대규모 언어 모델 시장 예측(-2030년) : 제공별, 아키텍처별, 양식별, 용도별, 최종사용자별, 지역별 세계 분석

According to Stratistics MRC, the Global Large Language Model Market is accounted for $1.6 billion in 2023 and is expected to reach $13.08 billion by 2030 growing at a CAGR of 35.0% during the forecast period. A large language model (LLM) is a type of artificial intelligence designed to understand and generate human-like text based on the vast amount of data it has been trained on. These models, like GPT-3, are built on deep learning architectures, particularly transformers, enabling them to process and generate text at an impressive scale. LLMs excel at various language tasks such as translation, summarization, and question-answering, often achieving human or superhuman performance on benchmark tests. They learn patterns and relationships from the data they are trained on, allowing them to generate coherent and contextually relevant responses across a wide range of topics.

Market Dynamics:

Driver:

Advancements in AI and machine learning

Advancements in AI and machine learning have propelled the large language model (LLM) market by enhancing the capabilities and performance of these models. With breakthroughs in algorithms, data processing, and computational power, LLMs can now understand and generate human-like text with unprecedented accuracy and coherence. These advancements have led to applications in various fields, from natural language processing to content generation and translation. Additionally, the scalability and efficiency of LLMs have improved, enabling businesses to leverage them for diverse tasks such as customer service automation, data analysis, and personalized content creation.

Restraint:

Bias and fairness

Bias and fairness constraints in large language models pertain to ensuring equitable and unbiased outcomes in their applications. This involves identifying and mitigating inherent biases within the data used to train these models. Addressing bias involves techniques such as data preprocessing, algorithmic adjustments, and diverse representation in training datasets. Fairness restraints aim to prevent discriminatory outcomes in LLM applications, particularly in sensitive areas like hiring, lending, or content moderation. Implementing these constraints requires a multidisciplinary approach involving ethics, sociology, and computer science to foster responsible and equitable deployment of LLMs in society.

Opportunity:

Content generation and personalization

The Large Language Model market offers significant opportunities in content generation and personalization. With the ability to comprehend and generate human-like text, LLMs can automate content creation across various industries, from journalism to marketing. Additionally, LLMs enable personalized experiences by tailoring content to individual preferences, behaviors, and demographics. This level of customization enhances user engagement and satisfaction, driving higher conversion rates and brand loyalty. Moreover, LLMs can dynamically adapt content based on real-time data, ensuring relevance and timeliness. Leveraging these capabilities, businesses can efficiently scale content production while delivering highly targeted messaging to their audience.

Threat:

Job displacement

The emergence of Large Language Models poses a significant job displacement threat due to their ability to automate various tasks traditionally performed by humans. LLMs can swiftly process vast amounts of text, potentially replacing roles in content creation, translation, customer service, and more. As businesses adopt LLMs for efficiency gains, there's a risk of reducing the demand for human labor in these sectors. This displacement could lead to job losses, particularly for roles that involve repetitive or routine cognitive tasks. Adapting to this shift may require upskilling or transitioning to roles that complement LLM capabilities rather than compete with them.

Covid-19 Impact:

The COVID-19 pandemic significantly accelerated the demand for large language models (LLMs) in various sectors. With remote work and digital transformation becoming imperative, organizations increasingly rely on LLMs for automating tasks, enhancing customer service, and streamlining operations. This surge in demand led to increased investments in LLM research and development, as well as adoption across industries such as healthcare, finance, and education. However, supply chain disruptions and economic uncertainties caused by the pandemic also posed challenges for LLM manufacturers and developers.

The services segment is expected to be the largest during the forecast period

The services segment in the large language model market is experiencing robust growth due to several factors. As organizations increasingly recognize the value of LLMs in improving efficiency and decision-making, there's a rising demand for specialized services to implement and customize these models to specific business needs. The complexity of LLM technology necessitates ongoing support and maintenance, driving the need for consulting, training, and managed services. Additionally, as LLMs become more integral to various industries, service providers are expanding their offerings to include domain-specific expertise, such as healthcare or finance, further fueling market growth.

The data analysis and business intelligence segment is expected to have the highest CAGR during the forecast period

The growth of the Data Analysis and Business Intelligence segment is driven by the increasing demand for advanced data processing and interpretation capabilities. LLMs offer powerful tools for extracting insights from vast datasets, enabling businesses to make data-driven decisions with greater precision and efficiency. As companies across industries recognize the value of harnessing data for competitive advantage, the adoption of LLMs for data analysis and business intelligence is on the rise. The evolution of natural language processing techniques within LLMs enhances their ability to understand and interpret complex data, further fueling market growth.

