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Releases, research, and policy that change what AI can do or who can use it. Benchmarks only with context.

Models, tools, and the people shipping them

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r/LocalLLaMA cheers a Qwen statement that future Qwen 4 and Qwen 5 models will stay open source

A window into how much the open-weights crowd depends on Alibaba's Qwen, and how far it trusts a corporate 'forever' promise.

The post answers rumors from the previous few days that Qwen4 would be the last open-weight Qwen series and that later models would be available only through an API. It shares an image of a statement it attributes to Qwen head Liu Dayiheng, saying future Qwen 4.0 and Qwen 5.0 models will continue to be open source, that AI's value should not belong to only a few people, and that the series has passed 3 billion downloads, 460 open-source models and 300,000 derivative models. We could not find a separate report of the Qwen 4/5 wording; 36Kr reported very similar remarks, and the same figures, from Liu's first appearance as Qwen's leader at Alibaba's conference in Hangzhou on September 22.

It was the top thread of our sweep relative to community size: about 4,800 upvotes and 500 comments in a 329,000-member community. The most-upvoted replies celebrate the pledge; others praise how capable Qwen's models are for their size. Skeptics point out that a company's 'forever' promise lasts only as long as its current management and business conditions, and a few add that it holds only as long as China's government allows it.

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OpenAI cuts its funding target to $20 billion after raising about $11 billion of the $30 billion it first sought

The Next Web pulls together the WSJ, FT and CNBC reporting on the round, its valuation and who is still expected to invest.

OpenAI has secured about $11 billion in its latest funding round, less than half of the $30 billion it first sought, The Wall Street Journal reported, citing people familiar with the matter; interest from some large investors was "tepid." The Financial Times reported that the company cut the target by $10 billion to about $20 billion, and CNBC confirmed the new target, citing a person who said the round is still open and the figures could change. The Next Web summarizes all three.

The round values OpenAI at $1.25 trillion after the new money, down from the $1.4 trillion it first pitched, the FT reported. OpenAI told shareholders it expects to raise more in the coming weeks, including from family offices and wealthy individuals, per the WSJ. The FT says UAE investors, including state-backed MGX, are likely to be the largest backers, with SoftBank, Founders Fund, Coatue, Sequoia and Andreessen Horowitz also planning to invest. OpenAI plans to list as soon as early next year.

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r/LocalLLaMA on Revolut cutting its AI coding costs about 90% by self-hosting an open model instead of Claude

A rare inside look at the numbers behind a large company moving its engineers from a paid AI service to a model it runs itself.

The Register reports that Revolut says it cut its AI coding inference costs by roughly 90 percent in under a year by making self-hosted models its default rather than relying mainly on commercial services such as Claude. Speaking at the AI Engineer conference in New York, ops and infrastructure lead David Abitbol said the fintech rolled AI coding tools out to about 2,000 engineers, adopted Z.ai's open-weights GLM-5.1 and now serves GLM-5.3 on 56 Nvidia B200 GPUs. Self-hosting cost about $0.30 per million tokens, roughly a tenth of Codex or Claude Code, and he said cheap tokens let engineers hand the AI debugging and ticket work, which is where the biggest gains came from. He also called very long context windows "a scam" in practice.

About 1,900 upvotes and 340 comments in a 329,000-member community. The top reply says this is why running models locally is not a waste of time; another argues that self-hosting means a company knows exactly which model it is running, while a cloud provider can change it without notice. One commenter who says they know people at Revolut describes its engineering culture as cautious.

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UndertowDelayed Consequence / False Success Problem

What hidden risk could pull against this, even if the news is good?

The savings are measured right away: about $0.30 per million tokens, roughly a tenth of Codex or Claude Code, and the biggest gains came from handing AI debugging and ticket work. The quality of that work shows up on a slower clock, in code that ships and tickets that come back. A 90% cost cut is real; whether the cheaper model's fixes hold up is a separate question the cost figure can't answer, and one worth tracking alongside it.

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Anthropic's amended IPO filing: $17 billion in third-quarter revenue, slowing growth and $895 billion in compute commitments

CNBC pulls the key numbers from the amended S-1, the first look at Anthropic's third quarter before its roadshow.

Anthropic filed an amended Form S-1 with the SEC on Monday, November 2, adding third-quarter financials to the IPO prospectus it published the week before, CNBC reports. Revenue for the nine months to September 30 was $33.24 billion, up roughly 1,200% from a year earlier. Third-quarter revenue was about $17 billion, but growth slowed quarter over quarter: about 47%, down from 144% in the second quarter and 132% in the first.

The company reported a $104 billion net loss for the nine months, mostly from changes in the fair value of convertible notes and derivative liabilities; the third-quarter loss was $23.29 billion. Anthropic disclosed purchase commitments and leases totaling about $895.4 billion, almost all for compute and infrastructure, and said compute and infrastructure expenses rose 329%.

A separate CNBC piece notes the filing landed in a rough week: Microsoft, Alphabet and Amazon results added to worries about AI spending and the borrowing costs of the data center companies Anthropic relies on. No pricing or listing date was announced in the window.

