What I Learned About AI Trust from Reconciling over 100B Transactions
An industry operator discusses the challenges of defining 'trust' in AI systems, using the example of how inconsistent data metrics can lead to misleading business insights. The author argues that transparency in system architecture is essential for genuine AI accountability.
Why it matters
It highlights the gap between high-level AI governance discussions and the practical, messy reality of data engineering and system metrics.
Over a month ago, I sat on a panel at AI Everything MEA in Cairo , discussing trust, transparency, and accountability in AI with two investors and one of the sharpest tech journalists in the business. I was the only operator on stage, the person who builds the systems rather than evaluating them for their investment opportunities and viability.
I want to discuss what AI trust actually looks like from the inside.
The moderator, Mike Butcher, asked a version of a question I hear constantly: What should investors look for when evaluating AI companies? The panel of investors -Yehia Houry from Flat6Labs and Abdelrahman Hassan from Enza Capital-offered thoughtful insights on governance frameworks and founder credibility.
Then it was my turn. And I decided to start with a story about SMS charges and their impact on computing Monthly Active Users (MAU).
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