Today we’re launching dreaming in Claude Managed Agents as a research preview. Dreaming extends memory by reviewing past sessions to find patterns and help agents self-improve. We’re also making outcomes, multiagent orchestration, and webhooks available to developers building with Managed Agents. Together, these updates make agents more capable at handling complex tasks with minimal steering. — Read More
Daily Archives: May 7, 2026
OpenAI Flips the Script
If you’re looking for evidence of AI’s unrelenting pace, here it is: In January, Dan wrote that whoever wins vibe coding wins how you work on your computer—and that OpenAI had some serious catching up to do.
Three months and the release of OpenAI’s latest model later, Codex is there, and in a new episode of AI & I, Dan and Austin get into why they do much of their knowledge work in Codex now. They cite the power of GPT-5.5, paired with a desktop app that is faster and more powerful than Claude Desktop or Cowork. — Read More
How AI agent memory works.
A language model on its own is stateless. You feed it a prompt, you get back a continuation, and the moment the response is finished the model forgets you ever existed. There is no “previous conversation” living inside the weights.
An agent, by contrast, is the orchestration around the model: a loop that decides what context to pass in next. Memory is the part of that loop that carries information forward. Everything in this essay is a different answer to the same question, what should we put in the prompt this time? — Read More
Beyond the hype: The enterprise AI architecture we actually need
he real future of enterprise AI is a structured architecture of private models and agent orchestration that works for teams without a complex training program.
My last few years working as a chief digital officer have been, in large part, a sustained exercise in separating what enterprise AI can actually do from what we as a world insist it is about to do. That distinction is not academic. It is the difference between a transformation program that delivers and one that produces a glossy internal report and a quietly shelved proof of concept.
Enterprise experimentation with generative AI has accelerated sharply over the past two years. The Stanford AI Index reports that more than half of organizations globally are now actively exploring or piloting AI-driven workflows — a signal that the conversation has moved from curiosity to operational pressure for many CIOs.
What follows is not a vendor blueprint or prediction. It is a working architectural sketch shaped by real enterprise constraints — the kind that has to survive contact with a real organization’s data governance function, its compliance team and its late-night incident queue. — Read More
Democratizing Machine Learning at Netflix: Building the Model Lifecycle Graph
As Netflix has grown, machine learning continues to support our ability to deliver value to members and drive excellence across multiple areas of our business. When Netflix began investing in machine learning over a decade ago, it was primarily focused on a single domain: personalization. Scala was the industry standard, our ML teams were relatively small, and optimizing member engagement was our primary use case. Fast forward to today, and machine learning has become the backbone of Netflix’s business transformation. We now apply ML across various business domains.
… Each domain operates with a different tech stack, different business metrics, and a distinct organizational structure. While this diversity is a testament to how machine learning has evolved to drive value across many verticals at Netflix, this growth introduces a new challenge: enabling cross-pollination of models and data across domains. — Read More
Rewiring the C-suite: The fast track to 2030
2026 is the year CEOs must rewire the C-suite—redesigning how decisions are made, how authority is distributed, and how AI reshapes influence—while preserving the decisiveness and clarity enterprises need to move fast. Getting there takes proactive leadership. CEOs will need to work with their C-suite leaders to build execution mechanisms, incentives, and operating models all focused on driving these outcomes.
Our research shows that CEOs who have the greatest success with AI are actively rethinking cross-functional collaboration and embedding AI across end-to-end workflows. They’re building organizations designed to thrive in uncertainty, where productive debate sharpens strategy and smart risk-taking is rewarded.
The 2026 CEO Study’s data, gathered in partnership with Oxford Economics, builds on our study, The enterprise in 2030, which identifies five predictions for the future of the organization. This study’s analysis, informed by our 2030 predictions, reveals five plays that CEOs must make to lead in an AI-first landscape. — Read More
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