Economics of Generative AI

🌈 Abstract

The article discusses the business model and financial challenges faced by OpenAI, the company behind ChatGPT, as well as the broader implications for the generative AI industry. It explores the differences between generative AI as a feature versus a standalone product, and how companies like Apple are approaching this technology. The article also raises concerns about the sustainability of the current research and development model in the generative AI space, and the potential missed opportunities due to the focus on profit-driven applications.

🙋 Q&A

[01] The Business Model of Generative AI

1. What are the key points made about the business model of generative AI?

• OpenAI, the maker of ChatGPT, could lose up to $5 billion this year, indicating the high costs of running such a business
• There are two main approaches to incorporating generative AI: as a feature within existing products, or as a standalone product
• Integrating generative AI as a feature, like Apple's approach with Siri and ChatGPT, carries less risk for the overall business compared to selling generative AI as a standalone product
• Building the underlying generative AI technology does not guarantee business success, as the challenge lies in creating a sustainable, profitable business model around it

2. What are the potential downsides of the "generative AI as a product" approach?

• If the generative AI product does not meet customer expectations, users may discontinue use and stop paying the provider
• The high costs of sustaining the research and development required for generative AI models means the pricing for standalone products must be very high to become profitable

3. How does the "generative AI as a feature" approach differ in terms of risk and business strategy?

• In the "generative AI as a feature" approach, the core business value proposition is not solely dependent on the AI, allowing companies to offer it as an additional selling point without taking on significant risk
• Companies like Apple are integrating generative AI features into their existing products, rather than selling the AI models as standalone products

[02] Challenges in Sustaining Generative AI Research

1. What concerns does the article raise about the sustainability of generative AI research?

• The high costs of training and running large language models make it difficult to sustain research for research's sake, as capitalism does not have a good channel for this type of work to be sustained
• Academic institutions, which traditionally value knowledge over profit, have been drained of resources and are unable to participate in this type of research without private investment
• There is a risk that only profit-driven applications of generative AI will receive investment, while potentially valuable but less lucrative applications may be overlooked

2. How does the article contrast the priorities of the private sector and academia when it comes to generative AI research?

• In the private sector, the focus is on creating profitable business models, which may prioritize applications that can generate the most revenue rather than those that are ethically or socially beneficial
• In academia, the culture and norms around research value knowledge over money, and can better prioritize ethical, security, and safety concerns in the exploration of generative AI

3. What is the article's view on the potential missed opportunities due to the current economic model governing technological progress in generative AI?

• The article suggests that applications of generative AI that make sense but do not generate the billions necessary to sustain the high costs may never get deeply explored, while less useful but more profitable applications receive investment