Look for ways in which we could be wrong.
In this case, we want to explore all the ways Nvidia might be vulnerable.
The first area is hardware.
Nvidia has been executing incredibly well for several years and holds a clear performance advantage.
Generally speaking, theydominatethe market for AI training.
This shift is likely to introduce a lot more competition.
Generally speaking, Nvidia dominates the market for AI training.
The closest competitor here is AMD with their MI300 series.
While this hardware is highly performant, it still lacks many features.
Overall, among major chip companies, Nvidia looks like it’s well-positioned.
Additionally, there are a number of startups going after this market.
The most advanced is probably Groq, which has released some fairly impressive inference benchmarks.
However, our assessment is that their solution is only suitable for a subset of AI inference tasks.
The most serious competition comes from the hyperscalers' internal silicon solutions.
Google is by far the most advanced in this area.
The other hyperscalers are further behind.
Another important element in all this is networking hardware.
The links between all the servers in a data center are a major constraint on AI models.
Nvidia has a major advantage in its networking stack.
Much of this comes from their acquisition of Mellanox in 2019, and their low-latency Infiniband solution.
This deal is likely to be remembered as one of the best M&A deals in recent history.
However, this advantage is a double-edged sword.
Recall thatNvidia admittednetworking is the source of their advantage in the inference market.
In short, Nvidia faces a large quantity of competitors, but remains comfortably ahead in quality.
A big reason for this lead remains its software stack.
We think this oversimplifies the situation.
From everything we can see,this moat remains incredibly solid.
These range from AMD’s ROCm to “open” alternatives like XLA and UXL.
Whenever the answer progresses past an awkward silence, we will revisit this position.
The biggest threat to Nvidia on this front comes from their own customers.
Beyond CUDA, Nvidia is also building up a whole suite of other software.
So far, it is unclear how widespread that adoption is.
As they grow more successful and more prominent, the industry’s discomfort level grows.
How far will these customers go, how much of Nvidia’s stack will they buy into?
More broadly, we think there are challenges to Nvidia’s overall business model.
And this poses a number of problems.
Whichleads us to AI factories.
This is Nvidia’s channel, and is likely to be a source of problems somewhere down the road.
Finally, the ultimate risk hanging over Nvidia is the growth of neural connection-based machine learning, a.k.a.
This should not be seen as a catastrophe for Nvidia, but it would spark a significant slowdown.
