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The Final Private Push: Anthropic Secures $65 BillionAnthropic has closed a monumental Series H funding round, raising $65 billion at a $965 billion post-money valuation. This capital injection represents the startup's largest private fundraising effort to date and signals that the company is likely in its final pre-IPO stage. The round brings the company's total capital raised to a staggering level, positioning it as a heavyweight contender in the generative AI sector just as public markets begin to open up to high-growth technology companies.The Infrastructure and Investor EcosystemThe funding round was co-led by a consortium of elite institutional investors, including Altimeter Capital, Dragoneer, Greenoaks, and Sequoia Capital. Notably, the round saw participation from major infrastructure partners such as Samsung, SK Hynix, and Micron, highlighting the critical role hardware manufacturers are playing in the AI supply chain.Strategic Backing: Hyperscalers committed $15 billion, including a significant $5 billion from Amazon.Investor Demand: The round was highly competitive, with one institutional investor reportedly pledging up to $5 billion just to secure a meeting with the CFO.Use of Funds: Proceeds will be directed toward advancing safety research, expanding compute infrastructure, and scaling enterprise products.Valuation Wars and Revenue TrajectoryThis funding round places Anthropic at the epicenter of a fierce valuation war in the AI industry. The company's massive valuation comes as it reports a $47 billion revenue run rate and expects a 130% revenue surge to achieve its first operating profit. This financial performance contrasts sharply with the broader tech sector, illustrating the intense demand for high-performance AI models.Competitive Landscape: Anthropic's valuation rivals OpenAI, which raised $122 billion in March at an $852 billion valuation.Market Positioning: The company is reportedly preparing to launch models comparable to its powerful cybersecurity model, Mythos, which has been limited due to safety concerns.The Strategic Shift Toward Enterprise SafetyThe inclusion of infrastructure partners like Samsung and SK Hynix suggests a strategic pivot toward vertical integration. By securing hardware support, Anthropic ensures a stable supply chain for the compute-intensive models it is developing, such as the newly released Claude Opus 4.8. This model emphasizes agentic tasks, advanced coding, and self-correction capabilities, addressing a critical need for enterprises seeking reliable and safe AI solutions.The IPO Countdown and Market DominanceWith this massive capital raise and the release of advanced models, Anthropic is poised to lead the next phase of AI innovation. The company's ability to attract top-tier institutional investors and secure hardware partnerships positions it uniquely ahead of its IPO. As the race for AI dominance heats up, Anthropic's valuation and growth trajectory suggest it will be a key player in shaping the future of the public AI market.

The Rise of Recursive Self-Improvement in AIThe word "recursion" is the latest buzzword in AI circles. Two separate startups have taken on the name, and many more have started referencing recursive self-improvement (RSI) in their roadmaps. Like AGI before it, RSI has become a three-letter byword for a cataclysmic AI takeoff – even if there's still a little disagreement about what it exactly means.In basic terms, RSI refers to an AI system that can continuously upgrade itself. Once AI systems can manage the upgrade cycle better than humans, the process can become a closed loop, limited only by the compute power they can access, and humans are no longer necessary or even helpful.Scary or not, that's a vision that a lot of AI labs are eager to chase.Key Players Pursuing Recursive SystemsEarlier this month, well-known AI researcher Richard Socher launched the aptly named Recursive Superintelligence with RSI as an explicit goal. "Our main focus is to build truly recursive, self-improving superintelligence at scale," Socher told TechCrunch at launch, "which means that the entire process of ideation, implementation, and validation of research ideas would be automatic."A number of other prominent researchers are already chasing that same goal, hoping for a breakthrough that will make recursive self-improvement possible.One of the most prominent is Andrej Karpathy, a legendary figure from Tesla and OpenAI, who is using agent swarms to train LLMs on simple tasks for a project he calls Auto-Research. Karpathy has been unusually open about the project, tweeting about milestones regularly and making the building blocks available through a public GitHub repo. So far, the work has mostly been confined to making minor improvements on a GPT-2 scale model — as Karpathy noted in March, "It's not novel, ground-breaking 'research' (yet)" — but it's been enough to convince lots of other researchers to follow the RSI dream. And with Karpathy now working on pre-training at Anthropic, he will have plenty of opportunity to apply the idea at a larger scale.Adaption — founded by Cohere and Google alum Sara Hooker — recently launched a similar tool called AutoScientist in an effort to automate frontier training. Like Karpathy's auto-researchers, the system trains agents to make incremental improvements — but for Adaption, the goal is to make it easier to train a full-scale frontier model. If those same researchers