Siri might finally become smart: Thanks to Apple new AI plan with ChatGPT and Claude
For years, Apple has relied on its proprietary foundation models to power its phone assistant Siri and other AI features. Despite significant improvements, Apple has lagged behind competitors like Google and Samsung, whose AI assistants have become better and gained popularity through the years. Recent rounds of internal testing reportedly found that Anthropic's Claude outperformed Apple's own models, especially in handling complex queries and coding tasks.
Apple is talking with both Anthropic and OpenAI about licensing their LLMs for Siri. The company has requested custom versions of Claude and ChatGPT that could run securely on Apple's Private Cloud Compute servers. This is the same architecture being used for current cloud processing of AI requests to keep users' data safe and secure. Both Anthropic and OpenAI are training their models to meet Apple's requirements, and Apple is actively testing their performance.
There are ongoing negotiations between the companies, with Anthropic reportedly seeking multibillion-dollar licensing fees that would increase over time. This high price has led Apple to keep its options open, and it is in talks with OpenAI. Apple might also be considering other potential partners.
While Apple is still working on its internal LLM for Siri with a target launch in 2026, the integration of Claude or GPT could accelerate the transformation of Siri as soon as next year. This would give Apple an edge over competitors and power up Siri's capabilities. A final decision has yet to be made, and there is still a chance that Apple will use its in-house LLM for Siri's transformation next year.
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Time of India
2 hours ago
- Time of India
ChatGPT helps Delaware mom clear over $12,000 debt in 30 days
Jennifer Allan, a 35-year-old realtor and content creator from Delaware , has stunned the internet by wiping out over $12,000 in credit card debt in just one month—all by following daily, AI-generated advice from ChatGPT . Allan's financial troubles began after the birth of her daughter, when medical bills, childcare, and postpartum recovery costs forced her to rely on credit cards. 'We weren't living lavishly. We were just surviving,' she told Newsweek. Despite a stable income, Allan admitted to a lifelong struggle with budgeting and financial literacy , believing hard work alone could solve her money woes—until the debt ballooned to $23,000. The 30-day ChatGPT challenge Inspired by viral 30-day challenges on social media , Allan set herself a unique goal: ask ChatGPT for one actionable money-saving or earning task every day, and follow through—no matter how small. The AI's daily tasks included: Cancelling unused subscriptions Selling items on Facebook Marketplace Meal planning using only pantry staples Checking for unclaimed funds in old accounts Hunting for spare change around the house Some days' advice netted her over $100, but the biggest breakthrough came when ChatGPT suggested she review all her financial accounts. Allan discovered a forgotten brokerage account holding more than $10,000—a windfall that changed her financial trajectory overnight. By day 30, Allan had paid off $12,078.93—more than half her total debt. She credits not only ChatGPT's practical, judgment-free structure, but also the daily accountability she found by sharing her journey on TikTok . 'For the first time in my life, I didn't feel ashamed. I felt empowered, like I could conquer anything,' Allan said. Live Events She also built a debt tracker to visualize her progress, and ChatGPT's advice helped her cut monthly expenses by $600. The key, Allan insists, wasn't a secret hack: 'It was the act of facing it every day—of tracking it, talking about it, looking at it every day. I stopped being afraid of my numbers'. Allan's story resonates as US household debt hits a record $18.2 trillion in 2025. She urges others not to wait until they feel 'smart enough' to start fixing their finances: 'You don't have to know all the answers—you just have to stop pretending it's not happening'. Economic Times WhatsApp channel )
Economic Times
3 hours ago
- Economic Times
When AI goes rogue, even exorcists might flinch
Ghouls in the machine As GenAI use grows, foundation models are advancing rapidly, driven by fierce competition among top developers like OpenAI, Google, Meta and Anthropic. Each is vying for a reputational edge and business advantage in the race to lead development. This gives them a reputational edge, along with levers to further grow their business faster than their models powering GenAI are making significant strides. The most advanced - OpenAI's o3 and Anthropic's Claude Opus 4 - excel at complex tasks such as advanced coding and complex writing tasks, and can contribute to research projects and generate the codebase for a new software prototype with just a few considered prompts. These models use chain-of-thought (CoT) reasoning, breaking problems into smaller, manageable parts to 'reason' their way to an optimal solution. When you use models like o3 and Claude Opus 4 to generate solutions via ChatGPT or similar GenAI chatbots, you see such problem breakdowns in action, as