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The Approval of the AmendmentMexico's lower house has approved a constitutional amendment to allow the nullification of elections in cases of foreign interference. The proposal passed the Chamber of Deputies with 307 votes in favour, 128 against, and one abstention.Defining Foreign InterferenceThe reform defines foreign interference as "illicit financing, propaganda, the systematic dissemination of disinformation, digital manipulation, and the intervention of foreign governments or agencies". It also covers acts of political, economic, diplomatic, or media pressure intended to influence public opinion.The Impact on ElectionsThe amendment, which is unlikely to affect the next federal elections in June 2027, still requires Senate approval to take effect. Electoral reforms must be enacted at least 90 days before the start of the election process in order to apply.Reactions from PoliticiansRicardo Monreal, the leader of the ruling Morena party in the lower house, defended the measure as a necessary safeguard of Mexico's democracy. Opposition lawmakers accused the governing party of overstating the threat to justify the reform.Concerns and CriticismsPresident Claudia Sheinbaum recognised previous instances of foreign funding for local candidates and organisations in Mexico. However, some politicians questioned how the new rules would be applied in practice, warning that the broad language of the amendment could create uncertainty.

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: Vertu's AI-Powered Foldable Targets Executive Market Luxury smartphone brand Vertu has unveiled the Alphafold, a foldable phone powered by an AI agent designed specifically for executives managing business operations on the move. The device represents Vertu's latest attempt to reinvent itself for the AI era, combining luxury materials with enterprise-focused AI capabilities to target the high-end business market. The Event Details: Luxury Meets AI: The Alphafold's Enterprise Capabilities The Alphafold features Hermes Agent, built on the open-source Hermes project by Nous Research, which can connect to enterprise systems like ERP and CRM. The AI agent coordinates tasks such as approvals, scheduling, sales tracking, travel planning, and operational reporting through natural-language prompts. The device can route requests across multiple AI models including OpenAI's GPT, Anthropic's Claude, Google's Gemini, and selected open-source models, while integrating with more than 80 apps and dozens of native phone functions for cross-platform workflows. Vertu has emphasized the device's privacy-focused architecture featuring a proprietary A5 security chip designed to isolate authentication keys, biometric credentials, and sensitive enterprise information from the main operating system. The company states that commercially sensitive data can be processed locally on the device, while prompts sent to external AI models are redacted or tokenized before leaving the phone. The Data Analysis: Premium Pricing Strategy in the Smartphone Market The Alphafold starts at $6,880 for the calfskin version, with higher-end models featuring bespoke finishes including alligator leather, 18K gold, and natural diamond accents. Vertu's highest-end standard model is currently priced at $46,800, with further customization options available. This pricing strategy positions Vertu firmly in the ultra-premium segment of the smartphone market. While foldable smartphones remain a niche segment globally—with IDC data showing approximately 20 million units shipped in 2025, accounting for less than 2% of total smartphone shipments—Vertu is betting that the combination of luxury materials and AI capabilities will justify its premium pricing. The average price of foldable smartphones was about $1,300 last year, roughly three times the price of non-foldable smartphones. The Impact Analysis: How AI is Transforming Executive Productivity Vertu CEO Molly Ma highlighted that existing AI features on smartphones from major manufacturers remain focused largely on consumer tools such as image editing and voice assistance, leaving room for more advanced AI-agent workflows tied to enterprise systems. The Alphafold aims to address this gap by providing executives with a device that can seamlessly integrate with their business operations and workflows. The device's larger foldable display (8.05-inch inner screen and 6.53-inch outer screen) is better suited for multitasking and productivity-oriented experiences, according to Kiranjeet Kaur, associate research director for mobile phones research at IDC. However, she noted that enterprise AI adoption on smartphones still lags behind computers, with most enterprise smartphone decisions continuing to be driven by ecosystem integration and device management support rather than AI capabilities. The Prediction: The Future of Luxury AI-Powered Mobile Devices The Alphafold represents Vertu's significant step forward from its previous AI-focused device, Agent Q, with Ma noting that AI-agent technology has matured rapidly over the past year, with improvements in memory, automation, and app integration. While the company has not yet undergone third-party security audits for the device, it has confirmed that independent audits and certification remain on its security roadmap. As the first 115-unit batch of Vertu's Alphafold begins shipping across major markets including the U.S., the device will serve as a test case for whether there's a market for luxury smartphones with enterprise AI capabilities. If successful, Vertu's approach could inspire other manufacturers to develop similar devices targeting the executive market, potentially accelerating the integration of AI agents into mobile workflows.

