Geoffrey Woo Exposes Extreme Chaos in Logistics Processes of San Francisco Startups
Renowned venture capitalist Geoffrey Woo observed a founder in San Francisco describing their logistics workflow as "cursed" to the point of sounding fake, and then opened his computer to show real screenshots.
The screenshots displayed 15 tabs, involving 4 personnel, 2 approval processes, and a spreadsheet named "final_final_v7," fully exposing the inefficiency of traditional manual operations and the accumulation of technical debt.
In market mechanisms, such logistical chaos highlights the insufficient penetration of AI automation, with funds continuously wasted on manual coordination and multiple version document management. Companies focusing on logistics automation and AI agent tools benefit, while startup teams relying on Excel and multiple manual approvals suffer in operational efficiency.
Source: Public Information
ABAB AI Insight
Geoffrey Woo, as a managing partner at Anti Fund, has previously shared observations on the transition from SaaS to AI, emphasizing workforce reduction and agent execution. His recent complaints in San Francisco continue his long-term tracking of operational pain points in startups, having discussed on multiple platforms how "cursed workflows" hinder growth.
From a capital perspective, Woo and other investors draw attention from LPs and founders through public cases, transforming logistical inefficiencies into investment signals for AI tools. This aims to push portfolio companies from labor-intensive operations towards automation, accelerating fund returns and providing real demand validation for logistics AI products.
Similar to how early Salesforce replaced Excel chaos with CRM, the current San Francisco startup ecosystem is in a control phase transitioning from the manual era of "final_final_v7" to full AI agent replacement, using such screenshots to expose the universality of operational technical debt.
Essentially, this reflects technological substitution and capital concentration: chaotic logistics processes directly illustrate insufficient AI penetration, and through public exposure, accelerate the shift of capital from wasteful manual coordination to automated agent tools, forcing startup operational resources to transition from multiple manual approvals to AI-driven workflows and reshape the logistics efficiency structure of the industry chain.
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The more "cursed" the process, the more inevitable the leverage of automation replacement.
The more version numbers, the more efficiency waste is magnified by screenshots.
The more manual approvals, the more undervalued the value of AI agents.