> ## Documentation Index > Fetch the complete documentation index at: https://meta.niceshare.site/llms.txt > Use this file to discover all available pages before exploring further. # Pretotyping > Pretotyping validates demand with cheap experiments before heavy engineering. Learn Google-era roots, methods, Palm’s wooden model case, and failure modes. **Category**: Strategies
**Type**: Innovation Validation and Learning Strategy
**Origin**: Popularized by Alberto Savoia (Google, Stanford teaching); pretotyping.org community
**Also known as**: Pretotype testing, Right It before Build It Right **Quick Answer** — Pretotyping is the discipline of testing whether people want an idea—and will behave accordingly—using fast, inexpensive artifacts before you invest in production-quality building. It prioritizes **evidence of demand** over opinion, and it pairs naturally with lean experimentation and modern product discovery. ## What is Pretotyping? Pretotyping is a family of tactics that simulate the core experience of a product or service with minimal cost—smoke tests, wizard-of-oz flows, landing pages, clickable mocks, or physical props—so teams learn **market risk** early. The term plays on “pretend prototype”: not a prototype that proves manufacturability, but an artifact that proves **desirability and usage intent**. > Build the smallest thing that can falsify your riskiest belief—then measure behavior, not applause. It aligns with `/strategies/minimum-effective-dose` and `/strategies/fast-follower` thinking: spend learning dollars before capital dollars. It differs from branding a beta as “MVP” when the team still skips demand validation. In product orgs, `/methods/design-sprint` workshops and `/strategies/optionality` portfolios are common complements when multiple hypotheses compete for scarce build capacity. ### Pretotyping in 3 Depths * **Beginner**: Before coding for months, run a one-week experiment that measures signup, pre-order, or repeated simulated use. * **Practitioner**: Tie each pretotype to one falsifiable hypothesis (price, workflow frequency, channel)—and pre-register what evidence would stop the project. * **Advanced**: Separate **idea risk** from **execution risk**; pretotyping cannot fix a bad market even with perfect craft. ## Origin **Alberto Savoia** advanced pretotyping while associated with Google’s innovation culture and later through public workshops, Stanford teaching, and the book *The Right It* (HarperOne, 2019). The Pretotyping.org community documents methods and historical artifacts; the slogan captures the priority: build **the right “It”** before you build **It right**. The intellectual lineage overlaps lean startup smoke tests and user-research prototypes, but pretotyping emphasizes ultra-cheap behavioral probes accessible to non-engineers. ## Key Points Pretotyping succeeds when metrics are honest and teams can kill ideas. Write the belief that, if false, makes the plan worthless—usually demand, frequency, willingness-to-pay, or distribution. Preference declarations are weak; repeated actions, deposits, or time-on-task are stronger—design accordingly. Do not fake regulated claims or mislead safety-critical users; simulate transparently where required. Define success numbers and timelines before results arrive—otherwise optimism will reinterpret noise as signal. ## Applications Pretotyping shows up in hardware, software, services, and internal enterprise tools. Run ads to a concise offer and measure click-to-waitlist conversion against cost thresholds. Manually deliver the future service behind the scenes to learn workflow friction before automation. Use materials models to test carry, pocketing, and daily use ergonomics before tooling. Shadow one team with spreadsheets and scripts before integrating APIs—validate job frequency and stakeholder pull. ## Case Study Before Palm Computing’s handheld became a silicon product, founder **Jeff Hawkins** used a **wood block** cut to the intended device size, carrying it for weeks and simulating interactions—an early famous pretotype of whether the form factor fit real life. The artifact is preserved in public collections such as the **Computer History Museum** (catalog artifact 102716262), documenting dimensions around **3⅜ × 4⅞ inches**—concrete evidence that the team invested in behavioral validation before expensive fabrication. The lesson: physical pretotypes can answer “Will I actually use this?” faster than a polished demo answers “Can we build this?” ## Boundaries and Failure Modes Pretotyping fails when teams confuse activity with learning—or optimize vanity metrics. Two boundary conditions matter. First, if your pretotype audience does not match the eventual market, you measure the wrong people. Second, if legal, safety, or trust requirements forbid simulation, you need compliant proxies—not cute shortcuts. A common misuse is endless pretotyping to avoid shipping; learning must have a decision deadline. ## Common Misconceptions Pretotyping is often mistaken for low-quality prototypes or “fake products.” An MVP often ships to real users; a pretotype may never ship—it exists to test hypotheses with minimum build. Ethical pretotyping is transparent about what is simulated—especially with money, health, and data. Pretotyping is designed to catch market failure early; polishing the wrong “It” accelerates losses. ## Related Concepts These frameworks pair with pretotyping—or replace it when the risk is purely technical. Keep interventions small enough to learn without locking in heavy commitments. Decide when validation should defer market entry versus when speed must follow a proven category. Understand cheap acquisition offers—cousin tactics in pricing experiments, with different accounting risks. See when demand tests must include multi-sided incentives, not single-user clicks alone. ## One-Line Takeaway Spend dollars on evidence of demand before dollars on scale—define one falsifiable metric and kill the idea when the data says no.