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Breaking Change Breaking Change

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The first time I visited GitHub's HQ2 in 2012, they had a TV showing off their first animations of Mona and were using it to push their new tagline: Social Coding. The phrase certainly captured the moment we were living in, so in 2014, I borrowed it for the title of one of my more popular conference talks, The Social Coding Contract. The goal of that presentation was to warn audiences of the long-term risks of all these tools making it so trivially easy to publish and consume open source dependencies. Sure enough, what followed was a decade defined by the productive and destructive chaos of a ceaseless deluge of useful, but poorly-understood and under-maintained dependencies.

Thanks to the upheaval being caused by coding agents, I believe we've entered a new era without realizing it, one that might be called Antisocial Coding.

Three things I've been chewing on lately:

• Agents blow out team communication costs. Last year, Scott Werner compared a developer orchestrating agents to an octopus with 8 autonomous arms and I replied to point out how this creates a hub-and-spoke communication crisis that would prove unwieldy for traditional multi-developer collaboration. Since then, I've been advising startups to stay single-developer for as long as possible and telling larger organizations to start moving to one-repo-per-human wherever they can—even if it means re-architecting their systems to align with this.

• Agent-built codebases calcify quickly. Last night, Caleb Hearth texted to tell me he'd created several issues on turbocommit, and I'm ashamed to admit my brain was so overwhelmed with six terminal tabs of unrelated WIP features that I brusquely denied all of them. Caleb shared a keen insight: many agent-led codebases are becoming impenetrably large and complex extremely early in their lifespan—even while the userbase is still limited to the original creator. And on reflection, I do sense a certain ossification in the projects I encounter lately—so tightly coupled to one person's hyper-specific needs that they lack room to grow or change by the time other users arrive. Beads is the perfect example: I spent two weeks trying to use it, but at 4 months old it was already far too complex and brittle to integrate into my workflow

• Open source is closing its doors. Yesterday, tldraw took the unusual step of removing the tests from their codebase, on the theory their continued presence would make it far too easy for anyone with an agent to build a cleanroom rewrite and undermine their business. Nothing says "closed for contributions" quite like not even having access to a test suite or CI to work with

I don't feel strongly about any of this, because I'm mostly a loner to begin with. But this trend will likely have troubling impacts for teams and organizations.

• If the last decade of social coding taught us anything, it's that pinning the continuity of your business operations on a bunch of software for which the bus factor is 1 is an unacceptable liability. If the next decade is typified by an explosion of solo projects, then the rational response will be to adopt fewer dependencies and have agents build more in-house implementations—even if it means shouldering more maintenance costs and security risks
• Unlocking the maximum productivity afforded by agents by sequestering individual human developers to their own discrete fiefdoms (whether at the repository, CODEOWNERS file, or some other boundary) will only accelerate the trend of discouraging contributions from others, compounding the above issue at scale and throughout engineering organizations
• While many people are (rightly) concerned about AI's impact on the job prospects of less-experienced developers, the continued erosion of collective code ownership coupled with a trend away from inter-person collaboration will further slam the door on opportunities for mentorship and apprenticeship, exacerbating the widening gap in potential value creation between juniors and seniors

I've never had more fun building stuff than I'm having right now, but that doesn't change any of the above. These are important things to be watching out for. Engineering leaders should probably consider the downstream effects discussed here before plowing ahead with plans to reorient their organizations around what's best for maximizing the productivity of semi-autonomous coding agents.