Why You Should Learn in Public and the Future of Generative AI Platforms

Why You Should Learn in Public and the Future of Generative AI Platforms

In today's digital age, learning in public has become increasingly popular and beneficial. Whether it's sharing your knowledge on social media platforms like Twitter or Medium, or creating content like blog posts and Youtube videos, learning in public allows individuals to accelerate their learning process. One of the main advantages of learning in public is the ability to receive feedback and knowledge from others. When you share your work, people are more than willing to share their insights and experiences with you, helping you to expand your knowledge and learn more efficiently.

Furthermore, learning in public also helps individuals to build their network effortlessly. By sharing what you have learned, you attract like-minded people who are interested in the same topics. This not only allows you to connect with individuals who share your passion but also provides opportunities for collaboration and further learning. The power of networking in the digital era cannot be underestimated, and learning in public is one of the fastest and most effortless ways to build a strong network of professionals and experts in your field.

In the realm of generative AI platforms, the landscape is constantly evolving. Infrastructure vendors have emerged as the biggest winners in this market, capturing the majority of revenue flowing through the stack. However, application companies, despite their rapid revenue growth, often struggle with retention, product differentiation, and gross margins. On the other hand, model providers, responsible for the existence of this market, have not yet achieved large-scale commercial success.

Nevertheless, there are already product categories within generative AI that have exceeded $100 million in annualized revenue, such as image generation, copywriting, and code writing. This demonstrates the staggering growth of generative AI applications, driven by their novelty and a wide range of use cases. However, without strong technical differentiation, both B2B and B2C apps rely on factors like network effects, data retention, and complex workflows to drive long-term customer value. It is not yet clear whether selling end-user apps is the only, or even the best, path to building a sustainable generative AI business.

One interesting aspect to consider is the role of hosting in the commercialization of generative AI. Demand for proprietary APIs, such as those offered by OpenAI, is growing rapidly. Additionally, hosting services for open-source models are emerging as useful hubs for sharing and integrating models, creating indirect network effects between model producers and consumers. The hosting aspect of generative AI platforms presents an opportunity for model providers to capture value and differentiate themselves in the market.

Moreover, the potential of generative AI is both great and potentially harmful. As a result, many model providers have organized as public benefit corporations (B corps), incorporating the public good into their mission. This has not hindered their fundraising efforts, as the promise of generative AI attracts significant investment. However, there is a valid discussion to be had about whether most model providers truly aim to capture value and if they should prioritize it over the public good.

When considering the financial aspect of generative AI, a significant portion of the market's revenue flows to infrastructure companies. For example, app companies spend a considerable amount of their revenue on inference and fine-tuning, either directly to cloud providers or to third-party model providers who, in turn, spend a significant portion of their revenue on cloud infrastructure. This indicates that a substantial percentage of total revenue in generative AI goes to cloud providers, highlighting the importance of the infrastructure layer in the stack.

Behind the scenes, running the majority of AI workloads, Nvidia has emerged as a major winner in the generative AI market. With billions of dollars in data center GPU revenue, including a significant portion from generative AI use cases, Nvidia has established itself as a lucrative and durable player in the infrastructure layer. While there are standard moats like scale, supply-chain, ecosystem, algorithmic, distribution, and data pipeline, it remains to be seen if any of these moats will be durable in the long term. Additionally, it is unclear if there will be a winner-take-all dynamic in generative AI or if both horizontal and vertical companies will succeed based on end-markets and end-users.

In conclusion, learning in public has become a powerful tool for accelerating knowledge acquisition and building networks. By sharing what you learn, you invite feedback and insights from others, fostering a collaborative environment for growth. In the realm of generative AI platforms, the landscape is constantly evolving, with infrastructure vendors currently dominating the market. However, the future of generative AI platforms remains uncertain, as the best approach may vary depending on the specific end-market and end-users. Nonetheless, it is clear that the hosting aspect of generative AI presents an opportunity for model providers to capture value and differentiate themselves.

Actionable advice:

1. Embrace learning in public by sharing your knowledge and insights on platforms like Twitter, Medium, or through creating content like blog posts and videos. This will not only accelerate your learning but also help you build a strong network of like-minded individuals.
2. Stay updated with the latest developments in the generative AI space, as this field continues to evolve rapidly. By understanding the current trends and dynamics, you can position yourself strategically and identify potential opportunities for growth.
3. Consider the potential ethical implications of generative AI and the public good. As the technology advances, it is crucial to have discussions and make informed decisions about how generative AI can benefit society while minimizing harm.