Summary:
Task discusses how the network effect of social media platforms is changing, particularly in the context of Web3 and the Hive blockchain. He argues that the traditional network effect based on user numbers is not the only or even the most important factor, and points to examples like Amazon and Elon Musk's X (Twitter) to illustrate how the network effect can come from building out features and integrating different services into an ecosystem.
He believes a similar dynamic is emerging with AI language models (LLMs), where the network effect comes from the data, compute power, and feedback loop of the model itself, rather than just user numbers. He suggests that Hive, as a technology platform, should be viewed similarly to other tech giants in terms of its potential to build out features and services that leverage AI and create their own network effects.
He emphasizes that as AI capabilities rapidly advance, integrating LLMs and other AI tools will become standard across social media and other digital platforms in the coming years. He believes this shift in network effects will have major implications for the future of Hive and other Web3 projects.
Detailed Article:
Task begins by noting that the traditional network effect of social media platforms, which has historically been based on user numbers, is undergoing a major change in his opinion. He argues that while user numbers are still important, there are other, more powerful network effects at play, particularly in the realm of technology companies and platforms.
He uses Amazon as an example, explaining that the company's network effect comes primarily from its features and the feedback loop created by users engaging with the various services and integrating deeper into the Amazon ecosystem. This is in contrast to the more straightforward user-based network effect of traditional social media platforms.
Task then draws a parallel to Elon Musk's plans for X (formerly Twitter), suggesting that Musk is not simply building a social media platform, but rather aiming to create a multi-faceted ecosystem that includes financial services, telecommunications, and potentially even media/broadcasting capabilities. He believes this type of integrated, feature-rich approach is the future of network effects, rather than relying solely on user growth.
Turning his attention to Hive, the host argues that the blockchain platform should be viewed through a similar lens as other major technology companies and platforms. While Hive may be constrained by factors like finances and developer resources compared to tech giants, the host believes Hive's potential is no different in terms of its ability to build out features and services that can create their own network effects.
He then delves into the network effects emerging within the realm of AI, particularly language models (LLMs). He explains how the data, compute power, and feedback loop of these models can create a self-reinforcing cycle, where the more the models are used and interacted with, the more they can learn and improve. This, the host argues, is akin to the network effect of Google's search engine, where the more people use it, the more valuable it becomes.
Task provides a specific example of how the Hive ecosystem can benefit from this AI-driven network effect. He discusses a tool called LeoLinker, which automatically inserts links to Hive's LeoGlossary pages within articles. When these articles are then used to train an LLM, the information from the LeoGlossary pages becomes part of the model's knowledge base. This data can then be further disseminated and incorporated into other AI systems, creating a feedback loop that strengthens the overall Hive ecosystem.
He suggests that this type of AI-driven network effect is not limited to Hive, but can be seen across various technology companies and platforms, including Tesla, X, Google, and Meta. He argues that as AI capabilities continue to advance, integrating LLMs and other AI tools will become a standard feature across social media and other digital platforms in the coming years.
Task concludes by emphasizing that the traditional rules of network effects are changing, and that Hive, as a technology platform, should be viewed in the same light as other major tech companies in terms of its potential to leverage AI and create its own self-reinforcing ecosystem.