Apparently, the providers of the major AI models are actively moving from flat-fee pricing to usage-based, token pricing. It’s creating some challenges for IT budgets. And that means, as this Troutman Pepper Locke alert describes, that this shift may also have disclosure implications for public companies, especially for MD&A.

Since “increased expenses incurred with AI token costs” may have a material impact on a company’s financial statements, MD&A disclosure may need to address:

– The reason for period-to-period changes in operating costs, discussed in quantitative and qualitative terms, to the extent AI costs are already having a financial statement impact;

– Known trends or uncertainties reasonably likely to cause a material change in the relationship between costs and revenues if the pricing shift and AI adoption results in the two-part “reasonably likely” assessment being met;

– AI investments or token consumption as known trends, demands, commitments, events or uncertainties reasonably likely to impact the company’s liquidity; or

– AI usage metrics (like token consumption rates, per-employee spend or AI ROI), if tracked, as key performance indicators (KPIs) used to manage the business.

The accounting — and disclosure — may be different industry by industry or company by company:

Different companies may account for AI token costs differently. For example, some companies may account for token costs in costs of revenue, while others may account for them as general and administrative (G&A) costs. For other companies, AI token costs might be accounted for research and development (R&D) expenses. For example, AI has started to significantly affect biopharmaceutical and biotechnology companies by rapidly transforming the drug development process, enhancing and speeding target identification, molecular design, clinical trials optimization, and regulatory processes — these companies are likely to record AI token costs as R&D expenses.

With the second quarter about to close for calendar year companies, the alert suggests four steps companies should take as they prepare for their next 10-Q, including assessing the materiality of AI costs, evaluating the cost structure shift, reviewing internal AI monitoring and governance and coordinating across legal, finance and IT to assess disclosure requirements.

– Meredith Ervine