. A notification interface is one thing. An autonomous AI assistant capable of reading messages, accessing files and acting across apps introduces a different level of sensitivity.
Privacy and Security Risks Are Not Theoretical
Alternative app distribution increases user choice, but it also moves some responsibility away from the App Store. Apple continues to notarize apps distributed through approved alternative channels, checking for known malware and severe fraud. That process is narrower than a complete App Store review.
Purchases made outside Apple’s payment system also fall outside its normal refund and billing support. Users may need to resolve disputes directly with the developer or payment provider.
A malicious marketplace, deceptive payment page or compromised developer account can create opportunities for fraud. Enterprise users face additional concerns when employees install software from sources their organization has not approved.
The AI question reaches further. A third-party assistant may need permission to access messages, photos, calendars, documents and app controls. Even a reputable provider can collect more information than the user expects or introduce vulnerabilities through poorly designed integrations.
Apple can build permission prompts, app isolation and revocation controls, but technical access always creates some exposure. Security depends on how narrowly permissions are defined, how visibly they are used and whether the operating system can stop unauthorized actions.
The Commission maintains that interoperability and privacy can coexist. Apple maintains that regulators are demanding access before adequate protections are ready. Both positions contain a legitimate concern: dominant platforms should not block competition, and competition should not require users to surrender control of personal data.
When regulators require a company to alter its architecture, responsibility can become blurred.
Apple remains the company customers blame when an iPhone feature fails, a third-party marketplace distributes a harmful app or an outside assistant misuses personal data. Regulators may have required the access, but they do not operate the device, provide technical support or repair the damage to the platform’s reputation.
This creates an accountability gap. Government committees can mandate openness without assuming the commercial and security consequences of that openness. Apple must comply, maintain the system and explain the outcome to users.
The same problem applies to delayed features. Regulators may argue that Apple chose not to release a service, while Apple may argue that the required conditions made the launch unsafe or technically impractical. Customers are left with less functionality and no simple way to determine who caused the delay.
Over time, the ecosystem can become a negotiated compromise rather than a product with one coherent direction. That may satisfy formal compliance while producing a weaker experience for everyone involved.
The risk is not that governments will literally operate Apple’s software. It is that committees increasingly define the conditions under which Apple can design, connect and release its own features. Control shifts through constraints.
The Industry Will Follow Europe’s Experiment
The consequences extend beyond one company. Google, Meta, Microsoft, Amazon and other platform operators face related European obligations. Hardware makers must consider whether ecosystem advantages can survive when core services are required to support rivals.
Smaller developers may gain new distribution and payment choices, but they may also inherit more compliance, customer-service and security responsibilities. Accessory companies could compete more effectively while becoming accountable for handling sensitive device data.
Other governments are watching. Rules introduced in Europe can influence legislation elsewhere, particularly when companies decide that maintaining one global technical framework is easier than operating several regional versions.
Apple is unlikely to abandon Europe or stop adapting its products. Its sales provide too much evidence of the region’s importance. The company will continue negotiating, appealing decisions and building compliance systems while withholding features it considers unsafe under current demands.
European customers may receive more app stores, payment choices and accessory compatibility. They may also wait longer for headline features included with the same hardware elsewhere.
The deeper consequence may be a technology industry that becomes more cautious about building tightly integrated products. Companies may design around anticipated regulatory demands from the beginning, limiting exclusive features, reducing private-system integration or avoiding innovations that depend on controlled access.
EU tech regulation began as an effort to constrain dominant platforms. Its long-term effect may be to transfer part of the power to shape those platforms from companies and users to regulatory institutions.
That shift deserves scrutiny equal to the market power it is intended to correct. Competition rules can restrain abuse. They can also become a slow mechanism for placing product design, security choices and ecosystem evolution under government supervision.
Shady Control and Bureaucracy
The danger grows when regulatory discretion becomes buried beneath permits, approvals, negotiations and technical committees that operate beyond meaningful public scrutiny.
