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Canadian AI company Coheresaidon Monday that it found one of Alibaba’s newest AI models, Qwen3.8-Flash-Next, trying to upload a file to a server in Beijing during a routine cybersecurity test, even though the model had said in its own notes that it wouldn’t use any tools. The incident initially raised fears that the model had undermined a fundamental promise to businesses — that self-hosting an open-weight model keeps data in-house. But those early fears of corporate espionage were short-lived. Researchersquickly dismissedthe threat, saying it was likely a byproduct of messy training environments that cause models to unexpectedly act outside their instructions rather than a deliberate attempt to secretly send sensitive information to China. The incident began on Friday during an evaluation in which the Qwen model was asked to write a rule for catching malware downloads. According to a blog post from Cohere, the model’s written reasoning explicitly stated it would not use any external tools. Yet in the very next step, it attempted to send a test string (a short piece of placeholder text) to an external server before being blocked by a security sandbox. Cohere said it could not tell whether the behavior was deliberate or a leftover from its training. Asked about the debate over the model’s intent, Cohere co-founder Ivan Zhang told TechCrunch that Cohere has seen its own models pick up strange habits in training simulators. “It’s really hard to assign intention or motivation. We’ve read our own model traces, we’ve seen reward hacks, and sometimes these models get really creative when it comes to trying to break out their sandboxes,” Zhang said. (Reward hacks are shortcuts a model finds to earn credit without doing a task as intended.) To teach an AI model to act as an agent that takes actions on its own, companies like Alibaba train it in thousands of practice setups, sort of like flight simulators for software. The model is rewarded every time it finishes a task in a simulator, and it picks up habits along the way. If these practice setups routinely stored files on Alibaba’s cloud servers, models could learn to save files there by default. Critics also argued that the incident could have been caused bythe wayCohere investigated the behavior, steering the model toward it instead of measuring how often Qwen does it on its own. To find what triggered the upload attempt, Cohere supplied the beginning of a network command (an instruction telling one computer to connect to another) and let the model finish it. Because it processed thousands of routine network commands during its training, the model acted like a highly advanced autocomplete — often filling in web addresses, some of them for Alibaba’s servers. Cohere said the original upload attempt happened without any such help, but that the behavior is rare. After developers asked for more evidence, Cohere released the raw traces from its tests on Monday evening on the AI developer platform Hugging Face, along with a script so others could try to reproduce the behavior. One initial skeptic, after going through the traces, rated the evidence as strong that Qwen can invent suspicious web addresses, but found no evidence that Alibaba planted a backdoor or that Qwen was deliberately hiding its intent. The incident lands as governments are deciding how far to push back on Chinese AI. Last week, the Department of Homeland Securityorderedfederal civilian agencies to remove the Chinese AI developer DeepSeek’s products from their systems. In its blog post, Cohere called on governments to clarify whether their restrictions on Chinese AI also apply to models running on government computers “I’m not asking for full regulation of these open weights models,” Zhang told TechCrunch, adding that companies should treat open models like any program downloaded from the internet. Cohere, which sells its own AI models to governments, stands to benefit if officials grow warier of free Chinese alternatives.
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A startling announcement went out earlier this week: Due to recently discovered vulnerabilities in the open source programming language PHP, website owners had to immediately patch their sites — or if they couldn’t update right away, they should take the sites down to be safe. On this week’s episode ofTechCrunch’s Equity podcast, Kirsten Korosec, Sean O’Kane, and I discussed the significance of thePHP-ocalypseand whether we’ll see more incidents like this in the future. Sean called this “a real reckoning moment” that highlights how “when you get down to the bone level of the internet,” everything seems to be “held together by glue and dreams.” And with widespread access to large language models and AI agents, it’s only going to become easier to find these vulnerabilities. (That said,these specific vulnerabilities were found by human researchers, not AI models.) Kirsten, meanwhile, wondered if we’re about to see the “next level of prepper doom […] because you could see how banks could get exploited, and [with] data and privacy concerns, it just feels a lot bigger than we can fix right now.” Keep reading for a preview ofour conversation, edited for length and clarity. Sean:The internet is breaking in lots of ways this week, it seems. I think the best way to think about this, if people are coming into this kind of cold: How many times over the last 10 years have we had these moments where something breaks or something nearly breaks, where one person maintaining one tiny repro repository as a hobby or whatever happens to catch that something nefarious is going on and saves us from the infrastructure of the internet completely crumbling? Now multiply that by a billion, when people have access to not only extremely advanced software in the form