"You cannot inspect quality into the product; it is already there."
Gino Ferrand, writing today from Santa Fe, New Mexico 🌞
In 2023, AI started writing code.
In 2024, it began reviewing it.
In 2025, it’s doing everything else.
CI/CD pipelines. Ticketing systems. Documentation. Testing frameworks. Even commit messages. The AI creep isn’t confined to IDEs anymore...it’s colonizing the entire SDLC.
And over the past few weeks, we’ve seen that shift accelerate.
What just happened?
Here’s a snapshot of how AI is going beyond the code editor:
Code Review: GitHub Copilot for Pull Requests now includes an “AI Coach” that can auto-summarize diffs, highlight potential issues, and even explain complex changes. Some teams are skipping manual summaries altogether.
Documentation & Ticketing: Atlassian’s AI upgrades in Jira and Confluence now let developers generate ticket descriptions, task breakdowns, or documentation from messy notes. LinkedIn is full of engineering managers praising how much time they’re saving on the “write it down” part of dev work.
Testing: Plugins for Selenium and Cypress are now using AI to auto-generate additional test cases. If your suite only covers the happy path, AI can guess the edge cases you forgot.
CI/CD Debugging: Companies are starting to use AI to analyze failed builds, performance regressions, and test flakiness. Instead of sifting through logs, AI can highlight the likely cause from a diff and flag related dependencies. Think: your build engineer’s instincts, but automated.
Version Control: Several Git clients now suggest commit messages based on diffs. One dev lead called it “spellcheck for your changelog.”
Infrastructure Optimization: Google’s DeepMind team internally deployed “AlphaEvolve,” an AI system optimizing data center configs and code execution paths. While it’s not yet customer-facing, these techniques tend to trickle down into features on GCP and other platforms.
Status Updates: Some startups are experimenting with AI-generated standups...using git logs, ticket movements, and PRs to auto-summarize what everyone did yesterday. It’s not perfect, but it’s getting closer. Fewer status meetings, more async clarity.
Why this matters
The software development lifecycle is no longer a sequence of human handoffs.
It’s becoming an AI-woven feedback loop...where tooling not only assists but participates.
And that shift demands a rethink.
Engineering leaders are starting to question old rituals:
If an AI writes your ticket and commits your code, do you still need a human standup?
If AI creates edge-case tests, does your QA process look different?
If AI points out bugs in PRs, should code review shift earlier in the cycle?
We’re not fully automated. But we are firmly in the era of AI-augmented pipelines.
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The new DevOps mindset
This isn’t just about efficiency. It’s about visibility, traceability, and accountability in a hybrid human-machine process.
Engineering managers are already:
Embedding AI checks into pipelines (e.g. commit message linters, AI PR flags)
Adding CI stages to verify AI-generated code for security and maintainability
Revising Definition of Done to account for AI contributions and artifacts
And in the process, they’re finding a new role: not just managing humans, but orchestrating machines with humans.
The next version of DevOps won’t just be about CI/CD.
It’ll be about AI/CD.
More to come…
Recommended Reads
✔️ Top 11 AI Tools For DevOps in 2025 (Spacelift.io)
✔️ How AI Can Supercharge Your CI/CD Pipeline (Meta Phase)
✔️ The Impact of AI in Software Development Life Cycle: A Stage-by-Stage Guide (Practical Logix)
– Gino Ferrand, Founder @ TECLA