Venture capital firms have identified a new frontier: leveraging artificial intelligence to extract software-like margins from traditionally labor-intensive professional services. This approach involves acquiring established service providers, integrating AI to automate core tasks, and using the resulting cash flow improvements to fund further acquisitions in a rapid roll-up strategy. General Catalyst (GC) exemplifies this model, dedicating $1.5 billion from its latest fund to a “creation” strategy. This initiative incubates AI-native software companies within verticals such as legal services and IT management, which then acquire mature firms and their client bases in the same sectors. GC aims to expand its reach to as many as 20 industries.
“Services globally is a $16 trillion revenue a year globally. In comparison, software is only $1 trillion globally,” said Marc Bhargava, GC’s head of this initiative. “The allure of software investing has always been its higher margins. As you get software to scale, there’s very little marginal cost and a great deal of marginal revenue.”
By automating 30% to 50% of tasks—and up to 70% in call centers—GC and its peers believe the margin math becomes compelling enough to fuel aggressive acquisitions.
Early Successes: Titan MSP and Eudia
Titan MSP, a GC portfolio company, received $74 million to develop AI tools for managed service providers (MSPs) and acquired IT services firm RFA. They demonstrated automation of 38% of typical MSP tasks, boosting margins and enabling further acquisitions. Similarly, Eudia targets in-house legal departments with AI-powered fixed-fee services, moving away from traditional hourly billing. It counts Fortune 100 clients like Chevron and Stripe and recently acquired legal service provider Johnson Hanna to expand its footprint. Bhargava notes the objective is to double EBITDA margins across these acquisitions, a significant uplift from traditional service business profitability. Other investors share this vision. Mayfield Ventures has allocated $100 million for “AI teammates” investments, backing startups like Gruve, which rapidly scaled a security consulting acquisition to $15 million revenue with 80% gross margins.
“If 80% of the work will be done by AI, it can have an 80% to 90% gross margin,” said Mayfield managing director Navin Chaddha. “Blended margins of 60% to 70% and net income of 20% to 30% are achievable.”
Investor Elad Gil similarly backs companies buying mature businesses to transform them with AI, emphasizing the advantage of owning assets to accelerate margin expansion.
The Hidden Cost of AI: “Workslop” and Its Impact on Productivity
Despite the enthusiasm, emerging evidence points to significant operational challenges. A recent Stanford Social Media Lab and BetterUp Labs study surveyed 1,150 full-time employees and found that 40% are burdened by “workslop”—AI-generated outputs that appear polished but lack substance, creating additional work to verify, correct, or redo tasks. Employees spend nearly two hours addressing each instance of workslop, resulting in an estimated hidden productivity tax of $186 per month per person. For a 10,000-employee organization, this translates to over $9 million in annual lost productivity, according to a Harvard Business Review analysis. This phenomenon complicates the assumption that AI implementation will straightforwardly improve efficiency and margins.
Bhargava counters concerns about AI hype by highlighting the complexity of successful AI integration. “It’s not easy to apply AI technology to these businesses,” he said. “The technical sophistication and applied AI engineering expertise are critical.”
GC’s approach pairs AI specialists with industry veterans to build companies from the ground up, a strategy designed to overcome the pitfalls of superficial AI adoption.
Scaling Amid Uncertainty: Staffing and Margin Risks
The risk posed by workslop also raises difficult questions about scaling. If firms reduce staff as AI efficiencies suggest, fewer employees remain to catch and fix AI errors. Maintaining staffing to manage AI-generated problems could erode the margin improvements that underpin the roll-up model. This tension may slow down aggressive acquisition plans or require recalibration of financial projections. Nonetheless, Silicon Valley investors appear undeterred, continuing to pour capital into AI-driven service transformations. General Catalyst emphasizes that its creation strategy companies are profitable from inception, leveraging existing cash flows while AI capabilities improve.
“As AI technology continues to evolve, we anticipate expanding into more industries and further incubating AI-native companies,” Bhargava said.
FinOracleAI — Market View
The venture capital-led AI transformation of professional services offers a compelling vision of dramatically improved margins and scalable roll-ups. However, operational complexities such as AI-generated “workslop” introduce material risks to productivity and profitability.
- Opportunities: Significant margin expansion by automating routine service tasks; accelerated roll-ups fueled by improved cash flow; scalable AI-native company models across multiple industries.
- Risks: Increased employee workload due to low-quality AI outputs; potential hidden productivity losses; challenges in technical AI integration requiring specialized talent; tension between staffing levels and margin improvements.
Impact: While the AI services transformation could reshape multiple industries with enhanced profitability, the presence of workslop and integration hurdles suggests a cautious outlook. Success will depend on sophisticated AI application and operational discipline to realize the promised financial benefits.
