Tech Council
Tech Council
Podcast Description
Are you a tech leader, architect, or engineer navigating the intricacies of building within the enterprise? Tech Council delivers the strategies and insights you need to succeed. Hosted by Duncan Mapes and Jason Ehmke, experienced leaders from the startup and banking tech arenas, this podcast dives deep into technology strategy and enterprise dynamics. Learn how to drive innovation, understand the bigger picture, and build impactful solutions from the ground up. Subscribe to Tech Council and gain the knowledge to shape the future of your enterprise, no matter your role.
Podcast Insights
Content Themes
The podcast tackles a variety of topics relevant to enterprise technology, including software project management, the impact of AI on software engineering, understanding organizational cycles, and value creation in tech contexts. For example, Episode 06 discusses why software projects fail and practical steps to correct their course, while Episode 05 delves into the ethical implications of AI in development.
Are you a tech leader, architect, or engineer navigating the intricacies of building within the enterprise? Tech Council delivers the strategies and insights you need to succeed. Hosted by Duncan Mapes and Jason Ehmke, experienced leaders from the startup and banking tech arenas, this podcast dives deep into technology strategy and enterprise dynamics. Learn how to drive innovation, understand the bigger picture, and build impactful solutions from the ground up. Subscribe to Tech Council and gain the knowledge to shape the future of your enterprise, no matter your role.
Everyone says AI is making software development easier.
That’s only partially true.
While AI accelerates the act of writing code, it also amplifies existing problems. Poor inputs produce poor outputs faster. Weak specifications lead to larger-scale inefficiencies. And teams that prioritize speed without clarity often end up with more rework, not less.
This episode challenges the idea that AI is purely a productivity multiplier. Instead, it explores how AI is exposing deeper issues in how software is planned, validated, and maintained.
AI is unlocking new capabilities, but it’s also introducing new complexity that teams must learn to manage.
Top Takeaways:
- Garry Tan's public statements about his coding activity and the backlash he received for the perceived quality of his code
- Relevance of lines of code as a metric for productivity, especially in the context of AI and automation
- Shifting focus from code quantity to business outcomes and customer acquisition
- How coding practices have evolved with more capable browsers and computers, reducing the need for extreme optimization
- Importance of aligning engineering efforts with business goals and outcomes
- Trend towards open-source models and the challenges of maintaining proprietary advantages
- Cost of AI models and the economic implications of using different models
- Strategies for managing technical debt through automation and the balance between shipping quickly and maintaining quality
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