4 AI industry chain map and typical companies

4.1 AI Industry Role

The main players driving the development of AI include large hardware companies (Nvidia), large technology companies (such as Google, Microsoft, Amazon), and a series of AI start-ups. These companies are leading in data processing capabilities, algorithm development, and market applications, driving the development of the entire AI ecosystem.

• Hardware companies : Hardware manufacturers such as NVIDIA have launched GPUs and AI chips. AI chips can support the learning and accelerated computing of deep neural networks, providing computing power support for AI.
• Technology giants : Google, Microsoft, Amazon, etc. have invested a lot of resources in the field of AI. They have not only developed powerful AI platforms, but also actively invested in AI startups and expanded their AI ecosystems through mergers and acquisitions. These companies have rich data, powerful computing resources and top talents, and can lead the development direction of AI technology.
• AI startups : AI startups (such as OpenAi, Nuro, Vicarious, etc.) tend to focus on innovation in specific areas, such as medical AI, autonomous driving AI, financial AI, etc. These companies are flexible and innovative, able to respond quickly to market demand and develop competitive products and services. Startups usually obtain funding through venture capital and achieve rapid growth in a short period of time, becoming an important force in the market.
• Academic institutions and research organizations : Universities and research institutions around the world (such as MIT, DeepMind, BAIR, etc.) are also important forces in the development of AI technology. They continue to conduct cutting-edge research and promote industry progress through open source code and academic papers. At the same time, they have trained a large number of professionals in the field of AI. Through open source code and academic publications, these institutions promote the dissemination of knowledge and the popularization of technology.

4.2 AI Industry Chain Map

The AI industry chain, from upstream hardware providers (such as chip manufacturers) to midstream software development and platform provision, and then to downstream application scenarios , constitutes a large and complex ecosystem. There are multiple key participants in each link, jointly promoting the advancement of AI technology and the widespread application.

4.2.1 Upstream: Infrastructure Layer

The upstream segment includes hardware manufacturers and cloud service providers.

• Hardware manufacturers : Provide the hardware support required for AI computing, including CPU, GPU, TPU, and dedicated AI accelerators. NVIDIA, AMD, Intel, and recently emerging dedicated AI chip manufacturers (such as Tesla's FSD chip) are all important players in this layer.
• Cloud service providers : such as Amazon Web Services (AWS), Google Cloud, Microsoft Azure, etc. These companies provide large-scale cloud-based computing resources and AI development platforms to support enterprises in developing, training, and deploying AI models. The popularity of cloud services has lowered the threshold for AI development, allowing small and medium-sized enterprises to use AI technology.

4.2.2 Midstream: Platform and Tool Layer

The midstream part includes AI model R&D companies, software development platforms, data services and management tools. This level provides algorithms, platforms and data support for the entire ecosystem, promoting the popularization and practical application of AI technology.

• AI model development companies: focus on developing and training large AI models , and provide basic algorithms and models for enterprises and developers to use. These companies have promoted cutting-edge research in artificial intelligence technology and commercialized their results through APIs or platforms. Representative companies include OpenAI , Google DeepMind , Anthropic , and Cohere . These companies have developed large language models (LLMs) such as GPT and BERT for tasks such as natural language processing and generative AI.
• AI software development platform : provides developers with tools to build, train, and deploy AI models . These platforms provide a flexible framework that allows developers to easily develop and deploy AI models. These platforms not only support high-performance model training, but can also be combined with hardware accelerators (such as GPUs and TPUs) to improve model training efficiency. Representative open source platforms such as TensorFlow , PyTorch , Keras, Hugging Face , etc. support developers to create and train various deep learning models, and can apply models to multiple scenarios from academic research to commercial applications.
• Data services and management tools : Data is the core of AI model training, and enterprises need a large amount of data to train AI models. Data services and management tools help enterprises efficiently manage and process large-scale data . Data service companies such as Snowflake and Databricks provide big data processing and analysis tools to help enterprises manage structured and unstructured data. In addition, data annotation service companies (such as Scale AI ) provide high-quality training data for AI models to ensure the accuracy and reliability of the models.

