The Future of Learning in the Age of AI and Who Owns the Generative AI Platform

The Future of Learning in the Age of AI and Who Owns the Generative AI Platform

In the age of AI, the future of learning is set to be transformed. With advancements in technology, students and teachers are becoming early adopters of software that utilizes chat-based conversational interfaces. According to psychologists Edward Deci and Richard Ryan, humans are intrinsically driven by autonomy, relatedness, and competence, which means they will continue to learn regardless of any shortcuts. AI has the potential to act as a live tutor, providing personalized learning experiences tailored to individual needs and preferences. This includes personalizing learning modalities, content types, and even curriculum. Additionally, AI can help teachers reduce their workload by creating drafts of lesson plans and syllabi, allowing them to focus on giving students personalized attention.

However, there are also concerns regarding the truth in the age of AI. Algorithms are trained on available data, which means societal biases can be baked into them. A study conducted by the University of Washington found that 72% of people reading an AI composed news article believed it to be credible, despite containing incorrect facts. This raises questions about the trustworthiness of user-generated content and the blind trust in personalities, brands, and "experts" that people already follow and respect.

Moving on to the ownership of generative AI platforms, it is clear that infrastructure vendors are currently the biggest winners in this market. While application companies are growing quickly, they often struggle with retention, product differentiation, and gross margins. On the other hand, model providers, responsible for the existence of this market, have yet to achieve large commercial scale. However, there are already product categories, such as image generation, copywriting, and code writing, that have exceeded $100 million in annualized revenue.

One challenge in the generative AI market is the lack of strong technical differentiation. Both B2B and B2C apps can drive long-term customer value through network effects, data retention, and complex workflows. Margins and retention are expected to improve as competition and efficiency in language models increase. Additionally, there is an argument to be made for vertically integrated apps having an advantage in driving differentiation. Model providers have also embraced the idea of capturing value by organizing as public benefit corporations (B corps) or incorporating the public good into their mission.

When it comes to the flow of money in the generative AI market, a significant portion goes to infrastructure companies. On average, app companies spend around 20-40% of their revenue on inference and fine-tuning, which is paid either to cloud providers or third-party model providers. This means that around 10-20% of total revenue in generative AI goes to cloud providers. Nvidia, a leading provider of data center GPUs, is a major player in the infrastructure space and has seen substantial revenue growth from generative AI use cases.

While infrastructure companies have certain advantages, such as scale, supply-chain, ecosystem, algorithmic, distribution, and data pipeline moats, it is unclear if these moats will be durable over the long term. It is also uncertain if there will be a winner-take-all dynamic in generative AI. Both horizontal and vertical companies are expected to succeed, depending on the end-markets and end-users. If the AI itself is the primary differentiation in the end-product, verticalization is likely to prevail. However, if the AI is part of a larger feature set, horizontalization is more likely to occur.

In conclusion, the future of learning in the age of AI holds great potential for personalized and enhanced learning experiences. Teachers can benefit from AI by reducing their workload and focusing on individualized attention for students. However, there are concerns about the biases and trustworthiness of AI-generated content. In the generative AI platform market, infrastructure vendors have emerged as the biggest winners, while application companies and model providers face their own challenges. It remains to be seen whether strong technical differentiation and a winner-take-all dynamic will shape the future of generative AI.