Venture capitalists are increasingly turning their attention to the application of artificial intelligence in traditionally labor-intensive service sectors, envisioning a future where these businesses can achieve profit margins comparable to software companies. This ambitious strategy involves acquiring established professional services firms, integrating AI solutions to automate key functions, and then leveraging the resulting enhanced cash flow to consolidate more businesses within the same industry. While this approach promises significant financial returns, it also introduces complexities that might prove more challenging than initially perceived by investors.
A notable proponent of this investment philosophy is General Catalyst (GC), which has allocated a substantial $1.5 billion from its latest funding round to what it terms a \"creation\" strategy. This initiative focuses on nurturing AI-native software companies in specific vertical markets, which subsequently act as vehicles for acquiring mature firms and their existing client bases within those sectors. GC has already made investments across seven diverse industries, including legal services and IT management, with plans to expand its reach to as many as 20 sectors. Marc Bhargava, who spearheads GC's efforts in this domain, highlights the immense market opportunity, noting that the global services industry generates $16 trillion annually, significantly dwarfing the $1 trillion software market. The primary appeal, he explains, lies in the potential to replicate the high margins characteristic of software, where marginal costs are minimal as products scale.
Bhargava suggests that automating a substantial portion of services—ranging from 30% to 50% of tasks with AI, and even up to 70% in areas like call centers—could yield irresistible financial benefits. The strategy appears to be gaining traction, as exemplified by Titan MSP, a General Catalyst portfolio company. GC provided $74 million to Titan MSP to develop AI tools for managed service providers, leading to the acquisition of RFA, a prominent IT services firm. Through pilot programs, Titan successfully automated 38% of typical MSP tasks, and now intends to utilize its improved margins for further acquisitions in a classic roll-up approach. Similarly, GC incubated Eudia, a company specializing in AI-driven legal services for in-house legal departments. Eudia, which offers fixed-fee services to Fortune 100 clients like Chevron, Southwest Airlines, and Stripe, recently acquired Johnson Hanna to expand its operational footprint. General Catalyst aims to at least double the EBITDA margins of the companies it acquires through these AI-driven transformations.
Other venture capital firms are adopting similar strategies. Mayfield, for instance, has committed $100 million to investments in \"AI teammates,\" including Gruve, an IT consulting startup. Gruve acquired a $5 million security consulting company and, within six months, expanded its revenue to $15 million, achieving an impressive 80% gross margin. Navin Chaddha, Mayfield’s managing director, envisions a future where AI performs 80% of tasks, leading to gross margins of 80% to 90% and net incomes of 20% to 30%. Independent investor Elad Gil has been pursuing a comparable strategy for three years, supporting companies that acquire established businesses and overhaul them with AI. Gil asserts that owning the asset allows for faster transformation compared to merely selling software as a vendor.
However, emerging data indicates that this AI-driven services transformation may be more intricate than venture capitalists anticipate. A recent study by researchers from Stanford Social Media Lab and BetterUp Labs, involving 1,150 full-time employees, revealed that 40% of participants are experiencing an increased workload due to what is termed \"workslop.\" This refers to AI-generated output that, despite appearing polished, lacks substantive value, thereby creating additional work for human colleagues. The study found that employees spend nearly two hours dealing with each instance of workslop, deciphering, evaluating, and often correcting the AI-generated errors. This \"invisible tax\" is estimated to cost $186 per month per person, translating to over $9 million annually in lost productivity for an organization with 10,000 employees, as detailed in a Harvard Business Review article.
Despite these challenges, Bhargava of General Catalyst remains optimistic, viewing these implementation hurdles as validation of their strategy. He contends that the difficulty in applying AI technology to businesses underscores the necessity of their approach, which involves pairing AI specialists with industry experts to build companies from the ground up. He emphasizes the technical expertise required to navigate the complexities of various AI models and integrate them effectively into software solutions. The risk posed by \"workslop\" to the core economics of this strategy cannot be ignored. If acquired companies reduce staff based on projected AI efficiencies, they may lack the human resources to address AI-generated errors. Conversely, if current staffing levels are maintained to manage these errors, the anticipated significant margin gains might not materialize. While these scenarios suggest a need for caution and potentially a slower pace of scaling, most Silicon Valley investors are unlikely to be deterred by a few studies. General Catalyst highlights that its \"creation strategy\" companies, by acquiring businesses with existing cash flow, are already profitable—a significant departure from the traditional venture capital model of funding high-growth, cash-burning startups. This shift is likely welcomed by limited partners, who have endured years of losses from companies that never achieved profitability. Bhargava believes that as AI technology continues to advance, the opportunities for incubating companies across an expanding range of industries will only grow.