4.2.3 Downstream: Application Scenario Implementation and Service Layer

The downstream part includes actual application scenarios of AI in various industries, intelligent products and services based on AI technology, and service companies that provide consulting services and operational maintenance for the implementation of AI technology.

• AI applications in vertical fields : AI technology is applied to various vertical fields, such as healthcare, finance, retail, manufacturing, etc., bringing customized solutions to different industries. For example, in the healthcare field , AI diagnostic tools such as IBM Watson Health and Zebra Medical Vision help doctors diagnose diseases faster and more accurately by analyzing medical images and electronic medical records. In the financial field , AI is applied to risk assessment, fraud detection and algorithmic trading. Typical cases include Kensho and Darktrace , which use AI to improve the efficiency of financial data analysis and enhance security. In the retail industry , AI-driven recommendation systems such as Amazon's personalized recommendation engine improve the online shopping experience by analyzing user behavior and preferences. In the manufacturing industry , AI is applied to smart factories to optimize production processes through automated equipment and predictive maintenance. Siemens and GE's Predix platform are representative companies that use AI technology to help factories improve production efficiency and reduce operating costs.
• Smart products and devices : AI technology is widely used in various smart products and devices, promoting the development of automation and personalization functions and significantly improving user experience. For example, in the field of smart homes , AI-driven devices such as Amazon Echo and Google Home can not only execute voice commands, but also provide personalized services by learning users' daily habits, such as automatically adjusting home lighting, temperature and other environmental settings. In the field of driverless cars , companies such as Tesla and Waymo rely on AI technology to develop autonomous driving systems, using cameras, sensors and deep learning algorithms to achieve automated driving and road navigation of vehicles. In the field of drones , companies such as DJI use AI technology to enhance the autonomous flight and target tracking capabilities of drones, which are widely used in filming, logistics and infrastructure inspection. Representatives in the field of robotics, such as Boston Dynamics , use AI technology to provide robots with perception and decision-making capabilities, enabling them to perform tasks in complex environments, such as warehouse automation and dangerous environment operations.
• AI consulting services and operation and maintenance companies : responsible for implementing the application of AI technology in the actual business of enterprises and providing long-term support and optimization. These companies provide enterprises with a full range of services from AI strategic consulting, technical implementation to model maintenance, and are a key link in promoting the application and development of AI technology in different industries. For example, IBM Watson and Accenture provide AI consulting services to help enterprises formulate AI strategies and implement AI solutions. AI models and systems need to be continuously maintained and optimized after deployment, which has spawned the AI operation service market (MLOps). Companies such as DataRobot and Algorithmia focus on providing enterprises with monitoring, maintenance and optimization services for AI models.

4.3 Typical AI companies (mid- and upper-stream)

4.3.1 NVIDIA

Founded in 1993, NVIDIA is a world-leading graphics processing unit (GPU) manufacturer, originally known for developing PC gaming graphics cards. Today, NVIDIA not only maintains its industry-leading position in graphics processing, but has also made important breakthroughs in artificial intelligence (AI), high-performance computing (HPC), autonomous driving, data centers, cloud computing and other fields.

Business areas : NVIDIA is the world's leading graphics processing unit (GPU) manufacturer and has played an important role in the field of AI. NVIDIA provides AI hardware (such as GPU, CUDA parallel computing architecture) and software platforms (such as NVIDIA AI and Deep Learning SDK). Its GPUs are widely used in autonomous driving, data centers, medical AI, image processing and other fields.

• GPU (Graphics Processing Unit): NVIDIA was first known for its GeForce series of graphics cards, which focus on games, image processing, 3D rendering and other fields, and are widely used in personal computers, game consoles and workstations. GPU has now become the core hardware for AI model training and reasoning, especially in deep learning. NVIDIA's GPU is widely used due to its powerful parallel computing capabilities.
• AI and Machine Learning : NVIDIA's GPU and CUDA (parallel computing architecture) have become standard hardware in the field of artificial intelligence and deep learning, helping large-scale AI models to achieve efficient training and reasoning.
• NVIDIA AI Platform : Software tools provided by NVIDIA (such as NVIDIA AI and NVIDIA TensorRT) support developers and enterprises to accelerate the development and deployment of AI models.
• NVIDIA DRIVE : NVIDIA has launched the NVIDIA DRIVE platform for autonomous driving, providing a complete solution from perception, decision-making to autonomous driving systems. It has cooperated with many automakers to promote the application of autonomous driving technology.
• NVIDIA Jetson Platform : Jetson is an edge AI platform designed for robots and Internet of Things (IoT) devices. It supports local AI processing and is used in areas such as smart cities, industrial automation, and smart devices.

