Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now Anthropic released an upgraded version of its flagship artificial intelligence model Monday, achieving new performance heights in software engineering tasks as the AI startup races to maintain its dominance in the lucrative coding market ahead of an expected competitive challenge from OpenAI. The new Claude Opus 4.1 model scored 74.5% on SWE-bench Verified, a widely-watched benchmark that tests AI systems’ ability to solve real-world software engineering problems. The performance surpasses OpenAI’s o3 model at 69.1% and Google’s Gemini 2.5 Pro at 67.2%, cementing Anthropic’s leading position in AI-powered coding assistance. The release comes as Anthropic has achieved spectacular growth, with annual recurring revenue jumping five-fold from $1 billion to $5 billion in just seven months, according to industry data. However, the company’s meteoric rise has created a dangerous dependency: nearly half of its $3.1 billion in API revenue stems from just two customers — coding assistant Cursor and Microsoft’s GitHub Copilot — generating $1.4 billion combined. “This is a very scary position to be in. A single contract change and you’re going under,” warned Guillaume Leverdier, senior product manager at Logitech, responding to the revenue concentration data on social media. The AI Impact Series Returns to San Francisco – August 5 The next phase of AI is here – are you ready? Join leaders from Block, GSK, and SAP for an exclusive look at how autonomous agents are reshaping enterprise workflows – from real-time decision-making to end-to-end automation. Secure your spot now – space is limited: https://bit.ly/3GuuPLF OpenAI and Anthropic both are showing pretty spectacular growth in 2025, with OpenAI doubling ARR in the last 6 months from $6bn to $12bn and Anthropic increasing

OpenAI has finally released its first open-weight large language models since 2019’s GPT-2. These new “gpt-oss” models are available in two different sizes and score similarly to the company’s o3-mini and o4-mini models on several benchmarks. Unlike the models available through OpenAI’s web interface, these new open models can be freely downloaded, run, and even modified on laptops and other local devices. In the company’s many years without an open LLM release, some users have taken to referring to it with the pejorative “ClosedAI.” That sense of frustration had escalated in the past few months as these long-awaited models were delayed twice—first in June and then in July. With their release, however, OpenAI is reestablishing itself as a presence for users of open models. That’s particularly notable at a time when Meta, which had previously dominated the American open-model landscape with its Llama models, may be reorienting toward closed releases—and when Chinese open models, such as DeepSeek’s offerings, Kimi K2, and Alibaba’s Qwen series, are becoming more popular than their American competitors. “The vast majority of our [enterprise and startup] customers are already using a lot of open models,” said Casey Dvorak, a research program manager at OpenAI, in a media briefing about the model release. “Because there is no [competitive] open model from OpenAI, we wanted to plug that gap and actually allow them to use our technology across the board.”
The new models come in two different sizes, the smaller of which can theoretically run on 16 GB of RAM—the minimum amount that Apple currently offers on its computers. The larger model requires a high-end laptop or specialized hardware. Open models have a few key use cases. Some organizations may want to customize models for their own purposes or save money by running models on their own equipment, though that equipment comes at a substantial upfront cost. Others—such hospitals, law firms, and governments—might need models that they can run locally for data security reasons. 
