"Coachable" is doing real work in real investment decisions.

No one has agreed what it means.

Fifteen years of research, term sheet language, accelerator screening forms, and coaching sales decks. Press any two practitioners for a definition and you will get two different answers.

Tatiana Somià published the most rigorous recent attempt last year in Cogent Economics & Finance. Fifteen competencies, five areas, stitched from four different literatures. When her investor panel and her coach panel disagreed about what counted as coachability, she resolved it by weighting the investors most. The final scale includes items the coaches in her own study said were coaching outputs, not coachability markers.

That is not a rigour problem. It is a construct problem. When you build a definition by negotiating across raters with different interests, what you produce is a map of what investors want to see in founders. Useful. Not the same thing as coachability.

Here is the definition I am willing to defend.
Three capacities, one precondition.

• Seek feedback: the capacity to incorporate signals that do not originate in your own perception.

• Reflect: the capacity to think past the mental models that constructed your current view.

• Act: the capacity to break habits that worked at an earlier stage and no longer fit.

The precondition is courage. Before any of these can operate, the founder has to be willing to deliberately expose a blind spot. Not discover one by accident. Not admit one after the consequences are undeniable. Go looking for the view they have been avoiding.

Most startup sales failures are not sales failures. They are positioning, ICP, or validation problems that surface at the point of sale. Every major sales methodology, from Challenger to MEDDIC to Sandler, assumes upstream work that most founders have not done. Fixing your sales process when the real problem is two layers upstream is the most expensive way to learn nothing.

Truth is, most startups die.
— 9 out of 10 fail (according to Genome Project)
— 199 out of 200 (according to THNK & Deloitte Fast Ventures)
It’s the elephant in the room.

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.