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Philosophy in Practice: GitLab’s Model of Accelerated Iteration ​

The theory that links the stability of boring technology to faster iteration is persuasive. Its true value, however, lies in practical application, and GitLab is a concrete example of how this philosophy can be implemented at scale to produce remarkable results. The company explicitly attributes its ability to ship 106 releases in 106 consecutive months to its commitment to “boring solutions”.

Complexity Limits Speed ​

GitLab’s philosophy is based on one fundamental principle: the speed of innovation is directly limited by the total complexity that accumulates. Every new technology, abstraction, or dependency adds incremental overhead that, over time, slows the entire organization. By actively resisting that accumulation of complexity and consistently choosing the simplest, “boring” solution, GitLab has maintained a high velocity of iteration over a decade of growth.

This approach is reflected in a series of pragmatic, often counterintuitive decisions that prioritize delivery speed over technical elegance.

Practical Examples of “Boring” ​

GitLab’s culture of choosing “boring” solutions is best understood through examples that show a consistent preference for leveraging what already exists rather than building something new:

  • Leveraging Existing Features: To implement issue boards (a complex feature) initially, the team opted to use the existing issue label functionality to drive the lists. That “boring solution” enabled a fast delivery of a new feature by reusing components that were already understood and maintained.
  • Avoiding Unnecessary User Interfaces (UIs): When authentication with Vault was required, instead of building a new UI or CLI, the team chose to use a JSON Web Token and document the use of curl against the API endpoints. That “no-code” solution solved the user problem with zero development and maintenance overhead.
  • The Simplest Tool Possible: A striking example of GitLab’s commitment to simplicity was the massive, critical migration from Microsoft Azure to Google Cloud Platform (GCP). To manage that complex project, they used the most “boring” solution of all: a checklist in a GitLab issue. Although that checklist was edited 140 times, the simple, transparent process allowed the migration to proceed without major incidents. They resisted the temptation to use sophisticated project management software, relying instead on a simple, visible, and familiar tool for everyone.
  • Disciplined Incrementalism: When improving the user experience to make an issue title visible while scrolling, the initial proposal was to fix only the title. A quick validation showed the MVP (Minimum Viable Product) should also include the issue state. Crucially, the team resisted expanding scope to include other fixed elements, like tab navigation, or applying the logic to other objects, like Merge Requests. They delivered the smallest possible change that added value, demonstrating a disciplined approach to iteration.

“Boring” Is Not Dogma, It’s Strategy ​

GitLab’s approach shows that choosing “boring” solutions is not permanent but strategic and contextual. Tools like Gitolite were adopted initially because they were effective and allowed focus on delivering value, and they were replaced as the company scaled.

This underscores that “boring” is not a value judgment but a choice for the simplest tool with the least overhead, optimized for iteration speed and with the flexibility for future revision.

This philosophy of simple solutions and rapid iteration is human-centered engineering. It values attention, focus, and engineers’ creative energy while minimizing cognitive fatigue caused by complex stacks. The MiG-15 pilot fatigue metaphor illustrates the mental exhaustion of dealing with unpredictable technologies, highlighting the importance of a stable environment.

A “boring” and stable technology stack fosters mastery and confidence, allowing engineers to enter flow. By reducing cognitive load and decision fatigue, engineers can direct their creativity toward solving customer problems and generating business value, focusing on the “what” instead of constantly worrying about the “how” of the infrastructure.

Now that we understand how choosing boring solutions can accelerate iteration and value delivery, let's explore a phenomenon that seems to be moving the industry in the opposite direction: AI. How do these new technologies fit into a philosophy that prioritizes stability and simplicity?