Venture capital firms are deploying a new investment strategy, pouring billions into acquiring traditional service-based companies with the goal of transforming them using artificial intelligence. This approach aims to automate labor-intensive tasks, dramatically increase profit margins, and create businesses that operate with the efficiency of software companies.
Firms like General Catalyst are leading this charge, betting that AI can unlock unprecedented value in sectors that make up a $16 trillion global market. However, emerging research on AI-related productivity losses suggests this transformation may face significant challenges.
Key Takeaways
- Venture capitalists are acquiring established service firms to integrate advanced AI and automation.
- The primary goal is to convert labor-heavy businesses into high-margin, software-like operations.
- General Catalyst has allocated $1.5 billion to this strategy, targeting sectors from legal services to IT management.
- A major risk is "workslop," where flawed AI output creates more work for employees, potentially negating efficiency gains.
- Despite challenges, this model is already producing profitable companies, a departure from typical high-burn VC investments.
A New Playbook for Venture Capital
A fundamental shift is underway in venture capital, moving beyond pure software startups to focus on the vast services economy. The core idea is to apply AI to automate a significant portion of the work in established professional services firms, such as legal, IT, and consulting.
Marc Bhargava, who leads these efforts at General Catalyst (GC), highlighted the scale of the opportunity in a recent interview. "Services globally is a $16 trillion revenue a year globally," he stated, comparing it to the much smaller $1 trillion software market. The allure of software has always been its high margins, where revenue grows much faster than costs.
By automating 30% to 50% of tasks in a typical service business, VCs believe they can replicate this financial model. In some areas, like call centers, automation potential could reach as high as 70%.
The Roll-Up Strategy Explained
The investment model often involves a "roll-up" strategy. It starts with an initial acquisition, which is then transformed with AI to improve cash flow. These newly improved profits are then used to acquire more companies in the same sector, creating a larger, more efficient entity over time.
General Catalyst's $1.5 Billion Bet
General Catalyst is one of the most prominent firms pursuing this strategy, dedicating $1.5 billion from its latest fund. Their approach, termed a "creation" strategy, involves building AI-native companies from scratch and then using them as platforms to acquire established players and their customer bases.
GC has already launched ventures across seven industries and plans to expand to as many as 20. According to Bhargava, the firm aims to at least double the EBITDA margin of the companies it acquires.
Case Study: Titan MSP
A clear example of this strategy in action is Titan MSP, a GC portfolio company. General Catalyst provided $74 million in funding to help Titan develop AI tools specifically for managed service providers (MSPs).
Following this development, Titan acquired RFA, a well-established IT services firm. Bhargava noted that pilot programs showed Titan's AI could automate 38% of typical MSP tasks. The company now plans to use its enhanced margins to fund further acquisitions.
Case Study: Eudia Legal Services
In the legal sector, GC incubated Eudia, which serves in-house legal departments. Instead of billing by the hour, Eudia offers fixed-fee services powered by AI. This model has attracted Fortune 100 clients, including Chevron, Southwest Airlines, and Stripe.
To expand its footprint, Eudia recently acquired Johnson Hanna, an alternative legal service provider. This move allows Eudia to integrate its AI technology into an existing business with an established client roster.
Other Firms Join the Trend
General Catalyst is not alone in this pursuit. The venture firm Mayfield has allocated $100 million for investments in "AI teammates." One of its portfolio companies, Gruve, an IT consulting startup, demonstrates the potential returns.
After acquiring a $5 million security consulting company, Gruve used AI to grow its revenue to $15 million within six months while achieving an 80% gross margin, according to its founders.
Navin Chaddha, Mayfield’s managing director, explained the financial logic. "If 80% of the work will be done by AI, it can have an 80% to 90% gross margin," he told TechCrunch. This could lead to net income of 20% to 30%.
Solo investor Elad Gil has also been backing companies with this model for three years. "If you own the asset, you can [transform it] much more rapidly than if you’re just selling software as a vendor," Gil said in an interview.
The Hidden Cost of AI: 'Workslop'
Despite the optimism, a significant operational hurdle has emerged: AI-generated work that is flawed or lacks substance. Researchers from Stanford Social Media Lab and BetterUp Labs have termed this phenomenon "workslop."
A survey of 1,150 full-time employees revealed that 40% are shouldering more work because of low-quality AI output from colleagues. This creates a chain of inefficiency, as employees must spend time deciphering, correcting, or redoing the AI-generated tasks.
"Employees involved in the survey say they’re spending an average of nearly two hours dealing with each instance of workslop," the study noted.
The financial impact is substantial. The study's authors estimate that workslop imposes an "invisible tax" of $186 per person per month. For a company with 10,000 employees, this translates to over $9 million in lost productivity annually.
A Challenge or an Opportunity?
The problem of workslop directly challenges the core assumption of the VCs' strategy. If AI creates more corrective work, the promised margin improvements might not materialize, especially if companies cannot reduce their headcount as planned.
However, Marc Bhargava of GC views this challenge as a validation of his firm's approach. "I think it kind of shows the opportunity, which is, it’s not easy to apply AI technology to these businesses," he argued. He believes the difficulty of proper AI implementation is precisely why a specialized, ground-up approach is necessary.
He emphasized the need for highly skilled "applied AI engineers" who understand the nuances of different AI models. This complexity, he suggests, is why simply buying off-the-shelf AI software often fails to deliver transformative results.
Despite the risks, this new investment model has one major advantage over traditional venture capital. Because the strategy involves acquiring businesses with existing customers and cash flow, many of these new AI-powered holding companies are already profitable. This is a significant departure from the cash-burning startups that have dominated the VC landscape for years, offering a potentially more stable path to returns for investors.
"As long as AI technology continues to improve... I think there’ll just be more and more industries for us to help incubate companies," Bhargava concluded.