We launched Character.AI about a month before ChatGPT came out in October 2022, and we scaled from 300 generations per second to over 30,000 in about 18 months.

The scaling laws in AI are still valid, but as we scale, challenges such as data management and system complexity become more pronounced, necessitating innovative solutions.

Scale AI acts as a refinery for data, converting large amounts of raw data into high-quality data that powers algorithms for various applications.

The scaling law in AI suggests that as models grow larger, they will require significantly more computational resources, making them more capital intensive to develop.

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

The scaling laws became a foundational principle for AI development after being popularized by researchers, including those at OpenAI and Google DeepMind, who emphasized that it's not just about making models larger, but also about training them on sufficient data.

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.

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 scaling laws we discovered were crucial to our success. Reflecting on the early days of AI, it seemed improbable that we would achieve what we have now, especially when the idea of chatbots felt like science fiction.