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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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  • OpenAI Raises 11 Billion Dollars at a 1.25 Trillion Dollar Valuation, and the Market Is Blinking
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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Claude Solved a 25-Year NSA Mystery in 30 Minutes: The Curve Seeds Came From a Clock

Photograph of the tall NIST administration building in Gaithersburg under a blue sky.

Anthropic said on October 16 that an internal Claude research model solved, in under half an hour, a cryptography mystery that had stood for 25 years. The seeds behind the NIST elliptic curves P-192, P-224, P-256, P-384 and P-521 — numbers the NSA supplied in the late 1990s with no explanation of where they came from — were not hashes of a secret phrase and, on this evidence, were not chosen to hide a weakness. They were output from lrand48, the random-number generator in old C libraries, seeded with a Unix timestamp.

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DeepSeek's New Flash Model Cuts the Cost of an Agent Task to Under 20 Cents

Aerial photograph of the modern office towers housing DeepSeek's headquarters across a river with an arched bridge in the foreground.

DeepSeek shipped an updated Flash model on October 17, and the headline is not raw intelligence but price per finished task. DeepSeek-V4.1-Flash-1017 is live now on the company's website, in its app, and through its API, with open weights published on Hugging Face and ModelScope. DeepSeek calls the release a Pareto improvement over the V4.1-Flash it replaces: better benchmark scores, faster responses, and shorter outputs at the same time, which is the combination that actually moves an agent workload's bill.

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China Wants a Licence for Its Own AI: Export Curbs Reach Model Weights

The offices of DeepSeek in Beijing, a modern glass office complex photographed on a clear day.

On October 16, China's Ministry of Commerce and Ministry of Science and Technology proposed adding 40 technologies to the national export-control catalogue, and 23 of them are artificial intelligence: large models, autonomous agents, computing clusters and chip designs, according to Reuters and Bloomberg reporting. The quiet revolution in the draft is scope. Until now, export controls on AI, from Washington and Beijing alike, mostly reached the hardware underneath the models.

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