AI and Engineering Closing the Gap Between Expectations and Reality 1
summary
Surveying 100,000 engineers finds 27% save 10+ hours/week and 54% save 5+ hours, yet case studies split: some individual authors report 5x speedups and EveryY reports 90% of code written by AI, while industry studies (DORA, Matter) show increased AI use can correlate with worse delivery and up to 20% slower outcomes; practical fixes are concrete: enforce static types, strong linting, tests, local context engineering, embed tooling (CodeScene, Playwright, MCP servers) and shift quality checks left.
AI-written summary · about
chapters (9)
- 0:00Introduction and welcome
- 1:14Why discuss AI today
- 3:21AI productivity survey results
- 7:13Mental models for AI adoption
- 9:19Good engineering for AI
- 15:05AI and code quality
- 17:55Beyond coding with AI
- 23:36Reflection on AI's role
- 25:22Q&A session
related talks
18mHumans + AI: The Future of Software Development Teams
27mEngineering in the Age of AI: Making AI Work for Us, Not Replace Us
22mThe AI Productivity Paradox in Engineering
26mBeyond the code: Leading engineering teams in the GenAI era
38mFrom code to culture: How AI is reshaping the engineering organization
39mAI-assisted development: Common pitfalls and smarter practices
37mAI-assisted development: Common pitfalls and smarter practices
44mSpeed Is Easy, Trust Is Hard: AI's Real Impact on Developer Productivity