# When code is abundant

DevFeed: [When code is abundant](<https://devfeed.tech/articles/when-code-is-abundant-101.md>)

Original publisher: [Read original article](<https://about.gitlab.com/blog/when-code-is-abundant/>)

Author: Bill Staples

Published: 2026-08-24T00:00:00Z

Content type: opinion

Language: en

Sources: [GitLab](<https://devfeed.tech/sources/gitlab.md>)

Topics: [AI Development](<https://devfeed.tech/topics/ai-development.md>), [Vibe coding](<https://devfeed.tech/topics/vibe-coding.md>), [Software Engineering](<https://devfeed.tech/topics/software-engineering.md>), [GitLab](<https://devfeed.tech/topics/gitlab.md>), [sdlc](<https://devfeed.tech/topics/sdlc.md>), [anthropic](<https://devfeed.tech/topics/anthropic.md>), [Concurrency](<https://devfeed.tech/topics/concurrency.md>), [Graphs](<https://devfeed.tech/topics/graphs.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [code](<https://devfeed.tech/tags/code.md>), [concurrency](<https://devfeed.tech/tags/concurrency.md>), [devsecops-platform](<https://devfeed.tech/tags/devsecops-platform.md>), [enterprise](<https://devfeed.tech/tags/enterprise.md>), [news](<https://devfeed.tech/tags/news.md>), [policy](<https://devfeed.tech/tags/policy.md>), [software-development](<https://devfeed.tech/tags/software-development.md>), [workflow](<https://devfeed.tech/tags/workflow.md>)

## AI overview

This opinion argues that AI agents and large language models are making code production cheaper and faster, shifting the main challenge for enterprise software development toward context, verification, governance, identity, policy, approval, and audit. It discusses how the software development lifecycle and its underlying architecture must adapt to machine-scale concurrency and automated workflows.

## Source excerpt

I returned from the holiday break in January convinced that something fundamental had changed. Large language models had reached the point where they could produce useful code reliably enough, and cheaply enough, to change the economics of software development. Engineers everywhere seemed to be experimenting with the same thing: not just asking an AI assistant for suggestions, but giving agents real work and seeing how far they could take it. I started thinking about what happens if that continues. What changes when producing code is no longer the primary constraint in building software? I wrote those thoughts down in a board memo in January. In May, I published part of that thesis in GitLab's Act 2: the cost and time of producing software was collapsing, machines would increasingly build software under human direction, and the architecture underneath software development would have to change with it. In June, at GitLab Transcend, we showed the first pieces of that architecture: source control rebuilt for machine-scale concurrency, GitLab Orbit as a context graph spanning the software lifecycle, and governance around agent identity, policy, approval and audit. Then, on August 21, Anthropic published The AI-Native SDLC Playbook. It opens with a simple statement: "Code is no longer the bottleneck." I agree. Anthropic's playbook is a practical description of how the development lifecycle changes when agents can move implementation dramatically faster: planning becomes machine-readable, handoffs become automated, verification moves into the loop, and human judgment concentrates at the gates. What interests me is what happens one level beyond the workflow. If code is no longer the primary constraint, what becomes scarce? What architecture does an enterprise need when people, agents and multiple models are all acting across the software lifecycle at machine speed? And where does durable value move when generating the code itself becomes increasingly abundant? Over the pas