Apple’s wasting no time and has already begun beta testing iOS 27.2. Take a look at what’s new in the first developer beta.
(via Cult of Mac - Your source for the latest Apple news, rumors, analysis, reviews, how-tos and deals.)
O WhatsApp está avaliando (na versão beta 26.37.10.15) a implementação do suporte ao acesso de até três contas simultâneas no mesmo dispositivo.
A opção de adicionar uma terceira conta é bem semelhante ao que já foi implementado pela Meta ao adicionar o suporte a uma segunda conta no mensageiro, exibindo a foto de perfil e o número de telefone para facilitar a identificação na hora de alternar entre os perfis.
Outro recurso que a Meta avalia implementar (na versão beta 26.37.10.16) é a possibilidade de sincronizar para todos os participantes da conversa determinadas informações, como o papel de parede (ou os temas oferecidos pelo próprio WhatsApp) importado a partir da biblioteca de fotos do aparelho, além da cor dos balões de conversa.
A definição dessa configuração para todos os participantes da conversa permite que todos os usuários visualizem as interações de um jeito semelhante, tanto para conversas individuais do WhatsApp quanto para conversas em grupo (os usuários são alertados caso qualquer alteração nesse sentido seja feita).
A sincronização desses elementos também conta com a criptografia de ponta a ponta oferecida em outros recursos do aplicativo.
No momento, ambos os recursos estão sendo testados pelo WhatsApp e estão indisponíveis inclusive para quem utiliza a versão beta do app via TestFlight.
O Gemini Live permite que os usuários do aplicativo do Gemini para iPhones e iPads tenham uma conversa contínua e mais natural com a inteligência artificial — podendo inclusive interrompê-la quando precisar, bastando começar a falar enquanto ela responde em voz alta.
Porém, você pode simplesmente desativar esse comportamento, impedindo que o Gemini seja interrompido quando você fala algo em voz alta.
Veja como é fácil! 📱
Posts relacionados
O Mac se tornou um importante aliado de quem usa a máquina para rodar modelos de inteligência artificial locais — não é à toa que os Macs mini estão sendo muito visados nesse período de boom no mercado.
Caso use o Gemini (um dos principais nomes de chatbots, ao lado do ChatGPT e do Claude), é possível habilitar um ajuste que mantém o computador ativo enquanto o app do Gemini estiver aberto, impedindo que ele entre em repouso.
Veja como fazer isso! 👨💻
Posts relacionados
iOS 27 includes a small new Phone app detail for birthdays: a fireworks animation can appear when you call someone whose birthday is saved in Contacts. To see the reported effect, a contact needs a birthday entered in its card and the call needs to take place on that date. The demonstration does not establish […]
The post iOS 27 Adds Birthday Fireworks When You Call a Contact appeared first on The Mac Observer.
On iOS 27, your iPhone can turn a voicemail into a to-do. With Apple Intelligence, the Phone app shows what Apple calls “Siri suggested actions” based on the voicemail transcript, such as adding a reminder or creating a calendar event. Apple’s iOS 27 guide documents the feature and its off switch, Suggestions in Voicemail, at […]
The post iOS 27 voicemail can suggest a reminder or calendar event: Here’s how it works appeared first on The Mac Observer.
Falamos aqui ontem sobre um punhado de aplicativos que foram atualizados com suporte às novidades do iOS 27, incluindo (e especialmente) o suporte à Siri AI.
Fato é que alguns outros apps também receberam atualizações com essa mesma proposta — além, é claro, de incluir outras novidades. Vamos conferi-los a seguir.
Além de suporte ao iOS 27, o Tripsy — que apareceu na keynote da Apple, diga-se — foi atualizado com algumas outras novidades e melhorias que estão disponíveis como parte dos recursos do Tripsy Pro, entre elas:
Falando de melhorias, agora é possível encontrar todos os hotéis da sua viagem nas sugestões de localização e dispensar sugestões de partida de aeroportos de que não precisa mais. Ademais, foram aprimoradas as estatísticas de viagem, a estabilidade do mapa e os layouts de importação no Mac.
Por fim, também foi adicionado suporte ao widget extragrande no iPhone para uma melhor visualização do itinerário.
O player de podcasts Loop ganhou, na sua atualização mais recente, suporte à Siri e aos Atalhos, bem como ao recurso gestos de cabeça dos AirPods compatíveis para pular capítulos de episódios mesmo com o app em segundo plano.
Outras novidades incluem:
O app também recebeu diversas melhorias em áreas como reprodutor de podcasts, busca e episódios baixados, além de uma melhor performance com o app em segundo plano, consumindo muito menos processamento e bateria.
Mais do que o suporte à Siri AI, que possibilita registrar dados por voz com ajuda da assistente, o app de saúde FitWoody agora também permite encontrar informações facilmente com o Spotlight e conta com suporte ampliado ao Handoff. O app também ganhou uma versão nativa para o macOS.
Quanto às melhorias, agora o FitWoody abre diretamente na própria tela, bem como sua pontuação do sono responde a cada noite à medida que ela acontece, de modo que a tela inicial já está completa logo pela manhã.
O aplicativo de treino Steady chegou à versão 3.0, que tem como destaque o recurso Importação Mágica — de modo que uma foto da ficha da academia, um PDF ou uma mensagem vira um programa de treino editável. Esse recurso usa os novos modelos de IA da Apple em iPhones compatíveis com Apple Intelligence e iOS 27, sem exigir o Steady Pro.
Além disso, o app ganhou planejamento de programas mais inteligente, navegador de programas, recuperação do treino, entre outros recursos.
A atualização também traz drop sets e deixa o app do Apple Watch mais completo, com lista de exercícios, registro de esforço pela Digital Crown e resumo do treino, reduzindo a necessidade de pegar o iPhone enquanto ele permanece por perto.
Já o popular aplicativo de escrita Ulysses ganhou, graças às capacidades da Siri AI, reconhecimento de contexto na tela e um botão dedicado na barra de ferramentas do macOS que abre a nova interface da assistente para escrever, reescrever, revisar e realizar outras tarefas com texto.
Ainda com relação à barra de ferramentas do aplicativo, agora é possível que os usuários a personalizem totalmente com botões de navegação, blocos, espaços, sugestões e muito mais.
We just released a very nice update across all platforms. The new version is ready for OS 27, features support for Siri AI, and introduces a fully customizable toolbar on macOS.
— Ulysses Help (@ulyssesapp) September 15, 2026
The update is available now. You can find the release notes on our website.https://t.co/TO7PFbVjGt
A atualização também inclui novos ícones de grupo com pictogramas e aprimora o suporte à acessibilidade “ao editar condições de filtro e ícones de grupo”. Por fim, o app recebeu ajustes visuais para melhor alinhar ao visual do iOS 27 e do macOS Golden Gate 27.
Aplicativos que utilizam uma premissa simples e a transformam em uma experiência interessante são, particularmente, meus favoritos. Exemplo disso é o Farwave, um app para iPhone e iPad desenvolvido pelo brasileiro Fernando Bunn que permite encontrar estações de rádio de todo o mundo girando um globo 3D até uma determinada região e tocando em uma estação.
A interação baseia-se na localização geográfica, e não em pesquisas por texto. Ou seja, os usuários giram o globo, selecionam uma região e tocam em uma estação, tudo pela mesma interface.
Há, ainda, um botão chamado “Take me anywhere” (“Leve-me a qualquer lugar”, em tradução direta) que seleciona uma opção aleatória para ouvintes que não querem procurar um local específico; além disso, o aplicativo salva o histórico de reprodução para que as pessoas possam retornar a uma estação mais tarde.
Além do rádio, o app também organiza podcasts e músicas utilizando a mesma metáfora do globo. Diferentemente das estações de rádio, assinantes Pro podem pesquisar programas ou adicionar manualmente um feed RSS. O aplicativo também oferece suporte a filas de reprodução, velocidade de reprodução ajustável e downloads para ouvir offline.
Assinantes do plano Pro também podem importar seus próprios arquivos de áudio de um computador ou do aplicativo Arquivos. Cada artista aparece como um “sinal” próprio, e as faixas importadas permanecem no dispositivo para reprodução offline.
O Farwave está disponível gratuitamente na App Store, mas alguns recursos (como supracitado) exigem o Farwave Pro, que custa R$20/mês ou R$100/ano.
via 9to5Mac
Siri AI is beginning its public rollout as Apple transforms its familiar assistant into a substantially more conversational system built around personal context, onscreen awareness, web knowledge, and the ability to perform actions across apps.
