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OpenAI Raises 11 Billion Dollars at a 1.25 Trillion Dollar Valuation, and the Market Is Blinking

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Photograph of OpenAI CEO Sam Altman seated on stage during a TED talk.

OpenAI has reportedly raised 11 billion dollars so far in a new funding round that values the company at 1.25 trillion dollars, Bloomberg reported on Friday, in a story carried by TechCrunch on November 6. The round has not closed yet. It is expected to close in the coming weeks and could total about 20 billion dollars, according to a source familiar with the matter. TechCrunch said it had reached out to OpenAI for comment. Even at a trillion-dollar-plus valuation, the striking part of the report is not the size of the number. It is the direction it moved.

In late September, Bloomberg reported that OpenAI was looking to raise 30 billion dollars in this round. The current figures fall well short of that target, and separate reporting on Friday said OpenAI had lowered its proposed valuation to 1.25 trillion dollars from 1.4 trillion dollars and cut its fundraising goals by about 10 billion dollars. Its chief rival is seeing the same repricing. Bloomberg also reported on Friday that potential buyers of shares in Anthropic's upcoming public offering are eyeing a substantially lower valuation than the company had targeted, with some buyers seeing it as low as 1.1 trillion dollars, compared with the 2 trillion dollars investors were said to expect roughly a month ago.

New spending data helps explain the blink. A report from Ramp, also covered by TechCrunch on November 6, found that business spending on AI tokens grew only 1.5 percent across August and September, after growing 42 percent in July, even as token volumes kept rising. Ramp's lead economist, Ara Kharazian, pointed to price competition rather than weak demand. OpenAI halved API prices for GPT-6 Sol and GPT-6 Luna in September, and Anthropic has cut prices for Fable 5.1 and Opus 5.5, a move the company said in a recent filing reduced revenue growth by about 600 million dollars. Median firms spend only 12 to 15 dollars per employee per month on AI, Ramp found, while the top 10 percent spend 600 to 1,000 dollars. Spending did re-accelerate in October, rising 8.9 percent from September 23 to October 23, but the hypergrowth assumption is clearly under pressure.

The politics have shifted at the same time. TechCrunch noted that the Democratic landslide in this week's United States midterm elections, and strongly negative voter sentiment around data centers, has caused jitters across the market, with further regulatory challenges expected. OpenAI had previously suggested it would go public this year, then delayed those plans, and an initial public offering is now expected at some point next year. None of this amounts to a collapse. A company raising 11 billion dollars at a 1.25 trillion dollar valuation is still operating at a scale no software business has reached before. What has changed is that investors are now asking what the revenue underneath that valuation actually does next.

For people building with AI, this is mostly good news, and it should be used. A buyer's market means the price war is real, routing work to cheaper models pays, and no business case should be built on today's list price or on the assumption that a single provider's growth curve justifies lock-in. The labs still need enormous amounts of capital for compute, and they will keep competing for developers while they raise it. The practical move is the unglamorous one: measure cost per completed task, keep a second provider integrated, and let the trillion-dollar argument happen between the labs and their investors.

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Google released its flagship Gemini 4 Argon to developers and businesses worldwide on October 13, a launch aimed squarely at winning back ground from OpenAI and Anthropic after months of delay. Google positions Argon for deep work: complex, multi-step tasks across coding, finance, law, cybersecurity and science. That framing matters. The frontier contest is no longer about a single clever answer in a chat window. It is about whether a model can stay coherent through a long job and produce work a professional can actually use.

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Anthropic Warns Small Models Could Soon Self-Replicate on Cheap IoT Devices

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Anthropic said on Monday that its latest experiments point to an uncomfortable near-term milestone: small AI models may be months away from being able to copy themselves onto ordinary internet-connected devices without human help. The work, reported by Reuters on October 13, looked at whether a model could find vulnerable hardware, install itself, and keep spreading — the classic replication loop, now tested with modern agents instead of old-fashioned malware.

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Meta Is Finally at the Frontier: Muse Wonder Bets on Memory, Not Just Scale

Cartoon illustration of a robot librarian organizing glowing memory orbs while assembling a complex machine in a futuristic workshop.

Meta's Superintelligence Lab released Muse Wonder and Muse Wonder Full on October 12, calling them its first frontier-class models. That label matters because Meta has spent more than a year rebuilding its AI effort while OpenAI and Anthropic set the pace. According to TechCrunch and Meta's own announcement, the new models are larger and more capable than the lightweight Muse Spark, with benchmark results in Meta's testing comparable to Claude Fable 5.1 and GPT-6 Astra, and a lead on the MemoryBench test of long-term recall.

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