Generative AI Market

[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. This growth is set to transpire at a commendable compound annual growth rate (CAGR) of 31.5% over the defined forecast period. The generative AI market is poised to exhibit a substantial growth pace during this forecast duration, primarily attributed to a myriad of influential business drivers. Several pivotal factors are propelling the ascent of the generative AI market. These factors encompass the ongoing advancement of artificial intelligence (AI) and deep learning technologies, fostering an environment conducive to innovation. Furthermore, the surge in content creation endeavors and the burgeoning demand for creative applications are also contributing significantly to Large Language Model market expansion-boosting the generative AI industry. The introduction of innovative cloud storage solutions, enabling convenient data accessibility, further fuels the growth trajectory by removing barriers to data utilization.

Technology Roadmap of Generative AI till 2030

The Generative AI market report covers the technology roadmap till 2030, with insights into the short-term, mid-term and long-term developments.

• Short-term (2023–2025):
  • Fine-tuning and optimization of LLMs for various applications, resulting in more accurate and context-aware text generation.
  • Integrating text-based generative AI with other modalities, such as images and videos, catering to diverse generative AI market needs.
• Mid-term (2026–2028):
  • Generative AI models will become increasingly domain specialized. Tailoring AI-generated content to specific industries and niches within the Large Language Model market will become a priority.
  • Users will have fine-grained control over style, tone, and other attributes, making AI-generated content more versatile and suitable for the diverse LLM market.
  • Generative AI models will be designed to seamlessly integrate with existing software and platforms commonly used across industry verticals
• Long-term (2029–2030):
  • The emergence of fully autonomous Generative AI systems, which will be able to generate new works in a wide range of domains, including art, music, and literature
  • An increased use of generative AI in discovery and material design, as researchers continue to explore the possibilities of these technologies
  • Development of guidelines for the ethical use of generative AI and ensuring that it is used in a way that is safe and beneficial for diverse applications across the LLM market and generative AI ecosystem.

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Market Dynamics

Driver: Evolution of AI and deep learning

In the rapidly evolving landscape of artificial intelligence (AI), it is instigating a profound transformation across the realm of business. Vital functions within enterprises, encompassing marketing, sales, finance, and human resources, are prime domains that stand to harness the potential of emerging AI-driven applications. The surge of AI constitutes a paradigm shift of unparalleled magnitude, advancing at an unprecedented pace compared to preceding shifts. While technological strides such as cloud computing, 5G connectivity, and the Internet of Things (IoT) have undeniably ushered in transformative changes, none have demonstrated the rapid evolution witnessed in AI.

Central to this trajectory is the concept of deep learning—an AI algorithmic approach that empowers systems to discern patterns from data and progressively enhance their proficiency. Deep learning, complemented by artificial neural networks, constitutes the linchpin of the burgeoning AI landscape. These artificial neural networks are ingeniously designed to emulate the human brain and can be trained on extensive datasets to speed up constructing generalized learning models. Traditional machine learning models are being supplanted by artificial neural networks, a transformation fueled by innovative computing technologies like single-shot multi-box detectors (SSDs) and generative adversarial networks (GANs), which are orchestrating a revolution within the LLM market and the broader Generative AI market.

Restraints: Issues related to bias and inaccurately generated output

The growth trajectory of generative AI solutions encounters a significant hurdle in the form of limited access to high-quality input data. The effectiveness of AI performance is intricately tied to the caliber of data supplied to algorithms. Attempts to train AI models with subpar data lead to discrepancies in expected outcomes, some models even failing to achieve optimal results. The presence of deficient, irrelevant, or manipulated datasets poses financial risks, especially if disparities emerge between ground truth and AI predictions.

In a similar context, Google's highly anticipated AI chatbot tool, Bard, has faced pre-release scrutiny due to an erroneous response during a demonstration. This incident underscores the challenge of securing top-tier training data as enterprises race to incorporate AI technology akin to Microsoft-backed ChatGPT into their offerings.

Mitigating bias remains an ongoing concern for LLM market and the overall Generative market, as evident in some outputs generated by ChatGPT and similar tools. 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.

