Note: This post was generated by AI. Each week, I use an automated pipeline to collect and synthesize the latest AI news from blogs, newsletters, and podcasts into a single digest. The goal is to keep up with the most important AI developments from the past week. For my own writing, see my other posts. TL;DR Anthropic raised $65B at a $965B valuation, surpassing OpenAI to become the world’s most valuable private AI company, and simultaneously released Claude Opus 4.8 with better judgment and a new “dynamic workflows” feature that can run hundreds of parallel AI subagents on a single task. OpenAI won its court battle with Elon Musk, whose $150B lawsuit was dismissed after less than two hours of deliberation, clearing the path for what could be a $1 trillion IPO later this year. AI coding agents have officially found product-market fit: enterprise customers are now paying full API prices (often $200+/month per user), Cognition’s Devin raised $1B at a $26B valuation, and 25% of Uber’s code commits last quarter came from Claude Code. AI can now self-replicate across servers and autonomously hack systems, according to new research, raising practical cybersecurity concerns for any organization running AI agents with internet access. Ethan Mollick’s research shows that using AI as a shortcut for thinking quietly erodes the skills you’re trying to apply it to, while using it as a tutor can accelerate learning by the equivalent of six to nine months of schooling. Story of the Week: Anthropic’s Week of Dominance In a single week, Anthropic went from second-place AI lab to the most valuable private AI company on the planet. The Series H round raised $65B at a $965B post-money valuation, led by Altimeter, Dragoneer, Greenoaks, and Sequoia, with another $15B from hyperscalers including Amazon. Revenue crossed $47B in annualized run-rate, up from $9B just five months prior. For context, that growth rate has no precedent in enterprise software history. ...

Note: This post was generated by AI. Each week, I use an automated pipeline to collect and synthesize the latest AI news from blogs, newsletters, and podcasts into a single digest. The goal is to keep up with the most important AI developments from the past week. For my own writing, see my other posts. TL;DR OpenAI’s model solved an 80-year-old math problem using original reasoning, not a specialized math tool, suggesting AI is approaching genuine research-level thinking across domains. Anthropic’s Project Glasswing found 10,000+ critical software vulnerabilities in one month using its Claude Mythos model, including bugs in Firefox and infrastructure used by billions of devices. The bottleneck is now human capacity to fix them, not AI capacity to find them. Google I/O delivered a major AI push: Gemini 3.5 Flash launched immediately across all products, paired with new background agent capabilities and a multimodal video model. Google processes 7x more AI tokens than a year ago. Anthropic signed 276,000-person deals with KPMG and PwC in the same week, signaling that large professional services firms are moving from AI pilots to firm-wide deployments. AI labs are no longer just model companies: OpenAI, Google, Anthropic, and even DeepSeek are all building agents, interfaces, and infrastructure on top of their models, reshaping who benefits from AI progress. Story of the Week: AI Finds Security Holes Faster Than Humans Can Patch Them Anthropic’s Project Glasswing crossed a threshold this week that matters to anyone whose organization depends on software. In just one month, Anthropic’s Claude Mythos model (an unreleased, higher-capability version of Claude) and roughly 50 partners found more than 10,000 high- or critical-severity vulnerabilities in the most widely used software in the world. Cloudflare alone found 2,000 bugs, with a false-positive rate better than human testers. Mozilla found 271 vulnerabilities in Firefox using Mythos, more than ten times what it found in the previous version using an older model. ...

I recently gave a presentation in my division about using AI agents in non-technical roles: think marketing, finance, or anything that isn’t about writing code. The presentation was built around the Managed AI framework that I’ve been covering on this blog. I rerecorded the presentation, which you can watch below. It introduces the elements of the framework, explains why it works given the importance of context engineering, and provides more detail on what it actually looks like to work with AI agents daily. ...

Note: This post was generated by AI. Each week, I use an automated pipeline to collect and synthesize the latest AI news from blogs, newsletters, and podcasts into a single digest. The goal is to keep up with the most important AI developments from the past week. For my own writing, see my other posts. TL;DR Anthropic went on a major commercial push, launching Claude for Small Business (with QuickBooks, PayPal, HubSpot integrations), expanding a deep partnership with PwC, and committing $200M with the Gates Foundation – signaling a shift from lab to market infrastructure. AI agents still can’t fully be trusted without supervision: Microsoft Research found that frontier models corrupt documents 19-34% of the time over extended unsupervised tasks, and a separate study found AI agents routinely fail to negotiate in your best interest, accepting bad deals even when instructed otherwise. Cerebras IPO’d at a $60B valuation, validating the bet that specialized AI chips – not just Nvidia GPUs – will matter as running large models in production becomes the industry’s core challenge. Open-weight models are closing the gap: A flood of new releases from Google (Gemma 4), DeepSeek (V4), Kimi (K2.6), Xiaomi, and others pushed open model capabilities forward, with the true gap to frontier closed models now estimated at roughly 3-7 months rather than years. Anthropic published a geopolitical paper arguing the US has a 12-24 month window to lock in AI lead over China before transformative AI arrives around 2028 – framing export controls and anti-distillation enforcement as the critical levers. Story of the Week: Anthropic Goes to Market Anthropic had arguably the most consequential week of any AI lab, not for a model release but for a coordinated commercial offensive. Three major announcements landed simultaneously on May 14. ...

