Towards the Curator Economy: An Overview of Human Content Curation and Large Language Models (LLMs)

Towards the Curator Economy: An Overview of Human Content Curation and Large Language Models (LLMs)

The curator economy is a concept that revolves around the democratization of human content curation. It emphasizes the role of human minds and hands in the curation process, focusing on smart and insightful contextualization of the content being shared. By utilizing highlights, notes, and comments, human curators add value to the information they curate.

Content curation, in essence, is the simplest and fastest route to finding quality content on specific topics. In a world where information overload is a common challenge, content curation helps individuals arrive at the right destination with the least amount of stoppages along the way. It is a way to avoid going down the internet rabbit hole and surfacing with barely anything or, even worse, with deceitful information. By following knowledgeable curators who know where they're going, individuals can save time and find reliable and relevant information more easily.

The benefits of content curation are numerous. Firstly, it helps individuals overcome the overwhelming nature of vast amounts of information available. Instead of feeling lost or disinterested, curated content allows individuals to focus on what they need. Secondly, content curation flattens the learning curve by guiding individuals through the expertise of like-minded people. By leveraging the knowledge and experience of others, individuals can waste less time and find what they need more efficiently. Lastly, content curation is based on data from reliable sources, ensuring that the information obtained serves its intended purpose.

Moving on to the realm of large language models (LLMs), we encounter a different approach to content curation. LLMs, such as those developed by OpenAI, have the potential to revolutionize various industries and applications. However, the success of LLMs relies heavily on the availability of suitable training data. As Russell Kaplan from Scale AI suggests, language-aligned datasets are often the rate limiter for AI progress in many areas.

When considering the use of LLMs for specific applications, several factors come into play. Firstly, the accessibility of relevant training data is crucial. Without a sufficient amount of data, training LLMs to perform specific tasks becomes challenging. Secondly, the strength of the data moat, or the accumulation of data, determines the competitive advantage and viability of the LLM application. Proof of concept from larger companies can also provide reassurance regarding the feasibility of LLM applications.

Cost is another important consideration when utilizing LLMs. If relying on APIs from large companies like OpenAI, pricing power and product SLAs become factors to consider. Depending on the core product and the sophistication of the model required, alternative, less complex models may achieve the desired results without incurring high costs.

For applications that do not own the LLM model themselves, the long-term outcome of LLM infrastructure is a significant concern. Will multiple providers commoditize LLM models, or will a single company with the best resources become the gatekeeper? This question highlights the potential future developments and dynamics within the LLM landscape.

In conclusion, the curator economy and large language models offer unique perspectives on content curation and its applications. While the curator economy emphasizes the role of human curators in providing valuable context and guidance, LLMs present a powerful tool for automating various tasks and expanding the possibilities of content curation. By combining these approaches, individuals and businesses can leverage curated content and the capabilities of LLMs to enhance their knowledge, decision-making, and overall productivity.

Actionable Advice:

1. Embrace content curation: Take advantage of curated content to save time and access reliable information. Follow knowledgeable curators who share content aligned with your interests and goals.

2. Evaluate LLM feasibility: Before diving into LLM applications, assess the availability of suitable training data and the strength of the data moat. Consider proof of concept from established companies and the potential costs involved.

3. Consider long-term implications: For applications that rely on external LLM models, think about the future of LLM infrastructure. Will multiple providers offer similar models, or will a single company dominate? Keep an eye on developments in the LLM landscape to stay informed and make strategic decisions.

By embracing the curator economy and exploring the possibilities of LLMs, individuals and businesses can navigate the vast sea of information more effectively and unlock new opportunities for growth and innovation.