why 99% of AI tutorials sell you a lie (mine did too)

This video is an honest reflection by an AI practitioner on hype versus reality in AI applications. The creator confesses to past overstatements, explains why industry hype exists, outlines a 90/10 rule (most claims are marginal), and proposes a practical employee analogy for deploying AI. He closes with concrete examples from running a B2B agency and advice to raise expectations about the work required to make AI production-ready.

– Why hype exists: Big labs cherry-pick benchmarks and startups/creators chase funding and views, producing inflated claims and sensational demos.
– 90% vs 10%: Most announced improvements are marginal; only a minority deliver transformative value. Use three deep domain-specific questions to evaluate new models.
– Employee analogy: Treat AI as a new hire — it needs context, training, iterative feedback and time before it reliably produces value in production.
– Practical agency experience: The speaker describes a B2B content pipeline that is heavily automated but required months of tuning, daily reviews and active maintenance to reach production quality.

Quotes:

There’s a huge gap between a shiny demo and something that works in production.

99% of AI-generated content gets zero views.

Treat AI like an employee: train it, give context, and iterate.