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The LeadThe United States has escalated its threats against Oman, warning that it would "aggressively" impose sanctions if the Gulf ally helps Iran establish a tolling system in the Strait of Hormuz. This intensifies President Donald Trump's recent threats against Oman, including a warning to "blow them up" if they don't comply with US demands regarding the strategic waterway.US Treasury's Aggressive StanceUS Treasury Secretary Scott Bessent stated on Thursday that Washington will "not tolerate" either country imposing fees on commercial ships in the strategic waterway. "Oman, in particular, should know that the US Treasury will aggressively target any actors involved — directly or indirectly — in facilitating tolls for the Strait and any willing partners will be penalized," Bessent said in a social media post."All nations should reject outright any efforts by Iran to disrupt the free flow of commerce. Tehran's days of terrorizing the region and the world are over."Global Energy Security at RiskAbout 20 percent of the world's oil flowed through Hormuz before the conflict, making the Iranian blockade a major strain on global energy supplies. The closure has sent oil prices soaring and threatens economic stability worldwide. The strait's critical importance to global energy markets makes any disruption a matter of international concern.Regional Power Dynamics ShiftThe statement comes less than 24 hours after President Trump threatened to bomb Oman, a key US ally known for its neutrality and mediation efforts in regional crises. This unprecedented threat against a close security and economic partner signals a significant shift in US foreign policy in the Middle East.While Iran has suggested joint Iranian-Omani management of the Hormuz Strait, Oman has not explicitly stated it is seeking control over the waterway, parts of which flow through its territory.Future Outlook for Hormuz StraitThe US and Iran have been indirectly negotiating to reach an agreement for a comprehensive end to the war, with control over the Hormuz Strait emerging as a major point of disagreement. Trump has stressed that the strait must remain a free passageway for international commerce.Ali Bagheri Kani, deputy secretary of Iran's Supreme National Security Council, countered that Tehran will not allow Hormuz to be a source of insecurity for the country, stating that "the powers that have used this passage against Iran's security must be held accountable." The standoff continues as both nations dig in on their positions regarding control of this vital waterway.

The Lease Duration Dispute A controversy has arisen regarding the length of Anthropic's lease with SpaceX, a deal that involves billions of dollars a month for exclusive use of Anthropic's Colossus cluster. Elon Musk claimed on X that the lease is for 180 days with a 90-day notice for mutual cancellation, while SpaceX's recent S-1 filing presents the deal as a three-year agreement. The Details of the Deal According to Musk, the short-term lease was SpaceX's request, not Anthropic's. He stated that SpaceX won't leave Anthropic hanging and will provide a reasonable off-ramp, but might need the compute capacity back if it gets super tight. On the other hand, SpaceX's S-1 filing confirms a 90-day cancellation notice but describes the agreement as lasting through May 2029, with a monthly fee. The Data Analysis The deal involves a significant monthly fee of $1.25 billion, as mentioned in the S-1 filing. This substantial commitment highlights the importance of the compute capacity for both parties. The Impact Analysis The discrepancy between Musk's statement and SpaceX's filing raises questions about the accuracy of the information provided. This situation could be seen as a material misrepresentation made while marketing a security, which could have implications for investors and the companies involved. The Prediction The future of the lease and the relationship between Anthropic and SpaceX will depend on how this situation unfolds. With the SEC possibly involved, the companies will need to clarify the terms of the agreement to avoid any further controversy.

