Which type of Generative-AI is more favored by investment institutions?

Introduction

With the continuous advancement of artificial intelligence technology, large language models (LLM) are increasingly used in various industries. Through the venture capital and accelerators I participate in, we receive many business proposals in the field of generative AI. Through the analysis of due diligence on these projects, we found several directions that are more likely to be favored by the capital market. Below I provide some personal insights into these innovation directions and predict future development trends:

The rise of AI aggregators

The rapid development of AI technology has brought a variety of generative AI tools, and users are often confused when faced with so many choices. AI aggregators emerged to provide a unified platform that allows users to easily access and use different AI services. This one-stop solution not only simplifies the user's operation process, but also improves efficiency and effectiveness through centralized management.

AI aggregators usually adopt a subscription model, and users pay a fixed monthly fee to use all AI services on the platform. The advantage of this model is that it can provide aggregators with a stable revenue stream and encourage users to use the platform more frequently, thereby increasing user stickiness. However, this also brings challenges, such as how to continuously provide high-quality services to maintain user satisfaction, and how to price to attract and retain users in a highly competitive market.

Integrating AI services from different sources requires solving compatibility and integration issues. Technical teams must ensure that different AI services can collaborate seamlessly while protecting the security and privacy of user data. In addition, the aggregator also needs to build a powerful back-end system to support access by a large number of users and handle complex data processing tasks.

Case 1: AI Hub Connect

AI Hub Connect is a successful AI aggregator that attracts a large number of users by providing an intuitive user interface and powerful back-end support. By partnering with multiple AI service providers, the platform provides users with an extensive toolset covering functions ranging from text generation to image recognition.

Case 2: IntelliAI Nexus

IntelliAI Nexus focuses on providing customized AI aggregation services for enterprise users. By deeply understanding the specific needs of enterprises, it provides a series of customized integrated solutions to help enterprises improve operational efficiency and reduce costs.

Capital markets are increasingly interested in AI aggregators as they offer an efficient way to solve the choice difficulties faced by users and provide new revenue channels for enterprises. As AI technology continues to advance, investment in this area is expected to continue to grow.

Development Prospects of Distributed AI Cloud

Centralized AI cloud services, such as OpenAI, provide powerful computing power and a wide range of services, but users often face inconsistent service quality and concerns about data privacy. Decentralized AI cloud aims to provide a more consistent service experience and enhance data security and privacy by deploying computing nodes in multiple physical locations. This model allows enterprise customers to tailor services to their needs while maintaining full control of their data.

Data privacy is one of the core advantages of decentralized AI clouds. By processing data on-premises or in a private cloud environment, enterprises can avoid exposing sensitive information to third-party service providers, thereby reducing the risk of data breaches. Additionally, a decentralized architecture helps comply with regional privacy regulations, such as the European Union’s General Data Protection Regulation (GDPR).

Enterprise customers, especially those with strict requirements on data privacy and service quality, are more inclined to choose decentralized AI cloud services. These customers are often willing to pay a premium for a higher level of customization and greater control over their data. Decentralized AI cloud providers meet the unique needs of these enterprise customers by providing specialized services.

The strategic significance of large model componentization

Decomposing a large language model into smaller sub-models (Sub LLM) can improve the model's manageability and flexibility. This approach allows developers to optimize model performance for specific application scenarios while reducing the consumption of computing resources. Componentization also facilitates rapid iteration and updating of models, as smaller models are easier to tune and retrain.

The mixed use of industrial models and specialized models provides customized AI solutions for specific industries. For example, in the medical field, AI models can be developed specifically for diagnosis and treatment recommendations; in the financial industry, models for risk assessment and fraud detection can be created. This customized approach increases the effectiveness of AI technology in solving industry-specific problems.

Componentized large-scale models provide greater flexibility, allowing companies to select and combine different model components according to actual needs. This flexibility not only increases the applicability of AI solutions, but also helps reduce development and deployment costs. In addition, componentization also promotes the development of open source communities because developers can more easily share, modify, and improve model components.

Innovation paths for personalized models and 2B services

Image AI technology has made significant progress in recent years, especially in the field of generative AI. Stable Diffusion's LoRA technology is a typical example, which generates high-quality images by using low-rank approximation while significantly reducing the computational requirements of the model. The application of this technology is not limited to artistic creation, but also shows great potential in the commercial field.

The B2B service market has a growing demand for customized AI models. Taking the clothing industry as an example, AI models can help designers quickly generate new clothing styles and shorten the product development cycle. In the packaging industry, AI can optimize packaging design, improve material utilization and reduce environmental impact. For small-batch, diversified manufacturing, customized AI models can provide flexible production solutions to meet market demand for personalized products.

Case 1: FashionAI Studio

FashionAI Studio uses AI technology to provide assistance to fashion designers and quickly generates clothing design sketches by analyzing fashion trends and consumer preferences. This service not only improves design efficiency, but also helps companies better predict market trends, thereby improving the market competitiveness of their products.

Case 2: PackAI Solutions

PackAI Solutions focuses on providing customized AI solutions for the packaging industry. By analyzing product characteristics and transportation requirements, PackAI's AI model can generate optimal packaging design solutions, helping companies reduce costs and improve the environmental performance of packaging.

The capital market’s interest in personalized models and 2B services continues to grow. As the need for businesses to improve efficiency and innovation capabilities continues to rise, investing in AI technology in these areas is seen as having high return potential. In addition, as the technology matures and application scenarios expand, the market size in this field is expected to further expand.

Investment Trends and Market Forecasts

In recent years, capital market investment in the AI field has continued to grow, especially for LLM projects that can provide innovative solutions. AI aggregators, decentralized AI clouds, large model componentization, personalized models and 2B services have attracted a large number of venture capital and private equity funds. Investors not only value the short-term returns of these technologies, but also pay attention to their profound impact on industries and society in the long term.

Based on current market data and development trends, the market size in the AI field is expected to continue to expand in the next few years. In particular, as technology matures and application scenarios expand, directions such as AI aggregators and decentralized AI clouds are expected to achieve significant growth. In addition, as enterprises' demand for improving efficiency and innovation capabilities continues to rise, the personalized model and 2B service market will also show huge growth potential.

Although the AI field is full of opportunities, there are also certain risks. The speed of technological development, the intensity of market competition, changes in regulations, and user acceptance may all affect return on investment. However, through in-depth market research and precise investment strategies, investors can minimize risks and seize opportunities.

Conclusion

This report reveals several directions favored by the capital market through due diligence on LLM projects supported by Singapore AI funds. AI aggregators, decentralized AI clouds, large model componentization, personalized models and 2B services, these directions not only demonstrate the innovation potential of AI technology, but also provide investors with abundant opportunities. As technology continues to advance and the market expands, these areas are expected to experience significant growth in the coming years. However, while pursuing returns, investors also need to pay attention to market risks and formulate corresponding risk management strategies.