Generative AI produces text & images, spanning blog posts, program code, poetry, and artwork. It uses complex machine learning models to predict the next word based on previous word sequences. In other words, it allows computers to abstract the underlying pattern related to the input, and then use that to generate similar content. In addition, generative AI produces a targeted summary after searching through various legal study sources. Therefore, countless hours of human investigation can be reduced through technology. Moreover, it generates code in-editor in real-time and offers whole functions, snippets, and even fully working modules, which is anticipated to help the industry expand in the approaching years. Generative AI makes use of unsupervised learning algorithms for spam detection, image compression, and preprocessing data stage, such as removing noise from visual data, to improve picture quality. Moreover, supervised learning algorithms are used for medical imaging and image classification. Furthermore, it has applications in various industries, such as BFSI, healthcare, automotive & transportation, IT & telecommunications, media & entertainment, and others. Generative AI is a powerful tool that can be used to create new ideas, solve problems, and create new products. In addition, it can help organizations save money and time, increase efficiency, and enhance the quality of content generated.
The generative AI market has experienced significant growth due to the advancements in deep learning. Generative models are type of machine learning that uses AI, probability, and statistics to produce a computer-generated representation of a targeted variable calculated from prior observations, input, or datasets. These improvements make deep learning models more intelligent and capable, enabling them to perform complex tasks such as image recognition, language translation, and content generation more accurately and effectively. In addition, the rise of generative AI is driven by the desire to provide users and consumers with more personalized, engaging and relevant content and experiences, have further fueled the market growth. However, ethical and privacy concerns pose significant challenges and barriers to the development and adoption of generative AI technologies, with one major concern being the creation of false content. These realistic-looking images or videos can mislead people into believing that things never happened. This can lead to the spread and manipulation of disinformation, which is harmful to individuals and society. On the contrary, the demand for generative AI applications in industries such as entertainment, healthcare, engineering, finance, and defense is driven by the growing use of innovative solutions such as super-resolution, text-to-image conversion, and text-to-video conversion. Moreover, the growing application of artificial intelligence is a result of its increased computing power and ability to solve problems in different industrial sectors. Expanding into these industries will provide major lucrative opportunities for the growth of the generative AI market.
The generative AI market is segmented on the basis of component, technology, end user, and region. On the basis of component, the market is bifurcated into software and services. By technology, it is segmented into generative adversarial networks (GANs), transformer, variational autoencoder (VAE), diffusion networks, and retrieval augmented generation. On the basis of end user, it is classified into media & entertainment, BFSI, IT & telecom, healthcare, automotive & transportation, and others. On the basis of region, it is analyzed across North America, Europe, Asia-Pacific, and LAMEA.
The report analyzes the profiles of key players operating in the generative AI market such as Adobe, Inc., Amazon Web Services, Inc., D-ID, Genie AI Ltd., Google LLC, IBM Corporation, Microsoft Corporation, MOSTLY AI Inc., Rephrase.ai and Synthesia. These players have adopted various strategies to increase their market penetration and strengthen their position in the generative AI market.
The generative AI market has experienced significant growth due to the advancements in deep learning. Generative models are type of machine learning that uses AI, probability, and statistics to produce a computer-generated representation of a targeted variable calculated from prior observations, input, or datasets. These improvements make deep learning models more intelligent and capable, enabling them to perform complex tasks such as image recognition, language translation, and content generation more accurately and effectively. In addition, the rise of generative AI is driven by the desire to provide users and consumers with more personalized, engaging and relevant content and experiences, have further fueled the market growth. However, ethical and privacy concerns pose significant challenges and barriers to the development and adoption of generative AI technologies, with one major concern being the creation of false content. These realistic-looking images or videos can mislead people into believing that things never happened. This can lead to the spread and manipulation of disinformation, which is harmful to individuals and society. On the contrary, the demand for generative AI applications in industries such as entertainment, healthcare, engineering, finance, and defense is driven by the growing use of innovative solutions such as super-resolution, text-to-image conversion, and text-to-video conversion. Moreover, the growing application of artificial intelligence is a result of its increased computing power and ability to solve problems in different industrial sectors. Expanding into these industries will provide major lucrative opportunities for the growth of the generative AI market.
