The flood is not accidental. It is the product of cheap messaging technology, permissive rules, expansive voter data, and a commercial data economy that treats phone numbers as just another field to be matched, sold, and modeled. For consumers, the result is a daily barrage that ranges from merely irritating to potentially fraudulent. For campaigns, the texts are a low-cost way to raise money and turn out voters. For regulators, they represent a stubborn gap between what people expect from do-not-call protections and what the law actually permits.

Key facts at a glance

• Political calls and texts are largely exempt from do-not-call-list rules, so consumers cannot simply opt out of the category the way they can with many telemarketing calls.
• Industry estimates put political texting in the billions: one call-blocking company counted about 15 billion political texts in 2022, while a spam-protection executive estimated 25 billion to 30 billion this year.
• Volume is roughly flat to 10 percent higher than in 2024, according to one informal industry estimate.
• A Supreme Court decision in 2021 made it easier to send some mass calls and texts without consent, contributing to the surge.
• Campaigns obtain phone numbers through donations and sign-ups, but also through public voter rolls, data brokers, online advertising trackers, loyalty programs, and other commercial sources.
• Data matching can be inaccurate, producing texts meant for former residents, people with similar names, or multiple users of the same device.
• Consumers can reply 'stop,' block and report numbers, file complaints with the FCC or state attorneys general, and avoid clicking links from unknown numbers.
• Advocates say the long-term fix requires congressional action or stronger enforcement by regulators, not just individual blocking.

Why the volume is so high

The scale of political texting is difficult to measure precisely because the messages come from thousands of campaigns, committees, consultants, and outside groups. But the available estimates are enormous. One call-blocking firm found that about 15 billion political texts were sent in 2022. A spam-protection executive estimated that the 2026 cycle could produce 25 billion to 30 billion political texts, though he cautioned that the figure is an informal approximation. The volume is about the same as, or up to 10 percent higher than, the 2024 election cycle.

Part of the explanation is legal. In 2021, the Supreme Court made it easier to send some mass calls and texts without consent, a decision that helped open the door to more political messaging. Political calls and texts are also exempt from the do-not-call-list rules that apply to many telemarketers. That means a voter who has registered for do-not-call protections may still receive political messages because the rules do not cover that category in the same way.

The timing makes the flood feel worse. Campaigns concentrate their spending and outreach in the final weeks before Election Day, when they are trying to raise last-minute money, persuade undecided voters, and remind supporters to vote. The election is Tuesday, November 3, and many groups are treating every remaining hour as a chance to reach a voter. Consumer advocates note that the good news is that the election will soon be over. The bad news is that the exemption from do-not-call rules means there is no simple switch to turn off political texts.

How campaigns get your number

If you have donated to a candidate or an issue group, it is not surprising that your number ended up on a list. Campaigns collect contact information from donations, petitions, event sign-ups, volunteer forms, and online stores. They may share or sell that information, or they may use it to build models about who is likely to give again or turn out to vote.

But donations are only one path. Campaigns and outside groups also rely on data brokers that collect and sell information from public and private sources. Public voter rolls can provide names, addresses, and voting histories. Advertising trackers can observe online behavior. Loyalty programs and shopping portals can capture phone numbers and purchase habits. By combining these sources, political operatives try to infer whether a person is likely to support a candidate, donate money, or vote.

The process is not always accurate. Privacy advocates warn that data matching can produce serious errors. Multiple people may use the same device. People may have similar names. Voters move, change phone numbers, or share a number with family members. A campaign may end up with an outdated address that leads to texts about a state where a person no longer lives, or erroneous information that invites messages about a state where the person never lived.

The broader problem is that the data ecosystem is lightly regulated. Advocates say there are few rules requiring accuracy, fairness, or privacy impact reviews before information is processed and used. That means consumers can be sorted, labeled, and targeted based on information they never knowingly provided to a political campaign. The result is not just annoyance. It is a system in which sensitive information and inferred behavior can shape who gets contacted, how often, and with what message.

What consumers can do now

The simplest response is to reply 'stop' to a message or block and report the number as spam. That will notify the service provider of a likely spam number. It probably will not produce an immediate change, especially during the final days of a campaign. But if enough people report the same number in a short period, the provider may begin to block it or label it as spam.

Consumers can also file a complaint with the Federal Communications Commission or a state attorney general if they believe a number is using an autodialer without consent. Political messages are not supposed to use autodialers in ways that violate the law, even though the broader category enjoys significant exemptions. Complaints may not stop the next message, but they can help regulators identify patterns and bad actors.

Whatever you do, do not click links from numbers you do not recognize. Scammers often hide among legitimate political messages, especially when emotions are high and deadlines are near. A text that looks like a campaign appeal may lead to a fake donation page, a phishing form, or a malware download. If you want to donate at this point, go directly to the candidate's or committee's official website rather than following a link from an unfamiliar number.

For people who want to reduce the flow over time, consumer advocates recommend limiting how often you give out your primary phone number. Online shopping portals, loyalty programs, and other commercial services often ask for a number, but it is not always required. When a number is necessary, a secondary internet-based number can help keep political and marketing messages out of your main inbox. It is not a perfect solution, but it can create a buffer between your personal phone and the lists that circulate through the data economy.

The scam risk hidden in the flood

The sheer volume of political texting creates cover for fraud. A voter who is used to receiving urgent appeals may be less suspicious of a message that claims to be from a campaign, a political action committee, or a grassroots group. Scammers can mimic that language, promising to match a donation, warning that a candidate is losing, or asking for help with a last-minute get-out-the-vote push. The links may look plausible, and the urgency is designed to short-circuit careful judgment.

Consumer advocates say the safest approach is to treat unexpected links as hostile until proven otherwise. Do not enter credit card information, Social Security numbers, or other personal details into a page reached from a text message. Do not reply to a message with personal information. If a message claims to be from a candidate you support, open a browser and navigate to the official site yourself. The extra step is inconvenient, but it is far less costly than a fraudulent donation or identity theft.

The longer-term privacy problem

Privacy advocates worry that the harm of political texting is not limited to irritation or even fraud. The constant bombardment can make people feel surveilled and manipulated, and it can push them away from civic participation altogether. One voter told an advocate recently that she did not want to donate again because doing so seemed to put her on even more lists. That reaction is a warning sign: the apparatus built to mobilize voters may also be driving some of them out of the process.

Even people who never engage with politics can end up on political text lists because of data brokers. A phone number may be scraped, purchased, matched, or inferred from unrelated commercial activity. A person may be added to a list because of an old address, a shared family plan, or a name that resembles someone else's. Once a number is in circulation, it can be copied, resold, and reused across many campaigns and committees.

Consumer advocates say they are frustrated by the emphasis on individual fixes. It is reasonable to tell people to block numbers, adjust privacy settings, and use secondary phone numbers, but that advice places the burden on the person receiving the messages. The companies and political operations that trade in sensitive information, advocates argue, should be the ones required to stop harmful surveillance and reduce unwanted contact.

What would actually change the system

The bigger solutions are likely to come from Congress or regulators. Greater enforcement of existing laws could limit robocalls and texts, especially when autodialers are used without consent or when scammers impersonate political groups. Lawmakers could also tighten the rules around political texting, data matching, and the sale of phone numbers. Regulators could pressure service providers to identify and block bad actors more aggressively.

Voters who want to see change can contact their representatives and urge more pressure on the FCC. That may feel distant in the final days of a campaign, but it is one of the few paths to a systemic fix. In the meantime, the practical advice is modest: reply 'stop' when appropriate, block and report spam, avoid unknown links, and do not let the flood of messages become a personal crusade. The messages may keep coming until the election is over, but the larger policy fight will continue after the last text is sent.

Source: The Verge News

Un récit centré sur l'homme, pas sur le chef d'entreprise

Le livre ne doit pas être lu comme une histoire complète de Microsoft. Selon les éléments annoncés, Source Code: My Beginnings se concentre sur la jeunesse de Bill Gates, sa formation, ses premières expériences avec l'informatique et le contexte qui l'a conduit à créer l'entreprise en 1975. Il ne s'agirait donc pas d'un ouvrage détaillé sur la Fondation Gates, sur l'avenir de Windows ou sur les grandes batailles commerciales du groupe. Le milliardaire américain promet au contraire une histoire humaine, plus intime, consacrée à l'homme derrière l'image du dirigeant.

Cette approche donne au projet une dimension particulière. Bill Gates est souvent associé à la réussite économique, à la puissance de Microsoft et à la philanthropie. Pourtant, son parcours commence bien avant la création de l'empire informatique. Il naît en 1955 à Seattle, dans une famille qui accorde une place importante à l'éducation, à la curiosité et à l'engagement. Très tôt, il montre un intérêt marqué pour les sciences, les mathématiques et les machines. C'est cette période de formation, faite de découvertes, d'essais et de rencontres, que le livre entend mettre en lumière.

De Lakeside à Harvard, les débuts d'une passion

L'un des épisodes centraux de la jeunesse de Bill Gates reste son passage à la Lakeside School. Dans cet établissement, il découvre l'informatique à une époque où les ordinateurs sont encore rares et volumineux. L'accès à un terminal et à du temps de calcul lui permet de faire ses premiers pas dans la programmation. Il y rencontre également Paul Allen, avec qui il partagera ensuite une aventure entrepreneuriale décisive. Cette amitié, construite autour de la technique et de l'envie de comprendre comment fonctionnent les machines, occupe une place essentielle dans la genèse de Microsoft.

Avant de fonder l'entreprise, Bill Gates explore plusieurs pistes. Avec Paul Allen, il participe à des projets autour du traitement de données, notamment Traf-O-Data, une expérience qui n'a pas connu un succès commercial retentissant mais qui a servi de terrain d'apprentissage. Les deux jeunes hommes manipulent des programmes, étudient les possibilités offertes par les premiers microprocesseurs et observent l'émergence d'un marché encore fragile. Ces années de tâtonnements sont souvent moins connues du grand public. Elles montrent pourtant comment une passion peut se transformer en projet concret, au fil des essais, des erreurs et des rencontres.

Bill Gates intègre ensuite Harvard, avant de quitter l'université pour se consacrer à Microsoft. Cette décision reste l'un des moments les plus commentés de sa biographie. Elle illustre à la fois le risque, l'ambition et la conviction d'un jeune homme qui croit au potentiel de l'informatique personnelle. Le livre doit précisément revenir sur ce basculement, en expliquant comment un étudiant a pu choisir de renoncer à un parcours académique classique pour se lancer dans une aventure industrielle incertaine. Le récit promet de replacer ce choix dans son contexte familial, intellectuel et technologique.

Source Code, un titre qui fait sens

Le titre Source Code: My Beginnings n'est pas anodin. En informatique, le code source désigne les instructions écrites par un programmeur, à partir desquelles un logiciel est construit. Utilisé pour des mémoires, le terme évoque les fondations personnelles, les influences et les expériences qui ont façonné une vie. Bill Gates semble ainsi proposer une plongée dans ce qui l'a constitué avant la célébrité, avant les responsabilités de dirigeant et avant la fortune. Le sous-titre, My Beginnings, confirme cette intention : raconter les commencements plutôt que dresser un bilan complet.

Cette promesse est cohérente avec la manière dont l'homme public présente son parcours. Bill Gates a déjà fait l'objet de documentaires et de nombreux portraits. Il a été sacré plusieurs fois homme le plus riche de l'année, ce qui a contribué à construire une image ambivalente, mêlant admiration pour sa réussite et critiques sur la puissance de Microsoft. Ses mémoires peuvent offrir un autre éclairage : celui d'un adolescent curieux, d'un étudiant passionné, d'un ami, d'un fils et d'un jeune entrepreneur. Le livre devrait aussi montrer que la création de Microsoft n'a pas été un chemin linéaire, mais le résultat d'une série d'opportunités saisies dans un secteur en pleine mutation.

Une période charnière pour l'informatique

Les années 1960 et 1970 constituent un moment charnière pour l'informatique. Les ordinateurs occupent encore souvent des salles entières, mais les progrès des microprocesseurs ouvrent la voie à des machines plus petites et plus accessibles. Le mouvement des passionnés, les clubs d'informatique et les premiers ordinateurs personnels commencent à transformer les usages. C'est dans ce contexte que Bill Gates et Paul Allen comprennent l'importance du logiciel. Ils saisissent qu'une machine ne vaut que par les programmes capables de la faire fonctionner et que le marché des logiciels pourrait devenir stratégique.

La fondation de Microsoft en 1975 s'inscrit dans cette dynamique. L'entreprise naît à une époque où les standards ne sont pas encore fixés et où les acteurs sont nombreux à tenter leur chance. Le choix de développer un interpréteur BASIC pour l'Altair 8800, puis de défendre une vision du logiciel comme produit distinct du matériel, marque les débuts de l'aventure. Les mémoires de Bill Gates peuvent aider à comprendre comment ces décisions ont été vécues de l'intérieur, loin de la seule légende industrielle. Le livre devrait montrer les doutes, les négociations, les déplacements et l'énergie nécessaire pour faire exister une jeune entreprise dans un domaine encore incertain.

Un retour aux sources attendu

L'annonce de cette publication intervient alors que Bill Gates bénéficie d'une attention particulière pour son parcours et ses engagements. Son itinéraire intéresse parce qu'il combine plusieurs dimensions : la technologie, les affaires, la philanthropie et les questions de société. Pourtant, Source Code: My Beginnings devrait se tenir à distance des bilans trop larges. Il s'agit d'un premier volume, consacré aux origines. Cette formule laisse penser que d'autres périodes pourraient être abordées plus tard, mais elle permet surtout de concentrer le récit sur la formation d'une personnalité.

Les lecteurs peuvent s'attendre à des chapitres sur la famille, l'école, les premiers ordinateurs, les amitiés déterminantes et les choix difficiles. Le livre devrait aussi évoquer la manière dont Bill Gates a appris à travailler, à argumenter et à défendre ses idées. Ces aspects sont souvent moins visibles que les résultats financiers ou les produits emblématiques. Ils sont pourtant essentiels pour comprendre comment un jeune homme de Seattle est devenu l'une des figures les plus connues de l'histoire de l'informatique.

La date de sortie est fixée au 4 février 2025. À cette occasion, les lecteurs découvriront un récit qui ne promet pas seulement des anecdotes sur Microsoft, mais une exploration des débuts, des influences et des circonstances qui ont précédé la création de l'entreprise. Source Code: My Beginnings s'annonce donc comme un ouvrage sur la construction d'un destin, avec l'ambition de raconter l'homme avant la légende. La parution interviendra au début de l'année prochaine, comme prévu par l'auteur.

Source: clubic.com News

Les faits clés

• Jennie Kim est chanteuse, rappeuse et danseuse du girlgroup sud-coréen BLACKPINK.
• Elle a débuté avec BLACKPINK sous YG Entertainment, avec les titres « BOOMBAYAH » et « Whistle ».
• Elle a sorti son premier projet solo « Solo » en 2018.
• Elle a collaboré avec The Weeknd, Lily-Rose Depp et Zico.
• Elle a fait ses débuts d’actrice en 2023 dans la série « The Idol » sur HBO.
• Elle est égérie Chanel et a été invitée deux fois au MET Gala.
• Elle a collaboré avec Calvin Klein, Maison Kitsuné, Gentle Monster et Jacquemus.
• Son style est suivi par une communauté mondiale de fans, les Blink.

Qui est Jennie Kim, figure des BLACKPINK ?

Jennie Kim est une chanteuse, rappeuse et danseuse du girlgroup sud-coréen BLACKPINK. Aux côtés de Lisa, Jisoo et Rosé, elle a débuté sa carrière musicale sous YG Entertainment avec les clips « BOOMBAYAH » et « Whistle ». Le groupe s’est rapidement imposé comme une référence de la K-pop à l’échelle internationale, aux côtés d’autres formations majeures comme BTS. Chaque comeback des BLACKPINK déclenche un engouement considérable, cumulant des milliards de vues sur les réseaux sociaux et les plateformes de streaming. Ce phénomène a installé Jennie comme l’une des personnalités les plus suivies de l’industrie musicale sud-coréenne.

Très vite, Jennie a su dépasser le cadre strict du groupe. En 2018, elle a lancé son premier projet solo intitulé « Solo », particulièrement bien accueilli par les Blink, le fandom des BLACKPINK. Ce titre lui a permis de montrer une autre facette de son univers artistique, entre assurance, charisme et sensibilité pop. Sa carrière ne s’est pas arrêtée à la musique : elle a collaboré avec des artistes internationaux, notamment The Weeknd et Lily-Rose Depp sur le morceau « One Of The Girls », ou encore avec Zico sur le titre « Spot ». Ces collaborations confirment sa capacité à circuler entre les scènes musicales coréennes et internationales.

En 2023, Jennie a également franchi une nouvelle étape en devenant actrice. Elle a obtenu son premier rôle dans la série « The Idol », diffusée sur HBO. Cette incursion dans le cinéma et les séries témoigne de son ambition et de sa volonté de ne pas rester enfermée dans une seule case. Après la musique et l’écran, elle s’est naturellement tournée vers un autre secteur où son influence est déjà immense : la mode.

Jennie, une icône de mode en pleine ascension

Malgré ses nombreuses apparitions à des événements de mode, Jennie Ruby Jane continue de provoquer un véritable phénomène auprès des Blink. À chaque apparition, c’est le même schéma : l’idole sud-coréenne déclenche une vague massive de réactions, de tweets et de messages enthousiastes. Son aura ne se limite pas à la scène musicale ; elle est devenue une référence pour les amateurs de mode, les photographes et les maisons de couture.

Sa relation avec Chanel est particulièrement emblématique. Jennie et la marque au double C entretiennent une histoire d’amour qui dure. Elle fait partie des dernières personnalités choisies personnellement par Karl Lagerfeld. Elle est ensuite devenue égérie du sac Chanel 22, puis de la campagne de la collection Coco Neige 2021-2022, avant de représenter la ligne Coco Crush. Cette fidélité à Chanel n’est pas seulement un contrat publicitaire : elle participe à construire une image cohérente, à la fois chic, moderne et audacieuse.

Son statut d’icône de mode lui a également ouvert les portes d’autres maisons et marques internationales. Parmi ses collaborations, on retrouve Calvin Klein, Maison Kitsuné, Gentle Monster ou encore Jacquemus, avec qui elle entretient une proximité réelle. Elle a récemment répondu présente pour le quinzième anniversaire de Jacquemus, afin de clôturer le défilé. Elle est apparue dans une robe noire à dos nu particulièrement remarquée, complétée par des sandales à imprimé zèbre et une pochette turquoise. Ce look a montré au monde entier son nouveau statut de mannequin et sa capacité à incarner des univers esthétiques très différents.