Google’s AI Overview Stumbles on Simple Letter Counting Google’s newly rolled‑out AI Overview feature in Search incorrectly counted letters in everyday words – claiming there are two “P”s in “Google”, one “r” in “poop”, and even misspelling “journalism”. The blunders highlight a long‑standing weakness of large language models (LLMs) when it comes to exact spelling. The Miscounted Letters Behind the New Search AI “Google” – AI said 2 Ps (actual: 0) “poop” – AI said 1 r (actual: 0) “journalism” – AI said 2 d’s (actual: 0) U.S. President’s last name – AI reported 1 P but rendered “t‑r‑p‑u‑m” Quantifying the Miscounts: Numbers Behind the Errors Beyond the anecdotal examples, the AI also produced a faulty definition for the word “disregard”, responding with “Understood. Let me know whenever you have a new prompt or question!” This illustrates that token‑based encoding can produce nonsensical outputs even when the input is a single word. Implications for Search Trust and AI Adoption Google’s AI‑driven overhaul aims to make generative responses the centerpiece of its 29‑year‑old search product. Repeated factual and spelling errors risk eroding user confidence, especially after earlier AI Overviews cited satirical sources and gave absurd advice such as “eat rocks”. Trust in AI‑generated answers remains a critical hurdle. What’s Next for Google’s Generative Search? Google told TechCrunch it is “working to fix this particular issue” and will likely refine its tokenizer and post‑processing pipelines. Industry observers expect incremental improvements rather than a complete architectural shift, meaning users may continue to see occasional glitches while the broader AI‑search strategy matures.

The Strategic Shift Toward Custom Silicon Snowflake's decision to deepen its reliance on AWS is driven by the explosive demand for AI processing power. The deal specifically targets AWS's proprietary Graviton ARM-based CPUs, which are increasingly vital for the inference and agent phases of AI workflows that GPUs cannot handle alone. By integrating Snowflake's Cortex AI tool, the partnership aims to streamline data operations, allowing enterprises to query databases using natural language and generate automated reports more efficiently. Financial Implications of the AI Boom This contract represents a massive financial milestone. While AWS has generated $7 billion from Snowflake since 2012, this new deal brings the total value to nearly the same level in a single contract. Furthermore, Snowflake reports that AWS spending has doubled in 2025 to $2 billion annually, highlighting the rapid monetization of AI tools. This data confirms that enterprises are aggressively accelerating their cloud spending to stay competitive in the generative AI era. Disruption in the AI Chip Market The move signals a broader trend where cloud providers are weaponizing their own hardware to undercut Nvidia. By offering "better price-performance," AWS aims to capture market share from Nvidia, a strategy already seen with Meta. This creates a bifurcated market where companies can choose between Nvidia's training dominance and AWS's cost-effective inference capabilities. The reliance on Graviton chips offers a more affordable option for cloud providers, allowing them to pass savings directly to customers. The Future of the AI Compute War As AI agents become more prevalent, the demand for high-performance CPUs will skyrocket. We can expect more multibillion-dollar contracts like this one, forcing Nvidia to innovate aggressively with its own Vera chip. The cloud giants are effectively building their own ecosystems, making it harder for third-party hardware vendors to maintain a monopoly. The winners in this space will be the companies that can optimize their data infrastructure for the specific chips they are using.

