Google and Microsoft just co-authored the spec that turns every website into an API for AI agents. The second-order effects here are massive.
Right now, browser agents work by taking screenshots, parsing the DOM, and guessing which buttons to click. It works about as well as you’d expect. Fragile, expensive, slow. WebMCP replaces all of that with a single browser API: navigator.modelContext. Websites register structured tools directly in client-side JavaScript. The agent reads a menu of available actions, calls them, gets structured data back. No scraping. No backend MCP server in Python or Node. The tools run inside the browser tab and share the user’s existing auth session.
Early benchmarks show ~67% reduction in computational overhead compared to visual agent-browser interactions. Task accuracy around 98%.
The second-order effect is where this gets wild. Today, when a browser agent visits two competing airline sites, it’s guessing at both interfaces equally. Once WebMCP adoption spreads, the site that exposes structured tools gives the agent a clean, reliable path to complete the task. The site that doesn’t forces the agent to fumble through the UI. Agents will prefer the cheaper path. Every time.
This means “Agent Experience Optimization” becomes a real discipline. Tool naming, schema design, description quality. Sound familiar? It’s the same shift that happened when meta descriptions and structured data became optimization surfaces for search engines. Except this time, the traffic source isn’t Google’s crawler. It’s every AI agent on the internet.
Bots already make up 51% of web traffic. Google just gave them a front door.
The AI space has moved so fast in just 12 months! This thread certainly show the true future potential of AI. Based on this (and looking at some of the companies covered) the future of enterprise companies is going to be so radically different in the next 12 - 24 months
"Service as Software" is Silicon Valley's hottest buzzword right now.
Everyone's talking about SaaS becoming service providers, but no one's explaining HOW. The answer? After 6 months of research and 100s of startup conversations, we have the answer: Systems of Agents.
We're looking at a $4.6T opportunity.
Significant progress in AI and Robotics this week.
Big developments from OpenAI, Serve Robotics, Meta, Microsoft, AtomLimbs, MIT, Liquid AI, EPFL, Luma, and more.
Here's everything you need to know and how to make sense out of it:
A lesson for everyone who asks people to hang on for 5 mins at the end of a teams / zoom call to discuss something. With auto-enabled transcripts and the warning flagging at the start of the meeting easy to see how this would happen.
A VC firm I had a Zoom meeting with used Otter AI to record the call, and after the meeting, it automatically emailed me the transcript, including hours of their private conversations afterward, where they discussed intimate, confidential details about their business.
The wait is over.
OpenAI just dropped o1, also known as Project Strawberry/Q*
This is new level of AI that can "think" and "reason" before responding to you.
10 wild demos:
1. Coding Video Game from a prompt
https://t.co/f60r4Q0uvy
I'm excited to share that we've built the world's most capable AI software engineer, achieving 30.08% on SWE-Bench – ahead of Amazon and Cognition. This model is so much more than a benchmark score: it was trained from the start to think and behave like a human SWE.
Exclusive: Meta just released Llama 3.1 405B — the first-ever open-sourced frontier AI model, beating top closed models like GPT-4o across several benchmarks.
I sat down with Mark Zuckerberg, diving into why this marks a major moment in AI history.
Timestamps:
00:00 Intro
00:38 Meta’s Llama 3.1 rundown
03:44 Real-world use cases for Llama 3.1
06:15 Educating developers on open-source AI tools
09:43 Societal implications of open-source AI
13:00 Balancing power and managing bad actors
14:40 Open source and global competition
16:59 Accelerating innovation and economic growth
20:04 Zuck on Apple and lessons from the past
24:22 Future of AI: Llama 3 and beyond
26:43 Prediction: Billions of personalized AI agents
31:32 Factors to changing anti-AI sentiment
I always hated that when I studied Physics, I had no intuition of what order I could study topics in, except linear.
The latest LLM use-case I love is feeding the Table of Contents of a textbook and asking it to create the dependency graph of topics!
Here are some: Physics
1/3
We CT scanned an Apple Vision Pro! We also scanned two Meta headsets. Here’s what we found inside, and what it says about the two companies’ approach to AR/VR and to hardware development in general. 🧵
"Everybody who is building these chatbots and Generative AI, when you are ready to run it, you need an AI factory and nobody is better at building end-end systems of very large scale for the enterprise than @DellTech . Any company and every company needs to build AI factory.."
Not sure why I found this so interesting this morning. I had no idea the caps were previously non recyclable. This actually is a bit of a feat of engineering, it’s the small things that make a difference.