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The Guardian editorial argues that the UK must treat the plight of NEETs as a national priority, linking rising youth unemployment to inadequate training, housing costs and a fragmented policy framework.Milburn Commission Highlights Over 1 Million UK NEETsThe commission’s report, due in the autumn, shines a bright light on the 1 million young people aged 16‑24 who are not in education, employment or training. It criticises political attacks on welfare and “kids‑these‑days” rhetoric, insisting that the problem is fundamentally a policy failure.The Scale of the Crisis: Over 1 Million Young People Out of Work or Study1 million NEETs – roughly one in eight of the 16‑24 cohort.60 % are economically inactive, meaning they are not actively seeking work.Health‑related universal credit claims have risen in regions with fewer entry‑level jobs.Apprenticeship starts have fallen 35 % over the past decade.Why the UK Is Falling Behind Europe on Youth EmploymentCompared with other wealthy European nations, the UK records one of the highest rates of young people not in work or study. Contributing factors include:Housing inflation limiting independent living for young adults.Restrictive GCSE combinations that disadvantage less academic pupils.Chaotic further‑education reforms and the poorly‑implemented apprenticeship levy.Automation and AI‑driven profit growth that do not translate into entry‑level opportunities.A National Participation System: Pathway to Re‑engaging Young WorkersThe report proposes a new “participation system” that would coordinate work and pensions, health, education and business departments to pull young people into the labour market. While ambitious, the editorial stresses that without a clear, cross‑departmental mission the UK will continue to lose a generation to inactivity.

The LeadStrictlyVC is set to host its exclusive Los Angeles event on Thursday, June 18, 2026, at The Aerospace Corporation Campus in El Segundo. The intimate gathering will bring together leading investors and entrepreneurs for high-signal conversations about venture capital and frontier technologies, with a special focus on defense technology and physical AI.The Event DetailsThe StrictlyVC Los Angeles 2026 event offers an evening of direct access to ideas and leaders shaping where technology and capital are headed next. The event will feature several key speakers discussing critical topics in the tech investment landscape.Date: Thursday, June 18, 2026Location: The Aerospace Corporation Campus, El Segundo, Los AngelesFocus: Defense technology, physical AI, venture capital, and frontier technologiesThe Value PropositionFor executives, investors, and founders navigating an increasingly complex market, this event provides a rare opportunity to step inside conversations that rarely happen in public. Attendees will hear directly from the people driving change across defense, AI, and advanced industry sectors.Featured Speakers and TopicsThe event will begin with Ethan Thornton, founder of Mach Industries, presenting "Built for a New Era of Defense Technology." Thornton will discuss building hard tech companies at speed and why defense innovation is undergoing a structural shift as autonomy, manufacturing, and national security become increasingly interconnected.The conversation will then turn to "backing the next frontier of physical AI," featuring Delian Asparouhov of Founders Fund and Saif Khawaja of Shinkei Systems. They will explore how advances in AI, robotics, and automation are reshaping both software systems and the physical world, and what it takes to move breakthrough technologies from concept to real-world deployment at scale.Additional speakers and conversations will be announced in the weeks ahead as the StrictlyVC Los Angeles agenda continues to take shape.The Impact AnalysisThis event reflects a growing trend of technological acceleration in traditionally slow-moving industries. The focus on defense technology and physical AI indicates a significant shift in venture capital priorities toward tangible, real-world applications of artificial intelligence. As these technologies mature, they have the potential to reshape national security, manufacturing, and automation sectors, creating new opportunities and challenges for investors and entrepreneurs alike.The PredictionAs the evening unfolds, the real value of the event will emerge from the conversations that continue beyond the stage. In an environment defined by access, focus, and proximity to industry leaders, introductions are likely to turn into insights, and insights often turn into opportunities. This event is poised to become a catalyst for new partnerships, investments, and technological breakthroughs in the defense and physical AI sectors, potentially setting the stage for the next wave of innovation in these critical areas.

