TL;DR Corporate leaders are restructuring their organisations around AI using a compression thesis: same work, fewer people, flatter hierarchy. The evidence increasingly points to expansion: more work, different work, new capabilities. Restructuring for compression when the actual phenomenon is expansion is a classification error with cascading consequences. Before redesigning your org chart, classify the change.

Your CEO just sent an all-hands email. The subject line contains the words "AI-first" and "restructuring." The deck references Block, Shopify, and Anthropic. There is a slide about flattening hierarchy, a slide about smaller teams, and a slide about everyone becoming a builder. The stock price ticked up 3% on the announcement.

"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.

TLDR;-) Large organisations don't resist innovation because they're broken. They resist it because they're working. Four mechanisms do the killing: procedural resistance, resource competing, standard dilution, passive waiting. Each is a legitimate organisational function operating in the wrong context. If the immune response is firing, you're probably working on the wrong layer.

Eighty-three per cent of companies rank innovation as a top-three priority. Three per cent are ready to act on it. That is not a typo. BCG's 2024 Most Innovative Companies report calls what remains "zombie innovation systems": organisations going through the motions of innovation without strategic commitment, waiting for certainty that will never arrive.

AI for VC & Founders: The playbook (dealbook?) has changed—and everyone is scrambling to keep up.

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.

TL;DR: Most conversations about sustainable performance start from the wrong premise—that the performance standards themselves are neutral. They're not. Before optimising for sustainability, ask: whose definition of "good" am I trying to meet? The answer might explain why it feels so hard.

You're exhausted. Not the kind of tired that sleep fixes—the kind that accumulates despite doing everything right. The productivity systems, the boundary-setting, the rest. You've tried it all.

The advice you get assumes the problem is execution. Work smarter. Delegate more. Manage your energy better.

But here's what that advice never questions: the performance standards themselves.

Most feedback is useless.

Not because people lack good intentions. Not because organisations don't invest in training. But because we've been taught to give feedback in ways that trigger defensiveness, focus on personality rather than behaviour, and leave people with nowhere to go.

Most businesses chase growth the hard way. They obsess over customer loyalty, lifetime value, and retention while ignoring the fundamental laws that actually drive sustainable expansion.

The result? Wasted budgets, stalled growth, and missed opportunities.

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.