Prepare data, train models, and build user experience

1. Prepare data : Machines need large amounts of high-quality data to learn. For example, to convert text to images, the ML model needs to learn from millions of images labeled with text. ML engineers typically spend 80% of their time manually cleaning data in a process called feature engineering.
2. Train the model : Next, the ML engineer splits the data into a training set and a test set. The machine uses the training set to build the model, and then uses the test set to improve the accuracy of the model.
3. Build the user experience : After training the model, the team needs to build a user UX experience where people can provide input to get the output they want. Even for ML engineers, how the model works is a black box, so the user experience needs to be clear, believable, and actionable.

Machine learning is further divided into three categories: supervised learning (training with labeled data, such as image classification), unsupervised learning (discovering patterns from unlabeled data, such as clustering), and reinforcement learning (optimizing by interacting with the environment to get rewards, such as game AI). These three types of learning methods form the core algorithmic foundation of modern AI.

2.3.2 Deep Learning

Deep learning is a machine learning technology based on neural networks . Its main feature is that it can automatically learn the characteristics of data , and the automatic learning process is achieved by assigning the feature learning task to the model for training.

Historical Development of Deep Learning

The multi-layer structure of the neural network is used to extract high-level features from the data. It is particularly suitable for processing unstructured data (such as images, speech, and text) and is applicable to scenarios such as image recognition, natural language processing, and medical image analysis.

2.3.3 Natural Language Processing (NLP)

NLP is a technology that enables computers to understand, process and generate human language by analyzing text or speech for semantic understanding and response.

In recent years, NLP technology has made significant progress, especially driven by generative pre-trained models (such as BERT and GPT-3), AI has performed well in language understanding and generation. These models are trained using large amounts of text data and can generate natural and coherent texts, which are applied to chatbots, intelligent customer service, language translation, content generation and other scenarios.

2.3.4 Computer Vision

Computer vision is the use of computer algorithms to enable machines to "understand" images or videos and automatically extract information from visual data.

Computer vision is mainly used for object detection and tracking, image recognition and processing, action recognition, etc. Its application scenarios include autonomous driving, security monitoring, medical image analysis, retail and advertising, etc.

2.3.5 Reinforcement Learning

Reinforcement learning is a technique that optimizes decision-making by obtaining feedback (rewards or penalties) through interaction with the environment . Learning strategies through interaction with the environment allows the AI ​​system to obtain the maximum reward through trial and error. After each operation, the system will receive rewards or penalties, and optimize decisions through long-term feedback.

Reinforcement learning is mainly used to train AI agents to make optimal decisions in dynamic environments. Application scenarios include game AI (such as AlphaGo), autonomous driving, robot control, etc.

The article "Human-level Control through Deep Reinforcement Learning" published by Google DeepMind in Nature implemented the end-to-end deep reinforcement learning model Deep Q-Networks for the first time. Its input is the pixel value of the game screen, and its output is the control command of the game. Its principle is shown in the figure below.

2.3.6 Generative AI

Generative AI is the process of using machine learning models to generate new content, such as images, text, or videos, that is similar to the training data.

Generative AI technology is changing the creative industry and promoting innovation in art, entertainment, advertising and other fields. Its application scenarios include artistic creation, image generation, game design, and steady generation.

2.3.7 Big Data and Data Processing

Big data technology is used to process and analyze large amounts of data, especially in AI for tasks such as data preprocessing, feature extraction, and model training.

Big data technology can provide effective training data for AI models, improving the accuracy and predictive ability of the models. Application scenarios include e-commerce analysis, market forecasting, sentiment analysis, trend analysis and forecasting, etc.

2.3.8 AI Hardware Acceleration (GPU/TPU/NPU)

AI hardware acceleration technology accelerates the neural network training and reasoning process by using dedicated hardware (such as GPU, TPU, NPU).

Its application scenarios include deep learning model training, smart device AI computing, data centers, etc.

3 AI Market, Application Scenarios and Business Models

3.1 Market size of AI industry:

The global artificial intelligence (AI) market is expanding rapidly, especially since the release of ChatGPT, with a significant growth momentum. The global AI market size is estimated to be between US$300 billion and US$400 billion in 2023.

According to Precedence Research, the global AI market size will be US$638.23 billion in 2024 and will reach US$3680.47 billion in 2034, with a CAGR of 19.1%, highlighting the huge potential and continued strong development of this field.

