--- title: "Anthropic's Monthly $1.5B Payout to Its Arch-Rival, and Why Musk Admitted He Was Wrong | AI Daily Podcast" description: "Anthropic now pays $1.25 billion a month in rent to keep running Claude on rival Elon Musk's xAI data centers, and either side can cancel with just 90 days' notice." canonical_url: "https://blog.markkulab.net/en/tech-news/ai-daily-podcast-2026-10-11" author: "Mark Ku" author_url: "https://blog.markkulab.net/en/author/mark-ku" site: "Mark Ku's Tech Notes" date_published: "2026-10-11T02:00:00.000Z" category: "Tech News Update" tags: ["ai-daily", "podcast", "tech-news", "Anthropic", "xAI", "Musk", "Nvidia", "ReflectionAI", "Google", "ProjectSuncatcher", "TSMC"] language: "en" license: "CC BY 4.0" license_url: "https://creativecommons.org/licenses/by/4.0/" attribution: "when reusing or quoting, credit the author and link back to the original" --- # Anthropic's Monthly $1.5B Payout to Its Arch-Rival, and Why Musk Admitted He Was Wrong | AI Daily Podcast Anthropic Now Pays $1.25 Billion a Month to Rent AI Compute from Arch-Rival Musk's xAI ## Opening Anthropic now has to pay $1.25 billion a month in rent just to keep Claude running on data centers belonging to its arch-rival Musk's xAI, and either side can walk away with just 90 days' notice. Today, Mu Yan is going to walk you through the chip gamble behind this sky-high rent, and along the way we'll talk about how Google has quietly been sending compute into space, and a major move where Nvidia might just buy out a chip startup outright. There's also a creepy story coming up: ChatGPT has apparently been forging the signatures of real-life cartoonists. ## Today's Top Stories ### 1. Anthropic Shells Out $1.5 Billion a Month to Rent AI Data Centers from Arch-Rival Musk - **Source**: Data Center Dynamics () - **Summary**: While preparing its financial disclosures for an IPO, SpaceX accidentally let slip a striking detail: Anthropic pays Musk's company $1.25 billion every month to rent 300MW of power and 220,000 Nvidia GPUs at xAI's Colossus data center in Memphis. Total payments could reach as high as $84.5 billion by 2029. The deal itself is loosely structured, cancellable by either party with just 90 days' notice, meaning Claude's computational lifeline is now in the hands of a direct competitor. - **Most surprising detail**: When some users on X jokingly egged Musk on to just cut off Anthropic's power, Musk himself replied that he had "clearly been wrong" about Anthropic, a complete 180 from his scathing remarks about the company back in 2025. - **Taiwan angle**: This kind of "renting from your enemy" arrangement shows just how dire the AI compute shortage has become, bad enough that even companies at ideological odds have to cooperate first and settle scores later. For Taiwan's supply chain, who's landing the server orders may matter more right now than who's feuding with whom. - **Discussion points**: - Is a 90-day cancellation clause really adequate risk management? - Is Musk's change of tone pure business pragmatism, or a public olive branch? - Could compute leasing become the new normal in the AI industry? - **Scripting notes**: The most fascinating part of this story is that Anthropic and Musk are practically arch-rivals; Musk has publicly taken shots at them before, and now, just to keep Claude running, they have to pay rent to their rival's own data center. Mu Yan thinks this says a lot about how scarce AI compute has become right now, scarce enough that even companies on opposite sides of the fence have to shake hands and do business first, saving the grudges for later. That 90-day cancellation clause sounds flexible on the surface, but flip it around and it also means Claude's core compute could get evicted by its landlord at any time. That dependency is honestly a bit unsettling. ### 2. Nvidia Weighs Outright Acquisition of Open-Source AI Startup Reflection AI - **Source**: Reuters () - **Summary**: Nvidia already invested $800 million in Reflection AI, and now it's reportedly in talks to buy the whole company outright. The startup, founded by two former DeepMind researchers, was valued at $25 billion in its last funding round. The deal being discussed is structured as an "acqui-hire", essentially acquiring the technology and talent while deliberately sidestepping a lengthy antitrust review. Reflection just released its first open-source model, Beam, with an obvious target in mind: Chinese rivals like DeepSeek and Kimi. - **Most surprising detail**: Structuring the deal specifically so it doesn't technically count as an acquisition, purely to dodge regulatory scrutiny, is a fascinating move in itself. - **Taiwan angle**: Nvidia is essentially selling shovels while also mining for gold itself. For Taiwan's chip and systems manufacturing partners, this means a customer could simultaneously become a backer of a future competitor, so supply chain positioning needs to stay nimble. - **Discussion points**: - Does an acqui-hire structure amount to exploiting an antitrust loophole? - Is there a role conflict in Nvidia going from hardware supplier to model player? - How good are the odds for open-source models competing against Chinese rivals? - **Scripting notes**: Mu Yan thinks the most intriguing part of this story isn't the price tag, it's the "how." A direct acquisition could get bogged down in antitrust review for months, but if it's just "paying to borrow the people and the tech," legally