How generative AI is transforming developer workflows at Amazon

Introduction

Software engineering stands at an inflection point. While previous technological shifts enhanced what developers could build, AI is fundamentally changing how we build. Amazon Q has driven a shift in how developers at Amazon approach software development. At re:Invent 2024, our breakout session Unleashing generative AI: Amazon’s journey with Amazon Q Developer (DOP214) shared insights from Amazon’s internal journey that reveal not just tactical benefits, but our strategic reimagining of the software development process itself. In the months since our talk, the capabilities of this technology have only accelerated in sophistication and reliability. Our hope is that these learnings can inform the approaches organizations are taking to adopt AI, through guidance on scaling implementation, measuring impact and considerations around meaningful data, and best practices every step of the way. For all of us, this is just the beginning of what is looking to be a very exciting journey as AI becomes not only an active assistant in our day-to-day innovation, but starts to become a peer and companion as AI agents take on full tasks.

Rethinking our mental models: The Productivity Paradox

For too long, we’ve approached software development optimization through the lens of industrial-age thinking – treating code like widgets on an assembly line. This mental model has led organizations to chase simplified metrics like lines of code or story points, missing the true nature of software development as knowledge work.

Our experience at Amazon has shown that the real opportunity isn’t just about making current processes faster – it’s about changing how developers interact with code, documentation, and knowledge. In the 2024 Stack Overflow Developer Survey, 53% of respondents agreed that waiting on answers disrupts their workflow, even if they know where to go find those answers. Similarly, 30% of respondents said knowledge silos impact their productivity ten times or more per week.

In 2024, to solve this problem for Amazon developers, we ingested our internal Amazon knowledge repository consisting of millions of documents into Amazon Q Business so our developers could get answers based on information spread across those repositories. By simply asking Amazon Q their questions in existing tooling instead of manually searching or needing to ask an expert, we reduced the time Amazon developers spent waiting for technical answers by over 450k hours and reduced the interruptions to “flow state” of existing team members.

Today’s developers face a striking paradox: while they’re equipped with more powerful tools than ever, we know that across the industry, developers can spend only 1-2 hours daily writing code. The rest is consumed by what we call “toil” – necessary but undifferentiated work that scales linearly with software complexity. This represents not just a productivity challenge, but a strategic liability for organizations trying to accelerate innovation.

Amazon’s scale has provided unique insights into the transformative potential of AI in software engineering. Using AI for software transformations tied tightly into our internal development tools, we didn’t just save 4,500 developer years of effort – we unlocked new possibilities for large-scale technical modernization that previously seemed impractical. This experience revealed something profound: AI isn’t just making existing processes more efficient; it’s making previously impossible tasks feasible. For instance, our ability to automatically handle complex dependency updates across thousands of services has fundamentally changed how we think about technical debt and system modernization. Over the coming years as these AI agents become increasingly capable and autonomous, we will get increasingly bold with the types of modernization work we ask them to perform, ensuring reliability by complementing them with other agentic capabilities such as correctness validation, testing, and even advanced anomaly detection and production system operations.

The evolution of developer cognition

Perhaps the most fascinating insight from our journey has been observing how AI is changing the way developers think about and solve problems. The traditional model of a developer working in isolation, relying solely on their own knowledge and documentation, is evolving into a more collaborative model where AI serves as an intelligent thinking partner. We’ve seen this manifest in unexpected ways. Developers report that having AI tools available changes not just how they write code, but how they approach problem-solving itself. The ability to rapidly explore multiple approaches or instantly access contextual knowledge has enabled more creative and experimental development practices.

In particular, we are seeing seasoned developers playing with new-to-them programming languages and coding techniques that previously would not have been worthwhile due to ramp time. One developer reported cutting their typical three-week ramp-up time for learning a new programming language down to just one week using Q Developer. This significant reduction in a developer’s initial time investment allows creativity with more suitable programming languages for nuanced project requirements, or jumping into work with unfamiliar and complex systems, without sacrificing code quality. For example, with our recently launched Amazon Q Developer Agentic CLI, another internal developer was able to work with an unfamiliar codebase to build and implement a non-trivial feature within 2 days using Rust, a programming language they didn’t know, stating, “If I’d done this ‘the old fashioned way,’ I would estimate it would have taken me 5-6 weeks due to language and codebase ramp up time. More realistically, I wouldn’t have done it at all, because I don’t have that kind of time to devote.”

As we look to the future, we see several emerging frontiers that will further transform software engineering. The rise of AI agents capable of handling increasingly complex development tasks is shifting the developer’s role from implementation to orchestration. We’re moving toward a model where developers spend more time defining what needs to be built and validating approaches, rather than handling every implementation detail. System architecture, traditionally considered too nuanced for automation, is emerging as a new frontier for AI assistance. Application security reviews or software testing, frequent bottlenecks to software release due to specialist bandwidth, will increasingly be accelerated by AI agents amplifying the capacity of those specialists. While we’re just beginning to explore this space, early experiments suggest AI could help architects evaluate trade-offs and identify potential issues in complex systems more effectively than ever before.

Strategic implications for organizations

We believe the most successful organizations will be those that view AI not just as a tool for automation, but as a catalyst for transforming how they approach software development entirely. The real strategic advantage will come from reimagining software development processes and culture to fully leverage AI’s capabilities. This includes rethinking traditional metrics, redefining developer productivity, and creating space and cultural change for teams to experiment with new ways of working. Amazon Q represents a new class of development tools that augment developer capabilities in fundamental ways, beyond just writing code more efficiently. Organizations that understand and embrace this transformation will be best positioned to lead in the next era of software innovation.

To learn more about Amazon Q Developer and explore innovative ways of accelerating software development refer to the Q Developer documentation. Individual users can get started with Q Developer in the AWS Console, CLI, or in their IDE on the perpetual Free Tier. And remember: give yourself and your team “Permission to Play!” We’re at the heart of a technological revolution; as technologists this a moment where we get to immerse ourselves in the unknown and learn and be curious!