Own your own AI or it will own you

We are closer than ever to AGI, but we are more focused on capabilities than ownership, who will benefit from the economic gains from AI systems and how do we ensure they are evenly distributed.

2023 marked the year AI entered the mainstream public discussion, this is good and has gotten the public interested in the topic, but we are still more focused on capabilities rather than ownership.

AI will create incredible economic opportunities but these gains would not be equally distributed and would be concentrated the hands of the few who have ownership of this technology. Fortunately the fundamental techniques for building these systems are generally known within the public domain shared as research papers and open source code, yet there is still a significant constraint for general participation: compute. Compute is the most important currency in the development of frontier AI system and accounts for a huge fraction of the performance gains over the years, simply making the models bigger and scaling them with more compute can significantly improve the performance of these models. This is why we have seen one of the greatest stock bull runs on the NVDA stock price.

The open source community struggles to keep up with these large compute requirements required for the highest performing frontier models and rely on the benevolence of large tech companies such as Meta and DataBricks to train these large models and make them open to the community. For these reasons the open source community is majorly focused on inference (making the models run faster after training), this has created incredible opportunities for startups and individuals through techniques like fine-tuning and RAG to make these models personalized to their use case and businesses. Even with this limited window of progress there is a strong push by the biggest AI labs and providers to regulate AI, citing reasons such as safety.

Individuals deep in the topic call this "regulatory capture". Citing future, unfathomed claims of doom should not allow individuals dictate the fate of such a powerful technology so early in its infancy. We need to investigate techniques to make training and inference of these foundation models more accessible to the public through techniques like grid computing, volunteer computing and decentralized incentivization.

"own your own AI or it will own you”