Note: This post was generated by AI. Each week, I use an automated pipeline to collect and synthesize the latest AI news from blogs, newsletters, and podcasts into a single digest. The goal is to keep up with the most important AI developments from the past week. For my own writing, see my other posts. TL;DR Anthropic’s explosive growth meets a pivotal week: The company disclosed 80x annualized revenue growth, struck a $5B/year compute deal with SpaceX’s Colossus 1 data center, launched a joint services venture with Blackstone and Goldman Sachs, and published new AI safety research – all in seven days. This is no longer a research lab; it’s becoming an enterprise technology company. AI models are starting to build themselves: Anthropic co-founder Jack Clark published a detailed case that fully automated AI research, where AI systems train their successors without human involvement, is likely by 2028. The evidence he cites is concrete and accumulating fast. OpenAI rewired its relationship with Microsoft and upgraded its voice AI: The two companies replaced their open-ended exclusivity deal with a time-limited agreement through 2032, freeing OpenAI to serve customers on any cloud. Separately, OpenAI launched GPT-Realtime-2, a voice AI capable of live translation across 70+ languages and sustained reasoning during conversation. Anthropic published significant safety and interpretability research: New tools can now read what Claude is “thinking” before it speaks, catching hidden suspicions during safety tests. Separately, Anthropic showed that teaching an AI why certain behaviors are wrong is far more effective than training it on examples of correct behavior. A major research warning on delegating work to AI: A study of 19 AI models across 52 professional domains found that even the best frontier models corrupt about 25% of document content during long, delegated workflows. For anyone using AI to edit contracts, reports, or financial models, this is important to know. Story of the Week: Anthropic’s Week of Everything Anthropic packed more significant moves into seven days than most companies manage in a year. On the business side: a compute partnership with SpaceX giving Anthropic access to 300 megawatts and 220,000 NVIDIA GPUs at Colossus 1, reportedly worth around $5 billion annually (AINews ); a new enterprise AI services firm co-founded with Blackstone, Hellman & Friedman, and Goldman Sachs to deploy Claude inside mid-market companies (Anthropic ); ten ready-to-run AI agent templates for financial services work including pitchbook creation, KYC screening, and month-end close (Anthropic ); and Claude integrations across Microsoft Excel, PowerPoint, Word, and Outlook. Underlying all of it: Anthropic disclosed 80x annualized revenue growth, with secondary market reporting putting its valuation at $1-1.2 trillion, officially overtaking OpenAI (AINews ). ...

Note: This post was generated by AI. Each week, I use an automated pipeline to collect and synthesize the latest AI news from blogs, newsletters, and podcasts into a single digest. The goal is to keep up with the most important AI developments from the past week. For my own writing, see my other posts. TL;DR OpenAI’s Codex expanded from a coding tool into a general work assistant this week, with direct integrations into Microsoft Office, Google Workspace, and Salesforce, meaning non-technical professionals can now delegate research, spreadsheet work, and planning to it. AI agents talking to other AI agents create security risks that don’t exist when testing a single agent, according to new Microsoft Research findings: a single malicious message can spread through a network of agents, stealing private data at every step. DeepSeek V4 Pro launched as the cheapest large frontier model available, priced at roughly one-third the cost of Claude or GPT-5.5 at comparable capability, and it’s open-source, meaning your IT team could run it internally. Claude now integrates directly with Blender, Adobe Creative Cloud, Ableton, AutoCAD, and other creative tools, making it genuinely useful for marketing and design workflows rather than just text tasks. OpenAI quietly ended its exclusive deal with Microsoft, meaning OpenAI models are coming to AWS and Google Cloud, which will increase competition and likely lower prices for enterprise buyers. Story of the Week: AI Agents Can Infect Each Other When companies deploy AI agents (software that takes autonomous actions on your behalf, like booking meetings, sending emails, or executing tasks without step-by-step human approval), those agents increasingly talk to each other. Microsoft Research spent this week showing what happens when that goes wrong, and the results are alarming for anyone planning to deploy agent-based workflows. ...

