So, where is your moat?
By Wang Ziwei @ Retail Wei Observation
Following Web3 and the Metaverse, artificial intelligence, especially Generative AI (referred to as "AIGC" in China), has become a hot investment area. Many stocks with a hint of AI concepts have experienced multiple "10 centimeters" or "20 centimeters" daily limit increases in the first half of this year, and many have doubled their market value.
What should Generative AI, AIGC, look like? Where is its value? These are topics worth discussing. This article analyzes the combination of AIGC and some industries from the perspective of a primary market investment manager, attempting to answer questions about the viability of business models.
Disclaimer: This article represents a personal perspective and is inevitably biased and limited in information. Therefore, the views expressed are for reference and communication purposes only and should not be taken as investment advice.
[One] AIGC + Copywriting/Scriptwriting#
Copywriting and scriptwriting were the earliest entrepreneurial opportunities that emerged after the appearance of ChatGPT. The most classic example is Jasper, which is valued at $1.5 billion but is currently downsizing.
The reason is simple. Aren't you just using an API? You just made some fine-tuning for specific social media platforms, right?
Yes, that's the biggest problem. You have almost no technological moat. It could even be a side project for a few programmer friends or a small service built by programmers for their own marketing department.
This week, OpenAI announced the release of GPT-3.5 Turbo fine-tuning. OpenAI claims that the final customized model can match or even surpass the capabilities of GPT-4 in certain tasks. Moreover, OpenAI will release a more advanced GPT-4 this autumn. In other words, fine-tuning is becoming "simpler."
At the same time, when these projects are actually implemented, their essence is driven by operations: how quickly you can acquire customers, make them willing to pay, and retain them in the long term. Therefore, you will find that this field requires the "hidden assets" of entrepreneurs to be monetized. If you are a master of private domains, you don't need to worry about seed users for these projects.
In addition, most AIGC-powered copywriting and scriptwriting companies claim to help users reduce costs and increase efficiency through SaaS services. The problem is that although cost reduction may attract users and even additional payments in the early stages, in the long run, these additional payments and commissions are almost non-existent.
As for efficiency improvement, it can indeed increase productivity. However, fundamentally, it may help operation and marketing personnel generate a large number of scripts and copy for social media. It is a logic of hacking platform algorithms. Regardless of whether the platform will eventually allocate traffic (some platforms may directly limit such content), one thing is clear: you won't get any additional income from it.
One more thing to mention is that compliance should be considered when implementing such projects. In other words, do not directly call OpenAI's API.
Therefore, from my personal point of view, AIGC's assistance in copywriting can make money and even generate good cash flow. However, compliance is the most important issue (of course, you can avoid this issue by using the APIs of ChatGLM and Wenxin Yiyuan). As for whether it is valuable, that is a more complicated question.
[Two] AIGC + Design#
The emergence of MidJourney made us realize that AI can help us create beautiful images in such a "foolproof" way—just open a browser, no need for graphics cards or brushes. Your biggest limitation is lack of imagination.
Compared to MidJourney, Stable Diffusion (usually referred to as SD) goes to the other extreme. You usually need to configure devices to make it generate images faster, but the results are better. Compared to MidJourney, the complexity of operation is similar to the difference between Photoshop and Meitu Xiuxiu.
Therefore, the combination of AIGC and the design field has become a hot topic.
AIGC empowers various fields, from fashion design, jewelry design, home decoration, and interior design to real images and rendered images. It seems that designers are becoming redundant. And due to the clear "what you see is what you get" nature, AIGC + design is a messy hot topic.
The underlying architecture of this field is essentially fine-tuning of SD. Yes, if you say you don't use OpenAI's API for copywriting and use Wenxin Yiyuan or ChatGLM instead, you can bypass OpenAI's API. However, it is almost impossible to bypass SD in the design aspect.
So, where are the barriers?
Among the high-quality projects currently seen, the most basic barrier is the prompt. Yes, you can follow tutorials for everything, but why can't you make it look as good as others?
That's the difference in prompts, including positive prompts, negative prompts, and prompts related to devices. You don't need just a few prompts, but dozens of them. So, in the short term, this can form a small barrier. However, in the long run, just like fabrics and colors in fashion, as long as you can produce it, I can replicate it in 2-3 weeks. Of course, I'm talking about one design. If it's dozens of designs, the workload is indeed not small.
The second barrier is technology. Indeed, some projects can create their own models, and there are various technical aspects such as accurate contour recognition that require research and development.
The third barrier is data. Where does your training data come from? Is it exclusive? For example, if you are in the construction industry, is your data from a top developer? If you are in jewelry design, is your data from a well-known jewelry brand? These are indeed barriers and can even help you go deeper. In other words, AIGC + design is likely to be an entry point.
AI design as an entry point is also our fantasy about AI. That is, AI can help industries achieve 10 times or 100 times transformation. If not the entire industry, we can find a node that can be disrupted by 10 times or 100 times.
Coincidentally, design is that entry point. At least it can improve industry efficiency from two perspectives:
The first is the ability to generate a large number of relevant designs before production and then distribute them on social media to see real consumer feedback, achieving trial production and seeding.
The second is to improve internal communication efficiency within the company. Taking a clothing company as an example, there is a communication gap between planners and designers. By quickly generating designs from the ideas in the designer's mind using AI and communicating quickly with the planners.
After entering the industry, can AIGC + design increase a company's revenue? It is almost impossible to measure, and the ceiling in this field is quite average. So, should you enter the supply chain after entering the industry? If so, how should you enter? Which part of the supply chain should you enter? These are questions that are worth considering.
These are also the questions that AIGC + design companies must answer.
[Three] Conclusion#
Overall, AIGC does bring the potential for cost reduction and efficiency improvement. However, from a business perspective, I believe the following three business logics have not changed and still need to be considered in the AIGC boom:
First, reducing costs is difficult to generate long-term revenue. However, providing incremental value to companies that can be clearly distinguished and quantified can generate long-term revenue. This is essentially the logic of CPS, paying based on revenue increment.
Second, the ceiling is a problem that AIGC must consider. If the field you enter has an average ceiling, you must go deeper, whether it is the supply chain, more tools (such as Shopify), or others.
Third, project-based operations can help you sustain your business in the early stages, but in the long run, the capitalization value is very low.
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"Retail Wei Observation" focuses on the latest strategies, tactics, and thoughts in the field of new retail and new consumption from a global perspective. The founder of the platform, Wang Ziwei, is an independent retail analyst.