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

Scaling and training very large neural networks has proven effective, as demonstrated by our training of a neural network 60 times larger than previous models using 16,000 CPU cores.

We realized that scaling laws in AI showed predictable improvements with increased data and compute, which encouraged us to focus on building a product for knowledge work as AI matured.

Our group investigates the hypothesis that large amounts of compute can solve interesting problems using neural networks, which has led to significant advancements in unsupervised learning at scale.

As we gather more data and build a larger platform, we can create more granular and personalized evaluations of AI models.

You need big models to train smaller models effectively, and while we have efficient smaller models, they often rely on insights from larger models.

When we started OpenAI, I was convinced that deep learning was genuinely working and that it improved with scale. This realization was crucial because it seemed like others weren't recognizing its potential.

Deep learning works and improves with scale; this belief was considered heretical at the time.

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.