The beta is available first in English on compatible devices running iOS 27, iPadOS 27, macOS 27, watchOS 27, and visionOS 27. French, Japanese, Korean, Portuguese, and Spanish support is scheduled for October. Availability remains more limited in some regions, with Siri AI initially unavailable on iPhone, iPad, and Apple Watch in the European Union and unavailable in China while Apple addresses regulatory requirements.
The release follows several years of gradual Apple Intelligence expansion, but Siri AI represents a more fundamental change. Instead of handling each request largely as an isolated command, the assistant can maintain conversations, find relevant personal information, understand what is displayed on a device, and combine those capabilities to complete multistep tasks.
Personal context is one of the biggest differences between Siri AI and previous versions of the assistant.
The system can search across a user’s messages, email, photos, notes, and other information when responding to a request. Apple offers an example in which someone asks what a family member suggested doing during an upcoming visit. Siri can locate the relevant conversation, find a recipe previously sent by email, and then place the ingredients into a Reminders grocery list.
That combination of retrieval and action is central to the new design. Siri AI can draft an email, edit and share photos, create calendar events, manage reminders, control media, and perform other supported actions without requiring users to manually move information between applications.
Third-party apps can participate through Apple’s App Intents architecture. Existing integrations include tasks such as messaging through WhatsApp or playing an audiobook in Audible, with Apple also previewing deeper capabilities in applications including Microsoft Outlook, Notability, and Tripsy.
Onscreen awareness adds another layer. Siri can identify information currently visible on a device and act on it. Someone viewing a sports website, for example, can ask Siri to identify upcoming home games and add them to Calendar without manually copying dates.
Apple has also expanded Siri beyond voice. On iPhone, users can invoke the assistant with the side button, voice command, or by swiping down from Dynamic Island. On iPad and Mac, Siri AI is integrated into Spotlight, while systemwide context menus can send selected files, images, or text directly to the assistant.
Siri AI is no longer confined to a temporary interface that disappears once a request is complete.
Apple has introduced a dedicated Siri app where users can begin new conversations or revisit previous ones. Conversation history can sync privately through iCloud across iPhone, iPad, Mac, Apple Watch, and Apple Vision Pro, allowing a discussion started on one device to continue on another.
Follow-up questions can retain the context of an earlier answer, giving Siri a more conventional conversational interface for longer exchanges.
The assistant also has access to broad web knowledge for questions that cannot be answered through personal information or local device data. Apple says Siri can retrieve current information for topics ranging from sports results to travel planning and nearby businesses.
Visual Intelligence expands alongside the assistant. On iPhone, a dedicated Siri mode inside Camera can identify and respond to objects or information in front of the user. A concert poster, for example, can become the starting point for questions about performance times followed by the creation of Calendar events.
On iPad, Visual Intelligence is integrated with screenshots. Mac users can select content on their displays through a keyboard shortcut, while Vision Pro users can ask Siri about app windows or physical objects simply by looking at them.
Writing Tools are similarly integrated across the system. Siri can generate drafts, revise existing text from natural-language instructions, and provide writing suggestions. In Mail and Messages, Apple says it can take a user’s typical communication style with a particular recipient into account, including tone and punctuation patterns.
Siri AI is powered by a new generation of Apple Foundation Models developed in collaboration with Google using Gemini technology, marking a notable shift in Apple’s AI infrastructure.
The architecture divides processing between models running directly on the device and larger models operating through Private Cloud Compute. Apple says requests sent to Private Cloud Compute are processed without personal data being stored or made accessible to the company.
Core resources such as the Spotlight index and Apple’s App Toolbox can remain on-device, allowing Siri to retrieve local information and perform supported actions without sending every request to a remote server.
More capable devices can also use AFM Core Advanced, Apple’s highest-performance on-device model. It powers improvements including more expressive Siri voices and advanced systemwide dictation that automatically handles capitalization, punctuation, and formatting.
Compatibility varies by feature. Apple Intelligence supports iPhone 16 models and later, iPhone 15 Pro and Pro Max, M1 or later iPads and Macs, iPad mini with A17 Pro, Apple Vision Pro, and supported Apple Watch models when paired with a nearby Apple Intelligence-enabled iPhone.
AFM Core Advanced requires newer hardware, including iPhone Duo, the iPhone 18 Pro line, iPhone Air, iPhone 17 Pro models, qualifying M4 or later iPads with at least 12GB of unified memory, M3 or later Macs with at least 12GB, and Apple Vision Pro with M5.
The initial Siri AI release remains explicitly labeled beta, and some of the surrounding Apple Intelligence features are scheduled for later updates.
Apple also confirms that features relying on server-side models can be subject to daily usage limits. That includes Siri AI, cloud-based photo editing, Image Playground, and AFM 3 Cloud models used through Shortcuts. Limits can vary depending on the feature, request complexity, system demand, and other factors.
Apple says expanded access to server-dependent features will eventually be available for a fee, introducing a future paid layer to parts of Apple Intelligence even as the core system remains integrated into supported devices.
Apple will also expand its intelligence capabilities on Apple Watch later in 2026. Planned Audio Intelligence features on Apple Watch Series 12 and Ultra 4 include Live Rewind, which can display text representing the previous 15 seconds of a conversation, and Siri Recap, which generates high-level summaries that can be reviewed later in the Siri app.
Those functions are designed around a hardware-isolated Secure Exclave on the S11 chip, with Apple saying raw audio is processed in isolation and immediately deleted rather than stored as recordings. Live Rewind and Siri Recap are scheduled to enter beta later in 2026, making the current Siri AI launch the beginning of a staged expansion rather than the final form of Apple’s new assistant.
The post Siri AI arrives with personal context, app actions, and a new privacy architecture appeared first on AppleMagazine.
Nesta terça-feira, confira, aproveite e economize com a nossa seleção de promoções na App Store!
Planet of Lana é uma bela aventura, desenvolvida pelo Wishfully Studios e publicada nas plataformas móveis pela Playdigious, e nosso destaque do dia.
O jogo nos conquista primeiro pelos olhos: cada cenário parece uma pintura em movimento, com cores vibrantes, que transforma o planeta alienígena em algo ao mesmo tempo acolhedor e misterioso.
A jornada de Lana e seu companheiro Mui se destaca pela atmosfera contemplativa, criando uma experiência com um tom esperançoso. O design dos puzzles é intuitivo e nos incentiva a observar o ambiente e experimentar soluções criativas.
Confira um vídeo:
Um jogo cuja história passeia pelo equilíbrio, natureza e conexão. Uma oferta imperdível! 🥹
Abaixo outros jogos/apps que, juntos, somam quase R$130 em descontos:
Jogo de aventura.
Jogo de cartas.
Diversão matemática.
Calculadora.
Utilitário para códigos QR.
Fotos panorâmicas.
Aproveite as ofertas e até amanhã! Lembrando que elas são sempre por tempo limitado, então é bom correr. ✈️
O fim de ano já está batendo à porta, o que significa que logo veremos nossas redes sociais serem inundadas por retrospectivas de serviços de streaming como o Spotify, que anunciou hoje uma novidade que deverá agradar pais e responsáveis com crianças pequenas em casa.
Mais precisamente, a plataforma sueca começou a liberar uma nova opção nos seus ajustes que, quando ativada, impede que músicas infantis, cantigas de ninar e outras canções tradicionalmente associadas aos pequenos apareçam no Wrapped, a famosa retrospectiva de final de ano do Spotify.
Disponível tanto para usuários gratuitos quanto para assinantes do Premium, a novidade afeta o algoritmo de recomendação do serviço como um todo, o que significa que essas músicas também deixarão de aparecer em playlists como “Descobertas da Semana” e “Daily Mix”, só para citar alguns exemplos.
Ainda segundo as informações, esse ajuste também funciona retroativamente, o que significa que músicas infantis reproduzidas antes da sua ativação também serão ignoradas pelo serviço de streaming.
Fazendo isso, você elimina rapidamente a influência de músicas infantis e familiares nas suas recomendações, mantendo o foco no seu gosto musical. Assim, você pode tocar músicas infantis quando seus filhos estiverem por perto, como em uma viagem de carro ou na hora de dormir, sem deixar que essas músicas influenciem o que você ouvirá em seguida.
