The rapid development of artificial intelligence has presented organizations with a new kind of challenge. Until recently, it was enough to provide employees with access to large language models such as ChatGPT or Perplexity. Now, however, we are rapidly moving from individual AI usage toward autonomous agent teams and broader workflows.<\/p>\n\n\n\n
This transition from \u201dPhase 1 to Phase 2\u201d brings not only a huge leap in productivity, but also a completely new cost structure. As AI starts working independently in the background, the traditional \u201dseat-based pricing\u201d no longer works as the sole business model for AI companies. For your organization to succeed in this new era and successfully take AI pilots into production, it is crucial to understand the new economics of AI consumption: tokens, credits, and different pricing tiers<\/strong>.<\/p>\n\n\n\n In this article, we unlock the secrets of AI pricing and introduce a model that helps you understand the different tiers of the AI market \u2013 all the way from cloud services to the application level.<\/p>\n\n\n\n In the video I published, I made a strong claim that the price of artificial intelligence will rise next year. I justified this by the fact that the current \u20ac20\/month fixed monthly fees are unprofitable for service providers under heavy use.<\/p>\n\n\n\n But when we delve into the latest market data, my claim about price increases turns out to be wrong<\/strong> \u2013 and that\u2019s the best possible news for your company! The price of tokens\u2014the raw material of AI\u2014is in free fall (a drop of up to 99 % in three years). In this blog post, I challenge the claim made in my own video and show why AI isn\u2019t actually becoming more expensive, but rather its pricing logic<\/em> is changing. Read below to find out what this \u201dAI deflation\u201d is all about.\n
\ud83d\uded1 Video challenge: Was I wrong about the price increases?<\/h3>\n\n\n\n
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