What valuation methods are used for AI assets?

Full Explanation

Valuing AI assets requires selecting the method best suited to the asset's characteristics and available data. The Cost Approach estimates the cost to recreate the AI asset — including data acquisition, cleaning, labelling, model architecture development, training compute, and fine-tuning. This works well for early-stage models without revenue attribution. The Income Approach is preferred for revenue-generating AI: the Multi-Period Excess Earnings Method (MPEEM) isolates the earnings attributable specifically to the AI asset after deducting contributory asset charges, while the With-and-Without method compares projected cash flows with and without the AI capability. The Market Approach uses comparable transaction data — AI model acquisitions, licensing deals, or API pricing benchmarks — but comparable data is still scarce in many AI sub-domains. In practice, valuers often use multiple methods and triangulate. Key challenges specific to AI valuation include rapid technological obsolescence (shorter useful lives), dependency on training data quality, and the difficulty of isolating AI's contribution from human expertise and other technology. Opagio's Valuator applies method selection logic based on the AI asset type and available financial data.

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