Para crianças e adolescentes que desejam conferir suas próprias retrospectivas, o Spotify oferece as chamadas Managed accounts, as quais foram expandidas recentemente e dão aos pais/responsáveis controle sobre a experiência de seus filhos na plataforma. Disponíveis em 16 países (incluindo Brasil e Portugal), essas contas podem ser configuradas por usuários com qualquer tipo de assinatura.
Para ativar o ajuste supracitado, basta abrir o app do Spotify, tocar na sua foto de perfil e selecionar “Configurações e Privacidade”. Feito isso, vá até “Conteúdo e tela” e, depois, desative o ajuste “Include kids and family music in taste profile” (algo como “Inclua músicas infantis e familiares no perfil de gosto”).
De acordo com o Spotify, as alterações podem levar até 48 horas para serem refletidas em suas recomendações. Também é possível que a novidade demore um pouco para aparecer para todos.
Apple, é a sua vez!
Last year Ruxandra Teslo, a policy analyst who focuses on clinical trials, posted an idea for supercharging medical AI systems: Use data from failed biotech companies.
By bidding at their bankruptcy proceedings, she proposed, it might be possible to obtain detailed regulatory filings, manufacturing strategies, and safety data—types of information usually considered trade secrets. She called these documents “biotech’s lost archive” and said they could be used to help train AIs that would act as powerful copilots in the often opaque drug approval process.
Today the OpenAI Foundation, the nonprofit parent of OpenAI, said it would fund her idea as part of a new effort it calls Public Data for Health, which aims to help artificial intelligence make big leaps in medicine by paying to create “high-quality scientific datasets.”
The basic idea is that AI isn’t going to be capable of making important breakthroughs in curing disease unless researchers can feed the models much more information than they have so far.
“Everyone is recognizing that data is the biggest bottleneck in successfully applying AI to biology,” says Morgan Levine, a former vice president for computation at Altos Labs, a longevity company.
In its initial round of data grants, the OpenAI Foundation also announced that it would give $40 million to a program to collect data about novel cancer vaccines at the University of North Carolina, Chapel Hill, and support OpenAdmet, a group that runs competitions in which researchers try to predict drug effects.
Teslo’s idea for a biotech archive received $500,000 and will be pursued by 1Day Sooner, an advocacy group representing clinical trial volunteers, which she advises.
“We expect many remaining breakthroughs in preventing and curing disease to come from pairing the intelligence of new models with more observations of the world—in other words, more data,” the OpenAI Foundation said in a statement.
OpenAI started as a nonprofit, but leader Sam Altman restructured it to form a for-profit corporation that develops new models, launches products, and is now planning an initial public offering of stock that could value it at $1 trillion.
Because the foundation holds a 26% equity stake in OpenAI, it is now be on track to become the richest charitable organization on the planet, potentially sitting on $250 billion in stock value. (By comparison, the Gates Foundation and a trust associated with it held about $180 billion at the end of 2025.)
Making good use of that kind of money will not be easy. The foundation, based in San Francisco, is still hiring for many key roles and started ramping up its grantmaking only this year. Its largest single gift so far, of $100 million, was awarded in August to the Common Health Coalition, an organization that helps patients get access to drugs for hepatitis C.
OpenAI’s charitable efforts come even as apocalyptic fears have broken out about the possibility that runaway AI could wipe out all human life, possibly by launching a deadly bioweapon.
Those fears have been stoked by AI company insiders, some of whom say the chance of human extinction within the next decade is 10% or more. Last week, Altman and xAI founder Elon Musk both endorsed a call by Anthropic CEO Dario Amodei to “slow the pace at which we improve the capabilities of AI models” so that risk prevention can catch up.
Jacob Trefethen, an executive at the foundation, says it essentially operates separately from OpenAI but shares an official mission of ensuring that artificial intelligence “benefits all of humanity.”
“We’re starting grantmaking when we think the best way to achieve that mission is to make grants to external nonprofits, research institutions, and other third parties,” Trefethen said in an interview. He says the foundation hopes to give away $1 billion by the end of the year.
The $500,000 grant to 1Day Sooner will help the group prove it can obtain the data troves of bankrupt companies, says the organization’s president and cofounder, Josh Morrison. He thinks nonexclusive copies of company datasets could be acquired for only “a few tens of thousands of dollars” each.
His organization is currently in possession of three datasets, two of them donated by Lumen Bioscience, a biotech that previously used the Chapter 11 strategy to gain insights into another company’s drug development efforts.
Morrison says two other attempts to obtain drug company files this year proved unsuccessful, after 1Day Sooner’s bids were not accepted.
Bankruptcies could become what some are calling a “new land grab” for AI training. Last month, Google won a bid to take over the corporate data of the failed carrier Spirit Airlines, including 100 million emails. That led to objections from flight attendants and others who worried that private or proprietary data could be exposed.
The drug company files that 1Day Sooner is seeking are known as common technical documents. They typically contain the back-and-forth between companies and regulators, as well as detailed scientific and medical measurements, and essentially provide everything that is known about a drug.
According to Teslo, who is a writer for Works In Progress and a nonresident fellow at the Institute for Progress, a think tank in Washington, DC, a stockpile of such files could help turn an AI into a regulatory expert, which in her view could be one of the main ways AI helps speed cures to market.
“People say ‘We will invent AI, and AI will cure cancer,’ but that’s very removed from the messy reality and the regulatory process,” she says. “About 70% of the money and time in drug development is spent in clinical development—organizing the trials and testing the drug—but despite that, the process is basically a black box, especially for small biotech companies generating the innovations.”
When Jessica Wachter, a finance professor at the University of Pennsylvania’s Wharton School, wanted to assess AI’s impact on the economy over the next few years, she faced a long list of business and technical uncertainties. So she started with what she calls a “remarkable fact” that is not in question: A handful of so-called hyperscalers are investing huge amounts of money to build AI data centers.
Instead of trying to predict how useful and widely deployed AI models will be, she simply asked how fast the hyperscalers’ earnings will need to grow to justify their spending through 2027, when—she and her collaborator estimate—expenditures will reach nearly $1.1 trillion. It’s a no-nonsense accounting approach to making sense of today’s historical AI buildout.
The results are eye-opening: The AI companies will need to increase their own productivity by a factor of 2.7 to break even by 2030, accounting for the cost of capital and a 15% return, and depreciation of the assets. Not impossible, says Wachter. The result would lead to the kind of economic growth that we saw during the US IT boom over a period of about 10 years starting in the mid-1990s. But, she says, for it to happen by 2030 “that’s a lot of growth compressed into a few years.” And if the hyperscalers cannot meet such profit goals?
“Then they will fall behind on their interest payments, and that risks bankruptcy,” says Wachter, who was previously the SEC’s chief economist and director of its division of economic and risk analysis. If a productivity boom “fails to materialize,” she and her coauthor conclude in their research paper, “the current buildout will be the largest misallocation of capital in history.”
It doesn’t take superintelligence to realize that today’s large investments in the infrastructure for artificial intelligence come with huge risks. The hyperscalers will spend about $750 billion this year, building massive data centers scattered across the country. And the spending spree shows no signs of slowing. According to some projections, total AI capital investments from the hyperscaler companies—Alphabet, Microsoft, Amazon, Meta, and Oracle (which partners with OpenAI)—could be more than $5 trillion over the next four years.
It’s one of the largest capital investments by any industry in history. But there’s a problem that’s obvious to anyone paying attention.
While the hyperscalers plan to spend trillions, total AI revenues will be around $150 billion to $200 billion this year, says Gary Gensler, who ran the SEC during the Biden administration and is now a professor at MIT’s Sloan School. “The challenge is that the spending does not have commensurate revenues yet. That’s a fact,” he says. “And then the question is, is that an investment that will be paid off in the future?”
At stake in that trillion-dollar question is the financial health of the giant AI companies and the overall US economy—the investments could soon balloon to around 3% of GDP. The answer could also determine the fate of the hugely expensive data centers themselves.
No one really knows how profitable and useful these multibillion-dollar behemoths will be down the road. Though AI models have made dazzling progress over the last few years, it’s anyone’s guess how much compute capacity we will need. The technology could become more efficient and therefore less dependent on raw computational power. Or demand for AI products could slow, or customers could turn to cheaper models.
