AI transformation is imperative! The implementation of GenAI applications from enterprises to quantitative trading is accelerating comprehensively.

Generative AI (GenAI) is changing the way the market operates, whether in internal processes, customer interactions, or high-frequency trading decisions. The application of AI is no longer just a concept but a key to competitiveness. The emergence of DeepSeek has not only made a significant impact in the GenAI field, but the background of its parent company, Huansquare Quantitative, has also sparked discussions on the feasibility and prospects of GenAI applications in the field of quantitative trading.

Kronos Research, AWS, Nurie AI, the Innovation Base of Lite-On Technology, Cathay United Bank, and other industry leaders unveiled at the AI Summit on February 21 how AI is expanding from enterprise applications to quantitative trading, and the significant impact it will have on future markets.

GenAI applications are everywhere, with LLM empowering innovation ready to take off.

The AI Summit is co-hosted by Kronos Research and Nurie AI, conveying the trend that AI has become the focus of global attention. Although AI is still in its early stages, the transformation towards AI is imperative, with innovative AI/GenAI applications gradually becoming ready, thus bringing new business opportunities.

From general business applications to quantitative trading, the presence of GenAI can be seen everywhere. Currently, corporate customer service centers, internal knowledge bases, and chatbots have already begun to utilize retrieval-augmented generation (RAG) in combination with LLMs, allowing the model to understand and answer questions more comprehensively. Financial institutions are also using GenAI to enhance customer experience, productivity, and innovation.

The quantitative trading company Kronos Research is actively utilizing GenAI to enhance market research and trading strategies. One of its core technologies is a price prediction model, which trains over 3,000 to 6,000 market features through AI to forecast price fluctuations over different time ranges. Additionally, Kronos also leverages GenAI to optimize the code review process and the development of trading strategies, accelerating decision-making efficiency.

▲ Kyle Tsai, Senior IT Infrastructure Manager at Kronos Research, stated that the team's primary application scenario for GenAI is code review, and they utilize RAG for efficient and comprehensive code analysis. (Source: Kronos Research)

Another noteworthy trend is that the benefits of investing in on-premises AI infrastructure lag far behind the rapid iteration of expensive GPU chips, making it impractical for companies to spend large amounts of money building related infrastructure. Liao Weikai, head of the AWS Solutions Architect team, stated that through the cloud, a wider variety of industry-leading foundational models can be chosen, allowing businesses of any size to seamlessly utilize various advanced models and GenAI services. This is also an important factor in promoting the ubiquitous application of GenAI and is a key foundation that accelerates the AI transformation journey for enterprises.

The five major stages of enterprise AGI advancement are initiating a comprehensive transformation from enterprises to trading markets.

With the widespread application of GenAI, it has brought impacts and changes to enterprises and trading markets. But is it causing short-term impacts or long-term transformations? As mentioned earlier, from general enterprise applications to quantitative trading, GenAI is used to enhance efficiency. Many companies have been committed to promoting digital transformation and sustainable transformation for years, and now they are further optimizing the implementation effectiveness of these two transformations through AI transformation.

OpenAI divides the journey of GenAI towards Artificial General Intelligence (AGI) into five stages: Chatbots, Reasoners, Agents, Innovators, and Organizers. The industry is currently preparing to enter the Agents stage to gain a competitive advantage in the market. As a result, companies are investing more resources and funds to pursue the most advanced AI/GenAI models and technologies to tackle more complex problems and achieve a leading position in the market.

For the Nurie AI team, which utilizes Artificial Intelligence Operations (AIOps) technology to reduce operating costs, simplify IT operations, and enhance customer experience, they are also further improving AIOps efficiency through GenAI, which clearly demonstrates that GenAI is driving long-term changes in business operations.

As technology giants continue to launch their own AI agents, such as Google Project Mariner, Anthropic Computer Use, and OpenAI Deep Research, Senior Research Manager Chen Yichang of the Lianfa Innovation Base stated that 2025 is even seen as a key year for the development of AI agents. Enterprises will also upgrade due to Agentic Workflow, which refers to the AI's ability to take actions based on existing resources and assist in completing tasks according to the situation.

In the world of quantitative trading, the efficiency of strategy and model innovation determines victory or defeat. Kronos Research is accelerating the innovation of trading models through GenAI technology, from code automation and quantitative research to price prediction, AI has become the core driving force of market decision-making. With the advancement of AI agents and LLM technology, the financial market will usher in a new wave of intelligent revolution, and in the future, it may evolve into an investment and trading model completely driven by AI agents, changing the traditional way of market operation.

Seeking key breakthroughs for the implementation of AI applications to establish a competitive advantage for enterprises.

Recently, the launch of DeepSeek RI has made it possible to develop high-performance AI with less data and lower costs. The previous situation where a few tech giants monopolized the market has been broken, allowing even small and medium-sized enterprises to conduct layered optimization based on existing LLM training frameworks, making training and inference more efficient and cost-effective. This brings a paradigm shift impact on the future development of LLM.

Taiwan's Chief Information Officer Cai Qiyan shared that in the future, companies will not only be able to scale pre-trained models in the first phase, but also in the second phase, through reinforcement learning (RL) combined with chain-of-thought, to elevate the model's "reasoning" capabilities to a higher level.

There are many factors driving the implementation of AI applications in enterprises. In addition to the key breakthroughs mentioned above, Fu-Ming Tsai, a technical manager at Cathay United Bank, stated that to address the time-consuming issue of LLM processing large amounts of text, enterprises can use Semantic Cache to accelerate the response speed of LLMs. Nowadays, applying GenAI in chatbots has become very common, and many companies have completed the first stage of their AGI journey through this.

With the continuous breakthroughs in technology and the reduction of AI inference and computing costs, cloud applications supporting various advanced AI models are becoming widespread. This includes the emergence of various groundbreaking technologies ranging from coding to predictive analytics, allowing for the proliferation of AI applications that meet the diverse needs of different enterprises. All of this will inevitably accelerate the pace at which AI agents disrupt industries, and the wave of the agent economy will also be upon us, ultimately realizing the grand vision of an AGI era.

▲ Hank Huang, CEO of Kronos Research (second from the right), shared the core applications of AI in quantitative trading at the event. (Source: Kronos Research)

This article emphasizes that AI transformation is imperative! The application of GenAI from enterprises to quantitative trading is accelerating comprehensively. First appeared in Chain News ABMedia.