In the past two years, artificial intelligence or AI has been portrayed as a revolution in productivity. From software coding to customer service, data analysis, resource creation and day-to-day office operations, the trend towards using AI is growing rapidly. Many organizations believed that using AI would reduce costs and increase both speed and efficiency.
But now another aspect of the situation has come to light. Major tech companies and corporate organizations are beginning to realize that the true cost of using AI can be much higher. Computing costs, cloud infrastructure and token usage costs are increasing rapidly, especially as AI begins to be widely used in organizations.
Recently, various international reports and online discussions have revealed that several tech companies have started investigating the use of AI by employees. After all, the cost of processing power and server capacity required to run AI in many cases exceeds the expected capacity.
AI certainly improves work efficiency, but running AI on a large scale requires massive computing power, cloud servers and storage capacity or storage. While the cost of using AI on a small scale is manageable at the beginning, when the use of AI across multiple departments of an organization grows simultaneously, the cost also increases very quickly.
At the heart of this whole problem is an important thing, called tokens. Many common users are not aware of this problem. The AI model uses this token when reading something, analyzing it, or generating an answer. In other words, each query, report, e-mail, coding, document processing or chatbot response costs a certain amount of tokens.
That cost may seem small in one or two requests, but if thousands of employees use AI tools throughout the day, the total token usage becomes huge. This adds to the cost of high-capacity servers and cloud services.
According to experts, the biggest challenge in using AI in the future is not only developing advanced technology, but sustaining that technology financially in the long run. Many organizations are now starting to think about setting limits on AI usage, creating policies and cost control strategies for using AI for specific tasks.