A market bifurcation is underway where investors prioritize AI startups with extreme growth rates over traditional SaaS companies. This creates a "changing of the guard," forcing established SaaS players to adopt AI aggressively or risk being devalued as legacy assets, while AI-native firms command premium valuations.

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.

Large enterprises navigate a critical paradox with new technology like AI. Moving too slowly cedes the market and leads to irrelevance. However, moving too quickly without clear direction or a focus on feasibility results in wasting millions of dollars on failed initiatives.

In AI M&A, recency is key. Companies pre-ChatGPT often had to rewrite their entire stack and relearn skills, making their experience less relevant. Acquiring a company with post-ChatGPT experience ensures their tech and knowledge are current, not already obsolete.

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.

In the current AI landscape, knowledge and assumptions become obsolete within months, not years. This rapid pace of evolution creates significant stress, as investors and founders must constantly re-educate themselves to make informed decisions. Relying on past knowledge is a quick path to failure.

The increased volatility and shorter defensibility windows in the AI era challenge traditional VC portfolio construction. The logical response to this heightened risk is greater diversification. This implies that early-stage funds may need to be larger to support more investments or write smaller checks into more companies.

While many firms are just now reacting to AI's impact, major credit investors like KKR have been actively underwriting AI-driven business model risk for nearly six years. This proactive, long-term approach to assessing technological disruption is a core part of their due diligence process, not a recent development.

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.