最新 AI 资讯
Cactus Wins MeitY Contract to Build AI-Powered Procurement Platform: Report
The NeGD project will use AI to help government departments draft, review and standardise procurement documents while retaining human oversight of decisions.
View
SK Hynix Raises $26.5 Bn as it Debuts on Nasdaq
Its ADRs are scheduled to begin trading on Nasdaq under the ticker SKHY on July 10.
View
Meta Unveils Muse Spark 1.1 AI Model With 1 Million-Token Context Window, Model API Preview
Meta Platforms on Thursday announced Muse Spark 1.1 as its latest multimodal reasoning model designed for agentic AI tasks. The company claims it brings significant improvements over its predecessor when it comes to tool use, coding, computer interaction, and multimodal reasoning. Developed by Meta Superintelligence Labs, the AI model supports a context window of up to one million tokens. Alongside, Meta also released a public preview of the new Meta Model API for developers.
View
After Medical Leave, OpenAI Applications CEO Fidji Simo Steps Down to Focus on Recovery
She said she will continue contributing to OpenAI as an adviser while supporting AI-driven healthcare initiatives through Chronicle Bio and Coda Research.
View
Elon Musk says He Was ‘Clearly Wrong’ About Anthropic, Calls it the AI Leader
Musk praised Anthropic’s Mythos and Fable models and said he would not use SpaceXAI’s infrastructure advantage to hurt the company despite competing in the AI race.
View
Meta Launches Muse Spark 1.1 Challenges GPT-5.5 & Opus 4.8
Meta also introduced the Meta Model API in public preview, allowing developers to build applications using Muse Spark models for the first time.
View
Why EY Built a 40,000 sq ft AI Simulation Centre in Bengaluru
Last month, EY Global Delivery Services launched the ey.ai Center for Reimagination in Bengaluru to help enterprises understand what AI transformation looks like.
View
Fidji Simo steps down from OpenAI’s no. 2 role
Fidji Simo, OpenAI’s No. 2 executive, is stepping down from her full-time role,the Wall Street Journal reports. In a staff note Thursday, Simo said her ongoing medical leave has proven longer and harder than expected, and that she’ll transition to a part-time advisory role instead. Simo joined OpenAI’s board of directors in 2024 and joined OpenAI in May 2025 as CEO of Applications, then a newly created role reporting directly to Sam Altman that consolidated the company’s business and product operations. Her appointment came with a broader reporting shift: COO Brad Lightcap, CFO Sarah Friar, and CPO Kevin Weil all began reporting to her, while Altman stepped back to focus on research, compute, and safety. Simo firstdisclosedher health issues in April, when she announced she was taking medical leave for a relapse of a neuroimmune condition; that same memo publicly announced that Lightcap was moving into a new “special projects” role and that CMO Kate Rouch wasleaving the companyto focus on cancer recovery. Weil has sinceleft the company, too. Simo came to OpenAI from Instacart, where she’d been CEO since 2021 and led the company through its 2023 IPO, and before that spent over a decade at Meta, including running the Facebook app. Simo’s decision to step back permanently leaves Altman searching for a successor right as OpenAI itself eyes a possible IPO. She’d been widely seen as a likely candidate to take on even more responsibility once OpenAI went public, making this a real vacuum for him to address. Simo was primarily focused on growing OpenAI’s consumer business. But ChatGPT’s growth cooled late last year, missing internal revenue targets, pushing the company to lean harder into coding tools instead, an area where it has been, and for now continues to be, trailing Anthropic. TechCrunch has reached out to OpenAI for more information. Soon after the Journal story broke, Simo shared the newsdirectlyon X, after which Altmanresponded, also on X: “i am really sad about this and very grateful for all fidji has done for openai, and even grateful for her friendship and who she is as a person. we all wish her the best for a speedy recovery. this sucks.” Simo’s announcement lands on a busy news day for OpenAI. Earlier Thursday, the company launched its newGPT-5.6 family of models— Sol, Terra, and Luna — alongside a new agent called ChatGPT Work, designed to handle multistep office tasks like drafting documents, spreadsheets, and presentations. Both releases were framed by OpenAI as directly targeting Anthropic. OpenAI’s executive ranks appear from the outside to be on the thin side for a company that was most recently assigned an $852 billion valuation. In addition to Altman, Lightcap, Friar, and co-founder Greg Brockman (who is also the company’s president and was overseeing product strategy while Simo was out), its bench includes Denise Dresser, who in December joined as the company’s chief revenue officer, overseeing its “global revenue strategy across enterprise and customer success,” per a release at the time. It wouldn’t be shocking to see Dresser take on a more expansive role, given she previously spent two years as the CEO of Slack and, before that, spent 14 years with Slack’s parent company, Salesforce. Simo’s departure comes against another backdrop worth understanding: OpenAI’s shifting approach to employee equity. In April of last year, the same month that Simo joined, the company shortened its vesting cliff — the waiting period before new hires’ stock grants begin vesting — from the industry-standard 12 months to 6 months. Then in December, OpenAI eliminated the cliff altogether for new hires, letting equity start vesting from day one. The move, described internally by Simo as a way to let employees “take risks” without fear of losing equity if let go early, came amid an escalating AI talent war and reflects just how aggressively OpenAI has been spending to retain staff. The company was projected to spend $6 billion on stock-based compensation in 2025 alone. None of the aforementioned exits appear tied to compensation. Executive equity packages are typically negotiated individually and could have entirely different vesting terms.
