SpaceX Bought Cursor. Now What?
On June 16, 2026, SpaceX acquired Cursor for $60 billion — four days after the AI coding tool's IPO. The number is attention-grabbing, but the context matters more: Cursor had reached $4B+ in ARR, was deployed inside 64% of the Fortune 500, and had built its moat on model-agnostic flexibility. Engineers chose it partly because they could route code through Anthropic or OpenAI without locking into a single vendor's ecosystem.
This is a landmark moment for AI-assisted development. It's also a useful signal to pause and ask which parts of the development workflow AI coding tools actually solve — and which parts they leave untouched.
The Gap the Deal Exposes
AI coding assistants are genuinely good at generation. They help engineers move faster through multi-service changes, handle boilerplate, and keep context across complex codebases. What they don't address is what happens after the code is written.
In a typical microservices organization, a single logical change might produce PRs across three or four repositories simultaneously — spanning GitHub and GitLab, touching shared libraries and downstream services. The reviewer has no unified surface. They're context-switching manually, holding the full picture in their head, hoping they catch the cross-service contract issue before it merges.
As AI coding tools accelerate the rate of code production, this review layer becomes the constraint. More PRs per week across more repos means more fragmented context for the humans responsible for catching what the AI missed.
What Engineering Teams Should Act On
The Cursor acquisition should prompt a straightforward audit: your team has a strategy for AI-assisted code generation. Do you have an equally deliberate approach for reviewing and tracking what gets generated?
Cross-repo visibility — seeing all open PRs across every repository in a unified view, with enough context to understand how changes relate — is the part of the stack that receives the least investment relative to its impact on engineering velocity.
That asymmetry is where review debt quietly accumulates, stale PRs pile up unnoticed, and the speed gains from AI coding tools get partially eaten back in coordination overhead.
For teams navigating this, Code Board provides exactly this layer: a unified multi-repo PR board aggregating GitHub and GitLab into one Kanban view, with AI-powered code review that understands your codebase's specific architecture and cross-repo relationships.
