§10. Work Credits: Energy-Anchored Claims on the Triad

Jason St George. "§10. Work Credits: Energy-Anchored Claims on the Triad" in Next‑Gen Store of Value: Privacy, Proofs, Compute. Version v1.0. /v/1.0/read/part-ii/10-work-credits/

§10. Work Credits: energy‑anchored claims on the triad

The triad gives us three capacities. To turn them into concrete monetary instruments, we need a unit of account for work: something that binds energy, hardware, and verification into a transferable claim.

Call these Work Credits (WC).

Informally:

A Work Credit is a claim on a standardized unit of triad work (privacy settlement, proof generation, or verified compute) that has been produced and attested under public SLOs.

Clarification. Throughout this section, “Work Credits,” “AI Money,” and “proof money” are generic, notional instruments (a class of designs that any future chain or protocol could implement), not product branding for a particular network. The point is to specify what kind of asset can sit on top of the triad, not to name one specific chain or ticker.

We can describe Work Credits along a few axes:

10.1 Definition: Work Receipts vs. Work Credits

A common source of confusion is conflating “proof that work was done” with “transferable claim on future capacity.” These are different financial objects. We split them cleanly:

Work Receipt (WR)

A Work Receipt is a PIDL artifact proving that a specific unit of work was completed under attested conditions.

• Content: Claim hash, proof hash, workload ID, SLA tier, timestamps, hardware profile, prover signature.
• Properties: Copyable, verifiable by anyone, not scarce.
• Analogy: A receipt from a completed transaction. It proves the past but confers no future rights.

Work Receipts are not money. They are evidence.

Work Credit (WC)

A Work Credit is a transferable instrument issued against Work Receipts under protocol-defined issuance rules.

• Issuance: Minted when (a) a valid Work Receipt is accepted by the network, and (b) telemetry confirms SLOs are met.
• Properties: Scarce (supply bounded by issuance rules), transferable, fungible within workload class.
• Rights: Depend on the design variant (see below).

Two design variants for Work Credits:

Recommendation: The thesis adopts WC-Base as the primary reference design for SoV analysis. WC-Voucher is useful for enterprises hedging cost volatility, but is explicitly not pitched as a store of value.

Issuance mechanics for WC-Base:

For a canonical workload $W$ (e.g., “MatMul of size $n$ with error bound $\varepsilon$,” “provenance proof for content type $C$,” “corridor settlement of size $S$ with anonymity set $\geq A$”), define:

• $p(W)$: production cost (energy + hardware amortization + opex) to generate one unit and produce a valid proof.
• $v(W)$: verification cost.
• $r(W) = v(W) / p(W)$: verification asymmetry.

A Work Credit of type W, tier T is issued only when:

• A valid Work Receipt for workload (W) at tier (T) is accepted by the network.
• Telemetry confirms that VerifyPrice(W,T) and other SLOs (latency, failure rate, decentralization) are within bounds.
• Issuance does not exceed the issuance envelope for the current period (see §10.6).

The credit does not represent a “future option” on its own—that is the WC-Voucher variant. WC-Base represents historical verified work and derives its value from being the fee/staking medium, not from redemption rights.

10.2 Energy anchoring

Like PoW, Work Credits are ultimately energy‑anchored:

• The marginal cost of producing one more credit is bounded below by the energy and hardware required to pass the verification threshold.
• Unlike SHA‑256 PoW, the work is useful: it powers privacy settlements, proofs of provenance, and AI computation.

This anchoring gives Work Credits:

• Credible scarcity: You cannot mint Work Credits without expending real resources to produce proofs and settle flows.

• Economic meaning: One credit corresponds to something the world actually cares about (anonymized payrolls, authentic media, verified inference), not just burned electricity.

10.2.1 Layer 0 maturity and economic consequences

Work Credits are only as trustworthy as the hardware that produces them. A world where all proving runs on opaque, vendor-controlled hardware is different from one with diversified, partially open designs.

§14 (Part III) defines a Layer 0 maturity ladder with grades L0-A through L0-D. Here we preview how those grades affect Work Credit economics:

How this affects issuance and pricing:

1. Tiered issuance caps: WC minted on L0-A profiles may face stricter issuance limits than L0-C/D profiles. This prevents the system from becoming dependent on opaque hardware.

