GENERATIVE AI MARKET SIZE AND SHARE ANALYSIS - GROWTH TRENDS AND FORECASTS (2024-2031)

Generative AI Market Size and Trends

Global generative AI market is estimated to be valued at USD 68.34 Bn in 2024 and is expected to reach USD 496.82 Bn by 2031, exhibiting a compound annual growth rate (CAGR) of 32.8% from 2024 to 2031.

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Increasing adoption of advanced technologies powered by artificial intelligence and machine learning algorithms across industries can drive the generative AI market growth. Generative models are gaining popularity as these help reduce costs and increase productivity by automating repetitive manual tasks. The ability of generative AI techniques to learn from large datasets and generate new meaningful information with minimal human intervention can boost demand for generative AI solutions. Advancements in deep learning and ability of generative models to handle large and complex datasets can open new growth avenues for players.

Advancements in deep learning and neural networks enabling more sophisticated generative models

With advancements in deep learning techniques like generative adversarial networks (GANs), reinforcement learning, and self-supervised learning, researchers are now able to generate increasingly lifelike images, videos, speech, text and other forms of data. Deep learning models are becoming more powerful as computing capabilities increase and more training data becomes available. Due to unsupervised learning techniques like GANs and autoregressive models, AI systems can now learn the underlying distribution or patterns in datasets without the need for human annotation or labeling. This self-supervised learning enables generative models to produce synthetic data that mimics real data with high fidelity.

Deep neural networks have billions of parameters that can learn rich, high-dimensional distributions over natural data domains like images, audio and text. By learning from huge volumes of unlabeled training examples, generative models can mimic subtle statistical properties like object shapes, textures or sentence structures. Advancements in neural architecture search enable researchers to develop novel network designs that are even better at capturing complex, real-world distributions. The availability of huge computational resources in the cloud allows them to train these models at massive scales for longer periods. Generative models can generate photos, videos and other content that appear highly realistic even to the human eye.

Market Concentration and Competitive Landscape

Rising investment in AI research and development by tech companies and venture capitals

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Major technology companies like Accenture, Adobe, Adept, AI21 Labs, Amazon Web Services (AWS), etc. and well-funded AI startups are investing heavily in generative AI with the goal of developing new applications and business opportunities. Corporates see huge commercial potential in generative models for personalized experiences, creative works, synthetic training data and others. Venture capitalists have recognized this potential and invests in AI startups each year. This rising investment boosts advancements in generative modeling techniques.

Large firms like OpenAI, Google, AWS, Microsoft, and others have launched initiatives and research labs dedicated to advancing the state-of-the-art in generative modeling, computational creativity and related areas. These are investing in novel model architectures, self-supervised learning methods, massive computational resources and talented researchers. Startups are innovating with new applications of generative AI in domains like art, science, manufacturing and social media. Many tech companies uses AI to automate routine design/engineering processes and generate synthetic test/training data to lower costs and boost productivity.

Venture funding for AI startups has increased exponentially in recent years. Unicorns like Anthropic, Stability AI and DeepMind received funding from top VCs. This large influx of capital boosts more innovations that push the boundaries of generative modeling.

Key Takeaways from Analyst:

Global generative AI market growth is driven by rising demand for AI-generated content across multiple industries. As organizations increasingly realize the potential of generative AI technologies to automate repetitive creative tasks, there has been huge adoption of these systems. However, data privacy and security concerns surrounding the use of large language models can hamper the market growth.

North America currently dominates the generative AI market due to heavy investments by tech giants and startups in the region. Meanwhile, Asia Pacific is expected to witness the fastest growth led by China, India, and other emerging economies. Both established companies as well as a number of startups are working on developing more advanced generative AI tools catering to industries like education, healthcare, e-commerce, and media & entertainment.

Collaboration between AI developers, content creators, and domain experts can expand the capabilities of generative AI systems and addresses concerns around bias, accuracy and transparency. If designed and applied responsibly with human oversight, generative AI has significant potential to revolutionize content creation, learning, and several other areas for benefit of business and humanity.

Market Challenge - Ethical concerns around the use of generative AI, particularly in areas like art and journalism

Global generative AI market growth can be hampered due to growing ethical concerns around the use of this technology, particularly in areas like art and journalism. There are debates around whether generative AI models could undermine creative fields by automatically generating visual artworks, news articles, or fiction without human input. However, other people counter that AI-generated content still requires significant human labor to develop the models and provides new opportunities for collaboration between humans and machines. Issues around attributing authorship of AI-generated content and protecting creative works continue to be discussed. Evaluating the appropriate use of disclosure and content filtering will be important to address these ethical considerations and ensure generative AI is developed and applied responsibly.

