Data friction protects industrial AI incumbents
The guest argued that proprietary data silos and highly specialized domain requirements create a powerful moat for incumbent industrial technology providers against generic AI disruptors.
The argument
Because industrial operational data is kept within proprietary systems rather than the public internet, generic tech companies cannot easily train their models on it. Additionally, the highly distinct operational needs of different sectors—such as refineries versus data centers—require deep domain expertise that horizontal software providers lack.
The thesis, stress-tested
✓ What validates it
- ✓Incumbents successfully monetizing proprietary datasets through exclusive AI partnerships
- ✓Sustained market share retention by industrial incumbents in automation software
▸ Risks discussed
- ▸Potential breakthroughs in synthetic data generation that bypass data friction
- ▸Tech giants developing highly customized vertical solutions
Hear it yourself
"You've been there so long since since the nineteen eighties. Mhmm. I'm curious. How has the culture of Honeywell changed? It's almost forty years, three and a half decades. Is it still essentially the same company, or has everything changed like so many other companies? Yeah. I think it it evolved a lot. I would say, you know, we there was a big change moment in early two thousand when Honeywell and Allied Signal merged together. I recall. Yep. So, little bit of, fun fact, Alight Signal acquired Honeywell and changed its name to Honeywell. Mhmm."
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