Apple's AI-Powered Siri App Set to Rival ChatGPT Apple is planning to unveil a new AI-powered Siri app at its Worldwide Developers Conference (WWDC) in June, according to leaked renders published by Bloomberg. The app is designed to rival popular AI chatbots like ChatGPT, Claude, and Gemini. The New Siri App Features The new Siri app will feature a rebuilt AI model that uses Google's Gemini AI technology under the hood for added intelligence. The app will allow users to search, launch apps, start messages, ask about the weather, add calendar appointments, search their notes, and trigger app shortcuts. Results will be displayed in a formatted text in a card-style interface that emerges from the iPhone's Dynamic Island. The Data Analysis 2.5 billion: Apple's install base across all devices 900 million: Weekly active users of ChatGPT The Impact Analysis Apple's approach to AI is similar to its earlier multibillion-dollar partnership with Google that made Google the default search engine on iPhone. By partnering with outside companies for AI technology, Apple can leverage its scale and unmatched runway to introduce AI to people who haven't yet adopted standalone AI tools. The Prediction With its massive install base and reputation for prioritizing user privacy, Apple is well-positioned to make a significant impact in the AI market. The new Siri app and AI-powered features are expected to be a major part of Apple's strategy to compete with popular AI chatbots and establish itself as a leader in the AI space.

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 Lead Enterprise organizations are not rejecting AI. They are rejecting operational instability. This is the shift many founders still misunderstand — and it is becoming one of the defining realities separating enterprise AI companies that scale from the ones that stall after early momentum. The Event Details At 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 unpack that shift during his AI Stage session, “The Enterprise Isn’t Broken. Your Assumptions About It Are.” The Data Analysis The enterprise AI market is full of successful pilots that never became real deployments. Not because the technology failed. But because the organization could not absorb the operational consequences of adopting it. The Impact Analysis Now the reality founders need to face is that startup AI deals rarely die because the model underperformed. They die because the enterprise lost confidence in what the deployment would require. The AI startups gaining traction inside large organizations increasingly share one thing in common: They reduce uncertainty. The Prediction The startups that succeed in enterprise AI over the next several years may not necessarily be the ones with the most advanced models. They may be the ones that best understand how enterprises actually absorb change. That is the kind of operational pressure that Tavakoli-Shiraji and other speakers on the AI Stage at Disrupt will explore.

The Flavor Crisis in American FoodThe article examines the phenomenon of declining flavor in American food products, noting that taste has been increasingly sacrificed for other factors in modern food production.Industrial Food ProductionOne key factor discussed is the impact of industrial food production methods on flavor quality, with large-scale operations often prioritizing efficiency and shelf life over taste.Processed Foods and Artificial FlavorsThe rise of processed foods and reliance on artificial flavors is identified as another significant contributor to the flavor decline in American cuisine.Cultural ImplicationsThe article explores how this flavor loss has affected American food culture and the relationship between consumers and their food.Looking ForwardDespite the challenges, the article suggests that there may be growing awareness and efforts to address the flavor crisis in American food.

A former senior CIA official, David Rush, was taken into custody on May 19 after a joint CIA‑FBI operation uncovered a cache of 303 gold bars valued at over $40 million, along with $2 million in cash and luxury watches. The alleged theft, spanning from 2009 to 2026, has ignited scrutiny of the agency’s internal oversight and the use of gold in covert government finance.Details of the Alleged Embezzlement and the Gold Bar CacheRush, a former senior executive‑service level employee with top‑secret clearance, is accused of misappropriating government assets for personal gain.The FBI affidavit states he claimed military leave and education credentials that were later proven false.From November 2025 to March 2026, he allegedly requested “significant quantity of foreign currency and tens of millions of dollars in gold bars for work‑related expenses.”Searches on May 18 revealed 303 gold bars (≈1 kg each), $2 million in U.S. currency, and 35 luxury watches, many Rolexes.Financial Scale: Valuation of Gold, Cash, and Luxury Watches303 gold bars – estimated market value > $40 million.$2 million in U.S. cash recovered.35 high‑end watches, primarily Rolex, estimated at several hundred thousand dollars.Potential additional undisclosed assets, given the “significant quantity” of foreign currency mentioned in the affidavit.Implications for CIA Oversight and Government Asset ControlsThe case highlights gaps in the CIA’s internal audit mechanisms, especially regarding high‑value commodity allocations for “work‑related expenses.” It also revives longstanding speculation about the agency’s use of gold as a covert funding tool, a practice documented in historical accounts such as Gold Warriors. If proven, the misuse could erode public trust and prompt congressional hearings on asset tracking and clearance protocols.What Comes Next: Legal Proceedings and Policy ReformsRush remains detained pending a detention hearing scheduled for Friday in Alexandria, Virginia.Federal prosecutors are likely to pursue charges of theft of government property, fraud, and false statements.Expect a review by the Office of the Director of National Intelligence (ODNI) to tighten controls on commodity disbursements.Congress may introduce legislation mandating stricter reporting and independent audits of any gold or foreign‑currency transactions within intelligence agencies.