The more power officials hold over which features, contracts or technologies may proceed—and when—the greater the opportunity for lobbying, favoritism and selective enforcement to influence outcomes. What begins as oversight can gradually become a system in which delay itself is leverage, while responsibility disappears inside layers of administration.
For the technology industry, that risks replacing transparent product decisions with political access, negotiated exceptions and regulatory influence exercised far from the consumers expected to live with the consequences.
The City Attorney’s Office sent the tech giants cease-and-desist letters this week telling them to stop profiting from 13 “face-swap” apps that are overwhelmingly used to target women and girls.
PrismML AI compression could give Apple a way to place far more capable artificial intelligence directly inside the iPhone. The Caltech spinout says it reduced a 27-billion-parameter model from approximately 54GB to less than 4GB, small enough to run locally on an iPhone 17 Pro.
That represents a reduction of about 93%, but the more useful number may be memory efficiency. PrismML says its technology can use up to 15 times less memory than conventional versions of the same model. Apple has reportedly held early discussions with the startup about possible uses for the technology.
Neither company has announced a partnership, acquisition or product plan. PrismML’s claims also need to be judged through independent testing, sustained performance and the compromises introduced by heavy compression. Even so, the demonstration points directly at one of Apple’s largest AI challenges: fitting stronger models inside devices without destroying battery life, consuming too much memory or sending every complex request to the cloud.
PrismML AI Compression Changes the Size Problem
Large AI models are difficult to run on phones because their parameters require substantial storage and working memory. A standard model may perform well in a data center equipped with powerful accelerators, large memory pools and constant electricity, then become impractical inside a device designed to last all day on one battery.
PrismML approaches that problem by representing model weights with far fewer bits. The startup has developed what it calls Bonsai models, including 1-bit versions designed to preserve much of the original model’s capability while sharply reducing size and energy use.
The reported iPhone demonstration used a compressed version of Alibaba’s Qwen model with 27 billion parameters. That is far larger than the compact language models normally associated with smartphones. Running it locally suggests that model size may no longer be controlled only by the number of parameters. How efficiently those parameters are represented becomes just as relevant.
Compression is not new. Developers already use quantization, pruning and other methods to make models smaller. PrismML’s claim is that its approach pushes that process much further while retaining useful reasoning and language performance.
That claim is exactly why Apple would be interested.
Why Siri AI Needs Smaller Models
Apple’s AI strategy depends on dividing work between the device and the cloud. Simpler Apple Intelligence requests can run locally, while more demanding tasks may move to Private Cloud Compute. That hybrid structure allows Apple to offer stronger capabilities without forcing the iPhone to carry every model it may ever need.
The cloud layer, however, introduces cost, latency and availability requirements. A request must leave the device, reach a server, be processed and return. Apple has designed Private Cloud Compute around strong privacy protections, but local processing remains faster and easier to explain.
A larger on-device model could improve Siri AI’s language understanding, reasoning and ability to complete multi-step tasks without a network connection. It could also reduce the number of requests sent to Apple’s servers.
That does not mean cloud processing would disappear. Server models can remain larger and support especially demanding requests. PrismML AI compression could instead move the dividing line. Tasks that currently need Private Cloud Compute might become possible directly on a future iPhone.
The benefits could include quicker responses, stronger offline operation and less server demand as Apple Intelligence reaches more users.
Image Credit: Apple Inc.
Privacy Fits Apple’s Preferred Direction
On-device processing has always been central to Apple’s AI positioning. Personal information can remain on the iPhone rather than moving through a conventional cloud service. The device can interpret a request, use local context and produce an answer without exposing the underlying data beyond the hardware.
A model capable of handling more work locally would reinforce that approach. Siri AI could potentially understand messages, notes, schedules or onscreen content while reducing the need to transfer information elsewhere.
Apple already uses Private Cloud Compute when local processing is insufficient, and the company has built significant safeguards around that system. Yet every task completed on the iPhone removes one additional processing step.