The Corporate AI MirageUK communications executives are reporting a surge in demand from non-tech companies to be rebranded as artificial intelligence specialists. Public relations professionals describe this trend as a desperate attempt to capitalize on the current technology buzz, often stretching the truth to secure media coverage for brands that have little genuine connection to the sector.The Mechanics of 'AI Washing'The phenomenon, often termed 'AI washing,' involves companies retrofitting the 'AI' label onto existing products or services that rely on basic automation rather than advanced generative intelligence. This rebranding effort has led to bizarre applications of the technology, such as AI-powered basketball hoops and lasers designed to protect women on underground platforms.AllBirds recently 'pivoted' to acquiring AI graphics processing units.Genetics companies are hyping AI-powered blood tests.Property firms are marketing handheld scanners that generate floor plans as AI tools.The PR Backlash and Market FatigueThe saturation of the market is causing significant friction within the PR industry. Account directors report that roughly 50% of the AI-related pitches they send out are unwanted, as journalists and executives become numb to the language. This fatigue is compounded by the skepticism surrounding claims of 'AI-driven' products that are merely better automation.Even high-profile corporate figures are under scrutiny. The chief executive of Standard Chartered recently apologized for describing workers displaced by AI as 'lower-value human capital,' highlighting the tension between corporate efficiency strategies and public perception.Future Outlook: From Hype to SubstanceWhile stock market investors have largely shrugged off recent jitters over the AI boom, the long-term viability of 'AI washing' is questionable. As the industry matures, the gap between genuine AI integration and superficial rebranding will likely widen, forcing companies to either innovate or face further reputational damage.

The Parent's Cricket Dream Every cricket-loving parent experiences that tiny flicker of hope that their child might become the next superstar. It's the irrational dream that the gods who blessed players like Sachin Tendulkar and Ellyse Perry might one day smile upon their own children. This hope begins the first time you wrap their chubby hands around a plastic bat or when they accidentally hit a tennis ball with surprising power. What parents truly hope for isn't fame or contracts, but simply that their children fall in love with the game. The author, a new father of two boys, already analyzes his children's physical attributes for cricket potential—long fingers for spin bowling, broad shoulders for powerful hitting. This is how cricket colonizes the mind, turning rational adults into amateur talent scouts studying toddler anatomy. Family Cricket Traditions Once cricket embeds itself deeply into your life, it becomes less a sport than a language through which everything else is understood. The author compares strategizing meal times and bedtime routines to captains discussing bowling changes, and positioning furniture to setting fields. This transformation of daily life through cricket's lens is a common experience for families deeply involved in the sport. The tradition of passing cricket through generations is highlighted by Sheahan Arnott, a club bowler in London whose father remains the record run-scorer at Bentley Cricket Club in Perth. They've played hundreds of games together, including a memorable moment when Arnott captained his father in his 500th game after he scored a century. For Arnott, the greatest cricket dream was playing alongside his father. The Joy of Shared Cricket Moments There is a unique joy in sharing cricket with family members that goes beyond individual achievement. The author inherited his love for cricket through his parents, who took him to the Wanderers stadium as a child. His mother drove him to endless coaching sessions, while his father offered infinite throwdowns in the garden despite working a full week. Their support transformed every small achievement into something significant. Mark Cooper, a 73-year-old cricketer, has played alongside his three sons and daughter with Millfields CC since the 1990s. He describes watching his children grow from young fielders to adults with their own lives, sharing magical moments like walking off together after hitting the winning runs. These shared experiences create bonds that transcend the sport itself. Balancing Dreams and Reality There is a delicate balance in passing on cricket passion without burdening children with parental expectations. The danger of projecting unfinished dreams onto children is as precarious as driving on the up in cricket. The trick is to pass on the obsession without passing on the burden, using cricket's intricacies as a guide. Cricket's grand tapestry is made with a million tiny stitches—properly filled-out scorecards, precisely packed cooler boxes, caring for an ageing ball. Sharing this wisdom with the next generation is both a responsibility and a privilege. The author acknowledges that his eldest son hasn't shown interest yet and his youngest can't even hold his head up, let alone a bat, but as a cricket tragic, he understands the value of patience and hope.

