TL;DR Grand Theft Auto VI now launches on November 19, 2026 for PS5 and Xbox Series X/S, after slipping first from 2025 to May 26, 2026 and then to November. No PC at launch. Development is widely estimated at $1 - 2 billion, with some reports of total production and salary spend climbing higher still - comfortably the most expensive game ever made, and arguably the most expensive single piece of media ever produced. For scale: GTA V cost roughly $265 million, has sold around 225 million copies, and made well over $10 billion across twelve years. First-year revenue forecasts for GTA 6 run from about $3.2 billion to over $7 billion, and Take-Two’s own FY2027 guidance points at $8.0 - 8.2 billion in net bookings. Pre-orders reportedly cleared $1 billion in the first hour. The thing now behaves less like a product launch and more like a small economy switching on. I’m a hobbyist who plays games and likes numbers, not an industry analyst - so treat this as one curious observer doing the arithmetic out loud. I should be upfront: I am not a games-industry analyst, and nothing here is insider knowledge. I’m someone who has sunk an embarrassing number of hours into Rockstar’s worlds over the years and who finds the economics of this particular launch genuinely hard to get my head around. GTA 6 has crossed a threshold where a video game stops being comparable to other games and starts being comparable to infrastructure projects. That shift is what I want to pick at. ...

TL;DR On June 29, 2026, Cursor released a native iOS app in public beta, available on all paid plans, for iPhone and iPad You can launch cloud agents from your phone - pick a repo, describe the task by voice or text, use slash commands, choose a frontier model, and let an agent run in an isolated VM Remote Control lets you take an agent already running on your desktop and keep steering it from your phone, with an option to keep the machine awake while you’re away Live Activities put agent status on your lock screen; you get push notifications, can review demos, screenshots and logs, inspect diffs, and merge pull requests without opening a laptop A launch promo gives 75% off Composer 2.5 runs in the mobile app through July 5, 2026 This lands months after SpaceX’s move on Cursor - and reframes the editor as an orchestration surface rather than a place you type code I’ve written about Cursor enough times on this blog that a phone app could have been a footnote. It isn’t. Not because the app itself is revolutionary - it’s a well-made mobile client - but because of what it quietly admits about how the work has changed. For most of software history, the editor was where you sat and typed. Cursor’s iOS app is built on the assumption that you mostly aren’t typing anymore. You’re directing. ...

TL;DR Boris Cherny, who built Claude Code at Anthropic, posted a short framing: as engineering, product, design, and data science melt into one role, he sees five archetypes on his team The five are Prototyper, Builder, Sweeper, Grower, and Maintainer - and crucially, none of them map cleanly to a job title The interesting claim is not the list, it is the decoupling: the archetype is a description of what energy you bring to a system, not what your contract says you do I think the framing is genuinely useful as a self-diagnostic, and quietly radical for how teams get staffed and rewarded Where it leaves me unsure: it describes a steady-state team that already exists, and says less about how you grow people into these shapes, or what happens to the people who do not fit any of them A short post on X has been rattling around my head for a few days. Boris Cherny, who built Claude Code at Anthropic, was reflecting on what happens to roles when the old functional boundaries stop meaning much. His observation: when he looks at the Claude Code team, he does not really see engineers, designers, PMs, and data scientists. He sees five archetypes that cut across all of them. ...

TL;DR On June 8, 2026, OpenAI filed a confidential S-1 with the SEC, targeting a September 2026 public listing with Goldman Sachs and Morgan Stanley as underwriters The private valuation sits at $852 billion, with analysts projecting a debut above $1 trillion - one of the five largest IPOs in US history The same week, ChatGPT’s market share fell below 50% for the first time - to 46.4%, with Gemini at 27.7% and Claude at 10.3% OpenAI’s Q1 2026 non-GAAP operating margin was negative 122%: it spends $2.22 for every dollar it earns Noam Shazeer - co-author of Attention Is All You Need and the AI talent Google paid $2.7 billion to retain in 2024 - just left Google to join OpenAI Anthropic filed its own S-1 a week earlier, on June 1, targeting October, at a $965 billion valuation - the two biggest AI labs are racing to Wall Street simultaneously The timing is almost too perfect to be coincidence - and yet it is. On June 8, 2026, OpenAI submitted a confidential S-1 registration with the SEC, beginning the legal process toward a public listing. The same week, for the first time since ChatGPT launched in November 2022, OpenAI’s flagship product held less than half of the global AI assistant market. The company is going to Wall Street at the precise moment it is no longer the only name in the room. ...