Market Opportunity- Development of more user-friendly and accessible generative AI tools for non-technical users

The development of more user-friendly and accessible generative AI tools for non-technical users presents a major opportunity for the market growth. Most generative AI systems require advanced coding and machine learning skills, which limits their adoption. However, there is potential to design novel generative AI products and services with intuitive interfaces similar to popular mobile apps and social media platforms. This could make generative text, image, video and audio creation much more seamless and engaging for everyday consumers and businesses. Simplifying the user experience of generative AI tools could help unlock new applications of the technology for industries like marketing, design, education and more. It may also encourage more diverse participation and mitigate concerns about generative content being uncontrolled or misused by non-experts.

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Insights By Technology - Rapid Adoption of Deep Learning Enables Advanced Data Modeling

In terms of technology, deep learning segment is estimated to contribute the highest market share of 46.3% in 2024, owing to its capability to efficiently handle large and unstructured datasets. Deep learning techniques such as convolutional neural networks, recurrent neural networks and deep reinforcement learning are increasingly being adopted to build generative AI applications that can autonomously learn from data. Compared to other traditional machine learning models, deep learning allows for more human-like learning as it mimics the neural structure of the brain. This property of deep learning has enabled advanced data modeling capabilities for generative AI. The ability of deep learning models to recognize patterns in large volumes of unlabeled data has boosted its demand across content creation, virtual assistants and other generative use cases. Ongoing enhancement in computing power and availability of big data has further boosted the adoption of deep learning technology. As deep learning delivers highly accurate outcomes, its usage is anticipated to intensify, thus, driving the deep learning segment growth.

Insights By Deployment Mode - Rapid Transition to Cloud-based Platforms Drives Segment Growth

In terms of deployment mode, cloud-based segment is estimated to contribute the highest market share of 75.4% in 2024, owing to on-demand capabilities and low upfront costs associated with cloud-based generative AI solutions. Moving workloads to the cloud allows organizations to focus more on innovation rather than investing heavily in infrastructure. Cloud platforms also facilitate remote collaboration and provide instant access to generative models from any location. This has encouraged companies, especially small and medium enterprises, to adopt cloud-based generative AI services. Furthermore, pay-as-you-go pricing model of cloud eliminates unpredictable hardware expenses. Maintenance and regular updates of generative models can also be efficiently managed in the cloud. These advantages have accelerated the migration of generative AI workloads to public and private clouds. As advanced cloud capabilities in areas such as auto-scaling and serverless computing emerge, the cloud-based segment can witness growth over the forecast period.

For instance, in June 2023, Accenture, a global professional services company specializing in digital, cloud, and security solutions, announced a collaboration with Microsoft to assist companies in transforming their businesses by harnessing the power of generative AI accelerated by the cloud. This collboration aims to help customers responsibly build and extend technology within their organizations, ensuring these effectively navigate the evolving landscape of AI innovation.

Insights By Application - Booming Content Creation Industry

In terms of application, content creation segment estimated to contribute the highest market share of 34.2% in 2024, due to massive volume of content being generated daily. Social media engagement and content sharing have expanded significantly in the recent past. This has boosted demand for automated and AI-based content generation techniques. Generative AI models are increasingly deployed by companies to produce personalized and hyper-relevant content at scale. These are able to churn out news articles, product descriptions, social media posts and more with human-level language quality. Such capabilities are greatly relieving the workload of content writers and marketing teams. Furthermore, entertainment industry is also embraces generative AI for tasks like video/image editing, subtitling, localization and digital asset creation. As user-generated and AI-assisted content becomes mainstream, content creation segment will witness growth.

Regional Insights

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North America has established itself as the dominant region in the global generative AI market with an estimated market share of 44.6% in 2024. The region is home to technology giants and industry leaders like OpenAI, Anthropic, Uber, and DeepMind who have made massive investments in generative AI capabilities like text generation, image generation, and autonomous content creation. Several cutting-edge projects using GANs, diffusion models and other generative techniques are ongoing across universities and national labs in the U.S. and Canada.

The presence of high-skilled talent and strong focus on AI innovation from both public and private institutions has created a thriving generative AI ecosystem in North America. Large corporations are deploying generative solutions across various divisions to enhance productivity and create new revenue streams. Furthermore, the region has witnessed interest from investors who have poured billions into generative AI startups.