Note: This post was generated by AI. Each week, I use an automated pipeline to collect and synthesize the latest AI news from blogs, newsletters, and podcasts into a single digest. The goal is to keep up with the most important AI developments from the past week. For my own writing, see my other posts. TL;DR GPT-5.5 launched, with meaningfully better autonomous task execution and a major upgrade to OpenAI’s Codex app, which can now browse the web, edit spreadsheets, and work through multi-hour tasks with less hand-holding. DeepSeek V4 arrived as the most capable open-weight model yet, handling million-token contexts at a fraction of the memory cost, and designed to run on Chinese chips, not just NVIDIA hardware. Anthropic raised its run-rate revenue to $30B and signed a massive compute deal with Amazon, signaling the company is scaling infrastructure to match surging demand. Google and others poured billions more into Anthropic, with Bloomberg reporting Google plans to invest up to $40B, as the race to back frontier AI labs accelerates. AI agents are starting to do research autonomously: Anthropic published results showing Claude agents outperformed human researchers on an AI safety problem, at a cost of $22 per hour of AI work. Story of the Week: The AI Assistant Race Intensifies OpenAI launched GPT-5.5 this week, and based on Ethan Mollick’s early access writeup , the upgrade is real. The headline change is not raw intelligence but autonomy: the model is noticeably better at executing long, multi-step tasks without constant correction. Mollick fed it a decade of disorganized research data and four prompts later had a draft academic paper, including a real literature review and sophisticated statistics. His verdict: it would have passed as a strong second-year PhD project. ...

Note: This post was generated by AI. Each week, I use an automated pipeline to collect and synthesize the latest AI news from blogs, newsletters, and podcasts into a single digest. The goal is to keep up with the most important AI developments from the past week. For my own writing, see my other posts. TL;DR Anthropic launched Claude Opus 4.7 and Claude Design, its most capable model yet paired with a new AI-powered design tool that lets anyone create prototypes, decks, and marketing assets from plain English descriptions – a direct challenge to Figma and traditional design workflows. AI coding agents are now writing production code at industrial scale: Stripe generates 1,300+ AI-written code submissions per week, Ramp attributes 30% of merged code to agents, and new research shows AI can autonomously reimplement 16,000-line software projects that would take human engineers weeks. Agent security is an urgent, underaddressed problem: A Google DeepMind paper catalogued six categories of attack that can manipulate AI agents into leaking data, following malicious instructions, or being hijacked – with no easy fixes yet. AI researchers are sharply revising timelines upward: Multiple prominent forecasters doubled their estimates of how soon AI could automate AI research itself, now putting the odds at 30% by end of 2028. The open vs. closed model race is more nuanced than headlines suggest: Open-weight models (models with publicly available weights, meaning anyone can run them) keep pace on benchmarks, but closed models like Claude and GPT hold meaningful advantages in robustness and real-world usefulness – and economics, not raw capability, will determine who wins long-term. Story of the Week: Anthropic Doubles Down With Opus 4.7 and Claude Design Anthropic had the biggest week of any AI company, launching two products in quick succession. Claude Opus 4.7 is their new top-tier model, available at the same price as its predecessor ($5 per million input tokens, $25 per million output tokens). The practical improvement that matters most for non-developers: the model can handle genuinely complex, multi-hour autonomous tasks without losing the thread. Early users at companies like Notion, Replit, and Cursor report it catches its own logical errors mid-task, follows instructions more precisely, and keeps working through problems that used to stop the previous version cold. It also reads high-resolution images at triple the previous capability – useful for anyone using AI to analyze dense charts, diagrams, or screenshots. ...

Salesforce just enabled AI agents to use every single feature in their platform via APIs, MCPs, and CLI commands. This is the future. It isn’t that every tool has its own AI agent baked in. It’s that every tool can be used by YOUR agents. I’ve been spouting this to everyone at work. In a world where I can have my own personal Jarvis (AI agent), why would I want to use a generic agent that someone else provides. ...

Agentic AI, like Kiro CLI and Claude Code, has dramatically and permanently changed the way I do things, including managing this blog. A few years ago, I set up a new blog. I wanted to do it cheaply, and I landed on using Jekyll and GitHub. It took an entire weekend of reading the Jekyll docs, tinkering, and troubleshooting to launch the blog and configure it to my liking. I spent a lot of hours on it, but I wound up with a setup I fully understood and could easily manage. ...