Region with largest share:

The growth of the Large Language Model market in North America can be attributed to the region's presence of several tech giants and leading AI research institutions, fostering innovation and development in language modeling technologies. The increasing demand for natural language processing applications across various sectors, such as healthcare, finance, and customer service, is driving the adoption of LLMs. North America boasts a robust infrastructure for cloud computing and data centers, facilitating the deployment and scalability of LLMs. Additionally, the presence of a skilled workforce and favorable government policies supporting AI research and development further propel the growth of the LLM market in the region.

Region with highest CAGR:

The Asia-Pacific region has seen a significant surge in the adoption and growth of large language models (LLMs) in recent years. This growth can be attributed to several factors, including the region's increasing technological infrastructure, burgeoning demand for AI-driven solutions across various industries such as finance, healthcare, and e-commerce, as well as a growing pool of skilled AI talent. Government initiatives aimed at promoting AI research and development have further fueled the expansion of the LLM market in the Asia Pacific. Furthermore, the cultural diversity and vast linguistic landscape of the region present unique challenges that LLMs are well-equipped to address, driving their widespread adoption.

Key players in the market

Some of the key players in Large Language Model market include AI21 Labs, Alibaba, Amazon, Anthropic, Baidu, Cohere, Crowdworks, Google, Huawei, Meta, Microsoft, Naver, NEC, OpenAI, Technology Innovation Institute (TII), Tencent and Yandex.

Key Developments:

In April 2024, Google is currently working on a centralized location-sharing feature for Android users. This new feature, known as "Google Location Sharing," was recently discovered in updates to Google Play Services. The primary objective of this development is to consolidate all active location-sharing services associated with a user's Google account, into one accessible page within the Settings menu.

In April 2023, Microsoft announced that it will invest US$2.9 billion over the next two years to increase its hyperscale cloud computing and AI infrastructure in Japan. It will also expand its digital skilling programs with the goal of providing AI skilling to more than 3 million people over the next three years by opening its first Microsoft Research Asia lab in Japan, and deepening its cybersecurity collaboration with the Government of Japan.

Offerings Covered:

• Software
• Services
• Other Offerings

Architectures Covered:

• Autoregressive Language Models
• Single-headed Autoregressive Language Models
• Multi-headed Autoregressive Language Models
• Autoencoding Language Models
• Vanilla Autoencoding Language Models
• Optimized Autoencoding Language Models
• Hybrid Language Models
• Text-to-Text Language Models
• Pretraining-finetuning Models
• Other Architectures

Modalities Covered:

• Text
• Code
• Image
• Video
• Other Modalities

Applications Covered:

• Information Retrieval
• Language Translation And Localization
• Content Generation And Curation
• Code Generation
• Customer Service Automation
• Data Analysis And Business Intelligence
• Other Applications

End Users Covered:

• Information Technology (IT)
• Healthcare & Life Sciences
• Law Firms
• Manufacturing
• Education
• Retail
• Media & Entertainment
• Other End-users

Regions Covered:

• North America
  • US
  • Canada
  • Mexico
• Europe
  • Germany
  • UK
  • Italy
  • France
  • Spain
  • Rest of Europe
• Asia Pacific
  • Japan
  • China
  • India
  • Australia
  • New Zealand
  • South Korea
  • Rest of Asia Pacific
• South America
  • Argentina
  • Brazil
  • Chile
  • Rest of South America
• Middle East & Africa
  • Saudi Arabia
  • UAE
  • Qatar
  • South Africa
  • Rest of Middle East & Africa

What our report offers:

• Market share assessments for the regional and country-level segments
• Strategic recommendations for the new entrants
• Covers Market data for the years 2021, 2022, 2023, 2026, and 2030
• Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
• Strategic recommendations in key business segments based on the market estimations
• Competitive landscaping mapping the key common trends
• Company profiling with detailed strategies, financials, and recent developments
• Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

• Company Profiling
  • Comprehensive profiling of additional market players (up to 3)
  • SWOT Analysis of key players (up to 3)
• Regional Segmentation
  • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
• Competitive Benchmarking
  • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

• 2.1 Abstract
• 2.2 Stake Holders
• 2.3 Research Scope
• 2.4 Research Methodology
  • 2.4.1 Data Mining
  • 2.4.2 Data Analysis
  • 2.4.3 Data Validation
  • 2.4.4 Research Approach
• 2.5 Research Sources
  • 2.5.1 Primary Research Sources
  • 2.5.2 Secondary Research Sources
  • 2.5.3 Assumptions

3 Market Trend Analysis

• 3.1 Introduction
• 3.2 Drivers
• 3.3 Restraints
• 3.4 Opportunities
• 3.5 Threats
• 3.6 Application Analysis
• 3.7 End User Analysis
• 3.8 Emerging Markets
• 3.9 Impact of Covid-19

