What Comes After Code? The Role of Engineers in an AI-Driven Future

Peter Wang—Chief AI Officer at Anaconda and a driving force behind PyData—challenges conventional thinking about AI’s role in software development. As AI reshapes engineering, are we moving beyond writing code to orchestrating intelligence? Peter explores why companies are fixated on models instead of integration, how AI is breaking traditional software workflows, and what this shift means for open source. He also shares insights on the evolving role of engineers, the commoditization of AI models, and the deeper questions we should be asking about the future of software.

1. AI is Eroding the Value of Coding—System Design is the New Engineering**

Writing code is becoming less valuable as AI takes over implementation. What separates engineers now isn’t syntax fluency—it’s the ability to design, integrate, and debug intelligent systems. The bottleneck has shifted from execution to defining the right problems.

2. Software Engineering is Becoming Debugging for Emergent Behavior**

Traditional software follows strict logic, but AI systems introduce uncertainty and unpredictability**. Instead of writing rules, engineers must now diagnose why** an AI system behaves the way it does. Debugging LLM-driven software is less about fixing syntax errors and more about steering complex, probabilistic outputs toward useful results**.

3. AI Models Are Converging—Stop Treating Them as a Moat**

Every major AI company is distilling competitors’ models, making them less of a differentiator. The real advantage isn’t in having a better model—it’s in how well a company integrates AI into its business workflows**. Companies that obsess over model performance while neglecting deployment and data pipelines will lose ground.

4. Stop Expecting Models to Be Correct—Build Workflows for Uncertainty**

Businesses still expect AI systems to behave like traditional software. But hallucinations, inconsistencies, and errors are inherent to generative AI**. Instead of chasing “fixes,” the real challenge is engineering workflows that handle uncertainty—using ranking, retrieval, and human validation where necessary**.

5. You Don’t Need AI Models for Everything—Better Retrieval Often Wins**

Companies overuse generative AI when classic search and structured knowledge retrieval** would be faster, cheaper, and more reliable. BM25, metadata filtering, and structured queries can often outperform a costly LLM call. The future isn’t just about bigger models—but smarter ways to use data**.

You can read the full transcript here.

00:00 Introduction to the Technological Revolution

00:55 Meet Peter Wang -- Chief AI Officer at Anaconda

01:16 Big Questions in AI and Software Development

03:44 Peter's Journey to Chief AI Officer

04:19 The Hype and Reality of AI

06:10 Exploring the Future of Descriptive Analytics

07:02 The Impact of Generative AI on Data Science

10:42 Navigating the Complex AI Landscape

16:26 The Role of Pioneers and Settlers in Business Innovation

28:52 The Importance of Open Source in AI Development

33:13 The Backbone of AI: Open Source Contributions

35:26 The Future of AI: Smaller and More Efficient Models

38:43 The Role of Data in AI Development

39:38 Legal and Ethical Considerations in AI

42:34 Challenges in AI Tooling and Frameworks

57:05 Human-AI Collaboration -- The Cybernetic Approach

01:01:18 Practical Advice for Data Science Leaders

01:05:08 Conclusion and Final Thoughts