The Future of Generative AI: Exploring Defensibility and Evaluation of Early-Stage Consumer Companies

The Future of Generative AI: Exploring Defensibility and Evaluation of Early-Stage Consumer Companies

Introduction:
Generative AI has witnessed remarkable growth, with applications such as image generation, copywriting, and code writing surpassing $100 million in annualized revenue. However, the question of who truly owns the generative AI platform remains unanswered. In this article, we will delve into the dynamics of the generative AI market, the role of infrastructure vendors, the challenges faced by application companies and model providers, and the importance of defensibility in evaluating early-stage consumer companies.

The Landscape of Generative AI:
The growth of generative AI applications has been fueled by novelty and diverse use cases. While infrastructure vendors have emerged as the biggest winners, capturing the majority of revenue, application companies face challenges in retention, product differentiation, and gross margins. Model providers, who are essential for the market's existence, are yet to achieve large-scale commercial success.

The Role of B2B and B2C Apps:
In the absence of strong technical differentiation, both B2B and B2C apps create long-term customer value through network effects, data retention, and complex workflows. However, it is not clear if selling end-user apps is the most effective strategy for building a sustainable generative AI business. Vertically integrated apps might hold an advantage in driving differentiation, while margins and retention are expected to improve as competition increases and AI tourists exit the market.

Commercialization and Hosting:
For model providers, the path to commercialization is closely tied to hosting. Demand for proprietary APIs is growing rapidly, and hosting services for open-source models are emerging as useful hubs for sharing and integrating models. This symbiotic relationship between model producers and consumers highlights the importance of hosting in capturing value within the generative AI market.

The Ethical Dimension:
Given the immense potential and potential harm of generative AI, many model providers have incorporated the public good into their mission. This approach, such as organizing as public benefit corporations, issuing capped profit shares, or emphasizing social and environmental impact, has not hindered their fundraising efforts. However, it sparks a discussion on whether capturing value aligns with the goals of most model providers.

The Dominance of Infrastructure Companies:
The flow of money in the generative AI market largely benefits infrastructure companies. On average, app companies allocate a significant portion of their revenue to inference and fine-tuning, either through cloud providers or third-party model providers. Consequently, a substantial portion of total revenue in generative AI is estimated to go to cloud providers. Nvidia, with its data center GPU revenue, stands out as a major winner in the infrastructure layer.

Defensibility and Evaluation of Consumer Companies:
In the realm of consumer companies, defensibility is a crucial factor. Founders must address questions regarding user acquisition, retention, engagement, and monetization. The frequency and time spent on the platform, along with features that drive user engagement, play a pivotal role. Additionally, factors such as switching costs, lock-in, virality, and economics over time determine the sustainability and defensibility of consumer companies.

Looking Ahead:
While the long-term winner-take-all dynamic in generative AI remains uncertain, both horizontal and vertical companies are expected to succeed based on the needs of end-markets and end-users. The AI's role in the end-product, whether as the primary differentiation or part of a larger feature set, will influence the dominance of vertical or horizontal approaches.

Actionable Advice:

1. Focus on differentiation: Whether through vertical integration or a robust feature set, finding ways to differentiate your generative AI product will be crucial for long-term success.
2. Harness the power of hosting: Consider developing proprietary APIs or leveraging hosting services for open-source models to capture value and foster collaboration within the generative AI ecosystem.
3. Embrace ethical considerations: Incorporating social and environmental impact into your company's mission can attract support and funding, while also addressing the potential harm associated with generative AI technologies.

Conclusion:
The generative AI landscape is evolving rapidly, with infrastructure vendors and hosting services playing significant roles. While the market lacks strong technical differentiation, the path to success lies in driving customer value through network effects, retention, and differentiation. As the industry matures, it is crucial for players to understand the dynamics of the market, evaluate defensibility, and make strategic decisions to thrive in the future of generative AI.