Digital Point vs Hiring an AI Engineer

Pros

• +Full-time focus on your domain and your stack
• +Tribal knowledge accumulates with one person over years
• +Direct reporting line, no contract or scope review
• +Equity-aligned incentives if early-stage company

Cons

• −Loaded cost typically $180K-$280K/year US ($120K-$200K base plus benefits, equity, taxes)
• −Recruiting cycle 8-16 weeks for a senior AI generalist
• −Single-person dependency. Sick days, vacation, attrition all halt the work
• −Engineer alone cannot ship a full ops layer (also needs ops people, designers, attribution analysts)
• −Skills gap: most "AI engineers" specialize in models, not in agent orchestration plus workflow automation plus reporting

Pros

• +No hiring cycle. Active engagement starts within 2-3 weeks of audit signoff
• +Bench includes AI-agent builders, automation engineers, marketing operators, and attribution analysts in one accountable counterparty
• +Pricing scales with scope (no fixed $250K headcount commit)
• +Operators backstop the AI when the agent plateaus, so workflows do not break in the wild
• +Cross-client pattern recognition: DPL has run 200+ growth audits and operated $50M+ in ad spend

Cons

• −Not a full-time embedded resource. Communication happens in scheduled cycles, not Slack-on-tap
• −No equity alignment
• −Specialized in growth ops and acquisition-side workflows. Pure ML research or model training is not the fit