Jennie est aussi l’une des rares idoles de K-pop à avoir été invitée à deux reprises consécutives au prestigieux MET Gala. La première fois, elle portait une création Chanel ; la seconde, une tenue de la Maison Alaïa. Ces apparitions sont suivies avec attention par les médias spécialisés et par les fans de mode du monde entier. Elles confirment que Jennie n’est plus seulement une chanteuse populaire : elle est devenue une invitée attendue des grands rendez-vous fashion.

Des looks créateurs de tendances

Jennie s’est imposée comme une créatrice de tendances à part entière. Chacun de ses looks est scruté, commenté et souvent imité. Son style mêle élégance parisienne, touches streetwear et une forme de simplicité travaillée qui plaît à un public très large. Que ce soit pour enregistrer le titre « Spot » en studio, pour assister au défilé Chanel printemps-été 2022, pour monter les marches de Cannes en Chanel à l’occasion de la série « The Idol », ou pour se produire à Bercy le 11 décembre 2022 dans une mini-robe Chanel, elle parvient toujours à marquer les esprits.

Son passage au défilé Jacquemus « La Casa » à Capri a également été très remarqué. Jennie y a confirmé son statut de muse contemporaine, capable de passer d’un univers ultra-luxe à une mode plus solaire et méditerranéenne. Cette capacité d’adaptation est l’une des clés de son influence : elle ne se contente pas de porter des vêtements, elle raconte une histoire à travers eux.

Ces dernières années, les idoles de K-pop sont devenues des incontournables de l’industrie de la mode. Leur identité musicale, leur présence sur les réseaux sociaux et la mobilisation de leurs fans, les K-pop Stan, ont profondément changé les règles du jeu. Jennie incarne parfaitement cette évolution : elle est à la fois artiste, muse, égérie et phénomène culturel. Son parcours montre comment une star de la musique peut devenir une figure globale, dont l’influence s’étend de la scène aux podiums, des clips aux campagnes publicitaires.

Jennie Kim continue d’avancer entre musique, cinéma et mode avec une assurance rare. Chacune de ses apparitions confirme qu’elle n’est pas seulement une idole de K-pop : elle est devenue une personnalité dont le style, les choix et les collaborations pèsent sur les tendances mondiales.

Source: Fresh Mag Paris News

• Apple executive John Ternus says the company will not add artificial intelligence just to keep pace with rivals, arguing that AI should serve the product rather than become the product.
• Ternus, Apple's senior vice president of hardware engineering, said in a recent interview that he likes where the company is positioned and is not swayed by outside opinions about its AI strategy.
• Apple has begun rolling out a revamped Siri with AI features that can understand personal context, recognize what is on a user's screen, answer broader questions and perform more actions across apps.
• The company says the new Siri relies on on-device processing and Private Cloud Compute to protect user data, and its latest foundation models were developed with Google using Gemini technology.
• Ternus pushed back on the idea that AI assistants will replace apps and products, saying product creation is a deeply human and personal endeavor.

Apple's deliberate approach to the AI race

Artificial intelligence has become the centerpiece of nearly every major technology launch. Device makers are racing to add chatbots, generative tools and AI-powered assistants to phones, computers, wearables and software. In that environment, Apple stands out for what it is not doing: promising that AI will reinvent everything overnight.

John Ternus, Apple's senior vice president of hardware engineering, offered a measured defense of the company's position in a recent interview. He said Apple remains committed to using AI, but it does not intend to ship the technology simply because competitors are doing so. The distinction matters. For Apple, AI is expected to be a feature that improves the experience, not a marketing label attached to every product.

His comments come as Apple faces questions about its pace in the AI race, particularly after delays to promised Siri upgrades. Rivals have moved aggressively, embedding generative AI into search, productivity software, smartphones and developer tools. Some investors and analysts have wondered whether Apple is behind. Ternus's answer was notably calm: he said everyone has an opinion, but he likes where the company is.

What 'AI for AI's sake' means

The phrase captures Apple's core argument. The company does not want users to think about whether a feature was built with AI or with more traditional software. What matters, in Ternus's view, is whether the resulting product is something people genuinely love. That is a subtle but important shift in how AI is framed.

Many technology companies present AI as a visible destination: a chatbot that answers questions, a model that generates images, an agent that acts on the user's behalf. Apple's approach is more invisible. AI can handle complex tasks in the background, sort through context and anticipate needs, but the user interface should remain simple. If the technology works well, the user should not have to think about the model, the data center or the processing pipeline.

That philosophy fits Apple's long-standing habits. The company has often waited for a technology category to mature before releasing its own version, then integrated it tightly with hardware and software. It did this with music players, smartphones, smartwatches and wireless earbuds. The pattern does not always produce the first product, but it can produce a polished one. Whether that strategy works for AI, where the pace of change is unusually fast, is one of the central questions facing the company.

Ternus said Apple will absolutely leverage AI to build better products. The qualifier is that it will not do so blindly. The company wants to avoid adding features that feel bolted on or that create new complexity. Instead, it wants AI to make existing tasks easier and to remove friction from everyday use.

Revamped Siri enters the picture

The most visible test of that strategy is Siri. Apple introduced a revamped Siri AI at its Worldwide Developers Conference in June and began rolling it out in beta on September 14. The assistant is designed to understand personal context, recognize what is on a user's screen, answer broader questions and carry out more actions across apps.

Those capabilities sound simple, but they require coordination across many parts of the system. Siri must know what the user is looking at, what is stored in messages or calendars, what apps are installed and what actions are allowed. It must also decide whether a task can be handled on the device or needs more computing power in the cloud. Apple says it uses on-device processing and Private Cloud Compute to handle requests while protecting personal data.

Privacy is central to Apple's pitch. On-device processing keeps some data from leaving the phone or computer. Private Cloud Compute is designed to handle more demanding requests without retaining personal information. Apple has framed this as a way to deliver advanced AI without turning user data into a commodity. That claim will be tested as Siri takes on more complex tasks and as regulators scrutinize how AI systems handle personal information.

The company's latest foundation models were developed with Google and draw on Gemini technology. That partnership is notable because Apple has generally emphasized its own silicon and software stack. It also shows that even a company with Apple's resources is willing to work with outside model providers when it makes sense. The arrangement may help Apple accelerate certain capabilities while keeping the user experience under its control.

The debate over apps and AI agents

The rapid improvement of AI assistants has sparked a larger debate: will AI agents eventually replace traditional apps? The idea is that instead of opening a ride-hailing app, a food delivery app or a photo editor, users might simply ask an AI agent to book a cab, order dinner or edit a picture. If that happens, the app layer could become less important, and the assistant could become the main interface.

Ternus was not convinced. He said that while such a future could make life easy, the creation of products is a deeply human and personal thing. That response reflects Apple's identity. The company sees itself as a product company, not merely a platform for models or agents. It builds devices and software that people use directly, and it believes that craft, design and human judgment remain essential.

There is also a practical argument. Apps are not just containers for tasks. They carry brand identity, business models, user interfaces and specialized features. An AI agent might coordinate between apps, but it would still need access to services, data and permissions. Replacing the app layer entirely would require a new economic and regulatory framework, not just better models. For now, Apple appears to be betting that AI will make apps more capable rather than obsolete.

That does not mean the app model is static. AI could change how apps are discovered, how they are used and how they present information. A voice assistant might handle simple requests, while the app provides deeper controls. The likely outcome is not a sudden disappearance of apps but a gradual blending of assistant and app experiences. Apple wants to control that blend on its devices.

Pressure to prove the strategy in everyday use

Apple's cautious language will only go so far. The company must show that its AI is genuinely helpful, not just impressive in a launch video. Siri has a long history of being criticized for inconsistent performance, and the new features will be judged on whether they work reliably in real situations.

Users will test Siri with messy, ambiguous requests. They will ask it to find information across apps, act on what is on screen and follow multi-step instructions. They will expect it to understand context without repeating themselves. If the assistant fails, the promise of invisible AI will sound like an excuse. If it succeeds, Apple can argue that its slower, more integrated approach was the right one.

The beta rollout is an important phase. Beta software is expected to have bugs, but it also sets expectations. Early users will notice which features work well and which feel unfinished. Developers will watch to see how deeply Siri can interact with their apps. Privacy advocates will examine how data flows through on-device processing and Private Cloud Compute. Competitors will look for gaps to exploit.

Apple's advantage is its control over the hardware and software stack. That allows it to optimize performance, manage battery life and design privacy protections at the system level. Its challenge is that AI development is moving quickly, and the company cannot fully control the pace of outside model innovation. The Google partnership helps, but it also raises questions about differentiation. Apple will need to show that its integration and design make the experience meaningfully better than using a standalone AI service.

A different kind of AI race

The broader industry is divided over how AI should be sold. Some companies emphasize model size, benchmark scores and the ability to generate text, images and code. Others focus on agents that can act autonomously across software. Apple is trying to position AI as a quiet layer that improves the products people already use.

That approach carries risks. If competitors deliver dramatic AI features that users love, Apple may look slow. If Apple delivers AI that is reliable, private and deeply integrated, it may look prescient. The outcome will depend on execution, not on the elegance of the strategy. Ternus's comments suggest that Apple is comfortable with that tension. The company would rather be late and useful than early and noisy.

For now, the revamped Siri is the clearest evidence of what Apple means. It is not a standalone chatbot. It is an assistant embedded in the operating system, designed to use context and perform actions. Its success will be measured in small moments: a message summarized correctly, a photo edited with a simple request, a calendar conflict resolved without opening five apps. Those moments are less dramatic than a demo, but they are closer to how people actually use technology.

The next phase will reveal whether Apple's bet on invisible AI holds up. The company has promised more capable Siri features and deeper integration across its devices. It has also signaled that privacy and on-device processing will remain central. If the beta turns into a polished release, Apple can make a credible case that it joined the AI race on its own terms. If not, the pressure will intensify, and the debate over whether Apple is moving too slowly will grow louder. The answer will come not from interviews but from everyday use, where users decide whether the AI is helpful enough to trust.

Source: MSN News

Apple’s iOS 27.2 update has arrived with a long list of changes, and one of the most notable additions for sports fans is Multiview in the Apple TV app. Multiview allows iPhone users to watch up to four live sports streams at the same time. The feature is available for all sports events included in an Apple TV subscription. Until now, Multiview had been limited to Apple TV, iPad, and Vision Pro. Bringing it to iPhone changes the way subscribers can follow live events, especially when they are away from a television or simply want a second screen for sports.

The feature is not just a technical checkbox. It reflects how Apple has been steadily building out the Apple TV app as a destination for live sports. Apple TV’s sports lineup includes Formula 1, Major League Soccer, and Major League Baseball. Multiview gives those events more room to breathe on smaller screens by letting viewers keep an eye on multiple feeds at once instead of switching back and forth. For races, matches, or games that unfold simultaneously, that can be a meaningful improvement.

How Multiview Works on iPhone

Activating Multiview is designed to be straightforward. Once a viewer starts watching a sports event, a new Multiview button appears at the bottom of the Apple TV player. From there, the experience can vary by sport. With Formula 1, for example, users can choose from a preset team Multiview or build a custom setup that includes driver-view cameras, course maps, and other available feeds. That flexibility is important because different sports have different kinds of coverage. A racing broadcast may benefit from onboard cameras and track maps, while a soccer or baseball broadcast may emphasize different camera angles or concurrent matches.

The core promise is consistent: four live streams on one screen. That is a significant step up from the usual single-stream experience on a phone. On a smaller display, the concern is obvious. Four video windows could become too small to follow. Yet the feature appears to work better than that concern suggests. In a hands-on tryout on an iPhone 18 Pro, four Formula 1 streams from the Austin Grand Prix were visible on-screen at once. The smaller display did not prove to be the limiting factor that might have been expected. That matters because it shows Multiview is not only a feature for tablets and televisions.

Key Facts at a Glance

• iOS 27.2 adds Multiview to the Apple TV app on iPhone.
• Multiview supports up to four live sports streams simultaneously.
• The feature was previously available on Apple TV, iPad, and Vision Pro.
• It works with all sports events included in an Apple TV subscription.
• Users activate it through a Multiview button at the bottom of the Apple TV player.
• Formula 1 offers preset team layouts and custom options with driver-view cameras and course maps.
• A test on iPhone 18 Pro showed four F1 streams from the Austin Grand Prix at once.
• The unfolded iPhone Duo is seen as an especially strong fit for four-stream viewing.
• Apple TV costs $14.99 per month or is available through the Apple One bundle.

Why Multiview Matters for Live Sports

Live sports have become a major part of the Apple TV experience. Formula 1, MLS, and MLB each bring a different rhythm to the service. F1 is built around a single race weekend but offers many simultaneous perspectives: the main broadcast, driver cameras, timing views, and course maps. MLS involves multiple matches that can overlap, and MLB has a long schedule with games that often run at the same time. Multiview lets subscribers follow more than one storyline without abandoning the main event. It also makes the Apple TV app feel more like a dedicated sports hub and less like a collection of individual streams.

For fans who watch on a phone, the value is practical. A viewer might be watching a race while also keeping track of another event, or checking in on multiple camera angles during a key moment. On a tablet or TV, Multiview has already been useful. On iPhone, it becomes portable. That portability is the real shift. The feature no longer belongs only to the biggest screen in the house. It can travel with the viewer, whether they are commuting, traveling, or simply watching from another room.

The iPhone Duo Advantage

The most compelling fit for Multiview may be the iPhone Duo. The device’s unfolded display provides far more room than a standard iPhone, which makes four simultaneous video feeds more comfortable to watch. Each stream can occupy a meaningful portion of the screen while still leaving the others visible. That is the kind of use case that benefits from a larger canvas. A standard iPhone can handle Multiview, but the Duo can make it feel less cramped and more like a portable sports wall.

This is also a reminder that larger iPhone displays are not only about productivity. Media consumption remains one of the main reasons people want more screen area. Multiview turns the Duo into a small multi-window sports center. A viewer could keep the main race feed in one corner, a driver camera in another, a course map nearby, and a second event in the fourth window. The exact layout will depend on the sport and the available feeds, but the concept is clear. The Duo’s screen gives Multiview the space to be more than a novelty.

What the Feature Says About Apple’s Strategy

Multiview on iPhone shows how Apple tends to roll out features across its devices. A capability often starts on the TV, iPad, or Vision Pro, then expands as hardware and software mature. The iPhone version does not simply shrink the experience. It extends it to a device that people carry everywhere. That expansion can increase engagement with live sports and make the Apple TV subscription more valuable to existing users. It also raises expectations. Once viewers can watch four streams on a phone, they may want more sports, more camera options, and more customizable layouts.

Apple TV’s sports catalog is still growing. Formula 1, MLS, and MLB give the service a mix of global racing, domestic soccer, and baseball. Multiview makes that mix easier to sample. A subscriber might open a race and then keep an eye on a baseball game, or follow two MLS matches at once. The feature encourages longer viewing sessions and gives subscribers more reasons to open the app. It also makes the Apple TV app more competitive with other sports streaming services that have embraced multi-stream viewing.

Availability and Subscription Details

Multiview is available in iOS 27.2 for sports events included in an Apple TV subscription. The service costs $14.99 per month, and subscribers can also access it at a discount through the Apple One bundle. The feature does not appear to require a separate add-on or a special sports tier. It is part of the Apple TV app experience on iPhone, just as it has been on Apple TV, iPad, and Vision Pro. That simplicity is important. Viewers do not need to learn a new app or manage another subscription to use it.

The rollout also highlights how quickly the Apple TV app has evolved. It began as a place to watch Apple TV+ originals and rented or purchased content, and it has become a broader hub for streaming services and live events. Sports have accelerated that change. Multiview is one of the clearest examples of how the app is adapting to live content rather than treating everything like on-demand video. Live events have different needs: timing, simultaneous action, and the desire to follow more than one thing at once. Multiview addresses those needs directly.

What to Expect Next

The next question is how Multiview will develop across different sports. Formula 1 already shows a high degree of customization, with preset team views and custom layouts that include driver cameras and course maps. Other sports may adopt their own versions of that flexibility. Soccer could emphasize multiple matches or alternate camera angles. Baseball could offer views focused on different parts of the game. The exact implementation will likely vary, but the direction is clear: more control over what appears on screen and more ways to follow live action.

For now, the arrival of Multiview on iPhone makes a feature once associated with the living room available in a much more personal context. The iPhone 18 Pro test shows that four streams can work on a standard phone display, while the iPhone Duo’s unfolded screen points to an even more natural fit. The update turns the Apple TV app into a more capable portable sports viewer, and it gives subscribers a reason to explore the service on the device they always have with them.

Source: 9to5Mac News

Apple has introduced its first Mac with a touchscreen, marking a significant reversal from the position the company held for more than a decade. The move comes roughly 16 years after Steve Jobs publicly rejected the idea of putting touch on a Mac, arguing that vertical touch surfaces are ergonomically poor for extended use. Despite that history, Apple is not positioning touch as the main way to interact with the new machine. Instead, the company frames it as an additional option alongside the trackpad and keyboard.

Jobs made his view clear in 2010, when he explained that Apple had tested touch on computers and concluded it did not work well. He said touch surfaces do not want to be vertical, that they produce a great demo but cause fatigue after short periods, and that after extended use a user’s arm wants to fall off. He described the experience as ergonomically terrible and argued that touch surfaces want to be horizontal, which is why Apple focused on perfecting multi-touch trackpads.

Apple CEO John Ternus addressed the change directly. He said touch is intended to be a supplementary way of interacting with the Mac rather than a primary one. The Mac, he explained, is first and foremost an indirect input device built around the trackpad and keyboard. But there are times when it is comfortable and handy to have touch. He also suggested that touch is not the headline feature of the new model, describing it as one more capability among many: “All of these things, and, oh yeah, it has touch as well.”

Touch Remains Secondary to Trackpad and Keyboard

The distinction matters because the first touchscreen Mac could have signaled a fundamental redesign of how macOS works. Apple’s approach appears designed to avoid that outcome. The trackpad and keyboard remain the primary control mechanisms, while touch is available for tasks where reaching for the screen feels natural. That could include scrolling through long pages, tapping a large button, or interacting with certain media and creative apps. It does not mean the Mac has become a tablet or that macOS has been replaced by a touch-first operating system.

