Value and Values Investing

What must you learn before your predictions should influence a shared future?

A value investor asks what an opportunity is worth relative to its price. A values investor asks whether the outcome is worth producing, who benefits, and which rights or harms cannot be traded away. A capable ecosystem participant must do both.

This method prepares people to allocate attention, trust, reputation, and capital before those judgments are expressed through futarchy. It does not promise that a market price reveals moral worth or that staking creates wisdom.

values → thesis → evidence → valuation → prediction → allocation → outcome → learning

Two Gates

Every opportunity must pass two gates:

1. Can it compound? Is there a credible path to durable, venture-scale value?
2. Should it compound? Would scaling this company strengthen human agency, legitimate relationships, and the ecosystem it affects?

The first gate tests investability. The second tests eligibility. Strength in one cannot compensate for a fatal failure in the other.

Values Lens

Use five lenses before forecasting returns:

Lens	Investor question	Evidence to seek
Truth	Are the claims, measurements, and uncertainties represented honestly?	Primary evidence, provenance, falsifiers, variance, and corrected claims
Identity	What distinctive company is forming, and what will it refuse to become?	Purpose, principles, founder behaviour, boundaries, and repeated choices
Trust	Why should customers, workers, partners, and capital continue choosing it?	Commitments kept, informed consent, recovery, retention, and reciprocity
Value	What worthwhile change is created, captured, and returned to those who enable it?	Beneficiary outcomes, willingness to pay, margins, and fair value flows
Spirit — Good Company	Will this venture attract people who strengthen goodwill and responsible agency?	Conduct under pressure, quality of relationships, stewardship, and exits

These are governance constraints, not brand adjectives. If evidence of fraud, coercion, preventable harm, or extraction crosses an agreed boundary, the opportunity is ineligible even when its expected financial return is high.

Venture Truths

Institutional AI diligence commonly probes technical depth, data provenance, business impact, defensibility, cost at scale, team quality, safety, and survivability. The questions vary; their shared purpose is to expose unknowns that could prevent a company from becoming durable and fund-returning. See the Rebel Fund diligence guide, Mayfield's investor questions, and Sentiero's founder questions as examples—not universal authorities.

Truth	What must become believable?	Disconfirming signal
Problem	The pain is urgent, frequent, costly, and still matters when AI language is removed	Admiration without changed behaviour or willingness to pay
Founder	The team combines technical depth, domain insight, judgment, and learning velocity	Borrowed insight, hidden gaps, or inability to recruit and adapt
Market	A reachable wedge can expand into a market large enough for the capital model	Top-down TAM with no buyer, budget, distribution route, or expansion path
Moat	Use compounds a defensible advantage in data, workflow, trust, or distribution	A thin interface whose supplier or competitor can copy the whole value
Economics	Retention, gross margin, service cost, and acquisition economics improve with scale	Inference, implementation, support, or CAC grows as fast as revenue
Timing	Adoption readiness and enabling technology create a window this team can exploit	The market is too early, already commoditised, or dependent on one event
Survivability	The venture remains valuable through model, platform, price, and regulatory shifts	One provider, subsidy, feature, customer, or regulatory assumption owns it

Do not total these into a comforting average. A fatal contradiction in problem truth, economics, integrity, or survivability blocks allocation regardless of strength elsewhere.

Learning Loop

Produce one values-investor memo:

1. Name the beneficiary and setpoint. State whose reality should improve, what good means, and which harms are unacceptable.
2. Write the thesis. Use: “I believe [venture] can create [valuable change] for [beneficiary] because [mechanism and evidence] by [review date].”
3. Test the five lenses. Record evidence, uncertainty, and any eligibility breach for Truth, Identity, Trust, Value, and Spirit.
4. Test the seven truths. For each, write the strongest supporting evidence and the strongest disconfirming evidence.
5. Value the opportunity. Separate intrinsic worth, expected economic value, market price, social value, and downside. Do not disguise one as another.
6. Make one prediction. Freeze an outcome, measure, time window, confidence, and causal assumptions before allocating.
7. Allocate within loss limits. Commit only the attention, reputation, or capital justified by evidence and reversibility.
8. Review the receipt. Compare the observed outcome with the prediction, update credibility, and retain the lesson for the next decision.

Memo Template

Beneficiary and desired change:
Non-negotiable values and harm limits:
Venture thesis:
Truth / Identity / Trust / Value / Spirit evidence:
Problem / Founder / Market / Moat / Economics / Timing / Survivability evidence:
Intrinsic worth, expected value, price, and downside:
Prediction, measure, window, and confidence:
Allocation and maximum acceptable loss:
Falsifier and stop condition:
Review date and outcome receipt:
Lesson retained:

Futarchy Boundary

Robin Hanson's formulation is “vote on values, but bet on beliefs”. In this Playbook:

• people and legitimate governance choose eligible values, beneficiaries, rights, setpoints, and harm constraints;
• prediction markets estimate which eligible proposal is most likely to improve a frozen measure;
• the market does not define human worth, moral legitimacy, or whose interests count;
• outcome evidence updates forecasts and credibility;
• revising the setpoint requires an explicit governance process, not a price movement.

This makes futarchy downstream of values formation. A participant who cannot write a falsifiable values-investor memo is not ready to influence consequential allocation through a prediction market.

Sui Role

Sui can make the coordination legible and enforceable by representing proposals, identities, positions, outcome windows, settlement rules, and receipts as programmable objects. The Sui futarchy pattern explains the conditional markets and settlement layer.

The chain supplies execution and verification. It does not supply the values, causal validity, oracle legitimacy, or human authority that make the result worthy of trust.

Proof of Done

The learning cycle is complete when:

• one participant can explain the difference between value and values;
• the memo includes both supporting and disconfirming evidence;
• eligibility breaches cannot be compensated by projected return;
• the prediction names one frozen measure, window, confidence, and falsifier;
• allocation has an explicit maximum acceptable loss;
• a dated review updates the thesis and the participant's forecasting record.

Failure Modes

• Values theatre — attractive principles appear in the memo but never block an investment.
• Price as truth — market price is treated as proof of worth or legitimacy.
• AI theatre — model language hides a weak problem, product, or business.
• Score compensation — strengths elsewhere conceal a fatal integrity, economics, or survivability failure.
• Whale morality — capital weight is mistaken for greater standing to define the good.
• Metric capture — participants optimise the settlement measure while degrading the beneficiary's real outcome.
• Oracle laundering — a contestable human judgment is presented as neutral on-chain fact.
• Permanent setpoint — governance becomes efficient at pursuing an outdated definition of value.
• Unpriced harm — benefits are captured privately while ecological, social, or operational costs are displaced.

Context

• parent Investing — treat attention, relationships, reputation, and money as predictions about the future.
• forms Investment Thesis — turn belief into a falsifiable claim before allocating.
• governed-by Goodwill — make good company both a method and a measure.
• implemented-by Futarchy on Sui — aggregate beliefs only after values and gauges are explicit.
• measured-through MEV Benchmark — prevent throughput or return from hiding lost agency or unacceptable harm.

Questions

Which opportunity would you still support if its price disappeared but its effects on people and the ecosystem remained visible?

• Which value is genuinely non-negotiable because it can stop allocation?
• What evidence would prove that the venture's advantage compounds with use?
• Which prediction could be settled without pretending the metric captures the whole good?
• Who bears the downside if the market is confidently wrong?

Changes my mind: Repeated reviews show that this two-gate method produces no better eligibility, forecasting, allocation, or learning decisions than a simpler investment memo.

Next question: Can you complete one values-investor memo before asking the ecosystem to bet?