OpenAI has facilitated such activity by releasing its open models under a permissive Apache 2.0 license, which allows the models to be used for commercial purposes. Nathan Lambert, post-training lead at the Allen Institute for AI, says that this choice is commendable: Such licenses are typical for Chinese open-model releases, but Meta released its Llama models under a bespoke, more restrictive license. “It’s a very good thing for the open community,” he says. Researchers who study how LLMs work also need open models, so that they can examine and manipulate those models in detail. “In part, this is about reasserting OpenAI’s dominance in the research ecosystem,” says Peter Henderson, an assistant professor at Princeton University who has worked extensively with open models. If researchers do adopt gpt-oss as new workhorses, OpenAI could see some concrete benefits, Henderson says—it might adopt innovations discovered by other researchers into its own model ecosystem. More broadly, Lambert says, releasing an open model now could help OpenAI reestablish its status in an increasingly crowded AI environment. “It kind of goes back to years ago, where they were seen as the AI company,” he says. Users who want to use open models will now have the option to meet all their needs with OpenAI products, rather than turning to Meta’s Llama or Alibaba’s Qwen when they need to run something locally. The rise of Chinese open models like Qwen over the past year may have been a particularly salient factor in OpenAI’s calculus. An employee from OpenAI emphasized at the media briefing that the company doesn’t see these open models as a response to actions taken by any other AI company, but OpenAI is clearly attuned to the geopolitical implications of China’s open-model dominance. “Broad access to these capable‬‭ open-weights models created in the US helps expand democratic AI rails,” the company wrote in a blog post announcing the models’ release.  Since DeepSeek exploded onto the AI scene at the start of 2025, observers have noted that Chinese models often refuse to speak about topics that the Chinese Communist Party has deemed verboten, such as Tiananmen Square. Such observations—as well as longer-term risks, like the possibility that agentic models could purposefully write vulnerable code—have made some AI experts concerned about the growing adoption of Chinese models. “Open models are a form of soft power,” Henderson says. Lambert released a report on Monday documenting how Chinese models are overtaking American offerings like Llama and advocating for a renewed commitment to domestic open models. Several prominent AI researchers and entrepreneurs, such as HuggingFace CEO Clement Delangue, Stanford’s Percy Liang, and former OpenAI researcher Miles Brundage, have signed on. The Trump administration, too, has emphasized development of open models in its AI Action Plan. With both this model release and previous statements, OpenAI is aligning itself with that stance. “In their filings about the action plan, [OpenAI] pretty clearly indicated that they see US–China as a key issue and want to position themselves as very important to the US system,” says Rishi Bommasani, a senior research scholar at the Stanford Institute for Human-Centered Artificial Intelligence.  And OpenAI may see concrete political advantages from aligning with the administration’s AI priorities, Lambert says. As the company continues to build out its extensive computational infrastructure, it will need political support and approvals, and sympathetic leadership could go a long way.

OpenAI is getting back to its roots as an open source AI company with today’s announcement and release of two new, open source, frontier large language models (LLMs): gpt-oss-120b and gpt-oss-20b.The former is a 120-billion parameter model as the name would suggest, capable of running on a single Nvidia H100 graphics processing unit (GPU) and the latter is only 20 billion, small enough to run locally on a consumer laptop or desktop PC. Both are text-only language models, which means unlike the multimodal AI that we’ve had for nearly two years that allows users to upload files and images and have the AI analyze them, users will be confined to only inputting text messages to the models and receiving text back out. However, they can still of course write code and provide math problems and numerics, and in terms of their performance on tasks, they rank above some of OpenAI’s paid models and much of the competition globally.

