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AI NewsGoogle has joined Amazon in attacking Nvidia’s stronghold with AI app developers

Google has joined Amazon in attacking Nvidia’s stronghold with AI app developers

3:34 AM IST · October 21, 2026

Google has joined Amazon in attacking Nvidia’s stronghold with AI app developers

On Tuesday, Googleannouncedthat TorchTPU, its long-awaited version of PyTorch, is now available as an open-source project. With this, Google joins Amazon in trying tobreak Nvidia’s strangleholdon the AI industry, where most AI apps are written by default to run on Nvidia’s chips, or GPUs. PyTorch is the popular open-source toolkit that AI app developers and startups use to build, train, and run AI models. It is, by some accounts, used by 85% of the AI apps. PyTorch was originally developed by Meta (then Facebook) in 2016, long before the world had an inkling that LLMs would change the world. At the time, Nvidia’s GPUs were already the standard hardware for AI research, so many of PyTorch’s features were built to work specifically with them. In 2022, Meta turned PyTorch over to the Linux Foundation, a nonprofit that manages many important open source projects. Google’s version, TorchTPU, lets AI apps run on Google’s AI chips, called Tensor Processing Units (TPUs). It also makes it easy to move existing PyTorch apps to TPUs and, therefore, to Google Cloud. Developers can do this with a simple configuration change and no re-writing of their apps. Amazon made asimilar announcement Monday,when it released its own version of PyTorch, called TorchNeuron. TorchNeuron allows AI apps to be ported to Amazon’s homegrown AI chip, Trainium, and to AWS with no rewriting. Amazon also said it going even further in its effort to loosen Nvidia’s hold on PyTorch. It is submitting code to the main PyTorch project that decouples various features from Nvidia’s chips, making it more open to any hardware by default. Google may be one-upping Amazon in a few ways, however. It says it worked directly with Meta, Hugging Face, Inferact (maker of vLLM) RadixArk (maker of SGLang), and others to ensure that TorchTPU’s features and code libraries work well with these popular AI development tools, just as the original PyTorch does. Plus the search giant has launched a dedicated tool, theTPU agent suite, to help developers move their apps to Google’s chips and cloud and fix errors along the way. It even updated its popular LiteRT tool, which runs AI on phones, laptops and embedded devices, to work better with PyTorch AI apps. None of this means AI app developers will automatically ditch Nvidia’s hardware en masse just because they can now easily move to alternative chips and clouds. But Amazon and Google are clearly working to end Nvidia’s advantage.

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ChatGPT now lets you lock your chats

ChatGPT now lets you lock your chats

OpenAIannouncedon Tuesday that users can now lock ChatGPT conversations to keep sensitive chats private when others may have access to their screen or device. Users can secure their locked chats with a four-digit PIN, or use Face ID or Touch ID on supported devices. Unlike the app’s Temporary Chats, which provide a separate session that doesn’t appear in chat history, users can return to Locked Chats at any time. The company says the feature is designed for situations where users might be discussing something personal or planning a surprise and want to keep those conversations private when handing their device to friends or family to show them something in another chat. The addition of locked chats adds a new layer of privacy to ChatGPT, as the app is increasingly used forpersonal conversations. Of course, while locking a chat can limit access, it doesn’t address concerns about how sensitive information is stored or handled. OpenAI notes that locking a chat doesn’t create end-to-end encryption or a separate encrypted vault, change how long it retains the chat, remove information that ChatGPT remembered before a chat was locked, or change the “Improve the model for everyone” setting. Once users lock a chat, it moves to a dedicated “Locked Chats” section, where it’s kept separate from regular chat history, pinned conversations, and chat search results. With Memory enabled, Locked Chats has its own separate memory shared across conversations within the section. New messages sent after locking a chat can update that memory, but don’t update regular Memory. That means new information from locked chats won’t leak into regular chats. OpenAI says resetting a PIN permanently deletes all locked chats, files, and other content protected by the previous PIN. Additionally, the Locked Chats option isn’t available for temporary chats, archived chats, group chats, chats created from a shared link, chats with scheduled tasks, and chats with meeting recordings. Locked Chatswill be available to users on ChatGPT Free, Go, Plus, Pro, Business, Enterprise, and Edu plans. The company notes that the feature is not available on ChatGPT for Healthcare. The feature is starting to roll out on Tuesday, so it may not be live yet for all users.

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Google has joined Amazon in attacking Nvidia’s stronghold with AI app developers

