Larridin CTO Ameya Kanitkar told Business Insider that AI costs should be treated as capital expenditure. In a separate Forbes piece, he calls token spend a variable operating expense and shows how quickly usage and cost mix can change. Those are two different arguments. Here’s what each tells finance.
In Business Insider, Kanitkar says organizations should evaluate AI as a capital investment and judge returns over a year or two rather than expecting immediate gains.
His Forbes Technology Council article addresses a different problem: token spend behaves as a “variable operating expense” that can be difficult to track and forecast.
The distinction matters because the Forbes data can explain the spending and budgeting problem without establishing the case for CapEx treatment.
The Forbes article identifies several reasons AI spending can be difficult to forecast.
A finance team looking only at total spend or prompt volume can also miss changes in model mix that materially affect cost.
The Forbes data does not establish that AI spending should be treated as CapEx.
The article explicitly calls token spend an operating expense. The data shows why that expense is unusually variable and difficult to track. It doesn’t make an accounting argument for reclassifying it.
Kanitkar’s CapEx position comes from his separate Business Insider comments. That argument asks leaders to evaluate AI against a longer investment horizon rather than judging it only by short-term cost.
Those ideas can be discussed together, but they should not be treated as the same claim. The volatility data explains the measurement and budgeting problem. It does not, by itself, prove the CapEx case.
For the practical argument for a longer-term investment framing, see AI Costs as CapEx, Not OpEx: The Framing Shift That Changes the Budget Conversation.
Whether finance ultimately treats a specific AI cost as an operating expense or a capital investment, leaders still need to know where the money is going.
Larridin’s Token Spend & Insights consolidates AI spend across tools, models, agents, and teams and connects that spend with the work and outcomes behind it.
That gives finance a clearer answer to the underlying questions Kanitkar raises in both pieces: What’s driving the cost? How quickly is it changing? Which models and teams account for it? And what is the organization getting in return?
How AI spend is classified doesn’t tell finance what’s driving it or what the organization is getting in return. That requires measurement.
Track spend by model, tool, team, and usage over time rather than relying on a single annual estimate. That makes changes in model mix and consumption easier to spot before they turn into unexplained budget variance.
Yes. The Forbes article explicitly describes AI token spend as a variable operating expense. His separate Business Insider comments make a broader case for evaluating AI as a longer-term capital investment. The two statements address different questions.
Yes. Formal treatment depends on the type of investment, applicable accounting standards, and jurisdiction. Organizations should confirm the appropriate classification with their accounting advisors rather than treating the strategic framing as an accounting determination.
Larridin’s Token Spend & Insights shows where AI spend is coming from, how it changes over time, and which teams, tools, and agents are driving it.
Book a discovery call to see what is driving your AI spend.