Standards

Standards drive consistency — without consistency, improvement is guesswork.

The Validated Virtuous Feedback Loop — Flow

Standards are the tide that rises all boats. Competition is zero-sum. Standards are positive-sum. When you standardize, everyone who adopts gains.

📄️ AI Standards

Who is setting standards the will develop the platform that sets the legacy for all of us?

📄️ Control System

The only thing you can control is how you choose to be. But there are systems that help you bring your best state to the moment.

Definition

Term	Meaning
Protocol	Specific rules for making progress
Standard	Widely adopted protocol
Glue	The ontology that connects protocols

Standards are the Ontology that transforms Protocols into a "Context Graph" of decision traces.

The Capital Loop

Standards create predictability. Predictability attracts capital.

STANDARDS → Consistency → Predictable Results
    ↓
PROTOCOLS → Methods that recreate expected outcomes
    ↓
PREDICTION MARKETS → Bet on what protocols will produce
    ↓
CAPITAL INFLOW → Money flows to accurate predictions
    ↓
FUTARCHY → Governance by prediction market outcomes
    ↓
GAMES → Practice prediction before real stakes
    ↓
BETTER STANDARDS → Learning compounds into new protocols

Stage	What Happens	Where It Lives
Consistency	Standards remove variance	Standards
Predictability	Protocols produce expected results	Protocols
Betting	Markets price beliefs about outcomes	Prediction Markets
Capital Flow	Money moves to accurate predictors	Investing
Governance	Decisions made by market outcomes	Futarchy
Practice	Games build prediction skills	Futurist Games

The insight: Standards aren't just about quality control. They're the foundation that makes outcomes predictable enough to bet on. When outcomes are predictable, capital flows. When capital flows based on predictions, you get futarchy — governance by putting money where your mouth is.

This is why game economics matters: games are where we practice prediction before stakes are existential.

Economic Foundation

Better standards → better predictions → better capital allocation → better economies.

Systems	Investing	Economic Result
Standards create consistency	Consistency enables prediction	Reduced uncertainty
Feedback loops improve quality	Calibration improves accuracy	Better decisions
Process over outcomes	Shield from results, focus on process	Learning compounds
94% are systems problems	Structured processes counter biases	Fewer errors

Japan's Miracle

Japan's manufacturing miracle wasn't cheap labor — it was quality standards creating feedback loops that compounded.

DEMING CYCLE (PDCA):
    PLAN → What result do we expect?
    DO → Execute the process
    CHECK → Does reality match prediction?
    ACT → Adjust the standard
    ↓
    Better standard → Better prediction → Repeat

This is Simkin's calibration applied to manufacturing:

Start with a prior (expected result)
Update with new information (actual result)
Seek feedback to improve calibration (adjust standard)
Compound over time (Japan's quality advantage)

Capital Flows

Capital flows to predictable outcomes. Predictable outcomes require standards.

Economy Type	Standards Quality	Predictability	Capital Flow
Frontier	Emerging	Low	High risk capital
Developing	Inconsistent	Medium	Selective
Mature	Robust	High	Deep liquidity

The arbitrage: Find domains with emerging standards before they mature. Own the choke points. See Atoms and Bits for where atoms meet bits.

Games Lab

Where do you test standards before deploying to reality? Games.

EVE Online has central bank economists testing monetary policy in simulation. Every patch is a standards experiment. Every player response is calibration data. This is why game economics matters — games are the lab where standards prove themselves.

What Games Test	Standard Being Validated	Graduation Path
Monetary policy	Central bank levers	Real economies
Market structures	Trading protocols	DeFi, exchanges
Governance	DAO voting	Futarchy
Coordination	Guild protocols	Hive-mind

Futurist games take this further: predict which standards will win, then bet on predictions. The skill isn't just playing — it's recognizing which protocols create predictable outcomes.

"The greatest danger in times of turbulence is not the turbulence — it is to act with yesterday's logic." — Peter Drucker

The economy isn't built on money. It's built on standards that make money meaningful.

Power

Standards compound faster than capital:

What Compounds	Mechanism	Example
Capital	Interest on interest	7% annual returns
Standards	Each adoption makes next adoption easier	HTTP → entire web in 15 years

Economists obsess over incentives. Finance obsesses over capital allocation. Crypto obsesses over tokenomics. All miss the foundation: standards drive adoption, adoption drives network effects, network effects drive value.

HTTP won not because it was technically superior, but because it was a standard everyone could build on. ERC-20 tokens have value because they're interoperable — the standard, not the token, creates the network effect.

"94% of problems in business are systems problems, not people problems." — W. Edwards Deming

Atoms and Bits

Standards are magical because they bridge matter and meta — physical atoms and digital bits. This is where the real money is.

The Bridge

Domain	What Standards Are	Examples
Bits	Rules for how software and protocols connect	TCP/IP, ERC-20, USB, file formats, API conventions
Atoms	Rules for how physical things fit together	Metric screws, shipping containers, power sockets, LTE/5G bands
Bridge	Standards that touch both	DePIN device specs + onchain protocols + oracle formats

When you design a standard that touches both, you create a bridge so physical supply can be financed, measured, and traded as digital assets.