View
OpenAI says GPT 5.6 is the ‘preferred model’ for Microsoft Copilot 365 amid breakup chatter
Earlier this week, Bloombergreported thatMicrosoft was replacing some of OpenAI’s software with its own in-house models in an effort to cut costs. Those in-house models, known as MAI, were increasingly being used to power apps like Word and Excel, the outlet noted. The story raised an increasingly common question about the two companies, which were once seemingly inseparable, and have recently sentmixed signalsabout the status of their situationship: Were the two companies drifting apart? Now, OpenAI is attempting to put any insinuations of such a break to rest. DuringOpenAI’s launch of GPT 5.6on Thursday, the company announced that it would become the “preferred model” powering Microsoft’s 365 Copilot. OpenAI noted ina blog postpublished Thursday that GPT 5.6 would support Microsoft users across the company’s suite of productivity apps, including Word, Excel, PowerPoint, and Cowork. “Our partnership with Microsoft has always been about bringing the benefits of advanced AI to more individuals and organizations, and we’re excited to continue building on that shared commitment,” OpenAI wrote in a blog post. What being a “preferred model” actually means isn’t entirely clear, other than that OpenAI’s software will continue to power Microsoft’s apps. That said, it was never reported that ChatGPT’s software would stop powering Microsoft’s apps — merely that Microsoft was relying increasingly on its own software in an effort to reduce costs. The new “preferred model” disclosure doesn’t appear to negate that previous reporting.
View
New York Times says OpenAI hid evidence in ChatGPT copyright trial
The New York Times and The Daily News claim that OpenAI has been lying about its ability to search customer chat log data and training datasets for their copyrighted works. It’s the latest escalation in a two-yearlawsuit against the AI firmfor allegedly violating copyright law by training its generative AI models on the Times’ content and reproducing that journalism in user outputs. Throughout the case, OpenAI has argued that it lacked the ability to search its own training corpus. It also argued that searching or producing its massive collection of ChatGPT conversations would be technically burdensome and would raiseuser-privacy concernsbecause the logs would need to be retrieved, processed, and de-identified. The outlets sought that data to determine whether their copyrighted journalism was present in OpenAI’s training dataset and whether and how often ChatGPT generated responses using or reproducing their content. In an April court-ordered deposition, OpenAI data privacy engineer Vinnie Monaco allegedly revealed that OpenAI had already conducted internal searches and evaluations of its training corpus to search for copyrighted journalism works. Monaco’s deposition also allegedly revealed that, beginning before the NYT filed its lawsuit, OpenAI had already amassed a database of about 78 million de-identified ChatGPT conversations that it was using internally to determine how much it was infringing on others’ works. On top of that dataset, OpenAI also allegedly implemented a “Bloom” filter as part of a set of tools called “Project Giraffe,” which detected and kept a record of regurgitation in outputs, shortly after the lawsuit was filed. Those last two revelations are particularly significant. The plaintiffs had originally asked OpenAI to provide a sample of 120 million chat logs, but OpenAI had negotiated to bring the sample down to just 20 million. OpenAI finally submitted that sample to the courts last December, but it had allegedly included so many redactions as to render the sample “unusable,” in the court’s words. The plaintiffs also claimed OpenAIdeleted billionsof ChatGPT outputs after they filed suit in direct violation of the court’s preservation order, and that the AI giant substituted millions of logs in the requested sample. In other words, they claim OpenAI made it needlessly difficult to obtain information that the company had already collected. “If OpenAI genuinely believed that copying our clients’ journalism was fair and legal, it wouldn’t have hid the truth about having done it,” Ian B. Crosby, lead counsel for the plaintiffs, said in a statement. Now, the NYT and The Daily News are asking the judge to discipline OpenAI for allegedly withholding evidence and messing with the discovery process. They are asking the court to prevent OpenAI from using the 20 million chat log sample as evidence, claiming it is unreliable; to accept as fact that ChatGPT logs would have shown major regurgitation and grounding of the plaintiffs’ content; to prevent OpenAI from arguing that its provided chat logs don’t demonstrate substantial regurgitation; and to make OpenAI pay legal fees for having to chase down this evidence. In a statement, OpenAI spokesperson Drew Pusateri denied the allegations, accusing the Times of trying to access private user conversations as its case weakens. “As the Times’ case weakens and they’ve been forced to drop claims against us, they’re persisting with their efforts to invade the privacy of people who have nothing to do with this case, including by making these blatantly false allegations,” Pusateri said. “We’ll continue defending our users’ privacy and the long-established principles of fair use.”
