The two forces that define this era
Raoul Pal's "Universal Code" thesis rests on a simple observation: two opposing forces are operating simultaneously in the global economy, and they don't cancel each other out — they compound.
The inflationary force is the money side: governments and central banks keep expanding the monetary base, printing stimulus, and running persistent fiscal deficits. This is the engine that pushes everything in financial assets higher.
The deflationary force is the intelligence side: AI is crashing the cost of knowledge work. Bitcoin has created a programmable, borderless settlement layer that doesn't depend on any central authority. Both are reducing the "scarcity" that inflationary money creation depends on.
You don't pick a side. The opportunity is in assets that benefit from both forces simultaneously. Bitcoin benefits from inflationary pressure (debasement hedge) AND deflationary pressure (fixed supply + protocol-level scarcity). AI benefits from falling intelligence costs AND rising capital inflow. That's the Everything Code.
Why intelligence costs matter more than you think
There's a 200-year pattern in economics: every technological revolution that crashed the cost of a fundamental input eventually restructured the economy. Crashing the cost of transportation (steam, oil, shipping) created the modern economy. Crashing the cost of information (internet, search, social media) created the platform economy. We're now in the early phase of crashing the cost of intelligence — and that is the most consequential of the three, because intelligence is the input that drives all other inputs.
The practical implication: if intelligence becomes cheap, services that were previously scarce (legal advice, medical triage, financial modeling, content production, software engineering) become abundant. Abundant goods are deflationary. That's a direct hit on the inflation that central banks are trying to create — which is exactly why the money side of the equation is pushing back harder, not less.
The energy constraint
Every "intelligence is getting cheaper" narrative has a physical cost: electricity. AI training and inference consume massive amounts of power. Data centers are among the fastest-growing energy consumers in developed economies. This creates two effects that most people overlook:
- Energy becomes a binding constraint on AI growth. It's no longer just about GPU supply. In many regions, the grid is the bottleneck. This creates a direct link between AI and energy infrastructure — solar, nuclear, grid upgrades.
- AI increases energy demand in deflationary conditions. Even as services get cheaper, the demand for compute is inelastic. More users, more agents, more inference calls. The energy bill doesn't go down even if the intelligence per dollar goes up. This makes AI a structural tailwind for energy stocks and a structural headwind for electricity consumers.
- The binding constraint moves. In 2020 the constraint was GPU supply. In 2024 it was data center capacity. In 2026 it's increasingly electricity and land. Understanding where the constraint is tells you where the alpha is.
When someone asks "will AI go to the moon?" the better question is: where is the physical constraint? If it's chips, look at semiconductor companies. If it's energy, look at power generation and grid infrastructure. If it's data centers, look at real estate and cooling. The constraint determines the allocation.
AI and the crypto layer
The connection between AI and crypto is deeper than "tokenized compute" (though that's real). There are three layers of integration that are actually happening in 2026:
- On-Chain Agents. AI agents that transact on-chain: swapping, staking, liquidity provision, executing trades. This removes the human from the loop and creates a new class of autonomous economic actor that doesn't sleep, doesn't panic, and doesn't need a bank account.
- Tokenized Compute. DePIN (decentralized physical infrastructure networks) projects are creating marketplaces for GPU compute where you can buy inference capacity with crypto. This is particularly important for regions where cloud providers are restricted or expensive.
- Bitcoin + AI as a hedge pair. In a portfolio context, Bitcoin is the monetary hedge (debasement). AI is the productivity hedge (deflation of services). They're not correlated in the same direction — they complement each other. A portfolio with both is structurally different from one with either alone.
The deeper point: the crypto layer provides the settlement and coordination mechanism that makes autonomous AI agents economically viable at scale. Without a borderless, permissionless payment layer, AI agents are just expensive software. With it, they become a new form of economic activity.
What this means for how you think
The Universal Code frame changes three common mistakes people make:
- Treating AI as a pure tech story. It's also a monetary story. The capital flowing into AI is the same capital being created by monetary expansion. You can't understand the valuation without understanding the money.
- Treating Bitcoin as a pure crypto story. It's also a macro story. Its primary driver is not "hype" but central monetary policy. The four-year cycle tracks M2 and the Fed balance sheet, not Twitter sentiment.
- Treating macro as an abstract story. It's the demand side of both the AI and crypto stories. If M2 is shrinking, risk-off hits everything. If M2 is expanding, the capital flows into both AI and BTC.
Read that flow left-to-right and you have the framework. The red thread connects all three clusters into one system. That's the point of Stage 2.
Next steps
Learning content. This is not investment advice. The Universal Code framework is a synthesis of publicly shared theses — primarily Raoul Pal's "General Intelligence" and "Everything Code" concepts, as well as analyses from Diamandis, Bankless, and the wider crypto-macro research community. Sourced from Raoul Pal, Anthony Pompliano, Jordi Visser, Peter Diamandis & Bankless. The World is Spinning Faster Than Before is an educational project.