The most imaginative possibility may not be the large-scale model, but AIGC itself.
Article/Prince Wei @ Retail Wei Observation
AIGC, or "Generative AI," is currently a very popular investment field. Recently, after communicating with a large number of projects and investing in some early-stage projects, I think it is necessary to reflect on and review the journey of the past few months.
The content of this article is only a personal summary and personal opinion, which is inevitably biased and may contain errors. I hope it can stimulate further discussion. Please do not use it as investment guidance, DYOR.
[One] Model#
The first investment dimension of AIGC is the model.
The explosion of Generative AI was accompanied by the emergence of ChatGPT in November 2022. Since then, a large number of large language models (LLMs) have gradually entered the market. There is no doubt that logically, the model is an investment perspective.
However, the problem is obvious, the investment is too huge: the cost of single training of the model is extremely high, and the talent requirements are also very high. Therefore, in China, it is mainly done by large companies such as Baidu, Alibaba, and iFlytek. Although some non-large companies have provided excellent large models, their valuations have mostly reached billions of yuan, which is not acceptable to ordinary VCs.
In other words, from the current perspective, the investment window for models in China has basically been closed.
[Two] Data#
The second investment dimension of AIGC is data.
Data needs to focus on both quality and quantity. For example, the data sources of Chat GPT include Wikipedia, specific books and journals, selected content from Reddit (WebText), and specific web crawling content (Common Crawl, which is a large dataset collected from websites from 2008 to the present, including raw web pages, metadata, and text extraction. Its text comes from different languages and fields). This is a typical case of having both quality and quantity. When combined with excellent large models, it can achieve good results.
For the domestic generative AI industry, companies with unique data sources are worth paying attention to. That is, if you have data from a specific industry, your model is customized for that specific industry. This is the so-called "one meter wide and one hundred meters deep" logic, focusing on vertical and segmented industries.
The question is, where does your data come from?
Public data, well, that's good, but please pay attention to the compliance of the data, otherwise it will be "good for web crawling, but end up in jail";
Private data, internal data, where do they come from? This depends on the team's accumulation. It can be said to be the ability to realize the "implicit assets" of the team over the years:
For example, an "AIGC+design" team comes from a platform that owns a large number of image and audio copyrights, but this design business may ultimately serve consumer product brands that sell products on e-commerce platforms;
Another example is an "AIGC+e-commerce" team that comes from top e-commerce giants such as Alibaba and JD.com, and serves enterprise e-commerce customer service or product selection and testing steps, with the ultimate goal of creating a small-scale fast-response platform like SHEIN, and so on.
[Three] Scale#
The third investment dimension of AIGC is scale.
Scale - or revenue - has always been the most important indicator for investors. Although scale does not necessarily mean a moat, it at least proves that you are a player worth paying attention to.
In fact, truly scalable projects in the field of generative AI are quite rare at present, and most projects are still in the early stages. These early-stage projects have fallen into two traps.
The first trap is project-based. Making money project by project is valuable for early-stage teams to create benchmark cases and understand customer needs, and it can also generate cash flow to support the team (we temporarily ignore accounts receivable issues). The trap here is how to prove that you are not project-based in the future. From the perspective of VCs, it becomes a troublesome issue when the words "project-based" come to mind.
The more troublesome trap is the second one. Many entrepreneurs claim that they are not project-based and will move towards subscription + on-demand payment in the future. However, this subscription is essentially mostly SaaS. No matter how you use AI to empower this SaaS, you are still essentially a SaaS.
There are two problems with SaaS:
First, SaaS for small and micro enterprises in China is relatively false proposition, which is fundamentally different from the US market. Moreover, if you are really doing SaaS, please pay attention to your various core indicators, such as CAC, LTV, ARPU, NDR, etc., and use these indicators to monitor your development in real time.
Second, if your SaaS only reduces costs and improves efficiency, I'm sorry, you will basically not receive any money; only by increasing revenue - yes, it must not be just "improving efficiency" - can you possibly take a commission from the increment and continue to do so in the long term.
In fact, when you can increase revenue, you may not only be a SaaS, but also a BaaS that enters the business and supply chain. This in turn puts new demands on your team.
In conclusion, in the field of AIGC investment, the model focuses on technology, and the investment window has almost closed; data focuses on the team's past accumulation, which has opportunities; and scale focuses on the model, but you must avoid deviating from the right path.
"Retail Wei Observation" takes a global perspective and focuses on the latest strategies, tactics, and thoughts in the field of new retail and new consumption. It has in-depth research on super membership systems and domestic and foreign new retail cases. The founder of the platform, Prince Wei, is an independent retail analyst.