Leadership in AI-Assisted Engineering

Time: 4:40 PM

Speaker Bio: Deputy CTO of DX. 20+ years in software roles. Author of “DX’s Guide to AI Assisted Engineering.” Platform engineering specialist.

Speaker Profile: Full Speaker Profile

Company: DX (getdx.com) helps organizations optimize developer productivity and AI adoption.

Focus: Leadership strategies for AI-enabled engineering orgs. How to establish best practices, guardrails, and psychological safety when using AI tools. Top-down enablement matters.

References:

• YouTube Link
• QCon Speakers Page
• https://getdx.com/research/measuring-ai-code-assistants-and-agents/
• https://getdx.com/guide/ai-assisted-engineering/

Notes

• What is their PDF?
  • https://getdx.com/research/measuring-ai-code-assistants-and-agents/
  • https://getdx.com/guide/ai-assisted-engineering/
• GenAI is not evenly distributed
• Integrate across the SDLC
• Open metrics
• Compliant and trust
• Unblocking usage
• Reducing fear of AI
• Employee success
• Google Project Aristotle
  • Overwhelming predictor was psychological safety
• Explain the why of everything and increase trust
• Treat software development as a systems problem, not a people’s problem
• What makes a bad developer day — from Microsoft
• DX AI Measurement Framework
  • Utilization
  • Impact
  • Cost
  • Like a maturity curve
• What about compliance and trust?
• Setting up a feedback loop for a system prompt
• Where should we have more determinism or non-determinism
  • For temp never use 0 or 1, use e.g. 0.00001
• How do we tie this to employee success
  • Guide to AI assisted engineering
• Find the bottleneck, fix the bottleneck
  • Read the code, get the specs, gather additional context, and generate development specs
• Zapier unblocks devs and as a result they are hiring more people
• Spotify - uses agents to diagnose and help SRE

Slides

Slide: 2025-11-20-17-08

Key Point: Bold predictions about the transformative impact of full AI adoption in engineering - suggesting dramatic productivity multipliers, individual capability enhancement, and compounding benefits that are not yet widely understood even in tech hubs like San Francisco.

Literal Content:

• Dark slide with white text listing 5 numbered points:
  1. 10x difference when you hit 100% AI adoption.
  2. A single engineer should be able to build and maintain a complex, production product.
  3. Compounding engineering makes each feature easier to build.
  4. There are many non-obvious second-order effects once you adopt it.
  5. Many people in SF don’t know this yet.