Leaders may ask for “radical candor” about AI. But the real test is whether they can handle an answer that challenges the strategy.
The biggest AI mistake founders make isn’t choosing the wrong tool. It’s spending so much time choosing that they never build a system around one.
We’re very good at measuring who follows the system. We’re much worse at measuring who notices when the system is wrong.
AI can accelerate your career. But it can’t decide where you’re going, build your reputation, or make you valuable.
Sometimes the biggest barrier isn’t a lack of ability. It’s the belief that you don’t have any.
AI can build models, write code, and generate insights. But it still needs humans to deploy it.
Some founders call their NVIDIA bill a moat. Investors call it an expense.
Your model performs perfectly in testing. Then production data arrives and everything breaks.
You remove one synthetic key. Qlik Sense creates another. The cycle never ends.
Stakeholders think it's one small change. Power BI developers know it's never just one small change.
Companies complain about a talent shortage while their AI filters are rejecting qualified candidates before a human even sees them.
AI isn’t replacing data scientists. It’s changing what makes a data scientist valuable.
