In this "Summer Catch-Up" episode of "How Many CTOs Does It Take?" podcast, hosts Scott Porad and Brad Hefta-Gaub swap stories from the field, covering everything from data pipeline migrations to the ongoing debate over centralized versus distributed data teams. They explore the critical importance of clear data definitions, the promise (and pitfalls) of data lakes, and how organizational structure impacts collaboration.

The conversation shifts into the fast-evolving world of AI in software engineering, with real-world examples of how AI tools are accelerating problem-solving, uncovering hidden performance issues, and boosting productivity across teams. Scott and Brad also reflect on the role of engineering leaders in helping their teams adapt to these tools, not just to deliver value faster, but to future-proof their careers in a rapidly changing tech landscape.

• 00:00 Introduction and Welcome
• 00:47 Company Updates and Data Pipeline Migration
• 01:12 Coldplay CEO Controversy
• 03:30 Debate: Distributed vs Centralized Data Teams
• 06:58 Data Definitions and Trust Issues
• 19:09 AI in Engineering and Toolset Discussion
• 25:38 Exploring AI in Collaborative Debugging
• 25:59 Defining Rubber Ducking and Whiteboarding
• 27:37 AI's Role in Codebase Analysis
• 29:58 AI Tools and Their Impact on Productivity
• 37:01 Security Concerns and Tool Selection
• 41:31 Encouraging AI Adoption in Teams
• 50:04 The Future of Software Engineering with AI
• 52:10 Conclusion and Final Thoughts

Resources:

• How Many CTOs Pod: https://howmanyctospod.com
• Scott Porad: https://www.linkedin.com/in/scottporad/
• Brad Hefta-Gaub: https://www.linkedin.com/in/bradheftagaub/

#TechPodcast #EngineeringPodcast #DevTalks #PodcastForDevs #HowManyCtos #Podcast #Ctos #CtoPodcast #Technology #Engineering #SoftwareDevelopment #Entrepreneurship #DataEngineering #DataLakes #AIProductivity #EngineeringLeadership #AIInTech #CentralizedVsDistributed #FutureOfEngineering #TechLeadership #SoftwareEngineering #MachineLearning #DataScience #AIWins