The Financial Times just made our case
Today’s FT paywalled article ‘AI agents are rewriting the economics of computing’ dropped just a day after we informed the world about our AI Economics platform. It lays bare how non-linear tokens consume so much of today’s business budgets, detailing how
- Uber hit its annual AI spending limit in four months
- One Amazon internal project ran 860% over budget
- A single coding agent can consume 1000x the tokens of a chatbot
The messy uncomfortable truth is that reasoning, retrying and calling sub-agents long before a human sees an answer makes token consumption invisible. Pricing is per-token. Business value isn’t. For businesses this is literally a dark art.
The uncomfortable truth buried in the AI hype is that neither the labs, nor the enterprises buying frontier AI from them can reliably forecast what any given AI workflow will cost next month. That makes the issue less of a token problem and more a pure finance-visibility problem.
This is precisely where Yarken AI Economics comes in. By refusing to treat tokens, GPU hours, agent runs and licences as scattered line items, Yarken shines a light on what is really going on, even normalising non-AI labour and cloud infrastructure costs.
We give CFOs and finance teams a single finance-grade view of their operating expenses tied to business outcomes. They can choose from daily cost allocations, anomaly detection before month-end, defensible capitalisation records, or whichever metric they require in the international language of accounting principles and treatments.
The FT recently asked how pricing can shift from tokens to outcomes. We anticipated the need to connect the raw cost of AI to the business value it is supposed to deliver. Bravo to the FT for translating hard concepts into visualizations and we look forward to similarly opening the eyes of finance professionals in the language of business accounting.