As AI accelerates its popularization, why has the database become the new foundation?

As large models sweep across various industries at a rapid pace, a more fundamental transformation is quietly taking place at the underlying level of computing power and data.

On January 18th, the fifth OceanBase Database Competition came to an end. Previously, this competition was included in the National College Student Computer System Ability Competition system in 2023, becoming a Class - A disciplinary competition recognized by the Ministry of Education. The attention of the education system to talent cultivation once again highlights the crucial position of databases in the AI era.

It is reported that this year's competition attracted 1,223 teams and 2,620 students from universities across the country. In the final, they faced two tasks: one was to optimize the performance of the hybrid query of "full - text retrieval + structured filtering"; the other was to build a traceable multi - modal RAG system based on the same database kernel.

The above competition topics directly address the real bottlenecks in the current implementation of AI in industries: no matter how intelligent a model is, without high - quality, efficient, and manageable data support, it is like a castle in the air.

And the trend of the competition also reveals a tendency: AI is not an isolated technological revolution but a systematic reconstruction. In the stage where AI is accelerating change and deeply reshaping the productivity of various industries, basic software will not be submerged; instead, it is moving towards an unprecedentedly crucial position.

The hotter AI gets, the more crucial databases become

In traditional perception, a database is like a digital warehouse, ensuring the accuracy, consistency, and persistence of data. Its core functions are "recording" and "safekeeping". However, the requirements in the AI era go far beyond this.

In 2020, data was first defined as the fifth factor of production, on a par with land, labor, capital, and technology, marking that data has transcended the technical scope and entered the fields of economics and sociology.

This transformation reveals the contradiction in the AI era: there is a gap between the capabilities of large models and the requirements for application implementation.

Theoretically, large models can understand, generate, and process various complex tasks. But in reality, enterprises face specific problems: how to quickly find relevant data, how to ensure the timeliness of information, and how to control the inference cost.

When an enterprise issues an instruction like "find work orders from VIP users in the past 7 days with the content containing 'payment failure'", what it needs is not just a simple data query, but a real - time "inference" process that integrates semantic understanding, keyword matching, and condition filtering.

This means that the database will affect the response latency, answer accuracy, and decision verifiability, and further affect the efficiency, quality, and credibility of AI in obtaining information. Therefore, in today's era of accelerated popularization of AI, the database must also evolve from a passive storage carrier to an active participant and entry point in the AI inference chain.

When data becomes the key driving force for large models, the database that manages and stimulates the value of data naturally upgrades from a background tool to the core engine of the production system. The hotter AI gets, the more urgent the demand for real - time processing of high - quality data becomes, and the more prominent the importance of the database as an underlying support platform is.

How AI workloads reshape the database technology architecture

The increasing importance of the database is directly reflected in its adaptation to and support for emerging AI workloads. For example, the "optimization of hybrid search performance" and "development of a traceable multi - modal RAG system" were set as the final propositions in this year's OceanBase Database Competition, which once again reflects this industry demand.

First, "hybrid retrieval" has become a high - frequency and essential requirement. Pure vector search often struggles when dealing with complex and precise structured conditions, while traditional databases have shortcomings in semantic understanding. In the future, mainstream AI applications will inevitably be in a hybrid mode that can simultaneously handle multi - modal queries of text, vectors, relational data, etc.

This workload forces the innovation of the database technology architecture, promoting it to "natively" recombine capabilities such as search, vector processing, and transaction processing at the underlying level, rather than simply stacking multiple independent systems. Thus, while simplifying the architecture, it can achieve a leap in performance.

Second, "traceability" has become a hard indicator for enterprise - level AI. If an answer generated by AI cannot indicate its source, it is almost unusable in serious enterprise scenarios. Especially in high - risk scenarios such as finance and healthcare, the decision - making process of AI must be transparent and verifiable. Therefore, the database needs to have this built - in ability to ensure that every intelligent Q&A is traceable.

This requires the database not only to be able to quickly retrieve information but also to accurately manage the version, source, and context of information, providing a reliable foundation for AI output.

AI workloads are driving the database to evolve from a closed data container to an open, manageable, and auditable intelligent data platform.

The breakthrough in the AI era: from "usable" to "user - friendly"

Changes in technological requirements often lead to the reshaping of the market landscape.

In the field of traditional databases, there are barriers between latecomers and leaders due to long - term ecological accumulation. However, the new requirements generated by AI, such as the extreme pursuit of hybrid query performance and the natural demand for unified management of multi - modal data, have to some extent created a new starting line.

This may provide potential overtaking opportunities for Chinese databases.

The key to the breakthrough of databases lies in whether they can leap from meeting the "usable" requirement to providing a "user - friendly" experience. This requires database products not only to operate stably but also to deeply understand the characteristics of AI workflows and form comprehensive advantages in terms of performance, usability, cost, and function integration.

Zhou Aoying, a professor at the School of Data Science of East China Normal University and the director of the CCF Database Special Committee, also mentioned at the final site of the OceanBase Database Competition that large models are data - driven. This means that those who can more efficiently manage, purify, and supply data will occupy a more favorable position in the AI ecosystem.

China has rich application scenarios and a large amount of data resources, which provides unique advantages for the development of database technology. Therefore, basic software such as databases is also one of the key sub - fields where China may form global influence more quickly.

The "talent foundation" beyond the hot topics

The evolution of technology depends on the support of talents. Behind the popularity of the AI era, there is a strong demand for development talents who can calmly understand the system, optimize the kernel, and balance engineering reliability and performance.

Through database - related competitions, it can also be found that in the future, the talent structure may gradually shift from application - oriented talents who "can use tools" to creative talents who "can build systems". In the future, composite talents with both system - level and AI engineering capabilities will be needed.

For example, the growth path designed for contestants in the OceanBase Database Competition - starting from the entry - level MiniOB project to lay the foundation for database kernel skills, and then conducting in - depth optimization and AI application development based on the more complex seekdb in the final - shows that the focus of industrial talent cultivation lies in system capabilities, engineering thinking, and long - termism. Young developers need to understand that beneath the glamorous surface of AI applications, the stability and efficiency of the underlying foundation will directly determine the upper limit of the application experience.

The wave of AI technology has not diminished the value of basic software. Currently, the AI technology ecosystem is returning to rationality from the "large - model - centric theory" and moving towards a "dual - center of data and system". The database, a technological field that has developed for decades, has been given a new mission in the AI era - it is no longer a behind - the - scenes hero but a core pillar driving the intelligent revolution together with algorithms and computing power.

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