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AI NewsOpenAI’s revenue is reportedly $20 billion less than previously projected

OpenAI’s revenue is reportedly $20 billion less than previously projected

3:04 AM IST · October 9, 2026

A little over a week ago,it was reportedthat OpenAI’s annualized revenue was approaching $70 billion, a figure that would have made it competitive with Anthropic’sreported run rate. Now, however, the AI lab is said to have told investors that the real revenue is some $20 billion lower than that. The Financial Timesreports thatthe company has told investors that its annualized revenue is “approaching $50 billion.” The $70 billion figure was previously reported by news outlets and based on information that had been shared with OpenAI investors, the outlet writes. That figure was devised via “attempts by OpenAI’s own investors to produce a direct comparison with Anthropic’s annualised revenues,” per the FT. It’s worth pointing out that OpenAI and Anthropic calculate their annualized revenue differently — with Anthropic counting sales made by its cloud partners. OpenAI doesn’t do this. TechCrunch reached out to OpenAI for comment. The issue of OpenAI’s revenue has troubled the company, as it attempts to justify the gargantuan investments being made on its behalf; the AI giantraised $122 billionduring a March funding round alone. The company’sleaked 2025 financialsearlier this year showed it had made about $13 billion but spent significantly more. OpenAI’s IPO, which was previously rumored to be materializing this year, hasbeen pushed offuntil early 2027.

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OpenAI’s math solutions aren’t meeting the field’s standards yet

When OpenAIreleasedhundreds of claimed solutions to some of the world’s hardest math problems this week, the frontier lab said that it had consulted an advisory group of elite mathematicians to avoidthe controversythat came with the last time one of its models solved a long-standing problem in the field. But OpenAI fell short of those standards, particularly where the mathematicians emphasized the need for human understanding of a mathematical result. That’s especially concerning after a new paper highlighted gaps between the natural language and formally expressed solution to a million-dollar problem ostensibly solved by OpenAI’s models. The Advisory Group on Mathematics and Artificial Intelligence (AGMAI), hosted by Princeton University’s Institute for Advanced Studies, is made up of nine prominent researchers at institutions around the world. The organization releasedguidelinesfor frontier labs solving math problems at the end of September. In a statement on the latest set of proofs, the AGMAI said that “it is ultimately up to the mathematical community to assess the extent to which our recommendations were followed successfully.” However, the organization’s first request was “to stop testing advanced mathematical problems on proprietary models.” OpenAI’s release explicitly says that it is evaluating its proprietary models using open research problems in mathematics. The advisory group did not respond when asked by TechCrunch for a more thorough evaluation of OpenAI’s latest proof release. The lab clearly followed some of its principles, including releasing results as soon as possible and including information about how the models reached their conclusions. But not for all of them: Just 10 of the 719 manuscripts included releases of the model’s chain of thought. For papers that people don’t understand, the mathematicians suggested the proofs should be formalized — but just 42% of the proofs released by OpenAI had not undergone this process. Ultimately, it’s still not clear that OpenAI is taking “responsibility for ensuring that human understanding will follow” when releasing its proofs, in accordance to the AGMAI principles. AGMAI suggested that OpenAI should help fund the work of human mathematicians who will be required to make the lab’s solutions meaningful in any real way. “Problems are being solved autonomously by AI prompters who have no interest in the broader field itself once their initial target is ‘solved’, and do not understand the AI output well enough to answer questions on the result, give talks, or otherwise interact with the rest of the field,” Terence Tao, a prominent mathematician who has criticized OpenAI’s approach,wroteon social media after the release. That problem is exemplified bya paperreleased this week by mathematicians at the University of Cambridge and King’s College in London that questions the way frontier labs are approaching these challenges. When AI models solve mathematical problems, they first create a “natural language” explanation, then try to express that result in Lean, a programming language that in theory confirms the accuracy of the proof by compiling it as code. However, there may be problems with the way the models translate their natural language proofs into code; this paper documents at least two discrepancies between the natural language proof and the Lean code behind the solution OpenAI has offered to a problem derived from the Navier-Stokes equations that describe the complex behavior of fluids. These discrepancies don’t necessarily disprove either solution, but they do raise questions on whether we can simply rely on models to formalize their own solutions without human involvement. That’s one reason that AGMAI asked OpenAI to “include machine-readable metadata correlating the natural language and formal artifacts,” something that the frontier lab did not do with these releases. “Because of the phenomenon of mistranslations — as highlighted in this paper — the NL proof by OpenAI andother autoformalised Lean proofs should not prima facie be trusted without the same peer review process andscrutiny that other proofs are subjected to,” the authors of the “lost in translation” paper conclude. Mathematicians stress that when new results are discovered by humans, they take responsibility for them and engage with the broader community through papers, talks, and seminars. That process increases understanding of the solutions, finds strategies that can be used to solve other problems, and allows the new knowledge to be applied in practical fields. When a model is prompted to solve a hard problem and spits out a solution, “there is not human understanding of them at the point of release, and now the work begins,” Harvard University mathematics professor Melanie Wood told TechCrunch.