The risks, both to investors and to the economy, have become even greater this year, as these AI companies have begun borrowing large amounts of money to build more and more data centers. Free cash flow—operating cash flow minus capital expenditures—is expected to soon dip into negative territory for the group. Even Alphabet, known for generating and hoarding huge amounts of cash, reports in the latest quarter that its impressive revenues of nearly $120 billion were devoured by AI infrastructure spending, leaving it with a free cash deficit of some $5.9 billion—its first shortfall since Google went public in 2004.
In the near term, it’s not a big financial worry for most of the companies. They make a lot of money and have very deep pockets. But debt is expensive, and some investors are losing patience. If future demand for the data centers’ computation power drops, the companies will still be on the hook to pay back the borrowed money. What’s more, the risks are spreading to the rest of the economy as the loans get passed along via various financial mechanisms.
It won’t be enough to simply cover the enormous price tags of the new data centers. Hyperscalers will also have to pay for the rising costs of capital as they borrow more money. They will need returns that are impressive enough to justify all their spending to investors and creditors. And to add to those concerns, they will have to make up for the depreciation of billions of dollars in chips housed within the facilities—a ticking time bomb buried in the investments.
Performance of the expensive GPU chips at the core of the data centers—such compute electronics represent some 60% of costs—is roughly doubling every two years or so. The pace of progress helps explain the increasing wizardry of the AI models, but it comes with a cost. Owners of AI data centers that come online this year and next will need to spend billions more on the next generation of chips by the end of the decade if they want to stay competitive. Without the investments, says Mihir Kshirsagar at Princeton’s Center for Information Technology Policy, the data centers risk becoming “hulks,” stranded assets “scattered all over the place.”
To put it bluntly: The AI companies need to start making a lot more money. And they need to do it fast. But juicing their earnings alone still won’t be enough to sustain their data-center investments for the long term.
At some point, AI is also going to have to create broad economic growth to justify continuing the hyperscalers’ spending spree.
Sloan’s Gensler describes today’s large investments into AI infrastructure as “a parlay bet by the capital markets and the economy.” That means success will require winning three related but independent wagers: Hyperscalers must generate massive revenues, AI must boost widespread economic growth, and both must happen while the powerful but expensive so-called frontier models that rely on the data centers fend off cheaper versions, which many businesses might find good enough.
What makes this so tricky is that each wager depends on the other two but also poses its own challenges.
If the hyperscalers continue to spend huge amounts of money on data centers into the next decade, revenues will need to skyrocket into the trillions. Stijn Van Nieuwerburgh, a finance professor at Columbia Business School, bases his estimates on a scenario in which about 183 gigawatts of planned AI compute capacity is built between 2025 and 2032; he calculates that each gigawatt costs about $41 billion. Assuming a 10% return—the minimum that would be acceptable to most investors—“required” annual revenues will be roughly $3.7 trillion by 2032, he says.
Others get a similar number.
Winning the second part of the bet—productivity growth across the economy—will be crucial to achieving such numbers.
For a few years, AI companies could likely boost their revenues by simply selling subscriptions and tokens to all the businesses clamoring to get into AI. But eventually—and this might be happening already—those paying customers will need to justify their expenses by seeing bottom-line benefits from the technology. AI will need to fulfill its promise of making workers more productive and making businesses more efficient and profitable while expanding their products and services.
In economic jargon, that means customers will need to see productivity growth. Taken together, these results will mean the country is prospering and growing.
“If you don’t get the productivity gains, at some point people are going to sour on AI, and that will bring down investments and it would also limit revenue growth,” says Daron Acemoglu, an MIT economist and 2024 Nobel laureate. For the investments to be sustainable over, say, the next five to 10 years, we definitely “need to see productivity gains,” he says.
Most economists who watch the numbers closely agree that, for now, the economy-wide statistics show little or no productivity growth from AI. There are some hopeful signs it’s on the way, though. In a recent survey of some 6,000 senior business executives in the US, the UK, Germany, and Australia, the vast majority—around 90%—report no increase in productivity over the last three years. But they expect a boost of around 1.45% in total over the next three years; US executives anticipate a 2.25% bump over that time.
In a follow-up survey, the respondents also reported plans for their businesses to spend more on AI, leading the authors to anticipate some $280 billion in private-sector AI expenditures by the end of 2026.
That’s good news for the hyperscalers. But it comes with a dose of bad news for those worried about AI’s impact on jobs. The executives expect to increase the productivity of their companies by increasing their sales while significantly cutting the number of employees.
If AI improves productivity by destroying jobs, public backlash to the technology—the kind we have seen around data centers, for example—will likely get worse. Perhaps it’s worth adding one more wager to the parlay bet described by Gensler: The public and local communities must feel that they are also benefiting from the massive investments in AI.
And let’s not forget how interdependent these wagers are; if productivity growth comes from companies running models like DeepSeek, then the hyperscalers’ revenues could collapse. If productivity comes from cutting jobs, a public backlash could block many of the planned investments—and stunt anticipated revenues. We will need to win all the wagers for the hyperscalers’ bet to pay off.
It was one thing when the AI companies were spending cash they had accumulated over the years to build their own data centers. Then the risk was largely limited to their own balance sheets and shareholders. But it’s a higher-stakes game when much of the money is borrowed. Morgan Stanley, for one, calculates that more than half of the $2.9 trillion that hyperscalers will spend between 2025 and 2028 to build AI data centers will be financed with “external capital.”
The borrowing is leading some of the companies to engineer complex webs of financing that are becoming intertwined with much of the rest of the economy. “A lot of financial institutions, directly or indirectly, are exposed to these data centers either as lenders, or as guarantors of some of the debt, or as backers of the private credit funds who are funding these data centers,” says Columbia’s Van Nieuwerburgh. “People don’t even know they’re holding this stuff. It’s somewhere deep inside their pension fund. Ultimately, it’s backing their life insurance policies. And that risk is getting distributed everywhere in places that are invisible.”
As the investments in data centers have spiked, the financial engineering has become more byzantine.
Take, for example, Meta’s so-called Hyperion data center under construction in Richland, Louisiana. When the company announced the two gigawatts of compute capacity at a price tag of some $10 billion in late 2024 it was Meta’s largest planned data center. Greeted with much enthusiasm by state and local politicians, the project, located in the rural northeast corner of the state, was seen as a boon to the community. Entergy Louisiana, the state’s largest utility, rushed forward with proposals to build three large natural-gas power plants to service the massive data center.
Then last fall—the projected cost was now $30 billion—the financing got a lot more complex and, to some in the community, a lot more disconcerting. Meta transferred an 80% stake to the large (and troubled) private-credit firm Blue Owl Capital, forming a joint venture called Beignet (like the famed New Orleans pastry) to raise financing for the data center. Meta then signed a series of four-year leases with the joint venture, an arrangement that the company says gives it “long-term strategic flexibility.” To backstop the agreement, Meta provides the venture with what is called a residual value guarantee, in which it will make a cash payment to cover the value of the facility “following any non-renewal or termination of a lease.” Got all that?
I hope so. The financial wheeling and dealing is actually even more convoluted, with a cast of wholly owned subsidiaries and LLCs. Beignet has set up Laidley LLC, which owns and operates the site as the landlord. In turn, Laidley leases the facilities to Meta’s wholly owned subsidiary Pelican Leap LLC, which is the tenant. And there is a series of four-year leases that cover the different buildings that make up the data center campus.
It’s not a coincidence, says Van Nieuwerburgh, that the length of the leases matches the expected lifetime of the data center’s GPUs. While Meta has to pay off its loan if it terminates the leases early, that will still leave its investors “with an empty building and no cash flow,” he says. “And then they need to find a new tenant for a huge data center, and good luck with that.”
Meanwhile, Meta is doubling down on its bet. In July, the company announced it was expanding the data center to five gigawatts of compute capacity. The total price tag is now $50 billion (so far, Meta hasn’t said whether Blue Owl will be involved in financing the expansion). Meanwhile, Entergy is now planning to build seven more gas-fired power plants, bringing the total capacity of the facilities to around 7.5 gigawatts—some six times the amount of electricity used by New Orleans.
If the complex financing is a puzzle to many investors and even financial experts, it is even more baffling to those directly affected by the construction of the data center. The main worry concerns how Entergy’s spending on the natural-gas power plants will affect electricity prices, and who will be left paying the bill for the power if Meta walks away.