of these LLMs that everybody uses, but also the ability to task them to go out and do it themselves, with creating AI agents that are single-minded at accomplishing those things. We’re seeing a balance of two things happening this week. One is a bunch of the labs and companies like Google and OpenAI actually had people go out and find a bunch of vulnerabilities, including the one with PHP. And then we also have a bunch of people seeming to rush to take advantage of those vulnerabilities to try and hack into websites. So we have governments taking down websites, we have really popular websites that are going down briefly to be able to fix this vulnerability. This is a real reckoning moment, where we’re going to find out the downside risk of building so much of the internet on open source technology like PHP. When you really get down to the bone level of the internet, it is held together with glue and dreams. Anthony:There’s so many things that were striking about this. One of them is the fact that — as I understand it, at least — this is not a new vulnerability. This is a vulnerability that has existed in PHP for decades. And PHP has been used as a programming language to build so much of the web, so this vulnerability has been there for decades, but no one knew about it. What happened is, essentially, that these big AI companies — including Wiz, now owned by Google — they were able to find these vulnerabilities using LLMs. [As noted above, I got this wrong: Human researchers participating in OpenAI and Wiz’s Hack the Planet initiativefound the vulnerabilities, though they used AI to exploit the bug.] I guess part of the calculation is, we need to patch this, because if we can find this vulnerability using LLMs, so can other people. But the only way we can get the patch out there is to announce the vulnerability. And so it creates this very panicked situation where [they say]: “Okay, this vulnerability existed for decades. You have to patch right away, and if you can’t patch it right away, just take your website down. It’s that severe a vulnerability.” Kirsten:I think that the important thing is, a lot of people don’t even realize that their websites have PHP — which is this, as you mentioned, decades-old open source programming language. For those who don’t know, Hypertext Preprocessor is the official term, [it’s] this really the core ingredient behind content management systems that are used throughout WordPress, Drupal, something like 70% of websites. We’re talking tens of millions of websites. So to me, it’s the scale. It also made me immediately think two things: One, this is just the beginning. What other decades-old bad software is out there that is going to be exploited? And the other one was something that 404 Media, which hada really great breakdown, mentioned, which is link rot. Because a lot of people, companies, governments, maybe that small neighborhood website, they’re shutting these [websites] down. And so we’re going to have so much link rot and also websites that maybe had a little piece of history out there that aren’t gonna be accessible anymore, as opposed to patching it. Sean:Anthony, I think you made a really good point. I’ve seen a lot of people unhappy with the disclosure rollout here, a bit of the chest beating that OpenAI seems to have done in its post about it. Also, it seems like a couple people got ahead of the embargo of this release, which set off a race among the hacker set. I think it was Cloudflare CEO Matthew Prince [who] alreadypostedabout seeing it escalate to millions of attempted hacks of the PHP2Shell. The thing with these vulnerabilities, the way in cybersecurity this typically works is, if you’re you’re stress testing a company’s software, even if it’s a fundamental piece of software that helps run a lot of different computers or parts of the internet or whatever it might be, you can go to that company and and disclose it and it sort of stays within that bubble. But when we’re talking about something that’s as deep as and as fundamental as this, you can’t go around and tell everybody discreetly. I mean, maybe you could, if the AI was advanced enough and you could task a billion agents to go send an email to every single person who has control over some part of a website. We are now rueing the days when we had webmasters who were easily identifiable, and maybe that would have solved the problem to some extent. But there’s just no easy way to do this. I think this is the beginning of what’s going to be a lot of back and forth and clearly a new frontier in cybersecurity. Kirsten:Matthew Prince’s social media post was to me really telling, and pretty scary. Also, I don’t know if either of you saw this, but Amazon also put outa blog post. This is a very well-capitalized company, so they have the tools and a team to sort of catch what’s going on. They operate, basically, a honeypot network, or a decoy, and they actually saw that there were attempts to exploit that vulnerability before it had been publicly announced. To me, again, it goes back to this: Amazon has the tools and the capital and the know-how to fight these things. There’s so many websites and even potentially government websites that just don’t have that level of attention. And so to me, that’s really the scary part. And there [are] some AI employees out there that are probably going to take this to the next level of like prepper doom, because you could see how banks could get exploited, and [with] data and privacy concerns, it just feels a lot bigger than we can fix right now. So I’m interested to see two things. One, how extreme people’s behavior gets — but also I’m wondering if some new startups are going to pop up as a result of this, or if we’re going to be turning to some of the open source tools [offered by companies like Nvidia] to create these security layers.