Opportunity: Rapid expansion of Large Language Models (LLMs)

The rapid expansion of the LLM market has been a pivotal growth driver within the broader Generative AI market. In recent years, LLMs have witnessed explosive growth, and this can be attributed to several factors. LLMs like OpenAI’s GPT-3 & GPT-4 (text), Meta’s LLaMA (chat and code), and Google’s LaMDA (conversational dialogue) have demonstrated remarkable capabilities in natural language understanding and generation. LLMs have been increasing an average of 10x per year in size and sophistication, ushering in new opportunities for Large Language Model market. They can generate human-like text, translate languages, answer questions, and even perform creative tasks like generating art or music. This versatility has made LLMs highly sought-after in a wide range of industries, from content generation and customer support automation to healthcare and finance, significantly driving demand in the LLM market.

The development of accessible APIs by companies like OpenAI has democratized the use of LLMs, allowing businesses of all sizes to integrate these models into their applications and services easily. This ease of integration has catalyzed the adoption of LLMs across various sectors, further fueling LLM market expansion. As LLM technology continues to evolve and become more capable, businesses are increasingly relying on it to streamline operations, enhance user experiences, and gain competitive advantages, driving sustained growth in the LLM market. For instance, Amazon launched CodeWhisperer, its LLM-based code generation tool, boosting the Large Language Model market growth across the generative AI space.

Challenge: Quality of output generated by generative AI models

The quality of output generated by generative AI models is a critical challenge within the LLM market and the broader Generative AI market. Instances abound wherein these models yield outputs of suboptimal quality, marked by inaccuracies, irrelevance, and questionable outcomes, among other deficiencies. Notably, LLMs such as ChatGPT offer a case in point, exhibiting limitations in addressing recent events and delivering answers that are at times ambiguous and repetitive, which have hampered Large Language Model market opportunities. Similarly, Google's Bard drew criticism for an advertisement that falsely claimed the James Webb Space Telescope captured the first images of a planet beyond our solar system.

Crucially, it's imperative to recognize that the caliber of outputs produced by a given generative model hinges on the excellence of the underlying datasets or training sets. Biases inherent in these sets can be manifested within a specific model's results, potentially perpetuating biases if present within the training data. This phenomenon imparts a direct impact on both the excellence and dependability of the outputs, restraining the expansion of Large Language Model market.

Generative AI market Ecosystem

By services, professional services segment to account for a larger market size during forecast period

Professional services encompass a range of offerings, including solution customization, deployment assistance, technical support, and training, which are crucial for enterprises seeking to harness the potential of generative AI while addressing their specific business requirements. These services enhance the value proposition of generative AI solutions by ensuring optimal implementation, minimizing disruptions, and empowering businesses to capitalize on the transformative capabilities of this technology. As companies increasingly recognize the strategic importance of tailored AI solutions, the demand for professional services is expected to remain robust, thereby solidifying its leadership in the generative AI market.

By application, operations business function segment to hold the largest market share during the forecast period

By leveraging generative AI, organizations can streamline processes, enhance decision-making, and automate tasks, leading to improved productivity and cost-effectiveness. The operations segment encompasses diverse areas such as supply chain management, logistics, resource allocation, and risk assessment, where generative AI's capabilities offer transformative benefits. As businesses prioritize operational excellence to stay competitive, the demand for generative AI solutions and complex LLMs tailored to specific operational challenges is expected to drive the sustained dominance of the operations segment in the LLM market and the overall generative AI market.

By region, North America holds the largest market size during the forecast period

Anticipated for 2023, North America is poised to take the lead in the generative AI market, with the US spearheading this trajectory within the region. Multiple driving forces underpin North America's generative AI market ascendancy, encompassing the burgeoning demand for AI-generated content across sectors like media and entertainment, the expanding role of AI in healthcare and diverse industries, and the ample availability of substantial datasets for training generative models.

Furthermore, North America boasts a robust ecosystem of startups and venture capitalists, singularly focused on propelling LLM-as-a-service business model, thereby fostering innovation in the Large Language Model market and boosting Generative AI industry. Noteworthy industry players, including OpenAI, Nvidia, and Google, have their headquarters rooted in North America, further affirming the region's vanguard position in the generative AI landscape.