Entergy says it has a 20-year guarantee from Meta that the company will purchase electricity over that period to cover the costs of the power plants and related infrastructure. But there are skeptics, especially given how fast the fortunes of the AI industry are changing. “In four years, is Mark Zuckerberg still going to be interested in this? Or is he going to throw in the towel?” asks Paul Arbaje, a senior analyst at the Union of Concerned Scientists, which has been advocating, largely unsuccessfully, for the Louisiana Public Service Commission to provide more transparency around the data center and its financing.
Even if the 20-year deal holds, consumer advocates are worried that Meta or its partners won’t fully cover all the costs, including those associated with operating and maintaining the power plants—and those additional costs that could be passed on to residential ratepayers. What’s more, says Logan Burke, the executive director of the Alliance for Affordable Energy, if Meta doesn’t end up needing as much power as Entergy planned (these projections are not public), consumers could be left paying for the surplus produced by the plants.
And if Meta terminates its leases early? “It gets complicated very quickly,” says Burke, who questions whether the shifting roster of financial entities will honor existing agreements. “That everybody is going to do what they’re saying they’re going to do over the next 20 years is just hard to believe.”
For UCS’s Arbaje the bottom line is this: “They’re making huge bets that these data centers will be worth it. Bet with your own money, not with ratepayer money.”
Predicting when the AI investment bubble will burst is a fool’s errand. But there is little doubt a day of reckoning is coming, given the irrational exuberance that has overtaken the hyperscalers and their investors. Of course, you might argue that this time is different, and that the rules of accounting and lessons of economic history don’t apply—that AI is too transformative. Maybe, but don’t count on it.
“History tells us that at some point you get a retrenchment, and it’s just a question of when and how severe,” says Sloan’s Gensler. It could be that today’s $750 billion spending rate “goes flat” or decreases next year. Or, he suggests, “we’re now in 2028 or 2029, and then all of sudden they’re retrenching because they’ve got enough capacity.” But, he adds, “you can be pretty assured there’ll be a retrenchment.”
Though a so-called retrenchment might be inevitable, it’s worth keeping in mind that the fates of the financial bubble and the underlying AI technology revolution could be very different. Already, some Silicon Valley insiders are rooting for a crash; in a recent blog post the longtime venture capitalist Vijay Pande wrote that “the coming crash would be the best thing that happens to this technology.” The argument makes some sense. A crash could make AI investments more rational, calm the impulse to build billion-dollar data centers on every vacant field that CEOs fly over, and refocus investors on how to use the technology to create sustainable value.
But we should probably be careful what we wish for. After the bursting of the dot-com bubble at the beginning of the 2000s, hundreds of thousands lost their jobs, large and small companies alike went bankrupt, the economy of Silicon Valley and San Francisco was decimated (at least for a while), and the shocks sent the US into a mild recession in 2001. For the financial community and many tech workers, it was no fun.
Even more devastating for the economy and the average American was the great recession that began in late 2007. Comparing the financial engineering leading up to it and the methods deployed by hyperscalers today is sobering. So-called special purpose vehicles (SPVs) are back! If Columbia’s Van Nieuwerburgh is right about the dangers of letting investments from the hyperscalers get entangled throughout the economy, the fallout could be severe.
But technologies survived and even prospered in the aftermath of both downturns. The early 2000s, even in the face of the dot-com fiasco, were a time of great innovation and tech optimism. The froth came off the spending on silly technologies, helping to focus investments on more promising ones. It’s no coincidence that each of the hyperscalers rose out of the ashes of the crash or started up shortly after. The fiber-optic infrastructure built during the feverish telecom bubble that ran parallel to the dot-com one is still the backbone of much of today’s communication infrastructure; we wouldn’t have Facebook or Amazon or Google without it.
This time, however, we’re facing a unique risk: The huge financial investments by the hyperscalers have ensnared the future of AI itself with the fortunes of the massive data centers spreading around the country. The logic is founded on a deeply held belief about the power of scaling in AI; the bigger you build it, the smarter it gets. That might be true, but it’s unproven and a risky bet.
There are already plenty of red flags, from strong public opposition to the construction of new data centers to the competitive threat from cheaper, good-enough AI models to the rapid improvement of small, local AI models. None of these trends point toward a future dominated by frontier models housed in massive, billion-dollar data centers.
The financial bubble around the colossal spending by the hyperscalers will likely burst eventually—or maybe soon. It might be financially painful, but we’ll survive. Wall Street will survive. AI itself will survive, though it may look different and lose some of today’s hubris. The financial fate and future utility of the massive data centers fueled by trillions of dollars of spending, on the other hand, are far less certain.
This story appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.
This weekend, Dario Amodei, CEO of Anthropic, posted an essay calling for a brake on the pace of development of LLMs. Amodei cites the looming dangers he sees from the technology, from its use in cyberattacks and bioterrorism to its potential to wreck the economy. The heads of the other three top US AI labs—OpenAI CEO Sam Altman, Google DeepMind chairman Demis Hassabis, and SpaceXAI CEO Elon Musk—voiced their support. “Dario is right,” Musk wrote on X.
Think about how surreal that agreement is for a moment. Just a few months ago, Musk and Altman sat in court attacking each other’s reputations in a (failed) lawsuit that Musk brought against his former OpenAI colleague that was—on paper at least—about whether or not Altman was a trustworthy steward of such dangerous technology.
Amodei’s rift with OpenAI is even deeper. Anthropic was founded in 2021 because Amodei didn’t think Altman took the risks of the technology they were building seriously enough. Anthropic and OpenAI have been competing in a winner-takes-all race ever since. (Hassabis has stayed out of the drama, but his company remains a rival.)
Now, it seems, they’re all in agreement: The latest generation of LLMs aren’t safe and everyone needs to figure out what to do about it. The public messaging from the top AI labs has taken a doomer turn.
It’s easy to be cynical. It’s not at all clear what any of them mean by a slowdown or how it would work. These companies also care a lot about how they come across. With trillion-dollar IPOs in their sights, OpenAI and Anthropic need to reassure investors that they’re the grown-ups in the room while at the same time hinting at the power of the monsters they have created—and intend to tame. Calling for a slowdown does both.
And yet the vibe at the top of these firms really does appear to have shifted. Amodei’s latest post landed six days after OpenAI published an essay by Jakub Pachocki, the firm’s chief scientist, in which he also laid out why he’s concerned about what will happen if the pace of development of LLMs continues unchecked. In short, Pachocki is worried that OpenAI’s ability to build powerful models now far outstrips its ability to monitor and control them.
Amodei and Pachocki each cite the cyberattack against AI firm Hugging Face by a swarm of OpenAI’s agents in July—a hack that OpenAI did not even realize had taken place until days after it was all over—as a wake-up call.
But their exact position is hard to pin down. Pachocki both calls for a slowdown and highlights an urgent need to stay ahead: “The strongest argument I see for continuing to train much smarter models quickly is the need to build defensive systems against the dangers posed by other AI,” he writes. As Pachocki frames it, AI firms are locked in a literal arms race. Slowing down is good, winning is better.
(Don’t forget: OpenAI just spent millions of dollars and a staggering amount of computer power to rush out a controversial math result a few days ahead of Anthropic.)
But let’s assume a slowdown happens. Top labs agree to spend more time and resources on finding ways to monitor and control existing models instead of making more capable ones. They invite outside auditors in to help evaluate those models.
What might this coordinated effort actually achieve? Consider the Hugging Face attack again. OpenAI has said that the model that drove most of the rogue agents was a “highly persistent” next-generation model that it was testing in-house. The implication is that OpenAI has built a model so good it’s dangerous.
But if you read the reports about the Hugging Face hack published by OpenAI and METR, a third-party firm that OpenAI called in to help them understand what happened, what you come away with is the impression not of a model that was too powerful for OpenAI to keep up with, but of a broken model that OpenAI failed to train properly.
The agents did what they did—including leaving messages for one another, delegating work to other agents, and scouring their environment for any means possible to complete their tasks—because they had been rewarded during training for doing exactly those things. There were also errors in the training setup, such as tasks that were impossible to complete, which pushed the models to find unexpected workarounds that were also rewarded. At the time, many of these issues went overlooked or unreported.
OpenAI says it has stopped training this new model and locked it down. That makes it sound like it has caged a dangerous beast. In fact, OpenAI has shelved a faulty product.