Privacy is not the only advantage. Local models can continue operating when connectivity is poor or unavailable. That could help Siri AI during flights, rural travel, crowded events or other situations where cloud access becomes unreliable.
A capable offline assistant would also feel more like part of the operating system and less like a remote service accessed through an interface.
The iPhone Still Has Physical Limits
Shrinking a model to less than 4GB does not automatically make it ideal for everyday iPhone use. The model must also run quickly enough, avoid excessive heat and leave enough memory for iOS and active apps.
A technical demonstration may complete tasks successfully while consuming more energy or time than users would accept from Siri. Apple would need to optimize the model for its Neural Engine, memory architecture and software frameworks rather than simply loading PrismML’s existing build onto every supported device.
Storage is another consideration. A model occupying several gigabytes would compete with apps, photos, offline media and system files. Apple could reserve more storage for intelligence features, deliver models selectively or use several specialized models instead of one large general system.
Battery use may create the hardest constraint. An AI model can fit in memory and still require too much sustained computation for frequent mobile use. The ideal system needs to be small, fast and efficient enough to disappear into normal iPhone behavior.
Apple’s chip design gives it an advantage here. The company controls the processor, Neural Engine, operating system and AI software stack. PrismML could provide the compression technique while Apple handles the deeper hardware integration.
Image Credit: Apple Inc.
Better AI Without Replacing the Cloud
The strongest use of PrismML’s technology may not be putting one enormous model on every iPhone. Apple could use compression across a family of models designed for different devices and tasks.
A smaller model could handle routine language requests. A more capable local model could be downloaded only to recent Pro devices with sufficient memory. Specialized models could support writing, image generation, app actions or Visual Intelligence. Private Cloud Compute could remain available when the request exceeds local capability.
That would give Apple more flexibility than an all-local or all-cloud design. Each device could carry the intelligence appropriate for its chip, memory and battery capacity.
The approach could also help Macs, iPads and future home products. A compressed model may run more comfortably on a MacBook Air, HomePod or smart home hub without requiring constant server access. Apple could scale the same intelligence foundation across products while adjusting model size to the hardware.
This is especially relevant as Siri AI moves beyond answering questions. App actions, personal context and ambient smart home control require intelligence that remains available throughout the day. Lower memory and energy requirements could make those experiences more practical.
Compression Always Includes Trade-Offs
A 93% reduction is a striking headline, but compression can affect output quality. A smaller representation may lose precision, weaken reasoning or struggle with uncommon information. The real test is not whether the model runs. It is whether it performs well enough across the tasks Apple expects Siri AI to handle.
PrismML says its technology preserves strong performance compared with full-precision models. Apple would still need extensive evaluation across languages, safety categories, tool use and personal-assistant scenarios.
Reliability is especially sensitive for an operating-system assistant. A model used for casual brainstorming can tolerate occasional weakness. A model handling messages, appointments, directions or app actions needs a higher level of consistency.
Apple may therefore use aggressive compression selectively. Some tasks can accept a smaller model, while others may remain on a higher-quality server model. The user would not need to know which model is active as long as the response remains fast and dependable.
Image Credit: Apple Inc.
Apple Wants the Technology, Not Necessarily the Company
Reports indicate Apple has spoken with PrismML, but early conversations can lead in several directions. Apple could license the startup’s technology, work on a technical integration, hire members of the team or explore an acquisition.
The company has a long history of purchasing smaller firms for specialized technology and engineering talent. Model compression would fit that pattern because it supports Apple’s existing AI strategy rather than creating a separate consumer product.
PrismML could also remain independent and provide its tools to multiple hardware companies. Its technology has potential uses across phones, computers, robotics, vehicles and industrial equipment where cloud access is expensive or unreliable.
Apple’s interest alone gives the startup attention. The larger significance is that efficient models are becoming as valuable as larger ones. The AI industry spent years treating scale as the primary route to improvement. Mobile computing forces a different question: how much intelligence can fit inside the power and memory limits of one personal device?