Business model : NVIDIA's business model relies on hardware sales, software platforms, and ecosystem building. NVIDIA makes profits by selling GPU hardware, which is mainly divided into four categories: consumer-level (GeForce series), professional-level (Quadro series), data center (Tesla series), and AI computing (A100, etc.). NVIDIA provides AI development and optimization support to developers and enterprises through software tools and platforms (NVIDIA AI, TensorRT, Omniverse, etc.), and NVIDIA earns revenue through software subscriptions and development tools.

It is estimated that NVIDIA has firmly occupied more than 90% of the data center GPU market in the past 7 years. In 2023, its share reached 98%. The operation of all large data centers and the training of large models need to rely on GPUs developed by NVIDIA.

4.3.2 OpenAI and ChatGPT

Founded in 2015 by Tesla and SpaceX founder Elon Musk, OpenAI is an American artificial intelligence research institute dedicated to developing general artificial intelligence (AGI) to ensure its safety and bring the greatest benefit to all mankind. OpenAI was originally a non-profit organization, but later transformed into a "limited profit" business model, attracting investment from large technology companies such as Microsoft. Its goal is to promote the development of AGI through research and development of AI technology, while paying attention to the safety, ethics and controllability of AI.

Business areas : The core business revolves around the research and development of AI models, especially large language models (LLM) and generative AI, which are widely used in natural language processing, generative content and other fields. OpenAI also provides access to commercial AI models through API services.

• GPT : The GPT (Generative Pre-trained Transformer) series of models is one of its core products. Models such as GPT-3 and the latest GPT-4 have demonstrated powerful natural language generation capabilities.
• DALL·E : The generative AI model developed by OpenAI can generate high-quality images based on text descriptions. It has broad application prospects in design, advertising, creative industries and other fields.
• Codex : A GPT-based programming language generator that can understand natural language instructions and generate corresponding code. It has been applied to GitHub Copilot to help developers automatically generate and write code.
• OpenAI API : OpenAI provides commercial API services that allow developers and companies to build applications based on its AI models. Through the API, companies can easily call GPT, DALL·E, Codex and other models for various business scenarios, such as natural language processing, content generation and automated workflows.

Business model: Revolves around providing API access to AI models and monetizing through partnerships with large tech companies .

• OpenAI API : OpenAI's core business model is to provide access to models such as GPT, DALL·E, Codex, etc. through its API platform. Developers and companies can subscribe to these services and use their AI models on demand for tasks such as natural language processing, image generation, and automated programming.
• Technology licensing and authorization : OpenAI works with other companies to license its technology and models for product integration and application development. Through this authorization, OpenAI is able to expand its technological influence and provide customized AI solutions to enterprises.

OpenAI's technology has had a profound impact on the world, especially in the field of AI content generation and automation. Through its open API platform, OpenAI provides AI solutions to thousands of companies, promoting innovation in natural language processing, automated creation, programming and other fields.

4.3.3 Tesla:

Tesla was founded in 2003 and is a world-renowned electric vehicle manufacturer that focuses on the development and production of electric vehicles, energy storage systems and solar products. In addition to its electric vehicle business, Tesla is also at the forefront of the industry in artificial intelligence (AI) and autonomous driving technology. Its AI-driven autonomous driving system and self-developed AI hardware give it a unique competitive advantage in the automotive industry.

Business areas : Tesla's business is not limited to electric vehicles, but also includes autonomous driving, energy solutions, AI hardware development and other fields. Tesla has built a strong infrastructure in the field of artificial intelligence, including AI chips (FSD Chip fully autonomous driving chip; Dojo Chip, Dojo training chip), Dojo supercomputers and AI data centers, providing underlying technical support for autonomous driving and robotics businesses.

