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Glean's Rapid Growth in Enterprise AI Search Glean, a company often described as the Google for enterprise, has reached a significant milestone: $300 million in annual recurring revenue (ARR). This represents a three-fold increase from the $100 million milestone it reached just 15 months ago. The Competitive Landscape of Enterprise AI Search Glean's progress is particularly notable given the increasing competition in the enterprise AI search market. Tech giants such as Google, Microsoft, OpenAI, Anthropic, Salesforce, and Atlassian are building rival products, but Glean's first-mover advantage and better product offerings have helped it accelerate growth. The Value of Context Graph in AI Glean's AI tools have a deep understanding of customers' business needs, achieved through its "context graph" concept. By connecting to and learning from enterprises' internal software systems, Glean's AI provides more accurate and relevant results. This approach also helps enterprises cut AI computing costs. Reducing AI Costs with Glean Glean's context graph helps enterprises reduce AI token costs. By connecting AI to Glean, enterprises get the information they need to perform tasks, reducing the number of tokens consumed. This results in significant cost savings for enterprises. The Business Model and Future Outlook Glean offers various pricing structures, including a consumption-based model and a hybrid model. The company's $300 million milestone is a significant achievement, although it's worth noting that a portion of its top line is more accurately described as an annualized revenue run rate due to its consumption model. What's Next for Glean? As the enterprise AI search market continues to evolve, Glean's focus on reducing AI costs and providing a better product will likely remain a key differentiator. With its strong growth trajectory and significant funding, Glean is well-positioned to continue its success in the market.

Bloomberg Leaks Reveal New Siri App Integrated with Dynamic Island Just before WWDC in June, Bloomberg published leaked renders of Apple’s upcoming AI upgrade for the iPhone. The screenshots depict a brand‑new Siri app that will launch from the Dynamic Island and replace the traditional button‑press activation in iOS 27. Users will also be able to swipe down on the home screen—leveraging the familiar Spotlight gesture—to invoke AI‑powered searches. Scale Advantage: Apple’s 2.5 Billion Devices vs ChatGPT’s 900 Million Users Apple’s install base: 2.5 billion active devices across iPhone, iPad, Mac, and wearables. ChatGPT weekly active users: 900 million. iOS version introducing the feature: iOS 27. New Siri app will support text, photo, and document uploads and retain chat history. Strategic Shift: Apple Leverages Google’s Gemini to Accelerate AI Rollout The leaked details confirm that Apple’s rebuilt AI model runs on Google’s Gemini technology under the hood. This mirrors Apple’s earlier multibillion‑dollar partnership that made Google the default search engine on iPhone. By tapping Gemini, Apple sidesteps the massive R&D costs of building a large‑scale model from scratch while still delivering cutting‑edge capabilities. Apple is also developing local AI models that run directly on devices, reinforcing its privacy‑first brand and reducing reliance on cloud processing. What Apple’s AI Push Means for the Mobile AI Landscape Post‑WWDC Analysts expect Apple to announce the standalone Siri app at WWDC, positioning it as a direct competitor to ChatGPT, Claude, and other generative chatbots. The integration with Dynamic Island and Spotlight suggests a seamless, on‑device experience that could accelerate user adoption, especially among the billions who have not yet embraced third‑party AI tools. If Apple rolls out the feature globally in the next iOS update, the company could leverage its massive ecosystem to set new standards for privacy‑preserving, AI‑enhanced mobile interactions.

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 Rise of AI-Powered Payroll Remote, a seven-year-old Amsterdam-based payroll service provider, has recently surpassed $300 million in annual recurring revenue and become cash-flow positive. However, the company's true achievement lies in its 50% increase in revenue per employee after adopting AI at every level of the organization. AI Adoption Across the Organization According to CEO Job van der Voort, the key to Remote's efficiency gains is AI adoption well beyond the CEO's office or engineering department. Employees across all functions have been launching apps in Remote Labs, an internal marketplace built on the company's own technology. The Data Behind the Growth Annual recurring revenue: over $300 million Revenue growth per employee: 50% Core payroll business growth: over 300% year over year Number of companies served: tens of thousands The Impact of AI on Remote's Business Remote's adoption of AI has not only increased revenue per employee but also improved the company's overall efficiency. The company has reduced its hiring plans and is instead focusing on upskilling its existing employees to use AI tools. The Future of AI in Payroll Remote is now opening up its AI capabilities to clients, allowing them to create custom workflows. The company has also launched Remote MCP, an interface based on the Model Context Protocol, which grants AI agents and external platforms direct access to payroll and compliance data. The Prediction As AI continues to transform the payroll industry, Remote is well-positioned to lead the charge. With its focus on AI adoption and innovation, the company is poised for continued growth and success in the future.