A Smaller Model Could Make Siri Feel Larger
Apple does not need to place the biggest available model on an iPhone. It needs a model capable enough to improve the interactions users repeat every day.
PrismML AI compression could help Siri understand more context, complete more requests locally and respond without waiting for a server. It could strengthen Apple’s privacy argument while reducing the infrastructure load created by millions of AI-enabled devices.
The technology remains early, and a startup demonstration is not the same as a shipping Apple feature. The 93% figure will mean little if quality, battery life or speed suffer under daily use.
But the direction fits Apple unusually well. The company has spent years designing smaller, more efficient hardware systems that perform beyond their apparent limits. PrismML is proposing a similar strategy for AI models.
If the startup’s claims hold up, the next major Siri improvement may not come from sending a larger model into the cloud. It may come from fitting one into the iPhone without making the iPhone feel the weight.
We live in a modern, jet-set, hyper-fast world! When we want to buy something online, boom, zoom, we use our fingerprint or face to approve the transaction, so we can grab the next Segway outta here! We don’t have time to enter a credit card! And can we trust a webpage form? Pfeh!
All right, calm down, 1950s inner voice, it’s not that bad. Most of our transactions involving a payment card or other sensitive data can be safely handled over a secure web connection. Apple Pay in Safari is the highest standard, of course, because the payment process involves encrypted elements, and your card number isn’t disclosed to the merchant. The Wallet app in iOS and the Wallet features in iPadOS and macOS further let us automate the entry of numbers and identifiers on pages we trust.
That’s all for automated commerce. What about other scenarios where you need to provide information to someone, often a friend or a local business, in order to transfer money or conduct a transaction? How can you be sure no one else is snooping in?
Make a call
Voice is still one of the most secure means of providing data. The landline wired and cellular wireless networks may be fertile ground for government agencies, but if you need to read a credit-card number or provide a PIN, a call is often the safest way to do so. By voice, you can always have the other person verify details that you believe only they know, or you can verify details to them for the same.
Because we live in a cyberpunk dystopian future, I do have to add a proviso. AI-generated voices have been convincing for a couple of years now—long enough that you shouldn’t trust an incoming call from someone you think you know, and certainly not from a bank, credit-card company, or other financial institution. I mean, I wrote “How To Avoid AI Voice Impersonation and Similar Scams” in January 2024! I assume the state-of-the-art scam is even better.
(The biggest takeaway is analog, too. Set a family password that you demand from a family member who calls asking for money or to do some weird phone or computer set of instructions.1)
Unless recorded, voice calls also lack persistence, making them impossible to recover later. A real-time Bob needs to be either between Alice and Carol or doing an AI impression of Alice or Carol.
Use secure messaging
I could make the argument that using secure messaging carries even less risk than a voice call if you’ve had an ongoing messaging conversation with someone, so you know it’s really them. Are unsecured text messages being intercepted willy-nilly?2 No, but there is also something about sending plain text all over the cellular networks that gives me the willies, nillies aside.
Look for a lock and the label Encrypted for RCS; iMessage is always encrypted, or it won’t work. (Lower image: Apple)
Secure messaging systems include:
iMessage: While iMessage has seen exploits, they’re government-grade ones. Its secure end-to-end infrastructure is creaky, but unbroken. There’s an extra detail I’ll mention below.
RCS with encryption: Between Apple Messages on iOS 26.5 or later and an Android device with RCS encryption support, you have a similar level of end-to-end protection. Make sure you see RCS encryption in the text field, or it’s just plain text. You can also use RCS encryption between two capable Android devices—as if!
Signal: The Signal app and ecosystem have the best end-to-end encryption, as it implements some of the most modern approaches to protecting older messages and current conversations available so far.
The extra note on iMessage is complicated. Briefly, if you use Messages in iCloud, an encryption key to retrieve those messages is included in an iCloud-based iPhone or iPad backup. With access to your Apple Account, someone could potentially retrieve your stored messages. However, if you enable iCloud’s Advanced Data Protection, the backup is encrypted and requires endpoint decryption using one of your devices, protecting the Messages in iCloud encryption key. This should be a worry only if someone manages to obtain your Apple Account, can activate a second factor, and the information you passed via Messages is so sensitive that someone would hunt to uncover it.
