Predict future values with exponential growth/decay. Doubling time, half-life, Rule of 70, milestone table, growth trajectory, discrete & continuous compounding.
Given an initial value and a constant growth or decay rate, this calculator projects future values with exponential models. It is useful anywhere the rate of change is proportional to the current amount, such as population growth, compound returns, decay, or adoption curves.
You can compare discrete compounding, P(t) = P₀(1+r)^t, with continuous compounding, P(t) = P₀e^(rt), and see the equivalent rate conversion. That makes it easier to check whether a scenario is being modeled as stepwise change or continuous change.
The growth trajectory table shows how the value evolves over time, while the milestone table answers practical questions such as when the value doubles, reaches five times its starting point, or hits a target threshold.
Exponential models are the right choice when the same rate applies to whatever amount currently exists. That simple rule produces very large differences over time, which is why this calculator is useful for forecasting, comparison, and sanity checks.
The milestone and trajectory tables make it easier to see how a rate compounds over repeated periods instead of forcing you to extrapolate the curve mentally.
Discrete: P(t) = P₀(1+r)ᵗ. Continuous: P(t) = P₀eʳᵗ. Doubling time: t₂ = ln(2)/ln(1+r) or ln(2)/r. Rule of 70: t₂ ≈ 70/r%.
Result: Final value: $76,122.55, Total growth: 661.23%, Doubling time: 10.24 years, Rule of 70: 10.0 years
$10,000 at 7% annual growth reaches $76,123 in 30 years — a 6.6× return. The investment doubles roughly every 10.2 years. The Rule of 70 gives a quick estimate: 70/7 = 10.0 years.
The exponential function P(t) = P₀eʳᵗ is the unique function satisfying dP/dt = rP — the rate of change is proportional to the current value. This fundamental property explains why exponential growth appears in so many domains: populations where births ∝ current population, investments where interest ∝ balance, and reactions where rate ∝ concentration.
The compound interest formula A = P(1+r/n)^(nt) generalizes to continuous compounding as n→∞: A = Peʳᵗ. Einstein (apocryphally) called compound interest the eighth wonder of the world. At 7% annual return (historical S&P 500 average), $10,000 becomes $76,000 in 30 years and $580,000 in 60 years — a 58× return.
Thomas Malthus predicted (1798) that exponential population growth would outstrip linear food production growth, leading to famine. The Green Revolution proved Malthus wrong by making food production approximately exponential too. But the underlying concern remains: on a finite planet, exponential growth must eventually saturate. Today's climate models, resource depletion forecasts, and pandemic models all incorporate exponential phases that eventually transition to logistic or collapse trajectories.
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Doubling time is approximately 70 divided by the growth rate in percent. It is a quick mental shortcut that works best for moderate rates.
For small rates (<5%), discrete and continuous give nearly identical results. The difference grows with larger rates. $1,000 at 100% for 1 year: discrete = $2,000, continuous = $2,718.28. Finance uses discrete; physics uses continuous.
Yes — negative rates model exponential decay: radioactive half-lives, drug metabolism, depreciation. At −5% per year, an asset halves in about 13.5 years. The trajectory and milestone tables adjust for decay automatically.
The mathematical calculation is exact, but the assumption of a constant rate is the weak link. No real-world quantity maintains exactly 7% growth for 30 years. Use the projections as scenario analysis, not precise forecasts.
Exponential regression fits a model to observed data (finding the rate from data). This calculator does the opposite: given a known rate, predict future values. Use regression to discover the rate, then this calculator to project forward.
When resources are finite. A city growing 3%/year would engulf the entire Earth in a few hundred years. In reality, growth slows as carrying capacity is approached. For realistic long-term modeling, use logistic growth models.