View
Meta enters the crowded AI coding battle with Muse Spark 1.1
Meta publicly launched a new version of Muse Spark on Thursday, a multimodal AI model designed for agentic coding that aims to compete with similar products offered by OpenAI and Anthropic. Spark 1.1, the first version of whichwas announced in April, can engage in multistep reasoning and handle complex processes, manage digital workflows, and deploy new features in enterprise systems, the company says. Meta is a bit behind its competitors here; Anthropic and OpenAI haveoffered similar modelsfor quite some time. But that doesn’t mean Meta’s entry into the market isn’t a threat. An ongoing source of competitiveness within the AI industry remains the cost of usage, and Meta appears to be offering a competitive rate. Reutersreports thatthe company will charge $1.25 per million input tokens and $4.25 per million output tokens. That puts it in line with (albeit slightly above) Anthropic’s Claude Haiku 4.5 and OpenAI’s GPT-5.6 Luna. Meta’s pitch to users is Spark’s ability to handle large agentic workloads, fix bugs, and help with large code migrations — the kind of automation that enterprises are increasingly turning to AI companies to provide. “Muse Spark 1.1 delivers exceptional performance in personal agentic tasks that require planning and orchestration across a range of external apps and services,” the companywrote in a blog post. Meta has released a handful of foundation AI models over the past few years. The Muse Spark release was apparently important enough to compel CEO Mark Zuckerbergto poston X for the first time in three years. Zuckerberg’s last post was in July 2023, around the time the platform rebrandedfrom Twitter to X. In his post, Zuckerberg called Spark “a strong agentic and coding model at a very low price,” noting that the model was “strongest at agentic performance, tool use, and computer use.” Zuckerberg also noted that there was “more to come soon” — implying that the company plans to release additional models. It’s been a big week for AI announcements — particularly for Meta, which alsounveiled a new AI image-generation modelon Tuesday, dubbed Muse Image. Other releases this week have includeda newversion of Grokfrom SpaceXAI and anew family of modelsfrom OpenAI, GPT-5.6, that also dropped Thursday. Suffice it to say that the competition within the AI industry is as healthy as ever, and companies that wish to stand out from their peers have their work cut out for them.
View
Can AI answer the $3 trillion question?
Three years ago, Sequoia partner David Cahn was one of the first people to do the math and put a number on the implications of Silicon Valley’s titanic spend on AI infrastructure. In2023, he was reacting to Nvidia’s reported annual GPU revenue of $50 billion. Starting with that figure, and adding in the implied costs of operating the data centers and the margins for their operators, he deduced that $200 billion in revenue would be required to pay back the up-front investment. He took it as a challenge, asking entrepreneurs to come up with AI products and services to make use of, and generate revenue from, all that infrastructure. Fast-forward to today, adding up three years of hyperscaling, and Cahn’s got anew numberon AI infrastructure spending for 2026: $1.5 trillion. All told, he calculates that the AI industry will have to earn $3 trillion to justify all those chips and other data center expenditures. And that’s probably an underestimate — the rising costs of memory and the increasing use of exotic or inference-specific chips will drive that number up. “Recently,” he writes, “the required revenue per GW of CapEx has sharply increased due to these bottleneck dynamics and rising costs of construction.” On the other side of the ledger, Anthropic is thought to have hit$60 billion in ARR, while OpenAI reportedly earned$13 billionin 2025 (although inNovember 2025, it said it was at $20 billion ARR) and is presumably making more this year. But there’s clearly a large gap to be closed. Someone minding that gap is Torsten Slok, the chief economist at Apollo, the giant asset manager. In arecent note, he points out that the hyperscalers — Google, Meta, Microsoft, and Amazon — are all predicting massive accelerations in their free-cash flow in 2028. That is, they expect to see the payback from all those chips they bought. What if they don’t? Slok notes a risk we’re currently seeing across AI usage: More organizations turning to cheaper open weight models, often Chinese, not those built by the frontier labs, and overall token prices falling. OpenAI’s latest model, per CEO Sam Altman, is54% more token efficienton coding tasks. That’s good for users fretting about the cost of their AI agents, but it may be bad for companies building token factories should users not wildly increase their overall token usage with them. Slok worries that if hyperscalers don’t meet their cash-flow goals, the market reaction could be severe —“with so much riding on so few names,” he writes, “a slower payoff wouldn’t just be a sector problem, it would risk tipping the economy into recession and the S&P 500 into a correction.” Just something to keep in mind as you’re herding your AI agents toward cheaper tokens.
View