Google has joined Amazon in attacking Nvidia’s stronghold with AI app developers

On Tuesday, Googleannouncedthat TorchTPU, its long-awaited version of PyTorch, is now available as an open-source project. With this, Google joins Amazon in trying tobreak Nvidia’s strangleholdon the AI industry, where most AI apps are written by default to run on Nvidia’s chips, or GPUs. PyTorch is the popular open-source toolkit that AI app developers and startups use to build, train, and run AI models. It is, by some accounts, used by 85% of the AI apps. PyTorch was originally developed by Meta (then Facebook) in 2016, long before the world had an inkling that LLMs would change the world. At the time, Nvidia’s GPUs were already the standard hardware for AI research, so many of PyTorch’s features were built to work specifically with them. In 2022, Meta turned PyTorch over to the Linux Foundation, a nonprofit that manages many important open source projects. Google’s version, TorchTPU, lets AI apps run on Google’s AI chips, called Tensor Processing Units (TPUs). It also makes it easy to move existing PyTorch apps to TPUs and, therefore, to Google Cloud. Developers can do this with a simple configuration change and no re-writing of their apps. Amazon made asimilar announcement Monday,when it released its own version of PyTorch, called TorchNeuron. TorchNeuron allows AI apps to be ported to Amazon’s homegrown AI chip, Trainium, and to AWS with no rewriting. Amazon also said it going even further in its effort to loosen Nvidia’s hold on PyTorch. It is submitting code to the main PyTorch project that decouples various features from Nvidia’s chips, making it more open to any hardware by default. Google may be one-upping Amazon in a few ways, however. It says it worked directly with Meta, Hugging Face, Inferact (maker of vLLM) RadixArk (maker of SGLang), and others to ensure that TorchTPU’s features and code libraries work well with these popular AI development tools, just as the original PyTorch does. Plus the search giant has launched a dedicated tool, theTPU agent suite, to help developers move their apps to Google’s chips and cloud and fix errors along the way. It even updated its popular LiteRT tool, which runs AI on phones, laptops and embedded devices, to work better with PyTorch AI apps. None of this means AI app developers will automatically ditch Nvidia’s hardware en masse just because they can now easily move to alternative chips and clouds. But Amazon and Google are clearly working to end Nvidia’s advantage.

1 hour ago

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OpenAI acquires startup Orchestra Bio⁠ to beef up its biotech business

OpenAI acquires startup Orchestra Bio⁠ to beef up its biotech business

OpenAI on Tuesday acquired a four-year-old AI biotech software startup called Orchestra Bio, the companyannouncedvia its website. Terms of the deal were not disclosed, but the startup had reportedly raisedaround $20 millionas of 2025 from backers like Andreessen Horowitz, Atria, and 8VC. Orchestra creates AI agents that help biotech companies manage the minutiae of drug discovery operations, from tracking finances to resolving scheduling issues with clinical trials. OpenAI says it’s adding Orchestra’s team and technology to work onRosalind, its AI model for life sciences research, and to its related Rosalind Workbench tool, a harness that aims to help researchers plan experiments and analyze results. OpenAI has already won some big names as customers for Rosalind, including, it says,Moderna⁠andNovo Nordisk⁠. The acquisition is a classic example of how the biggest AI labs (namely OpenAI and Anthropic) are building ever more software for the biggest industries, like pharma. In the startup world, there is an ongoing fear that at any moment, the labs will launch new features and become competitors. In this case, Orchestra is getting absorbed, rather than stomped out.

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SpaceX’s AI business is beating expectations

SpaceX’s AI business is beating expectations

SpaceX’s data centers and Grok agents were more profitable than investors expected in third quarter of the year, as the company leaves its roots as a space company behind to focus on artificial intelligence, which now makes up 52% of its income. CEO Elon Musk’s deep tech conglomerate reported $14.6 billion in third-quarter revenue on Tuesday, according to itsregulatory filing. That number was driven by growth in all segments, but especially its renamed “Super Intelligence” business line, which generated $7.5 billion in revenue from the recently acquired Cursor coding tool, Grok bot, and most importantly, the company’s burgeoning neocloud business, which now boasts 1.7 GW of compute. The AI business generated $431 million in profit, which would have been higher without $1.2 billion in one-time stock-based compensation expenses related to the Cursor acquisition. The top-line revenue number was also boosted by about $1 billion in income related to the recovery of Bitcoin prices and unspecified related-party income that might be attributed to a SpaceX investment in another company. The growing revenue is balanced against total capital expenditures of $19.6 billion, as SpaceX continues to build out a second data center project, Colossus II, and increase investment in Starship, the heavy-lift rocket it sees as vital to deploying a new generation of Starlink satellites and developing orbital data centers. CFO Bret Johnsen said the company would spend $35 billion to $40 billion on new capital investments in the fourth quarter. During a live-streamed discussion of the results with investors, Musk said he was spending four days a week in Memphis, Tennessee, the site of Colossus II, working to bring the data center online. “People are starting to realize [that] the constraint on deployment of of super intelligence is more in power and cooling and infrastructure than it is in chips,” Musk argued, saying that he had tasked a team of SpaceX engineers to solve that problem. “This is a bit like the New York Yankees playing in Little League. It’s not fair, but the net result is being able to deploy power cooling infrastructure at a rate that is vastly faster than any anyone else.” In one example, Musk noted that manufacturers of turbines to power data centers are facing 12 to 18 month delays in obtaining the metals blades and vanes required for those machines. Now, SpaceX is using the metal casting capabilities developed for SpaceX to make those components and obtain turbines more quickly. Musk also said that the next test flight of the Starship rocket, expected in November, would see SpaceX attempt to recover both the first-stage booster and the second stage. Recovering both segments of the vehicle would be a “massive technology milestone,” Musk said, but the true milestone will be reflying both stages. SpaceX also saw growth in subscribers to its Starlink space internet network, reaching 13.6 million consumers, with Musk predicting that the growth of AI agents would lead to more demand for connectivity. Despite of those capital expenditures, the company’s revenue boost was substantial enough to push it back into profitability. SpaceX reported net income of $4.4 billion, a turnaround from the $1 billion loss in the same quarter last year. It will still have some catching up to do if it wants to remain profitable for the year, however. In the first nine months of the year, SpaceX still had a net income loss of $413 million. Musk took a particularly bullish stance on the company’s Super Intelligence business segment, predicting that SpaceX’s annual recurring revenue by the end of 2027 will be “somewhere between $500 billion and $1 trillion.” More concretely, Johnsen forecast total revenue for the first quarter of 2027 in the range of $33 billion to $37 billion. This is a developing story. TechCrunch will update.

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