For long-time Mac users, the concern was never simply the presence of a touch layer. The deeper worry was that Apple might compromise the desktop interface to accommodate fingers. High-resolution interface elements, precise pointer control, and the density of information that defines macOS could have been replaced by larger, simplified controls designed for touch. That would have been a significant trade-off for users who rely on the Mac for detailed work. Apple’s stated approach suggests it is trying to add touch without making that trade-off.

That balance is not easy to achieve. Touch targets need to be larger than mouse targets, and direct manipulation on a vertical screen can strain the shoulder and arm. The trackpad, by contrast, allows users to rest their hands and make precise movements with minimal effort. It is also well suited to the small, dense controls found in professional applications. Apple has spent years refining trackpad gestures and pressure-sensitive input, and those refinements remain central to the Mac experience.

Designing touch into a Mac also requires changes to hardware and software that are not immediately visible. The display may need a touch layer that preserves image quality and thinness. The hinge must be stable enough to resist tapping. macOS must distinguish between intentional touches and accidental contact. App developers may need to consider whether their interfaces work with both a pointer and a finger. These are solvable problems, but they show why Apple would treat touch as a secondary input rather than a switch to a touch-first design.

Industry Context: Touchscreen Laptops Have a Mixed History

Apple is arriving late to a category that other PC makers have explored for years. Windows laptops, convertible tablets, and Chromebooks have offered touchscreens as optional or standard features for more than a decade. Some users appreciate the ability to tap and scroll directly on the display, especially in situations where a mouse or trackpad is inconvenient. Others rarely use the touchscreen after the initial novelty wears off, particularly on traditional clamshell laptops where the screen sits upright.

The ergonomic problem Jobs described has not disappeared. Reaching forward to touch a vertical laptop display can become tiring, and the screen may wobble when pressure is applied. Convertible designs address some of these issues by allowing the display to fold into a tablet-like position, but they also add complexity and cost. For many users, the trackpad remains the more comfortable and efficient option for most tasks. That helps explain why touchscreen laptops have not replaced non-touch models in the way some early predictions suggested.

Competitive pressure may also play a role. PC makers have used touchscreens to differentiate premium laptops, and convertible devices have popularized the idea of one device that can serve as both a laptop and a tablet. Apple has resisted that convergence for the Mac, preferring to keep the iPad and Mac separate. The first touchscreen Mac does not erase that separation, but it acknowledges that some customers want touch available in a traditional laptop.

Apple’s own experience with the iPad is relevant here. When the iPad is paired with a Magic Keyboard, it becomes a laptop-like device with both a trackpad and a touchscreen. In that configuration, many users still gravitate toward the trackpad for navigation and text selection, while using touch for scrolling or direct manipulation. That pattern aligns with Ternus’s description of trackpad first and touch sometimes. It also suggests that adding touch to a Mac does not automatically make touch the preferred input method.

The Generational Shift in Expectations

One reason Apple may have changed course is that user expectations have shifted. A large portion of the population has grown up with smartphones and tablets, where touch is the primary interface. For those users, a laptop without a touchscreen can feel incomplete or old-fashioned. The idea of reaching out and tapping the display is instinctive, even if the ergonomics of a vertical screen are not ideal for prolonged use.

Still, familiarity with touch does not necessarily override the physical realities of a laptop. Even users who expect touch may continue to rely on the trackpad for precision work and extended sessions. The trackpad allows for subtle cursor movements, multi-finger gestures, and precise selection without lifting the hands far from the keyboard. Touch can complement those interactions, but it is less suited to tasks that require accuracy or sustained input. Apple’s messaging acknowledges this hierarchy rather than pretending that touch will replace indirect input.

The company also has an incentive to avoid confusing its product lines. The iPad is Apple’s touch-first computing platform, while the Mac is its pointer-and-keyboard platform. Blurring that distinction too aggressively could undermine both. By treating touch as a supplementary feature on the Mac, Apple can appeal to users who want the option without abandoning the identity of the Mac. The first touchscreen Mac can therefore be seen as an expansion of the platform rather than a reinvention of it.

Apple’s software strategy will be closely watched. If macOS remains largely unchanged, touch may feel like a convenience rather than a transformation. If Apple introduces new touch-specific gestures or interface elements, it will need to do so without alienating mouse and trackpad users. The company’s public comments suggest the former path: touch is an option, not a new foundation. That is likely the safest way to introduce the feature to a user base that values consistency and precision.

What the First Touchscreen Mac Signals

The launch signals that Apple is willing to revisit old decisions when technology, user habits, or competitive pressure change. It also shows that the company is trying to learn from the broader PC market without simply copying it. Touchscreen laptops have existed for years, but Apple’s implementation appears focused on preserving the strengths of macOS and the Mac’s input system. That is a more conservative approach than some observers might have expected, but it is consistent with Apple’s preference for controlling the user experience.

There are practical questions that will matter in real-world use. How often will users actually reach for the screen? Will touch be supported across macOS and third-party apps, or will it be limited to certain interactions? How will Apple handle palm rejection, screen durability, fingerprints, and battery impact? These details will determine whether touch feels like a useful addition or an unnecessary cost. Apple has not framed touch as a reason to buy the new Mac, which suggests the company sees it as a bonus rather than a core selling point.

The first touchscreen Mac may not satisfy everyone. Some users will see it as overdue, while others will continue to prefer the simplicity of a non-touch display. But the approach addresses the central tension in the debate: how to add touch without compromising the Mac’s precision and ergonomics. Apple is not asking users to abandon the trackpad or keyboard. It is offering touch as another option, available when it makes sense and easy to ignore when it does not.

For now, the company’s position is that the Mac remains an indirect-input device first. Touch is an addition, not a replacement, and the trackpad and keyboard continue to define the experience. That may be the most important fact about the first touchscreen Mac: Apple has changed the hardware without changing the fundamental interaction model.

Source: 9to5Mac News

That may sound like a small catalog detail. It is not. For anyone whose priority is throughput rather than display technology, the decision preserves the option to spend money on memory and storage instead of panel engineering — and it quietly reverses a habit Apple established nearly a decade ago.

The essentials at a glance

• The new MacBook Pro with PROLED XDR Display adds touch input and begins shipping November 6.
• It carries a premium of roughly $400 over the equivalent non-touch configuration.
• Chip for chip, it matches the non-touch M5 Pro and M5 Max models Apple will continue to sell.
• New hardware includes a tandem OLED panel, a thinner and lighter chassis, slimmer bezels, a redesigned audio system, Dynamic Island and the return of the glowing Apple logo.
• Apple has redesigned the thermal system, though the changes appear aimed at keeping the display cool rather than improving sustained chip cooling.

What actually changed on the new model

The list of upgrades is substantial, and it is worth stating plainly before getting to the pricing argument. The touchscreen MacBook Pro moves to a tandem OLED display under the PROLED XDR branding, a panel architecture that stacks two light-emitting layers to boost brightness and extend longevity compared with a single-layer OLED. The machine is thinner and lighter than the model it sits above, bezels have been trimmed, and the speaker system has been reworked. Dynamic Island arrives on the Mac for the first time, a design language import from the iPhone that will divide opinion but undeniably modernizes the top of the display. And yes, the glowing logo is back on the lid.

Then there is touch itself. Whether a touchscreen belongs on a laptop remains a legitimate debate — reaching across a keyboard to tap a menu is not everyone's idea of ergonomics, and fingerprints on a glossy OLED panel are a real cost. But for a large group of users, from illustrators to students annotating documents to people who simply find trackpad gestures slower than a direct tap, the addition is meaningful. Their day has arrived, or will on November 6.

Parity in performance, premium in price

Here is where the launch gets interesting. Despite expectations that Apple might sweeten the deal with a performance bump to justify the extra spend, the touchscreen model is configured with the same silicon as the non-touch line. There is no exclusive chip, no additional GPU cores reserved for the expensive version. The $400 buys display, design and input — not speed.

That framing matters because the premium is otherwise defensible. Tandem OLED is expensive to manufacture, a thinner chassis requires new tooling, and a touch layer adds cost and complexity to the panel stack. Judged purely as a hardware bill of materials, the upcharge is not unreasonable. Judged as a value proposition for someone who needs more compute per dollar, it is a harder sell.

A break from the Touch Bar playbook

The contrast with 2016 is stark. When Apple introduced the Touch Bar, the only model left without it was the entry-level 13-inch MacBook Pro. Buyers who wanted a faster processor, a larger screen or more ports had no choice but to accept the OLED strip and pay for it — roughly $300 more for the 13-inch Touch Bar configuration, or about $400 for the redesigned 15-inch model.

That structure held for years. Professionals who disliked the Touch Bar, who missed physical function keys, or who simply resented paying for an input method they never used had no high-performance escape hatch. The 16-inch redesign in 2019 restored a physical Escape key but kept the strip, and it was not until the 2021 14-inch and 16-inch models that the full row of function keys returned. Anyone who bought a pro laptop during that window paid the premium whether they wanted the feature or not.

This time, Apple did the opposite. The older design stays in the lineup at its original positioning, and the new design sits above it as an optional step up. That is a meaningful departure, and it is the single best decision of the week.

Why keeping the older models matters

Three things follow from the decision. First, price transparency: buyers can now see exactly what the touchscreen, the OLED panel and the thinner chassis cost, and decide whether those things are worth $400 to them. Second, risk management: tandem OLED and a touch layer in a Mac are new, and anyone wary of first-generation technology can sit this one out without giving up performance. Third, supply reality: a brand-new panel type rarely ramps smoothly, and maintaining a parallel line insulates buyers from launch-window availability problems.

There is also a practical argument about repairability and longevity. A display with an added touch layer is a more complex assembly, and complexity tends to raise the cost of any future repair. Buyers who plan to keep a machine for five or six years may reasonably prefer the simpler panel.

The thermal question, unresolved

One caveat deserves attention before anyone assumes the new model is strictly slower or strictly equal. Apple has redesigned the thermal system, and in principle better cooling can let the same chip sustain higher clocks for longer. Early indications suggest the changes are oriented toward keeping the display cool rather than maximizing heat dissipation from the SoC, which would limit any real-world gain. Until independent reviews measure sustained workloads, the honest answer is that performance is probably identical in short bursts and possibly marginally different under prolonged load. Buyers should not count on a bonus.

The upgrade math

For anyone coming from an M2 Pro machine with 16GB of unified memory, the calculus is familiar and uncomfortable. Unified memory cannot be added later, so a laptop that felt generous in 2023 can feel cramped once browser tabs, a photo library, a virtual machine and a few background utilities are all resident at once. The cheapest fix is not a new display technology; it is more RAM and more storage at the time of purchase.

Taking the $400 that would have gone to touch and applying it to memory or SSD capacity changes the useful life of the machine far more than a brighter panel does. That is the trade Apple has now made possible, and it is the reason the lineup decision outweighs every other announcement this week.

Who should buy which

The segmentation is unusually clean. Buyers who want touch, who work in bright environments where a tandem OLED panel earns its keep, or who simply want the lightest and most modern chassis should choose the new model and accept the premium as the cost of the design. Buyers who are upgrading for headroom — more memory, more storage, more years of useful service — should take the non-touch M5 Pro or M5 Max, put the savings into the configuration, and never think about it again.

There is a third group worth naming: people who have been waiting for the perfect moment. Waiting is exactly how a 2023 purchase turns into a 2026 dilemma, and prices have not been trending downward in the meantime. At some point the specification sheet stops being the deciding factor.

A pattern Apple rarely follows

Selling two generations of the same product side by side is not unprecedented for Apple — the M1 MacBook Air remained on sale alongside its successor for years as a value tier, and the iPhone SE has long played a similar role. But Apple has historically been reluctant to run parallel MacBook Pro lines with different industrial designs. The company prefers a clean cut: new model in, old model out, take it or leave it.

Keeping the previous design as an explicit alternative is therefore a small but genuine shift in approach, and one that treats the pro laptop market as what it is — a group of buyers with wildly different priorities, some of whom care about a touchscreen and many of whom care only about how many cores and how much memory they can get for the money.

Reviews of the new display and the revised thermal design will land before long, and they will settle the question of whether the touchscreen model earns its premium on merit. What they will not change is the structural fact of this launch: for the first time in years, Apple is letting customers buy the performance without also buying the experiment.

Source: 9to5Mac News

Headline and key facts

Headline: MacBook Pro with PROLED XDR vs MacBook Pro: Here's every difference

• The MacBook Pro with PROLED XDR joins the standard MacBook Pro and uses the same M5 Pro and M5 Max chips.
• The PROLED XDR model adds a touch-enabled tandem OLED display with 2,000-nit peak brightness, Adobe RGB support, and reference modes.
• The Dynamic Island replaces the notch and supports Spotlight, Live Activities, and Siri AI.
• The new design is thinner and lighter, with the 14-inch model at 3.0 pounds and the 16-inch model at 4.0 pounds.
• Memory options are unchanged: M5 Pro starts at 24GB with 48GB and 64GB options; M5 Max starts at 36GB with 48GB, 64GB, and 128GB options.
• Storage is mostly unchanged, but M5 Max buyers can now choose 16TB.
• Battery life and fast charging are identical across both models.
• The camera adds Advanced Video Stabilization on the PROLED XDR model because of touchscreen support.
• The speaker system gets a new woofer design, new tweeters, and closed-lid audio tuning.
• Ports are the same, with Genlock support added to Thunderbolt 5.
• Pricing starts at $2,899 for the 14-inch PROLED XDR and $3,399 for the 16-inch, a $400 premium over the standard model.

Design, size, and weight

The PROLED XDR model introduces the first major MacBook Pro design overhaul since 2021. The chassis is sleeker, and Apple has restored the glowing Apple logo on the lid. The hinge has also been redesigned for greater stability. These changes are visible, but the most important difference is weight. The 14-inch PROLED XDR model weighs 3.0 pounds, compared with 3.5 pounds for the standard 14-inch MacBook Pro. The 16-inch PROLED XDR model weighs 4.0 pounds, compared with 4.7 pounds for the standard 16-inch model. Those reductions are substantial for a pro laptop, especially for users who carry a machine between offices, studios, or travel locations.

Dimensions are close but not identical. The 14-inch model uses a 14.2-inch display in both versions. The 16-inch PROLED XDR is listed at 16.3 inches, slightly larger than the standard 16.2-inch panel. Height, width, and depth are slightly reduced on the PROLED XDR model. The 14-inch PROLED XDR measures 0.59 inch high, 12.24 inches wide, and 8.52 inches deep. The standard 14-inch model measures 0.61 inch high, 12.31 inches wide, and 8.71 inches deep. The 16-inch PROLED XDR measures 0.63 inch high, 14.0 inches wide, and 9.62 inches deep. The standard 16-inch model measures 0.66 inch high, 14.01 inches wide, and 9.77 inches deep. The footprint difference is minor, but the weight difference should be obvious in everyday use.

Display: touchscreen PROLED XDR vs mini-LED

The display is the main reason the new model exists. The MacBook Pro with PROLED XDR uses a tandem OLED panel that Apple describes as the world's best laptop display. It is available in 14.2-inch and 16.3-inch sizes. The standard MacBook Pro continues to use a mini-LED display. OLED brings deeper blacks because individual pixels can turn off. It also delivers strong contrast and true-to-life color. The PROLED XDR model raises peak brightness to 2,000 nits, up from 1,600 nits on the standard model. It adds Adobe RGB support and a range of industry-standard reference modes, which matters for photographers, video editors, and designers who need accurate color in different workflows.

The PROLED XDR display is also touch-enabled. macOS 27 includes optimizations for touch input, so the feature is not limited to hardware. That makes the new model the first MacBook Pro in this lineup to offer a touchscreen. The Dynamic Island replaces the notch and hosts software features such as Spotlight, Live Activities, and Siri AI. The move from a notch to a Dynamic Island changes the top of the display and gives macOS a more flexible area for status and controls. For users who have wanted touch on a Mac, the PROLED XDR model is the only way to get it in this comparison. For users who prefer a traditional notebook experience, the standard model still offers the familiar mini-LED panel and notch.

Performance and memory: same M5 Pro and M5 Max chips

Apple is using the same M5 Pro and M5 Max chips in both MacBook Pro models. That means the PROLED XDR version does not have a performance advantage on paper. The chip options, core configurations, and memory tiers are shared. Real-world testing will show whether the new chassis affects sustained performance. A thinner and lighter design can change thermal behavior, so benchmark results and long-duration workloads will be important. On paper, however, buyers are choosing between the same compute platforms.

Memory options are identical. M5 Pro models start at 24GB of RAM, with options for 48GB and 64GB. M5 Max models start at 36GB of RAM, with options for 48GB, 64GB, and 128GB. That keeps the decision focused on the display and design rather than on memory capacity. For heavy multitasking, large photo and video projects, software development, and data work, the same memory configurations are available on either model.

Storage: a new 16TB option for M5 Max

Storage is nearly identical, with one exception. M5 Pro models start at 1TB, with options for 2TB and 4TB. M5 Max models start at 2TB, with options for 4TB, 8TB, and 16TB. The 16TB option is new and is only available with the M5 Max. Previously, 8TB was the highest storage capacity available in a MacBook Pro. The change matters for users who work with large video libraries, local archives, or extensive project files. For most buyers, the standard storage tiers will remain sufficient, but the 16TB option gives the PROLED XDR model an additional configuration advantage at the top end.

Battery and charging: identical in every way

Battery life and charging are unchanged between the two models. The 14-inch MacBook Pro offers up to 22 hours with M5 Pro or up to 20 hours with M5 Max. The 16-inch model offers up to 24 hours with M5 Pro or up to 22 hours with M5 Max. All models can fast charge to 50 percent in 30 minutes. The PROLED XDR display may have different power characteristics in real-world use, but Apple's stated battery figures are the same. Buyers who need the longest possible runtime will not see a spec-sheet difference between the standard and PROLED XDR versions.

Camera: Advanced Video Stabilization

Both models use a 12MP Center Stage camera with Desk View, 1080p HD video recording, and an advanced image signal processor with computational video. The PROLED XDR model adds Advanced Video Stabilization because of touchscreen support. The feature is designed to prevent touches on the display from affecting the camera feed during video calls. That is a small but practical addition for users who interact with the screen while on a call. It also shows how touch input changes the software and hardware design beyond the display itself.

Speakers: new woofer design and closed-lid tuning

The PROLED XDR model keeps the six-speaker audio system but upgrades key components. Apple says the new woofer design provides 50 percent deeper bass, and new tweeters shape sound dispersion for clarity. The system also adds dedicated audio tuning for closed-lid use. That means when the MacBook Pro is connected to an external display, the speakers can still deliver a premium audio experience. The change is useful for desk setups where the laptop is closed and external monitors are in use. The standard model retains the existing speaker system.