2. Market pricing: WC from different L0 grades can trade at different prices if markets distinguish them. Treasuries and institutions may pay premiums for L0-C/D-backed capacity.

3. Risk disclosure: Every Work Receipt includes the hardware profile (HID) used. Aggregated telemetry shows what fraction of total WC is backed by each grade.

4. Deprecation impact: If an L0-A profile is compromised (TEE backdoor discovered), WC minted on that profile can be quarantined, discounted, or excluded from certain uses. This is the “bond downgrade” analog.

Why this matters for SoV:

If 90% of Work Credits are minted on L0-A hardware and a major TEE is compromised, the SoV thesis takes a hit—not because the cryptography failed, but because the base assumption (honest machines) was violated.

By making L0 grade explicit and tying it to economic consequences, the system:

• Creates incentives to invest in open hardware.
• Bounds the impact of hardware compromises.
• Gives users and allocators the information to price risk appropriately.

This is covered in detail in §14.3.1 (Layer 0 Feasibility Ladder). The key point for Part II: hardware trust is not binary; it is graded, measured, and priced.

10.3 Robots, AI, and the demand for Work Money

In a robot‑ and AI‑heavy economy the demand story for Work Credits becomes almost embarrassingly straightforward.

Consider a warehouse or factory where most of the physical activity is carried out by robots, and most of the planning and oversight is handled by models. The day‑to‑day budget splits into:

• Energy to power robots and data centers.
• Compute to run models and generate proofs.
• Privacy and settlement to pay workers, suppliers, tax authorities, and investors without leaking trade secrets or exposing everyone’s graph.

Each of those budgets expresses itself as a recurring need for receipts:

• Proof of correct inference for high‑stakes decisions.
• Proof of compliance and risk calculations for regulators and insurers.
• Privately settled wages and vendor payments with lawful audit trails.

Today that flow is mediated through cloud bills, payroll files, bank wires, and audit PDFs. In the stack described here, it is mediated through Work Credits and receipts. The robots and services are paid in Work Credits; in exchange they produce receipts that can be verified cheaply and settled over privacy rails. The more of the economy runs this way, the more natural it becomes to hold savings directly in the unit that pays for these things instead of in a separate, compliance‑backed currency that must be constantly converted.

This is one concrete way to read remarks like “money will just be energy” in a non‑hand‑wavey way. Energy, compute, and hardware are the inputs that enable robots and AI to work; Work Credits are the units in which their work is paid for and stored. The credits do not magically become Joules; they become a convenient way to denominate future access to verifiable, robot‑mediated output.

10.4 Why this is still “money,” not just coupons

If we revisit the seven SoV criteria from §3, the mapping is straightforward:

• Credible scarcity. Issuance is programmed and publicly documented, either as an explicit schedule (halvings, caps) or as a capacity‑linked minting rule. Deviations are detectable on‑chain and in telemetry.

• Cheap, public verification. Every unit of capacity that credits buy is backed by receipts that any honest party can verify under published VerifyPrice targets.

• Censorship‑resistance & portability. Settlement uses non‑custodial privacy rails and atomic swaps; Layer 1 and 2 ensure clients can reach the network under filtering; Layer 3 ensures users are not doxxed by default.

• Neutrality & permissionlessness. Open admission for provers, miners, routers, and mirrors is enforced by decentralization telemetry and fairness tests.

• Native demand. The demand for Work Credits comes from real workloads: proofs, verified FLOPs, private settlement, robotics and AI tasks. These are line items in budgets, not speculative self‑reference.

• Lawful privacy by design. Viewing keys, receipts, and provenance proofs allow institutions to satisfy audits without re‑introducing custodians or surveillance chokepoints.

• Duration‑neutrality. Work Credits do not promise fixed nominal coupons that can be pinned negative by policy. Their value comes from the intersection of energy/hardware costs and buyer budgets for capacity.

Seen in this light, Useful Work Money is not a loyalty program or a scrip. It is a monetary instrument whose backing is precisely the capacities this thesis has been concerned with all along: privacy, proofs, and compute, grounded in verifiable machines and energy, and measured by VerifyPrice and FERs. It earns a store‑of‑value premium not because it tells a compelling story, but because in a dense digital civilization it is one of the few things the world must keep buying, on rails anyone can audit.

