My previous article, "The AI-Native Paradox," explored how AI has created new challenges for both VCs and founders. But there's a deeper issue we need to address: the metrics we use to measure success are broken.

The same forces that make AI startups hard to evaluate and differentiate have also made traditional software metrics useless. ARR growth rates, churn calculations, and unit economics—the foundation of SaaS investing—don't work anymore.

This isn't just about tweaking formulas. We're witnessing a complete metrics revolution that demands new frameworks for measuring AI startup success. As we've explored in our work on corporate innovation in the AI age, what new metrics or evaluation frameworks are needed to assess the real potential of AI-native startups and solutions?

The private equity industry has awakened to a harsh reality: financial engineering alone no longer creates value1. With elevated interest rates and historic valuations, the firms that will outperform over the next decade are those that can systematically transform portfolio companies into high-performance growth platforms1.

Yet there's a critical gap between recognizing this need and executing it effectively. Most PE firms are still trapped in what we call the Heroics Trap—relying on exceptional individual efforts, one-off initiatives, and unsustainable growth spurts rather than building the systematic engines that create repeatable, scalable value.