Expert thinking on productivity, intangible asset valuation, growth strategy, and building more valuable businesses.
I spent 30 years in structured finance, working across derivatives trading, cross-border M&A, and asset-backed securities. I watched trading floors evolve from human traders to algorithms to AI agents. Each transition created new intangible assets and new valuation challenges. Here is how financial services is building and valuing AI intangible assets.
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The commercial property sector has invested over £15 billion in PropTech in the past five years. Yet most property valuations fail to capture the intangible assets these technologies create. This is about to change — and it will radically reshape how commercial property is valued and traded.
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Private equity firms pride themselves on rigorous due diligence and data-driven value creation. Yet most PE investment processes are structurally blind to the intangible assets that account for the majority of portfolio company value. This gap creates both hidden risks and untapped opportunities.
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I spent thirty years valuing commercial property — tangible buildings with concrete returns and comparable sales. Then I moved into intangible asset valuation. At first, the transition seemed impossible. Then I realised: the skills transfer perfectly. Here is what property valuation teaches about valuing what you cannot see.
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Pharmaceuticals were among the first sectors to pioneer IP-backed financing through patent royalty securitisations. But the opportunity extends far beyond royalty streams. Drug patents, clinical data packages, regulatory approvals, and formulation know-how constitute a rich intangible asset base that most PE-backed pharma companies underexploit as collateral.
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When 92% of S&P 500 value is attributable to intangible assets but only 8% appears on balance sheets, there is a fundamental disconnect between reported financial position and economic reality. This thought experiment reconstructs a typical SaaS company's balance sheet as if intangibles were properly captured. The results are illuminating.
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Data is now formally recognised as productive capital by the System of National Accounts. But how do you value it on a balance sheet? Tony Hillier draws on 30 years of structured finance experience to map three valuation approaches — and where data assets fit into modern capital structures.
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National accounts now recognise R&D and software as capital assets. But the majority of intangible investment — organizational know-how, proprietary data, trained workforces, customer networks — still falls outside the measurement boundary. These are the seven categories that distort our understanding of productivity.
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Under current accounting standards, most AI development costs are expensed immediately — treated as period costs rather than capital investments. Yet new FASB guidance (ASU 2025-06) and SNA 2025 recognised data as productive capital. This creates a widening gap between economic reality and financial reporting. Here is how to navigate it.
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The intangible asset gap costs growth-stage companies millions in undervaluation. Learn how to identify and close it before your next fundraise.
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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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