Strive Asset Management strategist Joe Burnett has outlined an aggressive, high-end scenario in which AI-driven deflation and the policy response to it would push Bitcoin to roughly $11 million per coin by Q1 2036. In his framing, Bitcoin stops being a marginal hedge and becomes a reserve-grade asset that absorbs a meaningful slice of global wealth. The claim is intentionally extreme, but it’s useful as a stress test because it forces you to examine what assumptions must be true for Bitcoin to dominate capital allocation.
Burnett’s core mechanism is what he calls an “AI deflation engine,” where automation and productivity gains drive persistent cost declines. He argues that in a debt-heavy fiat system, durable deflation would pressure credit markets and increase the risk of a deflationary spiral. To avoid that, he expects central banks and fiscal authorities to inject large amounts of liquidity, and his model assumes that sustained monetary expansion would funnel demand into Bitcoin because its supply is capped.
The numbers Burnett is anchoring to
Burnett’s scenario sets a target of $11 million per BTC by Q1 2036, implying a market capitalization around $230 trillion. He also assumes Bitcoin captures roughly 12% of global financial assets, up from a cited current share near 0.2%. That is the real leap in the model: the price outcome is simply what happens if Bitcoin becomes a major destination for global asset allocation rather than a niche instrument.
Under the hood, the scenario assumes global wealth grows at about 7% per year through 2036, and it implies a Bitcoin compound annual growth rate near 53% over the period. Burnett contrasts those assumptions with a macro datapoint: U.S. M2 money supply at $22.442 trillion in January 2026, while acknowledging that Bitcoin’s price has shown weak correlation with new liquidity in some periods. The model’s bet is that the “liquidity-to-Bitcoin” linkage strengthens dramatically under sustained deflation-driven stimulus.
Why the critique is as important as the projection
The pushback you included is basically a challenge to the model’s transmission mechanism. Skeptics note that broad money growth has not reliably produced proportional BTC appreciation, even during periods of record M2 expansion, and they attribute the gap to factors like low money velocity and capital flowing into competing assets. That critique doesn’t disprove the scenario, but it raises the burden of proof: the model needs a regime change where liquidity consistently seeks Bitcoin rather than dispersing elsewhere.
A second critique attacks the stability of the AI-deflation premise itself. If AI adoption faces scaling constraints, funding shocks, or a valuation correction, the deflationary impulse could weaken, undermining the “policy must print” chain Burnett relies on. In other words, the scenario isn’t just “AI gets big,” it’s “AI gets big and stays economically deflationary without a disruptive stall.” Add in regulatory friction and infrastructure constraints, and the path to “reserve Bitcoin” becomes even more conditional.
Burnett also introduces a concept that cuts both ways: “digital credit,” meaning structured balance-sheet products that could create a reflexive loop where yield demand drives more Bitcoin accumulation. If that mechanism scales, it could amplify inflows and accelerate adoption, but it also introduces leverage and circular reserve risk that can destabilize markets when conditions tighten. In practical terms, it’s a turbocharger that can also become a failure point.
What institutions should take from a high-end scenario like this
Even if you treat $11 million as an upper-bound thought experiment, it highlights operational questions that matter in any “Bitcoin becomes more systemically important” world. Higher price levels would change treasury sizing, custody concentration risk, and the sophistication required in settlement and risk controls. The development of leveraged Bitcoin credit products would elevate counterparty diligence and stress testing, because reflexive structures can break abruptly. And if crypto begins to look reserve-like, regulatory scrutiny tends to intensify, which affects everything from disclosure obligations to custody standards.
The most useful way to read Burnett’s projection is as a conditional map: if AI-driven deflation is persistent, if policy responses are consistently expansionary, and if global portfolios reallocate meaningfully toward a fixed-supply asset, then the endpoint can be very large. If any of those “ifs” fail, the outcome compresses quickly. That sensitivity is precisely why long-range Bitcoin forecasts diverge so widely.
