playbook/antigravity-awesome-skills/skills/find-complementary-founders/references/evidence-model.md

Evidence model

Research basis

Treat the popular 0→1, 1→10, and 10→100 language as a practical stage metaphor, not a validated personality taxonomy.

• Peter Thiel popularized 0→1 for creating something new versus copying an existing model. The later three-stage extension is practitioner language.
• March's exploration/exploitation model supports a real distinction between searching for new possibilities and refining existing capabilities.
• D'Acunto, Tate, and Yang found that startups with more diverse collective industry skillsets grew faster; a one-standard-deviation increase in skill diversity was associated with 16% higher five-year employment growth and 10% higher sales growth from the mean.
• A systematic review of entrepreneurial-team diversity describes diversity as a double-edged sword: knowledge breadth can help while disparity, separation, and conflict can hurt.
• De Cooman et al. found the best team outcomes when members perceived both supplementary fit (important similarities) and complementary fit (different useful capabilities), mediated by cohesion.
• Lewis's transactive-memory research supports making expertise legible: effective teams know who knows what and can coordinate that expertise.

Sources:

• March, Exploration and Exploitation in Organizational Learning: https://doi.org/10.1287/orsc.2.1.71
• D'Acunto, Tate, and Yang, Entrepreneurial Teams: Diversity of Skills and Early-Stage Growth: https://doi.org/10.2139/ssrn.3750982
• Klotz et al., Entrepreneurial team diversity — A systematic review and research agenda: https://doi.org/10.1016/j.emj.2022.10.004
• De Cooman et al., Creating Inclusive Teams Through Perceptions of Supplementary and Complementary Person–Team Fit: https://doi.org/10.1177/1059601115586910
• Lewis, Measuring Transactive Memory Systems in the Field: https://doi.org/10.1037/0021-9010.88.4.587

Operational model

Assess two independent axes.

Startup-stage contribution

Vector	Observable evidence
zero_to_one	frames unmet needs; runs discovery; invents; prototypes under ambiguity; creates a first working artifact
one_to_ten	interviews users; iterates from evidence; wins early customers; establishes a repeatable product/GTM loop
ten_to_hundred	designs reliable systems; delegates; hires; manages quality and economics; scales repeatable operations

Functional contribution

Vector	Observable evidence
problem_discovery	finds important unmet needs and tests assumptions
product	chooses scope, sequences value, and integrates feedback
engineering	builds and operates technical systems
design	creates understandable, usable experiences
go_to_market	positions, sells, distributes, and learns from the market
operations	creates repeatable delivery and reliable processes
people_leadership	recruits, aligns, coaches, and resolves conflict
capital_partnerships	secures resources and durable external alliances

Do not equate a job title with evidence. One outcome may support several vectors, but state the linkage explicitly.

Evidence hierarchy

Weight evidence in this order:

1. verified customer or operational outcome;
2. shipped public artifact with clear ownership;
3. repeated responsibility with a concrete result;
4. specific peer or collaborator feedback;
5. self-reported preference.

Preference indicates energy and desired role, not demonstrated capability. Require at least two independent strong evidence items for a high-confidence strong label. Use unknown when evidence is absent.

Matching rule

Maximize:

1. coverage of explicit stage and functional gaps;
2. overlap in project purpose, collaboration mode, and operating principles;
3. credible, current evidence;
4. reciprocal usefulness.

Check separately for commitment, decision rights, risk tolerance, pace, communication norms, and conflict handling. These are not "soft extras"; complementary skills without operating compatibility can make a worse team.

Never infer compatibility from demographics or sensitive traits.