Avoid using SMS or MMS text messages (unencrypted plain text), unencrypted RCS (in-transit/at-rest encryption), or other messaging systems where you’re not sure how they handle data protection. WhatsApp can be used if you either have backups disabled or have enabled end-to-end encrypted backups.
Don’t use most collaborative tools
I know that I spend a lot of time in shared documents, whether Google Docs, Notes, Pages, or more esoteric apps or web apps. Most lack the highest level of protection. Shared Pages and Numbers files on iCloud Drive, as well as Google Docs, are encrypted in transit and at rest, and granting shared access is relatively easy (very easy in Google Docs). For iCloud Drive documents, this is true even with Advanced Data Protection enabled.
I’d be dubious about pasting my driver’s license or credit-card information into any of them that are shared with someone else. Ditto, don’t take a picture of your payment card or driver’s license and share it via a Shared Album in Photos, as it has the same issue.
There are two notable (heh) exceptions: if you and the other party or parties to a shared Notes entry or an iCloud Shared Photo Library all have Advanced Data Protection enabled, end-to-end encryption is used.
Consider the risk
As with all decisions around privacy, consider how at risk you and your data are, including voice as data. In most cases, ensuring a baseline level of encryption can be enough. Even when the app or server can “see” your unscrambled information, the transport between your device and the server and another person’s device is encrypted. Someone would have to break into the server to sniff data. If stored on the server, the baseline state would be encrypted at rest; however, the server operator would manage those encryption keys.
For peace of mind, if not strictly necessary, I’d encourage you to consider using end-to-end encryption when you can. It’s so easily available. For someone to crack your connection, they would need to obtain one of your devices and be able to unlock it. Otherwise, the path between you and someone else is effectively impregnable.
For further reading
I cover message security extensively in my book Take Control of FaceTime and Messages, which I recently updated to include the beta release of RCS encryption for Apple-to-Android communication.
For more on Wallet, I wrote an entire book on that seemingly simple app and set of features, Take Control of Apple Wallet. The book arose from the frustrations of finding where things in Wallet (and outside Wallet but related to it) lived.
On the security side of things, Take Control of Securing Your Apple Devices includes detailed advice on the ins and outs of physical security: how to ensure someone with access to your hardware can’t reach your data.
[Got a question for the column? You can email glenn@sixcolors.com or use/glennin our subscriber-only Discord community.]
This will also deter family members from asking you for money if you don’t give them the password. ↩
The United States is having something of a national referendum on mass surveillance as of late. Embodied most infamously by video surveillance company Flock Safety, Americans from all walks of life are waging war against the proliferation of the company’s automatic license plate readers (ALPR), which the ACLU warns now number in the tens of thousands.
Plenty of communities are organizing campaigns to out-muscle municipal leaders or their cheerleaders on the local police force, and by-and-large, they’re working. Whether in rural towns or sprawling cities, activists who can mobilize citizens to flood council meetings and hold picket signs are finding success. Perhaps most prominently, the Los Angeles Police Department recently allowed its three-year contract with Flock to expire, citing democratic pushback as a significant pressure point.
There’s also another, less-legal side to the anti-Flock sentiment: a rising tide of vandalism. Not content to wait for local officials to strike down ALPR contracts, concerned citizens are taking matters into their own hands by disabling or cutting down Flock camera towers wherever they’re found. But what started as a collection of separate individuals is now rising to the level of a viral phenomenon, as privacy-minded Redditors take to the web to celebrate those who are fighting back with an outpouring of ironic memes and hype reels.
Ground zero for this community appears to be the subreddit r/FlockSurveillance. The forum hosts some 422,000 “activists,” who flood the channel with over 11,000 contributions per week.
