Ian Thomas shares a case study on embracing AI-native engineering within Meta’s Reality Labs. He explains the "Assess and Grow" framework, a maturity model designed to move teams from manual toil to AI-integrated innovation. He discusses real-world wins - including hitting 90% code coverage in record time - while addressing senior concerns like "code slop," review fatigue, and maintaining quality. By Ian Thomas
The rapid acceleration of AI capabilities and tool integration in software development is forcing companies to adapt their engineering practices to remain competitive.
This presentation highlights a practical, successful framework for integrating AI into core engineering workflows, offering a blueprint for other organizations seeking efficiency and quality gains.
Traditional software development teams are evolving into 'AI-native' structures, leveraging AI for tasks like code generation and testing, fundamentally altering development cycles and output.
- · Meta's Reality Labs
- · Companies adopting AI-native engineering
- · AI tool developers
- · Software engineers embracing AI
- · Companies resistant to AI integration
- · Manual code reviewers
- · Outdated software development methodologies
Increased engineering efficiency and code quality through AI-augmented development.
A new competitive landscape among companies based on their ability to leverage AI in product development.
The definition of 'software engineer' will further evolve, focusing more on AI orchestration and less on rote coding.
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