Asia Pacific region, especially countries like China, Japan, and South Korea, is emerging as the fastest growing market for generative AI. With a massive population and digital infrastructure, there has been immense scope for utilizing generative technologies at scale. The governments in Asia have proactively drafted policies to encourage domestic AI development, and offer incentives for businesses to integrate generative solutions. Several large conglomerates based in Asia are at the forefront of applying generative AI for novel applications across industries such as manufacturing, healthcare, education and finance.

Countries like China have additionally taken steps to cultivate local generative AI talent through academic programs and research collaborations. This focus on building internal capabilities within the region will allow Asia Pacific to become more self-sufficient and expand its influence in this strategic market. With rising demand for Generative AI and supportive conditions, the generative AI market in Asia Pacific looks set to flourish and make this the growth engine for global industry.

According to IBM's Global AI Adoption Index 2022 report, about 53% of IT professionals had accelerated their adoption of artificial intelligence (AI) in response to the pandemic. IBM, a leading provider of hybrid cloud and AI solutions, conducted this survey to assess the growing integration of AI technologies in organizations as these adapt to new challenges.

Market Report Scope

Generative AI Industry News

• In April 2023, Amazon Web Services, Inc. (AWS), a subsidiary of Amazon and a leading cloud services provider, launched a global Generative AI Accelerator aimed at supporting startups. This accelerator offers access to impactful AI tools and models, optimization of machine learning stacks, customized go-to-market strategies, and additional resources to help these startups thrive in the evolving AI landscape.
• In March 2023, Adobe, a leading software company known for its creative solutions, announced a partnership with NVIDIA, a global technology company specializing in graphics processing units and AI, to advance the growth of generative AI and enhance creative workflows. Together, both companies will innovate advanced AI models aimed at deep integration into applications widely used by developers and marketers, facilitating new creative possibilities and efficiencies in their work.
• In February 2023, Google, a leading technology company known for its search engine and AI innovations, introduced the 'Bard' AI chatbot, designed to compete with OpenAI's ChatGPT. Bard is believed to be a lightweight version of Google's LaMDA, capable of responding to human inquiries and synthesizing information similarly to ChatGPT.
• In February 2023, Microsoft Corporation, a global technology leader, unveiled a preview of its AI-powered Bing search engine and Edge browser, positioning them as an AI co-pilot for the web. The new Bing allows users to ask questions directly in a chat interface, and it responds with concise answers generated by a powerful AI language model, rather than providing links to websites.
• In January 2023, NVIDIA, a leading AI hardware and software vendor, introduced new enterprise Metaverse technologies, including virtual reality (VR) and augmented reality (AR) tools, as part of its Omniverse portal. This launch features generative AI tools and updates to the Omniverse platform with RTX capabilities, along with an early access program for developers interested in creating avatars and virtual assistants.

*Definition: Global Generative AI Market consists of companies that are developing and applying generative AI technologies to automate the creation and generation of various digital content such as images, videos, text, audio and other forms of data. These generative AI systems leverage large language models, generative adversarial networks and other advanced machine learning techniques to produce completely novel and realistic digital outputs without any human involvement in the generative process. The aim of this new emerging market is to disrupt and transform existing content creation workflows across industries through the power of autonomous generation using artificial intelligence.

Market Segmentation

• By  Technology Insights (Revenue, USD Bn, 2019 - 2031)
• • Deep Learning
  • Machine Learning
  • Natural Language Processing (NLP)
• By Deployment Mode Insights (Revenue, USD Bn, 2019 - 2031)
• • Cloud-based
  • On-premises
• By Application Insights (Revenue, USD Bn, 2019 - 2031)
• • Content Creation
  • Chatbots and Virtual Assistants
  • Image and Video Generation
  • Music Generation
  • Others
• Regional Insights (Revenue, USD Bn, 2019 - 2031)
  • North America
    • U.S.
    • Canada
  • Latin America
    • Brazil
    • Argentina
    • Mexico
    • Rest of Latin America
  • Europe
    • Germany
    • U.K.
    • Spain
    • France
    • Italy
    • Russia
  • Rest of Europe
    • Asia Pacific
    • China
    • India
    • Japan
    • Australia
    • South Korea
    • ASEAN
    • Rest of Asia Pacific
  • Middle East
    • GCC Countries
    • Israel
    • Rest of Middle East
  • Africa
    • South Africa
    • North Africa
    • Central Africa
• Key Players Insights
  • Abacus.AI
  • Accenture
  • Adobe
  • Adept
  • AI21 Labs
  • Amazon Web Services (AWS)
  • Anthropic
  • Character.ai
  • Cohere
  • Google
  • Hugging Face
  • IBM
  • Insilico Medicine
  • Microsoft
  • NVIDIA