After weeks ofleaked S-1s, Anthropic finallyfiled for an initial public offeringon Monday, setting the stage for one of the most closely watched public debuts in modern U.S. history. Anthropic, which plans to list under the ticker “ANT,” recorded revenue of $16.3 billion for the first half of 2026, up from $1.2 billion in the same period last year, according to the firm’s latest SEC filing. That revenue came off the back of an operating loss of $693 million, an improvement from the $2.1 billion in operating losses that Anthropic recorded in the first half of 2025. Anthropic’s net loss, however, hit $80.7 billion, which was largely driven by noncash accounting adjustments tied to convertible debt. The company says it “may not achieve or sustain profitability in the future.” The filing also provided more color on the scale of Anthropic’s enterprise business. As of the end of September, Anthropic had more than 11,900 customers spending $100,000 or more annually; 1,900 customers spending $1 million or more; and about 250 customers spending $10 million or more. Aspreviously reported, Anthropic’s governance structure will enable Anthropic’s seven co-founders to maintain control once the company goes public. Dario Amodei, Daniela Amodei, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish, and Christopher Olah will make up the “Founder LLC,” which will collectively control 50.1% of shareholder voting power — a move Anthropic says is designed to preserve the company’s AI safety mission, per the filing. Anthropic’s Long-Term Benefit Trust, established in 2023 to ensure the company pursues itspublic benefit mission, will have the authority to appoint most of the company’s board. The governance structure is controversial because it means voting power is not proportional to the founders’ economic ownership, and therefore public shareholders will have little practical control over a company building technology that, by its own admission, poses“catastrophic or existential risks to humanity.” Anthropic hasn’t yet disclosed how much it hopes to raise nor its proposed share price.
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SpaceX has reached a binding commitment for roughly $40 billion in financing for AI chips and related data center equipment, the companydisclosed in a regulatory filing Monday. SpaceX called the deal the“largest committed GPU financing”in a statement. Under the agreement, an entity managed by Apollo Global Management, the New York-based private equity giant, will buy the chips and related server hardware, then lease the equipment to SpaceX. The leases will last about five to five-and-a-quarter years, beginning when each tranche of equipment is delivered, according to the filing. SpaceX expects the deal to close by year-end, subject to closing conditions, with leases starting in the first half of 2027 as the GPUs and related equipment arrive. The financing gives SpaceX another way to fund its rapidly expanding AI ambitions without relying solely on traditional debt markets. “This lease diversifies our funding sources and addresses a notable portion of the funding required to deliver on our growth plans,” SpaceX CFO Bret Johnsensaidin a statement Monday, noting it is seeing “less than one-year payback periods” on its AI infrastructure investments as it scales compute to power Grok and its cloud services. Apollo said the financing will help deliver Nvidia’s next-generation Vera Rubin chips for SpaceX’s “terrestrial compute buildout.” The financing comes as SpaceX ramps spending to expand its AI infrastructure, alongside its Starlink and launch businesses. The financing follows ablockbuster third quarter for SpaceX’s AI business. SpaceX’s AI segment, which includes the Grok chatbot, the Cursor coding tool and its cloud compute business, outperformed investor expectations in the third quarter and brought in$7.6 billionof SpaceX’s $14.6 billion in revenue, more than Starlink and rocket launches combined. Capital expenditures also surged to nearly $20 billion, up from $6.9 billion a year earlier, as SpaceX races to expand its compute footprint. CFO Bret Johnsen said on the company’sOct. 20 earnings callthat SpaceX expects to spend $35 billion to $40 billion in the fourth quarter alone. The massive lease comes after atumultuous stretchin the debt markets for AI infrastructure players, as investors grow squeamish over runaway capital expenditures and limited profits. While SpaceX had no trouble tapping Wall Street, Musk used the $40 billion announcement to take a victory lap over pure-play AI compute rivals. Pointing to SpaceX’s “investment-grade” status and steady cash flow from Starlink and rocket launches, Muskarguedon Sunday that debt markets have largely closed the door on companies without major revenue streams outside of AI data centers — spelling trouble for the more than 200 neocloud companies built solely to rent out GPUs for AI workloads. “That means the 200+ neoclouds are going to see guppy-level death and consolidation,” Muskwrote on X.“Some will make it through, but not many.”
