Wright's Law Cost Curve Forecasting
Predict when emerging technologies become economically viable using production learning rates.
Wright's Law states that for every cumulative doubling of a technology's key production metric, unit costs decline by a constant percentage—its learning rate. By identifying the right cumulative metric (e.g., kilograms launched to orbit, kWh of batteries produced), measuring the historical learning rate, and projecting future output doublings, practitioners can forecast cost trajectories and pinpoint the price thresholds at which entirely new applications become economically viable. ARC Invest applies this to rockets: every cumulative doubling of kilograms launched to orbit yields roughly a 17% cost decline, which predicted Falcon 9 reaching ~$1,000/kg and projects Starship reaching sub-$100/kg—the threshold that makes orbital data centers financially feasible.
- Costs in technology-driven industries are not random—they follow predictable learning curves tied to cumulative production.
- The key variable is the cumulative production metric, not time; growth rate determines how fast the curve is traversed.
- Each doubling unlocks new economic thresholds that make previously unviable applications suddenly feasible.
- Learning rates are empirically measurable from historical data and tend to remain stable within a technology domain.
- Identifying the correct unit metric is the most critical and error-prone step—wrong metric, wrong forecast.
- Incumbent advantage compounds: earlier producers accumulate doublings faster, widening their cost lead over time.
- Select the correct cumulative production metricIdentify the single metric that best captures accumulated 'experience' in the technology—for rockets it is total kilograms launched to orbit, for solar it is total GW installed. Choosing the wrong metric produces misleading forecasts.Pro tipWhen in doubt, test multiple candidate metrics against historical cost data and pick whichever produces the most stable learning rate over time.WarningUsing time as a proxy instead of cumulative output is the most common error; it conflates pace of adoption with the learning curve itself.
- Measure the historical learning ratePlot log-log cost vs. cumulative output using at least a decade of data. The slope of the regression line gives the learning rate—the percentage cost decline per doubling. For space launch this has measured around 17%.Pro tipCross-validate your learning rate against analogous technologies (e.g., compare rocket learning rate to analogous aerospace hardware) to sanity-check outliers.WarningShort data series with sparse doublings can produce artificially steep or flat rates; aim for at least three full doublings before trusting the number.
- Establish the current baseline cost and cumulative outputAnchor the forecast by documenting today's verified cost-per-unit and the current cumulative production level. For rockets, this means current $/kg to orbit and total kg launched to date industry-wide or for a specific provider.Pro tipUse the most capital-efficient producer's cost, not the industry average, if you are assessing competitive moats or best-case scenarios.
- Project future cumulative output doublingsEstimate how many times cumulative output will double over your forecast horizon using realistic growth-rate assumptions. Model conservative, base, and aggressive scenarios. Each additional doubling applies the learning rate discount to current cost.Pro tipTie growth-rate assumptions to concrete demand drivers (e.g., satellite constellation buildouts, government contracts) rather than extrapolating historical growth blindly.WarningElon Musk-style timelines—or any visionary founder's stated schedules—tend to embed optimism bias; stress-test your schedule assumptions.
- Calculate forecast cost at each doubling milestoneApply compound learning-rate discounts to today's cost for each projected doubling. For example, three doublings at a 17% learning rate yields: cost × (1−0.17)³. This gives a cost curve over the forecast period.Pro tipBuild a simple spreadsheet with cumulative output on the x-axis and cost on the y-axis; the curve becomes a powerful visual communication tool for investment theses.
- Map cost thresholds to use-case viability unlock pointsList target applications ranked by the cost level they require to become economically feasible. Overlay these thresholds on the forecast cost curve to identify when each use case is expected to unlock. For orbital data centers, the unlock is sub-$100/kg to orbit.Pro tipTreat the unlock point as an investment entry signal—position before the cost curve crosses the threshold, not after the market has priced it in.WarningEconomic viability is necessary but not sufficient; also check for regulatory, infrastructure, and demand-side readiness before concluding a use case will actually scale.
ARC Invest tracked cumulative kilograms launched to orbit across the commercial launch industry. With a measured ~17% learning rate, the Wright's Law model explained Falcon 9's arrival at roughly $1,000/kg—cheap enough to make Starlink's satellite internet economically viable. Projecting further doublings enabled by Starship's full reusability, ARC forecast sub-$100/kg launch costs, the threshold their model identified as necessary for orbital data centers to be economically competitive with terrestrial alternatives.
ARC Invest has applied the same Wright's Law methodology to solar PV, tracking cumulative GW installed globally. Each doubling of installed capacity has produced a consistent ~20% cost reduction for decades. Analysts who used this framework in 2010 correctly projected that solar would reach grid parity in most markets by the early 2020s, enabling massive investment positioning ahead of the inflection—years before consensus recognized the shift.
Using Wright's Law on cumulative GWh of lithium-ion batteries produced, analysts tracked the learning rate at roughly 18% per doubling. This allowed projection of the cost level at which EVs would reach upfront purchase-price parity with internal combustion vehicles—approximately $100/kWh pack cost. Investors who anchored their EV thesis to this threshold rather than to arbitrary calendar-year targets positioned more precisely in automakers and battery suppliers.
Originally formulated by engineer Theodore Wright in 1936 while studying aircraft manufacturing. ARC Invest has extended it across solar, batteries, genomic sequencing, and space launch, using it as a core tool for timing technology investment theses. Application to rockets discussed by Daniel Maguire on Milk Road AI.