Generative AI - Worldwide

Generative artificial intelligence (AI) has gained a lot of attention in the past months, establishing more and more tools for users. Current trends in generative AI include the development of midjourney models, which aim to bridge the gap between pretrained models like GPT-3 and fully customized models. Midjourney models enable fine-tuning and adaptation of existing models to specific tasks or domains, providing more control and flexibility in generating desired outputs. Additionally, models like ChatGPT have gained prominence, allowing for interactive and dynamic conversational experiences. These models utilize generative AI techniques to understand user inputs, maintain context, and generate responses that are coherent and relevant. The continued advancement of generative AI, with a focus on fine-tuning and interactive capabilities, holds potential for creating more personalized and engaging user experiences across various applications, from virtual assistants to creative content generation.

In the future, generative artificial intelligence (AI) will further witness growth, as the advancements in machine learning and deep learning seek to increase. Moreover, the models and capabilities will be trained with more and more data, leading to even stronger tools and possibilities for generative AI. This could lead to creating highly realistic virtual actors, generating personalized content for immersive experiences, or even assisting in the production of movies and video games. In design and creativity, generative AI can aid in the generation of novel artworks, architectural designs, or fashion trends. Moreover, in healthcare, generative AI may facilitate the synthesis of new drug compounds, assist in medical imaging analysis, or help in the generation of personalized treatment plans. However, a significant concern is the potential for malicious use, such as generating realistic but fake media content for spreading misinformation or conducting scams.

The Generative Artificial Intelligence market is expected to see significant growth in the coming years, with a forecasted CAGR of over 24.4% from 2023 to 2030.

Data coverage: The data encompasses B2B, B2G, and B2C enterprises. Figures are based on the funding values from different industries for the market.

Modeling approach / Market size:Market sizes are determined through a top-down approach with a bottom-up validation, building on a specific rationale for each market. As a basis for evaluating markets, we use annual financial reports, funding data, and third-party data. In addition, we use relevant key market indicators and data from country-specific associations such as GDP, number of internet users, number of secure internet servers, and internet penetration. This data helps us estimate the market size for each country individually.

Forecasts:In our forecasts, we apply diverse forecasting techniques. The selection of forecasting techniques is based on the behavior of the relevant market. For example, the S-curve function and exponential trend smoothing are well suited to forecast digital products and services due to the non-linear growth of technology adoption. The main drivers are the level of digitalization, the number of secure internet servers, and the revenue of the Public Cloud market.

Additional Notes: The data is modeled using current exchange rates. The impact of the COVID-19 pandemic and the Russian-Ukraine war are considered at a country-specific level. The market is updated twice a year. In some cases, the market is updated on an ad-hoc basis (e.g., when new, relevant data has been released or significant changes within the market have an impact on the projected development). Data from the Statista Consumer Insights Global survey is weighted for representativeness.