Tokenomics: Why Charging for AI Is a Challenge
Buyers grapple with costs; sellers still unsure how to price
The term tokenomics has gained prominence by highlighting the difficulty of defining a payment model for artificial intelligence services. The core of the discussion revolves around two opposing sides: those who acquire the technology and those who offer it.
Buyer challenges
Those who purchase AI solutions face a recurring obstacle: keeping costs under control. The scalable nature and usage variability of these services make expense forecasting a complex exercise, creating uncertainty about the available budget.
Seller uncertainties
On the other side of the equation, AI providers still lack clarity on the value to charge. The absence of consolidated pricing parameters creates doubts about the best way to monetize functionalities that can be consumed flexibly or on demand.
Why tokenomics complicates the scenario
Tokenomics proposes using digital units – or tokens – as a way to measure and charge for AI resource usage. This approach, while promising, introduces additional variables that influence both the cost perception by buyers and price definition by sellers.
- Usage flexibility: consumption can vary according to demand, making it difficult to create fixed rates.
- Scalability: services that rapidly increase in volume can generate unexpected expenses.
- Transparency: the lack of standardized metrics makes it hard to compare different offerings.
These combined factors result in an environment where negotiating values becomes a delicate task, requiring both parties to find a balance between risk and benefit.
In summary, tokenomics highlights the need for new strategies to align cost and revenue expectations in the AI market, reflecting the complexity of turning advanced technology into a sustainable business model.
With information from BBC News.
Source: BBC News