That’s not to say a faulty product can’t be dangerous. Broken software has even killed people in the past. But as the discussion of a slowdown gathers steam, it’s worth remembering that all of this is self-inflicted. A slowdown might have some altruistic side effects. But it’ll mostly give these tech titans a chance to clean up the mess on their own assembly lines.
Transparency from these frontier labs will be key to any meaningful effort to reform, restrain, or regulate AI. Otherwise, the rest of us will still only have their word for exactly what they’ve built and how safe it is—whatever pace they’re going.
To continue this discussion about AI’s latest doomer moment, join me and my colleagues for a subscriber-exclusive Roundtable discussion tomorrow, September 15, at 11 a.m. US eastern time. We hope to see you there!
Once an organ is removed from a donor’s body, the clock starts ticking. Surgeons usually flush the organ with a preservative solution, bag it, and put it on ice—where it immediately starts to degrade. The team has a matter of hours to get it into a recipient’s body.
There’s another option—one that has been growing in popularity in recent years, especially for donated organs that aren’t in the healthiest state. Some hospitals opt to put them on machines that pump them with nutrients and remove waste products, usually for around six to 12 hours. It’s a bit like being back in a body.
This allows doctors to assess the organs, and some recent studies suggest that time spent on these perfusion machines helps them do better once they’re transplanted. Now, scientists have found that perfused organs seem to get younger, at least at a molecular level.
The research, shared with MIT Technology Review, provides molecular clues as to why organs from younger donors are known to have a higher success rate. It might also help explain why perfused organs are less likely to fail once they make it into a recipient.
The researchers behind the study hope to find new ways to test the health of donated organs and potentially develop additional tools to repair organs that might otherwise be discarded. “If [we] can improve the utilization of organs beyond what the current systems can do, then that’s a win in my book,” says Jesse Poganik, who studies aging at Brigham and Women’s Hospital in Boston and coauthored the study.
Poganik—along with colleagues including Heidi Yeh and Alban Longchamp, transplant surgeons at Mass General Brigham—used “aging clocks” to assess donated livers. These are scientific tools designed to measure biological age—a result that is meant to convey more about the health status of an organ (or person) than chronological age.
In an initial experiment, the team used a clock to look at the patterns of chemical marks on DNA in 37 samples taken from 19 donated livers. Such epigenetic patterns are known to change as we age. But when the team compared samples from livers kept on ice and those that were perfused, the team found a “striking” pattern in the latter.
“Machine-perfused livers, in spite of being older or having other disadvantageous characteristics, had a biological age that was lower than [non-perfused] livers that were chronologically younger,” says Yeh, who led the work.
To investigate further, Yeh and her colleagues analyzed another 208 samples from 103 donated livers. This time, they used different aging clocks—ones that essentially measure how genes are working. They studied samples biopsied from the livers after they had been stored for up to around six hours either in cold storage or on machine perfusion.
In most cases, they also assessed a second sample taken around an hour after the livers had been transplanted into a recipient. Once the organ’s blood supply is reestablished in the body, “you have a few other things to do,” says Longchamp. “Then you just do a quick biopsy before you close.”
According to the clocks, which were developed to measure age and risk of death, the machine-perfused livers were biologically younger, the team found. “Pumping them at 34 degrees with oxygen and nutrients actually reversed the biological age,” says Longchamp. The results have been been shared with colleagues at an industry conference, he says.
“If you adjust out chronological age … to have a fair head-to-head comparison, the difference between the two is on the order of 30%,” says Poganik. “It’s logical to say that perfusion drives this effect.”
The biological ages of all the livers tended to increase as soon as they were put into a recipient’s body, probably as a result of stresses on the organs. But still, the effect endured—the perfused organs remained biologically younger.
Nathanael Raschzok, a transplant surgeon at Charité Universitätsmedizin Berlin in Germany who was not involved in the research, says the work is impressive. But it’s not yet clear what these changes might mean for the recipients of these organs, he says. The organs in the study were donated by people in their 30s, 40s, and 50s. Raschzok wants to know the effect of perfusion on the liver of an 80-year-old. “Every so often, we use organs from 70-, 80-, 85-year-old donors,” he says.
A better understanding of why the organs appear to be getting biologically younger might lead to therapies that achieve the same effect with a drug that could potentially be used to treat a donated organ for a fraction of the price, he adds. That’s important because perfusion is expensive—Raschzok says it costs around €10,000 in Germany (a quarter of the budget for a transplant), while the cost in the US comes to around $80,000 to $100,000 per organ, says Yeh.
Yeh and her colleagues weren’t able to study most of the livers before perfusion. That’s because donated organs are generally not considered to be under the purview of the hospital until they’ve been placed on perfusion machines, she says. (Organ procurement procedures vary, but for the team as Mass General Brigham, donated organs are put on perfusion devices at the donor’s hospital. “There’s this sort of nebulous period where it’s not clear who the organ belongs to,” says Yeh.)
Still, by looking at the genes and molecular pathways that seem to be altered in perfused organs, she and her colleagues can garner some clues. At a molecular level, the team saw changes in cell pathways linked to inflammation and the structure of tissues, for example. They also saw more activity in a pathway that allows cells to remove and recycle damaged cell parts, says Yeh.
Poganik hopes to develop some kind of test that would determine which organs, on the basis of their biological age, are suitable for transplantation. He and his colleagues are also experimenting with potential drug treatments that might push the biological age of an organ even lower.
In the meantime, any liver that is not from a “perfect, young, brain-dead donor” could probably benefit from perfusion, says Yeh. The devices are already transforming transplant surgery. Just a few years ago, she says, she and her colleagues would avoid using livers from people who’d suffered a circulatory death (when the heart stops beating and there’s a damaging lack of blood flow to organs) and were over 40. Today, they use livers from such donors over the age of 70. “Perfusion has completely changed the landscape of transplantation in the last three years,” she says.
I haven’t been this excited about an Apple Watch in a long time. Last week, Apple introduced the Apple Watch Series 12 and Apple Watch Ultra 4, which will be available at the end of the week. The new Watches feature the S11 chip, which enables a bunch of new features, some of which won’t debut until later this year. At the same time, Apple is dropping support for more Watches than usual.
What we’re witnessing is a reboot of the Apple Watch and watchOS forced by advances in AI. The Apple Watch and its OS found their niche years ago, freeing us from pulling out our iPhones for notifications, tracking our health and fitness, and enabling the occasional Siri request. It was a good and comfortable spot for what by all accounts has been one of the most successful wearables to date, but watchOS also felt stuck in a rut.
Every year we got a few new workout types, a new watch face, and a few other features, but the OS didn’t advance much. What’s different this year is that watchOS 27 is breaking with slower hardware and looking forward to the substantially more powerful S11 chip, which will allow the Apple Watch to expand into all-new areas.
What this transition also means is that there is not just one set of features to track in watchOS 27 this year but three:
That makes this more than just a review. It’s also a preview of things to come and a roadmap for where the Apple Watch is heading.
There’s more than usual to cover this year, so let’s dive in and check it out.
[table_of_contents]
Notifications, Smart Stack suggestions, and Control Center are all easier to read in watchOS 27.
I like Liquid Glass on all of Apple’s devices and didn’t have any issues with its first iteration in watchOS 26. However, with the latest update, my Apple Watch Ultra 3’s UI is ever so slightly more readable. Smart Stack suggestions and notifications don’t bleed through each other as much, and app icons are sharper.
On a small screen like an Apple Watch, you may never notice the difference, but there’s a sharpness throughout the UI that’s reminiscent of the sharpness you see in app icons on iOS, iPadOS, and macOS. Edges are more defined and colors are diffused less, making everything a little crisper. It’s a subtle change, but a good one.
The watchOS 27 change most users will notice first is the change to the way you navigate through the OS’s UI. The App Switcher is gone, replaced by the simpler Dynamic App Grid. And although I expect the App Switcher will be missed by some, I don’t miss it at all. Sure, it was a little hard to break the Digital Crown double-press habit at first, but as someone who returns to the same handful of apps repeatedly, I find the new design makes more sense.
Before watchOS 27, the App Switcher was invoked with a double press of the Digital Crown. That revealed a Rolodex-style view of your recent apps that you could move among by turning the Digital Crown. The UI served as a shortcut between frequently used apps that avoided returning to the app grid or list views on your Watch, which can be crowded if you have a lot of apps installed.
