GitLab CEO on why AI isn't helping enterprise ship code faster

AI coding assistants are boosting developer productivity, but most enterprises aren’t shipping software any faster. GitLab CEO Bill Staples says the reason is simple: coding was never the main bottleneck. After speaking with more than 60 customers, Staples found that developers spend only 10–20% of their time writing code. The remaining 80–90% is consumed by reviews, CI/CD pipelines, security scans, compliance checks, and deployment—areas that remain largely unautomated. Faster code generation only worsens downstream queues.

GitLab’s response is its newly GA’ed Duo Agent Platform, designed to automate the full software development lifecycle. The platform introduces “agent flows,” multi-step orchestrations that can take work from issue creation through merge requests, testing, and validation. Staples argues that context is the key differentiator. Unlike standalone coding tools that only see local code, GitLab’s all-in-one platform gives agents access to issues, epics, pipeline history, security data, and more through a unified knowledge graph.

Staples believes this platform approach, rather than fragmented point solutions, is what will finally unlock enterprise software delivery at scale.

Learn more from The New Stack about the latest around GitLab and AI:

GitLab Launches Its AI Agent Platform in Public Beta

GitLab’s Field CTO Predicts: When DevSecOps Meets AI

Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

Denne episoden er hentet fra en åpen RSS-feed og er ikke publisert av Podme. Den kan derfor inneholde annonser.

Episoder(300)

JetBrains is selling independence as the rest of AI coding picks sides

JetBrains is positioning itself as the last major independent AI coding-tool vendor in a market increasingly tied to hyperscalers and foundation model labs. Speaking at Google Cloud Next, JetBrains VP...

21 Mai 26min

Why Block handed Goose to the Linux Foundation

What began as an internal developer tool atBlockhas evolved into a broader open-source initiative with industry backing. Goose, Block’s AI coding agent, followed a path similar to Amazon’s transformat...

15 Mai 19min

Fivetran's CPO: closed data stacks won't survive the agent era

At Google Cloud Next 2026, Fivetran Chief Product Officer Anjan Kundavaram argued that enterprise data systems are unprepared for the scale of AI-driven analytics. Unlike humans, AI agents can generat...

13 Mai 22min

The new FinOps problem isn't cloud bills

At Google Cloud Next 2026, Finout co-founder and CEO Roi Ravhon and Google Cloud FinOps lead Pathik Sharma discussed how FinOps is rapidly evolving for the AI era. Ravhon argued that while cloud FinOp...

12 Mai 28min

How Microsoft is governing thousands of Kubernetes clusters without manual intervention

Managing Kubernetes at fleet scale introduces significant complexity, especially as organizations expand from a few clusters to hundreds or thousands across cloud, on-premises, and edge environments. ...

7 Mai 25min

Why long-running AI agents break on HTTP and how Ably is fixing it

In this episode ofThe New Stack Makers, Matthew O’Riordan, CEO of Ably, explains how infrastructure originally built for human collaboration is now well-suited for long-running AI agents. While Ably i...

6 Mai 31min

Why the Linux Foundation adopted MCP, with Jim Zemlin and Mazin Gilbert

Agentic AI is advancing rapidly, with open-source projects racing to keep pace with real-world deployment. To accelerate progress, the Linux Foundation consolidated key technologies—Model Context Prot...

6 Mai 32min

Fresh data has us asking, does AI demand Kubernetes?

Kubernetes is rapidly emerging as the de facto operating system for AI, with two-thirds of organizations using it for generative AI inference and 82% adopting it in production. Its ecosystem — includi...

1 Mai 23min