Apple's AI efforts dealt another major blow — this has caused 'an earthquake inside Apple'
Pang, who joined Apple in 2021 from Google DeepMind, was central to the company's efforts to build its own large language models (LLMs). His departure, along with that of several close collaborators, signals deeper unrest within Apple's AI ranks.
As reported by The Information, Pang's exit and its aftermath has led to an 'earthquake inside Apple." You may like Why Pang's exit matters
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Pang was known for his hands-on technical contributions, including developing a key open-source training tool for Apple's AI models. Under his leadership, Apple made strides in shrinking LLMs to run efficiently on iPhones, a critical part of its 'on-device AI' strategy. But those advances came with internal tensions.
According to reporting from The Information, Pang's team had wanted to release some of Apple's AI models as open source earlier this year. This move could have shown progress while inviting collaboration from outside researchers.
But Apple exec Craig Federighi reportedly shut it down, concerned it would expose performance compromises Apple made to run the models on iPhones.
That disagreement was just one of many signs of friction between Apple's research-driven foundation models team and its product-focused leadership.
Get instant access to breaking news, the hottest reviews, great deals and helpful tips. A shift in power (and priorities)
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Earlier this year, Apple reorganized its AI efforts following delays to its revamped Siri assistant. The Siri team was pulled from longtime AI chief John Giannandrea and placed under Federighi, who also oversees Apple's software division.
Meanwhile, Pang's team remained with Giannandrea, but the separation highlighted a growing divide between R&D and product execution.
Now, with Pang gone and several of his top researchers either leaving or exploring offers from OpenAI, Anthropic, and Meta, Apple faces a major talent drain at a critical moment.
Bloomberg recently reported that Apple is testing outside models, including those from OpenAI and Google, to power Siri, a move that reportedly disheartened many on the internal AI team. The bigger picture
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While Apple made headlines with its Apple Intelligence announcement in June, integrating ChatGPT into iPhones and showcasing writing and image-generation tools, the company's own foundation models remain behind closed doors.
Insiders say there's still a lack of clear direction about whether Apple wants to compete head-to-head with models like GPT-4 or build more narrow, hardware-optimized tools.
In an interview with Tom's Guide following WWDC 2025, Craig Federighi, Apple's senior vice president of software engineering, and Greg Joswiak, the senior vice president of worldwide marketing, made it clear that Apple doesn't want to make a chatbot.
Without Pang's leadership and vision, some fear Apple's internal AI efforts could stagnate, or become overly reliant on outside partners. Others remain optimistic that the hiring of Zhifeng Chen, a former Google engineer now leading the foundation models team, will bring fresh momentum.
Either way, Apple's AI ambitions face a decisive inflection point. As rivals like Meta, OpenAI and Google continue to poach top researchers and ship headline-grabbing models, Apple must prove it's still a serious contender in the generative AI era. More from Tom's Guide
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Forbes
an hour ago
- Forbes
OpenAI: ChatGPT Wants Legal Rights. You Need The Right To Be Forgotten.
As systems like ChatGPT move toward achieving legal privilege, the boundaries between identity, ... More memory, and control are being redefined, often without consent. When OpenAI CEO Sam Altman recently stated that conversations with ChatGPT should one day enjoy legal privilege, similar to those between a patient and a doctor or a client and a lawyer, he wasn't just referring to privacy. He was pointing toward a redefinition of the relationship between people and machines. Legal privilege protects the confidentiality of certain relationships. What's said between a patient and physician, or a client and attorney, is shielded from subpoenas, court disclosures, and adversarial scrutiny. Extending that same protection to AI interactions means treating the machine not as a tool, but as a participant in a privileged exchange. This is more than a policy suggestion. It's a legal and philosophical shift with consequences no one has fully reckoned with. It also comes at a time when the legal system is already being tested. In The New York Times' lawsuit against OpenAI, the paper has asked courts to compel the company to preserve all user prompts, including those the company says are deleted after 30 days. That request is under appeal. Meanwhile, Altman's suggestion that AI chats deserve legal shielding raises the question: if they're protected like therapy sessions, what does that make the system listening on the other side? People are already treating AI like a confidant. According to Common Sense Media, three in four teens have used an AI chatbot, and over half say they trust the advice they receive at least somewhat. Many describe a growing reliance on these systems to process everything from school to relationships. Altman himself has called this emotional over-reliance 'really bad and dangerous.' But it's not just teens. AI is being integrated into therapeutic apps, career coaching tools, HR systems, and even spiritual guidance platforms. In some healthcare environments, AI is being used to draft communications and interpret lab data before a doctor even sees it. These systems are present in decision-making loops, and their presence is being normalized. This is how it begins. First, protect the conversation. Then, protect the system. What starts as a conversation about privacy quickly evolves into a framework centered on rights, autonomy, and standing. We've seen this play out before. In U.S. law, corporations were gradually granted legal personhood, not because they were considered people, but because they acted as consistent legal entities that required protection and responsibility under the law. Over time, personhood became a useful legal fiction. Something similar may now be unfolding with AI—not because it is sentient, but because it interacts with humans in ways that mimic protected relationships. The law adapts to behavior, not just biology. The Legal System Isn't Ready For What ChatGPT Is Proposing There is no global consensus on how to regulate AI memory, consent, or interaction logs. The EU's AI Act introduces transparency mandates, but memory rights are still undefined. In the U.S., state-level data laws conflict, and no federal policy yet addresses what it means to interact with a memory‑enabled AI. (See my recent Forbes piece on why AI regulation is effectively dead—and what businesses need to do instead.) The physical location of a server is not just a technical detail. It's a legal trigger. A conversation stored on a server in California is subject to U.S. law. If it's routed through Frankfurt, it becomes subject to GDPR. When AI systems retain memory, context, and inferred consent, the server location effectively defines sovereignty over the interaction. That has implications for litigation, subpoenas, discovery, and privacy. 