Tokenomics: Why making AI pay is tricky
Tech companies are struggling to establish sustainable pricing models for AI services due to the unpredictable nature of token consumption. Because LLM responses vary in length and complexity, firms find it difficult to forecast costs for long-term service contracts.
Why it matters
The economic viability of AI integration depends on solving the volatility of compute costs for businesses.
Image source, Getty Images Image caption, The big AI firms sell paid-for versions of their tech
If you have used a free version of an ChatGPT or its AI rivals, then you are obviously getting a good deal.
Firms like Microsoft, Google and Anthropic have invested hundreds of billions of dollars in developing Large Language Models (LLMs) the tech behind those services.
So getting, ChatGPT, Claude or Gemini to help with your speech or holiday plans is a bargain.
But, naturally, those firms want to recoup their investment, so they offer paid-for versions of their AI, which have extra features for tasks like coding or billing.
Meanwhile, third party firms are building and selling services based on AI agents, usually based on an LLM, which are trained to do specific tasks.
But setting a price for those services is surprisingly difficult.
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