TL;DR SpaceX filed a $60 billion all-stock acquisition of Cursor on June 16, 2026 - marking one of the largest AI/developer tools acquisitions ever (confirmed via SEC filing) Cursor’s revenue metrics are impressive: ~$4 billion annualized revenue with $2.6 billion from enterprise customers, suggesting strong product-market fit Strategic pivot: SpaceX is moving beyond rockets and satellites into the software infrastructure layer that powers AI development itself Signal to the market: This acquisition suggests major tech companies are betting heavily on owning the entire stack - from hardware to the tools developers use to build AI systems Enterprise focus: The majority of Cursor’s revenue coming from enterprise (65%) indicates this is a B2B infrastructure play, not just a consumer developer tool Why SpaceX Acquiring Cursor Matters On the surface, it might seem odd that a company known for rockets and space exploration would acquire an AI code editor. But this acquisition reveals something fundamental about how the largest technology companies are thinking about AI development infrastructure. ...

TL;DR Endpoint evals miss the failure mode that hurts in production - an agent can reach the right answer through a reckless path: wrong tool first, lucky recovery, ignored constraints that did not bite this time Trajectory evaluation scores the run: which tools were called, in what order, with what arguments, and whether each step satisfied policy The minimum viable setup: 50–200 real examples, per-step rubrics, 10+ runs per example, statistical regression tracking, and a held-out set you never tune against Replay harnesses let you re-run a captured trace against a new model or policy without re-hitting production systems This is the measurement layer that connects broken public benchmarks to agent security - you cannot harden what you cannot observe AI Evals Are Broken argued that leaderboard numbers stopped measuring production capability. Securing AI Agents argued that the tool layer must enforce policy the model cannot be trusted to enforce. This post is the bridge: how you measure whether an agent actually behaves before and after you ship. ...

TL;DR Language-only models do not contain a reliable simulator of physical reality - they contain a statistical shadow of one, good enough for many tasks and dangerously wrong for others. A world model is a system that learns to predict how an environment evolves and can plan inside that prediction - not just describe it in text. The gap matters for agents that must act in physical space, manipulate objects, or reason about counterfactuals where the answer is not in the training corpus. The 2026 frontier includes generative world simulators, vision-language-action models for robotics, and sim-to-real pipelines - not one breakthrough but a stack assembling in parallel. For builders today: language agents with MCP tools are the right architecture for knowledge work. World models are the path to agents that can competently act in the physical world. Almost everything I have written about AI agents assumes a model whose understanding of the world arrives through text. That assumption has carried the field a long way. Context engineering, tool use via MCP, memory across sessions - all of it sits on top of language models that read, reason, and call APIs. ...

TL;DR On 12 June 2026 at 5:21pm ET, the US government issued an export control directive ordering Anthropic to suspend all access to Fable 5 and Mythos 5 - globally, for every user, including Anthropic’s own employees The stated reason is national security: the government believes it has identified a method of jailbreaking Fable 5. Anthropic says the evidence was verbal only and describes a narrow, non-universal technique - essentially asking the model to read a codebase and fix software flaws Anthropic reviewed a demonstration and found it surfaced a small number of previously known, minor vulnerabilities that are widely available from other models Anthropic disagrees that a narrow jailbreak justifies recalling a commercial model deployed to hundreds of millions of people, and warns the same standard would “essentially halt all new model deployments for all frontier model providers” All other Anthropic models are unaffected. The company says it believes this is a misunderstanding and is working to restore access Four days. That is how long Mythos-class capability lasted as a publicly available product before the US government ordered it off the shelf. ...

When a frontier lab releases a new model in 2026, the press release leads with a row of benchmark scores. The numbers are bigger than they were a year ago, the model is the new state-of-the-art on whichever evaluation the lab chose to highlight, and the headline writes itself. The honest summary is that most of these numbers have stopped measuring what they were designed to measure, and the gap between benchmark performance and real-world capability is now wide enough that the benchmark-led narrative is actively misleading. ...

TL;DR This is the sequel to What Does Expertise Mean When AI Can Pass Any Exam? - less about broken credentials, more about what expertise becomes next Reference knowledge and routine pattern recognition are being commodified; judgement, accountability, integration, and tacit skill are appreciating By 2030, “expert” likely means someone who can direct AI systems, bear professional liability for AI-augmented work, and teach skills that do not compress into training data A concrete example: the 2030 civil engineer signs off on AI-generated structural calcs but remains expert at spotting when the model missed soil conditions the drawings never captured The practitioners who win are the ones who classify their own work honestly and invest in the appreciating categories now Expertise After AI argued that exams stopped measuring what we thought they measured. This post asks what replaces them - not as policy, but as a working picture of what practitioners will need to be good at by 2030. ...