AI Visibility Podcast with Jason T Wade of BackTier18 Sep

I Tried to Explain AI. I Got It Wrong. So I Learned How It Actually Works.

What actually happens inside AI?

After asking a podcast guest to explain AI—and then realizing my own explanation wasn't quite right—I went back to the basics.

In this short episode, I break down AI in plain English: training data, data preparation, model weights, prediction, error, and how a trained model generates an answer from a prompt.

I also look at where concepts like ontologies, relationships, probabilistic outputs, and modern AI search fit—and where they don't.

No Stanford degree required. I do, however, own the shirt.

Why saying “AI is data” doesn't tell the whole story
How training data is cleaned and prepared
What model weights actually are
Prediction → error → weight adjustment → repeat
How models learn statistical patterns at scale
Training versus inference
What an ontology actually describes
Why LLMs are probabilistic
How AI search differs from traditional search
Why modern systems can understand much longer, messier questions

Jason T Wade is the founder of BackTier and NinjaAI and host of the AI Visibility Podcast. His work focuses on AI Visibility—how AI systems discover, understand, cite, include, and recommend entities.

BackTier — AI Visibility strategy and systems
NinjaAI — AI SEO, GEO, and AEO
OpenAI — AI research and models
Stanford HAI — Stanford Institute for Human-Centered Artificial Intelligence

In this episodeAbout Jason T WadeRelevant Links

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