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 LeadAs AI video models become increasingly sophisticated, YouTube is shifting from a voluntary to an automated approach for labeling AI-generated content. The platform announced on Wednesday that its internal systems will now automatically apply labels when detecting "significant photorealistic AI" in videos, marking a significant step in content moderation for synthetic media.YouTube's New AI Detection ApproachBeginning in May, YouTube will leverage new internal signals to identify AI-generated content and label it accordingly. This proactive approach means that even if creators fail to disclose their use of AI, YouTube will step in and label the video for them. However, creators will retain the ability to update the disclosure status if their content is misidentified. Notably, labels will be permanently attached to videos created with YouTube's own AI tools, such as Veo or Dream Screen, and those containing C2PA metadata indicating full AI generation.The Evolution of YouTube's AI PolicyYouTube's AI labeling system has been in development for over two years, following updates to the platform's AI policies that required creators to disclose when their videos included AI content that could be mistaken for real people, places, or events. Animated or clearly imaginative scenarios were exempt from these requirements. The company emphasizes that while its policy hasn't changed, it will now take a more active role in enforcement, particularly following Google's recent release of Gemini Omni—a new family of multimodal AI models capable of producing high-quality videos with sophisticated understanding of physics, culture, history, and science.Technical Implementation and VisibilityYouTube is making its AI labels more prominent and consistent across the platform. Previously, labels appeared in the expanded description unless the video touched on sensitive topics like health or news, in which case a prominent label would appear directly on the video. Now, labels will appear directly below the video player above the description for long-form videos and directly on YouTube Shorts. For content that is only slightly altered, animated, or unrealistic—such as fantastical scenarios—the label will continue to appear in the expanded description only. This enhanced visibility aims to make viewers immediately aware when they're encountering photorealistic, AI-altered, or AI-generated content.Industry Impact and Future OutlookThis move comes shortly after YouTube expanded its AI deepfake detection capabilities, now allowing any adult to scan YouTube specifically for face matches—a feature initially tested with celebrities, public figures, politicians, and other creators. The platform has also committed to ensuring that AI labels won't impact video recommendations or monetization, addressing potential concerns from creators. YouTube's initiative reflects broader industry efforts to address synthetic media, with other companies like OpenAI, Nvidia, Kakao, and Eleven Labs also committing to the C2PA standard for content provenance. As AI technology continues to advance, platforms like YouTube are increasingly implementing detection and labeling systems to maintain transparency and help users distinguish between authentic and AI-generated content.

The Pope’s First Encyclical Calls for AI DisarmamentPope Leo XIV released his inaugural encyclical, Magnifica humanitas: On Safeguarding the Human Person in the Time of Artificial Intelligence, urging that AI be “disarmed” to prevent domination, exclusion, and death. The document, spanning nearly 43,000 words, frames AI as a moral and spiritual challenge for the world’s 1.4 billion Catholics and beyond.Key Provisions of “Magnifica Humanitas” on AI GovernanceThe encyclical warns against a “race for ever more powerful algorithms and larger datasets” driven by geopolitical or commercial dominance. It calls for:Robust legal frameworks and independent oversight of AI systems.Political action that can “slow things down when everything is accelerating.”Developers to bear “ethical and spiritual responsibility” for every design choice.Protection of workers’ rights and child safety in AI deployment.During the Vatican presentation, AI expert Christopher Olah of Anthropic highlighted the tension between corporate incentives and ethical imperatives.Numbers Behind the AI Debate: Layoffs and Military Use16,000 Amazon employees laid off in January 2026 as AI automation expands.The encyclical’s length: ~43,000 words.U.S. military confirmed use of “a variety” of AI tools in the 2026 US‑Israel conflict over Iran.These figures illustrate the scale of AI’s impact on employment, defense, and societal structures.Implications for Tech Industry, Policy and Global EthicsThe pope’s stance adds a powerful moral voice to ongoing debates about AI regulation. By positioning AI alongside nuclear energy—“must be at the service of all and of the common good”—the Vatican urges:Tech firms to curb competitive escalation.Governments to enact stricter oversight, especially on lethal autonomous weapons.International bodies to consider AI’s role in war, job displacement, and child safety.Such a high‑profile religious endorsement could influence legislators, especially in regions where Catholic opinion shapes public policy.What May Follow: Anticipated Policy Shifts and Church InfluenceAnalysts expect the encyclical to spark:Increased lobbying by the Vatican for AI‑focused legislation in the EU and U.S. Congress.Greater collaboration between AI developers and ethicists to meet the “spiritual responsibility” standard.Potential adoption of the pope’s language in future UN discussions on autonomous weapons.While concrete regulatory outcomes remain uncertain, the moral weight of the Vatican’s message is likely to shape public discourse and pressure corporations toward more responsible AI practices.