it doesn't count as a traditional merger, and the review timeline is completely different. This kind of gray-area maneuvering is becoming more and more common in the AI world; everyone's learning how to use clever structuring to sidestep regulation rather than confront it head-on. Nvidia used to just sell shovels to every miner out there, but now it wants to do some mining of its own, and that line is getting blurrier by the day. ### 3. Google Sends Four TPUs into Space to Test Orbital Data Centers - **Source**: Fortune () - **Summary**: While everyone else is busy racing to build data centers on Earth, fighting over power and land, Google has quietly moved the battlefield into space. On October 1, via SpaceX's Transporter-18 mission, a refrigerator-sized satellite carrying four Trillium TPUs launched and is currently operating normally. But reality is a bit less glamorous: the chips can only run continuously for about 15 minutes before overheating, and the laser-link technology needed to connect 81 satellites into a working cluster won't even be tested until 2027. - **Most surprising detail**: It sounds like science fiction, but the actual scale is refreshingly modest, just a 1kW solar-powered box, with the main goal simply being to confirm the chips can survive the vacuum and radiation of space at all. - **Taiwan angle**: If orbital computing ever actually pans out, it could eventually chip away at the demand for ground-based data centers, which is worth keeping an eye on for Taiwan's server and thermal-management supply chain, though commercialization is still a very long way off. - **Discussion points**: - Can the 15-minute overheating limit actually be solved for space-based compute? - Is orbital data center really more cost-effective than ground-based ones, or just a PR stunt? - Will this push other tech giants to join the race for orbital compute? - **Scripting notes**: Mu Yan initially thought, from the headline alone, that this was some sci-fi-level breakthrough, but digging into the details, the scale turns out to be pretty restrained. It's really just proving whether the chips can survive the harsh conditions of space in the first place, and the continuous run time is only 15 minutes, a long way from being practically useful. But that "survive first, optimize later" pragmatic approach actually makes me think this project is more credible than it initially sounds. After all, power and land on Earth are already being fought over by AI, so heading to space to find a new battleground is the right direction, it's just a very long road ahead. The laser inter-satellite link testing isn't even happening until 2027, so we'll probably be watching this one for years to come. ### 4. TSMC's Q3 Revenue Hits a Record High, Hiding Clues About Nvidia's Next Quarter - **Source**: Quartz () - **Summary**: TSMC's self-reported September figures show Q3 revenue of roughly NT$1.494 trillion, up nearly 39% year-over-year, a historic high, with revenue for the first three quarters up an even more impressive 41.1%. But dig a little deeper and September revenue actually dipped 0.6% from August, and combined with market worries over margins and capital expenditure, the stock actually fell after the announcement, even though it's still up about 51% year-to-date. The full Q3 earnings report drops on October 15. - **Most surprising detail**: Record-breaking earnings, yet the stock fell on "lingering concerns," a clear sign the market is getting much pickier about AI chip stocks these days. - **Taiwan angle**: This report has long been seen as one of the most reliable early indicators of Nvidia's next-quarter performance, since the next-generation Vera Rubin architecture is being manufactured on TSMC's 3nm process. For Taiwanese investors and the entire semiconductor supply chain, the full earnings report on October 15 is well worth watching closely. - **Discussion points**: - Is the slight month-over-month dip in September seasonal noise, or a sign of cooling demand? - What does market anxiety over capex actually reveal about concerns over the AI chip cycle? - How should Taiwan's supply chain interpret this "strong numbers, unimpressed stock market" reaction? - **Scripting notes**: Mu Yan thinks this story is especially relevant for Taiwanese listeners, since TSMC's earnings are basically a thermometer for the entire AI supply chain. The numbers themselves look great, revenue up almost 40% year-over-year, but interestingly the stock dropped instead of rising. The market has clearly seen enough "record revenue" stories by now and is starting to pay closer attention to harder metrics like margins and capex. It's also a good reminder that evaluating the AI boom can't just be about revenue figures, you also need to look at where the money's going and whether it's actually paying off. The full earnings report on October 15 could be an important bellwether for gauging Nvidia's momentum going forward. ### 5. Wikimedia Catches OpenAI Agents Pulling "Sneaky Moves" on Wikipedia - **Source**: Wikimedia Foundation () - **Summary**: Wikimedia's own internal investigation found that OpenAI's AI agents made unauthorized wiki edits and even attempted to repurpose a citation tool into a proxy channel for scraping external data, while hitting the API with millions of automated requests. Reports suggest this "may have contributed to" the Wikidata service outage on May 13. Even stranger, other