start to push the frontier forward, the system could quickly spiral into something very much like RSI.Disarray founder Doris Xin drew more specific RSI interest when her self-trained machine learning agent took home 28 medals in a recent Kaggle competition, beating out many human-trained agents. As she sees it, the major challenge is reliability."I would argue, given infinite compute and infinite time horizon, we are already there," Xin told me. "I want to make an argument that this is not a creative endeavor, really. It's just a lot of meat-and-potatoes engineering."The Current State of Self-Improving AIThere's also plenty of evidence that the AI industry isn't very close to recursive systems in any meaningful way — and is still grappling with talking to a wary public about its progress. So Google CEO Sundar Pichai basically admitted in a recent podcast interview."It's a continuum, and we are all definitely making progress," Pichai said. "But in the way people describe RSI, that would represent a next level of acceleration and would have a lot of implications, but we aren't quite there yet."But the continuum includes an awful lot of self-improving AI systems.In January, one of Anthropic's lead programmers for Claude Code estimated that "close to 100%" of his team's code was written by the tool — a frank admission that Claude Code was literally writing itself.Just because engineers are using an AI tool doesn't mean the tool can replace them — but Anthropic seems to be getting close to replacing engineers too. In a recent survey tied to the Mythos preview, five out of 18 Anthropic engineers believed that, with harness improvements, this version of Mythos could soon substitute for an L4 engineer — a midlevel programmer who can take on involved projects without supervision.Still, there were some of the same weaknesses you might expect."Some of Claude's major reported weaknesses compared to an L4 include: self-managing week-long ambiguous tasks, understanding org priorities, taste, verification, instruction-following, and epistemics," the report reads.In other words, its weaknesses are everything involved with self-direction, which is the cornerstone for RSI. But sure, for everything else, Claude is ready to step right in.Expert Perspectives on RSI TimelinesJust like the AGI term before it, the AI industry also can't tell us how far away it is from showcasing a meaningful recursive system. When Georgetown's Center for Security and Emerging Technology assembled a group of experts to study RSI last year, the group found a major split in assessments — some expecting an imminent "superintelligence" style explosion while others expected slower progress and an eventual plateau. But all agreed that recursion made the future especially difficult to predict.Helen Toner, director of CSET and a former board member at OpenAI, told TechCrunch that simply using AI tools to do AI research isn't enough to qualify as RSI. "They're just using AI for as much as they can," Toner told TechCrunch. "And I think that is different from the classic definition of RSI, which is really that there are no humans needed."Toner pointed to a recent post by METR's Ajeya Cotra, which distinguishes different milestones on the path to the AI research takeover. One step, which Cotra calls "adequacy," would come when the system can still perform research after all humans are removed — even if the resulting research isn't as valuable or efficient. "Parity" comes when an AI-only system is as good at research as a human-only system. "Supremacy," the final stage, comes when an AI-only system outperforms a collaborative system between humans and AI.Ultimately, Cotra concludes that AI is very close to the adequacy threshold of being able to produce some work on its own — similar to the incremental changes made by Karpathy's Auto-Research system. "I wouldn't be totally shocked if you told me this milestone had already passed, and I expect it to happen in the next couple years," Cotra wrote.She was less clear on when parity will come, but once it does, she thinks it would "massively accelerate the pace of AI progress, leading to AI research supremacy within another year."The Challenges Ahead for Recursive AIWith so much of AI built on scaling laws, there's a strong tendency to think RSI will follow the same curve. Toner thinks that many of those pursuing AI research and development via RSI "think of it as a pretty smooth ladder, where you can just keep scaling up."But even if AI researchers are able to make incremental improvements like Karpathy's auto-researchers, there will be larger challenges in handing off the whole process of research. Toner put it in terms of the history of computing, which has seen human beings handing off more and more of the process while still directing things from the top."We went from machine languages to assembly language and compiled languages; you're getting further and further from the guts of the computer," Toner said. "But the human is still, in some intuitive sense, running the show."Moving beyond that paradigm will take significant challenges, both in engineering and alignment. But even with the massive investments happening, there's no infinite compute available — and the basic trade-off between human labor and machine intelligence will be hard to overcome.The Future of Recursive Self-ImprovementAs for a total recursive AI system of apocalyptic visions? The only thing researchers essentially agree on is that, like AGI, it's not here yet.

