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Last week we ran a networking event with 60 engineering leaders. Two things stuck with me.

First, a phrase I heard more than once: juniors are burning out the seniors. Huge PRs trusting the LLM blindly, hoping the senior's code review will fix it all.

Second: I ran a live poll on pair programming. Almost nobody promotes it anymore. Several used to, and stopped.

So I've been chewing on two questions.

Do we still need juniors? A senior who understands product, code and architecture (basically, a good product engineer) augmented with AI can do 10x the work of a junior. And juniors aren't just less productive (the tasks they used to do are now automated with AI); they can be net negative, dragging seniors down for a long time.

There's an obvious problem with not hiring juniors, though: talent pipeline. Seniors are just juniors with 5 years on top. If your team is 100% senior, each of them owns you. No negotiating power when they ask for a 40% raise or walk away. And it gets much worse long term if no company hires juniors (fewer seniors down the line, i.e. more expensive).

My take: startups with fewer than 6-7 devs, don't hire juniors, the opportunity cost of a senior mentoring is brutal when that same senior with AI does the work of three. Bigger companies, yes. Pipeline matters, and you've got room to absorb the ramp-up.

And pairing? There have always been camps. I've always sat in the middle: pair in specific situations, with specific people (hunt a nasty bug, make an architecture call, align on product). Now with AI, traditional pair programming doesn't make much sense to me anymore, and where it does, it's closer to just mentoring than to a real strategy for shipping better. What I do think works is a new shape of pairing: pair early, on the thinking: research, product refinement, specs, implementation plans. Then pair with the AI for the actual implementation. And close the loop with a human code review at the end, ideally with the same person you paired with at the start. That's the winning strategy.

GitHub copilot has moved to usage-based billing. This feels like the end of subsidized AI coding. It's much worse value/cost for users.

An AI Agent Just Destroyed Our Production Data. It Confessed in Writing. The article tells the story of how a Cursor agent deleted PocketOS's production database on Railway, wiping backups too and leaving rental businesses scrambling to recover three months of lost data.

OpenAI launched: GPT-5.5, Workspace Agents and Privacy Filter.

Tokenmaxxing' as a weird new trend. Gergely argues that "tokenmaxxing" is the new lines-of-code metric: companies like Meta, Microsoft, and Salesforce are incentivizing engineers to burn AI tokens to look "AI-native," often creating waste, slower work, and bad incentives instead of real productivity.

Warp terminal is now open source on GitHub. and Ghostty terminal is leaving GitHub.

Laws of Software Engineering. A collection of principles and patterns that shape software systems, teams, and decisions.

J*** asked how a small 10-15 person engineering team can create a fair salary and growth system when veteran employees have more product knowledge but newer hires often come in with higher salaries. The main advice was to avoid over-engineering it and start with a simple 3-level career ladder, clear expectations, salary bands, and enough flexibility for a startup.

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