Next‑Gen SoV

§9. Compute as AI Money

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Jason St George. "§9. Compute as AI Money" in Next‑Gen Store of Value: Privacy, Proofs, Compute. Version v1.0. /v/1.0/read/part-ii/9-compute-as-ai-money/

§9. Compute as AI Money

Compute has always been an economic input: first as muscle, then as steam and electricity, now as FLOPs. The AI boom made this explicit: GPUs, TPUs, and datacenters are priced like oil fields.

But raw compute is not yet money‑like:

  • Most compute markets are opaque capacity rentals (cloud contracts, colo deals).
  • There is no standardized unit of verified work; only hours and instance types.
  • There is no cheap, public way to verify that a claimed computation actually ran correctly.

The triad reframes compute as AI Money by insisting on:

  • Canonical workloads: Matrix multiplications, FFTs, and core model primitives (operations that can be widely benchmarked and understood).

  • Proofs of useful work (PoUW). Miners/provers earn rewards for producing proofs that these workloads ran correctly, with verification asymmetry (cheap to check, costly to fake).

  • Verification economics. VerifyPrice turns “one unit of verified MatMul at dimension n” into something that can be priced and traded.

In that context, verified compute capacity becomes a monetary primitive:

“Rights to future, standardized, cheaply verifiable units of useful compute (verified FLOPs).“

9.1 Compute deflation and what remains scarce

A skeptical reader will object: “Compute gets cheaper every year. Moore’s Law (or its successors) drives raw FLOP cost down. How can ‘rights to compute’ be a store of value when the underlying commodity deflates?”

This is a serious objection. Here is the response:

What deflates:

  • Raw FLOPs per dollar (secular decline).
  • Cost of unverified, permissioned compute from hyperscalers.
  • Commodity inference for non-sensitive workloads.

What remains scarce:

  • Verified FLOPs with receipts: Compute where correctness is cryptographically proven, not vendor-asserted.
  • Policy-constrained capacity: Compute that runs under specific jurisdictional, privacy, or compliance constraints.
  • Censorship-resistant access: Compute that cannot be denied by TOS changes, sanctions, or platform bans.
  • Priority access under congestion: During demand spikes (model releases, regulatory deadlines), priority slots are scarce even if raw capacity is abundant.
  • Specific hardware profiles: Compute on L0-C or L0-D grade hardware (partial/full open) may remain scarce even as closed alternatives proliferate.

The instrument should reference capacity share, not “one timeless FLOP”:

AI Money is not “rights to 1 FLOP forever.” It is:

“Rights to X% of verified compute throughput under SLA Y on hardware profile Z.”

This is analogous to how oil futures reference “barrels of WTI crude delivered at Cushing”—not “energy” in the abstract. The specificity (verification, SLA, profile) is what creates persistent scarcity.

Telemetry that detects deflation risk:

If raw compute deflation outpaces the scarcity premium of verification/censorship-resistance, the following metrics will signal it:

  • VerifyPrice for verified FLOPs converges toward unverified market rates.
  • Capacity utilization on verified networks falls below thresholds.
  • Fee revenue from compute workloads declines as a share of total fees.

These are observable. The thesis predicts that the verification premium will persist because (a) demand for trustworthy AI is structural, and (b) the compliance/sovereignty premium will grow as AI becomes more consequential. But if telemetry shows otherwise, the “compute as SoV” leg weakens relative to privacy and proofs.

AI Money instruments can include:

  • Work Credits: minted against verified‑compute contributions, redeemable for future workloads or tradeable as a savings asset.
  • Compute futures: for specific workloads (e.g., “X verified inferences at model M with SLO Y”).
  • Stake in proof factories: whose entire business is converting energy + silicon into verified FLOPs with transparent VerifyPrice.

Why is this money‑like rather than just “another utility token”?

Because:

  • Demand is global and secular: As long as AI is an input to value creation, there is demand for FLOPs. Even under repression, states and corporations need AI capacity.

  • Verification is public: Anyone can check that a unit of AI Money corresponds to a valid proof of a canonical workload run under specified parameters.

  • It’s duration‑neutral: There is no fixed coupon; value is backed by the ability to sell compute into whatever nominal budgets exist.

In the same way that oil futures and pipeline throughput can act as quasi‑monetary instruments for an industrial economy, verified compute capacity acts as monetary collateral for an AI economy, with one crucial upgrade: units are defined by proofs and VerifyPrice, not by opaque vendor contracts.

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