Factors driving this growth include increased corporate demand for automation and data-driven decision-making, government investment and support for AI technology, and the continued maturity and widespread application of AI technology (expanding from traditional Internet industries to various fields such as finance, healthcare, education, and manufacturing).

3.2 Application Scenarios of AI

Relying on several key capabilities of AI (image recognition, speech recognition, natural language processing, and embodied intelligence), AI technology has been applied to various vertical fields, such as healthcare (such as AI diagnostic tools), finance (such as risk assessment and algorithmic trading), retail (such as recommendation systems), and manufacturing (such as smart factories), to solve industry-specific problems, improve operational efficiency, and create new business models.

• Medical field: The application of AI in the medical field is gradually maturing and expanding to multiple aspects, including diagnosis, personalized treatment, drug development, and health management. By analyzing large amounts of medical data (such as medical records, gene sequences, and imaging data), AI can assist doctors in early diagnosis of diseases, accurate treatment decisions, and accelerate the process of new drug development. For example, AI tools in radiology can help doctors identify early signs of cancer, and AI-driven genetic analysis can provide patients with personalized treatment plans. The application of AI in the medical field not only improves the accuracy of diagnosis and the efficiency of treatment, but also significantly reduces medical costs. Especially in resource-limited environments, AI technology can greatly improve the accessibility of medical services.
• Financial field: In the financial industry, AI is widely used in risk management, algorithmic trading, customer service and fraud detection. By analyzing massive amounts of market data and historical transaction records, AI can predict market trends in real time and execute high-frequency trading strategies, thereby improving investment returns and market efficiency. In addition, AI is also used to develop smart investment advisory services to help individual investors formulate investment strategies based on their financial status and risk preferences. AI-driven anti-fraud systems monitor trading patterns, detect abnormal trading behaviors in a timely manner, and reduce losses to financial institutions.
• Education: The application of AI in education is changing the traditional teaching model and promoting the development of personalized learning. By analyzing students' learning behavior data, AI can tailor learning content and paths for each student, helping students learn at a pace that suits their learning speed and comprehension ability. AI is also used to develop automated homework grading and exam scoring systems to reduce teachers' workload and provide real-time feedback. In addition, AI-driven education platforms can recommend suitable learning resources and courses based on students' performance and interests to improve learning outcomes.
• Retail and e-commerce: In the retail and e-commerce fields, AI helps companies increase sales and customer satisfaction through personalized recommendation systems, inventory management optimization, customer relationship management (CRM), etc. AI analyzes customers' shopping behaviors and preferences, and can accurately recommend products and increase sales conversion rates.
• Supply Chain Management : In supply chain management, AI can help reduce out-of-stock or overstock situations by predicting demand fluctuations and optimizing inventory management. In addition, AI-driven chatbots and virtual assistants enhance consumers’ shopping experience and provide them with 24/7 personalized services.
• Smart products and devices : AI technology is widely used in smart home devices, driverless cars, drones, robots and other smart products. These products use AI to achieve automated and personalized functions, significantly improving the user experience. For example, AI-driven smart speakers (such as Amazon Echo and Google Home) can not only execute voice commands, but also learn user habits to provide more considerate services.
• Autonomous driving : Autonomous driving technology is a highlight of AI in smart devices. Through deep learning models and sensor data fusion, the autonomous driving system can make real-time decisions in complex road environments and improve driving safety and efficiency.

3.3 AI Business Model

There are various business models for AI, including software as a service (SaaS), data analysis services, AI-driven products (such as smart devices), etc. Enterprises can achieve profitability by providing AI solutions to simplify processes and improve efficiency.