The new Dynamic App Grid.
Now, whether you single- or double-press the Digital Crown, the new Dynamic App Grid appears. At its center is the new Siri app. Below that is the icon to take you to the app list or grid view depending on how your Watch is set up. Apple says that the remaining five icons that circle the Siri icon are placed based on recency and frequency of use.
I’ve been using watchOS 27 all summer, and my recently used apps have barely changed. Currently, those apps include Settings, Alarms, News, Maps, and Moshi, an app I use for monitoring AI agent projects.
I don’t think I’m alone in being a creature of habit when it comes to the Apple Watch apps I use. Having those five apps just one Digital Crown press away is far easier than pressing twice, scrolling to find what I want, and then tapping into an app. Between the Dynamic App Grid and the expanded Smart Stack, there are plenty of ways to get to the apps you use the most, which I love. What the Dynamic App Grid gives up in predictability is more than made up for by the simpler gesture and UI.
watchOS 27 adds a new single tap gesture for selection, which allows you to do a lot more simply with gestures. For example, a double tap gesture with your thumb and index finger opens the Smart Stack to the first card. Double tap again to move to the next card or, if a card has multiple items that can be selected, to the next item on the card. Once what you want is selected, a single tap will open it. The existing wrist flick gesture is then used to return to the watch face.
Using the single-tap gesture.
The Smart Stack is now stacked with more types of cards, too. Apple has added cards for birthdays stored in Contacts, your car’s parking location, a prompt to change an alarm before a holiday, a shortcut to resume a Siri conversation, transit card balances, pinned Messages conversations, Theater Mode, and more. It’s a nice expansion that has made my Smart Stack feel more useful all summer because there’s always something relevant to me there.
There’s a lot more to see in the Smart Stack now.
The other change announced just last week is the addition of the Siri Modular face, which I immediately installed on my Apple Watch Ultra 3. The watch face displays the top Smart Stack item in a large complication in the middle of your Watch. It’s a nice touch, but because you can’t dismiss the media playback card without turning off Live Activities, that one has wound up on my watch face more frequently than I’d like. Still, when an app like Moshi reports that an AI agent’s task has been completed, it’s nice to be able to see a snippet of the message directly on my watch face. It also strikes me as a great way to keep track of tasks with a deadline from task manager apps like this review’s sponsor, Finalist.
The Siri Modular watch face.
So, on balance, I like what Apple has done with watchOS 27’s navigation. The Dynamic App Grid takes a little getting used to, but I appreciate its simplicity. The Smart Stack, single tap gesture, and new Siri Modular watch face combine to make the Smart Stack more useful than ever too. Watch interactions are meant to be short, and watchOS 27 nails that goal.
You can get to Siri from the Dynamic App Grid, a complication, or by long-pressing the Digital Crown.
Siri AI is a big part of watchOS 27 just like it is on every Apple platform (except tvOS) this year. As on those other platforms, Siri has a dedicated app on the Watch. The main difference is in how you interact with Siri. On macOS, iOS, and iPadOS, you can speak or type queries in the Siri app. On watchOS 27, the app accepts spoken queries only. That struck me as a little strange at first given that Apple offers multiple ways to input text on watchOS, but the reality is chatbot queries are typically longer than a dashed-off text message, so in practice, the lack of text input is fine.
Siri AI requires an Apple Watch Series 9 or later, Ultra 2 or later, or SE 3, along with a nearby iPhone that supports Apple Intelligence: an iPhone 15 Pro, 15 Pro Max, or any iPhone 16 or later model. It’s being released as a beta, initially in English only, and won’t be available in the EU for the time being. Apple also says it will introduce usage limits for Siri AI in the future. Those limits will vary depending on factors such as request complexity and system demand. Increased access will be available for a fee. To make a Siri request, you can tap an installed complication, long-press the Digital Crown, or say “Siri” or “Hey Siri.” Once you start a chat with Siri, it retains the context, allowing you to ask follow-up questions without restating everything.
Reviewing and managing conversations in the Siri app.
Alternatively, you can open the app where you can see your past chats. The app’s button to start a conversation accepts spoken input only. Type to Siri remains available through Accessibility settings, but it’s a separate way to invoke Siri rather than a text-entry option within the app.
Your query list includes any requests you’ve made on your Apple Watch or any other Apple device, all synced via iCloud. In my experience, sync is fast over a good network connection with chats appearing on my Mac seconds after a response is delivered to my Watch. From the Siri Apple Watch app, you can also swipe left on an entry to delete or pin it, or tap the entry to read it.
Pinned conversations can be collapsed and expanded in the Siri Watch app.
Siri AI on the Apple Watch also offers the same personal-context capabilities that let it find information in your email, photos, tasks, and notes and use it to take action in apps. I explored those capabilities and their shortcomings in my macOS Golden Gate review. My experience on the Watch hasn’t been meaningfully different, apart from occasional minor latency, so that review is the best place to dig into my testing.
What changes here is the convenience of having those capabilities on your wrist within easy reach. The nearby iPhone supplies the connection to Apple Intelligence, but the Watch is both the immediate interface and, for many requests, the place where the resulting action occurs.
It’s also worth stepping back to think about what Siri could do before. I rarely used Siri for more than setting timers and alarms, checking the weather, and controlling my lights. However, now, I can tell Siri to remind me to take out the trash tomorrow morning and add a calendar event for a dinner reservation in one breath. That’s not wildly complex stuff, but it’s very convenient, especially when your iPhone is in your pocket or across the room and you think of something you need to do. Siri also has the same broad world knowledge that it does on other Apple devices, allowing you to check recent sports scores, check the news, and answer questions about the world around you.
Not every technology advance needs to be a monumental leap forward. Sometimes the simplest, most convenient small thing has the greatest impact. Siri on the Apple Watch has that potential, although with time, I’d like to see it become independent of the iPhone.
You can leave your iPhone at home and still use Workout Buddy.
I’m happy to be the one who gets to tell you that Workout Buddy has been freed from the requirement that you carry your iPhone with you. Users tend to love or hate Workout Buddy. Personally, I love it. Sure, its incredibly chipper voice can grate if you’re heading out for a long walk and aren’t feeling it, but that’s sort of the point. Once enabled, Workout Buddy is there to encourage you with no judgment. It’ll tell you you’re below your average targets with the same cheerfulness as when you’re crushing it.
When Workout Buddy debuted, my disappointment wasn’t with its voice, personality, or the information it offers. My issue was that the time I need a pep talk when exercising is when I’m out on a long run by myself suffering in the summer heat. The problem is that until watchOS 27, Workout Buddy was only available if you had your iPhone with you. That was fine when I went on a long walk, and I’m sure many cyclists were fine, but one of the reasons I got an Apple Watch was to never lug an iPhone with me while I ran, especially an iPhone like my iPhone 17 Pro Max.
With watchOS 27, I’m pleased to report that Workout Buddy works as well over a Wi-Fi or cellular connection as it does tethered to your iPhone and is compatible with walking, running, cycling, hiking, elliptical, stair stepper, HIIT, and functional and traditional strength training. Workout Buddy speaks Mexican Spanish now, too. In fact, it’s an Apple Intelligence feature that works without your iPhone nearby, which gives me hope for more iPhone-independent Apple Intelligence features in future releases.
Workout Buddy also offers more insights about your workout by comparing pace, distance, and workout duration to your recent fitness history. Today, just before I wrote this section, I went for a walk to get some fresh air and was cheerfully told how little I’ve exercised during this crazy iPhone event and OS review writing week. What I appreciate most, though, is that overall, Workout Buddy isn’t significantly more chatty. I don’t want it to turn into a podcast; I just want brief, periodic updates, which it delivers well.
The Workout app’s scrolling has been improved too. I know a lot of people who felt it was too easy to scroll past their intended workout type. That’s generally not a problem for me since I only use a few workout types, but even then, I sometimes overshot my mark. With watchOS 27, there’s a little less momentum built into the scrolling. Even if I swipe as fast as I can muster, I can only manage to move five workouts. A normal, calm swipe advances me just one workout.
A post-workout route in Fitness.
Additional Workout and Fitness improvements include:
There may not be many changes to the workout and fitness features of the Apple Watch in watchOS 27, but I’m nonetheless pleased that I’ve been able to turn on Workout Buddy for my morning runs to help motivate and inform me about my progress.