• Electric vehicles : Tesla's core business is the production and sales of electric vehicles, including Model S, Model 3, Model X and Model Y. They occupy an important position in the global electric vehicle market with their high performance, long range and autonomous driving functions.
• Fully Self-Driving Technology : Tesla's Full Self-Driving ( FSD ) technology is the core of its AI strategy. Relying on its self-developed computing platform and huge computing power support, it continuously optimizes its AI model based on the data accumulated from large-scale driving mileage. Tesla began exploring autonomous driving technology in 2013 and launched a fully autonomous driving computing platform equipped with its self-developed FSD chip in 2019. Since the release of Tesla FSD, it has achieved a driving mileage of more than 1.6 billion kilometers.
• AI hardware development : Tesla has independently developed a fully autonomous driving (FSD) chip , replacing the NVIDIA hardware it previously relied on. The chip is specially designed to improve the computing power and efficiency of autonomous driving, and is an important foundation for Tesla to achieve its vision of fully autonomous driving. Tesla is developing a supercomputer called Dojo , which is dedicated to training deep learning algorithms for autonomous driving systems. Dojo optimizes the speed and performance of AI model training by processing massive amounts of visual and sensor data, helping Tesla to commercialize FSD faster.
• Energy Solutions: Tesla also provides energy storage systems for home and commercial use, such as Powerwall, Powerpack and Megapack, to help users store solar energy and optimize energy use. By integrating with solar products, Tesla promotes the popularization of clean energy solutions.
• Optimus : Optimus is positioned as a general-purpose bipedal autonomous humanoid robot that can perform unsafe, repetitive or boring tasks to solve the problem of labor shortage. Tesla plans to deploy Optimus in its own Gigafactory to perform some repetitive tasks, such as moving materials and assembling parts. In the future, Tesla is committed to promoting Optimus into thousands of households to help ordinary families complete housework, such as cooking and cleaning.
• Autonomous taxis (Robotaxi) : In April 2024, Musk announced that Tesla plans to officially launch autonomous taxis ( Robotaxi ) in Q3, which will subvert traditional travel methods and enable efficient shared use of vehicles.

Business model: Tesla's business model covers multiple dimensions of electric vehicles, autonomous driving and energy solutions, and makes profits through both hardware sales and software subscriptions.

• Hardware sales: Tesla makes profits by selling electric vehicles (Model S, Model X, Model 3 and Model Y) directly to consumers. Tesla has expanded the energy market and promoted the application of renewable energy technology by selling products such as Powerwall and Solar Roof.
• Software and Subscription Services : Tesla's Fully Self-Driving (FSD) software is sold as a one-time purchase or as a subscription service, allowing car owners to access more advanced self-driving features. This model provides Tesla with an additional ongoing revenue stream.
• Energy Services : Tesla provides enterprise-level energy storage solutions through Powerpack and Megapack, and works with utility companies around the world to help optimize grid operations and promote the application and storage of renewable energy.

Tesla is the leader in the global electric vehicle market. Its high performance, long range and innovative electric vehicle products have enabled it to occupy a significant share of global electric vehicle sales, especially in the United States, Europe and China. Tesla is not only the leader in the global electric vehicle market, but its innovations in autonomous driving, energy solutions and AI technology have also had a profound impact.

4.3.4 Anthropic

Anthropic is an artificial intelligence (AI) research company founded in 2021, dedicated to developing safe and reliable large-scale AI systems. The company was founded by former OpenAI researchers with the goal of promoting the safe development of AI through more controllable and explainable AI models. Anthropic focuses on AI ethics, AI security, transparency and fairness , and is committed to reducing the social risks that models may bring while developing powerful AI models.

Business areas : The core business revolves around the safety, explainability and ethics of artificial intelligence systems, especially large-scale language models (LLM) and generative AI.

• Large-scale language model (LLM): Anthropic's Claude model series is its representative large-scale language model, similar to OpenAI's GPT model. These models are capable of complex natural language understanding and generation, and are widely used in dialogue systems, automated writing, question-answering systems and other fields.
• Claude API : Anthropic provides an API service based on its Claude model, allowing developers and enterprises to integrate its AI model for natural language processing tasks. Through the API, enterprises can call the Claude model for functions such as automated conversations, content generation, and data analysis.
• Secure AI Solutions : Anthropic provides customized AI solutions to enterprises, especially in fields with high security requirements, such as finance, healthcare, and law. Through its security-first AI model, it helps enterprises reduce the risks of AI applications.