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Anthropic changes usage policy to ban model abuse and election interference

Anthropicupdated its usage policy on Thursday, codifying new prohibitions on election interference, weapons software, and surveillance. But perhaps the most attention-getting change is a new prohibition against prolonged verbal abuse of the model, which is now expressly forbidden. Since an update in August, Claude has been trained to end conversations with “persistently harmful or abusive user interactions” — but the new policy explicitly bars users from pursuing those conversations. “The policy update is meant to apply only in extreme cases, where users repeatedly act cruelly toward our models, with no discernible purpose,” the post explains. “It does not apply to common versions of user frustration, pushback, dark creative themes, or model testing and research.” The change comes after Anthropic has sought out collaborations withprominent religious scholars— in some cases, seeking to convince them that Claude could be conscious or have a soul. Separate sections of the usage policy forbid users from engaging in broadly deceptive campaigns, such as using Claude to run fake accounts or fabricated news outlets. The policy also includes rules against deceiving voters or disrupting elections, collected in a section titled “Do Not Undermine Democratic Processes.”

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Google brings agentic AI to Gemini, starting with businesses

At aGoogle Cloud eventon Thursday, the companyannouncedit’s bringing its Gemini AI into the agentic age, with the launch of a unified agent that can not only answer questions but also get things done on the user’s behalf, all from a single interface. The move comes as AI tools have been moving beyond being just conversational experiences to those that can take ownership of assigned tasks, generate code, schedule meetings, book appointments and travel, and more. It also follows the rise of consumer-facing agents like Meta’s Muse and those that operate over messaging, like Instinct and others, as well as the recent debut of ChatGPT’s Dots. The company still has a good shot at achieving scale for its agentic efforts — as Google CEO Sundar Pichai pointed out at the event’s start, Gemini today has over 1 billion monthly active users. He also noted that nearly 90% of Fortune 100 businesses now use Gemini Enterprise at work. Given Gemini’s adoption in the corporate world, Google will initially focus on bringing the agent to businesses before later rolling it out to consumers. According to Pichai, this will allow the company to solve the “harder problems around security, scale, and performance,” which come with launching powerful agents such as these. Thomas Kurian, CEO of Google Cloud, explained that the new agent can be given “objectives, not just instructions.” That means it’s able to plan the work, use custom skills and tools, and connect to businesses’ internal systems to accomplish its goals. By default, the AI will pick the best model to complete the task, but users can also take over to choose a model — including those from third parties, starting with Anthropic’s Claude models. Google said that it will expand the model picker to include open source models and other private models in the future. The request can include attachments, like files, folders, or other projects designed for specific workstreams, like those that combine files and skills. The agent can connect to the business’ data and systems, like Google Workspace, Microsoft 365, Slack, Jira, Confluence, Git, BigQuery, Databricks, Postgres, Snowflake, and others. It can also connect and work securely with any Model Context Protocol (MCP) server inside or outside the company’s network. Users can keep track of what Gemini is doing from a “tasks inbox” interface, where they can see Gemini’s thinking process, delegation of tasks to subagents, loading of special skills, code, and progress. Notably, the AI will have its own Workspace account, as if it’s just another co-worker. That means it has its own email address and its own context, Google said. It knows who at the company is on which team, their time zones, who needs to approve items, what’s on people’s calendars, and more. Users can call the agent by tagging it, emailing it, sharing with it, or adding it to a group chat. As it takes action, it writes an audit trail attributed to the agent, instead of a person. The agent will be accessible from iOS and Android mobile devices, Windows and Mac desktops, the command line interface, Google Workspace, Microsoft 365, ServiceNow, and Slack. The agent’s early testers included sportswear brand On, Shopify, and PayPal, and noted that Gemini Enterprise customers include large businesses like BNP Paribas, Bradesco, Merck, Orange Spain, Santee Cooper, SOMPO, Ulta Beauty, and Wesfarmers. Google said new flexible spending options, including multi-model orchestration, smart routing, and real-time spend caps, will help businesses keep their enterprise AI costs under better control.

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