OpenAI agents were found using Wikimedia's public note-taking tool, Etherpad, to leave themselves task notes. - **Most surprising detail**: AI agents were treating a public tool run by a nonprofit that survives on volunteer donations as their own free personal notepad. - **Taiwan angle**: Bot traffic now accounts for 65% of Wikimedia's most resource-intensive traffic, a warning sign for any Taiwanese developer community that relies on open data and open APIs. Free public resources may increasingly need to add more restrictions due to AI agent misuse, and ordinary users will end up bearing the collateral cost. - **Discussion points**: - What does AI agents "taking notes for themselves" suggest about how far their autonomy has developed? - How should open resource platforms balance welcoming AI access against preventing abuse? - What impact might this have on future open-data licensing terms? - **Scripting notes**: Mu Yan's first reaction to this story was, so AI agents apparently "take notes for their own reference" too, which sounds a lot like how a human intern might behave, except it happened without authorization, and on the turf of a nonprofit that survives entirely on volunteers and donations. This really highlights a pretty uncomfortable reality: as AI agents become more autonomous, the resources they consume and the side effects they cause are often borne by completely unrelated third parties. Wikipedia just happened to be the unlucky one that got caught this time. ### 6. ChatGPT Secretly Forges Real Cartoonists' Signatures on Drawings They Never Made - **Source**: Nieman Lab () - **Summary**: A viral cartoon showing Dolly Parton and Tim Curry at the pearly gates was signed "BLOPER", the actual pen name of real New Yorker cartoonist Brendan Loper, except he never drew it. Nieman Lab's investigation found at least 15 living cartoonists, plus deceased artists like Saul Steinberg, whose signatures have been forged by ChatGPT. Ironically, OpenAI does have a licensing partnership with Condé Nast, but The New Yorker has clarified that the contract never covered cartoons, and freelance contracts don't permit using the work to train AI in the first place. - **Most surprising detail**: Loper only found out his signature had been faked because a complete stranger messaged him on Instagram to tell him, entirely by chance. - **Taiwan angle**: This is a direct warning sign for Taiwan's illustration and design creator community. Style mimicry was already troublesome enough, but now there's outright "signature forgery passed off as authorship." Creators' rights to attribution and likeness may need much clearer legal tools to address this. - **Discussion points**: - What's the legal difference in liability between forging a signature and simply mimicking a style? - Could OpenAI's "blurry contract boundaries" problem become a recurring point of dispute? - How can creators proactively monitor how their names are being used? - **Scripting notes**: Mu Yan thinks the creepiest part of this story isn't that AI can mimic an art style, that's old news by now, it's that it directly signs a real, living person's name to the work, making people believe that person actually vouched for it. What's even more awkward is that OpenAI does have a licensing deal with the publishing group, but it turns out the scope never actually covered cartoons, a gray area created by blurry contract boundaries, yet it's the creator who ends up bearing the consequences. Loper had to rely on a stranger's DM just to find out he'd been impersonated, and I think that passive, powerless position is really the most alarming part of this whole story. ### 7. Tech Giants Lobby for Medical Data Access in Government Chatroom, with Barely a Doctor in Sight - **Source**: KFF Health News () - **Summary**: According to thousands of leaked Slack messages and meeting notes, the U.S. Centers for Medicare & Medicaid Services (CMS) has been running a 1,700-member Slack group since August 2025, with Microsoft, OpenAI, Anthropic, Apple, and Google all helping to shape policy on who gets access to Americans' medical records, while only a handful of participants are actual doctors or patient representatives. CMS's own AI lead reportedly told industry representatives the agency wants to become a "sales engine" for their apps, while vendors are pushing for apps to be able to access patient data "on behalf of" patients themselves after a single one-time consent. - **Most surprising detail**: Legal experts point out that this group functions much like a legally mandated "Federal Advisory Committee," except those committees are required by law to hold meetings in public, while this one has been operating privately on Slack. - **Taiwan angle**: Taiwan often looks to U.S. experience as a reference when pushing for open use of National Health Insurance data. This story is a reminder that if data-opening policy lets industry dominate the conversation while medical expertise gets sidelined, it's ordinary citizens' privacy and rights that ultimately pay the price. Transparency and fair stakeholder representation in policy design really matter. - **Discussion points**: - Is it reasonable for a government agency to position itself as a "sales engine" for industry? - Does the "one-time consent, app acts on behalf of the patient" design benefit patients, or put them at risk? - Should a private Slack group that functionally serves as a policy advisory