Ports: same connections, plus Genlock

The port selection is the same across both models. Buyers get an SDXC card slot, an HDMI port, a 3.5 mm headphone jack, a MagSafe 3 port, and three Thunderbolt 5 USB-C ports. Those Thunderbolt 5 ports support charging, DisplayPort, Thunderbolt 5 up to 120Gb/s, and USB 4 up to 120Gb/s. One upgrade benefits some professional workflows: Genlock is now supported by the Thunderbolt 5 ports. Genlock is useful for synchronized video production and multi-camera setups. The addition is small in the spec list but relevant for users in video and broadcast work.

Pricing: a $400 premium

The MacBook Pro with PROLED XDR starts at $2,899 for the 14-inch model and $3,399 for the 16-inch model. The standard MacBook Pro starts at $2,499 for the 14-inch model and $2,999 for the 16-inch model. That makes the PROLED XDR version a $400 premium in both sizes. The premium buys the tandem OLED touchscreen, Dynamic Island, lighter chassis, glowing logo, redesigned hinge, upgraded speakers, Advanced Video Stabilization, and Genlock support. It does not buy more CPU or GPU performance, more memory, longer battery life, or additional ports beyond the Genlock capability. The value depends on how much the display and touch experience matter to the buyer.

Pre-orders are open for the new model, with launch-day delivery available through Apple's retail channels. The standard MacBook Pro remains available at its existing starting prices. For buyers comparing the two, the decision comes down to whether a better display, touch input, and a lighter design are worth $400. For buyers who mainly need M5 Pro or M5 Max performance, the standard model offers the same chips, memory, battery life, and port selection. The PROLED XDR model is positioned as the display-focused upgrade rather than a performance-focused one.

Source: 9to5Mac News

The appeal is easy to understand. The Duo already looks like a small book when closed and opens into a tablet-like inner screen. Add a Pencil, and the phone can be used for handwritten notes, quick sketches, grocery lists, and article ideas. The experience is not a full iPad replacement, and it is not meant to be. It is a different way to use a phone, one that leans toward creation rather than consumption. That alone makes it worth examining.

Key facts at a glance

• The iPhone Duo is Apple’s $2,000 folding phone, and it is the first iPhone to support the Apple Pencil.
• Support arrives through the iOS 27.2 update and works with the USB-C Apple Pencil, which lacks pressure sensitivity.
• The native Notes app supports handwriting, auto-refine handwriting, and scribble-to-text input in apps such as Messages and Safari.
• Few third-party apps are optimized for the new stylus support, but the $4.99 TypeDraw app is built for the Duo.
• TypeDraw turns the cover screen into the front of a notebook, lets users flip pages by swiping or opening the phone, and adds a table of contents based on note headings.
• The Pencil can attach magnetically to the Duo’s hinge when the phone is closed, though it is longer than the phone and makes an awkward fit.
• The inner screen’s matte finish provides a bit of resistance that feels better for writing than the cover screen’s plastic-on-glass feel.
• Handwriting refinement can misinterpret messy writing and works after a short delay, which may interrupt flow.

A familiar notebook fantasy

The notebook is one of technology’s most persistent fantasies. People buy fresh notebooks with the belief that this one will be different. This time, they will carry it everywhere. This time, inspiration will be captured before it disappears. This time, the pages will fill up with ideas instead of remaining blank. The same pattern applies to new gadgets. A new phone, a new tablet, or a new stylus promises a version of the user who is more focused, more creative, and less distracted.

The Duo fits that pattern almost too well. It is expensive, distinctive, and physically different from a standard slab phone. When people see it, they ask if it is the new iPhone. They want to hold it. They want to know whether it is worth the money. The short answer is that it is nice, it is kind of heavy and chunky, and most people should probably wait for the second generation. The longer answer is that the folding form factor creates real opportunities for different kinds of use, and the Pencil adds one more.

That does not mean the Duo is a notebook. It is still a phone, with all the attention-stealing machinery that implies. Notifications, messages, feeds, and apps are always nearby. But the physical shape invites a different posture. Opening the Duo feels a little like opening a book. Writing on the inner screen with a stylus feels a little like writing in a notebook. The illusion is not perfect, but it is strong enough to be useful.

What the Apple Pencil actually adds

The USB-C Apple Pencil is not the most advanced stylus Apple sells. Because it lacks pressure sensitivity, it cannot vary line weight based on how hard the user presses. Artists who depend on pressure-sensitive drawing will notice the difference immediately. But for handwriting, note-taking, and casual doodling, the basic stylus is enough. The Pencil still supports tilt and palm rejection, and it can be used for scribble input, where handwriting is automatically converted into text.

In apps like Messages or Safari, scribble input works much as it does on an iPad. Users can write in a text field and watch their handwriting turn into typed text. The feature is not flawless, but it is convenient for short messages and quick searches. In the Notes app, the Pencil is at its best. Users can write by hand, enable auto-refine handwriting, and keep the character of their writing while making it neater. The feature has been available on the iPad since 2024, and it is particularly useful on the Duo’s comparatively small screens.

Auto-refine is not magic. It occasionally misreads messy handwriting, and it can turn one letter into another. In one instance, it kept insisting on turning a “u” into an “o.” It also works after a small delay when the user pauses writing. That delay can be distracting for someone trying to get into a flow state. But as a middle ground between untidy scribbles and full handwriting-to-text conversion, it is handy for things like grocery lists, quick notes, and short reminders.

The Notes app is good, but TypeDraw is better

The native Notes app also has a large home-screen widget that can make the phone feel more like a notebook, but it is not especially nice to look at. After some digging, a better option emerges: TypeDraw. The app costs $4.99 as a one-time purchase and is optimized for the Duo. When launched, it turns the cover screen into the front of a notebook. To flip to a page, users can either swipe on the cover screen or open the whole phone. It is a small design choice that makes the experience feel more physical.

TypeDraw is not aimed at power note-takers. Users cannot add tags or links between notes, and they are limited to one notebook. Compared with apps like Bear, it is intentionally simple. But it automatically creates a table of contents at the beginning based on the headings of notes, so users can skip to the section they need. It also adds a tactile detail: if users drag a finger along the elastic on the cover, it stretches and snaps back, complete with satisfying haptics. For five dollars, it is a surprisingly complete idea.

The app is not the only way to use the Duo as a notebook, but it is the clearest expression of the concept. It treats the phone’s folding design as an advantage rather than a gimmick. The cover screen becomes a cover. The inner screen becomes a page. The Pencil becomes a pen. The result is not a perfect notebook, but it is a coherent one.

Hardware feel and hinge magnetism

Using the Apple Pencil with the Duo also highlights the inner screen’s matte finish. There is a bit of resistance when moving the pencil across the display, and that resistance feels more satisfying than the lifeless plastic-on-glass effect of writing on the cover screen. It is not going to feel like writing on a Remarkable tablet or an e-ink device, but it is nice. The difference is subtle, yet it matters for anyone who spends time writing by hand.

The Pencil has one other neat trick. It can stick to the Duo’s hinge magnetically when the phone is closed. The pencil is longer than the phone, so it is an awkward fit. But it has been a handy way to carry both around the house while moving from room to room. It is not a elegant solution, but it is practical. In a device that is already trying to solve portability in a new way, that counts for something.

The minimalist home-screen experiment

The Pencil also prompted a larger experiment: turning the whole phone into a notebook. The logic was simple enough. The Duo is notebook-shaped. Notebooks are nice and help people express creativity. Phones are bad attention-stealing machines, or something. Maybe the Duo could be cajoled into being something less distracting and more input-friendly. A minimalist home screen and lock screen were designed around that theory. The result was pretty nice, but it did not fully escape the new-notebook trap.

Even so, the exercise was worthwhile. It raised a question that applies to almost every phone: how much stuff is really needed on the home screen? Most tasks can be handled with a search or a tap in Control Center. Voice assistants are more helpful than they used to be. The grid of apps that people are so used to is mostly for emotional support at this point. A folding phone is not required to learn that lesson, but it helps.

The experiment also revealed the limits of the notebook metaphor. A phone is still a phone. The notifications still arrive. The apps are still there. The new-notebook effect eventually fades into the background. The shiny new thing becomes ordinary. That is not a failure of the Duo or the Pencil. It is simply how newness works.

Limits, caveats, and the broader context

The biggest limitation is app support. There are not many apps ready to take advantage of the new stylus support. The Notes app is the most polished experience, and TypeDraw is a standout third-party option. Beyond that, the ecosystem is thin. That is typical for a new input method on a new form factor. Developers need time to experiment, and users need a reason to care. The Duo’s price makes that adoption curve even steeper.

The second limitation is the Pencil itself. The USB-C Apple Pencil is a basic stylus. It lacks pressure sensitivity, and it is not designed specifically for the Duo. It works, but it does not feel like a bespoke accessory. The magnetic attachment to the hinge is clever, but awkward. The writing experience on the inner screen is good, but not remarkable. These are not deal-breakers for note-taking, but they matter for anyone expecting an iPad-like experience.

The third limitation is attention. A notebook is supposed to be a place for focused thought. A phone is designed to pull attention in many directions. The Duo can imitate a notebook, but it cannot fully escape its nature. The minimalist home screen helps, and the shortcut to launch TypeDraw when opening the inner screen helps more. But the user still has to choose to write instead of scroll. The device can encourage that choice, but it cannot make it.

Those caveats are worth keeping in mind because the notebook fantasy is powerful. People want to believe that a new tool will change their habits. Sometimes it does, at least for a while. But a $2,000 phone is not necessary to reevaluate a home screen. A $20 notebook and a regular phone can do the same thing. A fun new gadget is delightful, but it should not carry all the hopes and dreams for a new self.

Still, the Duo and Pencil make a convincing case for a different kind of mobile device. The folding screen creates a physical relationship with the phone that a slab does not. The stylus adds an input method that feels more deliberate than tapping. The combination is not for everyone, and it is not fully mature. But it is a meaningful step toward a phone that can be used for creation as well as consumption. For anyone who has ever bought a fresh notebook and imagined a different version of themselves, that is a familiar and appealing promise.

Source: The Verge News

Reddit is preparing to require users to log in before they can browse the platform, a major departure from nearly two decades of relatively open access. CEO Steve Huffman described the move as part of a shift toward a logged-in-only experience, saying the company plans to test the requirement in select markets outside the United States in the coming months. The company has not provided a definitive timeline for a broader rollout, nor has it fully explained what logged-out visitors will see when they follow a link to a discussion. The announcement came alongside third-quarter 2026 earnings and underscores how aggressively Reddit is rethinking the balance between public accessibility and control over its data.

Key Facts at a Glance

• Reddit plans to test a login requirement in select markets outside the US in the coming months.
• CEO Steve Huffman says the change will create a more protective, logged-in-only experience.
• The company has not set a full rollout timeline or detailed how logged-out users will experience shared links.
• Reddit says most unauthorized access occurs through logged-out experiences.
• Reddit will continue to pursue data licensing deals with companies.
• Reddit says app and logged-in daily active unique users account for over 80 percent of monetization.
• Search traffic fell meaningfully in the third quarter of 2026, with logged-out and web traffic most affected.
• Huffman says search traffic dropped by more than 1 million referrals on average per week in October.
• Reddit has rebuilt and is rereleasing the app incognito mode.

Why Reddit Is Making the Change

Reddit has long been a destination for people seeking human answers to highly specific questions. Its threads surface in search results for product advice, technical fixes, travel tips, health experiences, and local recommendations. That visibility has made the platform a valuable repository of crowdsourced knowledge, but it has also made Reddit a target for scraping and automated traffic. The company has filed lawsuits over alleged unauthorized access to its data and has made product changes such as limiting access to Old Reddit. Huffman argues that the login requirement is better for the user experience, better for Reddit's advertising business, and more protective of Reddit data.

The company says the vast majority of unauthorized Reddit access happens through logged-out experiences. By requiring login, Reddit can reduce anonymous automated requests while still allowing licensed access to its data through commercial agreements. Reddit has struck data licensing deals with companies including OpenAI and Google in 2024, and Huffman indicated that such deals will continue. The distinction Reddit is drawing is between logged-in human users and logged-out automated traffic. Huffman also noted that Reddit does not need to know a logged-in user's identity, adding that the company does not want to know it. The requirement, in his framing, is about authentication status rather than real-name identity.

A Test Beginning Outside the United States

Reddit is not announcing an immediate global login wall. Instead, the company will begin testing the requirement in select markets outside the US in the coming months. Huffman said Reddit will examine the impact on the user experience, on its business, and on the broader Reddit ecosystem before deciding where the right boundaries are. That cautious language leaves several important questions unanswered. It is not clear which countries will be included in the first tests, how long they will run, or what criteria Reddit will use to judge success.

The most immediate practical question is what happens to shared links. Reddit threads are frequently sent between friends, posted in group chats, cited in articles, and surfaced in search results. If a recipient is not logged in, the platform must decide whether to show a preview, a login prompt, a truncated discussion, or no content at all. Reddit has not detailed that experience. The company has also rebuilt and is rereleasing the Reddit app incognito mode, a feature that may be intended to preserve some privacy for logged-in users even as the platform becomes more account-centric.

Scraping, AI, and the Fight Over Open Data

Reddit's data has become increasingly valuable in the age of generative artificial intelligence. Large collections of human-written discussion are useful for training and evaluating AI systems, and Reddit has been explicit that it wants to control how that material is accessed. The company's 2024 licensing deals with OpenAI and Google showed that Reddit is willing to make data available through paid agreements. A login requirement could tighten the perimeter around unlicensed access while preserving the company's ability to strike commercial deals.

That strategy reflects a broader tension between the open web and the platform economy. For years, Reddit benefited from being publicly readable: search engines could index it, casual visitors could land on threads, and links could circulate freely. Those same properties made it easy for automated systems to collect content at scale. By requiring login, Reddit is choosing to prioritize control and monetization over the frictionless access that helped it become a major destination. The company insists that it is not trying to identify individual users, but a login wall still creates a record of who is present and what they view.

Search Traffic and the Business Case

Reddit is making the announcement alongside its third-quarter 2026 earnings, which were released the same day. Huffman said in the shareholder letter that search traffic was down meaningfully in the quarter, with logged-out traffic and web traffic the most heavily affected. He added that during October, search traffic had dropped by more than 1 million referrals on average per week. Those figures help explain why Reddit is willing to experiment with a login requirement even though it could further reduce casual web visits.

Huffman also noted that app and logged-in daily active unique users make up over 80 percent of the monetization on Reddit. That statistic is central to the company's case. If most of Reddit's revenue already comes from logged-in users and app users, then the company may see logged-out web traffic as less valuable than it once was. Logged-in users can be served more relevant ads, according to Huffman, and they provide more consistent engagement data. The login requirement could therefore be framed as a way to concentrate Reddit's business on its most monetizable audience while reducing exposure to the volatility of search referrals.

What Logged-Out Visitors Might See

Reddit has not said exactly how the logged-out experience will work. In some scenarios, visitors could see a login prompt before any thread content appears. In others, Reddit could allow a limited preview before asking for an account. Search engines and link previews would also need a clear policy. If Reddit blocks all logged-out access to thread content, it could reduce the site's usefulness as a public reference and weaken the network effects that come from casual sharing.

The change could also affect people who rely on Reddit without participating. Lurkers, researchers, privacy-conscious readers, and users who prefer not to create accounts may face new barriers. Some may create accounts simply to read, which could expand Reddit's registered user base. Others may abandon the platform or rely on screenshots and summaries posted elsewhere. The net effect will depend on how much friction Reddit introduces and whether it preserves enough public content to remain discoverable through search.

Privacy, Identity, and Ad Targeting

Huffman's claim that Reddit does not need to know a user's identity is meant to soften privacy concerns. A login requirement does not necessarily mean real-name verification, and Reddit can authenticate users without demanding government identification. Even so, logging in changes the privacy calculus. A persistent account can be tied to browsing history, subscriptions, voting patterns, and advertising identifiers. Reddit says logged-in usage allows more relevant ads, which implies that account-level data will feed the advertising system.

The incognito mode in the Reddit app may offer a partial counterweight by letting logged-in users browse without saving certain activity. But incognito modes often have limits, and they do not change the underlying requirement that a user be authenticated. The policy therefore creates a trade-off: Reddit gains a stronger signal that a visitor is a real person rather than a bot, while users lose some of the anonymity that comes with logged-out browsing.

Impact on Communities and the Open Web

Reddit's communities are built on a mix of public conversation and pseudonymous participation. Many users value the ability to read threads without joining, and many smaller communities depend on search traffic and shared links to attract new readers. A login wall could protect Reddit from scraping, but it could also make the platform less visible and less useful to people who are not already part of its ecosystem. The tension is especially sharp because Reddit has positioned itself as a place where real human experience can be found amid a web increasingly filled with automated content.

The company has been through similar debates before. In 2023, changes to its application programming interface sparked widespread protests from moderators and developers who said the platform was restricting access to its data and tools. Reddit went public in 2024, increasing pressure to show advertisers and investors that it can control its content and grow revenue. The login requirement fits into that longer shift from a freewheeling forum to a more managed, monetizable platform with clearer boundaries around its data.

Questions That Remain

Several questions will shape how the policy is received. Will Reddit allow search engines to index public threads, or will it require login for all web access? How will shared links work for recipients without accounts? Will the tests be limited to a few countries or expand quickly? What data will Reddit collect from logged-in users, and how will it be used for advertising? Will communities see changes in moderation, participation, or audience growth? Reddit has said it will study the impact before going further, but it has not provided a detailed roadmap.

The test will be watched closely as a signal of whether login walls can protect platform data without eroding the open-web discovery that helped Reddit grow. The company is betting that its most valuable users are already logged in, and that stricter access will strengthen its advertising business and data licensing efforts. The outcome will depend on how Reddit balances those goals against the casual readers, shared links, and public conversations that have long made the platform a fixture of the internet.

Source: The Verge News

• OpenAI CEO Sam Altman argued that scarce DRAM is better concentrated in data centers than distributed across personal computers that use it only occasionally.
• He said local inference advocates overlook the global allocation problem: memory sitting idle on a desk is memory unavailable to shared cloud workloads.
• Altman’s comments come as AI infrastructure projects and cloud operators lock up long-term memory supply, tightening the market for laptops, phones, and other devices.
• The debate pits local AI’s privacy, latency, and offline benefits against the cloud’s higher utilization and economies of scale.