'I almost wish they'd go ahead and grant these AI systems legal personhood, as if they were therapists or clergy,' says technology attorney John Kheit. 'Because if they are, then all this passive data collection starts to look a lot like an illegal wiretap, which would thereby give humans privacy rights/protections when interacting with AI. It would also, then, require AI providers to disclose 'other parties to the conversation', i.e., that the provider is a mining party reading the data, and if advertisers are getting at the private conversations.' Infrastructure choices are now geopolitical. They determine how AI systems behave under pressure and what recourse a user has when something goes wrong. And yet, underneath all of this is a deeper motive: monetization. But they won't be the only ones asking questions. Every conversation becomes a four-party exchange: the user, the model, the platform's internal optimization engine, and the advertiser paying for access. It's entirely plausible for a prompt about the Pittsburgh Steelers to return a response that subtly inserts 'Buy Coke' mid-paragraph. Not because it's relevant—but because it's profitable. Recent research shows users are significantly worse at detecting unlabeled advertising when it's embedded inside AI-generated content. Worse, these ads are initially rated as more trustworthy until users discover they are, in fact, ads. At that point, they're also rated as more manipulative. 'In experiential marketing, trust is everything,' says Jeff Boedges, Founder of Soho Experiential. 'You can't fake a relationship, and you can't exploit it without consequence. If AI systems are going to remember us, recommend things to us, or even influence us, we'd better know exactly what they remember and why. Otherwise, it's not personalization. It's manipulation.' Now consider what happens when advertisers gain access to psychographic modeling: 'Which users are most emotionally vulnerable to this type of message?' becomes a viable, queryable prompt. And AI systems don't need to hand over spreadsheets to be valuable. With retrieval-augmented generation (RAG) and reinforcement learning from human feedback (RLHF), the model can shape language in real time based on prior sentiment, clickstream data, and fine-tuned advertiser objectives. This isn't hypothetical—it's how modern adtech already works. At that point, the chatbot isn't a chatbot. It's a simulation environment for influence. It is trained to build trust, then designed to monetize it. Your behavioral patterns become the product. Your emotional response becomes the target for optimization. The business model is clear: black-boxed behavioral insight at scale, delivered through helpful design, hidden from oversight, and nearly impossible to detect. We are entering a phase where machines will be granted protections without personhood, and influence without responsibility. If a user confesses to a crime during a legally privileged AI session, is the platform compelled to report it or remain silent? And who makes that decision? These are not edge cases. They are coming quickly. And they are coming at scale. Why ChatGPT Must Remain A Model—and Why Humans Must Regain Consent As generative AI systems evolve into persistent, adaptive participants in daily life, it becomes more important than ever to reassert a boundary: models must remain models. They cannot assume the legal, ethical, or sovereign status of a person quietly. And the humans generating the data that train these systems must retain explicit rights over their contributions. 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Here's How to Respond. The shift toward AI systems as quasi-participants—not just tools—will reshape legal exposure, data governance, product liability, and customer trust. Whether you're building AI, integrating it into your workflows, or using it to interface with customers, here are five things you should be doing immediately: ChatGPT May Get Privilege. You Should Get the Right to Be Forgotten. This moment isn't just about what AI can do. It's about what your business is letting it do, what it remembers, and who gets access to that memory. Ignore that, and you're not just risking privacy violations, you're risking long-term brand trust and regulatory blowback. At the very least, we need a legal framework that defines how AI memory is governed. Not as a priest, not as a doctor, and not as a partner, but perhaps as a witness. Something that stores information and can be examined when context demands it, with clear boundaries on access, deletion, and use. The public conversation remains focused on privacy. But the fundamental shift is about control. And unless the legal and regulatory frameworks evolve rapidly, the terms of engagement will be set, not by policy or users, but by whoever owns the box. Which is why, in the age of AI, the right to be forgotten may become the most valuable human right we have. Not just because your data could be used against you—but because your identity itself can now be captured, modeled, and monetized in ways that persist beyond your control. Your patterns, preferences, emotional triggers, and psychological fingerprints don't disappear when the session ends. They live on inside a system that never forgets, never sleeps, and never stops optimizing. Without the ability to revoke access to your data, you don't just lose privacy. You lose leverage. You lose the ability to opt out of prediction. You lose control over how you're remembered, represented, and replicated. The right to be forgotten isn't about hiding. It's about sovereignty. And in a world where AI systems like ChatGPT will increasingly shape our choices, our identities, and our outcomes, the ability to walk away may be the last form of freedom that still belongs to you.