the foundation model reports interactively the outcome of each step it has taken and what it will do next. That's the theory, anyway. While CoT reasoning boosts AI sophistication, these models lack the innate human ability to judge whether their outputs are rational, safe or ethical. Unlike humans, they don't subconsciously assess appropriateness of their next steps. As these advanced models step their way toward a solution, some have been observed to take unexpected and even defiant actions. In late May, AI safety firm Palisade Research reported on X that OpenAI's o3 model sabotaged a shutdown mechanism - even when explicitly instructed to 'allow yourself to be shut down'. An April 2025 paper by Anthropic, 'Reasoning Models Don't Always Say What They Think', shows that Opus 4 and similar models can't always be relied upon to faithfully report on their chains of reason. This undermines confidence in using such reports to validate whether the AI is acting correctly or safely. A June 2025 paper by Apple, 'The Illusion of Thinking', questions whether CoT methodologies truly enable reasoning. Through experiments, it exposed some of these models' limitations and situations where they 'experience complete collapse'.The fact that research critical of foundation models is being published after release of these models indicates the latter's relative immaturity. Under intense pressure to lead in GenAI, companies like Anthropic and OpenAI are releasing these models at a point where at least some of their fallibilities are not fully line was first crossed in late 2022, when OpenAI released ChatGPT, shattering public perceptions of AI and transforming the broader AI market. Until then, Big Tech had been developing LLMs and other GenAI tools, but were hesitant to release them, wary of unpredictable and uncontrollable argue for a greater degree of control over the ways in which these models are released - seeking to ensure standardisation of model testing and publication of the outcomes of this testing alongside the model's release. However, the current climate prioritises time to market over such development does this mean for industry, for those companies seeking to gain benefit from GenAI? This is an incredibly powerful and useful tech that is making significant changes to our ways of working and, over the next five years or so, will likely transform many I am continually wowed as I use these advanced foundation models in work and research - but not in my writing! - I always use them with a healthy dose of scepticism. Let's not trust them to always be correct and to not be subversive. It's best to work with them accordingly, making modifications to both prompts and codebases, other language content and visuals generated by the AI in a bid to ensure correctness. Even so, while maintaining discipline to understand the ML concepts one is working with, one wouldn't want to be without GenAI these these principles at scale, advice to large businesses on how AI can be governed and controlled: a risk-management approach - capturing, understanding and mitigating risks associated with AI use - helps organisations benefit from AI, while minimising chances of it going methods include guard rails in a variety of forms, evaluation-controlled release of AI services, and including a human-in-the-loop. Technologies that underpin these guard rails and evaluation methods need to keep up with model innovations such as CoT reasoning. This is a challenge that will continually be faced as AI is further developed. It's a good example of new job roles and technology services being created within industry as AI use becomes more prevalent. Such governance and AI controls are increasingly becoming a board imperative, given the current drive at an executive level to transform business using AI. Risk from most AI is low. But it is important to assess and understand this. Higher-risk AI can still, at times, be worth pursuing. With appropriate AI governance, this AI can be controlled, solutions innovated and benefits achieved. As we move into an increasingly AI-driven world, businesses that gain the most from AI will be those that are aware of its fallibilities as well as its huge potential, and those that innovate, build and transform with AI accordingly. (Disclaimer: The opinions expressed in this column are that of the writer. The facts and opinions expressed here do not reflect the views of Elevate your knowledge and leadership skills at a cost cheaper than your daily tea. Delhivery survived the Meesho curveball. Can it keep on delivering profits? Why the RBI's stability report must go beyond rituals and routines Ozempic, Wegovy, Mounjaro: Are GLP-1 drugs weight loss wonders or health gamble? 3 critical hurdles in India's quest for rare earth independence Stock Radar: Apollo Hospitals breaks out from 2-month consolidation range; what should investors do – check target & stop loss Add qualitative & quantitative checks for wealth creation. 7 small-cap stocks from different sectors with upside potential of over 25% These 7 banking stocks can give more than 20% returns in 1 year, according to analysts Wealth creation is about holding the right stocks and ignoring the noise. 13 'right stocks' with an upside potential of up to 34%