ElevenLabs announced the launch of Music v2, its latest AI‑driven music‑generation model capable of switching genres mid‑track and handling complex vocal arrangements. The new tool is positioned as a response to a growing wave of AI music solutions from rivals such as Google, Stability AI, and Suno. Music v2 Introduces Real‑Time Genre‑Switching Capability The model can move from opera to heavy metal, deliver rapid rap verses, and embed sound‑effects without breaking musical coherence. Users can select a specific section of a song—intro, verse, or chorus—and rewrite it via prompts while leaving the rest untouched. Supports multi‑language lyrics and diverse vocal styles. Allows section‑by‑section composition, enabling a stitch‑together workflow. Built on licensed data, cleared for commercial use. Competitive Landscape of AI‑Generated Music In the past year, major AI labs have accelerated music‑generation research. Google showcased its Flow Music tool at I/O, offering cover creation and song‑section editing. Stability AI and Suno have also released models that produce longer, more intricate tracks. ElevenLabs’ emphasis on commercial licensing differentiates it from startups like Suno and Udio, which have faced copyright lawsuits. Implications for Creators and the Music Industry By integrating Music v2 into the ElevenCreative suite and the new ElevenMusic platform, the company targets marketing teams and independent artists seeking rapid, royalty‑free production. The ability to edit specific song sections could streamline soundtrack creation for ads, games, and social media, potentially reshaping how content is produced at scale. Looking Ahead: Future Developments and Market Adoption ElevenLabs plans to roll out Music v2 via its ElevenAPI, widening access for developers. As AI‑generated music becomes more sophisticated and legally vetted, we can expect broader adoption across media firms, a rise in AI‑assisted songwriting, and intensified competition to secure licensing partnerships with record labels.

The Lead A phenomenon dubbed "AI psychosis" is reportedly affecting tech executives, particularly CEOs, who are overestimating artificial intelligence capabilities while simultaneously implementing mass layoffs. This disconnect between perception and reality is creating organizational chaos in the tech industry. The CEO AI Delusion Box founder Aaron Levie has suggested that CEOs are uniquely prone to "AI psychosis" because they're sufficiently distant from the implementation details of AI systems. When executives "play with AI" by developing prototypes or generating contracts, they often make the leap to believing AI agents can fully handle complex work without understanding the limitations. Unlike their technical teams, CEOs aren't responsible for reviewing code, discovering bugs, or training AI models on company-specific requirements. This lack of firsthand experience with AI's limitations doesn't stop them from making decisions based on overoptimistic assessments of AI capabilities. The Layoff Numbers In the first five months of 2026 alone, the tech industry has already seen 115,430 people fired from 152 tech companies. This nearly matches the 124,636 people let go by 275 companies throughout all of 2025, according to industry tracker Layoffs.fyi. The majority of these layoffs have been attributed to AI, though many argue that companies are engaging in "AI washing" - crediting AI productivity gains when other business decisions are really driving the cuts. The ClickUp Experiment Zeb Evans, CEO of project management software startup ClickUp, proudly declared on X that he had laid off almost a quarter of his employees (22%) after implementing approximately 3,000 AI agents for internal work. Evans insisted this wasn't a cost-cutting measure but rather an attempt to create what he calls a "100x org" composed of people who run and review AI agents' work. The Productivity Paradox Research on AI and productivity presents a complex picture. A meta-analysis published in UC Berkeley's California Management Review found "no robust relationship between AI adoption and aggregate productivity gain." Meanwhile, research from the National Bureau of Economic Research concluded that while AI adoption does improve productivity, there's a "productivity paradox" in which perceived gains exceed measured improvements. MIT researchers studying thousands of AI agents found they aren't yet producing human-quality work in many cases. They predict that at the current rate of improvement, large language models will "be able to complete most text-related tasks with success rates of, on average, 80%–95% by 2029 at a minimally sufficient quality level," with additional time needed to outperform humans. The Executive Bottleneck Research published in the Harvard Business Review suggests that when everyone in an organization uses AI to produce more output, the bottleneck simply shifts to executives. Their work awaits authorization of all the content being generated by AI-empowered employees. If everyone is empowered to act, the system risks becoming overwhelmed, as evidenced by OpenAI's experience last year. As Levie advises, CEOs should use AI extensively to understand both its capabilities and limitations. However, with the current trend of mass layoffs and organizational restructuring based on overoptimistic AI assessments, the tech industry may face continued chaos until this balance is achieved.