Accelerated Change Curve Decision Rule
Act before accelerating change makes inaction your riskiest option
- Difficulty
- Moderate
- Time to result
- ~ongoing to results
- Steps
- 6
- Confidence
- 97%
The Accelerated Change Curve treats technological and market evolution as exponential rather than linear. Leaders first identify the force driving change, then assess whether they are in the deceptively slow early phase or the steep later phase. Because people naturally extrapolate in straight lines, they tend to expect too much too soon and too little once adoption accelerates. The framework therefore evaluates inaction as an active strategic choice, not a neutral default. Instead of waiting for certainty, leaders take a bounded, reversible shot that produces information and preserves their ability to participate. They then revise the move as real evidence replaces forecasts. The output is a sequence of informed actions that keeps the organization adapting ahead of the curve without requiring perfect prediction.
Origin
Doug Leone described the exponential curve as the single curve he consults when making consequential decisions, connecting it to Sequoia's willingness to expand and experiment as technology and markets changed.
Core principles
- 01Technological change accelerates rather than moving linearly
- 02Doing nothing carries increasing strategic risk
- 03Linear thinkers overestimate early progress and underestimate later progress
- 04Imperfect action creates learning that passive observation cannot
How to run it
- 1
Name the accelerating force
Define the technology, behavior, or market dynamic changing the environment. Describe what becomes possible as its adoption compounds.
Pro tip Choose a measurable force such as cost decline, adoption, performance, or distribution speed.
Watch out Do not label ordinary cyclical movement as exponential change.
- 2
Place yourself on the curve
Determine whether the force is still in its slow-looking early phase or has begun steep acceleration. Use multiple indicators rather than one headline.
Pro tip Look for changes in both capability and customer behavior.
Watch out Early exponential growth can appear insignificant when viewed in absolute numbers.
- 3
Correct the linear forecast
Challenge forecasts that extend the recent rate of change in a straight line. Model both an early overestimate and a later underestimate.
Pro tip Use scenarios instead of pretending to know the curve's exact slope.
Watch out False precision can disguise the same linear assumption in a spreadsheet.
- 4
Price the cost of waiting
Compare the downside of a failed experiment with the opportunity, capability, and position lost through delay. Treat inaction as a decision with consequences.
Pro tip Include the time required to build organizational learning.
Watch out Do not assess action risk while treating delay as risk-free.
- 5
Take a bounded shot
Launch a reversible move that creates evidence and strategic options. Keep the downside survivable while making the experiment meaningful.
Pro tip Define the learning objective before committing resources.
Watch out A test too small to generate useful evidence is merely symbolic.
- 6
Learn and recommit
Review what the action revealed and decide whether to expand, modify, or stop it. Repeat as the curve evolves.
Pro tip Increase commitment only when evidence strengthens.
Watch out Do not defend the original move merely because resources have already been spent.
In the wild
A software company sees local competitors emerging in a rapidly digitizing economy. Rather than waiting until its domestic business is mature, it funds a small local team with a six-month mandate to test demand, distribution, and hiring. The team provides evidence before global competition intensifies.
→ The company gains market knowledge and preserves an expansion option without committing to a full launch.
Common mistakes
Treating inaction as safety
Waiting can surrender learning, talent, distribution, and market position while the curve accelerates. The comparison must include those losses.
Betting everything on one forecast
Recognizing exponential change does not reveal its exact timing. Use bounded experiments and staged commitments rather than an all-or-nothing prediction.
Is it for you?
Best for
It is best for leaders making uncertain, time-sensitive decisions in rapidly changing markets.
Not ideal for
It is not ideal for irreversible decisions where experimentation cannot bound the downside.
From the transcript
“there's only one curve I look at for the decisions I have to make. It's the exponential curve of accelerated change.”
“Which means if you believe in that, which means that doing nothing is the worst thing you can do. It's the riskiest thing you can…”
“And then we also know that in the early days of the curve, you over forecast because you're linear thinker.”
From the episode
Sequoia Capital Part II (with Doug Leone)
Doug Leone