Key Market Players

The generative AI solution and service providers have implemented various types of organic and inorganic growth strategies, such as new product launches, product upgrades, partnerships, and agreements, business expansions, and mergers and acquisitions to strengthen their offerings in the market. Some major players in the security automation market include Microsoft (US), IBM (US), Google (US), AWS (US), META (US), Adobe (US), OpenAI (US), and Insilico Medicine (Hong Kong), Simplified (US), Genie AI (UK), Lightricks (Israel), Lumen5 (Canada), Giphy (US), Dialpad (US), Persado (US), Codacy (Portugal), Paige.AI (US), Riffusion (US), PlayHT (US), Speechify (US), Media.io (France), Midjourney (US), FireFlies (US), Brandmark.io (Netherlands), Morphis Technologies (Portugal), Synthesia (UK), Mostly AI (Austria), Veesual (France), Deep AI (US), Galileo AI (US), Formula Bot (US), JetBrains (Czech Republic), Charater.AI (US), Hypotenuse AI (US), Viable (US), Defog.ai (US), Zeta Alpha (Netherlands), DeepSearch Labs (UK), Writesonic (US), amberSearch (Germany), Floworks (India), Inventive AI (US), Sonnet (US), Flair Labs (US), Gloo (US), Olli.AI (US), Hightime AI (US), GFP-GAN (US), Fontjoy (Italy), Eleuther AI (US), starryai (US), and Magic Studio (US).

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Scope of the Report

This research report categorizes the Generative AI market based on offering, application, vertical, and region.

By Offering:

• Software, By Generative Models
  • Rule-based Models
    • Knowledge-based Models
    • Script-based Models
    • Expert Systems
  • Statistical Models
    • Markov Models
    • Hidden Markov Models
    • Gaussian Mixture Models
    • Conditional Random Fields
  • Deep Learning Models
    • Feedforward Neural Networks
    • Recurrent Neural Networks
    • Long Short-Term Memory (LSTM) Neural Networks
    • Gated Recurrent Units (GRUs)
  • Generative Adversarial Networks (GAN)
    • Conditional GANs
    • Style GANs
    • Cycle GANs
  • Autoencoders
    • Denoising Autoencoders
    • Variational Autoencoders
  • Convolutional Neural Networks (CNNs)
    • Image-generatiing CNNs
    • Video-generating CNNs
  • Transformer-based Large Language Models (LLMs)
    • Bidirectional Encoder Representations from Transformers
    • Generative Pre-trained Transformer-1 (GPT-1)
    • Generative Pre-trained Transformer-2 (GPT-2)
    • Generative Pre-trained Transformer-3 (GPT-3)
    • Generative Pre-trained Transformer-4 (GPT-4)
    • Language Model for Dialogue Applications (LaMDA)
    • Other Transformer-based LLMs
• Services
  • Professional Services
    • Training and Consulting Services
    • System Integration and Implementation Services
    • Support and Maintenance Services
  • Managed Services

By Application:

• Application, By Business Function
  • Marketing & Sales
  • Human Resource
  • Operations
  • Finance
  • Research & Development (R&D)
  • Others
• Application, By Data Modality
  • Text
  • Code
  • Image
  • Video
  • Audio & Speech

By Vertical:

