The scale of mega cluster buildouts for AI is unprecedented, with data center power consumption expected to rise significantly, driven by the demands of AI training and inference.

Elon Musk's xAI is building a data center with 200,000 GPUs, which is currently the largest cluster, surpassing those of Meta and OpenAI, which have 128,000 and 100,000 GPUs respectively.

Creating a giant model like GPT-4.5 requires a lot of people, time, and computational resources.

Training larger models requires substantial computing power, which translates to more GPUs running for extended periods and consuming increasing amounts of energy.

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.

OpenAI's recent $6.6 billion funding round indicates that AI models are becoming increasingly capital intensive, with future models expected to be orders of magnitude larger than current ones.

Generative AI and large language models, like ChatGPT, are incredibly powerful technologies that will soon become even more powerful.

In the future, we will see a proliferation of different AI models, with companies using the largest models for performance optimization.

A significant limitation to AI progress is the energy requirement; for instance, America needs an additional 90 gigawatts of power, equivalent to 90 nuclear power plants, to support current AI demands.