The generative AI market is segmented on the basis of component, technology, end user, and region. On the basis of component, the market is bifurcated into software and services. By technology, it is segmented into generative adversarial networks (GANs), transformer, variational autoencoder (VAE), diffusion networks, and retrieval augmented generation. On the basis of end user, it is classified into media & entertainment, BFSI, IT & telecom, healthcare, automotive & transportation, and others. On the basis of region, it is analyzed across North America, Europe, Asia-Pacific, and LAMEA.
The report analyzes the profiles of key players operating in the generative AI market such as Adobe, Inc., Amazon Web Services, Inc., D-ID, Genie AI Ltd., Google LLC, IBM Corporation, Microsoft Corporation, MOSTLY AI Inc., Rephrase.ai and Synthesia. These players have adopted various strategies to increase their market penetration and strengthen their position in the generative AI market.
Key Benefits for Stakeholders
- The study provides an in-depth analysis of the global generative AI market along with the current & future trends to illustrate the imminent investment pockets.
- Information about key drivers, restrains, & opportunities and their impact analysis on the global generative AI market size are provided in the report.
- Porter’s five forces analysis illustrates the potency of buyers and suppliers operating in the industry.
- The quantitative analysis of the global generative AI market from 2022 to 2032 is provided to determine the market potential.
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Key Market Segments
By Component
- Software
- Service
By Technology
- Generative Adversarial Networks (GANs)
- Transformer
- Variational Autoencoder (VAE)
- Diffusion Networks
- Retrieval Augmented Generation
By End User
- Media and Entertainment
- BFSI
- IT and Telecom
- Healthcare
- Automotive and Transportation
- Others
By Region
- North America
- U.S.
- Canada
- Europe
- UK
- Germany
- France
- Italy
- Spain
- Rest of Europe
- Asia-Pacific
- China
- Japan
- India
- Australia
- South Korea
- Rest of Asia-Pacific
- LAMEA
- Latin America
- Middle East
- Africa
Key Market Players
- Adobe.
- Amazon Web Services, Inc.
- D-ID
- Genie AI Ltd.
- Google LLC
- IBM Corporation
- Microsoft Corporation
- MOSTLY AI Inc.
- Rephrase.ai
- Synthesia
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Table of Contents
CHAPTER 1: INTRODUCTION
CHAPTER 2: EXECUTIVE SUMMARY
CHAPTER 3: MARKET OVERVIEW
CHAPTER 4: GENERATIVE AI MARKET, BY COMPONENT
CHAPTER 5: GENERATIVE AI MARKET, BY TECHNOLOGY
CHAPTER 6: GENERATIVE AI MARKET, BY END USER
CHAPTER 7: GENERATIVE AI MARKET, BY REGION
CHAPTER 8: COMPETITIVE LANDSCAPE
CHAPTER 9: COMPANY PROFILES
List of Tables
List of Figures
Executive Summary
According to this report, the generative ai market was valued at $8.15 billion in 2021, and is estimated to reach $126.5 billion by 2031, growing at a CAGR of 32% from 2022 to 2031.Generative AI has numerous applications, including creative arts, such as music and visual arts, data augmentation, simulation, and product design. There are two main types of generative AI: generative adversarial networks (GANs) and variational autoencoders (VAEs). GANs consist of two neural networks that compete against each other to generate and distinguish real and fake samples. VAEs, on the other hand, use an encoding-decoding process to generate new samples. In recent years, generative AI has made significant advancements and has been used to produce high-quality outputs that are difficult for humans to distinguish from real examples. Despite these advancements, there are still challenges in the field, such as controlling the diversity and quality of the generated outputs and addressing ethical concerns, such as the potential for these systems to produce biased or malicious outputs.
Key factors driving the growth of the generative AI market include the rise in usage of artificial intelligence-integrated systems across industry verticals, and the rising need to create virtual worlds in the metaverse and modernize the workforce in industry are driving the growth of the market. in addition, the rise in popularity of generative AI in the healthcare industry for rendering products, such as prosthetic limbs and organic molecules from scratch using 3D printing, CRISPR, and other technologies are considered to be the major driving factor for the market growth. It also enables early identification of potential malignancy to bring more effective treatment plans. Apart from this, the elevating requirement for this technology to assist chatbots in holding effective conversations and boosting customer satisfaction is often acting as another significant growth-inducing factor for the market growth. For instance, in January 2023, Nvidia, in partnership with Evozyne, a pharmaceutical startup, launched a new generative AI model capable of producing proteins for use in medicine and other industries. This new protein transformer variational auto-encoder (ProT-VAE) is built on Nvidia’s BioNeMo framework and uses generative AI to rapidly create synthetic protein designs that fit into the given parameters. Such tactical factors are accelerating market growth.
