Why the confusion isn't your fault — and what's actually happening when you interact with AI
If you've been using AI for a while, you probably recognize this feeling.
You use ChatGPT for writing. Claude for analysis. Maybe a coding assistant or two. You've learned some things that work, some that don't, and you've developed instincts you can't quite explain.
Sometimes the results are startlingly good. Sometimes they're confidently wrong. Sometimes restarting a conversation magically fixes everything — and you're not entirely sure why. You're not imagining it. And it's not your fault.
Most people are using AI every day without a clear model of what they're actually doing when they interact with it. We're all improvising — often mistaking the tool for something it isn't.
That gap is why I wrote a book.
The Problem No One Is Really Solving
Nearly every AI book I've read falls into one of a few predictable camps:
- "AI will change everything / take your job / end the world." Big predictions, little practical help.
- "Here are 47 prompt templates you can copy-paste." Tricks without understanding — useful until they suddenly aren't.
- "Let me explain transformers and backpropagation." Technically accurate, practically inaccessible for most people who just want to use the tool well.
None of these answer the question people actually run into once AI becomes part of daily work: What is actually happening when I talk to this thing — and how do I use it well without fooling myself?
So people fill in the gaps themselves.
- They treat AI like a search engine (it isn't).
- They treat it like a person (it isn't that either).
- They trust confident answers and distrust hedged ones (often backwards).
- They blame themselves when it fails (usually the wrong diagnosis).
The result: frustration, wasted effort, hallucinated facts accepted as truth, and real capabilities left unused.
Why I Wrote Recursionship
I've been working with modern language models since the early GPT-3 era — not just casually, but by watching how they behave under different constraints, prompts, and conversational dynamics.
What I kept noticing wasn't "better prompts." It was better mental models.
The people who got the most out of AI weren't the ones with clever tricks. They were the ones who understood — at a mechanical level — what these systems are actually doing, and just as importantly, what they are not doing.
Once that clicks, a lot of confusing behavior suddenly makes sense:
- why phrasing matters so much
- why conversations drift
- why confidence isn't reliability
- why metaphor works so well
- why restarting a chat isn't rude — it's functional
That understanding changes how you interact with these systems. You stop fighting them. You stop over-trusting them. You stop blaming yourself for structural failure modes.
This book exists to give people that understanding.
What “Recursionship” Means
Recursionship names a mode of interaction most of us are already experiencing, even if we don't have language for it yet.
When you interact with an AI system:
- your input shapes its output
- its output reshapes your next input
- meaning emerges through that loop
Not as a conversation with a persistent entity. Not as a simple tool invocation.
But as collaborative navigation through a space of compressed human meaning.
You're not retrieving answers from a database. You're not talking to a mind. You're steering a trajectory through semantic space — and the "conversation" is the path that emerges while you do it. Once you understand that, a lot of things stop being mysterious and start being usable.
What This Book Is
Recursionship: A Field Guide to Living With AI is exactly what the title says: a field guide.
It's written for people who already use AI — at work, in creative projects, for learning, or for problem-solving — and want clearer expectations and better judgment.
Inside, I cover:
- what these systems actually are (in a way that permanently fixes your mental model)
- why language quality steers outcomes
- practical methods with real examples, not prompt folklore
- common failure modes that quietly waste time or distort thinking
- where responsibility and human judgment must remain in the loop
It doesn't assume you're a beginner. It doesn't assume you're a developer.
It assumes you're already living with AI — and want to do it with your eyes open.
What This Book Is Not
This is not:
- a hype piece
- a prompt cheat sheet
- a manifesto
- a doomsday warning
- an argument that AI "understands" us
It won't help you outsource thinking or responsibility. In fact, it pushes firmly against that instinct.
The point isn't to replace human judgment. It's to work with these systems in a way that preserves it.
Why I’m Sharing This Now
AI isn't "coming." It's already embedded in daily life — in writing, research, decision-making, and cognition itself. The missing piece right now isn't capability.
It's literacy.
Not technical literacy — but cognitive and relational literacy: understanding how these systems shape thought, and how thought pushes back.
That's what this book is for.
If you want to read it, it's available now on Amazon in paperback and Kindle. If not, I still hope this framing helps you use these tools with clearer expectations, stronger judgment, and fewer illusions.
Either way, we're already living with AI.
Recursionship: A Field Guide to Living With AI is my first book, and it would mean a lot to me if you checked it out, read the preview, and shared your thoughts.