TheCryptoDegen

PlanB (@100trillionUSD)on X, a former institutional investor, published three foundational Medium articles developing the Bitcoin Stock-to-Flow (S2F) model. This scarcity-based framework quantifies an asset’s value primarily through its Stock-to-Flow ratio (S2F = existing stock / annual flow or new supply). Bitcoin’s programmed halvings every ~4 years cause its S2F to rise sharply, making it increasingly scarce like gold or silver. The articles form a logical trilogy: introduction of the core model, defense against Efficient Market Hypothesis (EMH) critiques, and refinement into a cross-asset version (S2FX).Below is a clear summary of each article, followed by an overall analysis of their contributions, evolution, strengths, and implications.

1. "Modeling Bitcoin Value with Scarcity" (March 22, 2019). This is the original article that launched the S2F model. PlanB argues that scarcity (not utility or other factors) is the primary driver of monetary value for assets like gold, silver, and now Bitcoin. He quantifies scarcity via the S2F ratio and applies it to BTC’s fixed supply schedule.Key concepts: BTC’s stock was ~17.5 million coins with ~0.7 million new coins/year (S2F ≈ 25) at the time—placing it in the same league as monetary metals. He compares S2F across assets in a table and shows that higher S2F correlates with higher market value.
Model: Uses historical monthly BTC data (2009–early 2019) to fit a power-law regression (log(price) vs. log(S2F)). The simplified formula is roughly BTC price ≈ 0.4 × S2F³ (exact coefficients vary slightly in citations). The fit is statistically strong.
Predictions and conclusions: Future halvings will drive S2F higher (e.g., to ~56 after the 2020 halving), implying substantial price appreciation. PlanB frames this as a working hypothesis rooted in scarcity, not hype.
Tone and impact: Straightforward and data-focused, introducing S2F as a simple, verifiable metric inspired by Nick Szabo’s “unforgeable costliness” and Saifedean Ammous’s work.

2. "Efficient Market Hypothesis and Bitcoin Stock-to-Flow Model" (January 17, 2020). This article directly addresses the most common critique of the original model: If S2F data is public and predictable, why hasn’t the market already priced it in per EMH?
Main thesis: Bitcoin markets are reasonably efficient (weak and semi-strong forms—no easy arbitrage across exchanges), but they systematically overestimate risks (e.g., miner death spirals, government bans, hard forks, 51% attacks, scams). This risk overestimation explains why BTC’s actual returns have far exceeded what a classic risk-and-return model (Markowitz portfolio theory / CAPM) would predict.
Evidence:Risk-return chart showing bonds, gold, and stocks on a line; BTC (high risk, extremely high return) sits “off the chart.” Even a tiny 1% BTC allocation beats the expected return line.
Derivatives markets (futures/options) showed no major pre-halving price spikes, implying the market was pricing in exaggerated fears.
Investor surveys highlighted perceived risks (e.g., 42% feared futures manipulation).
Conclusion: Because risks are overestimated, the S2F scarcity model remains a superior forecasting tool over pure EMH/risk-return frameworks. PlanB still “picks up that bitcoin” despite EMH logic.
This piece acts as a bridge, defending the original model while anticipating the cross-asset upgrade.

3. "Bitcoin Stock-to-Flow Cross Asset Model" (April 27, 2020) — aka S2FX. This is the most sophisticated refinement. PlanB removes the time-series element of the original model and treats Bitcoin’s post-halving phases as distinct “assets” with abrupt transitions (like phase changes in water or the U.S. dollar’s evolution). He then adds gold and silver to create a unified cross-asset formula.
Key innovation (S2FX): Uses a genetic algorithm to cluster BTC’s historical monthly data into four phases (each with its own S2F and market value):Proof of concept (S2F ≈ 1.3, market value ~$1M)
Payments (S2F ≈ 3.3, ~$58M)
E-Gold (S2F ≈ 10.2, ~$5.6B)
Financial asset (S2F ≈ 25.1, ~$114B)
Cross-asset regression: Adds silver (S2F ≈ 33.3, market value ~$561B) and gold (S2F ≈ 58.3, ~$10T). The six data points form a near-perfect straight line on a log-log chart.
Formula: Market Value = exp(12.76) × (S2F)^4.12 (R² = 99.7%, very low p-values). This single equation prices BTC phases, silver, and gold.
Prediction: For the next phase (2020–2024, S2F ≈ 56), market value ≈ $5.5 trillion → BTC price ≈ $288,000 (based on ~19M supply). This was significantly higher than the original model’s ~$55k forecast.
Conclusions: S2FX is more robust because it interpolates within a multi-asset dataset instead of extrapolating a single time series. It treats BTC halvings as phase shifts that create new scarcity narratives.

Overall Analysis. The three articles show clear intellectual evolution, from simple to sophisticated: Article 1 introduces a BTC-only power-law scarcity model. Article 2 defends it philosophically against EMH. Article 3 upgrades it into a general scarcity valuation tool (S2FX) that works across assets and phases, achieving an almost perfect statistical fit.
Core strength: All three rest on an elegant, quantifiable thesis—scarcity (S2F) drives value—backed by transparent data, power-law regressions, and high R² values. PlanB repeatedly emphasizes falsifiability and calls the models “working hypotheses.”
Handling criticism: The EMH piece is a masterful preemptive response that acknowledges market efficiency while highlighting behavioral/psychological factors (risk overestimation) that allow S2F to retain predictive power.
Impact and limitations (within the articles themselves): These pieces popularized S2F in the Bitcoin community, spawning charts, derivatives, and widespread discussion. They shifted focus from utility narratives to pure scarcity. PlanB notes limitations (small sample size in S2FX, need for peer review) but argues the framework is stronger than alternatives.
Collective contribution: Together they provide a complete scarcity-based valuation toolkit: measure it (Article 1), defend why it still works (Article 2), and generalize it across assets and phases (Article 3). The progression from time-dependent to phase-based and cross-asset thinking is the key innovation.

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