AI-Driven Roll-Up Strategy: Venture Capitalists Bet Big on Automating Professional Services to Scale Profits

Investment firms are embracing a novel strategy aimed at maximizing returns: leveraging artificial intelligence (AI) to automate traditionally labor-intensive service businesses, thereby boosting profit margins. This approach entails the acquisition of established professional services companies, followed by AI implementation to streamline operations and subsequently consolidating more companies through a roll-up strategy.

General Catalyst (GC), with $1.5 billion earmarked from its latest fundraise for this purpose, is spearheading this transformation. The firm’s strategy focuses on nurturing AI-native software companies in specific verticals and utilizing them as acquisition vehicles to buy established firms within the same sectors. GC has made bets across seven industries, including legal services and IT management, with plans to expand into up to 20 sectors.

Marc Bhargava, who leads GC’s related efforts, highlights the vast potential of this strategy, stating, “Services globally generate $16 trillion in annual revenue, while software stands at only $1 trillion.” The appeal of software investing has always been its higher profit margins due to minimal marginal costs and substantial marginal revenues. By automating service businesses, Bhargava contends that the allure of improved cash flow becomes even more attractive.

An example of this strategy’s success can be seen in Titan MSP, one of General Catalyst’s portfolio companies. GC invested $74 million across two tranches to develop AI tools for managed service providers and subsequently acquired RFA, an IT services firm. Through pilot programs, Titan demonstrated it could automate 38% of typical MSP tasks, setting the stage for a classic roll-up strategy.

Similarly, Eudia, another incubated company by General Catalyst, focuses on in-house legal departments rather than law firms. Eudia has secured Fortune 100 clients such as Chevron, Southwest Airlines, and Stripe, offering fixed-fee legal services powered by AI instead of traditional hourly billing. The company recently expanded its reach with the acquisition of Johnson Hanna, an alternative legal service provider.

General Catalyst aims to double – at least – the EBITDA margin of acquired companies, according to Bhargava.

Notably, General Catalyst is not alone in this approach. Venture firm Mayfield has allocated $100 million specifically for “AI teammates” investments, including Gruve, an IT consulting startup that grew a $5 million security consulting company to $15 million in revenue within six months while achieving an 80% gross margin.

Navin Chaddha, Mayfield’s managing director, explains the potential of this strategy, stating, “If 80% of the work will be done by AI, it can have an 80% to 90% gross margin. You could have blended margins of 60% to 70% and produce 20% to 30% net income.”

However, early warning signs suggest that this transformation of the service industry may be more complex than anticipated. A recent study by researchers at Stanford Social Media Lab and BetterUp Labs revealed that AI-generated work (workslop) is creating additional work for colleagues due to its polished yet lacking substance, leading to lower productivity and increased headaches.

The trend is impacting organizations, with employees reporting an average of nearly two hours spent dealing with each instance of workslop. Based on participants’ estimates of time spent and self-reported salaries, the study calculates an invisible tax of $186 per month per person for an organization of 10,000 workers.

Despite these concerns, Bhargava remains optimistic about the potential of AI, arguing that the challenges faced during implementation validate General Catalyst’s approach. He emphasizes the need for technical sophistication in AI as the most critical missing piece of the puzzle. As a solution, General Catalyst partners AI specialists with industry experts to build companies from the ground up.

However, the emergence of workslop poses a threat to the strategy’s core economics. If acquired companies reduce staff as anticipated due to increased AI efficiency, they may lack the necessary resources to manage and correct AI-generated errors. Maintaining current staffing levels to handle additional work created by problematic AI output might hinder the realization of the huge margin gains that investors are counting on.

Despite these potential challenges, the ongoing improvement of AI technology and substantial investments in AI models suggest a promising future for this strategy’s expansion into various industries.