Himanshu Vasishth, Mindaugas Mozūras, Jackie Brosamer & Lukas Pfeiffer
Engineering Productivity: Cutting Through the AI Noise
Your engineers' distrust of AI-generated code is often valid. Learn how to navigate the trade-offs between speed and complexity for real productivity gains.
#1about 2 minutes
Favorite AI development tools at leading tech companies
Leaders from Block, Vinted, and Grammarly share their most used AI tools, including Goose, Cursor, GitHub Copilot, and Claude.
#2about 4 minutes
Finding real productivity gains beyond the AI hype
AI tools show significant value in specific use cases like prototyping and greenfield projects, but engineers remain skeptical of AI-generated code quality.
#3about 4 minutes
How to measure developer productivity in the AI era
Focus on team-level metrics and qualitative insights rather than individual performance, as tools like GetDX show AI users create more complex pull requests.
#4about 4 minutes
Fostering a culture of AI adoption and experimentation
Encourage AI adoption through bottom-up approaches like weekly demos, dedicated experimentation time like 'AI Fridays', and hack weeks instead of top-down mandates.
#5about 3 minutes
The evolution from prompt engineering to context engineering
The role of the engineer is shifting from writing prompts to designing systems that provide rich context, turning developers into architects and reviewers of AI-generated work.
#6about 4 minutes
Key initiatives for boosting engineering productivity
Drive productivity by open-sourcing internal tools, deeply integrating AI into enterprise systems, and providing clear leadership guidance on tool usage.
#7about 3 minutes
Essential advice for developers in the age of AI
Engineers should adopt a low-ego, beginner's mindset to navigate rapid technological changes, while leaders must listen to on-the-ground feedback.
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