Business model : The business model revolves around the development and secure application of AI models , while providing AI technical support to commercial customers through API services and enterprise solutions .

• API service : Through the API platform, Anthropic opens its large-scale language model Claude to developers and enterprises, providing natural language processing and generation AI functions on demand. Developers and enterprises can pay for use through a subscription model to obtain the AI capabilities of the Claude model and apply it to business scenarios such as dialogue systems, automated workflows, and content generation.
• Customized AI solutions : Anthropic provides customized AI solutions for enterprises that need powerful AI capabilities, especially in industries with high security requirements. The company helps enterprises avoid potential risks when applying AI by providing safe and reliable AI models and ensures the transparency and explainability of AI systems.
• Security and Ethics Consulting : Due to Anthropic’s expertise in AI security and ethics, the company also provides AI ethics and security consulting services to businesses and governments to help them evaluate and improve the security of existing AI systems and prevent potential risks brought by AI.

Anthropic's technology and research have had a significant impact in the AI community and industry, especially in promoting discussions on AI safety and ethical issues. Through its Claude model and safety-first AI system, Anthropic is gaining more attention and application from enterprises.

4.3.5 Cohere

Founded in 2019 and headquartered in Canada, Cohere is an artificial intelligence (AI) company focusing on natural language processing (NLP) technology. Cohere is committed to developing powerful language models to help companies apply AI technology to text understanding, generation, translation and other natural language processing tasks. Unlike companies such as OpenAI and Anthropic, Cohere mainly focuses on enterprise-level NLP solutions, especially by providing flexible and customizable AI models to help companies effectively use natural language processing technology.

Business areas : The core business revolves around natural language processing (NLP) and generative AI , providing a variety of language models and development tools to promote the application of AI in enterprises.

• Natural Language Processing (NLP): Cohere focuses on developing large-scale language models that can understand and generate natural language. They are widely used in tasks such as text classification, sentiment analysis, automatic summarization, translation, etc., and are suitable for text processing needs in various industries.
• Generative AI : Cohere's generative AI technology can generate high-quality natural language text for tasks such as content creation, automated writing, summary generation, and data reporting. The content generated by AI can meet the needs of industries such as media and marketing for efficient content generation.
• API and development tools: Cohere provides API services and flexible development tools to help enterprises and developers quickly integrate AI technology. Cohere's toolkit supports a variety of programming languages and frameworks, making it easy for development teams of different sizes and technical levels to adopt.
• Enterprise solutions : Cohere not only provides general language models, but also customizes them according to the needs of enterprises, making the models more suitable for business scenarios in specific industries. These customized models are widely used in customer support, e-commerce, law, finance and other fields that require high-precision language understanding.

Business model : The business model revolves around API services , customized solutions and enterprise NLP consulting services , mainly providing advanced NLP tools and support to enterprise customers.

• API services : Cohere provides natural language processing and generation services through its API platform. Developers and enterprises can call these APIs on demand to perform text processing tasks. Cohere adopts a subscription-based and pay-per-use business model to flexibly meet the needs of enterprises of different sizes.
• Customized NLP solutions: Cohere provides customized NLP solutions for enterprises that need personalized language processing capabilities. Enterprises can customize models according to industry needs and optimize the performance of AI systems. Especially in industries such as finance, law, and customer service that require high text processing accuracy, Cohere's customized models have strong market competitiveness.
• Enterprise consulting and technical support: Cohere provides in-depth NLP consulting services to help enterprises optimize their AI and language processing systems to ensure that enterprises can maximize the use of NLP technology. Cohere also provides training for enterprises and developers to help them understand how to better use Cohere's API and language models to enhance the AI capabilities of internal teams.

Cohere's performance in the enterprise-level natural language processing market is eye-catching. Through its efficient API services and customized solutions, Cohere has won the trust of many companies and is widely used in multiple industries. Cohere's NLP technology has been applied to finance, law, medicine, customer service and other fields, helping companies to automate text processing, data analysis and customer support tasks through AI technology, and improve operational efficiency.