15:55
02.06.2026
Despite years of predictions about mass unemployment caused by the rise of artificial intelligence, reality has turned out somewhat differently. OpenAI CEO Sam Altman recently acknowledged that his earlier estimates regarding the speed at which AI would replace human workers were overly pessimistic. Instead, a growing number of companies are facing a different challenge — the extraordinarily high costs of implementing AI solutions whose effectiveness often falls short of expectations.
This was highlighted by AI adoption and digital transformation expert Vitalii Kiro in his recent column.
According to Kiro, when people refer to “artificial intelligence” today, they are usually talking about large language models (LLMs) such as ChatGPT, Claude, and Gemini. These systems can generate text, analyze information, and perform a wide range of tasks. However, they do not constitute true artificial intelligence in the scientific sense. Operating them requires substantial computing resources, making them increasingly expensive for businesses.
The first signs of disappointment are already emerging among major corporations. One of the largest Pizza Hut franchise operators on the U.S. East Coast has filed a $100 million lawsuit against its parent company over the AI-powered delivery management system Dragontail. According to the complaint, the technology failed to improve operations and instead reduced on-time delivery rates, negatively affecting sales.
Starbucks has faced similar challenges. The company abandoned an automated inventory management system less than a year after its launch. The decision followed numerous inventory tracking errors that prevented the algorithms from accurately determining stock levels across its stores.
Concerns about AI monetization are also being voiced throughout the technology sector. Representatives of Uber have stated that the costs associated with large language models are often difficult to justify with measurable business outcomes. The company reportedly exhausted its annual AI budget within just a few months. Similar issues have been reported at Microsoft, which, according to industry media, has begun restricting the use of certain AI tools because expenses exceeded planned budgets.
Analysts are also warning of a potential market correction. According to Gartner, more than 40% of AI agent projects could be abandoned by the end of 2027 due to high costs, unclear business value, and poor returns on investment. At the same time, global spending on information technology and artificial intelligence continues to rise and is already measured in trillions of dollars.
Experts stress that these challenges do not indicate a failure of the technology itself. Rather, the industry appears to be entering a typical “reality check” phase following a period of inflated expectations. Artificial intelligence is already helping doctors, lawyers, journalists, and software developers, but businesses are increasingly focused on measurable economic results rather than ambitious promises.
As a result, the primary issue with modern AI is not that it is rapidly replacing human workers. Instead, companies around the world are being forced to confront the costs of a technology that, at least for now, does not always deliver returns that justify the billions invested in it.