Soviet Shoe Factory Principle

Original Article ↗ Hacker News Discussion ↗

Measurement, Goodhart’s Law, and KPIs

  • Core idea: “What gets measured gets done” – but once a metric becomes a target, it’s easily gamed (Goodhart’s Law / “measurement dysfunction”).
  • Commenters note US education, enterprise software, and internal corporate KPIs as examples where measured quantities improve while actual quality worsens.
  • GitHub commit counts as a performance metric led to more commits, worse code, angry customers, and eventually business loss.
  • Several argue that designing truly ungameable metrics may be impossible; proxies always miss nuance.

Capitalism, Theranos, and ‘Soviet’ Corporations

  • One analogy: Theranos as a capitalist version of the Soviet shoe factory—chasing investor metrics and hype instead of real products.
  • Pushback: others say Theranos was straightforward fraud, and “capitalism killed it” once the fraud was exposed.
  • Debate over whether early Theranos was naïve optimism or fraud from the start.
  • Many see large corporations as Soviet-like: rigid hierarchies, internal propaganda, distorted metrics, and political infighting. Counterpoint: unlike states, corporations must eventually make payroll or die (except when bailed out).

Markets, Consolidation, and Free-Market Limits

  • Some argue “thousands of competing Soviets” (firms) keep capitalism healthier than central planning.
  • Others question whether the current system is “healthy,” citing inequality, housing costs, and corporate concentration.
  • Long thread on Sears/Kmart: was their collapse a success of market discipline or a failure where PE looting and consolidation hurt competition and communities?
  • Broader concern: capitalism tends toward monopoly/oligopoly; antitrust and regulation are seen as necessary correctives.

Metric Gaming in Tech and Products

  • Smartphone cameras: manufacturers optimize for spec-sheet metrics (megapixels, “sensor size”) rather than real image quality; AI upscaling and fake moon photos are cited.
  • ML benchmarks: once famous, they get overfit and stop reflecting real capability.
  • VO2 max and other fitness metrics mentioned as similar failure modes.

How to Respond to Metric Failure

  • Suggestions:
    • Spend more effort designing better measures, but accept they’ll still be partial.
    • Align incentives and shared fate so metrics are advisory, not the whole game.
    • Use metrics to inform, not as rigid targets, and prioritize human judgment and integrity over “data-driven” theater.