N10-4: AI Team Assessment
Evaluating the talent, structure, and retention risk of an AI engineering team during diligence.
🎯 What You'll Learn
- ✓ Assess ML team depth
- ✓ Evaluate key-person risk
- ✓ Calculate replacement costs
- ✓ Identify talent retention risks
Lesson 1: ML Talent Depth Analysis
A single ML researcher does not make an AI company. Evaluate: How many people can train models (not just fine-tune)? How many understand the full pipeline (data → training → evaluation → deployment)? How many are interchangeable? If the answer to any is "one person," you have a critical key-person dependency.
Number of engineers who can independently train and evaluate models.
How many people understand the complete ML pipeline end-to-end?
Is model architecture knowledge documented, or only in one person's head?
Map your AI team's capabilities: who can train, who can deploy, who can evaluate. Identify single-person dependencies.
Lesson 2: Key-Person Risk Quantification
For each key ML person, calculate: (1) Replacement time (6-12 months to recruit a senior ML engineer), (2) Knowledge transfer time (3-6 months for the new person to reach full context), (3) Revenue at risk if they leave (all AI features dependent on their expertise). The total key-person exposure is the sum across all key individuals.
Senior ML engineers: 6-12 months to find and close.
New hire needs 3-6 months to understand the proprietary model architecture.
Sum of ARR that depends on features only this person can maintain.
Calculate the key-person risk exposure for your 3 most critical ML engineers. Total the revenue at risk.
Lesson 3: Retention Risk Assessment
AI talent retention follows different economics: ML engineers are 2-3x more expensive to replace than average engineers, they have near-zero unemployment, and they are being aggressively recruited. Retention signals to evaluate: equity vesting schedules (cliff or continuous?), comp vs market rate (within 10%?), and publication/conference participation (is the team intellectually stimulated?).
Total comp within 10% of market rate for their level and location.
When the next major equity vesting event occurs.
Are ML engineers publishing, attending conferences, working on interesting problems?
Assess retention risk for your AI team: comp competitiveness, vesting schedules, and intellectual satisfaction. Grade each risk.
Continue Learning: Track 10 — AI Due Diligence
2 more lessons with actionable playbooks, executive dashboards, and engineering architecture.
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Module Syllabus
Lesson 1: Lesson 1: ML Talent Depth Analysis
A single ML researcher does not make an AI company. Evaluate: How many people can train models (not just fine-tune)? How many understand the full pipeline (data → training → evaluation → deployment)? How many are interchangeable? If the answer to any is "one person," you have a critical key-person dependency.
Lesson 2: Lesson 2: Key-Person Risk Quantification
For each key ML person, calculate: (1) Replacement time (6-12 months to recruit a senior ML engineer), (2) Knowledge transfer time (3-6 months for the new person to reach full context), (3) Revenue at risk if they leave (all AI features dependent on their expertise). The total key-person exposure is the sum across all key individuals.
Lesson 3: Lesson 3: Retention Risk Assessment
AI talent retention follows different economics: ML engineers are 2-3x more expensive to replace than average engineers, they have near-zero unemployment, and they are being aggressively recruited. Retention signals to evaluate: equity vesting schedules (cliff or continuous?), comp vs market rate (within 10%?), and publication/conference participation (is the team intellectually stimulated?).