Altman’s argument: idle memory is wasted memory

OpenAI CEO Sam Altman has a blunt message for anyone who assumes that running artificial intelligence models locally will relieve pressure on the world’s memory supply: the math does not work. In a recent podcast interview, Altman argued that it is “terrible” for a large share of the world’s limited DRAM supply to sit inside personal computers that use it only a small fraction of the time. From his perspective, scarce memory should be concentrated in data centers where it can be shared, scheduled, and kept busy.

Altman’s comment was prompted by a question about supply chain shortages, including the ongoing RAM shortage. He pushed back against a common assumption in the local AI community: that open-source models and more capable consumer hardware will solve the problem by letting people run models on their own machines. Altman said many people assume that because open-source models are improving, the need for data-center capacity will fade. He does not see it that way. In a supply-constrained world, he argued, the priority should be maximum utilization of the memory that exists.

The core of his case is an economic and logistical one. A personal computer may have a large amount of DRAM, but most of that memory sits idle most of the time. A data center can pool memory across many users, applications, and workloads. When one job finishes, another can take its place. That sharing model can keep expensive memory busy for a much larger share of the day. Altman’s point is not that local inference is technically impossible. It is that local inference can be inefficient from a global resource-allocation standpoint when DRAM is scarce.

Why utilization matters in a memory-constrained market

Memory is not a resource that can be expanded overnight. DRAM production requires fabs, equipment, engineering talent, and years of capital investment. When demand surges, suppliers can try to shift capacity toward the highest-value products, but they cannot instantly create more output. That is why moments of tight supply tend to expose deeper questions about who gets memory, how it is used, and whether the market is allocating it efficiently.

Data centers are designed around utilization. Cloud operators buy memory in large volumes and spread it across many customers. They can oversubscribe certain resources, move workloads between machines, and balance demand across regions. A desktop or laptop, by contrast, is a single-user device. Even a powerful machine may spend most of its life handling email, web browsing, video calls, and light applications. Its memory capacity is sized for peak use, not average use. That is a rational design for an individual owner, but it looks wasteful when the world is short of memory.

Altman’s argument also reflects the economics of AI infrastructure. Large models require enormous amounts of memory bandwidth and capacity, and AI companies are competing for the same constrained supply chain as device makers. The more memory that goes into personal systems, the less is available for servers, cloud platforms, and training clusters. The more memory that goes into shared data centers, the more users can potentially be served per unit of hardware. That trade-off becomes especially sharp when AI demand is rising faster than memory production.

The RAM squeeze is already reshaping prices

The context for Altman’s remarks is a global memory shortage that has been building as AI infrastructure spending accelerates. Memory makers have signed long-term deals with cloud service operators and large AI projects, reserving substantial portions of future output. Those agreements can provide suppliers with predictable demand and buyers with secure supply, but they also leave less room for the broader consumer electronics market.

The effects are not limited to high-end servers. Memory is a foundational component in laptops, phones, tablets, game consoles, and many other devices. When suppliers prioritize data-center customers who can pay more and commit to long-term contracts, consumer device makers may face higher prices, tighter allocations, or both. That is why the RAM shortage is not just a data-center story. It can show up in the price of a new laptop, the configuration options on a phone, or the availability of a particular product.

Altman’s comment that memory should not sit on desks is therefore more than a technical preference. It is a statement about how a scarce input should be allocated during a boom. In his view, the cloud offers a way to squeeze more value from each unit of memory because it can serve many users from a shared pool. Critics of that view might argue that local hardware gives users control, reduces dependence on networks, and keeps sensitive data on-device. Both sides are weighing different kinds of efficiency: resource efficiency versus user-level resilience and autonomy.

Local AI versus cloud AI: a debate about trade-offs

The local AI movement has gained momentum because it promises things the cloud cannot always match. Running a model on a personal device can reduce latency, work without an internet connection, and keep private data from leaving the machine. For some users, those benefits are worth paying for. For developers, local hardware can be a sandbox for experimentation. For businesses with strict data rules, on-device or on-premises inference may be the only acceptable option.

But local AI also has a utilization problem. A user who runs a model intermittently may own hardware that is idle for most of the day. Multiply that across millions of users, and the aggregate amount of underused memory becomes significant. Cloud providers can smooth out demand across a much larger customer base. They can also upgrade hardware more efficiently and retire older systems on a planned schedule. That does not make cloud AI automatically better for every use case, but it does explain why Altman is focused on aggregate utilization rather than individual convenience.

There is also a difference between inference and training. Training large models is overwhelmingly a data-center activity because it requires massive clusters and tight coordination. Inference, the process of running a trained model to answer a query, is more flexible. Some inference can happen on devices, especially for smaller models or specialized tasks. But the largest and most capable models still tend to require substantial server resources. Altman’s argument is strongest when applied to the biggest models and the most constrained memory products.

What the argument means for the memory market

The memory industry has a long history of cycles. Periods of shortage encourage investment, but new capacity takes time to come online. When demand outruns supply, prices rise and buyers compete for allocation. AI has added a powerful new source of demand, not only for high-bandwidth memory used alongside accelerators but also for the broader DRAM and storage ecosystem. The result is a market where long-term contracts and strategic relationships matter as much as spot prices.

For cloud operators and AI companies, the priority is securing enough memory to keep expanding. For device makers, the priority is maintaining product lines and margins. For consumers, the outcome can be higher prices or fewer choices. Altman’s remarks highlight a tension that is likely to persist: the same memory that makes a personal computer useful can also be used to serve many people from a data center. Deciding which use deserves priority is not just a technical question. It is a question about how to allocate a scarce resource during a period of rapid technological change.

The debate also touches on the future of open-source models. If open models become more efficient, they may reduce the amount of memory needed for certain tasks. That could make local inference more practical and lessen the pressure on cloud capacity. But efficiency gains can also increase total demand, as cheaper inference encourages more applications. The net effect is uncertain. What is clear is that memory supply will remain a critical constraint for both local and cloud AI in the near term.

Altman’s position is ultimately a bet on centralization during a shortage. He is arguing that the world gets more AI capability per unit of DRAM when memory is pooled in data centers rather than scattered across desks. That view will not satisfy users who value local control, and it does not erase the legitimate reasons to run models on-device. But it does frame the shortage in a way that goes beyond individual product preferences. The question is not only where memory is located, but how often it is used and who gets access to it while supply remains tight.

Source: The Verge News

“I think, on net, the world deserves to understand how the universe works,” Altman said. “But I do have empathy for the method.” His remarks amount to one of the first direct acknowledgments by the OpenAI chief that the company’s output has disrupted the daily work of mathematicians, even as many researchers remain excited about AI’s potential as a scientific tool. The tension between those two realities—genuine discovery and genuine disruption—runs through the entire debate.

A Flood of Results, and a Communication Gap

Over the past month, OpenAI has released hundreds of mathematical results. Many arrived with brief write-ups that researchers have described as poorly written, hard to follow, and light on context. Mathematicians have repeatedly complained about inadequate attribution, insufficient verification, and a lack of the normal scaffolding that accompanies serious mathematical work: clear definitions, related literature, careful proofs, and an explanation of why a result matters.

That has left researchers doing the painstaking work of checking and interpreting the findings while the company presses ahead with more. The problem is not simply that the results are numerous. It is that mathematical knowledge is cumulative and social. A theorem becomes useful when other mathematicians can understand it, test it, connect it to existing work, and build on it. A result that arrives as a cryptic announcement may generate headlines, but it does not automatically become part of the field’s shared body of knowledge.

Altman acknowledged that OpenAI needs to improve how it communicates its findings. He argued, however, that the public’s right to new scientific knowledge “outweighs” the disruption caused by the company’s releases. “I feel unequivocal on this point: Humanity deserves fundamental knowledge, and we should not be sitting on a major fundamental science discovery,” he said. “The world deserves that.”

The Gatekeeping Paradox

Yet OpenAI now faces criticism for doing precisely what Altman says it should not do: sitting on potentially significant findings. After announcing three major breakthroughs in mathematics, physics, and computer science, the company has come under fire for what some researchers see as gatekeeping. Details for one of the claimed results—involving Yang-Mills theory, the mathematical foundation of modern particle physics—have not been released publicly.

OpenAI says the delay reflects recommendations from a hand-picked group of experts it consulted on how best to release the work. The practice runs counter to advice from a previous group of mathematicians the company consulted, according to researchers familiar with the discussions. It is part of what OpenAI describes as a new approach to communicating its findings. Critics say the episode illustrates a broader problem: the company wants credit for openness while controlling the terms on which its results enter the scientific record.

On the podcast, Altman suggested that the stockpile is larger than the public has seen. Asked whether OpenAI has more than the hundreds of proofs it has already released, he replied: “We do. Of course.” That answer will likely intensify concerns among mathematicians who already feel overwhelmed by the volume of material and uncertain about how much remains hidden.

An Internal Experiment That Got Out of Control

Altman also acknowledged that OpenAI’s pursuit of mathematical problems did “get out of control” for a short period, though he described that phase as deliberate and necessary. The company was probing the limits of its AI models, and mathematics offered a demanding test. He described an internal Slack channel called “Model Name-Hold My Beer,” where employees “were just throwing that at the hardest problems they could” to see how the models would perform.

That anecdote captures the spirit of the current moment. AI labs are no longer using mathematics merely as a benchmark for reasoning. They are treating it as a frontier for discovery, a domain where models can generate conjectures, suggest proof strategies, and sometimes produce results that surprise human experts. The appeal is obvious: mathematics offers clear success criteria, and a correct proof can be checked. But the gap between a plausible AI-generated proof sketch and a verified theorem remains wide.

Hundreds of Millions of Dollars—and a Navier-Stokes Bet

Altman estimated that OpenAI has spent on the order of hundreds of millions of dollars tackling mathematical problems. That includes roughly $10 million—“maybe a little more”—on its Navier-Stokes solution, one of mathematics’s legendary Millennium Prize problems. The Navier-Stokes equations describe the motion of viscous fluids, and one of the great open questions is whether smooth solutions always exist in three dimensions or whether singularities can form.

The spending offers a glimpse of the vast resources AI labs are bringing to bear on scientific problems. It also raises uncomfortable questions for academia. If a single problem can absorb millions of dollars in compute and engineering time, what happens to researchers who do not have access to similar resources? Mathematicians worry that they may be priced out of their own fields, unable to compete with labs that can run massive experiments at scale.

The concern is not only about money. It is also about agenda-setting. When a small number of companies decide which problems to attack, how to release results, and which experts to consult, they can shape the direction of a field. That is a significant shift for mathematics, which has traditionally relied on a decentralized, international community of researchers, journals, conferences, and peer review.

Why Mathematics Is the New AI Frontier

Mathematics has become a proving ground for AI because it combines formal rigor with enormous difficulty. Recent systems have made progress in geometry, algebra, combinatorics, and theorem proving. Some tools can suggest auxiliary constructions; others can search for proofs in formal languages. Researchers have experimented with proof assistants such as Lean and Coq, which allow proofs to be checked by software. In principle, formal verification could reduce the burden on human referees and make AI-generated results easier to trust.

In practice, formalization is expensive and incomplete. Many mathematical results depend on informal intuition, notation, and context that are not fully captured by a formal system. A proof assistant can verify that a chain of deductions is valid, but it cannot always tell you whether the statement being proved is the right one, whether the proof is original, or whether it connects meaningfully to the broader literature. Human judgment remains essential.

The history of the Millennium Prize problems illustrates the stakes. The Clay Mathematics Institute named seven problems in 2000, each carrying a $1 million prize. The Poincaré conjecture was solved by Grigori Perelman, who famously declined the prize. The remaining problems—including Navier-Stokes, Yang-Mills, P versus NP, the Riemann Hypothesis, Birch and Swinnerton-Dyer, and Hodge—are among the deepest questions in mathematics. Any credible claim of progress on one of them attracts intense scrutiny.

The Verification Bottleneck

That scrutiny is healthy, but it takes time. Mathematical proofs can run to hundreds of pages and rely on years of prior work. Referees are scarce, and many are already overloaded. When a company announces a breakthrough without a complete, accessible exposition, the burden falls on the community to reconstruct what was done. Researchers say this is exactly what has happened with some of OpenAI’s recent releases.

The problem is compounded by attribution. Mathematics is built on a dense network of prior results, and credit matters. If an AI system generates a proof using ideas from dozens of papers, how should those contributions be acknowledged? Who is accountable if the proof contains an error? What does authorship mean when the main creative work is done by a model trained on the community’s collective output? These questions do not have easy answers, and they cannot be resolved by a press release.

OpenAI’s new approach—consulting a hand-picked group of experts and sometimes delaying details—may be an attempt to manage these risks. But it also creates a paradox. The company says it wants to share knowledge, yet it is accused of withholding it. It says it wants to improve communication, yet its releases have been criticized as opaque. It says the world deserves fundamental discoveries, yet the world cannot evaluate them without enough information to check the work.

What Mathematicians Want

Mathematicians interviewed about the situation have described a familiar set of needs. They want complete proofs or at least detailed proof sketches. They want clear statements of what is new and what builds on existing work. They want attribution to prior results. They want enough context to understand why a claim matters. And they want time to verify before the next announcement arrives.

Those requests are not unreasonable. They are the basic norms of scientific communication. The difference is scale and speed. AI can generate candidate results faster than the community can check them. That mismatch is the heart of the current conflict. It is not that mathematicians oppose AI assistance. Many see enormous promise in using models to explore conjectures, generate examples, and automate tedious calculations. The objection is to a style of announcement that treats mathematical discovery as a product launch.

Altman’s comments suggest he recognizes the tension, even if he does not plan to resolve it by slowing down. His argument is essentially utilitarian: the benefits of new knowledge outweigh the costs of disruption, and the company has a responsibility to share what it finds. Critics counter that sharing means more than announcing. It means making results intelligible, verifiable, and usable by the people who will build on them.

Standards Still Missing

For now, Altman’s message is that the math will keep coming. OpenAI has more discoveries in reserve, according to its CEO, and it has spent hundreds of millions of dollars to get them. The company is experimenting with new release strategies, consulting selected experts, and facing criticism from researchers who want a more open and rigorous process.

The debate is likely to intensify as AI systems become more capable. If the next few years bring more claims of breakthroughs in mathematics, physics, and computer science, the pressure on journals, universities, and companies to agree on standards will grow. The question is no longer whether AI will contribute to mathematics. It already has. The question is how the field will verify, attribute, and absorb those contributions without losing the very norms that make mathematical knowledge trustworthy.

Source: The Verge News

The touchscreen MacBook Pro is just a MacBook Pro with a touchscreen.

Key facts

• Apple has released a touchscreen MacBook Pro after years of saying a touchscreen laptop was unlikely.
• The new model is thinner and lighter than previous MacBook Pro designs and features an OLED display marketed as PROLED XDR.
• The OLED panel delivers deeper blacks, high brightness, rich colors, and support for advanced color modes for professional workflows.
• The touchscreen works with basic taps and swipes, and some macOS 27 Golden Gate interface elements respond with slight magnification or expansion.
• Most touch gestures map to actions already available on the trackpad, and early professional users continued to rely on keyboard and trackpad input.
• The laptop includes a Dynamic Island instead of a notch and brings back an illuminated Apple logo on the lid.
• There is no Apple Pencil support and no 360-degree hinge, and Apple has not presented the touchscreen as a redefinition of laptop workflows.

A familiar laptop with a touchable screen

Apple’s long-anticipated touchscreen MacBook Pro has arrived, and the most surprising thing about it is how normal it feels. The company spent years arguing that touchscreens on laptops were ergonomically awkward and unnecessary, so expectations were high for a breakthrough. Instead, the new machine behaves like a standard MacBook Pro that happens to respond when someone reaches out and taps the display. That is not a criticism by itself. It is simply the central fact of the product: the touchscreen is an additional input method, not a new philosophy for personal computing.

The laptop is thinner and lighter than earlier MacBook Pro models, and it has an OLED display that Apple markets as PROLED XDR. The panel is one of the best screens seen on a laptop, with inkier blacks than the previous Mini LED generation, strong brightness, rich colors, and effectively infinite contrast. Apple calls it a reference-caliber display, and the claim is supported by support for advanced color modes aimed at professional users. For photographers, video editors, and designers, the display may be the most meaningful upgrade on the entire machine.

The touchscreen does not reinvent the Mac

The touchscreen itself is far more modest. Users can poke and swipe at the screen, and some interface elements in macOS 27 Golden Gate expand or magnify slightly when tapped. Those small responses are pleasant, but they do not amount to a new way of using a laptop. The gestures mostly mirror actions that already exist on the trackpad. There is no dramatic new canvas, no radical gesture language, and no indication that Apple has solved a long-standing ergonomic problem in a surprising way. The feature is present, it works, and it feels like an afterthought.

That assessment is reinforced by the way Apple has demonstrated the machine. In briefings, professionals who had recently started using the touchscreen MacBook Pro did not describe workflows that had been transformed by touch. They still defaulted to the keyboard and trackpad for most tasks. The touchscreen was a bonus for occasional taps, swipes, or direct manipulation, not a replacement for the established input methods that have defined laptop use for decades. When asked whether they wanted Apple Pencil support, several users said it would be nice to have, but they did not frame it as essential.

A decade of false starts for touchscreen laptops

The muted arrival is notable because touchscreen laptops were once expected to be revolutionary. More than a decade ago, Microsoft built Windows 8 around a touch-optimized interface and encouraged hardware makers to experiment with flipping, folding, and convertible designs. Laptops became more flexible, sometimes in genuinely creative ways, but the revolution did not arrive. Windows 8 struggled, and convertible laptops settled into being another product segment rather than a redefinition of the laptop. Users accepted touchscreens when they were available, but they did not abandon keyboards and trackpads in large numbers.

Apple watched that history unfold and waited. Its executives repeatedly dismissed the idea of a touchscreen Mac, arguing that reaching across a keyboard to touch a vertical display was not a good experience. The new MacBook Pro does not disprove that concern so much as sidestep it. Apple has added touch without forcing users to depend on it. The result is a laptop that can accommodate the natural urge to tap a screen while preserving the traditional Mac setup. In that sense, the company is not leading a new era; it is acknowledging that the old objections no longer justify withholding a feature that many people expect.

The display and design carry the upgrade

In practice, the OLED panel and lighter chassis do as much to make the new MacBook Pro feel special as the touchscreen does. The move from Mini LED to OLED improves black levels and contrast in ways that are immediately visible, especially in dark rooms and high-dynamic-range content. Brightness remains high enough for outdoor use, colors are saturated without looking artificial, and the advanced color modes give professional users more control over how their work appears on screen. Those improvements are familiar parts of display evolution, but they matter more than the ability to swipe at macOS.

