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Read "Artificial Intelligence Revolution" report.

Only summarize the essence of it.

The first wave of the Internet... the second wave of smartphones...

In the first half of 2023, the AI ecosystem exploded! We are currently in the early stages of artificial intelligence, building on the foundation of previous technological waves such as the Internet and smartphones.

AI development is much faster than previous waves. Compared to previous technologies such as personal computers, the Internet, and mobile technology, the adoption rate of artificial intelligence is accelerating, indicating that AI is integrating into mainstream use faster.

In a study, Boston Consulting Group (BCG) consultants using AI performed better in all task indicators, including a 40% improvement in work quality and efficiency.

An AI-centered ecosystem has emerged.

AI models are the core of this ecosystem, with typical representatives such as OpenAI and Meta.

In the future, "GPU + AI models = Brain," the speed at which artificial intelligence models reach human-level performance in various fields such as language understanding, image recognition, and code generation will continue to increase.

As the demand for computing resources by AI models increases, the importance of data centers, hardware, and power supply also increases. These are critical infrastructure to support AI operations.

As AI applications continue to expand and deepen, the demand and influence of NVIDIA's GPUs and related products and technologies in the field of cloud computing are also increasing. NVIDIA's revenue in the past 12 months exceeded 32 billion US dollars.

The current AI wave is highly dependent on computing resources, which also drives the demand for higher-performance GPUs and influences the development direction and innovation trends of the entire semiconductor industry.

AI has already begun to create a new developer tool ecosystem. AI Ops is a new type of tool for AI development engineers, and data management tools are crucial for improving models. Fine-tuning models becomes easier with LLM Ops and Vector DBs, which are new support layers for AI applications.

AI applications are seen as a key layer for interacting with AI. These applications include various tools and services that can be widely applied in different industries and scenarios.

AI applications are exploring innovations in multimodal data processing, such as combining text, images, and sound processing capabilities. This will open up new application scenarios and experiences.

Under the influence of AI, the expansion of enterprise scale no longer means just increasing manpower, but increasing computing resources. This means that AI can play a role similar to Autopilot at various organizational levels, such as executive leadership, senior management, product, sales and support, engineering, growth and marketing, thereby transforming traditional organizational structures and ways of operation.

In the AI era, a new type of "AI super application" may emerge, which will provide one-stop services and operations, integrating multiple functions and potentially changing the way users interact with applications. It may integrate hardware and operating systems, as well as other intelligent services, creating a new field with a market value of approximately $5 trillion.

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