Venture capital firms like General Catalyst and Mayfield are heavily investing in AI to automate labor-intensive service businesses, aiming to improve profit margins and facilitate acquisitions.

General Catalyst has committed $1.5 billion to incubate AI-native companies across various sectors and acquire established firms to leverage AI-driven efficiency gains.

Other VC firms, such as Mayfield, are allocating significant funds—like $100 million—to startups developing AI 'teammates' that promise high gross margins of 80% to 90%.

The core strategy involves automating 30% to 70% of tasks in service industries like legal and IT management, with early successes demonstrated by companies such as Titan MSP and Eudia.

AI automation has the potential to significantly boost efficiency, automating up to 70% of tasks in call centers and 30% to 50% in other service sectors, thereby increasing cash flow and valuation.

Successful examples include Titan MSP, which developed AI tools and acquired RFA, and Eudia, which offers AI-powered legal services and acquired Johnson Hanna to expand its legal offerings.

By improving margins through AI automation, these companies can afford to acquire additional firms at higher prices, creating a potentially lucrative flywheel effect.

Despite optimistic projections, recent research indicates that AI-generated work often results in 'workslop'—polished but substance-lacking outputs—that increases employee workloads and costs organizations millions annually in lost productivity.

This challenge of 'workslop' and increased employee frustration poses risks to the effectiveness of AI-driven automation strategies.

While efforts to automate and consolidate service industries are progressing, the complexity of AI implementation and the risk of increased workslop present significant hurdles to achieving the projected margin improvements.

There are concerns that AI-driven automation could reduce staffing levels, which might undermine the economic benefits if fewer employees are available to correct AI errors, threatening the core economics of these VC strategies.

General Catalyst aims to double or more the EBITDA margins of its portfolio companies through AI-driven transformations, focusing on acquiring profitable businesses with existing cash flow.

Employees report spending nearly two hours managing AI workslop per instance, leading to an estimated $186 monthly productivity loss per person, which could total over $9 million annually for a 10,000-worker organization.