4 Porters Five Force Analysis

• 4.1 Bargaining power of suppliers
• 4.2 Bargaining power of buyers
• 4.3 Threat of substitutes
• 4.4 Threat of new entrants
• 4.5 Competitive rivalry

5 Global Large Language Model Market, By Offering

• 5.1 Introduction
• 5.2 Software
• 5.3 Services
  • 5.3.1 Consulting
  • 5.3.2 LLM Development
  • 5.3.3 Integration
  • 5.3.4 LLM Fine-tuning
    • 5.3.4.1 Full Fine-tuning
    • 5.3.4.2 Retrieval-augmented Generation (RAG)
    • 5.3.4.3 Adapter-based Parameter Efficient Tuning
  • 5.3.5 LLM-backed App Development
  • 5.3.6 Prompt Engineering
  • 5.3.7 Support and Maintenance
• 5.4 Other Offerings

6 Global Large Language Model Market, By Architecture

• 6.1 Introduction
• 6.2 Autoregressive Language Models
• 6.3 Single-headed Autoregressive Language Models
• 6.4 Multi-headed Autoregressive Language Models
• 6.5 Autoencoding Language Models
• 6.6 Vanilla Autoencoding Language Models
• 6.7 Optimized Autoencoding Language Models
• 6.8 Hybrid Language Models
• 6.9 Text-to-Text Language Models
• 6.10 Pretraining-finetuning Models
• 6.11 Other Architectures

7 Global Large Language Model Market, By Modality

• 7.1 Introduction
• 7.2 Text
• 7.3 Code
• 7.4 Image
• 7.5 Video
• 7.6 Other Modalities

8 Global Large Language Model Market, By Application

• 8.1 Introduction
• 8.2 Information Retrieval
• 8.3 Language Translation And Localization
  • 8.3.1 Multilingual Translation
  • 8.3.2 Localization Services
• 8.4 Content Generation And Curation
  • 8.4.1 Automated Journalism And Article Writing
  • 8.4.2 Creative Writing
• 8.5 Code Generation
• 8.6 Customer Service Automation
  • 8.6.1 Chatbots And Virtual Assistants
  • 8.6.2 Sales And Marketing Automation
  • 8.6.3 Personalized Recommendation
• 8.7 Data Analysis And Business Intelligence
  • 8.7.1 Sentiment Analysis
  • 8.7.2 Business Reporting And Market Analysis
• 8.8 Other Applications

9 Global Large Language Model Market, By End User

• 9.1 Introduction
• 9.2 Information Technology (IT)
• 9.3 Healthcare & Life Sciences
• 9.4 Law Firms
• 9.5 Manufacturing
• 9.6 Education
• 9.7 Retail
• 9.8 Media & Entertainment
• 9.9 Other End-users

10 Global Large Language Model Market, By Geography

• 10.1 Introduction
• 10.2 North America
  • 10.2.1 US
  • 10.2.2 Canada
  • 10.2.3 Mexico
• 10.3 Europe
  • 10.3.1 Germany
  • 10.3.2 UK
  • 10.3.3 Italy
  • 10.3.4 France
  • 10.3.5 Spain
  • 10.3.6 Rest of Europe
• 10.4 Asia Pacific
  • 10.4.1 Japan
  • 10.4.2 China
  • 10.4.3 India
  • 10.4.4 Australia
  • 10.4.5 New Zealand
  • 10.4.6 South Korea
  • 10.4.7 Rest of Asia Pacific
• 10.5 South America
  • 10.5.1 Argentina
  • 10.5.2 Brazil
  • 10.5.3 Chile
  • 10.5.4 Rest of South America
• 10.6 Middle East & Africa
  • 10.6.1 Saudi Arabia
  • 10.6.2 UAE
  • 10.6.3 Qatar
  • 10.6.4 South Africa
  • 10.6.5 Rest of Middle East & Africa

11 Key Developments

• 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
• 11.2 Acquisitions & Mergers
• 11.3 New Product Launch
• 11.4 Expansions
• 11.5 Other Key Strategies

12 Company Profiling

• 12.1 AI21 Labs
• 12.2 Alibaba
• 12.3 Amazon
• 12.4 Anthropic
• 12.5 Baidu
• 12.6 Cohere
• 12.7 Crowdworks
• 12.8 Google
• 12.9 Huawei
• 12.10 Meta
• 12.11 Microsoft
• 12.12 Naver
• 12.13 NEC
• 12.14 OpenAI
• 12.15 Technology Innovation Institute (TII)
• 12.16 Tencent
• 12.17 Yandex