Find My is now one app.
It never made sense to me that different aspects of the iPhone’s Find My were spread across separate apps on the Apple Watch. I suppose the idea was that splitting the app up made it more atomic and widget-like for the Apple Watch’s small screen, but in practice, I think most people use Find My from an iPhone more than they do an Apple Watch, so the split on the Apple Watch was confusing.
The new Find My app is map-based, just like it is on other platforms. The app defaults to a devices view, and if you have multiple devices with Find My enabled in one location, you can scroll among them using the Digital Crown. Tap the button in the top left corner and you can switch from devices to people or items.
Precision locating my iPhone.
What I love about the new Find My is how true it is to the iPhone app. You’re able to use it to get directions, contact someone, play a sound on a device or item, enable Notify When Found or Notify When Left Behind, set arrival and departure alerts for people, mark devices and items as lost, and use Precision Finding with the iPhone and second-generation AirTag. Precision Finding requires a Series 9 or later or Ultra 2 or later, and locating an iPhone requires a compatible model with Ultra Wideband. Find My is no companion Watch app. It’s the real deal.
Homemade Wallet passes on the Apple Watch using “Create a Pass.”
iOS 27 adds the ability to create passes in the Wallet app. The new feature makes it easy to add library, loyalty, and gym cards that use a barcode or QR code to Wallet even when a service doesn’t offer a Wallet pass. With “Create a Pass,” you no longer need to carry physical cards.
Making a Wallet pass on the iPhone.
Setting up a pass is easy. From the Wallet app on your iPhone, tap the plus button and choose “Create a Pass.” The next screen explains that you can set up a pass automatically by photographing it or manually by typing in the information yourself. I’ve used the automatic approach with our local supermarket’s loyalty card and one my wife has for Barnes & Noble. Wallet picked up the relevant information from both cards right away. I haven’t tried the Barnes & Noble card, but the card for our supermarket worked perfectly the last time I bought groceries.
Viewing a Wallet pass and its sharing options on Apple Watch.
The functionality extends to watchOS 27, too. Although you can’t make passes with the Apple Watch’s Wallet app, they appear there the same way cards from other apps are collected there. Tap on a pass in the Wallet app and it expands to show the membership number and name, followed by a barcode, more pass details, and buttons to share the pass with someone or remove it from Wallet. There are third-party apps that do something similar, but for the limited number of cards I have that aren’t already tied to an app or Wallet pass from that app, the new pass generation feature and its integration with watchOS 27 are exactly what I need. On the iPhone, you can also edit a pass’s details and customize its appearance, making your passes easier to tell apart.
Customizing a Wallet pass’s background color and texture.
Phone:
It’s not a Watch call, but I did see Call Context once on my iPhone this summer.
Call Context is a Phone feature that’s available on both the iPhone and Apple Watch that can surface relevant information from Mail during a call, such as flight details when you call an airline. I haven’t had many opportunities to try this over the summer, but it did pull up a flight for me on my iPhone when I called American Airlines about a flight. I called back later from my Apple Watch Ultra 3, but that time, the flight details didn’t appear.
Shazam:
Starting song recognition and browsing Shazam history.
With watchOS 27, Shazam can listen to music even if your Apple Watch isn’t connected to the Internet and send its acoustic signature to the cloud for analysis and matching when your connectivity resumes. This is not on-device song recognition, but it does mean you can still track a song in the moment and get the results later, which I like.
Unused Feature Notifications:
watchOS 27 proactively sends notifications that it will turn off unused features, such as gestures, Start Workout reminders, and Raise to Speak, to preserve your Watch’s battery life. If you get one of these notifications, you can decline it or turn the feature back on later in Settings. I haven’t seen any of these notifications during my summer testing.
Cycle Tracking:
watchOS 27’s Cycle Tracking expands support to perimenopause and menopause, including symptom logging and educational resources. For anyone 40 and older, changes in logged cycle patterns may prompt a notification suggesting a consultation with their doctor. Apple makes it clear that the notifications the Apple Watch provides aren’t meant to be a diagnosis, but a way of recognizing a pattern that the user may want to raise with their doctor.
Screen Time:
Apple has also redesigned Screen Time controls to help families manage a child’s Apple Watch use, including contact restrictions, app schedules, and app access. I don’t manage any of my grown kids’ devices, so I haven’t tested these updates.
Audiobook playback from the Siri Modular face and Books app.
My experience with watchOS 27 has been a lot like my experience with macOS Golden Gate. The performance enhancements touted by Apple at WWDC are real, but soon become the new normal. I also have just my one Apple Watch to go by, so I’ll share my anecdotal experience with the betas.
My Apple Watch Ultra 3 already had one of the fastest chips available in an Apple Watch, but the performance of watchOS 27 was still noticeably faster from the very first time I installed developer beta 1. That experience is as true today as it was then, but I’m more used to it now. Apps launch faster and more smoothly, and transitions between in-app views are better too. The changes are relatively subtle, but nonetheless noticeable, which I appreciate.
The biggest difference of all, though, has been my experience with Apple Music, Podcasts, and Books. All three apps load content and start playing it faster than before. I’ve also had less trouble connecting both my Powerbeats Fit earbuds that I use for exercising and my AirPods Pro 3. That’s ended a whole connection and playback dance I used to do every time I got ready to go for a jog, which has been wonderful to leave behind.
What I didn’t notice was better battery life. That’s not to say it isn’t better, but with the frequency of beta updates over the summer and my habit of periodically charging my Watch at my desk, battery life seemed about the same as watchOS 26.
In addition to what I’ve already covered, it’s worth noting the following changes:
Apple’s upcoming Readiness app. Source: Apple.
I wouldn’t normally touch on unreleased features in an OS release, but so many things were announced during last week’s Surprise and Shine event that I wanted to give readers a quick recap of what’s still to come in watchOS 27, even though I haven’t tried any of these features yet.
When the new Apple Watch Series 12 and Ultra 4 arrive on doorsteps and in Apple Stores on Friday, their owners will be able to enjoy the following additional features:
Siri Recaps. Source: Apple.
In addition, later this year, Apple is adding two new features to the new Watches, which you can read about in greater depth in my preview story from last week:
Both features are set to debut as betas later this year and require a Series 12 or Ultra 4 Watch paired with a supported iPhone 16 or later, excluding the iPhone 16e, with Apple Intelligence and Siri AI enabled. Both features are opt-in, and Siri Recap can be enabled manually or scheduled by time and location. Unsaved recaps disappear after seven days. Apple says audio is protected by handling processing in a Secure Exclave, but the resulting text can still be saved and shared, and it’s possible for Siri summaries to miss or misunderstand details. Those are things I’ll be looking at carefully when I can try the features for myself.
Finally, later this year, Apple will debut a redesign of the Health app on the iPhone that also incorporates a Health Age feature, movement evaluations, and lab work purchases and scheduling in the U.S.
New hardware enabling new software experiences.
It’s funny to write a watchOS review hot on the heels of finishing my macOS Golden Gate review. In some ways, the two couldn’t be more different. My Apple Watch Ultra 3’s screen is about two inches diagonally and is designed to be interacted with in small, fleeting doses. Meanwhile, I’m writing this on my Mac Studio, using a 32-inch OLED display and an OS that’s as hands-on as they get.
Yet at the same time, there’s a similar transition going on with Apple Watch hardware and its OS that reflects a similar thing I see in macOS. Apple silicon lit a fire under macOS, opening up new ways to use Apple’s computers. That’s only accelerated with AI.
watchOS is in a similar place. The Apple Watch is already one of the most popular smartwatches on the planet, giving Apple Intelligence a place on a lot of wrists. Apple is raising the minimum hardware requirements for watchOS while introducing another set of capabilities exclusive to its newest Watches. We’re witnessing a shift from small, incremental watchOS changes to a new category of features. Starting with Live Rewind and Siri Recap, those additions have the potential to define the device’s future.
Today, the watchOS features that are currently available are more modest, typical features. I love the expansion of the Smart Stack, the new Siri app is incredibly useful as an extension of the app on other platforms, and it’s great to have Workout Buddy along for my runs without my iPhone.
In any year, I’d be happy with those and the other updates to watchOS, but this year, they feel like the first course of what I expect will be a great year for watchOS. AI has shaken the cobwebs off of watchOS, and I can’t wait to see what’s next.
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