Jim Woodroffe

Scaling your engineering team from 1 to 50 (and beyond) is one of the biggest tests for any founder. Too many leaders wait until problems force change instead of making proactive decisions that set their teams up to grow. In this micro memo, Bessemer Operating Advisor and long-time CTO Jessica Popp shares lessons from decades leading engineering orgs through every stage of scale. If you’re growing fast, this is your playbook for avoiding decisions you can’t easily undo and building a team that grows with you. 📖 Read the full guide: https://lnkd.in/gfFSgz8b #startups #engineering #techleadership #engineeringmanagement #scalingstartups

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New: I spoke with the head of Claude Code, Boris Cherny, about how the viral AI coding tool is reshaping Anthropic. The buzz around Claude Code has reached a fever pitch recently, but some developers say AI coding products in general are reaching an inflection point. A few years ago, AI coding tools basically amounted to autocomplete, suggesting lines of code after developers started typing. Now, the space looks completely different. Developers can spin up AI agents from their phone that will spend hours coding up a feature. Just how good are these tools? Well, engineers inside of Anthropic seems to be using Claude Code for almost everything, and its business is growing like wildfire. - Claude Code made up ~12% of Anthropic's ARR by the end of last year, WIRED has learned. The product soared past $1 billion in ARR, by at least $100 million, in December, and could play a significant role in the company's revenue growth moving forward. - Cherny says nearly everyone at Anthropic, even its sales team, is using the agent for almost everything. That said, competition in AI coding is fierce. Cursor is at least as large as Claude Code, in terms of revenue. OpenAI and Google are hot on their tails. Definitely a space to watch in 2026. Read more in my newsletter, Model Behavior. https://lnkd.in/gCUzcjd2

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In large technology transformations, the most dangerous decision is often the one that looks “cleanest” on paper. I’ve seen teams rush toward full rewrites of legacy systems because the architecture feels messy, outdated, or hard to reason about. In practice, the safer question is rarely “What’s the best design?” It’s “What keeps options open if we’re wrong?” Incremental extraction, controlled reuse, and preserving operational continuity may look inelegant—but they dramatically reduce irreversible risk. Senior technology decisions aren’t about elegance. They’re about survivability under uncertainty

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