AI-native software engineering teams operate very differently than traditional teams. The obvious difference is that AI-native teams use coding agents to build products much faster, but this leads to many other changes in how we operate. For example, some great engineers now play broader roles than just writing code. They are partly product managers, designers, sometimes marketers. Further, small teams who work in the same office, where they can communicate face-to-face, can move incredibly quickly. Because we can now build fast, a greater fraction of time must be spent deciding what to build. To deal with this project-management bottleneck, some teams are pushing engineer:product manager (PM) some teams are pushing engineer:product manager (PM) ratios downward from, say, 8:1 to as low as 1:1. But we can do even better: If we have one PM who decides what to build and one engineer who builds it, the communication between them becomes a bottleneck. This is why the fastest-moving teams I see tend to have engineers who know how to do some product work (and, optionally, some PMs who know how to do some engineering work). When an engineer understands users and can make decisions on what to build and build it directly, they can execute incredibly quickly. I’ve seen engineers successfully expand their roles to including making product decisions, and PMs expand their roles to building software. The tech industry has more engineers than PMs, but both are promising paths. If you are an engineer, you’ll find it useful to learn some product management skills, and if you’re a PM, please learn to build! Looking beyond the product-management bottleneck, I also see bottlenecks in design, marketing, legal compliance, and much more. When we speed up coding 10x or 100x, everything else becomes slow in comparison. For example, some of my teams have built great features so quickly that the marketing organization was left scrambling to figure out how to communicate them to users — a marketing bottleneck. Or when a team can build software in a day that the legal department needs a week to review, that’s a legal compliance bottleneck. In this way, agentic coding isn’t just changing the workflow of software engineering, it’s also changing all the teams around it. When smaller, AI-enabled teams can get more done, generalists excel. Traditional companies need to pull together people from many specialties — engineering, product management, design, marketing, legal, etc. — to execute projects and create value. This has resulted in large teams of specialists who work together. But if a team of 2 persons is to get work done that require 5 different specialities, then some of those individuals must play roles outside a single speciality. In some small teams, individuals do have deep specializations. For example, one might be a great engineer and another a great PM. But they also understand the other key functions needed to move a project forward, and can jump into thinking through other kinds of problems as needed. Of course, proficiency with AI tools is a big help, since it helps us to think through problems that involve different roles. Even in a two-person team, to move fast, communication bottlenecks also must be minimized. This is why I value teams that work in the same location. Remote teams can perform well too, but the highest speed is achieved by having everyone in the room, able to communicate instantaneously to solve problems. This post focuses on AI-native teams with around 2-10 persons, but not everything can be done by a small team. I'll address the coordination of larger teams in the future. I realize these shifts to job roles are tough to navigate for many people. At the same time, I am encouraged that individuals and small teams who are willing to learn the relevant skills are now able to get far more done than was possible before. This is the golden age of learning and building! [Original text: ]

How AI-native law firms use "management services organisation" structures to access capital historically barred from US law firms, including PE and VC funds (@stephenfoley / Financial Times) (Visit Techmeme dot com for the link and full context!)

What "AI-native" actually means in 2026: → Generated by AI → Deployed by AI → Running on infra built for it Most L1s meet none of these criteria. Canopy was built for all three 🌿

🖋️ Kami is an AI-native tool for typesetting professional documents. Been wanting this for a while: architecture diagrams that live with your repo. Redraw them and they keep their original intent, and roadmap plans never look like shipped facts.

Robinhood Chain is quickly becoming a top AI-native Layer 2 blockchain for on-chain finance and real-world assets. More builders are going live, and every one of them needs reliable node infrastructure behind the scenes. ZAN Node Service now fully supports @RobinhoodApp Mainnet, with sub-30ms response time (the fastest in Asia), 99.9% uptime SLA guarantee, and 40% lower cost than market average. Whether you're building DeFi, RWA tokenization, or AI trading agents, one API key unlocks Robinhood Chain and more. Get started: #Web3# #Blockchain# #Robinhood# #NodeService#

Coordination is the new bottleneck in AI-native development. See how we’re evolving Jira to be the system for the AI SDLC—and why teams like @Reddit are using it to ground their agentic workflows: