I sit down with Fabien Cros, who runs the AI practice at Ducker Carlisle, to talk about how he is seeing decentralized AI models inside organizations work much better than a top-down, centralized approach. He has found the key is giving employees a platform to build their own agents, letting the best use cases bubble up, and using a central engineering team for creating resiliency and managing risk.
We get into why companies keep confusing researchers with engineers, why cheap deterministic code often beats an expensive frontier-model call, and why 200 people experimenting daily will always out-build 5 experts.
Enjoy.
Recording date: June 9, 2026
Chapters
00:00 Introduction
04:44 Who Is Ducker Carlisle?
06:13 What Is Decentralized AI?
09:58 Example Of Centralized vs Decentralized AI
14:46 How The Model Works Across Multiple Clients
17:18 The AI Talent Gap
24:07 Infuse With AI or Start From Scratch?
28:30 How Ducker Carlisle Works With Clients
32:31 Hiring Interns to Build AI Systems Doesn’t Work
38:58 Training Employees To Build With AI
43:50 Fabien's Background
45:54 Future Of Consulting
49:31 Choosing The Best LLMs
54:19 US vs European Market
57:57 Decentralization Is Up To Us
01:01:31 How Do You Incentivize Internal Builders?
01:03:21 Rapid Fire
Connect with Jordan:
* Follow Jordan on X: https://x.com/jrwolfe
Connect with Fabien Cros:
* Ducker Carlisle: https://www.duckercarlisle.com/
* LinkedIn: https://www.linkedin.com/in/fabien-cros/
Remember to go direct!
Jordan
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