• Software as a Service (SaaS) : AI SaaS platforms provide cloud-based AI services that enterprise users can subscribe to on demand without having to develop or maintain AI infrastructure themselves. For example, Google's AI platform, Amazon's AWS AI services, Microsoft Azure's AI tools, OpenAI's ChatGPT, etc. Users can call these services (including machine learning, natural language processing, and computer vision) through APIs and pay based on usage.
• AI hardware sales : AI hardware manufacturers such as NVIDIA have developed AI-specific chips and generate sales revenue by providing AI chip computing power to various manufacturers and users. NVIDIA's AI chip customers include CSP manufacturers (Microsoft, Amazon, Google, etc.), Internet, and consumer technology companies (Meta, Tesla, etc.).
• Data analysis services : AI data analysis companies analyze the data of enterprises to provide valuable business insights and help optimize business processes and decisions. For example, companies such as Palantir help enterprises identify patterns, predict market trends, and develop more effective strategies by analyzing huge data sets. Such services are usually charged on a consulting or project basis.
• Smart devices : AI technology is embedded in various hardware products (such as smart speakers, drones, self-driving cars, etc.), which use AI to achieve key functions and create unique user experiences. For example, Tesla's autopilot system and Amazon's Echo smart speakers are all products enabled by AI technology. These smart devices not only make profits through hardware sales, but also may obtain continuous income through additional services or content subscriptions.
• AI application product services : AI applications for typical application scenarios are developed based on AI large language models (such as GPT-4, Codex, etc.). Enterprises and users use these AI application products by subscribing to AI services. For example, OpenAI launched ChatGPT to help users generate content, articles, Q&A, etc.; MidJourney provides artists and designers with the ability to generate artistic images of different styles; Runway provides AI video editing functions, allowing users to automatically generate video clips, apply style conversion, and perform quick editing. DoNotPay provides automated legal services to help users handle simple legal matters such as parking ticket appeals and refund applications, greatly reducing the threshold for legal services.

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 manufacturer of graphics processing units (GPUs), 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 many fields such as artificial intelligence (AI), high-performance computing (HPC), autonomous driving, data centers, and cloud computing.

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 technologies 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.

4.4 AI Application and APP (Downstream)

In the downstream of the AI ​​industry chain, AI applications are mainly AI solutions for specific industries or corporate needs. The core goal of this type of application is to integrate AI technology into industry workflows and promote the intelligent transformation of the industry. AI downstream applications cover a wide range, including both enterprise-level solutions and the consumer market.

4.4.1 OpenAI — ChatGPT

ChatGPT is an artificial intelligence chatbot based on a large language model launched by OpenAI in November 2022. It is capable of natural language processing and generation and provides a variety of intelligent services. Just two months after its launch, ChatGPT's monthly active users exceeded 100 million at the end of January 2023, making it the platform with the shortest time to reach 100 million users worldwide.

• Function : ChatGPT uses a generative pre-trained model (GPT) to understand and generate natural language text, supports multi-round conversations, answers questions, provides suggestions, and generates content. Its application scenarios cover areas such as customer support, writing assistance, and knowledge question and answer.
• AI technology : natural language processing (NLP), generative pre-trained models, and deep learning.
• Typical application scenarios : intelligent customer service, content generation, educational support, and writing assistance.

4.4.2. Zebra Medical Vision — Medical Image Analysis

Zebra Medical Vision is a company that uses AI technology to analyze medical images to help doctors diagnose diseases such as cancer, heart disease, pneumonia, etc.

• Function : Zebra Medical Vision's AI system automatically identifies potential pathological changes and provides diagnostic suggestions by analyzing medical images such as X-rays, CT scans, and MRIs, helping doctors identify diseases faster and more accurately.
• AI technology : deep learning, computer vision, and medical image processing.
• Typical application scenarios : cancer screening, heart disease detection, and lung disease diagnosis.

4.4.3. Zoom — Smart Meeting Function

Zoom is a video conferencing platform that is widely used for remote work, online education, and social interaction. Its video conferencing system provides high-quality remote collaboration experience through cloud computing and AI functions (such as real-time subtitles and background blur).

• Function : Zoom uses AI functions to provide intelligent conference services such as real-time subtitles, background blur, and noise suppression to improve the remote collaboration experience.
• AI technologies : natural language processing (NLP), machine learning, and computer vision.
• Typical application scenarios : remote conferencing, online education, and real-time subtitle generation.

4.4.4. Lemonade — AI-driven insurance claims

Lemonade is a company that uses AI technology to optimize insurance services. It simplifies the insurance claims process through AI and chatbots and provides fast and personalized insurance services.

• Function : Lemonade's AI system uses natural language processing and machine learning to automatically process insurance claims requests, quickly analyze customer needs and make claims decisions.
• AI technology : natural language processing (NLP), machine learning, and automated decision-making systems.
• Typical application scenarios : automated insurance claims, risk assessment, and customer service.

