Generative AI Market Share, Size & Growth Research Report 2023-2028

Lockdowns and industrial closures have disrupted the supply chain, delaying the development and implementation of new products. Additionally, businesses’ budget cuts during the economic crisis resulted in a decline in income for market participants, which resulted in a decreased demand for the generative AI goods and services. By tracking the development of the disease and aiding in the development of treatments, this technology had been utilized to create predictive models for the spread of COVID-19. In spite of the difficulties, there has also been an increase in investment in the research and development. Businesses are aggressively working to create solutions for brand-new issues brought on by the epidemic.

• In digital marketing, AI-generated personalized advertisements can cater to specific customer preferences, leading to higher conversion rates.
• Large language models may aid in reducing the time and expense required to develop NLP applications.
• Prominent research centers and universities in the region conduct cutting-edge research, publish influential papers, and contribute to the development of generative AI techniques.
• In recent years, there has been a remarkable surge in the popularity of virtual worlds within the Metaverse.
• Notably, generative AI has sparked a revolution in content creation, reshaping fields like marketing, entertainment, and journalism by automatically producing text, images, videos, and music.
• As individuals and businesses in China explore opportunities in automated content generation, they require reliable and fast platforms to fulfill the industry requirements.

For instance, in the gaming industry, generative AI can be used to generate game levels, characters, and assets, thereby reducing the burden on human designers and enhancing the variety and replay ability of games. In digital marketing, AI-generated personalized advertisements can cater to specific customer preferences, leading to higher conversion rates. As companies seek ways to optimize content creation and cater to individual preferences, the adoption of generative AI is expected to witness substantial growth. The chatbots & intelligent virtual assistants segment is expected to register a robust revenue CAGR during the forecast period. Chatbots and virtual assistants that can handle complex discussions are made using generative AI platforms.

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Moreover, generative AI techniques for image generation have made significant advancements in recent years. These techniques have become more sophisticated and capable of generating high-quality, realistic, and diverse images. The generative AI market from Generative Adversarial Networks (GANs) segment held over USD 3 billion revenue in 2022.

Continued research, regulation and collaboration will all play a role in shaping its trajectory forward. Scaling up generative AI systems to handle real-time and large-scale applications can be an enormously daunting task. Producing high-quality content such as dynamic video synthesis or interactive chatbots requires efficient algorithms and optimized architectures that meet computational demands while still offering user friendly experiences.

Generative AI Platforms and Applications: Market Trends and Forecast, 2Q23

Generative AI has numerous applications in creative industries, such as music and art, as well as in industries that require the creation of new content, such as marketing and advertising. The rising demand for AI-generated content across various industries has been a driving the expansion of the generative AI market. In sectors such as media and entertainment, gaming, and advertising, there is a constant need for fresh and engaging content to captivate audiences and consumers. Generative AI technologies offer a scalable and efficient solution to meet this demand by automatically producing content, including images, videos, music, and even text.

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.

Cloud storage provides scalable computing power, enabling access to resource-intensive Generative AI model training for businesses without heavy capital spending. Furthermore, it guarantees high efficiency in data accessibility and collaboration, Yakov Livshits enabling storage and sharing of various datasets across global teams. The cost-effective pay-as-you-go model of cloud storage reduces economic restraints and accelerates secure management of sensitive Generative AI projects.

NEW YORK, June 8, 2023 /PRNewswire/ — The popularity of ChatGPT spawned an array of startups and kicked off a race by major technology providers to compete for mindshare. Generative AI models, especially those based on deep learning architectures, are computationally intensive and resource-demanding. Training and running large-scale GANs or VAEs often require powerful GPUs or specialized hardware, making them inaccessible to organizations with limited computational resources. The high computational complexity presents a challenge for small and medium-sized enterprises and individual developers who may not have the financial means or infrastructure to invest in such hardware. In addition, the energy consumption of training these models can be considerable, leading to environmental concerns. Thus, overcoming this restraint involves ongoing research into model optimization, efficient parallelization techniques, and cloud-based AI services that can make generative AI more accessible to a broader audience.

Ensuring fairness demands constant diligence, with bias stemming from various sources such as skewed training data or model architecture. If unaddressed, biased AI models could perpetuate inequities, yielding responses that are discriminatory, offensive, or inaccurate for specific demographic groups. North America is expected to dominate the market during the forecast period due to rising research and development activities, advanced technological infrastructure, and leading AI companies’ presence in the region. Popular generative AI tools include ChatGPT, GPT-3.5, DALL-E, MidJourney, and Stable Diffusion. Generative AI is at a developing stage, which will require a skilled workforce and high investment in implementation for development.

Technology Analysis

By providing these options and insights, generative AI helps decision-makers make well-informed choices. Due to advancements in processing power and the expansion of the number of available huge datasets, more advanced generative AI models can now be learned and applied. If generative AI models have access to more data and processing power, they may learn from a variety of sources and generate results with greater accuracy and complexity. The transformative potential of Generative AI technology is clear for individuals, businesses, and society as a whole. Its swift advancement can democratise various industries and revolutionise content creation and creative processes.

The software segment was the largest segment and was valued at USD 1,881.55 million in 2017. Some of the main applications of this software across enterprises include content creation and customer service automation. Another prime example of AI-based software is StyleGAN which utilizes machine learning to generate realistic human faces and has wide applications in the fashion and beauty industry to generate virtual models for clothing and makeup. Runway is also another AI-based tool that is used by designers and artists to create new designs and artwork. Hence, such applications are expected to drive the growth of this segment which in turn will drive the global generative AI market growth during the forecast period.