Can LLMs replace structured systems to scale enterprises? Jason Ganz, Senior Manager DX at dbt Labs , joins Simon Maple to unpack why, despite the rapid rise of AI systems, enterprises still rely on structured data for consistency and reliable decision making. They also discuss: the invisible edge cases LLMs can’t see difference between software engineering and data engineering in AI the mismatch between AI output and business logic what the data engineer of the future actually does AI Native Dev , powered by Tessl and our global dev community, is your go-to podcast for solutions in software development in the age of AI. Tune in as we engage with engineers, founders, and open-source innovators to talk all things AI, security, and development. Connect with us here: 1. Jason Ganz (LinkedIn)- https://www.linkedin.com/in/jasnonaz/ 2. Jason Ganz (X)- https://x.com/jasnonaz 3. dbt Labs- https://www.getdbt.com/ 4. dbt Fusion engine- https://www.getdbt.com/product/fusion 5. dbt Community- https://www.getdbt.com/community 6. Simon Maple- https://www.linkedin.com/in/simonmaple/ 7. Tessl- https://www.linkedin.com/company/tesslio/ 8. AI Native Dev- https://www.linkedin.com/showcase/ai-native-dev/ 00:00 Trailer 01:01 Introduction 01:41 dbt Labs 04:39 Data engineers 07:39 LLMs understanding 13:15 AI isn’t as lazy as humans 15:29 Problem: the scaffolding to get data 17:38 Best contextual results 19:40 Dealing with security 25:00 Structured data 27:37 Problems with LLMs and data 29:47 Exact numbers 32:10 Hallucinations 34:28 Human validation 36:20 MCP servers 39:09 UX bottlenecks 42:27 Quality of data 44:00 The future of data engineers 47:02 getdbt.com 48:09 Outro Join the AI Native Dev Community on Discord: https://tessl.co/4ghikjh Ask us questions:
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