Lead-Domino Technology Sequencing
Solve the enabling constraint before scaling dependent solutions
- Difficulty
- Advanced
- Time to result
- ~months to results
- Steps
- 5
- Confidence
- 95%
Lead-Domino Technology Sequencing starts by identifying the prerequisite that unlocks the largest number of downstream solutions. Holman uses energy as the central example: abundant clean energy would make recycling, carbon capture, computing, and other processes more feasible, while attempting to scale those processes first can waste fuel and attention. The method maps desired outcomes to dependencies, tests those dependencies with basic arithmetic, and concentrates effort on the shared enabling constraint. After that constraint changes, downstream choices are reassessed because some will become easier and others irrelevant. The framework is deliberately skeptical of feel-good activity that ignores physical inputs. Its key decision rule is not simply to prioritize the highest-profile problem, but to solve problems in the logical order imposed by their mechanisms.
Origin
Extracted from The Tim Ferriss Show, where Holman argues that energy is the lead domino for multiple technology and environmental problems.
Core principles
- 01Order can matter more than the number of problems addressed
- 02An enabling constraint can make downstream problems easier or irrelevant
- 03Basic arithmetic can expose solutions that cannot scale
- 04Do not scale a dependent solution before its prerequisite exists
How to run it
- 1
Define the downstream outcomes
List the solutions or capabilities you want to make possible. Keep them concrete enough to identify their physical, technical, or institutional inputs.
Pro tip Separate desired outcomes from the activities currently associated with them.
- 2
Map the dependencies
For each outcome, identify what must already be abundant, reliable, or permitted. Look for one prerequisite that appears across many branches.
Pro tip Include energy, materials, regulation, and capital rather than mapping only software dependencies.
Watch out A missing prerequisite can make an otherwise attractive intervention performative rather than effective.
- 3
Find the lead domino
Select the enabling constraint whose removal would simplify or unlock the most downstream work. Give it priority over visible but dependent activities.
Pro tip Prefer a constraint that solves several other problems for free.
- 4
Run the arithmetic
Estimate the concentration, energy, cost, throughput, and scale involved. Reject sequences whose inputs become implausible when multiplied to real-world scale.
Pro tip Use rough arithmetic early; precision is unnecessary when orders of magnitude decide the answer.
Watch out Do not let moral appeal substitute for a workable resource equation.
- 5
Build in logical order
Expand or solve the lead constraint before scaling dependent solutions. Then revisit the map because the option set and economics will have changed.
Pro tip Measure whether the downstream tasks actually became cheaper or easier.
Watch out Do not keep scaling the old sequence after its assumptions change.
In the wild
Holman argues that recycling works better after clean, abundant energy exists. If recycling plants and collection systems burn enough fuel, scaling them before fixing energy puts the cart before the horse.
→ Energy becomes the first intervention, and recycling is evaluated again under the improved input constraint.
Holman describes atmospheric carbon as only 400 parts per million, making direct capture an energy-intensive search through dilute material. Cheap, abundant energy changes whether pumping air through filters is practical.
→ The sequencing test exposes why the energy supply must precede large-scale atmospheric capture.
Common mistakes
Solving visible problems first
The most visible intervention may depend on a less visible prerequisite that deserves priority.
Skipping scale arithmetic
A solution can sound compelling while failing on concentration, energy, or throughput at real scale.
Scaling the wrong thing
More activity does not help when the sequence itself is wrong.
Is it for you?
Best for
It is best for complex technology, infrastructure, and policy problems with strong dependencies.
Not ideal for
It is not ideal for independent tasks where no shared bottleneck or enabling constraint exists.
From the transcript
“We don't have time to keep scaling the wrong thing.”
“We got to pick something that's going to work and then go build that.”
“You can just do basic arithmetic to get those answers a lot of the time.”
From the episode
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