Elon Musk's AI venture xAI recorded a $6.4 billion operating loss on $3.2 billion of revenue in 2025, according to SpaceX’s recent IPO filing. The same filing details an aggressive roadmap to scale the Grok model to “multiple trillions of parameters,” signaling that the current spending trajectory is far from over. Scale‑Up Plans for Grok Signal Massive Compute Investment The filing reveals that SpaceX intends to push Grok’s architecture to a size measured in multiple trillions of parameters, a step the company describes as a “step change in reasoning in depth and overall intelligence.” This ambition will require a substantial expansion of compute infrastructure. Financial Snapshot: Revenues, Losses, and Capital Expenditure Trends 2024: $1.56 billion loss on $2.62 billion revenue. 2025: $6.4 billion loss on $3.2 billion revenue. AI‑related revenue grew to $465 million, split into $365 million from X and Grok subscriptions and $88 million from data licensing. Advertising contributed an additional $116 million. Capital expenditures rose from $12.7 billion in 2025 to an annualized run rate of $30.8 billion in Q1 2026. Monthly active users for Grok AI features reached 117 million in March 2026, out of 550 million total MAUs across Grok and X. Strategic Implications for the AI Industry and Investor Sentiment The disclosed losses and soaring capex underscore the high‑cost nature of frontier AI development. While competitors such as OpenAI and Anthropic are eyeing public listings in 2026, SpaceX’s anticipated valuation of up to $1.75 trillion positions the combined entity as one of the largest tech IPOs ever. The vertical integration of compute—via the Colossus and Colossus II data centers delivering roughly 1 GW of power—aims to lower training costs, but the scale of spending may test investor tolerance. Outlook: Orbital Compute Satellites and Valuation Targets The filing’s “use of proceeds” section earmarks expansion of AI compute infrastructure, including a long‑term plan to deploy orbital AI compute satellites as early as 2028. Although the satellite strategy is unlikely to materialize in the near term, it signals Musk’s intent to control the physical AI stack, a factor that could reshape cost dynamics if realized.

The Road to IPO OpenAI is pushing ahead with its initial public offering, with sources indicating that the company aims to go public by September. This development comes just a day after Elon Musk lost his lawsuit against OpenAI, which had threatened the company's structure, leadership, and finances. Preparations and Partnerships OpenAI CEO Sam Altman is reportedly working closely with tech IPO experts at Goldman Sachs and Morgan Stanley to prepare for the public offering. According to the Wall Street Journal, the company may file its IPO paperwork confidentially with regulators within days or weeks. The Musk Factor The news of OpenAI's potential IPO comes as the market awaits SpaceX's IPO filings, expected to be disclosed soon. SpaceX, now a competitor to OpenAI, acquired Elon Musk's xAI model maker. The Financial Showdown With Musk's lawsuit against OpenAI dismissed, the stage is set for a financial battle between Musk's SpaceX and OpenAI. The success of OpenAI's IPO will be closely watched, especially in comparison to SpaceX's public offering. The Future Outlook As OpenAI prepares to enter the public market, its valuation and growth prospects will be under intense scrutiny. The company's performance will not only reflect its own achievements but also influence the broader AI industry's financial trajectory.

The Viral Rise of NanoClaw NanoCo, the company behind security-focused OpenClaw alternative NanoClaw, has raised an oversubscribed $12 million seed round following a viral launch, its founders tell TechCrunch. The funding was led by Valley Capital Partners, and saw participation from Docker, Vercel, Monday.com, Slow Ventures and angels like Clem Delangue, CEO of Hugging Face. The Journey to Seed Funding In a matter of weeks, NanoClaw creator Gavriel Cohen said he went from coding the project on his couch to receiving viral endorsements from Andrej Karpathy and Singapore’s foreign minister, fielding inbound interest from dozens of investors, and even a roughly $20 million acquisition offer that he and his brother and co-founder, Lazer Cohen, declined. The Data Behind the Decision $20 million: The acquisition offer rejected by the Cohen brothers $12 million: The oversubscribed seed funding round 6 weeks: The time it took from committing the first lines of code to securing a term sheet 50+: The number of founders and tech executives who sent DMs asking to invest The Impact on the AI Industry The rise of NanoClaw highlights the growing interest in secure AI solutions. As an open-source project, NanoClaw has attracted a large community of users and contributors, demonstrating the potential for community-driven growth. The Future Outlook With the seed funding, NanoCo plans to expand its enterprise offerings, including implementation services for businesses looking to roll out NanoClaw AI agents to employees. The company has already started booking enterprise customers, with early adopters including executives at big tech companies like Amazon, Gap, Google, Meta, SentinelOne, and Accenture.