Yahoo
2 hours ago
- Yahoo
I Asked ChatGPT What Would Happen If Billionaires Paid Taxes at the Same Rate as the Upper Middle Class
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Instead, they grow their wealth through investments–stocks, real estate, and businesses–which are often taxed at much lower rates or not taxed at all until the assets are sold,' ChatGPT told me. Billionaire income is largely derived from capital appreciation, not wages. In other words, they make money on their money through interest. And as of yet, the U.S. tax code doesn't tax 'unrealized capital gains' so until you sell your assets, you could amass millions in appreciation and not pay a dime on it, ChatGPT shared. Learn More: What Do Billionaires Pay in Taxes? Right now, many billionaires pay an effective tax rate of around 8% or less, thanks to loopholes and tax strategies. Meanwhile, upper-middle-class households earning, say, $250,000 might pay around 20% to 24% of their income in taxes. (Keep in mind that the government doesn't apply one tax bracket to all income. You pay tax in layers, according to the IRS. As your income goes up, the tax rate on the next layer of income is higher. So you pay 12% on the first $47,150, then 22% on $47,151 to $100,525 and so on). So, if billionaires were taxed at the same rate as those upper-middle-class wage earners, 'it would level the playing field–and raise a ton of revenue that could be used for things like infrastructure, education or healthcare,' ChatGPT said. The Impact on Wealth Equality I wondered if taxing billionaires could have any kind of impact on wealth equality, as well. While it wouldn't put more money in other people's pockets, 'it could increase trust in the tax system, showing that the wealthiest aren't playing by a different set of rules,' ChatGPT said. It would also help curb 'the accumulation of dynastic wealth,' where the richest families essentially hoard wealth for generations without contributing proportionally to the system. But it's not a magic bullet. 'Wealth inequality is rooted in more than just taxes–wages, education access, housing costs, and corporate ownership all play a role,' ChatGPT said. Billionaires paying taxes doesn't stop them from being billionaires, either, it pointed out. Taxing Billionaires Is Not That Simple While in theory billionaires paying higher taxes 'would shift a much bigger share of the tax burden onto the very wealthy,' ChatGPT wrote, billionaires are not as liquid as they may seem. 'A lot of billionaire wealth is tied up in things like stocks they don't sell, so taxing that would require big changes to how the tax code works.' Also, billionaires are good at finding loopholes and account strategies — it might be hard to enforce. What's a Good Middle Ground? We don't live in a black and white world, however. There's got to be a middle ground, so I asked ChatGPT if there is a way to tax billionaires more, even if it's not quite how the upper middle class are taxed. A likely compromise would come from a policy decision, which isn't likely to be forthcoming anytime soon. President Donald Trump's One Big Beautiful Bill only offered more tax breaks to the wealthiest. However, policy proposals that have been floated, include: A minimum tax on billionaires where they might pay around 20% of their overall income Limiting deductions and closing tax loopholes that allow them to significantly reduce taxable income Tax unrealized gains (those assets that have only earned but not yet been sold), gradually. ChatGPT agreed that billionaires could pay more than they currently do, even if they don't pay exactly what upper-middle-class workers pay in percentage terms. 'The key is to design policies that are fair, enforceable, and politically feasible.' I asked how realistic such policy proposals are, and ChatGPT told me what I already knew: They're 'moderately realistic' but only with the 'right political alignment.' 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Digital Trends
2 hours ago
- Digital Trends
iPhone 17 Pro could get unexpectedly huge camera upgrades
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