Anthropic Scion: Git Breaks at 30,000 Merges a Day Anthropic built an internal version-control system called Scion in five months after its monorepo's merge volume rose from 2,500 to 30,401 code changes per day by September 2026, a 12x increase driven by AI coding agents that broke Git under the load. A seven-person team replaced Git's snapshot-based, stateful have/want negotiation with a diff-first write-ahead log and a three-tier content-addressed cache, cutting developer status checks from 3.9 seconds to 19 milliseconds (207x) and agent worktree creation from 7.1 minutes to 5.1 seconds (84x). Anthropic said Scion will not be open-sourced, and the team reported that every engineer on the project would now rate Claude as better at writing code than themselves, with their job becoming redirecting the model and managing scope. In September 2026, Anthropic’s monorepo was accumulating 30,401 code changes per day. Four months earlier, that number was 2,500. AI coding agents had multiplied their merge throughput 12x, and Git — the tool the entire industry runs on — broke under the load. A seven-person team built a replacement called Scion in five months. It won’t be open-sourced. But what broke Git, and what it took to fix it, is a preview of infrastructure problems the rest of us will face. Git Wasn’t Designed for This The numbers are worth sitting with. At 30,401 merges a day, the main branch was moving every three seconds. With 25 million CI checkouts per day, naively replaying all of that traffic through standard Git would have generated over 600 petabytes of daily data transfer. Anthropic ran “several hundred” internal mirrors at peak before admitting the approach was fundamentally broken. They didn’t jump straight to building their own system. Anthropic exhausted every Git optimization first: shallow clones, sparse checkouts, promisor remotes, internal mirrors, blob storage seed snapshots. Each one helped — and each one still assumed Git’s core model: offline-capable, independent peers holding local copies of repository history. That assumption doesn’t hold when your “developers” are AI agents that exist for 20 minutes, never go offline, and don’t need the last 100 GB of commit history to do their job. What Scion Actually Does Scion inverts Git’s storage model. Git stores snapshots and computes diffs. Scion stores diffs — changesets in a write-ahead log — and derives trees on demand. Every commit, rebase, and merge happens on the server. Clients fetch only what they need via a three-tier content-addressed cache: a local daemon, a regional cache, and a central server backed by Spanner and Cloud Storage. All three tiers answer the same RPCs. Cache hit rate exceeds 99% at every tier. The key insight is in the protocol design. Git’s have/want negotiation is stateful — it requires knowing what each client already has, which forces whole-repository mirrors. Scion names content by its hash, so any proxy or CDN can answer a request without repository awareness. That’s what makes stateless caching at every tier possible. The performance results are dramatic: - Developer status check: 3.9 seconds to 19 milliseconds 207x faster - Agent new worktree: 7.1 minutes to 5.1 seconds 84x faster - Pull latest: 87 seconds to 3.5 seconds 25x faster - CPU/memory footprint: 200x smaller than the previous mirror fleet - CI capacity: 25% savings Operations that scaled with repository size saw the largest gains. Operations already proportional to change size barely moved. The problem was always the per-repository overhead, not the per-change cost. The Human Role Has Already Changed Here’s what the Scion team reported after migration: every engineer on the project would now rate Claude as better at writing code than themselves. Their actual job became telling Claude no — redirecting it from querying commit logs instead of the review tool, stopping unnecessary defensive validation, managing scope. The code review model changed too. Pre-Scion: serialized merges behind full test runs, one global queue. Post-Scion: post-merge and pre-deploy testing, with intent-based review replacing line-by-line inspection. The stated reason: “Claude can typically fix conflicts faster than humans can even identify them.” So they removed the pre-merge gate, rebuilt governance around post-merge testing, and leaned into AI-assisted conflict repair. This is the real signal in the Scion story. It’s not the VCS technology — it’s the governance model that had to be completely rebuilt to accommodate the new velocity. What You Can Do Right Now Anthropic is direct: Scion is not for general use and will not be open-sourced. But the design principles transfer. If you’re running AI coding agents today, start with these: Blobless sparse clone — only fetch file contents you actually need git clone --filter=blob:none --sparse