10.5 Monetary role

Work Credits sit at the junction between base capacity and financial representation:

• For operators, they are revenue in kind: miners/provers/routers earn credits by contributing triad capacity.
• For users, they can be pre‑paid capacity or savings: hold credits to ensure future access to triad services, or to speculate on increased demand.

Their SoV behavior depends on:

• Demand for specific workloads: Work Credits tied to high‑value workloads (e.g., compliance proofs, LLM inference) may command higher premia.

• Governance & telemetry honesty: If VerifyPrice and decentralization metrics are falsified or gamed, the link between credits and real work weakens.

• Repression intensity: As repression rises, demand (and thus value) for Private Money credits (privacy/settlement workloads) and Truth Money credits (provenance workloads) should increase.

In portfolio terms, Work Credits are the equity‑like layer of the triad; they express exposure to the growth and usage of Privacy, Proofs, and Compute under verifiable rules.

10.6 Issuance: tying credits to real capacity

There are many possible issuance schemes; what matters here is not choosing a particular curve, but enforcing two principles:

1. Issuance is legible.
2. Issuance is constrained by real capacity.

One extreme is the Bitcoin model: a fixed schedule, regardless of demand, with the understanding that price will equilibrate. Another extreme is a pure capacity-linked model: Work Credits are minted only when new proving, compute, and settlement capacity comes online and is registered with telemetry; they behave almost like tokenized capacity reservations. In practice, a hybrid is likely: a predefined issuance envelope over time, modulated by capacity growth and burn.

In such a system, adding a new proving cluster, plant, or corridor is not just a marketing slide; it is an event that expands the envelope of Work Credits the system can credibly support. The converse is also true: if plant retires or corridors die and are not replaced, issuance that continues on autopilot will show up as VerifyPrice drift, SLA breaches, and deteriorating energy metrics. The governance layer’s job is not to guarantee any particular price, but to keep issuance and capacity in rough proportion and to make any departures visible.

From an allocator’s perspective this yields a familiar pattern: Work Credits look like equity in (or claims on) a portfolio of infrastructure (proving farms, AI chains, privacy corridors, and plants) rather than a purely arbitrary balance sheet. The difference from conventional “infra tokens” is the insistence on receipts and KPIs: if claimed capacity and observable behavior diverge, the discrepancy is not a rumor; it is a datapoint.

10.6.1 The supply balance equation

A critical question for any SoV claim is: why does demand not simply expand supply, neutralizing scarcity?

If Work Credits are minted whenever verified work is done, and demand for triad services grows, supply grows too. This could make WC behave like a labor-backed scrip rather than a scarce reserve asset.

The answer lies in the net supply dynamics. We can express this as a balance equation:

$\Delta \text{Supply} = \text{Issuance} - \text{Burns} - \text{Lost} \pm \text{Governance}$

Where:

For WC-Base to behave as SoV, the design must ensure:

$\text{Burns} + \text{Lost} \geq \text{Issuance} \quad \text{(at steady state)}$

This means:

• During growth phase: Issuance > Burns, but capped by schedule/capacity. Supply grows, but at a predictable, declining rate (like BTC halvings).
• At maturity: Burns ≥ Issuance. Supply is flat or declining. Holders benefit from scarcity.

What prevents runaway issuance?

1. Issuance envelope: Total WC mintable per epoch is capped, regardless of work submitted. Excess work earns priority, not extra tokens.
2. Capacity linkage: Issuance envelope expands only when new verified capacity (FERs, hardware profiles) is registered. You can’t mint by decree.
3. Halving schedule: Many designs use BTC-style halvings to ensure long-run issuance approaches zero.
4. Governance friction: Changes to issuance rules require supermajority and are visible in dashboards before activation.

Telemetry that detects supply risk:

• Inflation rate: Issuance / circulating supply. Should decline over time.
• Burn rate: Burns / fee volume. Should stay within target range.
• Net supply change: ΔSupply per epoch. Positive during growth, flat or negative at maturity.
• Issuance vs. capacity: If issuance grows faster than verified capacity, this signals potential dilution.

