Generative AI Software Market Forecast to Expand Near 10 Times by 2028 to $36 Billion, S&P Global Market Intelligence Says Jun 8, 2023

More complex generative AI models may now be trained and used because of improvements in computing power and the expanding availability of massive datasets. Generative AI models can learn from various sources and produce outputs with higher accuracy and complexity if they have access to additional data and computing power. Europe is a leader in research and development for artificial intelligence because of its creative businesses, industrialists, and digital start-ups based on scientific discoveries. With a strong industrial base, Europe makes many of the world’s industrial and professional service robots and tools in health, transportation, security, farming, and energy. Artificial intelligence is often used in almost all-important businesses, such as manufacturing, healthcare, transportation, e-government, and space technologies. Europe’s Generative AI market trend is continuously evolving and is expected to grow quickly in the coming years.

The market is currently worth more than $13 billion globally, but it is expected to approach $22 billion by 2025, with a stunning Compound Annual Growth Rate (CAGR) of 27.02%. This exponential rise may be ascribed to a variety of causes, including the availability of massive amounts of digital data, the widespread use of generative AI tools, and amazing advances in machine learning techniques.. Notably, North America dominates the generative AI market, holding a significant revenue share of 41%. The growth can be ascribed to the extensive applications of Generative AI in film/music production, fashion, and gaming, which unlock a myriad of possibilities. In music, Generative AI tools permit the remixing of already-existing songs and the creation of unprecedented compositions. For video production and editing, Generative AI tools streamline the procedure by adding special effects and generating fresh videos like animations and even full movies.

Market Restraining Factor

Based on Technology, the Generative AI Market segmentation includes Generative Adversarial Networks (GANs), Transformers, Variational Auto-encoders (VAEs), Diffusion, and NeRFs. The Transformers segment held the majority share in 2022, contributing around ~37.5% to the market revenue. Transformers, in particular, have propelled much of the recent research and hype surrounding generative models. Transformers, a ground-breaking neural network that can analyze massive data sets at scale to construct large language models (LLMs), debuted in 2017. The models in OpenAI’s Generative Pre-Trained Transformer series are among the largest and most powerful in this category, with one of the most recent, GPT-3, including 175 billion parameters. Video content consumption has also been on the rise across various platforms, including social media, streaming services, online advertising, and virtual communication.

From my first contact, I was grateful for the professionalism shown by the whole IMARC team. Asia-Pacific region exhibited fastest growing CAGR for generative AI during the analysis period of 2022 to 2030. The transformer segment is expected to dominate the generative AI industry with a CAGR of 26.4% from 2023 to 2033.

Market Size Estimation

This system employs generative models, such as large language models, to statistically sample new data based on the training data set. These models use machine learning algorithms to learn patterns in existing data & generate new content based on those patterns. LLMs expand content production, language translation, sentiment analysis, and data analysis. LLMs may aid sentiment analysis, which analyzes massive text data to estimate public opinion and customer feedback. The growing generative AI market and the need for AI-powered chatbots and virtual assistants indicate LLMs’ commercial potential. In the secondary research process, various sources were referred to for identifying and collecting information for this study.

The latest developments in deep learning algorithms and hardware, such as Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs), have been the primary driving forces of generative AI. These developments have made it possible to produce generative models that are more sophisticated and complicated. Generative AI utilizes deep learning algorithms and neural Yakov Livshits networks to discover patterns and generate new outcomes based on them. Generative AI grasps the context of a source text and linguistically creates those phrases in a different language using sophisticated deep-learning techniques. Deep learning methods, such as neural networks, are used by generative AI models to create new content, such as pictures, movies, or texts.

Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

Many organizations outsource these services to ensure on-time delivery to their clients. Personalised suggestions for goods, content, or entertainment are another application for generative AI that excels because it takes into account user preferences and behaviour. Additionally, generative AI gives artists and designers the freedom to push the frontiers of artistic expression by enabling them to create original works of art, experiment with innovative visual styles, and explore new creative horizons. Deep learning segment was leading the generative AI market in terms of technology in 2022.

• Shifts in workflows triggered by these advances could expose the equivalent of 300 million full-time jobs to automation, Briggs and Kodnani write.
• The software segment held the majority share in 2022, contributing around ~65.3% to the market revenue.
• In addition, the incorporation of AI improves analytics, which aids companies in using sentiment analysis, visual recognition, and dialogue capabilities to boost segment growth.
• Henceforth, it would accelerate the market growth of this technology in the forecast period.

Significant interest has been shown in generative AI across a range of sectors, including design, gaming, and healthcare. It is essential to the healthcare industry’s ability to identify new drugs, analyze medical images, and prescribe tailored treatments. Generative AI facilitates the creation of compelling virtual worlds and realistic characters in the game industry.

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Because generative AI has so many possible uses, and because MedTech is a highly regulated field where people’s lives are on the line, some companies may take a wait-and-see strategy. In-person visits to customers have been increasingly restricted for MedTech sales teams in recent years. Generative AI can help to accelerate those efforts Yakov Livshits by enabling mass personalization and adapting marketing messages to resonate more successfully with diverse client demographics, resulting in higher conversion rates. Rising complexities in information technology and other technologically advanced industries are the leading generative AI market trend that is fueling the industry demand.

The world is poised to see an explosion of growth in the generative AI sector over the next ten years that promises to fundamentally change the way the technology sector operates. The technology is set to become an increasingly essential part of IT spending, ad spending, and cybersecurity as it develops. Generative AI has experienced a substantial surge in demand from North America, particularly in the United States. The region’s robust technological ecosystem, investment in AI research, and the presence of leading tech companies have contributed to this demand. By automating laborious coding, generative AI has also had an impact on the software development industry. Rather than totally coding the software, IT professionals may now swiftly design a solution by communicating what they are searching for to the AI model.

Key Players in the Generative AI Industry

This can be attributed to the rise in demand for pre-training models on large amounts of data and fine-tuning them for specific tasks. Furthermore, language models such as GPT-3 have been shown to be highly effective in tasks such as language translation, summarization, and text completion, and their use is expected to increase in various industries. On the other hand, the Asia-Pacific region is forecasted to be the fastest-growing segment during the forecast period. [541 Pages Report] A notable expansion trajectory is anticipated within the generative AI market, forecasting an escalation from its 2023 valuation of USD 11.3 billion to a substantial valuation of USD 76.8 billion by the year 2030.

The market is expected to grow faster due to the increasing demand for generative AI in all industries. At the same time, the automotive and transportation segment had a market share of 22%. The multi-modal generative model is predicted to be the fastest-growing segment with a growth rate of around 41.6% during the outlook period. The multi-modal generative model can achieve greater Yakov Livshits accuracy and robustness by merging data from multiple modalities, augmenting the segment’s growth. Integrating generative AI successfully into various industries requires strong collaboration between humans and AI systems. Fostering trust, understanding, and acceptance of generative AI among users, employees, and stakeholders is paramount to realizing its full potential.

The growing number of partnerships, collaborations, and product launches that are offering lucrative opportunities to the market players in this domain will also lead to a rise in the growth of the generative AI market. Furthermore, the increasing investments in Artificial Intelligence research and development across the globe will also cause an increase in the generative AI market growth. Generative AI deploys machine learning techniques to create content like images, text, video, and audio.