Unlike private equity's 3-5 year model focused on debt and cost-cutting, GC's AI roll-ups are structured like venture-backed tech companies. The 7-10 year goal is to build a public "compounder" (like Danaher) that uses AI for operational improvements and reinvests cash flow into more acquisitions.
Because boards lack deep expertise in AI's seismic impact, they are pursuing scale-driven M&A. The goal is to accumulate diverse assets ('cards in a deck') to maintain flexibility and strategic options in an unpredictable, AI-driven future, rather than making specific bets on the technology itself.
Instead of selling software to traditional industries, a more defensible approach is to build vertically integrated companies. This involves acquiring or starting a business in a non-sexy industry (e.g., a law firm, hospital) and rebuilding its entire operational stack with AI at its core, something a pure software vendor cannot do.
GC is shifting from a traditional venture fund to a company that incubates and holds "transformation companies" like a hospital system and an AI consultancy indefinitely. These businesses are designed for long-term value creation, not quick exits, and also serve its portfolio founders.
Just as Kaizen and “China cost” revolutionized physical product businesses over 40 years, AI is initiating a similar, decades-long optimization cycle for intellectual property and human-centric processes. Companies that apply this “digital Kaizen” to lean out workflows will gain a compounding cost and efficiency advantage, similar to what Danaher achieved in manufacturing.
For fragmented, tech-averse industries, GC funds startups to first build an AI automation platform. Then, instead of a difficult sales process, the startup acquires traditional service businesses, implementing its own AI to dramatically boost their margins, providing immediate distribution and data.
The success of an AI roll-up hinges on effective technology implementation. Therefore, the primary filter for acquiring a company is not just its financials but whether its leadership and culture are genuinely eager to adopt AI and transform their operations. This cultural fit is non-negotiable.
Private Equity value creation has evolved. In the 2000s, it was driven by leverage; in the 2010s, by digital transformation. Today, AI serves as the new foundational "operating system" for growth, embedding intelligence into every process, contract, and customer touchpoint to drive returns.
The rapid evolution of AI means traditional private equity M&A timelines are too slow. PE firms and their portfolio companies must now behave more like venture capitalists, acquiring earlier-stage, riskier AI companies to secure necessary technology before it becomes unaffordable or obsolete.
Recent acquisitions of slow-growth public SaaS companies are not just value grabs but turnaround plays. Acquirers believe these companies' distribution can be revitalized by injecting AI-native products, creating a path back to high growth and higher multiples.
Viewing acquisitions as "consolidations" rather than "roll-ups" shifts focus from simply aggregating EBITDA to strategically integrating culture and operations. This builds a cohesive company that drives incremental organic growth—the true source of value—rather than just relying on multiple arbitrage from increased scale.