VCs Are Underestimating the AI Transformation Challenge
▼ Summary
– Venture capitalists are using AI to automate professional services firms and improve margins, then using the increased cash flow to acquire more companies in a roll-up strategy.
– General Catalyst has allocated $1.5 billion to incubate AI-native software companies and use them to acquire established firms in sectors like legal services and IT management.
– AI automation targets 30-70% of tasks in services businesses, aiming to transform low-margin industries by achieving software-like margins of 60-90%.
– Early challenges include “workslop,” where AI-generated output creates extra work for employees, potentially costing organizations millions in lost productivity.
– Despite implementation difficulties, proponents argue that the technical complexity of AI validates their approach, and they plan to expand into more industries as AI technology improves.
Venture capital firms are pursuing an ambitious strategy to transform traditional service industries through artificial intelligence, aiming to replicate the high-margin economics typically associated with software businesses. This approach involves acquiring established professional services companies, implementing AI automation to reduce labor costs, and using the resulting improved cash flow to acquire additional firms in consolidation plays.
General Catalyst has allocated $1.5 billion from its latest fund specifically for what it terms a “creation” strategy. The firm incubates AI-native software companies targeting specific industry verticals, then uses these platforms to acquire established service providers within the same sectors. Their current portfolio spans seven industries including legal services and IT management, with plans to expand to approximately twenty sectors total.
Marc Bhargava, who leads these initiatives at General Catalyst, highlights the enormous market opportunity. “Global services represent approximately $16 trillion in annual revenue, while software accounts for only about $1 trillion,” he explained. “Software’s appeal has always been its scalability with minimal marginal cost. If we can automate 30% to 50% of service company operations, reaching up to 70% in call center environments, the financial potential becomes extremely compelling.”
The improved profitability from automation creates capital for acquiring additional companies at premium valuations, establishing what supporters describe as a powerful growth cycle.
Early implementations demonstrate the model’s potential. Titan MSP, backed by General Catalyst with $74 million across two funding rounds, developed AI tools for managed service providers before acquiring established IT services firm RFA. Through pilot programs, Titan demonstrated automation of 38% of typical MSP tasks, positioning the company to acquire additional providers using enhanced margins.
Similarly, Eudia, another General Catalyst portfolio company, focuses on corporate legal departments rather than traditional law firms. The AI-powered legal service provider has secured Fortune 100 clients including Chevron, Southwest Airlines, and Stripe, offering fixed-fee arrangements instead of hourly billing. Eudia recently acquired alternative legal service provider Johnson Hanna to expand its market presence.
General Catalyst aims to at least double the EBITDA margins of acquired companies through this transformation process.
Other investment firms are pursuing similar strategies. Mayfield has dedicated $100 million specifically for “AI teammates” investments and led Series A funding for Gruve, an IT consulting startup that acquired a security consulting company and grew revenue from $5 million to $15 million within six months while achieving 80% gross margins.
Navin Chaddha, Mayfield’s managing director, notes that “if AI handles 80% of the work, companies can achieve 80% to 90% gross margins, with blended margins of 60% to 70% yielding 20% to 30% net income.”
Solo investor Elad Gil has pursued this approach for three years, backing companies that acquire mature businesses and transform them with AI. “Owning the asset enables much faster transformation than selling software as a vendor,” Gil explained. “Increasing gross margins from 10% to 40% creates tremendous value.”
However, emerging challenges suggest this services industry transformation may prove more complex than anticipated. A recent study surveying 1,150 full-time employees identified “workslop”, AI-generated content that appears polished but lacks substance, creating additional work for colleagues. Researchers found 40% of employees are handling increased workloads due to this phenomenon.
The organizational impact is significant. Employees report spending nearly two hours addressing each instance of workslop, deciphering output, deciding whether to return it, and often fixing it themselves. Based on time estimates and self-reported salaries, researchers calculate workslop creates an invisible tax of approximately $186 monthly per employee, translating to over $9 million annually in lost productivity for a 10,000-person organization.
Bhargava contends these implementation challenges actually validate General Catalyst’s approach. “The difficulty of applying AI technology to these businesses demonstrates the opportunity,” he argued. “If Fortune 100 companies could simply hire consultants, implement some AI, and transform their operations, our thesis would be less robust. The reality is that transforming companies with AI is genuinely difficult.”
He emphasizes the technical sophistication required, noting the need for applied AI engineers with experience across different models who understand their nuances and how to integrate them effectively into software solutions.
Despite these arguments, workslop threatens the strategy’s fundamental economics. If companies reduce staff as AI efficiency models suggest, fewer people remain to catch and correct AI errors. If they maintain staffing levels to handle problematic AI output, the anticipated margin improvements may never materialize.
Either scenario could potentially slow the scaling plans central to venture capital consolidation strategies and undermine the financial metrics that make these deals attractive. However, given that General Catalyst’s “creation strategy” companies reportedly achieve profitability through acquiring businesses with existing cash flow, most Silicon Valley investors remain undeterred by early warning signs.
“As AI technology continues advancing with massive investment in model improvement,” Bhargava predicted, “we’ll discover increasing numbers of industries where we can incubate transformative companies.”
(Source: TechCrunch)
