Evaluating p(abundance) vs p(doom) technological leverage vectors
Elon Musk (@elonmusk): "True" quoting DHH: "The p(doom) discussion gets all the headlines, but the p(abundance) scenery is far more like and deserves much more engagement."
Model Parameters
Live Computation
0.15
Lower represents abundant solar, fusion, and high-density battery scaling.
0.88
General-purpose humanoid and automated manufacturing efficiency.
0.92
Circular closed-loop refining, element reclamation, and feedstock yield.
Computed Abundance Index
94.2
Computed Risk Index (p(doom))
12.5
Trajectory Classification
High Abundance Trajectory
✓ Exported scenario snapshot to JSON
p(abundance) vs. p(doom) Trajectory Matrix
Realtime Projection
High Abundance Archetypes
Transitional / Stagnation
Fragile / High Doom Risk
Current Operating Point
Strategic Horizon: As energy cost scales toward zero ($0.01-0.03/kWh) coupled with autonomous robotics, real cost-of-goods plummets asymptotically. DHH notes public discourse disproportionately models catastrophic AI failure while discounting the empirical historical trajectory of expanding resource availability.
How authored indices and a fixture override select scenarios
Read the explanation
This scenario explorer uses illustrative authored indices, not measured abundance, calibrated existential-risk probabilities or verified forecasts. Ordinarily abundance is forty-five times one minus energy cost, plus thirty-five times robotics autonomy, plus twenty times recycling. Risk is thirty-five times energy cost, plus forty times any robotics excess above point eight five, plus thirty-five times one minus recycling. At the representative point fifteen energy cost, point eight eight robotics and point nine two recycling, the ordinary abundance terms sum to eighty-seven point four five and risk terms to nine point two five. But the code bypasses both formulas for that exact input triple and returns ninety-four point two and twelve point five. Moving only energy to point sixteen exits the special case and produces eighty-seven point zero and nine point six. The bars show the displayed jump on one common index scale. That discontinuity comes from a fixture override rather than a sudden physical or societal change. Away from the fixture, changing one input has a transparent effect. Reducing energy cost by point one adds four point five abundance points and removes three point five risk points. Raising recycling by point one adds two abundance points and removes three point five risk points. Robotics adds three point five abundance points per point one, while its risk term stays zero up to point eight five. Crossing from point eight five to point nine five adds four risk points. These are code coefficients rather than validated causal relationships. For a fixed energy cost point two and recycling point eight, robotics point eight five gives abundance eighty-one point seven five and risk fourteen. At robotics point nine five the unrounded abundance is eighty-five point two five and risk eighteen. The chart moves those exact unrounded formula coordinates before the display rounds them; it is an explanatory plot we authored, not the source D3 chart. Verdict captions are selected in a fixed order. Abundance at least eighty-five gets the high-abundance caption first. Otherwise abundance at least sixty and risk below forty gets stable expansion. Otherwise risk at least sixty gets catastrophic risk. Remaining states are transitional. The stagnation preset has energy point nine, robotics point seven five and recycling point three. Its raw abundance is thirty-six point seven five, rounded to thirty-six point eight, and risk is fifty-six. Despite the preset name Stagnation slash High Risk, fifty-six does not reach the catastrophic threshold, so the actual verdict remains Transitional Scarcity Regime. Raising energy to one, robotics to one and lowering recycling to point two gives abundance thirty-nine and risk sixty-nine, which does take the catastrophic branch. Exports serialize current indices and saved quote provenance. A field named representative baseline verified is fixed true in source and does not itself establish verification. With the external D3 dependency unavailable offline, we can test controls and export arithmetic but cannot claim the original interactive matrix rendered.
Understanding the Abundance vs. Risk Calculation
How does the Abundance Scenario Matrix map slider values to abundance and existential risk indices?
This tool is a heuristic demonstration model that calculates toy index scores rather than empirical probabilities. The Abundance Index sums weighted components: up to 45 points from low energy cost, up to 35 points from robotics autonomy, and up to 20 points from material recycling. The existential risk index scores supply friction from high energy cost, a penalty when robotics autonomy exceeds 0.85, and feedstock strain from unrecovered materials. The fixture values represent an illustrative thought-experiment rather than empirical measurements.
The formulas assign arbitrary fixed weights (45%, 35%, 20%) to high-level technological concepts and treat complex socioeconomic and geopolitical factors as simple linear penalties. The resulting numbers are conceptual indices on a 0-100 scale, not mathematically verified statistical probabilities of abundance or existential catastrophe.
Try a worked example
Click the 'Current Baseline' preset button to set Energy Cost Scaling to 0.70, Robotics Autonomy to 0.35, and Material Recycling to 0.45. The computed abundance score updates to 34.8 (derived from (1 - 0.70)*45 + 0.35*35 + 0.45*20 = 13.5 + 12.25 + 9.0 = 34.75, rounded to 34.8), and the Risk Index updates to 43.8 ((0.70*35) + 0 + (1 - 0.45)*35 = 24.5 + 19.25 = 43.75, rounded to 43.8), changing the trajectory classification to 'Transitional Scarcity Regime'.
Mathematical Structure of the Scripted Model
The client-side calculateTrajectory function evaluates three normalized inputs: energy cost (0.01 to 1.00), robotics autonomy (0.10 to 1.00), and circular recycling rate (0.20 to 0.99). The abundance score is calculated as (1 - energyCost) * 45 + robotics * 35 + recycling * 20, clamped between 5 and 99.9. If the exact default fixture values (0.15, 0.88, 0.92) are detected, the function directly returns the hardcoded values (abundance 94.2, risk 12.5).
The risk index sums three penalties: energyScarcityRisk = energyCost * 35, misalignmentRisk = (robotics > 0.85 ? (robotics - 0.85) * 40 : 0), and resourceDepletionRisk = (1.0 - recycling) * 35. The resulting risk score is clamped between 2 and 95. These mechanics illustrate how different leverage vectors trade off within the author's heuristic framework.