放弃融资幻想，今年的目标是搞钱

🌈 Abstract

The article discusses the author's (Wu Laosi) recent business activities, including hosting an AI application conference, launching an AI think tank, and writing a book about AI with the help of AI tools. It also covers the different perspectives of the "faith camp" and the "realist camp" on the development of large language models (LLMs) and the challenges faced by AIGC (AI-Generated Content) startups in terms of financing and commercialization.

🙋 Q&A

[01] The author's recent business activities

1. What are the author's recent business activities?

• The author hosted an AI application conference for 1,000 people at the beginning of the year.
• The author recently launched an AI think tank and productized the company's internal data and research.
• The author used AI to assist in writing a book about AI, which is now available on JD.com.

2. What are the author's thoughts on the experience of writing a book with AI assistance?

• The author found the experience of writing a book with AI assistance to be very different from writing for a public account.
• The author plans to share their insights on how they, as someone who had never written a book before, used AI to write a book about AI.

[02] Perspectives on the development of large language models (LLMs)

1. What are the key differences between the "faith camp" and the "realist camp" in their views on LLMs?

• The "faith camp", represented by Yang Zhilin, believes that LLM companies have huge potential and transformative possibilities, but profitability is a long-term goal that requires massive capital investment.
• The "realist camp", represented by Zhu Xiaohu, focuses more on the practical application and commercialization of AI technology, and is skeptical of the high valuations of LLM companies without clear business scenarios and sufficient data support.

2. What are the challenges faced by AIGC startups in terms of financing and commercialization?

• AIGC projects are not suitable for the VC investment structure, as they require a long investment cycle, massive capital needs, and uncertain exit paths.
• Most VC funds have failed to invest in LLM companies, and even those who invested early on have limited ability to continue increasing their investments.
• The author suggests that AIGC startups should focus on finding product-market fit (PMF) and generating revenue, rather than being consumed or "exploited" by the lack of financing.

[03] Recommendations for AIGC startups

1. What are the author's recommendations for AIGC startups in terms of financing and commercialization?

• For startups without PMF, the author suggests focusing on customers and generating revenue, rather than being obsessed with financing.
• For overseas expansion, the author recommends considering reverse 2B entry, where the algorithm engineering team is in Silicon Valley and the sales/delivery team is in China, or focusing on single-point products for the overseas creator ecosystem.
• The author also suggests that AIGC startups consider investing in the secondary market to hedge against the low probability of their own company going public.

2. What are the key elements for building an AI 2B MVP (Minimum Viable Product)?

• Deep understanding of the target industry and scenarios, and providing customized AI models to solve specific business problems.
• Seamless integration into the enterprise's existing workflows and providing a user-friendly experience.
• Ensuring data security and privacy protection to meet the high standards of enterprises.
• Demonstrating clear economic benefits for the enterprise, such as improved efficiency, cost reduction, or revenue increase.