Generative AI Market Analysis (2023 - 2030)
Global Generative AI Market is estimated to be worth USD 8.28 Billion in 2022 and is projected to reach a value of USD 99.79 Billion by 2030, growing at a CAGR of 36.5% during the forecast period 2023-2030.
The COVID-19 pandemic positively impacted the growth of the Global Generative AI Market. This can be ascribed to the augmented demand for Generative AI among enterprises. This demand arose from the need to elevate customer experiences and cater to individualized needs. Generative AI empowers businesses to devise personalized music playlists, news feeds, and product recommendations, among other applications. This trend has contributed to the growth of the Global Generative AI Market. Generative AI-specialized companies like OpenAI, Cohere, Synthesia, and Mostly AI have been at the forefront of devising cutting-edge solutions in this domain.
Text Generative AI platforms like ChatGPT have gained immense popularity owing to their ability to generate an extensive array of content such as articles, blog posts, dialogues, text summaries, translations, and website text. These platforms undergo training on extensive datasets to ensure the creation of authentic and up-to-date content. Leveraging Natural Language Processing (NLP) and Natural Language Understanding (NLU) techniques, text-generation AI comprehends text prompts, discerns context, and produces intelligent responses. Beyond content generation, these AI tools excel in tasks like question answering, text completion, text classification, content improvement, and engaging in human-like discussions. Generative AI models for text generation have applications in creative writing for crafting fiction pieces, developing conversational agents like virtual assistants and chatbots, accurate language translation, and generating marketing and advertising materials such as product descriptions, ad copy, and social media content. Therefore, this factor propels the demand for Generative AI. In addition, Generative AI tools excel at generating images based on text descriptions that function as text-to-image systems. Users can input specifics like subject, setting, style, object, or location to generate lifelike and vivid images. Image enhancement tools offer functions like image completion, where absent elements of an image are filled in, encompassing creating backgrounds, filling in missing pixels, or mending torn photos. Semantic image-to-photo translation permits the generation of photo-realistic images from sketches or semantic representations. Image manipulation allows tweaks in style, lighting, color, or shape while preserving the original elements. Image super-resolution tools refine image quality without sacrificing specific details like elevating CCTV image clarity. Therefore, this factor also propels the demand for Generative AI.
The Global Generative AI Market is encountering challenges regarding output imprecisions and legal entitlement. Consequently, it is essential to have human-in-the-loop safeguards in place to guide, monitor, and validate the generated content. Inaccuracies in Generative AI are referred to as hallucinations, where the model produces output that is not precise or pertinent to the original input. These inaccuracies can occur due to multiple factors such as incomplete or ambiguous input, flawed training data, or inadequate model architecture. Moreover, legal ownership of both machine-generated content and the data needed to train these algorithms is also a major apprehension with Generative AI. Thus, these challenges inhibit the growth of the Global Generative AI Market.
Key Market Insights:
- Based on the Offering, the Software segment occupied the highest market share in the year 2022. The growth can be ascribed to the growing utilization of Generative AI-based software owing to its several advantages, including improved image quality, faster conversion times, better performance, and readily available results. Currently, Generative AI software is utilized in diverse fields like natural language processing, computer vision, image creation and enhancement, and generative design. As ML models continue to advance and become more potent, Generative AI software is anticipated to make a notable impact across diverse industries and sectors, encompassing entertainment, gaming, fashion, and transportation.
- Based on the Technique, the Transformer Models segment occupied the highest market share in the year 2022. The growth can be ascribed to the significant role of transformers in NLP and other Generative AI applications. Transformers have become a critical component of NLP models. They excel in learning contextual relationships between words in a text sequence using self-attention. Unlike conventional recurrent neural networks (RNNs), transformers process input sequences in parallel, which enables faster processing and a global understanding of the sequence. This advantage, combined with their effectiveness across diverse NLP tasks like language modeling, text classification, question answering, and machine translation, led to the development of large-scale pre-trained language models like GPT and BERT.
- Based on the model type, the large language model segment held the highest market share and is projected to grow at the highest rate during the forecast period. This is due to various applications, such as various chatbots and data generation tools capable of conversing with users. It greatly reduces the time and cost associated with the development of NLP applications.
- Based on the Application, The NLP segment occupied the highest market share in the year 2022. The growth can be ascribed to the diverse applications of Generative AI in NLP, which is a branch of AI focused on computer-human language interaction. NLP employs ML algorithms to analyze and comprehend human language, as well as generate text that closely mirrors human-generated content in both style and substance. A prevalent usage of Generative AI in NLP entails the automated generation of news articles or social media posts. These systems are trained on extensive datasets of human-generated text and utilize that knowledge to generate fresh, authentic text that aligns with the training data about style and content. Furthermore, Generative AI can be leveraged to generate responses to customer inquiries or craft individualized marketing messages.
- Based on the End User Industry, the Entertainment segment occupied the highest market share in the year 2022. 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. This diminishes time expenditure for content creators and influencers. The gaming industry benefits profoundly from the implementation of Generative AI, permitting the creation of new characters, levels, and storylines, which results in immersive and rewarding gaming experiences.
- Based on the region, the region of North America dominated the Global Generative AI Market in the year 2022. The early and vast adoption of avant-garde technologies, the augmenting demand for Generative AI across diverse sectors, including entertainment, BFSI, healthcare, and real estate, the presence of a well-established IT industry in nations, such as the United States and Canada, and the rising investments by governments in research and development activities are some of the pivotal factors propelling the region's growth.
- Google LLC, OpenAI Inc., Synthesia Ltd., Anthropic, Theai Inc., Cohere Inc., and Kensho Technologies, Inc. are among the major players in the Global Generative AI Market. The Generative AI Market is estimated to become more competitive as new players enter the industry.
MARKET SEGMENTATION:
By Offering:
By Technique:
- Generative Adversarial Networks (GANs)
- Transformer Models
- Variational Autoencoders (VAEs)
- Diffusion Networks
By Models:
- Large Language Models
- Image and Video Generative Models
- Multi-modal Generative Models
- Others
By Application:
- Natural Language Processing (NLP)
- Automated Text Generators
- Language Translation
- Sentiment Analysis
- Code Generators
- Image Generators
- Others
- Robotics 7 Automation
- Computer Vision
- Object Recognition
- Image & Video Analysis
- Surveillance
- Chatbots & Intelligent Virtual Assistants
- Synthetic Data Generation
- Autonomous System Training
- Medical Imaging
- Cybersecurity
- Precision Agriculture
- Product Design
- ML-based Predictive Modelling
- Predictive Analytics
- Personalized Recommendations
- Others
- 3D Modelling and Reconstruction
- 3D Model Simulations
- 2D to 3D Model Generation
- Image and Texture Synthesis
- Music and Art Generation
- Automated Music Composition
- Video Generators
- Design Generators
- Voice Generators
- Others
By End User Industry:
- Aerospace and Defense
- Automotive
- Banking, Financial Services, and Insurance (BFSI)
- Education
- Entertainment
- Healthcare
- Manufacturing
- Real Estate
- Others
By Region:
- North America
- Europe
- Asia Pacific
- South America
- Middle East & Africa
For More Info, Request Sample Copy Of This Report @ https://virtuemarketresearch.com/report/generative-ai-market/request-sample