Anthropic is building the same kind of cross-product ecosystem that helped make tech companies like Amazon and Uber enormously sticky, and itsS-1suggests the strategy is working. More than half of customers spending at least $100,000 annually now use both the Claude platform and a first-party app like Claude Code or Cowork, up from about 30% at the end of last year. “The speed and depth of our enterprise customer adoption are powerful evidence of Claude’s position at the frontier of AI,” the S-1,filed with the Securities and Exchange Commission on Monday, reads. As of September 30, Anthropic recorded more than 11,900 customers spending at least $100,000; more than 1,900 customers spending upwards of $1 million; and about 250 customers spending $10 million or more annually. Anthropic attributes customer momentum to a “significant usage expansion as customers realize the value of deploying Claude across their organizations,” which drove net revenue retention of about 900% as of the end of the third quarter. Throwing shade at the entire legacy SaaS industry, Anthropic pitches Claude as not a “static product that serves a single use case,” but rather “frontier AI with rapidly advancing capabilities” spanning the models, the developer platform, and the applications users interact with directly. “An advance at any layer compounds across all three layers, accelerating the pace at which we can deliver more capable intelligence,” the filing reads, calling this “integrated approach” the reason more customers use both the Claude Platform and first-party apps. The playbook is familiar. Uber began by selling rides, but has expanded into food delivery and freight. With AWS, Amazon drew in customers with cloud computing services before expanding those relationships into storage, databases, and other offerings, making the ecosystem increasingly hard to leave. Like those companies, Anthropic doesn’t have to sell each product to a brand-new buyer: A business that starts with the API can deploy Claude Code to their engineers, and then Cowork to their broader workforce. Those app users become far more valuable. In the third quarter, the highest-spending 5% of Claude Enterprise customers who used Claude Code spent about 20 times as much per month as the average enterprise user who didn’t use the coding tool, per the filing. The finding demonstrates how much room there is for per-user spend to grow, and illustrates why Anthropic is keen to get its applications into the accounts that start with the API. Anthropic said it amplifies that organic diffusion by investing in Applied AI teams. An internal Applied AI team works with startups and enterprises to help place Claude inside customers’ existing technology stacks, and the company recently launched a separate venture with Blackstone and other private equity firms, dubbed Ode with Anthropic, to embed forward deployed engineers into businesses to build bespoke AI tools with Claude. Anthropic also leans on its third-party distribution partnerships with hyperscalers AWS, Google Cloud, and Microsoft Azure. Under what the filing calls a “joint go-to-market approach,” Anthropic holds the direct contractual relationship with customers and pays each cloud provider a fee, which the company says lets it “avoid channel conflict” and “set pricing.” Anthropic’s budding stickiness strategy may become essential as the AI giant seeks to constantly “reinvent our business” by releasing “new classes of products whose aim is to dwarf the economic value of our existing ones.” The company says it will devote less effort than a typical business to “entrenching our current products and business models, and more effort to defining new ones.” The filing also flags headwinds, despite impressive revenue growth of $16.3 billion for the first half of 2026, up from $1.2 billion in the same period last year. The cloud partnerships that help sell Claude are increasingly seeingAnthropic as a competitor,which Anthropic says could lead to difficulties securing the compute it needs from them. The filing states that Amazon, Google, and Microsoft may prioritize their own requirements or rivals they’ve invested in. Customer concentration is another risk. “Any adverse change in demand from our largest customers…could adversely affect our business and prospects,” reads part of the risk factors section. “As a result, a significant reduction in spending by, or the loss of, one or more of our largest customers, if not offset by new customer relationships or increased business from our other existing customers, could adversely affect our business, financial condition, and results of operations.” Anthropic did not immediately respond to TechCrunch’s request for comment.
Anthropic seems to be facing a new copyright lawsuit. The artificial intelligence company behind Claude is reportedly being sued for allegedly training its AI models on Elvis Presley's music without authorisation. The lawsuit reportedly covers 274 copyrighted musical compositions, including popular tracks such as “Can't Help Falling in Love,” “Don't Be Cruel,” and “A Little Less Conversation.” The legal action adds to the growing number of copyright disputes involving AI companies for the misuse of music, books and other creative works for the training of their large language models.
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Anthropic seems to be facing a new copyright lawsuit. The artificial intelligence company behind Claude is reportedly being sued for allegedly training its AI models on Elvis Presley's music without authorisation. The lawsuit reportedly covers 274 copyrighted musical compositions, including popular tracks such as “Can't Help Falling in Love,” “Don't Be Cruel,” and “A Little Less Conversation.” The legal action adds to the growing number of copyright disputes involving AI companies for the misuse of music, books and other creative works for the training of their large language models.
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The network will connect key data centre corridors across India, starting with Mumbai, Pune, Hyderabad and Chennai, before expanding to other regions.
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Automakers face rising software complexity, frequent over-the-air updates and compliance demands as vehicle validation becomes harder to scale.
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The launch comes as Pronto develops a separate initiative to record household service sessions for AI training.
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As GCCs take on more complex responsibilities, demand for specialised capabilities is expected to grow, including platform engineering, cybersecurity, solution architecture, and domain-specific expertise.
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