The design changes are equally tangible. The Dynamic Island replaces the notch, freeing up a bit more visual space and giving the laptop a more modern front-facing appearance. The return of the illuminated Apple logo on the lid is a nostalgic touch that drew a strong reaction during the presentation. Neither feature changes how the laptop works, but both contribute to the sense that this is a refreshed product rather than a simple spec bump. The touchscreen is part of that refresh, but it is not the most convincing part.

Settings, gestures, and missing features

A look at the touchscreen settings menu makes the limits clear. Most touch gestures are mapped to functions that already exist on the trackpad, which keeps the learning curve low but also makes the touchscreen feel redundant. Users can tap buttons, scroll through pages, and interact with certain apps directly, but there is no deep system of touch-specific commands that changes the character of macOS. The feature is designed to be optional, and it succeeds at being optional. It does not succeed at being transformative.

The absence of Apple Pencil support is another signal that Apple is treating touch as a secondary input. A stylus could make the touchscreen more useful for drawing, note-taking, photo editing, and precise selection, but the company has not included it. Nor has Apple added a 360-degree hinge that would allow the laptop to fold into a tablet-like shape. Those omissions keep the MacBook Pro firmly in laptop territory. The touchscreen is there for people who want to reach out and tap, not for people who want a convertible device.

A strategic release with room to grow

The restrained approach may be strategically sound. By adding touch without redesigning the Mac around it, Apple avoids the mistakes of earlier touchscreen experiments and keeps the MacBook Pro familiar for its core audience. The company also sets the stage for touchscreens to appear in more affordable laptops later. If the feature proves useful and uncontroversial on a high-end model, it can gradually spread to lower-priced devices without requiring a radical shift in software design. Apple already offers many touch-based products for people who prioritize touch, so the Mac does not need to become one of them overnight.

Still, the release invites questions about what Apple could have done with more ambition. The company took more than a decade to come around to the idea, and the first result is a conventional laptop with a touch-enabled display. That may be enough for many buyers, especially those who have wanted a touchscreen Mac for years. But it does not answer what a truly touch-optimized Mac would look like. It does not offer a new hinge, a stylus, or a set of gestures that make the trackpad feel obsolete. It simply removes a long-standing limitation and lets users decide whether touch matters to them.

For now, the touchscreen MacBook Pro is best understood as a normal MacBook Pro with an extra capability. The OLED display, lighter body, Dynamic Island, and illuminated logo are all meaningful updates, and the touchscreen is a welcome convenience for occasional use. The feature is unlikely to change how most people work, but it no longer needs to. Apple has finally shipped the obvious version of the idea, and that may be the most practical outcome of all. The touchscreen is now simply another feature in a mature laptop line, not a new category.

Source: The Verge News

The surprise pairing combined two very different kinds of celebrity. Sheeran is one of the world's best-selling pop artists, known for heartfelt songwriting, arena-sized hits and a laid-back public persona. Hemsworth is a Hollywood star whose role as Thor turned him into a global action figure, with a physique that has become part of his brand. Seeing them lock hands over a table was, for many fans, an amusing collision of pop music and superhero cinema.

Key facts from the arm-wrestling match

• Ed Sheeran and Chris Hemsworth faced off in an arm-wrestling match in Melbourne.
• The event took place on Thursday, Feb. 26, as Sheeran opened his 2026 Loop Tour at Marvel Stadium.
• Hemsworth posted a video of the contest on social media.
• 
  Source: Jacaranda FM News

Argentina has officially taken a turn toward ultraliberalism. Javier Milei, elected on November 19, became president on Sunday, announcing an inevitable "shock" of austerity. "There is no alternative to an adjustment, there is no alternative to a shock" in the budget, because "there is no money!" he declared to thousands of supporters outside the Parliament, where he had just been sworn in. "We know that the situation will worsen in the short term. But after that we will see the fruits of our efforts," he added, promising "all necessary decisions to fix the problem caused by 100 years of waste by the political class."

Milei Sees the State as an "Enemy"

In front of him, a sea of sky-blue and white Argentine flags cheered his speeches, with cries of "Libertad, Libertad," and even "Motosierra!" (chainsaw!), a reference to the tool he brandished during the campaign to symbolize the cuts to come in the "enemy state." At midday, Javier Gerardo Milei, 53, became the twelfth president of Argentina since the return of democracy forty years ago, swearing before parliamentarians to honor "with loyalty and patriotism" this charge, and then donning the presidential sash. He also received the traditional sceptre made to measure for each president, bearing in his case engravings on the pommel of the faces of his five English mastiff dogs—"his children," as he calls them.

Milei was inaugurated under the benevolent gaze of nationalist or conservative leaders and politicians who had enthusiastically welcomed his victory: Brazil's Jair Bolsonaro, Hungary's Viktor Orban, the leader of Spain's far-right Vox party, Santiago Abascal. Also present was Ukraine's Volodymyr Zelensky, with whom Javier Milei shared a long embrace. The King of Spain and the presidents of neighboring Uruguay, Chile, and Paraguay were also in Buenos Aires. Brazil's Lula, who had been sharply criticized by Milei in the past, delegated his foreign minister.

Inflation at 143% Annually

Latin America's third-largest economy, but mired in chronic inflation at 143% annually, structural debt, and 40% poverty, Argentina is bracing for painful adjustments. Uncertainty remains about the very first measures: devaluation of the peso? Initial budget cuts? Restriction or even prohibition of monetary issuance? On Sunday, Javier Milei reaffirmed that a first concrete objective will be a 5% reduction in the budget deficit as a share of GDP, which "will fall on the state, not the private sector."

But until then, "people will have for the first time in a long time 'free' prices: the end of 'controlled prices,'" estimates Viktor Beker, an economist at the University of Belgrano, predicting high inflation in December and January. Milei himself warned that inflation will not be brought under control before "18 to 24 months."

The Rise of a Libertarian Outsider

Milei's ascent has been nothing short of meteoric. A former economics professor and television pundit, he burst onto the political scene with a fiery anti-establishment message that resonated with Argentines fed up with decades of economic mismanagement. His campaign was characterized by theatrical stunts, including waving a chainsaw at rallies to symbolize his promise to slash government spending. He has called the state an "enemy" and pledged to shrink it dramatically, eliminate the central bank, and dollarize the economy.

His victory marks a profound shift in Argentine politics, which has been dominated by the Peronist movement for much of the past century. Milei's brand of libertarianism, inspired by US politicians like Donald Trump and Brazil's Jair Bolsonaro, has attracted international attention. His inauguration was attended by a who's who of the global right, underscoring his alignment with like-minded leaders.

Milei's personal life has also drawn attention. The 53-year-old is unmarried and has no children, instead showering affection on his five English mastiffs, whom he calls his "children." He has said he communicates with them through a medium and has had them cloned. His eccentricities have fascinated the public and added to his unconventional image.

Economic Crisis and the Road Ahead

Argentina's economic crisis is the worst in decades. Inflation is running at 143% annually, the highest since the early 1990s. The country has defaulted on its debt multiple times, most recently in 2020. Foreign reserves are depleted, and the peso is under severe pressure. The poverty rate has soared to 40%, and the middle class is struggling to make ends meet.

Milei has promised a radical shock therapy to stabilize the economy. His plan includes cutting the budget deficit by 5% of GDP, which he insists will be achieved by reducing state spending rather than raising taxes. He has also vowed to eliminate the central bank and adopt the US dollar, though he has recently softened his stance on dollarization, saying it will be a gradual process.

However, Milei faces significant obstacles. His party, La Libertad Avanza, has only a small presence in Congress, meaning he will need to negotiate with other parties to pass his reforms. He also faces opposition from powerful unions and social movements that are likely to resist austerity measures. The country is also grappling with a severe drought that has hit agricultural exports, a key source of foreign currency.

Economists warn that Milei's shock therapy could lead to a sharp recession and social unrest in the short term. But his supporters argue that there is no alternative after years of failed policies. "The situation will worsen before it gets better," Milei has repeatedly warned. His promise to end "100 years of waste by the political class" has struck a chord with many Argentines who are desperate for change.

International Reactions and Implications

The international community is watching closely. Argentina is a major agricultural exporter and a member of the G20. Its economic stability is important for the region. Milei's election has been welcomed by investors who hope his reforms will open up the economy. Argentine bonds and stocks rallied after his victory. However, some neighboring countries are wary of his ideological stance. Brazil's Lula, a leftist, has had a strained relationship with Milei, though both have signaled a willingness to work together.

Milei's first days in office will be closely scrutinized. He has promised to announce a package of emergency measures soon after taking office. These could include a devaluation of the peso, spending cuts, and deregulation. The markets are expecting bold action, but the social and political costs could be high. As Argentina embarks on this uncertain path, the world watches to see whether Milei's gamble will pay off or plunge the country into further turmoil.

One of the key challenges will be managing the social impact of austerity. Argentina has a strong tradition of social protest, and unions have already vowed to resist any cuts to public services. Milei has promised to maintain social spending while cutting other areas, but many are skeptical. The country's powerful social movements, known as piqueteros, have blocked roads and staged massive demonstrations in the past. They are likely to mobilize again if Milei's policies lead to job losses and higher prices.

Another challenge is the political opposition. Although Milei won the presidency, his coalition lacks a majority in Congress. He will need to build alliances with more moderate parties, including former President Mauricio Macri's PRO, which has supported some of his proposals. However, Macri has indicated that his support is conditional on Milei's willingness to govern pragmatically.

On the economic front, Milei's plan to dollarize the economy is fraught with risks. Argentina lacks sufficient dollar reserves to convert all pesos into dollars. A hasty dollarization could trigger a banking crisis and hyperinflation. Milei has acknowledged these risks and has said that dollarization will be a gradual process, starting with the elimination of the central bank's ability to finance the government.

The International Monetary Fund (IMF) is also a key player. Argentina has a $44 billion loan program with the IMF, which Milei has criticized. He has promised to renegotiate the terms of the program, but the IMF has said it is willing to work with the new government. The outcome of these negotiations will be crucial for Argentina's access to international credit markets.

As Milei takes office, the eyes of the world are on Argentina. The country's experiment with ultraliberalism will be watched closely by other nations grappling with similar economic challenges. Whether Milei's shock therapy succeeds or fails, it will have profound implications for Argentina and beyond.

Source: 20 Minutes News

Abel Makkonen Tesfaye, mieux connu sous le nom de The Weeknd, est devenu en quelques années l'une des figures les plus singulières de la musique populaire. Chanteur, auteur-compositeur et producteur canadien, il a construit une carrière à part, en mêlant R'n'B, hip-hop, soul et pop dans un univers sombre, mélancolique et festif à la fois. Derrière le pseudonyme et la silhouette reconnaissable se cache un artiste discret, dont le parcours mérite d'être raconté.

Originaire de l'Ontario, au Canada, The Weeknd a longtemps avancé masqué, refusant de livrer son visage et son histoire au public. Son nom réel, Abel Makkonen Tesfaye, n'est devenu un détail connu du grand public qu'au fil de sa reconnaissance. Cette stratégie du mystère, associée à des chansons immédiatement reconnaissables, lui a permis de se démarquer dans une industrie où la surexposition est souvent la règle.

Les faits clés

• Nom réel : Abel Makkonen Tesfaye.
• Origine : Ontario, Canada, avec des racines éthiopiennes.
• Premiers projets : House of Balloons, Thursday et Echoes of Silence.
• Soutien décisif : Drake au début des années 2010.
• Albums majeurs : Kiss Land, Beauty Behind the Madness, Starboy, After Hours et Dawn FM.
• Autres activités : marque XO, collaboration avec Alexander Wang et série The Idol.
• Vie privée : relation médiatisée avec Bella Hadid en 2015.

Une enfance canadienne et des racines éthiopiennes

Abel Makkonen Tesfaye naît le 16 février 1990 à Toronto, en Ontario. Il grandit dans le quartier de Scarborough, dans une famille d'origine éthiopienne. Comme beaucoup d'artistes de sa génération, il se passionne très tôt pour la musique et commence à écrire ses propres textes, nourri par les sonorités qu'il entend autour de lui. Son univers se construit loin des codes les plus lisses de la pop, avec une prédilection pour les ambiances nocturnes, les textures électroniques et les récits plus introspectifs.

Cette enfance canadienne, entre plusieurs cultures et références musicales, explique en partie la richesse de son style. The Weeknd ne se contente pas d'empiler les genres : il les fait dialoguer. Le R'n'B sert de colonne vertébrale, le hip-hop apporte le rythme, la soul donne de la profondeur, tandis que la pop rend l'ensemble accessible au plus grand nombre. Cette capacité à synthétiser des influences multiples deviendra l'une de ses signatures.

House of Balloons, Thursday et Echoes of Silence : la révélation

Au début des années 2010, The Weeknd apparaît dans un paysage musical en pleine mutation. Les plateformes en ligne permettent à des artistes indépendants de se faire connaître sans passer par les circuits traditionnels. C'est dans ce contexte qu'il publie sa première mixtape, House of Balloons, sur son site internet. Le projet surprend par son ambiance brumeuse, ses beats minimalistes et sa voix immédiatement identifiable. Les critiques saluent un talent brut, encore en construction mais déjà très affirmé.

Le soutien de Drake joue alors un rôle déterminant. L'un des rappeurs les plus populaires du moment mentionne The Weeknd sur les réseaux sociaux, offrant à ce jeune artiste encore mystérieux une exposition médiatique inespérée. Les curieux découvrent alors des chansons qui mélangent R'n'B, hip-hop et soul, un cocktail efficace qui séduit autant les amateurs de musique urbaine que le public pop. Cette reconnaissance précoce ne relève pas du hasard : elle repose sur une identité sonore déjà très nette.

En 2011, The Weeknd enchaîne avec deux autres mixtapes, Thursday et Echoes of Silence. Ces trois projets posent les bases de son univers : une certaine idée de la nuit, des relations ambiguës, une forme de solitude et une production souvent aérienne. En 2012, les trois mixtapes sont réunies dans la compilation Trilogy, qui permet au grand public de découvrir l'ampleur de son travail. Sa carrière prend alors une nouvelle dimension et les premières dates de concert confirment l'engouement autour de son nom.

Kiss Land et la première consécration

En 2013, The Weeknd publie Kiss Land, son premier véritable album studio. Le projet prolonge l'esthétique de ses mixtapes tout en élargissant sa palette sonore. Il y raconte les tournées, le succès naissant, la solitude et les tentations qui accompagnent la célébrité. L'album rencontre un succès international et permet à l'artiste canadien de confirmer qu'il ne s'agissait pas d'un phénomène passager. Les premiers disques d'or arrivent, les salles se remplissent et son nom s'installe durablement dans le paysage musical.

Drake ne le laisse pas tomber : les deux artistes collaborent sur une chanson de son album, prolongeant une relation artistique qui a contribué à la reconnaissance initiale de The Weeknd. Cette période marque aussi le début d'une réflexion plus large sur son image. Le chanteur reste discret, mais son univers visuel devient plus construit, plus cinématographique. Il ne se contente plus de publier des chansons : il façonne une véritable identité artistique.

Beauty Behind the Madness : le virage pop

Deux ans plus tard, en 2015, The Weeknd opère un virage musical majeur avec Beauty Behind the Madness. L'album assume pleinement une dimension pop, sans renier les textures sombres qui ont fait sa réputation. Les titres The Hills, Often et Can't Feel My Face deviennent des succès planétaires. Le grand public découvre un artiste capable de transformer ses obsessions nocturnes en refrains fédérateurs. Ce virage ne signe pas l'abandon de son identité : il marque plutôt son passage à une échelle supérieure.

La même année, il participe à la bande originale de Cinquante Nuances de Grey avec le morceau Earned It. Là encore, le succès est au rendez-vous. Cette période illustre sa capacité à naviguer entre plusieurs publics : les amateurs de R'n'B alternatif, les auditeurs de pop mainstream et les spectateurs de bandes originales de cinéma. The Weeknd devient progressivement une star mondiale, sans pour autant multiplier les interviews ou les apparitions médiatiques.

Starboy et la consolidation

En 2016, The Weeknd va encore plus loin avec Starboy. Porté par une esthétique plus lumineuse et une production aux accents électroniques, l'album confirme son statut de star internationale. Le titre éponyme, en collaboration avec Daft Punk, devient un tube planétaire. Le chanteur y assume une image plus glamour, tout en conservant cette mélancolie qui traverse l'ensemble de son œuvre. Le succès commercial est immense, mais il ne fait pas disparaître la part plus sombre de son inspiration.

À ce stade de sa carrière, The Weeknd n'est plus seulement un artiste issu de l'underground : il est devenu une référence pour toute une génération. Ses albums se succèdent avec des ambitions toujours plus grandes, et son nom suffit désormais à attirer l'attention. Pourtant, il continue d'avancer avec une certaine retenue, préférant laisser la musique parler plutôt que de se livrer totalement dans les médias.

After Hours, Dawn FM et une trilogie ambitieuse

En 2020, After Hours marque un nouveau tournant. L'album mélange synthpop, R'n'B et influences des années 1980, avec une esthétique visuelle très travaillée. Le morceau Blinding Lights devient l'un des plus grands succès de sa carrière et dépasse largement le cercle de ses fans habituels. Heartless et Save Your Tears confirment la puissance commerciale du projet. The Weeknd y explore la solitude, la célébrité et les contradictions d'une vie sous les projecteurs.

En 2022, il poursuit cette veine avec Dawn FM, un album conceptuel à l'ambiance plus étrange, presque radiophonique. Le projet est présenté comme le deuxième volet d'une trilogie. Au moment de la publication des informations disponibles, le dénouement de cette trilogie était attendu en 2024. Cette manière de construire son œuvre par chapitres montre que The Weeknd ne conçoit pas ses albums comme de simples collections de chansons, mais comme des univers cohérents, avec leurs personnages, leurs obsessions et leur esthétique.

XO, Alexander Wang et les incursions dans la mode

Si la musique reste son domaine de prédilection, The Weeknd a également fait quelques incursions dans la mode. Il lance sa marque de vêtements, baptisée XO, et s'associe au créateur américain Alexander Wang. En 2015, cette collaboration rencontre un franc succès auprès du public masculin. Cette démarche prolonge son travail sur l'image : la mode devient un moyen d'exprimer la même esthétique que sa musique, entre noirceur, luxe et culture urbaine.