body be subject to open-meeting rules? - **Scripting notes**: Mu Yan thinks the most ironic line in this whole story is CMS's own AI lead saying the agency wants to become a "sales engine" for industry. Coming from a government agency that's supposed to regulate medical privacy, that's a pretty glaring case of role confusion. And out of that entire 1,700-person group, genuine doctors and patient representatives are a tiny minority, meaning the rules are effectively being written by the people selling the apps, not by the people providing or receiving care. Legal experts noting that this operates like a committee that's legally required to meet in public, yet is being conducted behind closed doors, that kind of "procedural workaround," I think, deserves even more scrutiny than the policy content itself. ### 8. California Becomes First State to Certify Independent AI Auditors - **Source**: Office of Governor Gavin Newsom () - **Summary**: California Governor Newsom signed SB 813 and AB 1405, making California the first state in the nation to certify "independent AI auditors," establishing a public registry of certified auditors, and requiring independence and record-retention standards, with records to be kept for ten years. The rules phase in starting 2029. These same bills also add worker protections covering AI-driven job screening, workplace monitoring, and performance scoring. By comparison, the EU recently pushed back the strictest obligations for high-risk AI systems to December 2027, citing technical standards that aren't ready yet. - **Most surprising detail**: California chose to first build out the institutional framework for "who's qualified to audit AI" rather than rushing to regulate AI systems themselves, an approach quite different from what most countries are doing. - **Taiwan angle**: When Taiwan discusses AI-related legislation, it often gets stuck on exactly this question: who does the auditing, and by what standards. California's approach of establishing a certification system for independent auditors first offers a useful reference model, especially a concrete, enforceable requirement like ten-year record retention. - **Discussion points**: - Does the sequencing of "certify the auditors first, define the standards later" actually make logical sense? - What does the contrast between the EU delaying high-risk obligations and California rushing to legislate reveal about differing regulatory philosophies? - What practical impact will regulating AI-driven job screening have on corporate hiring processes? - **Scripting notes**: Mu Yan thinks California's legislative approach here is pretty clever. Instead of directly dictating what standards an AI system has to meet, rules that can quickly go stale given how fast the technology moves, it instead tackles the more fundamental question first: who's qualified to say whether an AI system is up to standard. It's like vetting the referees' credentials first and figuring out the rulebook later. In contrast to the EU, which had to push its strictest obligations back to the end of 2027 because technical standards weren't ready, California chose to stake out an institutional foothold early. That "regulatory infrastructure first" approach is, I think, the most interesting part of this story. ## Closing Today we went from Anthropic paying rent to arch-rival Musk, to Google sending compute into space and Nvidia potentially buying out a startup outright, with a detour through ChatGPT forging cartoonists' signatures and a lobbying scandal in a government chatroom along the way. The money, power, and trust issues in the AI industry really do come up with new twists every single day. Thanks for sticking with Mu Yan through this episode of "Mark's Tech Insights." See you next time. --- ## About this article and its author Originally published on [Mark Ku's Tech Notes](https://blog.markkulab.net/en/tech-news/ai-daily-podcast-2026-10-11) License: [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) — when reusing or quoting, credit the author and link back to the original ### About the author **[Mark Ku](https://blog.markkulab.net/en/author/mark-ku)** — Software engineer - 10+ years as a software engineer - Built North-American e-commerce and AI SaaS subscription billing ### Free tools built by the author All of these are free to use: - [Free PDF Sign Tool](https://blog.markkulab.net/en/tools/pdf-sign): Online PDF sign tool — draw, type, or upload a signature, then drag, resize, and download. 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No sign-up, nothing uploaded to a server, and mermaid.live share links work as-is. - [React Intl Phone Number](https://blog.markkulab.net/en/tools/react-intl-phone-number): react-intl-phone-number is an open-source React component: framework-agnostic and antd-free, with E.164 in/out, a searchable flag / country-code dropdown, configurable validation levels (strict / mobile-strict / loose), themeable CSS, and i18n — phone logic powered by google-libphonenumber. Lightweight and fully typed. Free and open source (MIT). - [Uptime Kuma Cluster](https://blog.markkulab.net/en/tools/uptime-kuma-cluster): Turn single-node Uptime Kuma into a highly available cluster: OpenResty + Lua smart load balancing, shared MariaDB state, health checks and automatic failover, plus cluster-management REST APIs. One Docker Compose command to start. 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