Operating Partner perspectives,
from an operator who built before he wrote.
Field notes on AI value creation, kill-rate discipline, and the operating model rewrites that protect the multiple at exit. Sharpened by 27 years of operating across the digital and AI waves.
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The board metrics that matter in the AI transition.
Three numbers determine whether AI investment becomes a multiple at exit. Adoption, capex, and maturity are not among them.
Most boards monitoring AI in their portfolio companies report adoption, capex, and maturity. Those numbers confirm activity. They do not measure value. Three other numbers determine whether AI investment becomes a multiple at exit: EBITDA absorption rate, run-rate of the asset created, and repricing gap. Each is harder to compute than the metric it replaces. Each is the one that determines whether AI investment has reached the multiple. This article details the calculation method for each, names the trap of activity-based reporting, and explains why the board pack is not neutral: it trains the company to optimize what it reports. In the AI transition, the multiple is shaped by what the board chose to measure four years earlier.
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Technology is no longer valued for what it is. It is valued for what it changes.
For fifteen years, when a mid-cap company built tech to operate its business, that tech had standalone value. That logic is being repriced. The artifact is no longer the asset. The change is the asset.
AI has collapsed the cost of building functional technology. When the production of an asset becomes commoditized, value migrates to what remains scarce. McKinsey documents that 6 percent of companies extract 5 percent or more EBIT impact from their AI investments. Bain documents that one in five strategic buyers walked away from a 2025 deal because of AI exposure on the target. This article names the pivot, sources the data, and reframes the question that defends the multiple at exit. Technology assets that demonstrate no measurable change in service depth, operational productivity, or customer experience have shifted from asset to cost in the buyer's view.
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Your most valuable asset is depreciating. No balance sheet shows it.
Boards know how to detect the depreciation of machinery, software, inventory, and working capital. Most cannot detect the depreciation of the operating capability of the teams that run the business. The asset sits on no balance sheet, yet it determines whether the company compounds during the hold, or quietly erodes until the multiple at exit reflects the gap.
Operating capability is a balance sheet asset that depreciates with no alert, no measurement, and no line in the Value Creation Plan. This article names the depreciation mechanism, identifies the three indicators already sitting in the data room that detect it in motion (manager-to-operator ratio drift, high-performer voluntary turnover, internal promotion ratio for management hires), and reframes operating capability as an economic asset boards can defend before the buyer sees the gap at exit.
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AI is not the bottleneck. Your operating model is.
Boards are seeing AI productivity gains across every function. Engineers ship more. Analysts produce more. Decision cycles accelerate at the edge. The dashboards turn green. EBITDA does not move.
Most mid-cap operating models were architected for a pre-AI information flow that no longer exists. AI changes the speed at the edge. It does not change the architecture at the core. The productivity gains of AI are captured by individuals. The losses are absorbed by the organization. This article names the dissipation mechanism, explains why headcount reduction is the wrong response, and reframes the right question for boards that want AI productivity gains to compound into a multiple at exit.
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AI portfolios fail when boards use the wrong economic model.
Most AI portfolios in mid-cap companies are not underperforming because the technology is immature. They are underperforming because nobody is allocating capital as if returns matter.
Boards are applying venture exploration logic to AI capex when they should be applying capital allocation logic. The two look similar from a distance. They are not. This article names the dispersion pattern, explains why the mental frame is wrong, gives a four-criteria grid to restore allocation discipline, and offers a 90-day window for boards that want to defend their multiples at exit.
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Inaction is the most expensive decision of the hold.
Wrong action shows up in three quarters and gets corrected. Inaction shows up at exit, when correction is no longer possible.
Inaction is not neutral. It is a decision that compounds against the multiple, quarter after quarter, in ways that rarely surface before the mid-hold review. This article names the three hidden costs of inaction, gives a grid to separate disciplined patience from disguised procrastination, and offers one rule for Partners who intend to defend their multiples.
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The end of the org chart. The beginning of value.
Boards that treat AI as another digital program will not discover the mistake during the hold. They will discover it at exit.
AI is a margin reset, not a technology shift. Eight convictions on what value creation in PE-backed mid-caps looks like when the cycle compresses to quarters.
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