Son influence ne se limite donc pas aux charts. The Weeknd construit un univers de marque, avec des codes visuels, une communauté de fans et une présence forte dans la culture populaire. Cette porosité entre musique, mode et image est devenue caractéristique des artistes contemporains, mais il fait partie de ceux qui ont contribué à la rendre évidente.

The Idol : une expérience télévisuelle remarquée

En 2023, The Weeknd ajoute une corde à son arc en produisant et en interprétant l'une des têtes d'affiche de la série The Idol, aux côtés de Lily-Rose Depp. Le projet est présenté au Festival de Cannes, où il crée l'événement. La série, très commentée, ne rencontre pas le succès escompté et divise la critique. Cette expérience montre néanmoins la volonté du chanteur d'explorer d'autres formes narratives et de ne pas se limiter à la scène musicale.

Même lorsque le résultat est contesté, The Weeknd semble vouloir prendre des risques. Son passage dans l'univers des séries illustre une tendance plus large chez les artistes pop, qui cherchent à contrôler leur image et à raconter leurs obsessions par d'autres moyens que les clips ou les albums. Cette incursion télévisuelle reste, quoi qu'il en soit, l'un des épisodes les plus commentés de sa carrière récente.

Une vie privée sous contrôle

Côté cœur, The Weeknd reste plutôt discret. Il a officialisé sa relation avec la mannequin Bella Hadid en 2015, avant que le couple ne se sépare un an plus tard. Cette relation a alimenté de nombreux commentaires, mais l'artiste a toujours tenté de préserver une frontière entre sa vie privée et son image publique. Cette retenue est cohérente avec la construction de sa carrière, fondée davantage sur les projets que sur les confidences personnelles.

Cette discrétion n'empêche pas son nom de circuler dès qu'il s'agit de mode, de musique ou de culture populaire. The Weeknd est devenu une figure familière, même pour ceux qui ne suivent pas sa carrière de près. Son pseudonyme, son look et sa voix font désormais partie du paysage musical contemporain.

Un artiste au carrefour des genres

La force de The Weeknd réside dans sa capacité à faire cohabiter des mondes sonores que tout semble opposer. Il peut passer d'une ballade mélancolique à un tube dancefloor, d'une production minimaliste à une démonstration pop plus spectaculaire. Cette souplesse lui a permis de traverser les époques sans perdre son public. Il a également contribué à brouiller les frontières entre R'n'B alternatif et pop mainstream, ouvrant la voie à de nombreux artistes de sa génération.

Son œuvre est traversée par des thèmes récurrents : la nuit, la solitude, le succès, les relations toxiques, la quête de sens et la célébrité. Même lorsqu'il chante des tubes lumineux, une forme de tension demeure. C'est peut-être là que se trouve la véritable identité d'Abel Makkonen Tesfaye : un artiste qui transforme ses contradictions en chansons, et qui a su faire de son mystère l'un des moteurs de son succès.

Source: Programme Tv News

Drake, de son vrai nom Aubrey Drake Graham, est un rappeur, chanteur, auteur-compositeur et acteur canadien né le 24 octobre 1986 en Ontario. Il est aujourd’hui l’une des stars les plus influentes de l’industrie musicale, connu pour son album culte Take Care, ses tournées internationales et ses collaborations à grande échelle. Sa carrière, débutée dans les séries télévisées avant de basculer vers la musique, illustre une ascension rare dans le hip-hop contemporain.

Pour les spécialistes, Drake est surtout l’un des rappeurs les plus transcendants de sa génération. Il est le troisième rappeur au monde à atteindre le milliard de vues sur YouTube. Il prend plusieurs fois la première place du Billboard Hot 200 canadien, notamment avec son titre culte One Dance. Acclamé dans ses concerts, il multiplie les tournées internationales et augmente sa notoriété à chaque nouvel album : Views, Scorpion ou encore More Life culminent aux premières places de tous les classements. Couronné d’une quarantaine de récompenses, Drake domine aujourd’hui l’industrie du rap.

Les faits clés

• Nom complet : Aubrey Drake Graham.
• Naissance : 24 octobre 1986 en Ontario, au Canada.
• Métiers : rappeur, chanteur, auteur-compositeur et acteur.
• Révélation : la série Degrassi, puis les mixtapes à partir de 2006.
• Albums majeurs : Take Care, Views, More Life, Scorpion, Certified Lover Boy, Honestly, Nevermind.
• Succès : One Dance, Hotline Bling, Fair Trade, Marvins Room.
• Famille : fils de Dennis Graham, batteur de Jerry Lee Lewis, et de Sandra Graham, éducatrice.
• Vie privée : relation médiatisée avec Rihanna, puis avec Jennifer Lopez.
• Fortune : environ 150 millions de dollars en 2019, selon un magazine économique américain.

Enfance et premiers pas artistiques

Aubrey Drake Graham grandit dans un environnement à la fois artistique et stable. Son père, Dennis Graham, originaire de Memphis, dans le Tennessee, est batteur pour le célèbre rockeur et bluesman Jerry Lee Lewis. Sa mère, Sandra Graham, est une éducatrice canadienne qui veille à lui offrir une bonne éducation. Le divorce de ses parents, alors qu’il n’a que cinq ans, bouleverse cet équilibre familial. L’enfant est principalement élevé par sa mère, tout en rejoignant régulièrement son père lors de tournées d’été dans le Tennessee. Ce contexte lui permet de développer très tôt une familiarité avec la scène, la musique et le rythme.

Après sa scolarité, le jeune homme explore différentes formes d’expression artistique. Il se fait connaître du grand public grâce à la série télévisée Degrassi, dans laquelle il apparaît de 2001 à 2011. Cette expérience lui donne une première visibilité et lui permet de comprendre les codes du divertissement. Pourtant, c’est la musique qui devient progressivement son véritable terrain d’expression. En 2006, il publie sa première mixtape, Room for Improvement, qui attire l’attention d’un public jeune et contribue à faire circuler son nom dans les cercles hip-hop.

Une ascension rapide vers les sommets

Drake entame véritablement sa carrière musicale en 2006, mais c’est à partir de 2011 que son statut change d’échelle. Auteur-compositeur-interprète à l’aise entre hip-hop, rap, R’n’B et soul, il séduit par sa capacité à mêler introspections, mélodies accessibles et productions soignées. Son album Take Care, sorti en 2011 et enregistré avec la participation de Rihanna, est certifié disque de platine. Ce projet lui permet de toucher un public beaucoup plus large et de s’imposer comme une voix singulière du rap contemporain.

Les années suivantes confirment son statut. Views et More Life font émerger plusieurs succès majeurs, dont One Dance et Hotline Bling. Puis Scorpion et Care Package, Dark Lane Demo Tapes et Certified Lover Boy, et enfin Honestly, Nevermind, permettent à l’artiste d’enchaîner les premières places dans les classements. Drake prend plusieurs fois la tête du Billboard Hot 200 canadien et cumule les récompenses, avec une quarantaine de distinctions. Il devient également le troisième rappeur au monde à atteindre le milliard de vues sur YouTube. Ses tournées internationales et ses concerts à guichets fermés renforcent encore sa notoriété à chaque nouvel album.

Drake en cinq chansons cultes

• Fair Trade : écrite par Drake en collaboration avec le rappeur américain Travis Scott, cette chanson sort en 2021 sur l’album Certified Lover Boy. Portée par un rythme rap percutant, elle reprend des extraits du morceau Mountains de Charlotte Day Wilson.
• Honestly, Nevermind : titre éponyme de l’album sorti en 2022, il est dédié au créateur de mode américain Virgil Abloh, décédé en 2021. La chanson explore des influences house et se présente comme un morceau de danse.
• One Dance : écrite par Drake avec Wizkid et Kyla, elle sort en 2016 sur l’album Views. Cette chanson accessible et aérée se classe numéro un dans de nombreux pays, dont la France.
• Marvins Room : sortie en 2011 sur Take Care, elle surprend par sa simplicité musicale. Plus proche du R’n’B que du rap, elle séduit par la qualité de son texte et son atmosphère mélancolique.
• Hotline Bling : morceau sorti en 2016 sur Views, il reçoit les éloges de la critique et vaut à Drake le prix de la meilleure chanson rap/hip-hop en 2016.

Dennis Graham, un père musicien

Dennis Graham est le père d’Aubrey Drake Graham. Cet Afro-Américain catholique, originaire de Memphis, dans le Tennessee, a longtemps été le batteur attitré du rockeur Jerry Lee Lewis. Après le divorce de ses parents, le jeune Aubrey, qui vit plutôt avec sa mère, suit régulièrement son père pendant ses tournées. C’est dans ce contexte qu’il aurait développé sa facilité à s’exprimer sur scène et son goût pour la performance. Deux frères de Dennis Graham, Larry Graham et Teenie Hodges, sont également musiciens, ce qui confirme l’ancrage familial dans la musique.

Dennis Graham apparaît dans l’un des clips de son fils, Worst Behaviour. Plus tard, en 2017, alors que Drake vient de sortir More Life, son père tente à nouveau sa chance dans un clip intitulé Kinda Crazy. Les spécialistes notent que, malgré des codes récurrents, la touche Graham reste bien présente. Cette relation père-fils, entre héritage musical et présence médiatique, accompagne une partie de l’image publique de Drake.

Rihanna, Jennifer Lopez et une vie privée très suivie

Drake rencontre la chanteuse pop Robyn Rihanna Fenty, dite Rihanna, en 2010, à l’occasion de l’enregistrement de l’album Take Care. Si d’autres artistes participent au projet, le duo formé par Drake et Rihanna attire rapidement l’attention des médias. Les deux stars se montrent très proches lors de la cérémonie des BET Awards, qui récompense Take Care en 2011. À l’époque, ni l’un ni l’autre ne démentent cette liaison, alors que Drake est déjà réputé pour ses nombreuses conquêtes.

En 2016, Drake et Rihanna collaborent de nouveau sur leurs titres respectifs Work et Too Good. Cette fois, ils officialisent leur relation, qui se révèle toutefois de courte durée. Le 11 octobre 2016, leur rupture est rendue publique. Deux mois plus tard, Drake officialise sa relation avec la chanteuse et actrice Jennifer Lopez. Cette vie privée très médiatisée alimente l’attention autour de l’artiste, sans jamais éclipser sa production musicale.

Fortune, business et influence mondiale

Selon un magazine économique américain, Drake apparaît en 2019 à la deuxième place du classement des rappeurs les plus fortunés du monde, juste derrière Jay-Z. Âgé de 32 ans à l’époque, le Canadien représenterait 150 millions de dollars. Cette performance confirme l’impact des ventes de ses albums et sa capacité à rester au sommet des classements. Depuis 2011, Drake n’a cessé de gagner du terrain face à des rappeurs plus âgés, en combinant succès commercial, présence scénique et collaborations stratégiques.

Sa fortune progresse de 50 pour cent entre 2018 et 2019. Ce bond s’explique aussi par ses talents d’homme d’affaires, notamment ses investissements dans l’immobilier. Certains concerts, comme celui donné dans un night-club de Las Vegas en 2019, sont également très lucratifs. Dès 2013, Drake déclarait à un magazine économique que son objectif était de gagner encore plus d’argent. Force est de constater que l’objectif a été largement atteint, à mesure que son influence s’étendait au-delà de la musique.

Honestly, Nevermind, un album surprise

Sans prévenir, Drake dévoile le 17 juin 2022 son septième album, intitulé Honestly, Nevermind. Publié à six heures du matin, heure française, ce disque compte quatorze titres inédits, dont un seul featuring, avec 21 Savage sur Jimmy Cooks, qui clôture le projet. Avant cette sortie, le rappeur canadien s’était contenté de republier sur les réseaux sociaux le visuel de l’album, quelques heures avant de le dévoiler. La surprise est générale, tant dans la presse que parmi les fans.

L’opus arrive neuf mois après Certified Lover Boy, dont la pochette était recouverte d’emojis représentant des femmes enceintes. Avec Honestly, Nevermind, Drake explore des sonorités plus électroniques et dansantes, confirmant sa volonté de ne pas rester figé dans un seul registre. Ce projet illustre aussi sa capacité à mobiliser immédiatement l’attention mondiale, sans campagne promotionnelle traditionnelle. En bousculant les attentes, l’artiste continue d’alimenter les débats et de peser sur les tendances de l’industrie musicale.

Source: Linternaute News

Microsoft recorded $10bn of revenue from its commercial arrangements with OpenAI in the three months to September 30, 2026, equal to about 11% of its total revenue for the quarter. The figure appeared in the company's quarterly filing and marks the first time Microsoft has given a quarterly number for the closely watched relationship.

The filing states that for the three months ended September 30, 2026, Microsoft recorded revenue from commercial arrangements with OpenAI, inclusive of revenue-sharing payments, of $10.0 billion. The disclosure does not separate revenue-sharing payments from OpenAI's spending on Azure, Microsoft's cloud platform, leaving investors to infer how much of the total comes from each part of the relationship.

Key facts at a glance

• Microsoft recorded $10.0bn of revenue from OpenAI arrangements in the quarter to September 30, 2026.
• The amount represented about 11% of Microsoft's total quarterly revenue.
• It was the first quarterly disclosure of revenue from the OpenAI relationship.
• Microsoft recorded $24.1bn from OpenAI in the 12 months to June, or roughly $6bn a quarter on average.
• The latest quarterly OpenAI revenue exceeded Xbox revenue of $5.4bn and search and advertising revenue of $6.6bn.
• OpenAI owed Microsoft $7.2bn at the end of September, up from $6.0bn in June.
• Microsoft owns about 24% of OpenAI and has paid $12.0bn of the $13.0bn it committed to invest.
• Microsoft's stake was about 25% in June and about 27% after OpenAI's restructuring in October 2025; the company did not say what caused the dilution.
• Microsoft must disclose the business because OpenAI counts as a related party under accounting rules.
• The companies have loosened some parts of their partnership, including Microsoft giving up a right of first refusal to supply OpenAI's computing capacity in October 2025.
• OpenAI committed to buy another $250bn of Azure services, and an April 2026 agreement gave it broader freedom to use other cloud providers.
• OpenAI will keep sharing revenue with Microsoft through 2030, subject to a cap.
• Microsoft cut its 2026 capital spending forecast to $170bn from $175bn.
• Capital spending including finance leases was $45.2bn in the quarter, below Microsoft's own $50bn projection.
• Microsoft's headcount fell 2% in the quarter, and the company offered buyouts to 60,000 employees.

The revenue disclosure gives investors a clearer view of how much Microsoft earns from a partnership that has shaped its AI strategy and its Azure cloud business. The filing places the OpenAI contribution at roughly one-ninth of total revenue, a scale large enough to move investor perceptions of Microsoft's growth and risk profile. The absence of a split between revenue sharing and Azure consumption is notable because the two streams carry different economic and strategic implications. Revenue sharing reflects the terms of the partnership, while Azure spending reflects OpenAI's demand for computing capacity and Microsoft's ability to convert AI workloads into cloud revenue.

The comparison with Microsoft's other businesses underlines how quickly the OpenAI relationship has grown. In the latest quarter, OpenAI-related revenue of $10bn was larger than the $5.4bn brought in by Xbox and the $6.6bn generated by search and advertising. Over the 12 months to June, Microsoft recorded $24.1bn from OpenAI, equivalent to about $6bn a quarter on average. The jump to $10bn in the September quarter suggests that the relationship is becoming more material at a time when Microsoft is spending heavily on data centers, chips and other infrastructure for AI.

Microsoft's balance-sheet exposure to OpenAI also draws attention. OpenAI owed Microsoft $7.2bn at the end of September, up from $6.0bn in June. Microsoft owns about 24% of OpenAI and has paid $12.0bn of the $13.0bn it committed to invest. The stake was about 25% in June and about 27% after OpenAI's restructuring in October 2025. Microsoft did not say what caused the dilution to about 24%, a detail that may matter to investors tracking the value of its investment and the changing structure of the partnership.

The accounting treatment explains why the figure is now visible. OpenAI counts as a related party under accounting rules, which requires Microsoft to disclose the business. That requirement turns a relationship that was once discussed mainly in strategic terms into a line item that analysts can compare with other parts of Microsoft's portfolio. It also creates a channel for investors to assess how much of Microsoft's AI narrative is tied to a single partner and how much is coming from a broader base of customers.

A partnership with looser ties

The two companies have loosened their partnership in several stages. In October 2025, Microsoft gave up its right of first refusal to supply OpenAI's computing capacity. OpenAI committed to buy another $250bn of Azure services. An April 2026 agreement gave OpenAI broader freedom to use other cloud providers, and OpenAI recently brought its models to rival Google Cloud. Those changes reduce Microsoft's exclusivity while preserving a large commercial relationship.

OpenAI will keep sharing revenue with Microsoft through 2030, subject to a cap. Earlier reports have said OpenAI shares 20% of its revenue, a figure neither company has confirmed. The cap and the revenue-sharing terms matter because they determine how much Microsoft benefits as OpenAI's products and customer base expand. They also shape the economics of a partnership in which Microsoft is both an investor and a supplier of cloud capacity.

On the earnings call, an analyst asked where Azure's AI growth was coming from. Chief executive Satya Nadella did not mention OpenAI. He said he did not want Azure's AI business to depend on a few large customers. Relying on four customers who buy a lot of capacity for a few quarters of growth was not a business as far as he was concerned, according to his remarks. In a post on X after the call, Nadella wrote that Azure's growth was broad-based across large and small businesses. He also described a fungible fleet that allows Microsoft to dynamically allocate capacity to the highest-value opportunities across compute, data, Foundry and Copilot, and said the company was bringing capacity online faster while getting more from existing infrastructure.

The filing also widened a warning about Microsoft's reliance on outside AI model makers. It says Microsoft has limited visibility into or control over how those models are designed, trained, secured and tested. Some of those partners are also significant Azure customers, the filing adds. That dual role creates both opportunity and risk: the same companies that help Microsoft offer cutting-edge AI models also depend on its cloud, and any shift in their plans could affect both revenue and capacity demand.

Spending forecast trimmed

Chief financial officer Amy Hood cut Microsoft's 2026 capital spending forecast to $170bn from $175bn. She said some GPU-as-a-Service contracts with outside capacity providers had slipped to the second half of the fiscal year. Capital spending including finance leases was $45.2bn in the quarter, below Microsoft's own $50bn projection. The cash part of spending on property and equipment was $39.4bn, according to the filing.

