Tech takes the Pareto principle too far

Meaning and Misuse of the Pareto Principle

• Several commenters argue the article treats Pareto as a sequential “first 80% of work / last 20% of work” rule, whereas it originally describes uneven distributions (e.g., 20% of causes → 80% of effects).
• Others say the analogy to features and effort is still useful, as a heuristic about diminishing returns and prioritization.
• Multiple posts stress that “80/20” is a rough power‑law intuition, not a law of nature, and warn against using it to justify social hierarchies or fatalism.

MVPs, Vertical Slices, and Product Strategy

• Strong debate over whether a game “vertical slice” is equivalent to an MVP.
  • Some say a polished, limited game slice is just one kind of MVP.
  • Experienced game developers counter that MVP ≈ prototype/first playable, whereas vertical slice is production‑quality and used to validate pipelines, not markets.
• In startups, MVP is framed as testing “should we build this at all?” vs. “can we build it?”, with failures like advanced hardware (AR/VR devices) cited as over‑investing pre‑validation.

Value and Cost of the “Last 20%”

• Many agree the final polish delivers emotional satisfaction, brand differentiation, and timelessness, but is expensive.
• Some see normal employment as denying developers that completion satisfaction, reserving it for hobbies (e.g., woodworking).
• Others emphasize opportunity cost: the time to perfect one feature could ship many “good enough” features customers actually value more.

User Expectations and “Good Enough”

• Multiple comments: most users accept “passable” quality in housing, food, apps, etc.; perfection is overkill.
• Counterpoint: in crowded markets, the extra 80% of refinement on core 20% of features is exactly what creates competitive advantage.

Domains Where Pareto Fails

• Safety‑critical systems (medical devices, power plants, serious drones, flight control, some robotics) are cited as examples where you must aim for near‑perfection.
• Concern that fast‑and‑loose web/SaaS cultures are bleeding into domains like self‑driving cars and AI.

AI and 80% Reliability

• Some see current “AI” as an archetypal 80% solution: impressive demos, but 20% error rates make it hard to rely on.
• Others note that for many less‑skilled users, that 80% already exceeds their own baseline and delivers real value (e.g., writing, explanations).