Sources: CS Cornell, Kevin Kelly, AVC

Network Effects

Network effects: The more people adopt a standard, the more valuable it becomes for every new participant. Fax machines, Windows, TCP/IP, ERC-20 — all classic examples where value compounds with adoption.

Plentitude over scarcity: As marginal cost of another compatible device approaches zero, value shifts to controlling or sitting near the standard. Exchanges, wallets, and infrastructure around ERC-20. Routers and CDNs around TCP/IP.

The key to abundance is open standards. Closed standards create scarcity (proprietary lock-in). Open standards create abundance (anyone can build). Protocols that elevate standards from proprietary to open unlock network effects that compound without permission.

Deep liquidity: In crypto + DePIN, common standards for measuring usage, paying contributors, and proving work unlock easier financing. Capital can underwrite a repeatable pattern instead of bespoke one-offs.

Sources: PSU Network Effects, A16Z Critical Mass

The Play

For someone building AI + crypto, the play is to deliberately design and own small but crucial standards that others build on:

Strategy	What It Means	Example
Pick a scarce intersection	Find where atoms meet bits with no standard	Proof of data quality format, onchain AI inference receipts
Make core open, own the choke points	Give away spec/SDK, monetize the default infrastructure	Free spec → paid indexer, oracle, explorer, financing vault
Target where capital wants standards	InfraFi investors need verifiable, comparable metrics	"GAAP of DePIN" — sit where the money flows
Align with network effects	If your standard makes everyone richer, they adopt voluntarily	Standard that increases addressable capital wins

Sources: Forbes DePIN, Rapid Innovation

Usage Receipts

A "Standard Usage Receipt" for AI or DePIN services:

RECEIPT = {
  service_type: "inference" | "bandwidth" | "energy",
  quantity: X tokens | Y GB | Z kWh,
  timestamp: T,
  counterparty: Q,
  proof: verifiable_hash
}

Common, onchain, verifiable log entry
Wrap in Hex-architected module (domain = usage receipts, app = indexer + oracle, infra = chain adapters)
Ship as default library others drop into their protocols

Monetization: Indexing fees, oracle subscriptions, analytics, risk scoring for InfraFi lenders, structured products (tranches on standardized receipts).

Source: TDE Interoperability

Dream-Engineering

DREAMINEERING: The balance between two modes. A time to sell. A time to build.

Mode	Question	System	Nature
Dream	"What should exist?"	Belief	Probabilistic — sell the vision
Standards	"What's the spec?"	Value	The Handoff — agree on terms
Engineering	"Build to spec"	Control	Deterministic — execute precisely

Standards enable the handoff between modes. Without standards, you can't switch cleanly from dreaming to building. With standards, the vision becomes a spec, and the spec becomes reality.

The blockchain use case: Public, immutable logs prove standards were followed.

DREAM (Probabilistic)           ENGINEER (Deterministic)
   Time to sell                    Time to build
        ↓                                ↓
   Explore possibilities          Execute precisely
        ↓                                ↓
        └────→ STANDARDS (The Handoff) ←─┘
              Agree on the spec
                        ↓
              VERIFY (Immutable Log)
                        ↓
              PROOF enables TRUST
                        ↓
              Trust enables COORDINATION
                        ↓
              Coordination COMPOUNDS

This is why prediction markets + smart contracts work: dreamers sell the vision, standards define the spec, engineers build precisely, and the blockchain proves it was done right.

See VVFL: The Tight Five of Value Creation for how standards compound through feedback loops.

Protocols and Constraints

Standards emerge from two forces working together:

Force	Function	P&ID Symbol	Question
Protocol	Creates movement	▷ PUMP	How do we proceed?
Constraint	Sets boundaries	● GAUGE	What's acceptable?

Protocols = Rules for making progress (steps, sequences, triggers) Constraints = Limits that shape behavior (thresholds, permissions, policies)

Together they produce decision traces — the record of why something was allowed to happen.

Constraint Types

Type	What It Is	Where It Lives
Threshold	Numeric limit	Benchmarks
Permission	Role-based access	Access control systems
Policy	Rule statement	Documentation
Precedent	Past decision	Context graph
Exception	Approved deviation	Decision trace

Current systems capture thresholds, permissions, and some policies. Precedent and exceptions usually die in Slack.

Non-Expert Problem

The expert knows which exceptions apply. The non-expert doesn't.

Without captured decision traces:

New hires re-solve problems the team solved last quarter
AI agents hit walls of tacit knowledge
Auditors can't verify why something was allowed

With captured decision traces:

Exceptions become searchable precedent
Precedent informs future decisions
The loop compounds instead of repeats

See Matrix Thinking for the test. See Context Graphs for the technical implementation.

Value Migration

Science discovers protocols that repeatedly recreate expected results. Standardization leads to industrialization — scale of transformation and distribution with lowest possible unit economics.

