Menlo Venturesmenlovc.com · 16 September 2026

Key Findings Consumer AI adoption hardly grew even as spending tripled. The share of U.S. adults using AI rose just three points, from 61% to 64%, while global consumer spend reached an estimated $40 billion in 2026, more than 3x last year. The real growth came from existing users going deeper and paying more: 55% of AI users now pay for at least one AI product, and 46% of payers spend more than they did a year ago, versus 12% who spend less. Power users are driving consumer AI market growth: 55% of AI users now pay for AI, but spending is highly concentrated; the top 14% of payers account for 60% of spending. Payers are also nearly twice as likely to use AI daily (50% vs. 26%) and 5x as likely to use AI agents (65% vs. 13%). AI is becoming a source of income: Nearly half (48%) of AI users have earned money with AI, equivalent to roughly 30% of Americans. Consumers are handing AI more control: 41% of AI users have tried an AI agent, 24% use one regularly, and 32% have let AI act on their behalf without final approval. Among agent users, 92% pay for AI, and 63% use it daily. AI-generated conte

Sidebar.iomaggieappleton.com · 15 September 2026 · not counted as a party

This talk is about collaborative planning with agents: divided worlds, boundary objects, and thicker interfaces.The European navigator would begin every journey by making a plan. They would plot a course from maps and general, abstract principles, trying to do most of their thinking in advance.Once at sea, they would compare every move against the plan and work to stay on the course they had set. When something unexpected happened, they would have to revise the plan.The Chuukese navigators work differently: they do not make plans ahead of time.They instead begin with an objective, like reaching a certain island, and set off without a firm route.Instead, they respond to conditions as they arise, reading the wind, waves, currents, sun, and birds and steering accordingly. They’re continuously thinking about how to reach the objective and making ad hoc decisions, constantly improvising based on the context.Suchman is a famous HCI researcher and cultural anthropologist who worked at Xerox PARC https://en.wikipedia.org/wiki/Xerox_PARC in the 80s and 90s. Her book is all about planning and communicating with machines, and despite being written in 1987, it’s still very

Maggie Appletonmaggieappleton.com · 14 September 2026

This talk is about collaborative planning with agents: divided worlds, boundary objects, and thicker interfaces.The European navigator would begin every journey by making a plan. They would plot a course from maps and general, abstract principles, trying to do most of their thinking in advance.Once at sea, they would compare every move against the plan and work to stay on the course they had set. When something unexpected happened, they would have to revise the plan.The Chuukese navigators work differently: they do not make plans ahead of time.They instead begin with an objective, like reaching a certain island, and set off without a firm route.Instead, they respond to conditions as they arise, reading the wind, waves, currents, sun, and birds and steering accordingly. They’re continuously thinking about how to reach the objective and making ad hoc decisions, constantly improvising based on the context.Suchman is a famous HCI researcher and cultural anthropologist who worked at Xerox PARC https://en.wikipedia.org/wiki/Xerox_PARC in the 80s and 90s. Her book is all about planning and communicating with ma

OpenAI newsopenai.com · 14 September 2026

Fyxer uses OpenAI models, fine-tuning, memory, and real user feedback to organize inboxes and draft emails in each user’s voice.

GitHubgithub.blog · 10 September 2026 · 2 posts

Checking agent-generated code usually means hopping between tabs. Learn how to view diffs, run terminal commands, and preview web apps side by side in the GitHub Copilot app. September 10, 2026 | 3 minutes Share: When an agent makes a change to your code, you want to review it, run it, and see what changed. Great news: now you can do all three without having to leave the GitHub Copilot app. Before, doing those three jobs would mean having to bounce between your editor, terminal window, and web browser. But when these steps live side by side as built-in panels in the Copilot app, checking your agent’s work is simple. Let’s walk through each of the panels in the app and how we’ll use them to complete the AI coding loop. Using the diff panel to review changes The diff panel (a diff is a before and after comparison) shows exactly what lines were added, removed, or changed, with additions highlighted in green and deletions in red. This gives you complete clarity, so you can choose what happens. You can accept changes, lea