If these metrics drift outside healthy ranges, it becomes visible in dashboards—holders and operators can respond before the SoV thesis is undermined.

10.7 Energy-priced, not energy-pegged

In popular discourse one often hears that “money will just be energy,” especially in the context of robotics and AI. As shorthand, this is attractive: robots and data centers run on electricity, so why not quote everything in kWh and be done with it? The problem is that not all kilowatt-hours are created equal. Time of day, grid node, reliability, carbon intensity, and siting constraints all affect their economic and political meaning. A winter-peaking kWh on a stressed urban grid is not the same as a curtailed hydro kWh in a remote valley. If we pretend otherwise, we smuggle a lot of hidden politics and risk into the unit of account.

The discipline in this thesis is to admit that complexity and route around it with receipts.

Energy is measured and wrapped into Facility Energy Receipts (FERs), which record, for each facility and time window:

• kWh in,
• kWh delivered to IT,
• heat reused,
• PUE/ERE/WUE,
• water use,
• carbon intensity,
• and outages/curtailments.

On top of that, we measure verified work per kWh:

• ηᵥFLOP for FLOPs,
• proofs-per-kWh for proof workloads,
• swaps-per-kWh for settlement.

VerifyPrice then tells us how much it costs, in time and money, for an independent verifier to check that a given amount of work was done.

The result is an implicit conversion path:

energy → FERs → verified work (proofs/FLOPs/swaps) → receipts → Work Credits

We never pretend that one Work Credit is one kilowatt-hour; instead we make it easy for anyone to estimate, at any given time, how many kilowatt-hours of which quality and where in the world sit behind a portfolio of Work Credits, via the receipts.

Pricing in energy then becomes an inference problem for markets:

• Work Credits are implicitly energy-priced because their production and redemption depend on energy-intensive workloads whose cost curves are public.
• FERs and VerifyPrice make those curves legible without forcing a brittle kWh peg.

What distinguishes Work Credits from naive “energy tokens” is precisely this separation:

• The unit of account is denominated in work, not in kWh.
• Energy enters through FERs, ηᵥFLOP, and VerifyPrice, not through a one-dimensional peg.

Markets, regulators, and builders can look at those receipts and say, with some confidence, “a Work Credit currently corresponds to about this much capacity, with this energy and carbon profile, under these SLAs.” That is enough to make the asset priceable and analyzable without forcing it into a crude energy standard.

10.8 Why Work Credits are SoV, not just a utility token

It is easy to misread Work Credits as another “utility token” with a story stapled on top. §3 and §10.4 already showed that they inherit the full SoV checklist (scarcity, cheap public verification, censorship-resistance, neutrality, native demand, lawful privacy, and duration-neutrality) from the stack. Here we make that contrast explicit.

Four properties do most of the work:

1. Issuance is constrained by energy and installed capacity.

   Work Credits do not float on arbitrary governance votes. Issuance is bounded by measured energy and verifiable capacity: FERs and plant telemetry cap the budget of subsidized work over a given interval. To print more, someone has to build more verifiable machines or increase real capacity. Narrative cannot override thermodynamics.

2. Demand is driven by budgeted workloads, not vibes.

   The “use” of a Work Credit is paying for proofs, inference, settlement, and related workloads that already sit on OPEX lines. AI labs, exchanges, custodians, and regulators must keep buying these workloads each quarter, regardless of token price. That places Work Credits upstream of compulsory spend, not downstream of hype.

3. Verification asymmetry keeps cheating costlier than honesty.

   For each workload W, the verification ratio r(W) is explicitly bounded: it is always much cheaper to check a PIDL receipt than to fake it at scale. A Work Credit backed by a portfolio of such workloads inherits this property: to counterfeit the base, you must fake receipts, and faking receipts is provably more expensive than doing the work. Most “utility tokens” rely on soft notions of “activity”; Work Credits rely on receipts that anyone can cheaply reject if they are bogus.

4. Returns are duration-neutral and fee-driven, not coupon-driven.

   Work Credits do not promise fixed coupons or redemptions at par. Their economic value comes from a share of fee+burn and spread on a growing, compulsory workload budget. As more proofs, compute, and private settlements clear over the stack, more fees flow in and more units are retired. That makes them duration-neutral: they behave more like equity in a necessary utility than a bond whose real yield can be pinned negative.