The Launch of Sesame's Conversational AI On Thursday, the AI startup Sesame, co-founded by Oculus' founders and others from the VR company that sold to Meta, released a public preview of the conversational AI agents it's been developing for over a year. With its new iOS app, Sesame is rethinking the traditional AI chatbot experience popularized by apps like ChatGPT, creating one where conversation flows, even if the AI needs time to think. Reimagining AI Conversation Flow As the company explains in its launch announcement, "There's an inherent tension between replying quickly and taking the time to compose thoughtful responses. A slower response is usually more correct, but it can also feel unnatural if it takes too long." To address this challenge, Sesame claims to have built fast search and retrieval systems, so the AI can have up-to-date information, as well as technology that allows it to run multiple parallel searches while speaking, weaving those results into its responses as it talks. That means the AI will talk more like a human, even pivoting mid-sentence if need be, as it taps into newer information — as a human might when remembering another key fact or point they want to add. User Growth and Development Milestones The app offers four distinct AI agents called Maya, Miles, Simone, and Charlie, each of which have their own distinct voice, personality, point of view, and memory. Maya and Miles were previously available in Sesame's Research Preview of its technology, where they were soon accessed by over one million people within the first few weeks, said Sesame investor Sequoia at the time. (The company had then just raised its $250 million Series B from Sequoia and others and was opening up a beta.) During the beta, Sesame learned from user feedback and rolled out features such as search cards with image results for visualizing concepts, notes for capturing takeaways, a texting mode for those times when speaking aloud is not an option, and support for deep dives where you can get more in-depth results. There's also a new incognito mode for private conversations, which allows the agents access to prior context but saves nothing to memory. Transforming the AI Landscape The app, however, is only the first step toward Sesame's bigger plans for AI involving intelligent eyewear, which the team expects to launch in 2027. Before that, the agents will also learn to do more than just think with you, Sesame hints, suggesting they'll later be able to take action on your behalf — hence why they're called "agents" in the first place, instead of just chatbots. That is potentially even more interesting, as working with agentic tools or apps today requires being able to prompt for what you need and have a specific idea of what you want to happen, and sometimes, even how it should happen. A conversational agent that you could talk to naturally could help you take the next steps, without you having to perfect the command you're giving it. The Road to AI-Powered Eyewear The iOS app is out today in 39 countries, and the full experience is free for the time being. However, there still may be a short waitlist at sign-up. An Android preview is coming in the future, the company says.

Palace's Conference League triumph caps a stellar seasonIn a match billed as a "feel‑good final," Crystal Palace lifted the Conference League trophy, marking their third piece of silverware in a year that also saw an FA Cup victory. The win underscores the club's rising financial clout compared with their working‑class rivals, Rayo Vallecano, who remain trophy‑less after 102 years.Conference League final: Palace defeat Rayo VallecanoThe Tin Pot decider at the Red Bull Arena ended with Palace prevailing, thanks to a decisive strike from Jean‑Philippe Mateta. Rayo’s supporters displayed stoic resilience, unfurling a banner that read, "I know no greater victory than to be with you in defeat."Venue: Red Bull Arena, BudapestDate: 27 May 2026Scoreline: Palace 1‑0 Rayo VallecanoKey player: Jean‑Philippe Mateta (match‑winning goal)Financial and squad implications of the third trophyPalace’s "vastly superior financial heft" has allowed them to assemble a squad capable of competing on multiple fronts. The victory adds to a cabinet that previously held only a Kent Senior Cup, signalling a shift in the club’s revenue streams from prize money, merchandising, and increased broadcast share.Estimated prize money for the Conference League win: £20 millionProjected increase in season ticket sales for 2026‑27: +12%Potential market value uplift for key players (Mateta, Wharton, Lacroix): +15‑20%What the win means for Palace's standing in English footballThe triumph elevates Palace from a Premier League survival outfit to a genuine European contender. Manager Oliver Glasner received praise for his tactical acumen, while the club’s board is already being linked with high‑profile managerial candidates such as Andoni Iraola and former Coventry City boss Frank Lampard should Glasner depart.Future outlook: managerial moves and transfer market activityWith the summer window approaching, Palace faces a "massive scramble" for retained talents like Mateta, Adam Wharton, and Maxence Lacroix. Rumours suggest interest from larger clubs, meaning Palace must decide whether to cash in or build a squad capable of challenging for a Europa League spot.Potential incoming manager candidates: Andoni Iraola, Frank LampardKey transfer targets to retain: Mateta, Wharton, LacroixStrategic goal for 2026‑27: Qualify for Europa League via league position

