AI Investment Methodology: Soukaïna Abelhad on Verifying "70% Automated" Claims in Pitch Decks

This recommendation is based on publisher episode information. In a video published on October 13, 2026, at the AI Engineer conference, Soukaïna Abelhad presented a technical due diligence framework for evaluating automation claims in pitch decks. Abelhad argues that when a deck states a workflow is "70% automated," the critical step is not to accept the number but to ask what it counts, what human work remains, and who checked the result.

According to the episode description, Abelhad outlines three core tests: first, comparing workload growth with fully loaded labor costs to distinguish "assistance" from "labor removal"; second, defining the automation boundary within a named workflow to separate AI-attributed work from autonomously completed tasks; and third, examining the validity of evaluation methods, particularly the limits of model-on-model assessment compared to domain expert review. These checks culminate in an economic question: what is the cost per accepted autonomous task after accounting for model costs, human review, escalations, and rework?

For venture capital and corporate strategy investors evaluating AI startups, this resource offers a concrete checklist. It does not require deep model expertise but instead uses observable operational metrics—such as reviewer counts and rework rates—to verify automation claims. Readers should focus on how Abelhad translates abstract percentages into auditable operating costs and process boundaries, preventing the misclassification of high-labor-dependent assistance systems as efficient automation solutions.