Taken together, these properties push Work Credits into the store-of-value bucket described in §3 and §10.4:

• constrained by energy and capacity,
• demanded by necessity rather than fashion,
• protected by verification asymmetry, and
• paid in a fee stream tied to indispensable workloads.

In that sense they are not tickets to a theme park; they are metered claims on the power plant that keeps the park (and the rest of the AI + ZK economy) online.

Work Credits are “utility tokens” only in the most literal sense: they buy utility the world cannot stop buying. Under the SoV lens, that is exactly what you want: an asset whose backing is quantitatively visible in receipts and KPIs, and whose long-run value is rooted in capacities that must be purchased through every regime.

10.9 Economic linkage: how triad demand becomes asset value

This subsection answers the question that separates a systems manifesto from a monetary thesis: Why does demand for Privacy, Proofs, and Compute raise the value of holding the asset, rather than merely rewarding consuming a service?

The answer has four parts.

1. What exactly is the asset?

The asset in this framework can take several forms, but all share a common structure:

• Network tokens: Native units of the protocol (analogous to ETH or BTC) that are required to pay fees, post collateral, and participate in governance.
• Work Credits: Claims on verified capacity, minted against energy-anchored work.
• Corridor/Pool shares: LP positions or staking rights in specific privacy corridors, proof factories, or compute networks.

What matters is that all uses of the triad must flow through the asset. You cannot get a proof, settle a private payment, or buy verified compute without either holding or acquiring the native unit.

2. Why is it scarce in a monetary sense?

Scarce capacity does not automatically imply a scarce asset. The link is forged through:

The result: supply is bounded by physics (energy, hardware) and shrinks with usage (burns), while demand is driven by compulsory workloads. This is the scarcity structure of a commodity, not an IOU.

3. Why does demand for triad capacity raise the value of holding the asset?

The causal chain:

Demand for Privacy/Proofs/Compute
        ↓
Users must acquire native tokens to pay fees
        ↓
Fees paid → portion burned, portion to stakers/provers
        ↓
Burns reduce supply; staking rewards require holding
        ↓
Increased demand + reduced supply → price appreciation
        ↓
Holding the asset captures the economics of the triad

More concretely:

• Fee revenue: Every proof, every private settlement, every verified inference pays a fee denominated in the native asset. This creates continuous buy pressure.
• Burn mechanics: A fraction (e.g., 30–70%) of fees is burned. As throughput grows, more units are destroyed than minted, creating deflationary pressure during growth phases.
• Required collateral: Provers, routers, and LPs must stake tokens proportional to their capacity. This locks supply and aligns incentives with network health.
• Priority/governance rights: Holding grants access to premium SLA tiers, governance votes, and first-mover allocation of scarce capacity.

Key insight: The holder is not buying “exposure to price” (reflexive speculation). The holder is buying a share of the fee stream from indispensable workloads, denominated in an asset whose supply shrinks as those workloads grow. This is closer to equity in a utility than to a collectible.

4. What prevents capacity providers from capturing all value while holders get diluted?

This is the classic “utility token trap”: if operators earn all the fees and governance can inflate supply, holders are just exit liquidity.

The stack avoids this through:

The economic design goal: At steady state, fee burns ≥ new issuance, so holders benefit from both yield (staking rewards) and capital appreciation (supply contraction).

Summary: The Value Accrual Lemma

Demand for triad capacity (Privacy, Proofs, Compute) translates into store-of-value premium for the native asset because:

1. All usage requires the asset (fee medium).
2. Usage burns supply (deflationary pressure).
3. Capacity provision requires staking (supply lockup).
4. Issuance is energy-constrained (no governance inflation).
5. Workload demand is structural (AI, commerce, compliance budgets, not hype cycles).

If these five conditions hold, and VerifyPrice/Reach/Settle remain within SLOs, holding the asset captures the economics of the triad rather than merely consuming a service.

This is the bridge between “these capacities are indispensable” and “therefore they can function as monetary primitives and earn a durable store-of-value premium.”

Tip: hover a heading to reveal its permalink symbol for copying.