Preparing your Pipeline for the AI Revolution
āAI codes instantly, flawlessly!ā š¤©
āAI breaks everything, obviously!ā š¤
Whether you're an AI optimist, a pessimist, or somewhere in-between, there's no getting around it: if you aren't shipping AI-generated code yet, you will be. Are you and your team prepared for it?
The traditional code review process wasn't designed for machine-generated code. When AI can produce hundreds of lines in seconds, human reviewers become the bottleneckāand more critically, they miss things. AI generates seemingly perfect code that can harbor subtle business logic errors, security vulnerabilities, or integration issues that only surface under real conditions. No amount of peer review is sufficient for AI-generated code.
The solution? Test everything before it merges.
But here's the thing most teams get wrong: they're optimizing their testing for human development speed, not AI development velocity. When AI can generate features in minutes, your testing strategy needs to match that paceāor risk becoming the new bottleneck. In this session, we'll explore how to build testing pipelines that scale with AI, and why your deployment strategy might determine whether AI becomes your superpower or your liability.
This talk is ideal for CTOs, engineering managers, and DevOps leaders who are evaluating or implementing AI development tools and need to evolve their testing and deployment processes to match the speed of machine generated code. If you're responsible for maintaining code quality while your team adopts AI-assisted development, this session will give you the strategic framework to scale safely.
āAI breaks everything, obviously!ā š¤
Whether you're an AI optimist, a pessimist, or somewhere in-between, there's no getting around it: if you aren't shipping AI-generated code yet, you will be. Are you and your team prepared for it?
The traditional code review process wasn't designed for machine-generated code. When AI can produce hundreds of lines in seconds, human reviewers become the bottleneckāand more critically, they miss things. AI generates seemingly perfect code that can harbor subtle business logic errors, security vulnerabilities, or integration issues that only surface under real conditions. No amount of peer review is sufficient for AI-generated code.
The solution? Test everything before it merges.
But here's the thing most teams get wrong: they're optimizing their testing for human development speed, not AI development velocity. When AI can generate features in minutes, your testing strategy needs to match that paceāor risk becoming the new bottleneck. In this session, we'll explore how to build testing pipelines that scale with AI, and why your deployment strategy might determine whether AI becomes your superpower or your liability.
This talk is ideal for CTOs, engineering managers, and DevOps leaders who are evaluating or implementing AI development tools and need to evolve their testing and deployment processes to match the speed of machine generated code. If you're responsible for maintaining code quality while your team adopts AI-assisted development, this session will give you the strategic framework to scale safely.