AI Services Transformation: Challenges for Venture Capital

The Rise of AI-Driven Investment in Services Businesses

Venture capital firms are increasingly focused on a new investment strategy: leveraging artificial intelligence (AI) to enhance the profitability of traditionally labor-intensive service businesses. This approach centers on acquiring established firms and integrating AI to automate processes, ultimately boosting cash flow and facilitating further acquisitions.

General Catalyst's Pioneering Strategy

General Catalyst (GC) is at the forefront of this trend, dedicating $1.5 billion to a “creation” strategy. This involves developing AI-focused software companies within specific industries, then utilizing these entities to acquire existing firms and their customer bases.

GC has already made investments across seven sectors, including legal services and IT management, with ambitions to expand into a total of 20 industries. Marc Bhargava, leading these efforts, highlighted the significant revenue potential of the services sector – a $16 trillion global market – compared to the $1 trillion software industry.

The Appeal of Software-Like Margins

The core idea is to replicate the high margins characteristic of software businesses. As software scales, marginal costs remain low while revenue increases substantially. Applying AI to automate 30% to 50% of tasks within service businesses, and potentially up to 70% in areas like call centers, could yield similarly attractive financial results.

Early Successes and Roll-Up Strategies

Titan MSP, a GC portfolio company, exemplifies this strategy. Receiving $74 million in funding, Titan developed AI tools for managed service providers and subsequently acquired RFA, an established IT services firm. Pilot programs demonstrated a 38% automation rate for typical MSP tasks, paving the way for further acquisitions.

Similarly, Eudia, incubated by GC, focuses on in-house legal departments, offering AI-powered, fixed-fee legal services to clients like Cargill, Del Monte, and Stripe. Eudia expanded its reach through the acquisition of Johnson Hana, an alternative legal service provider.

GC aims to at least double the EBITDA margins of acquired companies. This focus on profitability represents a shift from traditional venture capital models.

Other Firms Join the Trend

Mayfield has allocated $100 million for “AI teammate” investments, including Gruve, an IT consulting startup that achieved an 80% gross margin and significant revenue growth after acquiring a security consulting company. Navin Chaddha of Mayfield suggests that AI-driven automation can lead to blended margins of 60% to 70% and net income of 20% to 30%.

Solo investor Elad Gil has also been pursuing a similar strategy for three years, supporting companies that acquire and transform mature businesses with AI. He emphasizes the advantages of owning the asset for faster transformation.

Challenges and Potential Pitfalls

Despite the promising outlook, emerging concerns suggest that the transition may be more complex than initially anticipated. A recent study revealed that 40% of employees are experiencing increased workloads due to “workslop” – AI-generated content that requires significant revision and correction.

The Cost of "Workslop"

Employees spend an average of nearly two hours per instance of workslop, deciphering, correcting, or rejecting the AI output. The study estimates this “invisible tax” at $186 per month per person, totaling over $9 million annually for an organization of 10,000 workers.

GC's Response and the Importance of Expertise

Marc Bhargava dismisses the notion of AI being overhyped, arguing that implementation challenges validate GC’s approach. He stresses the need for highly skilled AI engineers who understand the nuances of different models and their applications.

GC’s strategy of combining AI specialists with industry experts to build companies from the ground up is seen as a key differentiator.

Potential Impact on Economics and Scaling

However, workslop poses a threat to the core economics of the strategy. If companies reduce staff based on anticipated AI efficiency gains, fewer personnel will be available to address AI-generated errors. Maintaining staffing levels to handle workslop could negate the expected margin improvements.

These challenges may necessitate a slower scaling pace than initially planned, potentially impacting the attractiveness of these deals to investors.

Looking Ahead

GC’s “creation strategy” companies are already profitable, a departure from traditional VC investments in high-growth, cash-burning startups. This is likely to be welcomed by limited partners seeking more stable returns.

Bhargava remains optimistic, believing that continued advancements in AI technology will unlock opportunities in more industries. As long as AI models continue to improve with substantial investment, the potential for expansion remains significant.