A synthesis of Theory of Constraints, causal inference, reliability engineering, and second-order cybernetics into a unified methodology for engineering systems under resource constraints. The framework provides formal constraint identification, causal validation protocols, investment thresholds, dependency ordering, and explicit stopping criteria. Unlike existing methodologies, it includes the meta-constraint: the optimization workflow itself competes for the same resources as the system being optimized.
Users tolerate slow loads. They don't tolerate lost progress. A 16-day streak reset at midnight costs more than 300ms of latency ever could. At 3M DAU, eventual consistency creates 10.7M user-incidents per year, putting $6.5M in annual revenue at risk through the Loss Aversion Multiplier. Client-side resilience with 25x ROI prevents trust destruction that no support ticket can repair. This is the fifth constraint in the sequence.
New users arrive with zero history. Algorithms default to what's popular - which on educational platforms means beginner content. An expert sees elementary material three times and leaves. The personalization that retains power users actively repels newcomers. This is the fourth constraint in the sequence.
While demand-side latency is being solved, supply infrastructure must be prepared. Fast delivery of nothing is still nothing. GPU quotas - not GPU speed - determine whether creators wait 30 seconds or 3 hours. This is the third constraint in the sequence - invest in it now so it doesn't become a bottleneck when protocol migration completes.
Once latency is validated as the demand constraint, protocol choice determines the physics floor. This is the second constraint - and it's a one-time decision with 3-year lock-in.
Users abandon before experiencing content quality. No amount of supply-side optimization matters. Latency kills demand and gates every downstream constraint. Analysis based on Duolingo's business model and scale trajectory.