Nvidia is a victim of the compute marketplace it created
Having proven how valuable compute can be, the company finds itself at the center of a market everyone wants to be in - while simpler technologies and less interesting companies get rich on the sidelines. Long the leading light of the industry, Nvidia has had a bad couple of months. Bloomberg has the ugly details , but the upshot is that the company's stock price has fallen 15% since its peak in May, even as projected revenue continues to grow.
Key Takeaways
- Compared with expected earnings, the company is now cheaper than the S&P average; investors are paying less per dollar of Nvidia's projected profit than they do for the typical large American company.
Money is still flooding into AI infrastructure stocks, but it's mostly going into memory companies.
- For anyone who appreciates Nvidia's technological accomplishments, this can feel a bit deflating.
There's a lot of genuinely impressive technology behind Nvidia's rise, both in developing CUDA, its widely adopted programming platform that made Nvidia GPUs the default engine for AI research, and in pushing the pace of GPU development to a speed few thought possible.
- Without the chips or the companies changing too much, the service they provide suddenly became very valuable - and since demand is growing faster than anyone can scale up supply, they have been able to increase prices tenfold over the past year.
- When I talked to Ornn co-founder and CTO Wayne Nelms about the forces driving that disparity, he framed it as a simple issue of supply and demand.
Google , Amazon , Microsoft , and even OpenAI have launched their own custom processors to lessen their dependence on Nvidia; even if those chips aren't as good as the latest model from Nvidia, they're good enough to drive down the price of compute.
- It's a frustrating state of affairs for Nvidia, and largely a product of its own success.
Stats & Key Facts
- #Bloomberg has the ugly details , but the upshot is that the company's stock price has fallen 15% since its peak in May, even as projected revenue continues to grow.
Compared with expected earnings, the company is now cheaper than the S&P average; investors are paying less per dollar of Nvidia's projected profit than they do for the typical large American company. Money is still flooding into AI infrastructure stocks, but it's mostly going into memory companies. Over the same period, Micron - one of the world's largest makers of DRAM, the standard type of memory chip found in computers and servers - has nearly tripled in value, establishing memory as the new bottleneck for data centers and the hot new AI trade.
The basic reason is simple: The GPU shortage that looked so alarming last year has eased off a bit. At the same time, data centers need all the memory money can buy. For anyone who appreciates Nvidia's technological accomplishments, this can feel a bit deflating.
There's a lot of genuinely impressive technology behind Nvidia's rise, both in developing CUDA, its widely adopted programming platform that made Nvidia GPUs the default engine for AI research, and in pushing the pace of GPU development to a speed few thought possible. Nvidia's success is the kind of thing you can write whole books about, and the GPUs themselves are among the most complex devices ever produced, right at the bleeding edge of human capability. For memory companies like Micron, the story is much simpler.
For more details please read the original article at TechCrunch AI.
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