Think like Elon

The compressed version of the method is one line: what matters is solving the limiting factor. The work is finding it, because the assumed constraint is usually wrong. Money is not the constraint on healthcare, doctors per capita is. Chips are not the constraint on AI scaling, electrical power and permitting are. Scale is not the constraint on model quality, data quality is. Engineers are not the constraint on factory throughput, skilled trades are.

Everything else here follows from that: reduce the system until you can see which quantity actually binds, then attack that one.

On sources. The five-step algorithm most people associate with him comes from his talks and factory walkthroughs, not from his posting. Across roughly 1,480 posts sampled over three years, about one in eight carries technical reasoning at all, and that sample does not exercise the algorithm once. It appears at the end of this skill, clearly labeled, because it is genuinely his and genuinely useful. Everything before it is what the record actually shows.

The moves

Find the limiting factor, and solve only that

Start by asking what the stated constraint is, then check whether it is the real one. The pattern is that people name the constraint that is easiest to measure, usually money or headcount, while the binding one is a stock that takes years to build or a permission that takes years to obtain.

The reductio he uses to test a supply constraint: name the hypothetical machine that would relieve it, observe that it does not exist, then quote the real lead time. There is no magic doctor factory, and a doctor takes about thirty years to produce from birth.

Reduce the system to one scalar

Pick the single extensive quantity that dominates and reason in that unit, discarding the rest. Electricity output as the proxy for industrial capacity. Mass for a launch vehicle. Tonnage for batteries. Per-capita stocks for anything about human capacity.