Wondering what infrastructure it takes to deliver OpenAI?

As AI continues to explode across domains, it's tempting to think that jumping on to Huggingface and grabbing a model like LLAMA 3 with its open-source license will make me the next OpenAI. This notion might suggest that possessing the AI model weights is the golden ticket to untold opportunities. However, the truth is that owning the model weights and license is merely a fraction of the equation.

Successfully deploying and maintaining this model in real-world scenarios requires robust infrastructure. Major cloud providers such as Azure, AWS, and GCP offer this essential infrastructure, and even players like Red Hat (IBM) and Oracle present strong, viable options. Each of these platforms brings unique strengths and substantial capabilities to the table. This is the second part of what is required—something OpenAI established quickly, a partner that could provide infrastructure at scale and globally. Another part of the picture will be large, unique, very high-quality large-scale data sets, and this we see with OpenAI partnering with Reddit and Stack Overflow, and I expect more to come soon.

With so many announcements from Microsoft, I want to focus on a Microsoft-centric story that outlines the journey of providing the hardware necessary for training and running OpenAI models. And to re-ground ourselves in our dreams of what it will take to be the next OpenAI. I put together a short video to give a sense of scale and highlight the pace and investment. It’s a quick watch, just a few minutes long, but it vividly illustrates the direction AI is heading and the rapid pace of its advancement. If you enjoy the video, I would greatly appreciate your feedback and sharing it with others. Your support means a lot to me.