But where does value accrue?

SCIENCE (discovers what's repeatable)
    ↓
PROTOCOLS (methods that recreate expected results)
    ↓
STANDARDS (protocols adopted by many)
    ↓
INDUSTRIALIZATION (scale of transformation)
    ↓
UNIT ECONOMICS COMPRESSION (margins shrink in the middle)
    ↓
VALUE CAPTURE AT EDGES (DePIN thesis)

Stage	Where Value Lives	Why	Industry Examples
Early (Science)	Discovery, IP, patents	Scarcity of knowledge	Quantum, Materials
Middle (Standards)	Platform control, network effects	Coordination advantage	Telecom, Cloud
Late (Industrial)	Edges — data capture, energy, physical	Only scarcity left	Utilities, Commodities

The pattern: As industries mature from frontier → infrastructure → commodity, margins compress in the middle and value migrates to the edges.

This is the DePIN thesis: When everything in the middle is standardized and competed away, the only remaining scarcity is:

Physical presence (sensors, robots, satellites at the edges)
Ground truth data (what actually happened, not what was predicted)
Energy capture (solar, compute, bandwidth at source)

Investment implication:

Frontier (Robotics, Space) — Position for discovery value
Standards (Platform plays) — Position for network effects
Edges (DePIN) — Position for the long-term value capture

See Industries for the full maturity spectrum from Frontier to Infrastructure.

Evolution

Science discovers what is possible, technology converts that knowledge into know-how while standards set expectations for quality control.

The Lindy Effect (Time) and Metcalfe's Law (Adoption)
Successful protocols often become standards through industry adoption and formal recognition
Standards organizations (e.g., ISO, IEEE, IETF) play a crucial role in formalizing and maintaining protocols as standards
Standards ensure interoperability, consistency, and quality across different implementations
The standardization process involves rigorous review, testing, and consensus-building among experts and stakeholders

People who understood this:

Dr. W. Edwards Deming — Quality standards as feedback loops
Mitch Rales / Danaher — Built $200B+ by applying consistent operational standards (DBS) to acquisitions
William S. Knudsen — Mass production standardization

Danaher Business System (DBS): Acquired companies, applied Toyota/Deming standards, compounded value. Proof that standards aren't boring infrastructure — they're the multiplier.

Scalability

Standards enable scalability by driving consistency, without consistency improvement is guesswork.

Stable
Consistent
Composable
Interoperable
Reliable

The HTTP standard for example catapulted the internet to ubiquity by creating a uniform way to exchange information.

ISO Standards

ISO standards are agreed internationally by experts. Key examples: ISO-9001 (Quality), ISO-69315 (Innovation), plus standards for energy, environment, food safety, workplace safety, and IT security.

Space Example

Rocket Lab's internal systems encode standards for:

Component traceability (ISO 9001 quality management)
Configuration management (version control for hardware + software)
Mission requirements (testable specs that compound)

These aren't just compliance — they're the operational standards that make coordination automatic. Internal tools become the third space where plans meet execution. See Space Industry for the full convergence thesis.

Crypto Standards

Regulation happens when good standards fail to evolve.

Newer protocols can establish better standards from inception. The opportunity: prove crypto provides greater capital efficiency through transparency and self-regulation.

The DX thesis: Platforms that engineer risk out at the base level — through type systems, object models, and native safety primitives — enable developers to iterate faster with less capital. Faster iteration produces more experiments. More experiments surface proven patterns. Proven patterns become standards. Standards attract liquidity.

See Developer Experience for the measurement framework.

What's emerging:

Self-regulatory organizations (industry FINRA equivalent)
Crypto-specific GAAP accounting principles
Standardized disclosure of team wallets and allocations
Quarterly financial reporting

tip

Eat your own dog food

Financial Standards

ISO-20022 will support 80% of transaction volumes and 87% of transaction value worldwide. See also CESR for Ethereum staking yield benchmarks.

Web Standards

Verifiable Credentials enable portable online identity—the foundation for decision trace ownership.

Context

First Principles — Naming is the first principle
Naming Standards — The meta of the matter (implementation)
Protocols — Rules for making progress
Developer Experience — DX as leading indicator
Game Economics — Test standards before reality
DePIN — Value capture at edges
Process Optimisation — Without consistency, improvement is guesswork

Mantra

The greatest potential value of blockchain to humanity is an immutable single source of truth

📄️ AI Standards

📄️ Benchmarks

📄️ Control System

Definition​

The Capital Loop​

Economic Foundation​

Japan's Miracle​

Capital Flows​

Games Lab​

Power​

Atoms and Bits​

The Bridge​

Network Effects​

The Play​

Usage Receipts​

Dream-Engineering​

Protocols and Constraints​

Constraint Types​

Non-Expert Problem​

Value Migration​

Evolution​

Scalability​

ISO Standards​

Space Example​

Crypto Standards​

Financial Standards​

Web Standards​

Context​