The Bitcoin Heuristic: A Mental Model for AI
If you've been in or around Bitcoin for more than one cycle, you know the feeling.










It starts with dismissal. "Magic internet money." "Ponzi scheme." "No intrinsic value."
Then, the shift. You sent a transaction on a Saturday night and it settled instantly. You read The Bitcoin Standard. Or you watched the money printer go brrr and realized the game was rigged.
You fell down the rabbit hole.
You went from Skeptic to Tourist to Student to Maximalist. You stopped looking at the price (Noise) and started looking at the protocol (Signal). You realized "Sound Money" wasn't a marketing slogan - it was an engineering requirement.
If you've spent time in Bitcoin circles, you've seen versions of this journey mapped before - it's almost canonical at this point.
Here is my observation: You are about to do it again.
The journey from "AI Skeptic" to "AI Native" follows the exact same map. The stages are identical. The traps are identical. And the endpoint - finding signal in the noise - is identical.
If you found your way to "tick tock next block," you already have the playbook for this too.
1. The Skeptic
In this stage, we dismiss the tech intellectually. We look at current flaws and assume they are permanent features rather than temporary bugs.
- The Bitcoin Skeptic points to volatility and says "this can't be money."
- The AI Skeptic points to hallucinations and says "this can't be intelligence."
Both are betting against the rate of change.
2. The Tourist
The "Toy Phase." We engage with the tech, but only as a parlor trick.
- The Bitcoin Tourist enters the casino. They don't care about censorship resistance; they care about "number go up."
- The AI Toy User enters the magic show. They don't care about leverage; they care that "the computer can talk."
A lot of people get stuck here. They play with the toy, get bored, and leave.
3. The Speculator
The Peak of Inflated Expectations. We see the potential, but we drown in the noise. We mistake speculation for utility.
- The Bitcoin Speculator gets wrecked trading altcoins, chasing meme coins, and leveraging everything.
- The AI Prompt Engineer gets wrecked building "wrappers" with no moat, chasing AGI hype, and shipping vaporware.
Greed and impatience define this phase. We want the outcome without the work.
4. The Student
The pivot point. You stop consuming noise and start studying signal.
- The Bitcoin Student stops trading and reads The Bullish Case for Bitcoin. The asset stops looking like a stock and starts looking like a truth machine.
- The AI Pragmatist stops browsing "Top 10 Prompts" threads and learns about Context Windows. The tool stops looking like a chatbot and starts looking like a reasoning engine.
You move from "using" the tool to understanding the system.
5. The Native
The endpoint. The Native doesn't "use" the tech - they think through it. Implementation doesn't matter. Mindset does.
- The Bitcoin Maximalist holds their own keys, verifies instead of trusts, and realizes money is just information you can control.
- The AI Native stops "chatting" with the bot, starts orchestrating outcomes, and realizes intelligence is just compute you can orchestrate.
The Tourist asks a question and hopes for a good answer. The Native looks at a problem and sees a system that can be dismantled, reasoned through, and rebuilt.
The First-Mover Advantage
The world is splitting.
A lot of people are stuck in Stage 1 or 2. They dismiss AI as "slop" or play with it as a toy. They are waiting for it to be perfect before they take it seriously.
You don't have that luxury.
If you are a Bitcoiner, you have already trained your brain to:
- Ignore the FUD.
- Look past the volatile hype cycle.
- Study first principles.
- Optimize for leverage.
You don't need to "believe" in AI. You just need to recognize the map. You've walked this path before.
You have the rarest skill in tech right now: The ability to ignore the price and study the protocol.
Don't waste it playing tourist.
Further Reading
- The Bullish Case for Bitcoin - The essential map for understanding the asset's trajectory.
- The Bitter Lesson - Rich Sutton's proof that in AI, leveraging computation always beats human cleverness.
- Software 2.0 - Andrej Karpathy on why AI is a fundamental shift in how we engineer systems.