The market also offers growth opportunities to the key players in the market. Deep learning is part of a broader category of machine learning methods based on artificial neural networks with representation learning. Generative models are the type of ML that uses artificial intelligence, probability, and statistics to produce a computer-generated representation of a targeted variable calculated from prior observations, input, or datasets. This implied that it could generate new or synthetic data through ML and deep learning after being trained on a real dataset. As a result, the demand for machine learning and deep learning also increases to provide a better computer-generated representation, which significantly paves the implementation of generative AI. These aforementioned factors are projected to strengthen the market growth during the forecast period.
The global generative AI market is segmented into component, technology, end user, and region. Depending on the component, the market is divided into software, and services. By technology, it is divided into generative adversarial networks (GANs), transformers, variational auto-encoders, and diffusion networks. Based on end user, it is bifurcated into media and entertainment, BFSI, IT and telecom, healthcare, automotive & transformation, and others. Region wise, it is analyzed across North America (the U. S., and Canada), Europe (the UK, Germany, France, Italy, Spain, and the rest of Europe), Asia-Pacific (China, India, Japan, Australia, South Korea, and rest of Asia-Pacific), and LAMEA (Latin America, Middle East, and Africa).
The key players profiled in the study are Adobe, Inc., Amazon Web Services, Inc., D-ID, Genie AI Ltd., Google LLC, IBM Corporation, Microsoft Corporation, MOSTLY AI Inc., Rephrase. ai, and Synthesia. The players in the market have been actively engaged in the adoption of various strategies such as partnership, collaboration, acquisition, product development, and product launch to remain competitive and gain an advantage over the competitors in the market. For instance, in October 2022, Adobe introduced Generative AI, an AIbased technology to develop an approach that centers on the needs of creatives by integrating Generative AI into Adobe creative tools.
Key Market Insights
By component, the software segment was the highest revenue contributor to the market and is estimated to reach $82.45 billion by 2031, with a CAGR of 31.4%. However, the services segment is estimated to be the fastest-growing segment with a CAGR of 33.3% during the forecast period.By technology, the transformer segment dominated the global market and is estimated to reach $41.42 billion by 2031, with a CAGR of 29.9%. However, the diffusion networks segment is expected to be the fastest-growing segment during the generative AI market Forecast.
Based on end user, the media and entertainment segment was the highest revenue contributor to the market, with $2.7 billion in 2021, and is estimated to reach $31.07 billion by 2031, with a CAGR of 28.1%.
Based on region, North America was the highest revenue contributor and fastest growing region, accounting for $3.3 billion in 2021, and is estimated to reach $46.27 billion by 2031, with a CAGR of 30.7%.
Companies Mentioned
- Adobe.
- Amazon Web Services, Inc.
- D-ID
- Genie AI Ltd.
- Google LLC
- IBM Corporation
- Microsoft Corporation
- MOSTLY AI Inc.
- Rephrase.ai
- Synthesia
Methodology
The analyst offers exhaustive research and analysis based on a wide variety of factual inputs, which largely include interviews with industry participants, reliable statistics, and regional intelligence. The in-house industry experts play an instrumental role in designing analytic tools and models, tailored to the requirements of a particular industry segment. The primary research efforts include reaching out participants through mail, tele-conversations, referrals, professional networks, and face-to-face interactions.
They are also in professional corporate relations with various companies that allow them greater flexibility for reaching out to industry participants and commentators for interviews and discussions.
They also refer to a broad array of industry sources for their secondary research, which typically include; however, not limited to:
- Company SEC filings, annual reports, company websites, broker & financial reports, and investor presentations for competitive scenario and shape of the industry
- Scientific and technical writings for product information and related preemptions
- Regional government and statistical databases for macro analysis
- Authentic news articles and other related releases for market evaluation
- Internal and external proprietary databases, key market indicators, and relevant press releases for market estimates and forecast
Furthermore, the accuracy of the data will be analyzed and validated by conducting additional primaries with various industry experts and KOLs. They also provide robust post-sales support to clients.
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