Engineering Judgment in the Age of AI: What becomes more valuable when code generation becomes abundant?

Wednesday, July 15 | 5:00–6:00 PM ET / 2:00–3:00 PM PT | Online

AI has quickly moved from experimentation to expectation. For many engineering teams, the question is no longer whether AI can help write code faster. It is what happens when code itself becomes easier to produce.

The real challenge begins when output accelerates: when more ideas can be prototyped, more changes can be shipped, and more code can enter the system faster than before.

For engineering leaders, the hard part is not just helping teams move faster. It is helping them make better calls as the cost of creating more code drops.

We’ll discuss

1. What becomes more valuable when code gets easier to generate: Where engineering judgment, product sense, technical taste, architecture, and restraint matter most.
2. Choosing what is worth building: How leaders help teams decide where to apply AI, where to slow down, and where more code may not mean more progress.
3. Quality when output accelerates: What risks emerge when teams can generate and ship more code faster, and how leaders maintain reliability, maintainability, and system health.
4. Review and decision-making in an AI-assisted workflow: How planning, design reviews, code reviews, and technical decisions need to evolve when AI becomes part of everyday engineering work.

Hosted by

Ken Pickering, CTO @ Scripta Insights

Ken Pickering is CTO at Scripta Insights, where he applies AI and large-scale data systems to bring transparency to pharmacy pricing, one of the most complex and opaque areas in U.S. healthcare.

With over 20 years of experience leading engineering through platform rebuilds, hypergrowth, and market shifts, Ken has held leadership roles at Hopper, Starburst, Going, and Rue La La. His work spans predictive pricing, distributed data systems, experimentation platforms, and real-time personalization.

Join this conversation with engineering leaders working through one of the most important questions in AI adoption today:

When code becomes easier to generate, what forms of engineering judgment become more valuable than ever?