Flywheels

Compounding loops that accelerate with momentum; popularised by Jim Collins.

Author

Jim Collins (popularised); modern growth-loop practice in product and marketplaces



A flywheel is a repeatable sequence of actions whose outcomes feed back to make the next cycle stronger. Unlike one-off funnels or campaigns, flywheels compound: momentum builds, costs per result fall, and performance improves with each turn. Classic examples include marketplace loops (more selection → better experience → more buyers → attracts more sellers) and product-led loops (usage → content/data → better product → more usage).

How it works


Loop definition – name 3–6 nodes that feed one another and return to the start (A → B → C → A’).

Loop gain (g) – the multiplicative effect per cycle. If g > 1, the loop accelerates; if g < 1, it stalls.

Latency – time for one full turn; shorter cycles compound faster.

Momentum and push – early turns need external energy (sales, promos, manual ops) until the loop carries itself.

Friction and leakages – drop-offs, delays, quality issues, and handoffs reduce g; fix the weakest link first.

Counter-loops – pair helpful loops with balancing loops (quality, trust, cost) to avoid runaway harm.

Doom loops – the same logic can spiral down (slower delivery → worse reviews → fewer orders → slower delivery).

Use-cases


Product-led growth – usage → content/data → better recommendations/features → more usage.

Marketplaces – more supply → better choice → more demand → attracts more supply.

Content/SEO – publish quality pieces → organic traffic → email/community growth → more signals and contributors → more quality pieces.

Developer platforms – more apps/integrations → more users → more APIs and tooling → more apps.

Operations – better reliability → trust/adoption → more data and automation → lower incident rate.

Brand – great experience → referrals/reviews → higher conversion → more customers experiencing greatness.

Pitfalls & Cautions


Calling funnels “flywheels” – if outcomes don’t meaningfully feed the next turn, it isn’t a flywheel.

g < 1 – pretty diagrams with leaky steps; quantify or you’ll fool yourself.

Long latencies – slow cycles blunt compounding; bring value forward.

Perverse incentives – growth that harms quality or trust; pair with counter-metrics.

Over-seeding – permanent discounts or manual hacks that mask a weak loop.

Single-node obsession – improving one step while another throttles the loop.

Ignoring saturation – loops flatten at constraints (supply, attention, channel caps); design the next loop.

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