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.

The principle 'bigger model, more data, better results' has held true for the last 12 to 15 years in AI development.

The last two years have been dominated by large AI models, which have proven to be popular and effective, but we are now entering an era focused on complex workflows around these models.

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.

The era of supervised learning relied heavily on human-labeled data, but recent advancements allow us to train models without requiring explicit human input, unlocking new possibilities.

This decade is characterized by the addition of generative AI tools, which build on the foundations of supervised learning.

While specialized models can be effective for specific tasks, the overall trend is towards general models that incorporate learnings from specialized iterations.

In AI, every approach has performed better when more scale is applied, especially as foundational problems are addressed over time.

The scaling laws paper revealed that increasing parameters, data, and compute leads to a smooth, consistent improvement in model performance, demonstrating that performance is more dependent on scale than on the algorithm used.