Last week, an intriguing discussion caught my attention at a fantastic event organized by Leni. The panel discussion revolved around an interesting comparison: Will the Generative AI industry resemble the Coffee industry, with a dominant player like Starbucks, or the Winery industry, characterized by a multitude of providers offering differentiated products?
This thought-provoking question led me to delve deeper into the dynamics of the Generative AI industry. Here are my thoughts.
In any industry, two key factors significantly influence its structure – the fixed and marginal costs of producing the product and the price for each unit of service. Let’s consider the Coffee and Winery industries for context.
In the Coffee industry, the high fixed cost – primarily branding – incentivizes scaling. Starbucks, for instance, has invested heavily in establishing a formidable brand and hence, scales up to distribute the cost. On the contrary, the Winery industry thrives on differentiation, with numerous wineries offering unique products.
Now, let’s apply these factors to the Generative AI industry. The industry can be divided into three essential layers as per the framework described by A16z:
1) The Infrastructure layer, which runs training and inference workloads for generative AI models.
2) The Foundational Model Layer, which provides the Foundational model via a proprietary API or open-source model checkpoints.
3) The Application Layer, where companies transform generative AI models into user-facing products, either by running their own model pipelines (“end-to-end apps”) or relying on a third-party API.
For the Foundational Model vendors, there’s a high fixed cost involved in training the models, and the marginal cost of providing a unit of service (API call) is quite low. Moreover, most sales are made through API calls, which have a low unit sale price. This dynamic, coupled with the fierce competition and the rise of competitive open-source alternatives, is causing the pricing power of Proprietary API vendors to shrink rapidly. As a result, the Foundational Model market is likely to resemble the Coffee industry, where you either go to Starbucks (OpenAI), or you make your own coffee (Open Source). Infrastructure layer has very similar dynamics as the Foundational Model layer so I will skip it in this discussion.
Moving to the Application Layer, it’s essential to differentiate between consumer and enterprise applications. Consumer applications are likely to follow the Coffee industry’s pattern due to the significant fixed cost of creating a consumer-facing brand and the strong incentive to scale.
However, enterprise applications might mirror the Winery industry. With the wide availability of LLM APIs, creating an enterprise AI application no longer requires a substantial fixed cost. Although there are some fixed costs required for enterprises (e.g., data compliance), they are not on the same level as training LLM and can be sequenced in the iteration with customers. Moreover, the price for enterprise applications can be quite high (up to 6 or 7-figure for a single account), fostering an expectation for differentiated services.
In conclusion, the Generative AI industry presents a unique blend of the Coffee and Winery industries’ dynamics. The Foundational Model Layer and consumer applications at the Application Layer are akin to the Coffee industry, while enterprise applications at the Application Layer resemble the Winery industry. As the industry evolves, it will be fascinating to see how these dynamics play out.
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Jing Conan Wang 