The AI Security Imperative At a recent event in Los Angeles, Google Cloud COO Francis deSouza stressed that security can't be an afterthought in AI adoption. He advocated for a platform approach to security, warning against 'shadow AI' where employees use consumer tools without organizational oversight. The Risks of 'Shadow AI' DeSouza highlighted the risks associated with employees using unauthorized AI tools, which can lead to security breaches and data exposure. He emphasized that companies need to demand security, governance, and auditability from their platforms from the start. The Challenge of Keeping Pace with AI Threats The threat landscape has changed fundamentally, with the average time between an initial breach and the next stage of an attack dropping from eight hours to 22 seconds. The attack surface has expanded beyond the traditional network perimeter, and companies need to adapt to this new reality. Google's Own AI Security Challenges Despite deSouza's sound advice, Google itself faces challenges with AI security. The company has refunded developers who incurred large bills due to unauthorized API calls to Gemini models. Google's automated systems had upgraded their billing tiers without explicit consent, leading to surprises for developers. The Future of AI-Native Defense DeSouza sees the emergence of AI-native, fully agentic defense as a solution to the challenges posed by AI threats. This approach involves using agents to drive defense, allowing humans to oversee and focus on high-level decision-making. The Skills Gap in AI Security The industry faces a shortage of people qualified to oversee AI security, and the vulnerabilities introduced by AI are multiplying faster than security teams can address them. According to LinkedIn's CISO Lea Kissner, it may take several years for the industry to understand AI security in a sustainable way.

The Lead: A Personal Manifesto Against AI ConvenienceWendy Liu explains why she deliberately avoids generative‑AI tools, insisting that the struggle of thinking is what makes us human. In an era where large language models can produce code and prose in seconds, Liu contends that the convenience comes at the cost of cognitive sovereignty.The Early Coding Journey: Learning by Hand in the Mid‑2000sGrowing up with unmonitored access to a family computer, Liu taught herself to build websites using only a basic text editor. The process involved countless hours of debugging and poring over documentation, which she describes as “painstaking” but ultimately rewarding.Mid‑2000s: Self‑taught web development using a simple editor.Result: Deep appreciation for the craft of coding despite imperfect outcomes.The Rise of AI‑Assisted Development: From “Vibe‑Coding” to Mass RedundanciesToday, tools like OpenAI’s Codex and Anthropic’s Claude Code enable anyone to generate functional code through natural‑language prompts. Liu notes that this “vibe‑coding” trend has led many tech firms to justify large‑scale layoffs, using AI as a pretext for workforce reductions.The Cognitive Off‑Loading Concern: Protecting Our Thinking MusclesLiu warns against “cognitive off‑loading,” the habit of delegating mental tasks to AI for convenience. She cites emerging research suggesting that even brief interactions with AI chatbots can negatively affect problem‑solving abilities.The Societal Implications: From Corporate Greed to Environmental TollThe article links AI’s rapid expansion to broader issues:Trillions of dollars projected for data‑centre construction.Corporate revenues used to fund mass redundancies while pushing AI adoption.Environmental concerns tied to the energy consumption of massive AI models.Potential widening of socioeconomic inequality as AI becomes a “utility” controlled by a few corporations.The Path Forward: Embracing Inefficiency as a Moral ChoiceChoosing to work without AI, Liu argues, is a deliberate act of preserving humanity and building character. She acknowledges the personal trade‑offs—being a less efficient coder and writer—but frames the inconvenience as a safeguard against corporate‑driven efficiency that threatens individual agency.