Seizure of $232 Million Targets Cosa Nostra’s Financial EmpireOn Thursday, 2026‑05‑28, Italy’s financial police, the Guardia di Finanza, announced the confiscation of assets worth over $232 million that were tied to the late Mafia boss Matteo Messina Denaro. The operation traced funds through a web of companies, luxury properties, and offshore accounts that had been built since the 1980s.Scale of the Asset Freeze Across Europe and Offshore HavensCountries involved: Spain, Switzerland, Luxembourg, Monaco, LebanonOffshore jurisdictions: Cayman Islands, GibraltarKey asset types: luxury villas on Spain’s Costa del Sol, diversified financial portfolios, corporate holdings in various sectorsThe investigation also led to the arrest of three individuals who were suspected of managing the concealed wealth.Implications for Mafia Money Laundering and Regional SecurityChief anti‑Mafia prosecutor Giovanni Melillo described the seizure as a “major step in dismantling the group’s financial base.” By striking at the money‑laundering channels, authorities aim to cripple the Cosa Nostra’s ability to reinvest illicit proceeds into legitimate businesses, thereby reducing its influence over the Sicilian economy and beyond.Future of Anti‑Mafia Operations in Italy and EuropeThe use of advanced surveillance tools—drones, aircraft, and thermal scanners—demonstrates a shift toward high‑tech policing in organized‑crime cases. Analysts expect that the success of this operation will encourage further cross‑border cooperation, tighter monitoring of offshore flows, and more aggressive asset‑freezing measures throughout the EU.

General Compute, a new inference neocloud, raised a $15 million seed round at a $60 million post‑money valuation and booked a $300 million order for SambaNova’s upcoming SN50 chips. The company promises 600‑700 tokens per second per chip and a deployment model that fits into existing, air‑cooled data‑center infrastructure. General Compute’s Funding and Strategic Partnerships Seed round led by FUSE VC with participation from Carya Venture Partners and Village Global Ventures. Co‑founders Finn Puklowski (CEO) and Jason Goodison (CTO) partnered with SambaNova, an Intel‑backed chipmaker focused on inference. General Compute will be the first neocloud to deploy SambaNova’s SN50 chips, ordering $300 million worth of hardware. Colocation strategy includes traditional data‑center providers and repurposed crypto‑miner facilities. Financial Snapshot: $15 Million Seed and $300 Million Chip Order Seed funding: $15 million raised, valuing the company at $60 million post‑money. Chip commitment: $300 million of SN50 chips on order, enough to power a large inference fleet. Comparable market moves: Nvidia’s $20 billion acquisition of Groq (Dec 2025) and Cerebras’ $57 billion IPO (May 2026) illustrate the scale of inference‑focused investments. Implications for the AI Inference Landscape The shift from GPU‑centric training to specialized inference hardware is accelerating. SambaNova’s memory‑rich, flexible architecture claims to outperform GPUs, Groq, and Cerebras on token‑throughput, delivering 600‑700 tokens/sec versus ~250 tokens/sec for GPUs. Air‑cooled, low‑power chips lower the barrier to entry for colocation, enabling rapid deployment in existing facilities and even in repurposed crypto‑mining sites. This could democratize high‑speed inference, pressure pricing, and spur a wave of niche cloud providers focused on agent‑to‑agent workloads. What the Next Year May Hold for Inference‑First Cloud Providers When SambaNova releases its next‑gen chips later in 2026, General Compute’s early access positions it to capture a sizable share of the fast‑inference market. Expect: Increased competition among inference‑only clouds (e.g., CoreWeave, OpenRouter) to offer multi‑model routing and token‑cost optimization. More venture capital flowing into inference‑focused startups, mirroring the recent $113 million Series B for OpenRouter. Potential consolidation as larger players (Nvidia, Intel) seek partnerships or acquisitions to secure the most efficient inference stacks. Speed and cost efficiency will become the primary differentiators, shaping the architecture choices that dominate the AI future.