Engineering Bitcoin Conviction

A product manager's deep dive into building a predictive model for Bitcoin adoption. For premium members & research participants, here's the full research stack.

Bitcoin’s Core Problem Isn’t complexity, It’s Product Management

Why is explaining Bitcoin to intelligent people so impossibly hard?

We’ve all been there. You pour your heart out, explaining the future you see so clearly—the freedom, the potential, the necessity... Only to be met with that familiar, polite smile. The slight head tilt. And the blank stare that screams, “I have no idea what you’re talking about, and frankly, I think you’re crazy.”

It’s isolating. It’s frustrating. And after years of this, it can feel hopeless.

For years, the common wisdom was that this is a marketing problem. We just need catchier slogans or simpler explanations. Or maybe it’s a technical problem. We just need to simplify the UI.

They’re both wrong.

Bitcoin’s single biggest barrier to mass adoption is a product management problem.

We are hoping that the most revolutionary “product” of all time... Gets adopted by the next billion users navigating the chaos through hundreds of random YouTube videos, podcasts, and books, just like the pioneers did.

That’s not a strategy; it’s wishful thinking.

The strategy that worked to get us here is not the same strategy that will work for the next wave of growth. We aren’t just failing to market the product; we are failing to meet the user where they are and onboard them with an ultra-personalized approach.

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A $52B TradFi PM’s Obsession with Bitcoin’s User Experience

My name is Miguel Abascal, and I’m a product manager.

Before I dedicated my life to Bitcoin, I spent a decade in TradFi. My job was managing $52B+ savings portfolios across two of Canada’s top banks. My entire world was a relentless obsession with three things: growth, user journeys, and friction removal.

When I found Bitcoin, I saw two things almost instantly.

First, the most important “product” ever created for humanity. Second, the single worst user experience in modern history.

I am now applying my entire TradFi PM playbook to solve Bitcoin’s most critical challenge.

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Why a Map is the Key to Ultra-Personalized Onboarding

Without a map, we are guessing. We’re hoping. We’re throwing random information at new users and wondering why it doesn’t stick.

This is why we are building the “Waze for Bitcoin Learning.”

We are building a data-driven, predictive model that can guide anyone from a state of Confusion to a state of Conviction in the most efficient way possible.

To build this map, you don’t rely on one person’s anecdotal path. You reverse-engineer the journey. You must apply a systematic, PM-level approach to deconstruct the cause-and-effect of conviction from the pioneers who have already made the trip.

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The 7 Frameworks We’re Using to Build the Map

This is why we launched our research project.

It’s not just a survey. It’s a full-scale qualitative and quantitative analysis built on the same models I used in banking. We are decoding the cause-and-effect of conviction.

We’re running hundreds of in-depth interviews through a multi-layered analytical stack. We are systematically analyzing every story through seven distinct lenses:

1. Jobs to be Done (JTBD): To find the core ‘why’ a person ‘hires’ Bitcoin.

2. Customer Forces Canvas: To map the psychological push, pull, and friction of the switch.

3. Stages of Change: To plot the user’s internal psychological timeline from dismissal to conviction.

4. Fogg Behavior Model: To isolate the exact ‘prompt’ that triggers the first tangible action.

5. Diffusion of Innovations: To understand the social risks and trust signals required for the mainstream.

6. Education Design Lens: To identify what actually makes complex ideas simple and actionable.

7. Narrative & Framing Lens: To deconstruct the ‘Hero’s Journey’ of adoption.

This is the “how.” This is the blueprint for engineering bitcoin conviction.

For our premium members and research participants, I am opening up the hood on the entire research methodology.

Below, you will find each framework, why we chose it, how they stack together, and how we are using them to build the predictive model that will become the Art of Orange Pilling playbook.

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