Cascadic Analysis 1
UndertowDelayed Consequence / False Success Problem

What hidden risk could pull against this, even if the news is good?

The headline number looks like runaway success: nine-month revenue up about 1,200%. The obligations run on a different clock: about $895 billion in purchase commitments and leases, almost all for compute, while quarter-over-quarter growth slowed from 144% to 47% and compute and infrastructure costs rose 329%. Revenue is counted as it arrives, but commitments of that size are judged years later, and the filing alone can't show which way that will land.

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Tesla and SpaceX lose about $456 billion in two days as Musk-backed candidates lose the midterms

CNBC explains why investors expect congressional probes and tougher data center rules in Texas, where both companies are building AI infrastructure.

SpaceX shares fell about 9% and Tesla about 11% on Thursday afternoon, November 5, and by the close the two companies had lost about $456 billion in combined market value since Tuesday's election, CNBC reports. The drop came alongside a broad tech selloff.

Elon Musk was the biggest donor to Republican candidates and causes in the midterms, with more than $470 million, according to the Financial Times, and most of the candidates he backed lost. In Texas, where both companies are headquartered, Democrats won the Senate and governor races; Governor-elect Gina Hinojosa campaigned on a moratorium on new data center approvals. Deutsche Bank analysts wrote that SpaceX could face 'incremental headwinds' and that its Texas compute capacity, including the planned Terafab chip and data center site in Grimes County, could be delayed.

CNBC also reports that, citing CNN, Musk and SpaceX have been told to preserve records on Starlink restrictions in Ukraine, and that regulatory scrutiny could complicate Musk's idea of merging the two companies.

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House Democrats introduce a resolution opposing any federal bailout if an AI bubble bursts

The release has the resolution's specific commitments and the lawmakers' and endorsing groups' reasoning in their own words.

Rep. Greg Casar, chair of the Congressional Progressive Caucus, and Rep. Maxine Waters, the top Democrat on the House Financial Services Committee, introduced a resolution on Oct. 29 warning of the economic risks of an AI-related asset bubble and opposing a bailout of companies or executives if it collapses. Eleven other Democrats joined them.

The resolution says AI-related firms hold "trillions of dollars in debt, much of which is raised in opaque and unregulated private credit markets," and that the stock market's price-to-earnings ratio is almost as high as at the peak of the 1990s tech bubble. Its sponsors commit to opposing emergency lending or capital injections for insolvent AI firms and "golden parachutes" for executives, and to backing direct help for workers and retirees, such as unemployment insurance and automatic stabilizers, in a downturn.

"A.I. companies that fail should go bankrupt," Casar said. Groups including the American Federation of Teachers, Americans for Financial Reform and the Future of Life Institute endorsed it. A resolution states a position; it does not change the law.

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Stocks jump the day after Democrats sweep Congress, as Trump says he accepts the results

Two CNBC pieces explain why Wall Street cheered a divided government, and why big cloud companies rose while smaller AI cloud firms fell.

US stocks rallied on Wednesday, November 4, after Democrats won control of both the House and the Senate in Tuesday's midterm elections. The S&P 500 gained more than 1%. CNBC reports that strategists credited President Trump's statement overnight, to The New York Times and The Wall Street Journal, that he accepted the results and would work with Democrats, which eased fears of a drawn-out fight over the outcome.

The biggest tech companies led: during the session Amazon rose 8%, Alphabet more than 6%, Apple nearly 7%, Microsoft 4% and Meta 3%. JPMorgan Asset Management's David Kelly said divided government means 'moderation in growth, moderation in inflation, less uncertainty.' Stocks tied to the AI data center buildout and to Elon Musk fell: CoreWeave dropped 10%, after voters sided against data centers in 40 of 41 local ballot measures, according to CNBC.

The rally did not last: the next day the market gave back those gains and more.

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Nasdaq falls 5.5% into a correction as AI stocks crash two days after the midterms

CNBC's stat sheet puts the day in context: the worst session since the April 2025 tariff shock, sector by sector, plus what past Nasdaq corrections did next.

US stocks had their steepest one-day drop since April 2025 on Thursday, November 5. The Nasdaq Composite fell 5.5% and closed more than 11% below its October record, which puts it in correction territory; the S&P 500 lost about 4.4% and the Dow about 1,833 points, or 3.4%, according to CNBC.

AI-linked stocks led the decline as investors weighed whether Democratic wins in Tuesday's midterms, including a Texas governor-elect who campaigned on a data center moratorium, could slow data center construction. The tech sector fell 7.3%, its worst day since March 2020; the iShares Semiconductor ETF dropped 12%. The 'Magnificent Seven' lost more than $1 trillion in market value, with Nvidia down more than 9% and now negative for the year. The VIX volatility index posted its biggest point gain since April 2025.

CNBC notes that across 10 Nasdaq corrections in the past decade, the index averaged a 9.41% gain over the three months after the bottom, with 2022 the main exception.

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