The AI Price Collapse Is the Best Case for Bitcoin You’ve Never Heard

Chain of Thoughts — Side Episode

GPT-4 cost $30 per million tokens in 2023. Today it’s $0.25. That 120x price drop is the most underrated macro argument for a fixed-supply asset.

When was the last time a technology got 120 times cheaper in three years — and nobody called it a crisis?

That is exactly what has happened to AI compute. And if you follow the logic of what deflation does to economies, and what governments do when economies deflate, you arrive at a conclusion most crypto analysts aren’t talking about: the AI price collapse may be the strongest structural argument for Bitcoin that has appeared in a decade.

Photo by Igor Omilaev on Unsplash

The Setup

Here is the price table for GPT-class AI:

Model Year Cost per 1M tokens
──────────────────────────────────────────────
GPT-4 (launch) 2023 Q1 $30.00
GPT-4 Turbo 2023 Q4 $10.00
GPT-4o 2024 Q2 $5.00
GPT-4o mini 2024 Q3 $0.15
GPT-4.1 2025 Q2 $2.00
GPT-5 mini 2026 $0.25

#1 That is a 120x price reduction in approximately three years. Not 20%. Not 50%. 120 times cheaper.

For context: when electricity became 120x cheaper at the turn of the 20th century, it restructured every industry that touched it. When digital storage became 120x cheaper across the 1990s and 2000s, entire business models — film, music, publishing, photography — either transformed or died.

AI is doing the same thing now. The difference is speed. Electricity took decades. Digital storage took a generation. AI intelligence is repricing every three to six months.

Macro investor Jordi Visser said it plainly at Bitcoin Investor Week: “No company now has a moat. Their businesses are no longer defensible.” #2 He named software and SaaS stocks — the backbone of the S&P 500 and Nasdaq ETFs most retail investors hold — as structurally impaired. The premium pricing that made software businesses so profitable for two decades was a moat built on proprietary expertise. AI is turning that expertise into a commodity. “We have a deflationary situation happening,” Visser said, “and the reason is because AI is driving that deflation as we speak.”

The Thesis

Deflation sounds like good news. Cheaper AI, cheaper software, cheaper services — that is progress. But macroeconomics has a specific problem with deflation that goes beyond pricing.

When goods and services get cheaper, people wait to buy. Why spend today when it will cost less tomorrow? Consumption delays pile up. Corporate revenue falls. Businesses cut costs, which means cutting jobs. Unemployed people spend less. Deflation deepens. This is the spiral that destroyed Japan’s economy for three decades, and that the Federal Reserve spent the entire 2010s fighting to avoid.

When deflation takes hold, governments have one primary tool: print money. Increase the money supply fast enough, and you can offset deflationary pressure and keep the economy moving. This is what the Fed did after 2008. This is what every major central bank did after COVID. The playbook is established, well-rehearsed, and politically popular because inflation is a slow and diffuse tax while deflation triggers immediate unemployment.

AI deflation is structural — it does not turn off when the Fed raises rates or when the political cycle shifts. The cost of AI compute will continue to fall as hardware improves, energy costs drop, and competition between model providers intensifies. That means governments will face a sustained, structurally-driven deflationary pressure unlike anything in the modern era.

Their response — the only response they have — is money printing.

Bitcoin has 21 million coins. That number is not going up.

The Evidence

The deflationary dynamic is already visible in earnings calls. Companies across logistics, customer service, legal, and software are beginning to replace recurring labor costs with AI tools that are 10–20x cheaper per unit of output. When that cost deflation flows through to pricing, it will show up in CPI. When it shows up in CPI, the Fed will interpret it as progress and cut rates. Lower rates mean easier money. Easier money means more dollars chasing a fixed supply of scarce assets.

Gold has played this role for centuries — it is the classical hedge against monetary debasement. And it is working: gold crossed $5,000 per troy ounce in early 2026, its strongest multi-year run since the post-2008 expansion of central bank balance sheets.

But gold is a commodity. Its supply expands when prices rise — miners extract more. It has no programmable scarcity. It cannot be verified in seconds by anyone on earth with an internet connection.

Bitcoin’s supply schedule is written in code and enforced by every node on the network. The most recent halving reduced the block reward from 3.125 BTC to 1.5625 BTC. No CEO, no central bank, and no government can change that schedule. When the Fed expands its balance sheet to fight AI-driven deflation — and they will — every new dollar printed makes the next Bitcoin marginally scarcer.

The key insight Visser is pointing at: the assets most vulnerable to AI deflation are those built on proprietary expertise — precisely the software and services companies that dominate the major stock indices. The asset that benefits from the government’s response to AI deflation is the one with a mathematically fixed supply.

The Implication

If this thesis is right, the investment rotation over the next decade is not complicated. It is the same rotation that followed 2008 and 2020, but driven by a structural force rather than a cyclical shock: out of growth equities built on defensible moats (which AI is eroding), and into genuinely scarce assets that government money printing makes more valuable in real terms.

This does not mean Bitcoin goes up every month. It means the macro argument for owning it is stronger in a world of AI deflation than in a world without it. The thesis is structural and long-dated — measured in years, not weeks.

It also means the framing that positions crypto as a “risk asset” correlated to the Nasdaq is becoming less accurate over time. BTC may trade like a risk asset in acute crisis moments, but the macro argument underneath it is the opposite of an equity long — it is a bet against the ability of any central bank to manufacture scarcity.

What Could Be Wrong

The strongest counter-argument is containment. AI deflation may stay narrow — confined to software, content, and knowledge work — rather than spreading through the entire goods and services economy. If CPI stays elevated due to energy costs, housing, and manufacturing — all sectors AI deflates slowly if at all — the Fed may not need to ease aggressively, and the money-printing response may not materialise at the scale the thesis requires.

A second risk: Bitcoin may not hold its position as the primary fixed-supply hedge if another asset class or protocol takes that role. This seems unlikely given network effects, institutional custody buildout, and regulatory legitimacy, but it is not zero.

Third, the timeline. Structural deflation theses have a poor record as short-term trade signals. Japan’s deflation lasted thirty years. You could have been right about the thesis in 1993 and waited three decades for resolution. The AI deflation thesis is most useful as a framework for long-horizon allocation, not as a timing tool.

Bottom Line

AI is making intelligence 120 times cheaper every few years. Governments respond to deflation with money printing. Money printing benefits assets with fixed supply. Bitcoin has 21 million coins and that number is not changing.

The technology making everything cheaper may be the most powerful macro argument for a fixed-supply asset since the 2008 financial crisis — not despite the deflationary environment, but because of it.

Sources

• #1 — API Pricing — OpenAI
• #2 — Jordi Visser at Bitcoin Investor Week — YouTube

This analysis was developed from the Big Picture section of Chain of Thought — a daily crypto and macro market digest. New edition every morning.

Not financial advice.

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The AI Price Collapse Is the Best Case for Bitcoin You’ve Never Heard was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.