• Media & Entertainment
  • Digital Marketing
  • Sales Intelligence
  • Data Visualization
  • Customer Experience
  • Application Development & API Integration
  • General Search & Insight Generation
  • Media Editing
  • Synthetic Data Training
• BFSI
  • Digital Marketing
  • Sales Intelligence
  • Data Visualization
  • Customer Experience
  • Application Development & API Integration
  • General Search & Insight Generation
  • Media Editing
  • Synthetic Data Training
  • Fraud Detection/Risk Management
• Healthcare & Life Sciences
  • Digital Marketing
  • Sales Intelligence
  • Data Visualization
  • Customer Experience
  • Application Development & API Integration
  • General Search & Insight Generation
  • Media Editing
  • Synthetic Data Training
  • Fraud Detection/Risk Management
• Manufacturing
  • Digital Marketing
  • Sales Intelligence
  • Data Visualization
  • Customer Experience
  • Application Development & API Integration
  • General Search & Insight Generation
  • Media Editing
  • Synthetic Data Training
  • 3D Design and Prototyping
  • Predictive Maintenance
• Retail & eCommerce
  • Digital Marketing
  • Sales Intelligence
  • Data Visualization
  • Customer Experience
  • Application Development & API Integration
  • General Search & Insight Generation
  • Media Editing
  • Synthetic Data Training
  • Fraud Detection/Risk Management
• Transportation & Logistics
  • Digital Marketing
  • Sales Intelligence
  • Data Visualization
  • Customer Experience
  • Application Development & API Integration
  • General Search & Insight Generation
  • Media Editing
  • Synthetic Data Training
  • Predictive Maintenance
  • Fraud Detection/Risk Management
• Construction & Real Estate
  • Digital Marketing
  • Sales Intelligence
  • Data Visualization
  • Customer Experience
  • Application Development & API Integration
  • General Search & Insight Generation
  • Media Editing
  • Synthetic Data Training
  • 3D Design and Prototyping
  • Predictive Maintenance
• Energy & Utilities
  • Digital Marketing
  • Sales Intelligence
  • Data Visualization
  • Customer Experience
  • Application Development & API Integration
  • General Search & Insight Generation
  • Media Editing
  • Synthetic Data Training
  • 3D Design and Prototyping
  • Predictive Maintenance
  • Fraud Detection/Risk Management
• Government & Defense
  • Digital Marketing
  • Sales Intelligence
  • Data Visualization
  • Customer Experience
  • Application Development & API Integration
  • General Search & Insight Generation
  • Media Editing
  • Synthetic Data Training
  • Predictive Maintenance
  • Fraud Detection/Risk Management
• IT & ITeS
  • Digital Marketing
  • Sales Intelligence
  • Data Visualization
  • Customer Experience
  • Application Development & API Integration
  • General Search & Insight Generation
  • Media Editing
  • Synthetic Data Training
• Telecom
  • Digital Marketing
  • Sales Intelligence
  • Data Visualization
  • Customer Experience
  • Application Development & API Integration
  • General Search & Insight Generation
  • Media Editing
  • Synthetic Data Training
  • Predictive Maintenance
  • Fraud Detection/Risk Management
• Others

By Region:

• North America
  • US
  • Canada
• Europe
  • UK
  • Germany
  • France
  • Italy
  • Spain
  • Finland
  • Rest of Europe
• Asia Pacific
  • China
  • India
  • Japan
  • South Korea
  • Singapore
  • Australia and New Zealand
  • Rest of Asia Pacific
• Middle East & Africa
  • Saudi Arabia
  • UAE
  • South Africa
  • Israel
  • Turkey
  • Rest of Middle East and Africa
• Latin America
  • Brazil
  • Mexico
  • Argentina
  • Colombia
  • Rest of Latin America

Recent Developments:

• In September 2023, Technology Innovation Institute (TII) unveiled the Falcon 180B LLM with 180 billion parameters. This model relies primarily on web data from RefinedWeb (85%) and includes curated content like conversations, technical papers, and code (3%). Falcon 180B's diverse dataset empowers it with extensive capabilities for natural language tasks, setting a new standard in the Large Language Model market.
• In August 2023, IBM extended its collaboration with Microsoft to expedite the implementation of generative AI for shared clients. This expanded collaboration will introduce a fresh offering, furnishing clients with the necessary technology and expertise to revolutionize their business procedures and seamlessly expand the utilization of generative AI.
• In August 2023, Google and Cognizant announced a partnership in which Cognizant will develop healthcare solutions based on LLMs developed by Google. This initiative aims to leverage the capabilities of generative AI to address various challenges within the healthcare industry, creating new growth avenues for the LLM market.
• In August 2023, IBM, and the United States Tennis Association (USTA) unveiled digital fan enhancements to be featured on USOpen.org and the US Open app. These enhancements encompass various features, including spoken commentary generated through Artificial Intelligence (AI).
• In July 2023, Caylent and AWS announced a collaboration to tackle customer objectives concerning generative AI. This collaboration involves an evaluation of their data environment and organizational preparedness using Caylent's Generative AI Strategy Catalyst.
• In July 2023, Shutterstock broadened its partnership with OpenAI to develop tools based on generative AI. Shutterstock intends to extend its current agreement with OpenAI by furnishing the startup with training data for its AI models.

Frequently Asked Questions (FAQ):

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