AcknowledgmentsGenie 3 was made possible due to key research and engineering contributions from Phil Ball, Jakob Bauer, Frank Belletti, Bethanie Brownfield, Ariel Ephrat, Shlomi Fruchter, Agrim Gupta, Kristian Holsheimer, Aleks Holynski, Jiri Hron, Christos Kaplanis, Marjorie Limont, Matt McGill, Yanko Oliveira, Jack Parker-Holder, Frank Perbet, Guy Scully, Jeremy Shar, Stephen Spencer, Omer Tov, Ruben Villegas, Emma Wang and Jessica Yung.We thank Andrew Audibert, Cip Baetu, Jordi Berbel, David Bridson, Jake Bruce, Gavin Buttimore, Sarah Chakera, Bilva Chandra, Paul Collins, Alex Cullum, Bogdan Damoc, Vibha Dasagi, Maxime Gazeau, Charles Gbadamosi, Woohyun Han, Ed Hirst, Ashyana Kachra, Lucie Kerley, Kristian Kjems, Eva Knoepfel, Vika Koriakin, Jessica Lo, Cong Lu, Zeb Mehring, Alex Moufarek, Henna Nandwani, Valeria Oliveira, Fabio Pardo, Jane Park, Andrew Pierson, Ben Poole, Helen Ran, Nilesh Ray, Tim Salimans, Manuel Sanchez, Igor Saprykin, Amy Shen, Sailesh Sidhwani, Duncan Smith, Joe Stanton, Hamish Tomlinson, Dimple Vijaykumar, Luyu Wang, Piers Wingfield, Nat Wong, Keyang Xu, Christopher Yew, Nick Young and Vadim Zubov for their invaluable partnership in developing and refining key components of this project.Thanks to Tim Rocktäschel, Satinder Singh, Adrian Bolton, Inbar Mosseri, Aäron van den Oord, Douglas Eck, Dumitru Erhan, Raia Hadsell, Zoubin Gharamani, Koray Kavukcuoglu and Demis Hassabis for their insightful guidance and support throughout the research process.Feature video was produced by Suz Chambers, Matthew Carey, Alex Chen, Andrew Rhee, JR Schmidt, Scotch Johnson, Heysu Oh, Kaloyan Kolev, Arden Schager, Sam Lawton, Hana Tanimura, Zach Velasco, Ben Wiley, and Dev Valladares. Including samples generated by Signe Norly, Eleni Shaw, Andeep Toor, Gregory Shaw, and Irina Blok.Finally, we extend our gratitude to Mohammad Babaeizadeh, Gabe Barth-Maron, Parker Beak, Jenny Brennan, Tim Brooks, Max Cant, Harris Chan, Jeff Clune, Kaspar Daugaard, Dumitru Erhan, Ashley Feden, Simon Green, Nik Hemmings, Michael Huber, Jony Hudson, Dirichi Ike-Njoku, Bonnie Li, Simon Osindero, Georg Ostrovski, Ryan Poplin, Alex Rizkowsky, Giles Ruscoe, Ana Salazar, Guy Simmons, Jeff Stanway, Metin Toksoz-Exley, Petko Yotov, Mingda Zhang and Martin Zlocha for their insights and support.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. These protocols will help AI agents navigate our messy lives A growing number of companies are launching AI agents that can do things on your behalf—actions like sending an email, making a document, or editing a database. Initial reviews for these agents have been mixed at best, though, because they struggle to interact with all the different components of our digital lives. Anthropic and Google are among the companies and groups working to fix that. Over the past year, they have both introduced protocols that try to define how AI agents should interact with each other and the world around them. If they work as planned, they could give us a crucial part of the infrastructure we need for agents to be useful. Read our story to learn more.  —Peter Hall
A glimpse into OpenAI’s largest ambitions —James O’Donnell
OpenAI has given itself a dual mandate: on the one hand, it’s a tech giant rooted in products, including of course ChatGPT, which people around the world reportedly send 2.5 billion messages to each day. But its original mission is as a research lab that will not only create “artificial general intelligence” but ensure that it benefits all of humanity.  My colleague Will Douglas Heaven recently sat down for an exclusive conversation with the two figures at OpenAI most responsible for the latter ambitions. The whole story is worth reading for all it reveals—about how OpenAI thinks about the safety of its products, what AGI actually means, and more—but here’s one thing that stood out to me. This story is from The Algorithm, our weekly newsletter all about the latest goings-on in AI. Sign up to receive it in your inbox every Monday. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 OpenAI is adding mental health guardrails to ChatGPTIt’s set to give less direct advice, and encourage users to take breaks from lengthy chats. (NBC)+ What happens when doctors fail to spot AI’s mistakes? (The Verge)+ OpenAI has released its first research into how using ChatGPT affects people’s emotional well-being. (MIT Technology Review)2 The US wants to build a nuclear reactor on the moonAnd it hopes to do that before Russia and China, who are planning to do exactly the same. (Politico)+ NASA’s latest mission to the moon just failed. (Engadget)+ Nokia is putting the first cellular network on the moon. (MIT Technology Review)3 How to live forever (or at least get rich trying) 👴🤑Love them or hate them, the people behind the explosion in longevity research are a fascinating bunch. (New Yorker $)+ Longevity clinics around the world are selling unproven treatments. (MIT Technology Review)4 Welcome to Silicon Valley’s ‘hard tech’ eraGoodbye, consumer software. Hello, massive military contracts. (NYT $)+ Phase two of military AI has arrived. (MIT Technology Review)5 There’s a big problem with the Gulf’s trillion-dollar AI dreamBuilding data centers in a region that already has water scarcity issues seems…unwise. (Rest of Water)+ There’s a data center boom in the US desert too. (MIT Technology Review)+ Google has promised to scale back its energy usage during certain times to reduce stress on the grid. (Quartz $)6 Tesla’s board awarded about $30 billion of shares to Elon Musk“Retaining Elon is more important than ever before,” they wrote in a letter to shareholders yesterday. (FT $)+ Tech CEOs pay packets are reaching stratospheric new records. (WSJ $)7 What happens if you respond to those scam job texts?You get exploited, obviously—but you’d be surprised just how weird it can get along the way. (Slate) 8 Why there’s so much uproar over Vogue’s AI-generated adIt’s the latest flashpoint in the war over when AI should (and shouldn’t) be used. (TechCrunch)

