This is one of the best summaries of current AI through the lens of nvidia, one of the most important players in hardware. There is also a podcast of this.

Here are some key milestones from the conversation about Nvidia’s history:

• In 2012, the AlexNet algorithm that used GPUs to greatly improve image classification performance was a breakthrough moment for AI and machine learning. Nvidia GPUs were key to its success.
• In 2017, Google’s Transformer paper introduced a new machine learning model architecture that enabled dramatic improvements in natural language processing tasks like translation. It also enabled training on much larger datasets.
• In 2019, OpenAI pivoted to focus on large Transformer-based language models after Elon Musk’s departure. They created GPT-3 in 2020.
• In late 2022, OpenAI launched ChatGPT, sparking huge interest in generative AI applications. This led to surging demand for Nvidia data center GPUs to train these models.
• Over the past 5 years, Nvidia has invested heavily in its data center platform – new GPU architectures like Hopper, acquiring Mellanox for networking, building its own Grace CPU, and advancing its CUDA software platform. This positioned them perfectly for the AI boom.
• In 2022/2023 Nvidia saw explosive data center revenue growth to over $10B per quarter due to demand for AI model training, cementing their lead in accelerated computing.

So in summary, breakthroughs in AI models created huge demand for Nvidia’s specialized data center products, which they had focused on developing for years prior. This allowed Nvidia to capitalize on the AI revolution.