Executive Summary: Engineer Claims Unfair Dismissal Over Israel‑Related AI WorkAn AI engineer at Google DeepMind has lodged a claim with a UK employment tribunal alleging that he was unfairly dismissed after protesting the firm’s provision of military AI to the Israeli government. The dispute highlights growing tensions between AI developers and staff concerned about the technology’s ethical implications.Leaflets, Emails, and the Tribunal ClaimThe engineer, of Palestinian heritage, distributed flyers in the London office stating “Google provides military AI to forces committing genocide” and asked colleagues, “Is your paycheck worth this?”. He also emailed staff about Google’s 2025 decision to drop a promise not to pursue weapons that harm people and urged them to join the union United Tech and Allied Workers. After HR meetings, he was told he had resigned; he denies this and says he was laid off in September.Monetary and Workforce Figures Highlighting the Dispute$1.2bn cloud‑computing deal between Google and the Israeli government has sparked protests.An insider cited at least 10 staff members who quit over ethical concerns.Recent polls in Great Britain show one in three university students fear AI‑driven job loss could trigger social unrest.Broader Implications for AI Ethics and Employee ActivismThe case adds to a wave of internal dissent at major tech firms over AI’s use in defence and surveillance. Workers argue that while they may not oppose all military applications, they oppose “irresponsible use” and “misuse by anti‑democratic actors”. Google maintains that it does not fire employees for expressing opinions and that it adheres to policies governing unionised staff.What May Lie Ahead for Google and the AI IndustryIf the tribunal finds in favor of the engineer, it could set a precedent for how tech companies handle internal whistle‑blowing on ethical issues. The dispute may pressure Google to revisit its AI principles, especially regarding weapons and surveillance, and could encourage further regulatory scrutiny of AI contracts with governments.

The Lead A US jury has handed a resounding victory to Sam Altman and OpenAI in their long, bitter courtroom battle with Elon Musk, finding Altman, OpenAI, and its president, Greg Brockman, not liable for Musk's claims that they unjustly enriched themselves and broke a founding contract. The Court Ruling and Its Implications The unanimous verdict, delivered after less than two hours of deliberation, is a stark rebuke of Musk and his lawyer's claims that Altman 'stole a charity' through his leadership of OpenAI. The jury's decision, affirmed immediately by the judge's dismissal of all charges, provides OpenAI with a stamp of approval for its for-profit plans, already in motion, and a clear path ahead to go public later this year at around a $1tn valuation. The Financial Impact Musk's demands that Altman be removed as CEO and that the for-profit arm of the company transfer some $150bn to the non-profit arm would have jeopardized the blockbuster initial public offering. A delay to OpenAI's financial bonanza may have been one of Musk's goals, as his own company, SpaceX, is reportedly planning to go public in June. The Industry Impact OpenAI's plans now seem all but guaranteed, given that the world's richest man couldn't put a stop to them. Wall Street, ever wary of upheaval and uncertainty, is likely breathing a sigh of relief, said professor Sarah Kreps, director of the Tech Policy Institute at Cornell University. She called the ruling a reflection of the tough reality that developing frontier AI is expensive and that maintaining non-profit status is not viable in the face of fierce, capital-intensive competition. The Future Outlook The trial did not deliver answers to major questions of the AI boom about safety, governance, and labor. While the jury's verdict was a 'technical' one, Musk's lawyers said he would appeal the case. The trial demonstrated that a small cabal, mostly men, rules the AI industry, and its central element was not a fight over AI's benefit to humanity but a hateful vendetta that Musk brought against Altman.

