Rohan Light

We tend to think the value of AI is about model quality. In practice, the payoff we get from AI depends just as much on our own knowledge and how accurately we judge what we know. We can think of this as an AI Knowledge Payoff (AIKP): how much useful

If we have lined up our management, accounting and economics cases, have moved our data governance to a profit centre and know how to get data assets onto the balance sheet, then we’ve avoided the floor of our habitable range and worked out our maturity pathway to using portfolio

The modern AI economy has redefined the ontological status of data, shifting it from a secondary byproduct of digital interaction into a factor of production that functions as the lifeblood of institutional intelligence. However, the valuation of data as an asset class remains incomplete without a rigorous framework for pricing

Most data governance programmes are designed to reduce risk and increase consistency. But under the wrong conditions, good governance overly done can create systemic fragility—the digital equivalent of London’s Millennium Bridge wobble. In Infonomics terms, this happens when we maximise compliance metrics while also uncritically synchronising CVI (Cost

The global economic architecture in 2025 reached an inflection point where the customary boundaries between macroeconomic statistics and microeconomic financial reporting began dissolving under the weight of the digital economy. For decades, data has been an invisible production factor—vital for value creation but largely absent from the formal ledgers

[Note: I've got another edit round lined up for this post, final to be confirmed. The main elements are tidying up the formulae. For the originals, refer to Laney's original work] The global economy has undergone a structural transformation where the centre of gravity of value