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

By allowing models to think longer, large language models can leverage more compute on the fly, enhancing their intelligence for complex problems.

Fine-tuning AI models on new types of data is crucial to maintain accuracy, especially as trends and terminology evolve rapidly.

The reasoning models being developed by DeepSeek are designed to utilize more compute, emphasizing the growing importance of inference in complex AI tasks.

DeepSeek's recent models, including V3, highlight the importance of balancing training and inference compute resources to optimize performance and efficiency.

The diminishing returns in pre-training mean that significant increases in intelligence will require exponentially more compute resources, making it harder to achieve breakthroughs.

For supervised tasks with clearly defined inputs and outputs, having sufficient training data is crucial for success, and the amount of computational power required varies by task.

The GPT architecture excels at efficiently absorbing and generalizing data, but as compute resources grow, data becomes the bottleneck, necessitating algorithmic innovations to learn more from the same amount of data.

The last decade focused on large-scale supervised learning, where increasing data and model size led to improved performance.