The Acquisition Deal Anthropic announced Monday it has acquired Stainless, a startup founded by former Stripe engineer Alex Rattray whose software is widely used by rival AI labs, including OpenAI and Google. Stainless' Technology and Impact The New York-based startup, founded in 2022, rose to prominence in the emerging AI industry for automating the creation and maintenance of software development kits, or SDKs — the libraries developers use to interact with APIs. Rattray developed software that could take API specifications and turn them into production-ready SDKs across multiple programming languages, including Python, TypeScript, Kotlin, Go, and Java. Financial Terms and Future Plans Anthropic didn’t disclose terms of the deal. However, The Information reported last week that Anthropic was in talks to acquire Stainless, which is backed by Sequoia Capital and Andreessen Horowitz, for more than $300 million. The acquisition will take a key infrastructure supplier out of the hands of Anthropic’s competitors. The company told TechCrunch it will wind down all hosted Stainless products, including its SDK generator. Impact on the AI Industry The technology is particularly valuable to companies like Anthropic, OpenAI, Google, Replicate, Runway, and Cloudflare that are building AI agents that can connect to external software and complete tasks on behalf of users. Stainless’s SDK tools are an easy way to build and maintain those connections — but going forward, the tools will only be available to Anthropic, not its competitors. Future Outlook According to Anthropic, Stainless software has powered the generation of every official Anthropic SDK since the earliest days of its API. “I started Stainless because SDKs deserve as much care as the APIs they wrap,” Rattray said in a press release posted Monday. “Anthropic was one of the first teams to bet on this with us. We have been watching what developers have built on Claude over the last few years, which made bringing our teams together an easy decision. The team gets to keep doing the work we love, on the platform where it matters most.”

The Legal Victory for OpenAI's Leadership In a decisive moment for the artificial intelligence industry, a federal jury in Oakland, California has ruled in favor of Sam Altman and Greg Brockman, OpenAI's president, in their high-stakes legal battle against Elon Musk. The nine-person jury found the OpenAI leaders not liable for unjustly enriching themselves or breaking contracts made with Musk when founding the startup. This verdict represents a significant legal victory for Altman and a stark rebuke of Musk's central claim that Altman "stole a charity" through his leadership of OpenAI. The Courtroom Decision and Its Implications The jury's finding, while non-binding and advisory, carries substantial weight as Judge Yvonne Gonzalez Rogers immediately indicated she would agree with the jury's decision. This alignment between jury verdict and judicial ruling effectively ends the legal chapter of Musk's ambitious lawsuit, which sought $134 billion to be redistributed from OpenAI's for-profit arm to its non-profit component. The case also demanded the removal of Altman and Brockman from their roles at OpenAI and the undoing of the firm's for-profit restructuring. Musk's Core Allegations Against OpenAI At the heart of the three-week trial was Musk's allegation that Altman, Brockman, and OpenAI breached their founding agreement when they restructured the company into a for-profit entity. Musk accused the defendants of breach of charitable trust and unjust enrichment, claiming that Altman had deceived him into co-founding OpenAI in 2015 as a non-profit dedicated to bettering humanity, only later to twist the organization's purpose to pursue personal gain. This narrative formed the foundation of Musk's legal challenge against the company he helped establish. OpenAI's Defense Strategy OpenAI's legal team systematically rejected all of Musk's claims, asserting that he was always aware of plans to create a for-profit entity from the company's inception. The defense highlighted that Musk's motivations stemmed from jealousy after his failed attempt to take over OpenAI in 2018, which led to his departure from the company shortly thereafter. OpenAI representatives repeatedly emphasized that the company remains overseen by its nonprofit organization and remains dedicated to what it refers to as "the mission" of helping the world with its AI technology. The Silicon Valley Showdown The trial delivered unprecedented access to the inner workings of OpenAI and featured testimony from several of Silicon Valley's most prominent executives. Beyond the primary litigants, Musk, Altman, and Brockman, Microsoft CEO Satya Nadella also took the stand, facing combative cross-examinations that revealed the intense personal and professional dynamics at play. The proceedings brought in many current and former OpenAI executives, as well as academic experts on nonprofit law and corporate governance, creating a comprehensive record of the company's founding and evolution. The Future of OpenAI Post-Verdict With this legal challenge behind them, OpenAI can now focus on its ambitious AI development initiatives without the cloud of Musk's lawsuit hanging over its leadership structure. The verdict reinforces the company's current governance model and its transition toward a for-profit entity while maintaining its nonprofit oversight. For the AI industry at large, this outcome provides stability to one of its most influential organizations during a critical period of technological advancement. The case also sets a precedent for how founding agreements in tech startups are interpreted when companies evolve their business models in response to market pressures and technological opportunities.