Researchers at HSE University analysed how effectively the global generative artificial intelligence market converts investment into real revenue, concluding that AI is currently developing faster than it is paying off. The results have been published in the journal Foresight and STI Governance .
In recent years, generative artificial intelligence (GenAI) has become one of the main areas of technological investment. Companies are pouring billions of dollars in chips, servers, and data-centre infrastructure, expecting rapid economic returns from large language models.
However, market expectations may be overstated. HSE University Academic Supervisor Yaroslav Kuzminov and Ekaterina Kruchinskaia , Associate Professor at the Faculty of Social Sciences and Senior Lecturer at the Department of Higher Mathematics, set out to assess the balance of the generative AI market and whether a gap exists between infrastructure investment and revenues from AI technologies.
The authors applied the DEA method—a model used to analyse the efficiency of complex economic systems based on multiple input and output parameters. In this case, the 'input' consisted of revenues of AI hardware manufacturers (chips, servers, semiconductors, and data-centre infrastructure), including companies such as AMD, Intel, and NVIDIA. The 'output' was the revenue of companies developing and monetising AI solutions, including Sony, OpenAI, Google DeepMind, Amazon, and Apple. In essence, the model simulates the AI market at both the input and the output stages, assuming that these players set the main agenda.
The analysis covers the period from 2016 to 2024. Importantly, the years themselves were treated as the units of analysis—although companies typically serve as units in this method. This decision was deliberate: the authors aimed to evaluate the efficiency of AI development overall in each specific year, rather than within individual companies. To test the robustness of the results, calculations were performed both in absolute terms and with adjustments for global GDP. This approach made it possible to assess the relative efficiency of the generative AI market across different years.
The analysis showed that the development of the GenAI market is nonlinear. As generative models emerged and underwent initial commercialisation between 2016 and 2021, efficiency increased. However, beginning in 2021 the trend changed: efficiency indicators declined despite a sharp rise in investment. After a short-term surge in 2023, efficiency again returned to the level recorded in 2022.
'From a purely methodological perspective, the results suggest that the AI solutions market is developing according to a catch-up model: revenues from software products do not yet compensate for the massive investment in hardware infrastructure. Increased demand for chips and computing power is driven by the development of large language models, but their commercial returns remain limited and do not offset the cost of hardware technologies or further investment in them,' said Ekaterina Kruchinskaia.
According to the researchers, the current development model strengthens the position of hardware manufacturers but produces limited economic returns, as computing power becomes an end in itself. The market for AI solutions and applications capable of influencing social processes—for example, by increasing labour productivity—faces not only constraints such as the high cost of hardware and training runs, shortages of qualified personnel, and technological limits of the models, but also struggles to generate sufficient revenue, especially when compared with the scale of investment required.
'AI is indeed transforming not only the economy and companies' business models but also everyday social life. This is evident in our daily lives. At the same time, its influence is actually spreading more slowly than it may appear and is less productive than many would like. Many people speak of a bubble in the AI market—a phenomenon not new to the global economy. It would be fair to say that such risks do exist. Our model opens the door to a more practical discussion in this direction. It is important to have not only analytical tools but also an applied plan, and a straightforward one at that. Without improving the efficiency of applied solutions, expanding their adoption and pursuing more balanced investment planning, further positive progress will be difficult,' noted Yaroslav Kuzminov.
The authors emphasise that studies of this kind are important not only for the academic community but also for businesses, investors, and the development of balanced science and technology policy in the field of artificial intelligence.