9 Earth’s core seems to be up and leaking out of Earth’s surface 🌋It’s a finding that’s forcing geoscientists to rethink some long-held assumptions. (Quanta $)+ How a volcanic eruption turned a human brain into glass. (MIT Technology Review)10 Could lasers help us see inside people’s heads?It seems possible, but big hurdles remain to this new method being adopted in clinical settings. (IEEE Spectrum) Quote of the day  “Hate it! Don’t want anything to do with it.” —Weezy Simes, a 27-year-old florist, sums up her feelings about AI to Business Insider. One more thing ANDREA D’AQUINO What happened to the microfinance organization Kiva? Since it was founded in 2005, the San Francisco-based nonprofit Kiva has helped everyday people make microloans to borrowers around the world. It connects lenders in richer communities to fund all sorts of entrepreneurs, from bakers in Mexico to farmers in Albania. Its overarching aim is helping poor people help themselves.
But back in August 2021, Kiva lenders started to notice that information that felt essential in deciding who to lend to was suddenly harder to find. Now, lenders are worried that the organization now seems more focused on how to make money than how to create change. Read the full story. —Mara Kardas-Nelson
We can still have nice things A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.) + I want this guy to draw my portrait. + Highly recommend making these lemongrass chicken lettuce wraps. So tasty and easy!+ This encyclopedia teaches you about ancient gods and forgotten deities from around the world.+ Some of the architecture in Iran looks breathtakingly beautiful.

OpenAI has given itself a dual mandate. On the one hand, it’s a tech giant rooted in products, including of course ChatGPT, which people around the world reportedly send 2.5 billion requests to each day. But its original mission is to serve as a research lab that will not only create “artificial general intelligence” but ensure that it benefits all of humanity.  My colleague Will Douglas Heaven recently sat down for an exclusive conversation with the two figures at OpenAI most responsible for pursuing the latter ambitions: chief research officer Mark Chen and chief scientist Jakub Pachocki. If you haven’t already, you must read his piece. It provides a rare glimpse into how the company thinks beyond marginal improvements to chatbots and contemplates the biggest unknowns in AI: whether it could someday reason like a human, whether it should, and how tech companies conceptualize the societal implications.  The whole story is worth reading for all it reveals—about how OpenAI thinks about the safety of its products, what AGI actually means, and more—but here’s one thing that stood out to me. 
As Will points out, there were two recent wins for OpenAI in its efforts to build AI that outcompetes humans. Its models took second place at a top-level coding competition and—alongside those from Google DeepMind—achieved gold-medal-level results in the 2025 International Math Olympiad. People who believe that AI doesn’t pose genuine competition to human-level intelligence might actually take some comfort in that. AI is good at the mathematical and analytical, which are on full display in olympiads and coding competitions. That doesn’t mean it’s any good at grappling with the messiness of human emotions, making hard decisions, or creating art that resonates with anyone. 
But that distinction—between machine-like reasoning and the ability to think creatively—is not one OpenAI’s heads of research are inclined to make.  “We’re talking about programming and math here,” said Pachocki. “But it’s really about creativity, coming up with novel ideas, connecting ideas from different places.” That’s why, the researchers say, these testing grounds for AI will produce models that have an increasing ability to reason like a person, one of the most important goals OpenAI is working toward. Reasoning models break problems down into more discrete steps, but even the best have limited ability to chain pieces of information together and approach problems logically.  OpenAI is throwing a massive amount of money and talent at that problem not because its researchers think it will result in higher scores at math contests, but because they believe it will allow their AI models to come closer to human intelligence.  As Will recalls in the piece, he said he thought maybe it’s fine for AI to excel at math and coding, but the idea of having an AI acquire people skills and replace politicians is perhaps not. Chen pulled a face and looked up at the ceiling: “Why not?” Read the full story from Will Douglas Heaven. This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.