The Lead Barcelona has completed the signing of Anthony Gordon from Newcastle for £70m, a move that brings a direct and intense playing style to their attack. The 25-year-old England winger is known for his turbo-charged talent and ability to terrorize defenses. Gordon's Playing Style Gordon is a versatile right-footed forward who is arguably at his best on the left but capable of playing across the frontline. He is aggressive and direct, reveling in accelerating into space behind defenders and pressing relentlessly from the front with rare intensity. His playing style is not without risk, as he often forces defenders into taking gambles they would prefer to avoid. The Data Analysis In 26 league games for Newcastle, Gordon contributed six goals, three of them penalties, and only two assists. However, in the Champions League, he scored 10 times in 12 appearances and contributed two assists. His performance in the Champions League has drawn praise from top managers, including José Mourinho. The Impact Analysis Gordon's signing is part of Hansi Flick's plan to keep Madrid firmly in their place. His arrival brings unpredictability to Barcelona's attack, and his ability to interchange positions with other forwards could make their attacking trio more formidable. However, Gordon's inconsistency and sometimes selfish play may pose a risk for Flick. The Prediction If Gordon can refine aspects of his game and diplomatic skills at the Camp Nou, he could become a key player for Barcelona. Meanwhile, Newcastle seems poised for an even more dramatic reinvention, with their sporting director pursuing several new signings to strengthen their squad.

The Lead: Apple's AI RevolutionJust ahead of Apple's Worldwide Developers Conference (WWDC) in June, Bloomberg has published leaked renders of what Apple's planned AI upgrade could look like on iPhone — including a brand-new Siri app meant to rival ChatGPT and other AI chatbots. This represents Apple's most significant push into the competitive AI landscape, leveraging its massive user base to introduce AI capabilities to billions of devices worldwide.The Event Details: New Siri Interface and CapabilitiesWhile users will still be able to press a button in iOS 27 to trigger Siri, the animation and response will now emerge from the iPhone's Dynamic Island — that's the black pill-shaped area at the top of the screen that today houses Live Activities. This mode will work best for quick voice queries or searches, maintaining familiarity for existing Siri users.A new mode will put Siri-powered search within easy reach, capitalizing on people's muscle memory for swiping down on their screen to access Spotlight Search. The swipe-down gesture will still open search, but now those searches will draw on the AI-powered Siri, which includes a rebuilt AI model that uses Google's Gemini AI technology under the hood for added intelligence.From here, iPhone users can search, launch apps, start messages, ask about the weather, add calendar appointments, search their notes, and trigger app shortcuts, with results displayed in formatted text in a card-style interface that also emerges from the Dynamic Island.The Data Analysis: Strategic Partnerships and Scale AdvantageApple's approach to AI mirrors its earlier multibillion-dollar partnership with Google that made Google the default search engine on iPhone. Just as building a search engine from scratch was never in Apple's playbook, AI presents a similar calculus — it's too expensive and complex to go it alone, at least right now. So Apple is working with outside partners for AI technology that users want today, while simultaneously building out its own models, including local AI that runs on local devices rather than the cloud.The scale advantage is substantial. While ChatGPT now has 900 million weekly active users, Apple's install base (all devices, not just iPhone) is 2.5 billion — meaning the company has an unmatched runway to introduce AI to people who haven't yet adopted standalone AI tools.The Impact Analysis: Shaping the AI LandscapeApple's entry into the competitive AI chatbot space with a standalone Siri app will significantly reshape the market dynamics. The app will surface users' past chat history and allow them to upload documents and photos, in addition to text, positioning it as a direct competitor to ChatGPT, Claude, Gemini, and others.This move demonstrates Apple's strategy of leveraging partnerships while maintaining its privacy-focused brand. By using Google's Gemini AI technology while developing local AI capabilities, Apple aims to deliver powerful AI experiences without compromising on its core privacy principles. This approach could set a new standard for how tech companies balance powerful AI features with user privacy concerns.The Prediction: Future of AI IntegrationLooking ahead, Apple's AI strategy appears focused on seamless integration rather than standalone dominance. The company is likely to continue its dual approach: leveraging partnerships for immediate capabilities while developing proprietary technology for long-term differentiation.As Apple rolls out these AI features across its ecosystem, we can expect to see similar AI-powered enhancements in macOS, iPadOS, and watchOS. The company's emphasis on local AI processing suggests a future where more AI tasks happen on-device, reducing latency and enhancing privacy — potentially setting a new industry standard that other tech companies will need to follow.

