The barriers to training large language models are primarily capital and talent, as specialized expertise and substantial financial resources are required.

The new technology of large language models is creating unprecedented opportunities for startups, enabling tasks that previously required extensive research teams to be accomplished more easily.

As we scale large language models (LLMs), we will eventually encounter a data wall, which limits the effectiveness of current scaling techniques.

I believe we are nearing the upper limits of large language models, as the future will likely involve many smaller, specialized models rather than a few large ones. We're at the upper end of large language models, and they're rapidly becoming a commodity as they mature.

Further research confirmed that scaling laws apply to various models, including those for text-to-image and image-to-text tasks, while in early 2020, these laws for large language models (LLMs) were largely unknown outside of OpenAI, except for one researcher who focused on the scaling hypothesis, suggesting that increasing the size of data and compute would lead to the emergence of intelligence.

Large language models are becoming significantly larger and smarter, with AI labs adopting a strategy of scaling more parameters, data, and compute to improve model performance.

At Scale, we power the data infrastructure for the entire AI industry, with every major large language model built on top of our data engine, and we partner with enterprises and governments to implement this technology for their unique challenges.

The influential paper on scaling laws for neural language models published in January 2020 established that increasing model size, data, and compute leads to consistent improvements in performance, emphasizing the importance of scale over algorithm.

Using large language models as judges, combined with human review, allows for scaling without overwhelming human subject matter experts.