Why most AI implementations fail: they skip the data layer
AI projects rarely fail at the model. They fail at the inputs. The case for building an organized, permissioned, current data layer before you deploy anything intelligent.
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Notes from the bridge
Weekly writing on data layers, AI operations, and live-verified platform facts. Every technical claim is dated and tested before it is published.
Airtable's docs said 10. The live API said 25. Test everything.
While building an expert Airtable skill, I found that a limit everyone agrees on had silently changed. The docs had not caught up. Neither had the AI models. A case for live verification as a working discipline.
Every sentence was true. The system was still unsafe.
A security section where nothing was false still described three cross-tenant data leaks. The fix was not better facts. It was a reviewer whose job was to attack the text, not read it.