How AI Agents Reshape Knowledge Work

Frontier AI systems are closing the gap between model intelligence and real-world utility. New models, compute architectures, and orchestration patterns are enabling these systems to accomplish tasks deemed impossible just a few months ago.

This rapid innovation has proved a boon to AI users by magnifying their leverage and agency. Yet it has also created a lag between the technological frontier and our understanding of precisely how knowledge work is evolving in response. How does frontier AI change the nature of knowledge work across professions? Which structural and economic transformations in this work might we expect?

… This article presents the highlights from our study. Detailed methodology and findings are available in our technical report. — Read More

#strategy

China’s Xiaomi MiMo Is Now 15X Faster Than ChatGPT and Claude

Most people know Xiaomi as the Chinese phone brand. The one that makes cheap electric scooters and air purifiers. Not exactly the company you’d expect to break a major AI inference speed record on a Monday morning.

And yet. Xiaomi just released MiMo-V2.5-Pro-UltraSpeed, a serving mode for its trillion-parameter flagship that hits over 1,000 tokens per second—peaking near 1,200 in demos. — Read More

#china-ai

MXC Internals: How Microsoft’s eXecution Containers Actually Isolate Agent Code

When an agent decides to run code, where does that code run, and what can it touch? Every coding-agent vendor now has an answer. OpenAI’s Codex CLI sandboxes locally through OS-native primitives: macOS Seatbelt, Linux Landlock + seccomp; Anthropic’s Claude Cowork runs the agent inside a full local Linux VM layered with seccomp and a network allowlist; hosted offerings like Google’s GKE Agent Sandbox and LangSmith Sandboxes wrap the workload in a VM or container. But so far, no OS vendor has provided a native solution.

At Build 2026, Microsoft open-sourced MXC, the Microsoft eXecution Container, under the MIT license: “a sandboxed code execution system for running untrusted code (model output, plugins, tools) on Windows, Linux, and macOS.” — Read More

#cyber

Anthropic’s Project Glasswing Update

In April, Anthropic initated Project Glasswing. The idea was to let companies use their new model to find and fix vulnerabilities in their own software. It was a fantastic PR move, and so many press outlets have uncritically parroted Anthropic’s claims that it’s now common wisdom that Mythos is better at finding software vulnerabilities than other models. Which is just not true. — Read More

#cyber

Models inherit a stale web, and they set us back a year

… [T]he models we now write code with learned from a web that is already old. I made the model gap to show this concretely: measured in Chrome releases (I know, I know, the web is far broader than just Chrome, but also Chrome has easy data to access on chromestatus.com), even the freshest model is several versions behind, and most are ten to twenty behind. The “knowledge” cutoff is a serious issue for the web platform, and the ecosystem of libraries and tools that are being launched but are not easily available to these models is massively gaining traction (Claude Code).

That connects to model half-life, where I looked at how quickly models are superseded, and to dead framework theory: if a framework stops appearing in fresh training data, the models stop reaching for it, and the framework quietly dies regardless of its merits. I wrote this thesis at least 6 months ago, and I think I’ve been proven correct (which is why we built Modern Web Guidance). The flip side, though, is that I’ve found guided output getting better than what people create (I think auto-research loops to optimize web performance, as an example, will massively raise the bar for the quality of the web people experience). — Read More

#devops

ChatGPT failed to kill Google Search

Ayear ago it wasn’t clear how AI was going to work out for Alphabet — GOOGL $365.95 (-1.35%) — , which missed out on the first-mover advantage held by OpenAI’s ChatGPT.

The fear was that AI competition would eat into traffic for Google’s all-important Search business. And that incorporating AI answers into its own searches could cannibalize revenue, since customers would be less likely to pay for their blue-linked pride of place if people got all their answers up top. Those fears have not materialized. — Read More

#big7