Venture Capital Giants Bet on AI Transforming Traditional Service Industries: General Catalyst Invests $1.5 Billion to Achieve Software-Level Profit Margins

The venture capital industry is brewing a new investment revolution. Several top VCs are confident they have found the next big investment opportunity: transforming traditional labor-intensive service businesses into efficient machines with software-level profit margins using AI technology.

The leader of this trend is General Catalyst, which has allocated $1.5 billion from its latest fundraising specifically for its "create strategy." The core of this strategy is to incubate AI-native software companies in specific vertical industries and then use these companies as acquisition platforms to buy mature companies and their customer bases within the same industry. GC has already made investments in seven industries, including legal services and IT management, and plans to eventually expand to 20 niche areas.

Marc Bhargava, who leads the relevant business at General Catalyst, said in an interview with TechCrunch, "The global service industry generates $16 trillion in annual revenue, compared to only $1 trillion for the global software industry." He pointed out that the appeal of software investment has always been its higher profit margins, "when software scales, the marginal cost is very low while the marginal revenue is substantial."

Bhargava believes that if service businesses can be automated, with AI handling 30% to 50% of company operations, or even up to 70% of core tasks in scenarios like call centers, the return on investment will become highly attractive.

This strategy seems to be working. Take Titan MSP, part of General Catalyst' s portfolio, for example. The firm invested $74 million in two rounds to help develop AI tools for managed service providers, and later acquired the well-known IT services company RFA. Bhargava said that through pilot projects, Titan proved it could automate 38% of typical MSP tasks. The company now plans to use the improved profit margins to adopt a classic snowball strategy to acquire more MSP companies.

Similarly, the firm also incubated Eudia, which focuses on internal legal departments rather than traditional law firms. Eudia has signed Fortune 100 clients such as Chevron, Southwest Airlines, and Stripe, offering AI-based fixed-fee legal services instead of traditional hourly billing. The company recently acquired Johnson Hanna, an alternative legal services provider, to expand its business.

Bhargava explained that General Catalyst's goal is to at least double the EBITDA profit margin of the acquired companies.

This investment giant is not alone. Venture capital firm Mayfield has set aside $100 million for "AI team member" investments, including IT consulting startup Gruve. The company acquired a $5 million security consulting firm and increased its revenue to $15 million within six months, achieving an 80% gross margin.

Navin Chaddha, a managing director at Mayfield, told TechCrunch this summer, "If 80% of the work is done by AI, you can achieve a gross margin of 80% to 90%. Combined profit margins can reach 60% to 70%, resulting in net income of 20% to 30%."

Independent investor Elad Gil has been pursuing a similar strategy for three years, supporting companies that acquire established businesses and transform them with AI. Gil told TechCrunch this spring, "If you own assets, you can transform faster than just selling software as a supplier."

However, early warning signals suggest that this transformation of the service industry may be more complex than expected by VCs. A recent study by the Stanford Social Media Lab and BetterUp Lab surveyed 1,150 full-time employees across industries and found that 40% of employees take on more tasks because of AI-generated "work garbage"—tasks that look polished but lack substantive content, creating more work and trouble for colleagues.

This trend is causing losses for organizations. Employees involved in the survey reported spending nearly two hours per instance of "work garbage," including first interpreting the content, deciding whether to return it, and often needing to fix the issue themselves.

Based on the estimated time spent and self-reported salaries, the study authors estimated that "work garbage" causes an implicit cost of $186 per person per month. In their new article in the Harvard Business Review, they wrote, "For an organization with 10,000 employees, considering the estimated prevalence of work garbage... this means over $9 million in productivity losses annually."

Bhargava refuted the view that AI is overhyped, believing that these implementation failures actually validate General Catalyst's approach. He said, "I think this shows where the opportunity lies—that applying AI technology to these businesses is not easy. If all Fortune 100 companies could simply bring in consulting firms, apply some AI, and sign a contract with OpenAI to transform their businesses, our theory would not be as strong. But the reality is that transforming companies with AI is really hard."

He pointed out that the complexity of AI technology is the most critical missing piece. "There are many different technologies, each good at different areas. You really need application AI engineers from companies like Rippling, Ramp, Figma, and Scale, who have experience with different models, understand their nuances, know what fits what use, and know how to package them into software."

This complexity is precisely why General Catalyst's strategy of pairing AI experts with industry experts to build companies from scratch makes sense.

Nevertheless, there is no denying that the threat of work garbage could undermine the core economic logic of this strategy. Even if a holding company is created as a starting point, if the acquired companies reduce staff according to the AI efficiency theory, they will lack enough people to capture and correct errors generated by AI. If companies maintain current staffing levels to handle the extra work caused by AI outputs, the huge profit margin growth that VCs expect may never be achieved.

These situations theoretically should slow down the expansion plans at the core of VC's snowball strategy and may weaken the attractiveness of these deals. However, the reality is that just a few studies are unlikely to make most Silicon Valley investors slow down.

In fact, since General Catalyst usually acquires companies with existing cash flows, the firm says its "create strategy" companies are already profitable—clearly deviating from the traditional VC model of supporting high-growth, money-burning startups. This change might also be welcomed by limited partners of venture capital firms who have paid for years of unprofitable companies.