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.

Engineering Productivity: Cutting Through the AI Noise
#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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