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Discover insights from analyzing workflow data across 200,000 engineers and 20 million pull requests from a thousand companies to understand the real impact of AI coding tools on engineering productivity. Learn about significant productivity gains observed at scale, including 2x increases in pull request throughput and 24% faster cycle times on average, while exploring what effective AI adoption looks like in practice and why autonomous agents aren't yet delivering on their promise. Examine the critical role of code architecture as an often-overlooked factor in AI tool effectiveness, understand what productivity gains are realistically possible, identify potential side effects of AI tool adoption, and discover why some engineering teams fail to see meaningful improvements despite significant investments in AI coding tools.