4.4.5. Alibaba — Smart Retail

Alibaba's unmanned supermarket is a fully automated retail model built using artificial intelligence (AI), the Internet of Things (IoT), big data and biometrics. The core of the unmanned supermarket is to achieve "unmanned" operation through technical means, so that consumers can complete the shopping process without relying on traditional store clerks.

• Function : Alibaba's smart retail system uses AI and RFID technology to achieve functions such as automatic checkout, inventory management, and personalized recommendations, allowing consumers to complete their shopping without human intervention.
• AI technologies : computer vision, Internet of Things (IoT), machine learning.
• Typical application scenarios : unmanned supermarkets, automated checkout, and personalized product recommendations.

4.4.6. Apple Siri — Intelligent Voice Assistant

Siri is Apple's intelligent voice assistant, which uses natural language processing (NLP) technology to help users complete various tasks, such as setting reminders, navigation, and sending messages.

• Function : The intelligent voice assistant in Apple devices can help users complete operations through voice commands, including sending messages, setting reminders, navigation, and querying information.
• AI technology : NLP, speech recognition, machine learning.
• Typical application scenarios : voice command execution (making calls, sending text messages, setting reminders), navigation, and information query.

4.4.7. Spotify — Music Recommendation System

Spotify uses AI and machine learning algorithms to analyze users' listening habits and provide personalized music recommendations. Through user behavior data, Spotify can predict songs and artists that users may like.

• Function : Spotify's AI-driven music recommendation system analyzes users' listening habits and preferences to provide personalized music recommendations and daily playlists.
• AI technology : collaborative filtering, deep learning, and machine learning.
• Typical application scenarios : personalized music recommendations, generating daily music recommendation lists, and discovering new music.

4.4.8. Grammarly — AI writing assistance tool

Grammarly is an AI-based writing assistance tool that uses natural language processing technology to help users detect spelling, grammar, and writing style errors and provide improvement suggestions.

• What it does : Grammarly helps improve writing by analyzing your text and providing suggestions for grammar, spelling, and style improvements.
• AI technology : natural language processing, machine learning, and text analysis.
• Typical application scenarios : text proofreading, writing suggestions, grammar and spelling checking.

4.4.9. Replika — AI Chatbot

Replika is an AI chatbot that allows users to have personalized conversations and build emotional connections. Replika uses NLP and sentiment analysis technology to simulate human conversations, helping users relieve stress and engage in self-reflection.

• Function : Replika's chatbot allows users to establish an emotional connection by having a conversation with the AI. It mimics human conversational style, provides emotional support, and can help users reflect on themselves.
• AI technology : NLP, deep learning, sentiment analysis.
• Typical application scenarios : emotional companionship, dialogue interaction, and self-reflection.

4.4.10. Youper — Emotional Health Assistant

Youper is an AI-driven emotional health app that helps users manage their emotions and mental health through emotional diaries and conversation analysis. AI analyzes the user's emotional state and provides suggestions and meditation exercises.

• What it does: Helps users manage their emotions and mental health through emotional journaling, conversation analysis, and meditation techniques.
• AI technology : NLP, sentiment analysis, machine learning.
• Typical application scenarios : emotional diary, meditation guidance, and mental health management.

4.5 AI Agent

AI Agent refers to an autonomous computing system that can perceive its environment and make decisions and actions based on the information in the environment . Agents usually have the ability to perceive, reason, learn and act, and can interact with the environment or other agents driven by a certain goal or task. AI agents can be applied to simple rule systems to complex deep learning models, and are widely used in automation, robotics, game AI and other fields.

The various AI consumer applications we see every day, such as Apple's Siri assistant and ChatGPT chatbot, are actually AI Agents. These AI Agents provide AI products and services directly to ordinary consumers , and provide users with convenient, personalized services and entertainment experiences through AI technology.

Currently, most of the AI ​​applications facing the C-end in the market are essentially a form of AI Agent. The figure below is an AI Agent market map drawn by Insight Partners, covering various types of Agents from many companies and industries.

4.5.1 AI Agent Technical Architecture

A typical AI Agent technology architecture consists of a data layer , iPaaS (integrated service platform layer) , an automation layer , and a user interface layer . These four layers together support the perception, decision-making, action, and interaction capabilities of