Expert thinking on productivity, intangible asset valuation, growth strategy, and building more valuable businesses.
Technology and SaaS companies sit on vast portfolios of intangible assets — proprietary code, patents, customer contracts, data — yet most still rely on equity dilution or unsecured debt to fund growth. Structured lending against these assets offers a capital-efficient alternative that PE firms and fund managers are only beginning to exploit.
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The frontier of structured finance is lending against the intangible assets that constitute AI company value: proprietary models, training datasets, and customer contracts. The question is no longer whether this is possible, but how to structure it and price the risk appropriately.
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PE operating partners managing 5-15 portfolio companies face a new dimension of value creation: assessing which portfolio companies are building genuine AI intangible assets and which are pursuing fashionable but value-destructive AI-washing. Here is the assessment framework that separates signal from noise.
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Traditional productivity metrics were designed for an economy dominated by physical capital and tangible output. In a world where the most valuable firms derive their competitive advantage from software, data, brand equity, and organizational know-how, those metrics are increasingly unreliable. Here is why the gap matters — and what we should do about it.
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The San Francisco Fed's recent research on AI and productivity is among the most rigorous work in the space. But it also reveals critical blindspots in how economists measure the impact of AI on economic growth. Here is what they got right, what they missed, and why the measurement gap itself is the most important finding.
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Board oversight of AI investment is becoming as critical as financial controls. Here is the 10-point accountability checklist that separates effective board governance from box-ticking.
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Mergers and acquisitions involving artificial intelligence companies fail at a 62% rate in terms of value creation. The root cause is not market misjudgment — it is due diligence frameworks designed for physical assets, applied to intangible-asset-heavy technology companies. Here is the anatomy of that failure, and a framework that fixes it.
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Before we asked others to run their businesses through our AI intangible asset valuation framework, we ran Opagio itself through it. Here is what the data revealed about our own strengths, gaps, and surprises.
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After 30 years of helping businesses grow, scale, and sell to private equity, I have seen what separates businesses that achieve premium valuations from those that leave value on the table. Increasingly, the difference is intangible asset visibility. Here is how to prepare.
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Individual developers using AI copilots write code 30-55% faster. Individual analysts produce reports in half the time. Yet organizational productivity remains flat. The gap between task-level AI gains and enterprise-level outcomes reveals a measurement failure — and an intangible asset opportunity — that most firms are missing entirely.
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Every significant AI investment creates or enhances the same six categories of intangible assets I built during 15 years at IG Group. The problem is not that these assets do not exist — it is that no one is measuring them. Here is the framework that connects AI spending to enterprise value creation.
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I was at IG Group when Facebook's IPO was mispriced. The market overvalued the platform and undervalued the intangible assets — the data, the network effects, the advertiser relationships. Today's AI market is making the same mistake in reverse. Here is how to tell the difference between genuine AI value and bubble pricing.
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