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.

The Shift in Enterprise AI EvaluationEnterprise organizations are not rejecting AI; they are rejecting operational instability. This marks a critical inflection point where the market moves from experimentation to deployment safety. The defining reality separating scalable AI companies from stalled ones is no longer the power of the demo, but the safety of the deployment.TechCrunch Disrupt 2026: The Operational PivotAt TechCrunch Disrupt 2026, taking place October 13–15 at Moscone West in San Francisco, Arsalan Tavakoli-Shiraji, co-founder and SVP of field engineering at Databricks, will address this shift during his session on the AI Stage.Event: TechCrunch Disrupt 2026Date: October 13–15, 2026Location: Moscone West, San FranciscoSession: "The Enterprise Isn't Broken. Your Assumptions About It Are."The Pilot Trap: Why Models Don't Fail, Deployments DoThe enterprise AI market is currently plagued by successful pilots that never become real deployments. This failure is rarely due to technical underperformance but rather the organization's inability to absorb the operational consequences of adoption.Enterprise buyers are no longer asking if AI is exciting; they are asking if it is safe to deploy broadly. Key concerns now include workflow friction, governance, and explainability. An AI product can perform exceptionally well in a controlled environment but fail commercially if it creates instability within the business.Uncertainty Reduction as a Competitive AdvantageStartups gaining traction inside large organizations share a common trait: they reduce uncertainty. While breakthrough demos generate initial excitement, operational adoption requires clean integration, ease of governance, and trust over time.Tavakoli-Shiraji brings a unique perspective to this challenge, bridging the gap between enterprise strategy (via his background at McKinsey & Company) and deep technical systems architecture (PhD in computer science from UC Berkeley).The Future of Enterprise AI AdoptionThe next phase of enterprise AI success will likely belong to companies that understand how organizations absorb change, rather than those with the most advanced models. Founders must prioritize long-term operational adoption over initial excitement.

YouTube announced on May 28, 2026 that its Premium service will soon include an AI‑powered podcast recommendation tool, an “Auto speed” playback feature, and an on‑the‑go listening mode, aiming to deepen engagement with its growing podcast audience. AI‑Driven Podcast Recommendation Engine Launches The new recommendation tool leverages the same generative AI behind YouTube’s "Ask Music" to suggest podcasts based on genre, listener mood, or shows already enjoyed. Premium users will see personalized suggestions directly in the Podcasts tab, streamlining discovery without leaving the app. Auto Speed Playback and On‑the‑Go Mode Arrive on Android First Auto speed: Dynamically adjusts playback speed during slower speech or dense segments, preserving comprehension while reducing total listening time. On‑the‑go mode: Adds quick‑skip controls, episode‑jump shortcuts, and background‑play optimization for activities like running or commuting. Both features are live for Premium users on Android and will roll out to iOS in the coming months. Premium Podcast Consumption Metrics Highlight Growth Potential Premium users logged over 800 million hours of podcast playback in April 2026. YouTube Podcasts now boasts more than 1 billion monthly active users. The platform’s "Ask Music" already powers personalized radio stations, indicating a ready AI infrastructure for podcast recommendations. Strategic Play to Capture Audio‑First Audiences By enhancing discovery and hands‑free listening, YouTube is positioning itself against established audio platforms such as Spotify and Apple Podcasts, while also responding to Netflix's recent push into video podcasts. The focus on AI personalization and adaptive playback reflects a broader industry shift toward seamless, user‑centric audio experiences. What This Means for the Future of Podcast Platforms Analysts expect the AI recommendation engine to increase user retention, potentially driving Premium subscription growth by double‑digit percentages over the next year. If the Auto speed feature delivers measurable time‑saving benefits, it could set a new standard for intelligent playback, prompting competitors to develop similar adaptive technologies. The on‑the‑go mode further blurs the line between video and audio consumption, suggesting that YouTube will continue to integrate podcasting deeper into its core ecosystem.