Analysis believes that cloud computing giants increase not only the revenue of their cloud platform but also the valuation of chip companies by investing in AI startups and requiring them to develop models on their own cloud platform. Currently, large-scale cloud platforms support half of Nvidia's datacenter revenue. Once this investment cycle ends, Nvidia's growth engine may be severely affected.
There may be a huge bubble in the AI field, which has become a basic consensus on Wall Street. Most people believe that investors' frenzy has caused this bubble. In contrast, Drew Breunig, a well-known enterprise strategy consultant, offers a completely different perspective in an article published on Monday.
In the article published on Monday, Breunig specifically mentioned the role of large cloud computing companies in the AI bubble.
These cloud computing giants have huge cash reserves and have invested billions of dollars in companies that build basic models. More importantly, they require these companies to develop models on their cloud platform, thus creating cloud computing revenue while investing.
Afterwards, they use their newly added cloud computing revenue to purchase a large number of high-performance GPUs and rent them to AI model building companies and other market participants. This large-scale procurement inadvertently pushed up the valuation of GPU manufacturers, further exacerbating the bubble phenomenon in the entire AI industry.
Breunig believes that it is precisely this model that has led to the distortion of the AI market valuation. He pointed out that the revenue obtained by cloud platform companies through investment is completely different from the valuation method of traditional venture capital. This difference to some extent magnifies the bubble in the AI field.
For example, large cloud platforms have supported half of Nvidia's data center revenue. Once this investment cycle ends, Nvidia's growth engine may be severely affected.
The article also proposed several situations that may break this bubble:
Investor pressure, a drop in startup valuations, and efficiency gains from technological advances could all be factors to break the bubble.
If investors begin to question the cost-effectiveness, energy consumption, and reliability of AI technology, or if start-ups run out of money before finding clear use cases, even cloud platforms may have a hard time maintaining this bubble.
Breunig also mentioned the potential impact of technological progress on the bubble. He believes that as AI models become more efficient, the demand for computing resources may decrease, which may lower the demand for high-performance GPUs and affect the entire ecosystem.