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The relationship between applications and infrastructure should be close, with mutual support and shared growth. As the network evolved, we began to connect the world, transitioning from Infrastructure 1.0 to 2.0, and now moving toward smart systems. The era of machine learning and artificial intelligence has arrived, driven by big data, large-scale storage, elastic computing, and advanced algorithms—especially in deep learning. This has led to a wave of innovative applications.
In complex games like Go, machines have already surpassed humans. Applications such as image and speech recognition are becoming increasingly vital, with voice assistants and autonomous vehicles entering everyday life. However, much of the current discussion around AI focuses on algorithms and use cases, while the underlying infrastructure often goes unnoticed.
In the early days of computing, only experts in assembly language, compilers, and operating systems could build simple applications. Today, similar challenges exist: only those with PhDs in statistics or distributed systems can truly develop and scale AI systems. This highlights a missing link—the need for abstraction tools that make AI development faster and more accessible. As a result, only elite engineering teams can handle this work effectively.
Meanwhile, infrastructure development lags behind AI innovation. Current systems and tools aren't well-suited for future intelligent applications. Looking ahead, the industry needs new tools to unlock AI’s full potential, making it more accessible and practical. In the field of infrastructure entrepreneurship, providing the modules needed for intelligent system development is a huge opportunity.
From Infrastructure 1.0 to 2.0, the relationship between applications and infrastructure has always been dynamic—interdependent and mutually reinforcing. Hardware and software advancements have enabled new applications, which in turn push infrastructure to evolve. For example, the shift from slide shows to PPTs and social platforms like Pinterest reflects this cycle.
In the early 2000s, the commercial internet was built on Intel x86, Microsoft OS, Oracle databases, Cisco networking, and EMC storage. Companies like Amazon, eBay, and Google relied on these infrastructures, marking the "Infrastructure 1.0" era. But as the web grew, the client-server model became insufficient in terms of scalability and cost-effectiveness.
This led to the rise of self-reliant tech companies like Google, Facebook, and Amazon, who developed scalable, open-source, and cost-effective infrastructure. Technologies like Linux, Docker, Kubernetes, Hadoop, and Spark defined the cloud computing era—Infrastructure 2.0. These innovations allowed the internet to reach billions of users and capture massive amounts of data, paving the way for modern machine learning.
Now, the question is no longer just "How do we connect the world?" but "How do we understand the world?" This shift from connection to cognition marks a key difference between AI and traditional software. Unlike conventional programming, where logic is hardcoded, AI learns from data, enabling decision-making and prediction.
However, building smart applications requires vast amounts of data and powerful computing resources, making it difficult to generalize. This challenges the traditional von Neumann architecture and calls for a fundamental rethink of infrastructure, tools, and development practices.
Most AI research today focuses on algorithms and training techniques, but the real challenge lies in data preparation, function development, and scaling infrastructure. To fully realize AI's potential, we need new abstractions, interfaces, and systems that simplify the process.
This transformation isn’t gradual—it’s disruptive. It will reshape how we design and develop systems at every level. Emerging technologies include specialized hardware for high-concurrency computations, optimized system software, distributed frameworks, efficient data management, low-latency services, and end-to-end platforms that streamline the AI workflow.
Looking ahead, a new ecosystem of infrastructure and tools will emerge around AI, just as cloud computing did in the past decade. This is Infrastructure 3.0, a modular foundation that empowers the creation of intelligent systems. We’ll see new projects, platforms, and startups shaping the future of AI.