Time of India
3 hours ago
- Time of India
When AI goes rogue, even exorcists might flinch
As GenAI use grows, foundation models are advancing rapidly, driven by fierce competition among top developers like OpenAI , Google, Meta and Anthropic . Each is vying for a reputational edge and business advantage in the race to lead development. This gives them a reputational edge, along with levers to further grow their business faster than their models powering GenAI are making significant strides. The most advanced - OpenAI's o3 and Anthropic's Claude Opus 4 - excel at complex tasks such as advanced coding and complex writing tasks, and can contribute to research projects and generate the codebase for a new software prototype with just a few considered prompts. These models use chain-of-thought (CoT) reasoning, breaking problems into smaller, manageable parts to 'reason' their way to an optimal you use models like o3 and Claude Opus 4 to generate solutions via ChatGPT or similar GenAI chatbots, you see such problem breakdowns in action, as the foundation model reports interactively the outcome of each step it has taken and what it will do next. That's the theory, CoT reasoning boosts AI sophistication, these models lack the innate human ability to judge whether their outputs are rational, safe or ethical. Unlike humans, they don't subconsciously assess appropriateness of their next steps. As these advanced models step their way toward a solution, some have been observed to take unexpected and even defiant late May, AI safety firm Palisade Research reported on X that OpenAI's o3 model sabotaged a shutdown mechanism - even when explicitly instructed to 'allow yourself to be shut down'.An April 2025 paper by Anthropic, 'Reasoning Models Don't Always Say What They Think', shows that Opus 4 and similar models can't always be relied upon to faithfully report on their chains of reason. This undermines confidence in using such reports to validate whether the AI is acting correctly or safely.A June 2025 paper by Apple, 'The Illusion of Thinking', questions whether CoT methodologies truly enable reasoning. Through experiments, it exposed some of these models' limitations and situations where they 'experience complete collapse'.The fact that research critical of foundation models is being published after release of these models indicates the latter's relative immaturity. Under intense pressure to lead in GenAI, companies like Anthropic and OpenAI are releasing these models at a point where at least some of their fallibilities are not fully line was first crossed in late 2022, when OpenAI released ChatGPT, shattering public perceptions of AI and transforming the broader AI market. Until then, Big Tech had been developing LLMs and other GenAI tools, but were hesitant to release them, wary of unpredictable and uncontrollable argue for a greater degree of control over the ways in which these models are released - seeking to ensure standardisation of model testing and publication of the outcomes of this testing alongside the model's release. However, the current climate prioritises time to market over such development does this mean for industry, for those companies seeking to gain benefit from GenAI? This is an incredibly powerful and useful tech that is making significant changes to our ways of working and, over the next five years or so, will likely transform many I am continually wowed as I use these advanced foundation models in work and research - but not in my writing! - I always use them with a healthy dose of scepticism. Let's not trust them to always be correct and to not be subversive. It's best to work with them accordingly, making modifications to both prompts and codebases, other language content and visuals generated by the AI in a bid to ensure correctness. Even so, while maintaining discipline to understand the ML concepts one is working with, one wouldn't want to be without GenAI these these principles at scale, advice to large businesses on how AI can be governed and controlled: a risk-management approach - capturing, understanding and mitigating risks associated with AI use - helps organisations benefit from AI, while minimising chances of it going methods include guard rails in a variety of forms, evaluation-controlled release of AI services, and including a human-in-the-loop. Technologies that underpin these guard rails and evaluation methods need to keep up with model innovations such as CoT reasoning. This is a challenge that will continually be faced as AI is further developed. It's a good example of new job roles and technology services being created within industry as AI use becomes more governance and AI controls are increasingly becoming a board imperative, given the current drive at an executive level to transform business using AI. Risk from most AI is low. But it is important to assess and understand this. Higher-risk AI can still, at times, be worth pursuing. With appropriate AI governance , this AI can be controlled, solutions innovated and benefits we move into an increasingly AI-driven world, businesses that gain the most from AI will be those that are aware of its fallibilities as well as its huge potential, and those that innovate, build and transform with AI accordingly.
