VCs Underestimate AI Services Transformation Challenges

Venture capitalists believe they've discovered the next major investing advantage: leveraging AI to achieve software-like profit margins from traditionally labor-intensive service businesses. Their approach involves acquiring established professional services firms, deploying AI to automate operations, and then using the resulting enhanced cash flow to acquire additional companies in a roll-up strategy.

Pioneering this movement is General Catalyst (GC), which has allocated $1.5 billion from its latest fund to a "creation" strategy. This initiative focuses on developing AI-native software companies within specific sectors, then using these companies as platforms to acquire well-established firms—and their customer bases—in the same industries. GC has already made investments across seven sectors, including legal services and IT management, with ambitions to eventually expand into as many as 20 different fields.

"The global services industry represents $16 trillion in annual revenue," Marc Bhargava, who leads GC's related initiatives, stated in a recent TechCrunch interview. "In contrast, the global software market is only $1 trillion," he observed, highlighting that software's appeal has always been its superior margins. "When software scales, the marginal cost is minimal while the marginal revenue is substantial."

He explained that if you can similarly automate service businesses—handling 30% to 50% of their operations with AI, and up to 70% of core tasks in areas like call centers—the financial prospects become incredibly compelling.

This strategy is already demonstrating success. Consider Titan MSP, one of GC's portfolio companies. The investment firm provided $74 million across two funding rounds to help develop AI tools for managed service providers, followed by an acquisition of RFA, a prominent IT services firm. Through pilot programs, Bhargava reports that Titan automated 38% of standard MSP tasks. The company now intends to use its improved margins to acquire more MSPs in a classic industry consolidation play.

In a parallel move, the firm incubated Eudia, which targets corporate 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 acquired Johnson Hanna, an alternative legal service provider, to broaden its market presence.

Bhargava clarified that GC aims to at least double the EBITDA margin of the companies it acquires.

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GC isn't the only firm embracing this philosophy. Venture firm Mayfield has dedicated $100 million specifically to "AI teammates" investments, including Gruve, an IT consulting startup that acquired a $5 million security consulting company. According to its founders, Gruve then scaled this acquisition to $15 million in revenue within six months while achieving an 80% gross margin.

"If AI handles 80% of the work, it can generate 80% to 90% gross margins," Navin Chaddha, Mayfield's managing director, told TechCrunch this summer. "You could achieve blended margins of 60% to 70% and produce 20% to 30% net income."

Solo investor Elad Gil has been pursuing a similar strategy for three years, backing companies that acquire mature businesses and transform them with AI. "If you own the asset, you can implement changes much faster than if you're merely selling software as an external vendor," Gil explained in a TechCrunch interview this spring.

However, early indicators suggest this services-industry transformation might be more complex than venture capitalists anticipate. A recent study by researchers at Stanford Social Media Lab and BetterUp Labs surveyed 1,150 full-time employees across various industries and found that 40% are burdened with extra work due to what the researchers term "workslop"—AI-generated output that appears polished but lacks substance, creating additional work and complications for colleagues.

This trend is negatively impacting organizations. Survey participants reported spending nearly two hours on average addressing each instance of workslop, including time to decipher it, decide whether to return it for revision, and often simply to correct it themselves.

Based on participants' time estimates and self-reported salaries, the study authors calculate that workslop imposes a hidden cost of approximately $186 per employee monthly. "For an organization with 10,000 workers, given the estimated prevalence of workslop... this translates to over $9 million annually in lost productivity," they write in a new Harvard Business Review article.

Bhargava challenged the notion that AI is overhyped, arguing that these implementation challenges actually reinforce GC's approach. "I think it highlights the opportunity, which is that applying AI technology to these businesses isn't straightforward," he said. "If every Fortune 100 company could simply hire a consulting firm, implement some AI, sign a contract with OpenAI, and transform their business, then obviously our thesis would be less compelling. But the reality is, transforming a company with AI is genuinely difficult."

He identified the required technical expertise in AI as the most crucial missing component. "There's a wide variety of AI technologies, each with different strengths," he noted. "You truly need applied AI engineers from companies like Rippling, Ramp, Figma, and Scale, who have hands-on experience with different models, understand their nuances, know which models are suitable for specific tasks, and comprehend how to integrate them into software." He maintained that this complexity is precisely why GC's strategy of pairing AI specialists with industry experts to build companies from scratch is so logical.

Nevertheless, workslop undoubtedly threatens to partially undermine the strategy's core economics. Even if a holding company is established as a foundation, if acquired companies reduce staff as the AI efficiency model suggests they should, fewer people will be available to identify and correct AI-generated errors. If companies maintain current staffing levels to handle the extra work created by flawed AI output, the substantial margin improvements that VCs are banking on may never materialize.

One could argue that these factors should temper the aggressive scaling plans that are fundamental to VC roll-up strategies—plans that potentially jeopardize the financial metrics that make these deals attractive. However, realistically, it will take more than a couple of studies to slow down most Silicon Valley investors.

In fact, because they typically acquire businesses with existing cash flow, GC states that its "creation strategy" companies are already profitable—a significant departure from the traditional VC model of backing high-growth, cash-burning startups. This likely represents a welcome change for the limited partners funding venture firms, who have endured years of losses from portfolio companies that never achieved profitability.

"As long as AI technology continues advancing, and we observe this massive investment and improvement in the models," Bhargava said, "I believe there will be increasingly more industries where we can help incubate companies."