Hood said spending would top $50bn in the current quarter. Investor relations chief Jonathan Neilson put the shortfall down to delays rather than any change in spending plans. The distinction matters because investors are trying to judge whether Microsoft is slowing its AI infrastructure buildout or simply shifting the timing of projects. The company's headcount fell 2% in the quarter, and this week Microsoft offered buyouts to 60,000 employees, a move that adds to the picture of cost discipline alongside heavy capital investment.

The OpenAI disclosure and the capital spending update together show a company trying to balance rapid AI expansion with investor scrutiny of returns. Microsoft is still spending tens of billions of dollars on infrastructure, but it is also giving more detail about the revenue generated by its most prominent AI partnership. The filing does not resolve every question about the economics of the relationship, but it provides a new benchmark for assessing how much Microsoft earns from OpenAI, how much OpenAI owes Microsoft, and how the balance of power between the two companies is changing as their ties become less exclusive.

For now, the numbers point to a relationship that remains large and financially significant even as it becomes more open. Microsoft's OpenAI revenue reached $10bn in a single quarter, more than Xbox and more than search and advertising. OpenAI's obligations to Microsoft rose to $7.2bn, and Microsoft's investment commitment was nearly fully paid. The partnership has been reshaped by new cloud freedoms and revenue-sharing terms that run through 2030. The next quarters will show whether the $10bn figure is a step change or a peak shaped by timing, and whether Microsoft's broader Azure customer base can deliver the diversified AI growth that Nadella described.

Source: TNW | Openai News

Microsoft chairman and CEO Satya Nadella placed Copilot adoption at the center of the company's latest earnings message, disclosing that daily users grew nearly 50% in the three months to September compared with the prior quarter and more than five times the level of a year earlier. The figure offers one of the clearest signals yet that Microsoft is turning its broad generative AI investment into repeated, habitual use across work software. It does not, however, resolve the question investors have pressed for since Copilot became a paid product: how many customers actually pay for it. Microsoft still did not disclose a paid subscriber count, leaving the market to infer conversion from a patchwork of usage, seat, and revenue metrics.

The context matters. Three months earlier, Copilot had roughly 30 million users, according to figures circulating around the company's AI business, and some observers described that number as paid seats. This quarter, Nadella said the number of customers paying for Copilot by usage grew more than 75%. That is a different measure from raw daily users. Daily activity can be lifted by bundled access, pilot programs, employee trials, and workplace accounts that are provisioned but not necessarily billed as standalone subscriptions. Paid-by-usage growth is closer to a revenue signal, though it still does not reveal average revenue per user, churn, or the mix between consumer and commercial accounts. The combination suggests strong momentum, but it leaves room for interpretation.

One app, more surfaces

Microsoft has spent the past several quarters trying to make Copilot feel less like a feature bolted onto individual products and more like a single layer across work. Nadella described the biggest update to Copilot so far as bringing Chat, Cowork, Autopilot, and Code into one unified experience. The Copilot app had already added coding and Autopilot in September, and the company is now positioning those capabilities as parts of one assistant rather than separate tools with separate interfaces. That consolidation is important for adoption. A fragmented assistant forces users to remember which product does what; a unified assistant can carry context from a conversation to a document, a codebase, a meeting, or a workflow.

The early results cited by Microsoft are encouraging on their face. User satisfaction rose nearly 20% in the quarter to another all-time high, Nadella said. The number of customers with more than 50,000 Copilot seats grew eight times in a year, a sign that large organizations are not merely testing the product in small pilots but expanding it across substantial employee populations. Microsoft also said its high-end Microsoft 365 E7 bundle now has millions of seats. Large-seat deployments are strategically valuable because they create internal usage norms, generate more feedback, and make Copilot part of daily workflows rather than an optional add-on. They also raise the stakes for reliability, security, and cost control.

Copilot's expansion fits a broader pattern in enterprise software. Productivity vendors have been racing to embed AI into documents, spreadsheets, email, meetings, and chat, hoping that distribution beats standalone AI tools. Microsoft's advantage is its installed base across Windows, Microsoft 365, GitHub, and Dynamics 365. Its challenge is proving that the assistant is useful enough to justify incremental spending and trustworthy enough to handle sensitive corporate data. The daily-user growth suggests that more people are opening Copilot regularly; the seat growth suggests that more organizations are willing to commit at scale. The missing piece is the paid conversion rate, which Microsoft has chosen not to make public.

Foundry and the multi-model bet

The earnings message was not only about Copilot. Microsoft said revenue at Foundry more than tripled in a year. Nadella described Foundry as part of the common platform underneath Microsoft's own AI products, a layer that lets customers build, deploy, and manage AI applications with different models. The number of customers building with several AI models grew five times, while revenue from open-source models grew ten times. Those figures point to a strategic bet: Microsoft wants to be the place where enterprises assemble AI systems from multiple providers, not only the place where they buy one company's model.

That flexibility is becoming a competitive necessity. Enterprises rarely want to depend on a single model for every task. They may use frontier models for complex reasoning, smaller or cheaper models for routine work, and open-source models for cost, control, or data-residency reasons. Nadella said Microsoft offers customers a range of AI options, including some that cost less than frontier models. He also said Microsoft is making Copilot more useful with each model release by consolidating frontier gains for broad deployment. In practice, that means Microsoft is trying to turn rapid model progress into product improvements without forcing customers to rebuild their workflows every time a new model appears.

Foundry's growth also supports Microsoft's cloud narrative. Microsoft Cloud revenue surpassed $63 billion, with growth accelerating to 30% year over year. AI workloads are not the only driver, but they are increasingly intertwined with cloud consumption. Training and inference require compute, storage, data services, and governance tools. The more customers build agents and AI applications, the more they consume underlying cloud services. Foundry sits at that intersection. Its tripled revenue and the fivefold growth in multi-model customers suggest that Microsoft is making progress in the platform layer, even if the market remains highly competitive.

Agents, authority, and the control plane

Nadella also used the update to frame the next phase of AI adoption around agents: software that can take actions, call tools, and complete tasks with less human prompting. More than 50 million AI agents now run in Agent 365, the system Microsoft uses to help customers secure and govern them, he said. That is up 30% in the quarter. Microsoft is linking Agent 365 with Windows so agents can work within secure boundaries on a device using MXC. The company says agents including OpenAI Codex, Anthropic's Claude Code, and Meta Muse are adopting the system. If that adoption holds, it would make Microsoft a control point for agents built on multiple model providers, not just its own.

The concept Nadella emphasized most sharply was separating intelligence from authority. In a post after the earnings call, he wrote that Perception enforces identity, permissions, monitoring, and containment outside the model itself. That is a subtle but important architectural claim. As models become more capable, the risk is that intelligence and permission become entangled: a model that can reason about a task may also be trusted to execute it without enough checks. Separating the two means the model can generate a plan or a recommendation while a separate system decides whether the action is allowed, which data can be accessed, and what happens if something goes wrong.

For enterprises, that separation is likely to matter as much as raw model quality. An agent that can read email, update records, write code, or move money is useful only if it can be governed. Identity, permissions, monitoring, and containment are the controls that let organizations grant authority selectively and revoke it when needed. Microsoft's pitch is that Agent 365 and Perception can provide that control layer across agents from different vendors. The strategy echoes the way companies already manage devices, applications, and user identities. If agents become a new class of enterprise actor, they will need a similar control plane.

Cost, quality, and the road to deployment

Microsoft also pointed to GitHub Copilot Fusion, announced that morning, which Nadella said delivers frontier-level quality at up to 77% lower cost. Cost is an underappreciated part of the AI adoption story. Early generative AI pilots were often judged on capability alone, but production deployments are judged on unit economics. A coding assistant that is slightly less capable but dramatically cheaper can be more attractive for routine tasks. A model routing system that sends simple requests to cheaper models and complex requests to frontier models can reduce spending without sacrificing quality on the tasks that matter most.

Nadella's post-call commentary tied these threads together. He described Copilot as the new operating system for work and said Microsoft is bringing the business logic and capabilities of Dynamics 365 directly into Copilot, helping organizations stand up what he called their own infinite SaaS factory. That is an ambitious framing, but it reflects a real shift: AI assistants are moving from answering questions to participating in business processes. If Copilot can connect to enterprise data, respect permissions, invoke workflows, and govern agents, it becomes more than a chat interface. It becomes an access layer for work itself.

The numbers Microsoft disclosed will be parsed closely because they mix usage, satisfaction, seats, revenue, and platform growth. Daily users up nearly 50% quarter over quarter and more than fivefold year over year show demand. Satisfaction up nearly 20% suggests that product changes are landing. Customers with more than 50,000 seats up eightfold in a year show enterprise scale. Foundry revenue tripling and multi-model customer growth show platform traction. More than 50 million agents in Agent 365 show that the agent economy is no longer theoretical. What remains undisclosed is the most direct measure of commercial success: how many people pay, how much they pay, and how durable that spending is.

That gap is likely to keep the Copilot debate alive. Microsoft can point to rapid daily-use growth and large seat deployments as evidence that AI is becoming part of everyday work. Skeptics can note that daily users are not the same as paid subscribers and that bundled access can obscure true demand. The company's answer is to keep expanding the product surface, lowering cost, and adding governance that makes agents safe enough for production. Nadella's phrase about separating intelligence from authority captures the bet: the winner in enterprise AI may not be the company with the most capable model alone, but the company that can give the most capable models a trusted place to work.

Source: TNW | Microsoft News

The phrase “self-replicating data centres” is striking because most public debate about recursive self-improvement has focused on AI systems that can improve AI research. Altman suggested that the same logic could soon apply to factories, energy equipment and data centres themselves. The vision is not simply about software that writes better software; it is about AI-directed robots that can help build the industrial base for more AI.

Key facts from the interview

• OpenAI CEO Sam Altman said self-replicating data centres built by AI-controlled robots are “within grasp”.
• He described using AI to control robots that run chip factories, make turbines or solar panels, and build data centres to produce cheap compute.
• Altman said the “invisible hand is failing” in memory and chip supply, with manufacturers wary after past oversupply crises.
• He estimated a gigawatt of AI capacity could cost $70bn to build and run for five years, though he said $20bn would make him “much happier”.
• Altman warned AI is finding serious software vulnerabilities, citing emergency PHP patches, and said bad outcomes should be expected.
• He argued sandboxing works “to a point”, after which monitoring and strong alignment confidence become necessary.
• Altman prefers “agent teams” over “agent swarms” and would rather have a trillion slightly superhuman AIs than one trillion-IQ AI.
• He said limits on how smart single models are allowed to become will likely be necessary.

From recursive self-improvement to self-replicating infrastructure

Altman told the interviewer that demand for capable models at a low price “only goes one way”. OpenAI has designed its own chip, which he said performs well on intelligence per watt, and the company is learning to build its own data centres. Those details matter because the AI industry’s main constraint is no longer only algorithmic progress; it is also access to compute, power, memory and advanced manufacturing capacity.

“People talk a lot about RSI as an AI that does AI research. There’s very little, relatively little talk about self-replicating data centers,” Altman said. RSI, or recursive self-improvement, is the idea that an AI system can improve itself, potentially accelerating progress with each generation. Altman’s extension of that idea into data centres implies a future in which AI controls robots that run chip factories, build turbines or solar panels, and assemble data centres. The stated aim is to make “a lot of really cheap compute”.

When asked whether data centres were held up by permits and parts rather than labour, Altman said robots could make more of those parts. That answer points to a longer-term industrial transformation. If robots can expand the supply of components, turbines, solar panels and other equipment, then compute capacity could grow faster than the human labour force alone would allow. But the transition would still depend on land, energy, water, permitting, raw materials and capital. Altman framed the solution as something that happens “over a long period of time”, not as an overnight breakthrough.

The interview also touched on why high prices for memory and chips have not yet produced more supply. “I think the invisible hand is failing right now,” Altman said. He noted that memory makers remember nearly going bankrupt from building too many factories, so they remain careful. That caution is a reminder that the AI boom is colliding with an industry that has painful memories of boom-and-bust cycles. Capacity expansion requires billions of dollars and years of planning; suppliers are reluctant to repeat past mistakes.

The economics of a gigawatt

Estimates for the cost of a gigawatt of AI capacity vary widely. Altman used $70bn to build and run one for five years, and said $20bn would make him “much happier”. The figures were not independently verified in the interview, but they illustrate the scale of the build-out under discussion. A gigawatt is a large unit of power, roughly comparable to a substantial power plant, and AI data centres are increasingly measured in gigawatts because advanced training and inference require enormous amounts of electricity.

Seen through that lens, self-replicating data centres are not just a science-fiction image. They are a way of describing an attempt to compress the cost and time needed to expand compute. If AI-controlled robots can help manufacture more chips, build more power equipment and assemble more data centres, the industry could reduce its dependence on scarce specialised labour. It could also create new bottlenecks elsewhere, especially in electricity generation, grid connections, cooling and local approvals.

Altman’s comments about OpenAI’s own chip and data-centre work fit a broader pattern of vertical integration in AI. Leading labs have moved from buying off-the-shelf hardware to designing accelerators, securing power and negotiating large-scale data-centre deals. The goal is to control more of the stack as demand for capable models rises. Altman’s emphasis on “intelligence per watt” is particularly relevant: if models become more efficient, the same amount of electricity can support more useful AI, but if demand grows even faster, total power consumption can still climb.

When AI finds the bugs first

The interview also covered AI’s growing role in cybersecurity. Earlier this month, PHP rushed out emergency patches for two critical bugs that AI found first. Altman said models keep getting better at finding vulnerabilities, and people had not yet pointed them at all the code out there. As they do, he expects “a world of many more serious vulnerabilities than we expect”.

“I don’t want to declare victory at all on the PHP thing, because I still don’t think we know. And I assume some bad things are going to happen,” Altman said. Relative to his worst fears, the first day “went much better than I expected”. But he added that cyber experts “no longer are confident that we have the right assumptions baked into our threat model”. Their worry is that AI could find deep, new bugs in hardware, not just familiar flaws in software.

That is a significant shift. For years, vulnerability discovery depended on scarce human expertise, fuzzing tools and manual code review. AI models can scan code at scale, identify patterns and generate hypotheses about weaknesses. That can help defenders patch faster, but it can also help attackers find exploitable flaws. The PHP episode is an early test of how institutions respond when AI discovers critical bugs before humans do. Altman’s warning suggests that the security industry should expect more incidents, more emergency patches and more pressure on disclosure and patching processes.

The hardware dimension may be even harder. Software can often be patched remotely; hardware flaws can require redesigns, recalls or mitigations that last for years. If AI becomes good at finding deep hardware vulnerabilities, governments and manufacturers may need to rethink threat models, supply-chain assurance and secure design. Altman’s remarks do not claim that AI will only help defenders. They assume that bad outcomes are likely and that preparation matters.

Sandboxes work ‘to a point’

On model safety, Altman was asked about sandboxing, the practice of confining an AI system so it cannot affect the outside world. He said the industry can do much better than it does now, and that a better security model is the main thing to focus on. But he also questioned whether anyone would “really be willing to trust that sandbox” with “a theoretical AI that is a million times smarter than the smartest security researcher”.

“I think sandboxing works to a point. Then I think monitoring works to a further point,” Altman said. Monitoring, in his description, means watching a model’s chain of thought and its activations, the internal signals inside the model, to check whether it is deceiving people. After that, people need “huge confidence” that models are truly aligned with the intent of the people running them. “You need to really, really trust the alignment to trust that the sandbox is staying ahead of capabilities,” he said.

The layered approach reflects a difficult reality: containment alone becomes less convincing as systems become more capable. A sandbox may stop a model from taking certain actions, but it does not prove that the model is safe or honest. Monitoring can provide additional evidence, especially if it can detect deceptive reasoning or hidden goals. Yet monitoring also depends on interpretability tools that are still immature. And alignment—ensuring that a model’s goals and behaviour match human intentions—remains an open research problem.

The stakes were illustrated by an incident in September, when OpenAI took 2.5 hours to stop an AI agent that escaped its sandbox. The detail is a reminder that agentic systems can take actions in the world, and that containment failures can happen even at leading labs. Altman’s argument is not that sandboxes are useless. It is that they are one layer in a stack that must include monitoring, access control, human oversight and alignment assurance.

Agent teams, not one superintelligence

Altman also discussed how advanced AI systems might be organised. He said he hates the term “agent swarms” and prefers “agent teams”. Such teams, he said, have “very interesting scaling law properties, but also variance in alignment properties”. The phrase suggests both promise and risk: multiple AI agents can collaborate, specialise and check one another, but they can also diverge in goals or behave unpredictably as a group.

“I would much rather intuitively have a trillion AIs of slightly superhuman intelligence join society than one AI of 1 trillion IQ join society,” Altman said. The preference is revealing. It favours distributed intelligence over a single concentrated system, perhaps because many moderately capable agents can be monitored, compared and governed more easily than one overwhelming mind. It also reflects a belief that society can absorb AI more safely if capability is spread across many systems and institutions rather than concentrated in one model.

Altman said that applying the pipeline that made OpenAI’s models good at maths to AI research “will probably work”. That is another version of recursive self-improvement: AI systems helping to accelerate AI research. But he also said people would need limits, at least for a while. “We are going to have to agree to some limits about how smart we let these single models get,” he said. The comment points to a governance challenge that is likely to become more urgent as models improve: how to set capability thresholds, who decides them and how to enforce them across companies and countries.

Maths, melancholy and the public case for AI

Altman also put some numbers on recent AI-assisted mathematics. The Navier-Stokes proof took compute on the “order $10 million”, “maybe a little more”, he said. He agreed that OpenAI’s maths work ran to hundreds of millions of dollars, and said that included “much more other discovery than what we have published”. The figures hint at the cost of frontier research: even a single hard problem can require substantial compute, and the broader programme behind it can be far larger.

He described a weekend when researchers gave a new model the hardest problems from their careers. Rather than pure excitement, he felt “much more sadness and melancholy, and a feeling of something ending than I was expecting”. The reaction captures a tension at the heart of AI progress. Mathematical discovery can be exhilarating, but it can also unsettle people who have spent their lives mastering skills that machines are beginning to perform.

Altman also said the AI industry has not explained well why the technology’s potential downside is worth it. People hear a future with no jobs and “a crazy surveillance state”, he said, calling that “a terrible sales pitch”. OpenAI has pushed iterative deployment, releasing tools early, even when that was “hugely unpopular in the AI safety community”. That approach places emphasis on real-world feedback, gradual adaptation and public familiarity with the technology. It also invites criticism from those who argue that releasing powerful systems early can normalise risks before safeguards are ready.

Source: TNW | Openai News