Realize that nearly $200 billion USD is involved in the gaming industry annually. But how do I come to this prediction? Read on:

First of all, realize that Nvidia GPUs are essential for AI training. They process complex calculations more efficiently than CPUs and can handle large amounts of data simultaneously. Nvidia specializes in designing powerful GPUs for AI applications such as image recognition, natural language processing, and speech recognition. Due to the high demand for AI models and GPUs, Nvidia has a dominant position in the AI market. They play a crucial role in further advancing the AI market.

The Generative AI wave has accelerated after Nvidia's Q2 earnings guide and surpassing expectations. This signals a high demand for GPUs and AI model training.

Prior to Nvidia's announcement, consumer launches like ChatGPT, Midjourney, and Stable Diffusion AI received attention. With Nvidia's results, evidence has been provided that AI can generate billions of dollars in net revenue, which accelerates investments in AI.

Although there is a lot of investment in AI, the question remains about what customers are doing with all these GPUs and how much value needs to be generated to recoup the investment.

Assuming Nvidia generates $50 billion in annual GPU revenue, this means approximately $100 billion in data center costs. The end users of the GPUs also need to make a profit margin. Assuming they need to achieve a 50% margin, this means $200 billion in lifetime revenue needs to be generated to recoup the initial investment.

Major technology companies such as Google, Microsoft, and Meta are expanding their data centers, along with Bytedance, Tencent, and Alibaba as major customers of Nvidia. In the future, companies like Amazon, Oracle, Apple, Tesla, and Coreweave will also be major contributors.

The question is how much of this expansion is based on actual demand from end customers and how much is done in anticipation of future demand. This is an important question that needs to be answered.

Although some fictional assumptions have been made about the revenue of companies like OpenAI, Microsoft, Google, Meta, Apple, and others, it is clear that a gap of $125 billion+ needs to be filled to meet current CapEx levels.

There is an opportunity for startups to fill this gap by leveraging AI technologies and creating value for end customers. The goal of this analysis is to highlight that there is a gap between the current expansion of AI infrastructure and the value it can provide to end customers.

Startups should focus on creating value for end customers and improving their lives. The expansion of AI infrastructure and foundational models is already underway, so the question is how this infrastructure can be deployed and how it can change people's lives. 

Conclusion

The Generative AI wave has gained momentum following Nvidia's impressive results, indicating a high demand for GPUs and AI model training. While there is significant investment in AI, the question remains about what customers do with all these GPUs and how much value needs to be generated to recoup the investments. Nvidia plays a crucial role in the AI market thanks to their specialized GPUs for AI applications. Major tech companies are expanding their data centers, and there is an opportunity for startups to leverage AI technologies and create value for end customers. It is essential for startups to focus on improving people's lives and creating value for end customers using the available AI infrastructure and foundational models.