Why LLMs Keep Missing This One Thing | Jason Ganz

The AI Native Dev - from Copilot today to AI Native Software Development tomorrow

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

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Fejezetek

1. Trailer (00:00:00)

2. Introduction (00:01:01)

3. dbt Labs (00:01:41)

4. Data engineers (00:04:39)

5. LLMs understanding (00:07:39)

6. AI isn’t as lazy as humans (00:13:15)

7. Problem: the scaffolding to get data (00:15:29)

8. Best contextual results (00:17:38)

9. Dealing with security (00:20:21)

10. Structured data (00:25:41)

11. Problems with LLMs and data (00:28:18)

12. Exact numbers (00:30:28)

13. Hallucinations (00:32:51)

14. Human validation (00:35:09)

15. MCP servers (00:37:01)

16. UX bottlenecks (00:39:50)

17. Quality of data (00:43:08)

18. The future of data engineers (00:44:41)

19. getdbt.com (00:47:43)

20. Outro (00:48:50)