In short: A constructive call with the position disclosure attached. "GitLab this week potentially could beat just given there's so much more code that's coming out in the market. And I think people are just using GitLab more than ever. It's kind of like the Jevons paradox — the cheaper coding becomes, the more you use it, and GitLab is a way to record it. So we'll see how that plays out. I don't have a position right now." The cleanest statement of the AI-as-tailwind-not-threat case for developer tooling: agentic coding does not reduce the number of commits that need version control, review and audit — it multiplies them.
GitLab is where software teams store their code, review each other's changes, and keep a record of every version. It reports this week, and Singh thinks it could beat expectations — while being explicit that he does not own it.
His argument is an economic one with a name. The Jevons paradox describes what happened when steam engines became more efficient: coal use went up, not down, because cheaper power meant far more machines. Singh applies it directly: "the cheaper coding becomes, the more you use it, and GitLab is a way to record it."
The fear about AI and developer tools is that if machines write the code, you need fewer programmers and therefore fewer licences. The counter-argument is that AI does not reduce the amount of code — it multiplies it. Every AI-generated change still has to be stored, reviewed, tested and traced back to whoever approved it, and the more code is produced the more essential that record becomes. "There's so much more code that's coming out in the market… people are just using GitLab more than ever."
Full passage: premium transcript (PDF).
In short: Q1 rev +23% to $264M, operating loss narrowed to $16M — but the headline was a 14% workforce reduction (350 staff, exiting 22 countries, footprint −37%, $30–35M restructuring). Duo Agent Platform (DAP) showing "promising traction" but won't meaningfully contribute to FY27 revenue; expanded Google Cloud / Vertex AI partnership. Cautious: can the leaner cost base fund enough DAP go-to-market to reaccelerate into FY28, and does AI-code-generation displacement risk keep the multiple compressed.
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