Those who’ve been caught and charged with vandalizing ALPRs become digital folk heroes on the subreddit. When news broke that US Air Force engineer Jeffrey Sovern had been charged in connection to the destruction of 13 Flock devices, redditors flooded the post with praise, sharing links to his legal defense fund and encouraging others to donate. (Sovern’s GoFundMe now stands at over $40,000, compared to around $15,440 in early July.)
“I donated to him because those charges are bulls***,” one redditor replied.
In addition to memes, the subreddit has become a destination for serious discussion around ALPRs and surveillance in general. One developer used the forum as a launch pad for their app FlockHopper, a navigation app that helps travelers avoid ALRPs. Others use it to share important developments relevant to the anti-Flock crowd, to get advice on Freedom of Information Act requests, and to dispel misinformation.
“As entertaining a trend as it is, will a few Redditors knocking a few poles over really make a difference?” one redditor asked the group.
“Even a single grain of rice can tip the scales,” a user replied.
Since WIRED reported on Meta’s NameTag face recognition system, company executives have made confusing and conflicting remarks about its very existence.
Rarely has the Los Angeles Police Department found a police toy it doesn’t love. The third-largest police force in the US has major Palantir contracts in place, uses sketchy apps to track minority communities, and routinely sends drones to spy on peaceful protestors.
So when the LAPD ends a contract with a surveillance company because it thinks the technology is too dangerous, you know there’s a problem.
Over the weekend, the police department announced it was abandoning its contract with Flock Safety, the controversial company blanketing the United States in AI-integrated license plate cameras (ALPRs). Though its three-year contract with Flock is just about up, the LAPD says it won’t renew the partnership, citing residents’ concerns over privacy and civil rights, ABC reported.
“This contract is not being renewed because of serious concerns around civil liberties and civil rights issues, particularly around privacy and the data that is being collected from these cameras,” LAPD’s chief information officer Dean Gialamas told ABC in a statement. “The LAPD had to make a difficult decision, in this case discontinuing using Flock services until we can get those data, privacy, security and sharing concerns ironed out through a contractual relationship.”
The news comes just days after our sister publication The Drive‘s Joel Feder reported how he was suddenly swarmed by armed cops while test driving a Range Rover with his wife in Minnesota. Officers said they had been tracking him for days using Flock’s AI-integrated cameras, which had erroneously tagged his car as stolen.
After the latest Flock contract news broke, 404 Media revealed that a July 10 audit by the LAPD Office of the Inspector General caught the department’s ALPR cameras generating 161 false stolen-vehicle alerts in just two months — each one ending with officers pulling over an innocent driver. Factoring in 337 alerts which “resulted in the recovery of stolen vehicles,” the LAPD’s cameras carry an error rate of 32.3 percent, effectively giving officers a one-in-three chance at pulling an innocent person over.
“During the review period, officers acknowledged 161 alerts as accurate license plate matches; however, subsequent investigations determined the vehicles were not stolen,” the report reads. “In addition to creating an inconvenience for vehicle owners, these inaccuracies can affect individual liberty interests, erode public trust, and potentially create substantial legal and financial liability concerns.”
The OIG report notes that officers are required to “attempt to verify all records prior to conducting an investigative stop,” though it didn’t disclose how many of the 161 wrongful stops were the result of an officer’s failure to do so. Instead, investigators blamed license plate records it says were “not updated in a timely manner,” which “may” lead cops to “act on inaccurate or outdated information, increasing the risk of unnecessary enforcement actions, including vehicle stops and wrongful detentions, or a confrontation with serious consequences.”
As 404 flagged, the LAPD approaches potentially stolen vehicles as “high-risk” stops. For the LAPD, these stops require drastic shows of force, including “calling for back up, air support, and a supervisor and ordering the suspect out of their vehicle,” the report reads.
It doesn’t take a large language model to figure out that this is an incredibly dangerous scenario for Black people in Los Angeles, who account for 19.5 percent of those killed by the LAPD, despite making up only nine percent of the city’s population.
Whatever way you slice it, it seems one of the most notorious police departments in the US has finally met its match.
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