At the height of the dot com bubble, Yahoo was printing money from selling ads. Enticed by Yahoo’s success, more money was invested in startups. These startups, in turn, bought ads on Yahoo.
Many of these startups failed when the bubble burst, and Yahoo’s market capitalization dropped dramatically.
Is there a similar dynamic going on with AI partnerships and investments? Much of OpenAI’s $1 billion investment from Microsoft was returned as Azure usage. Other hardware providers (Google, NVIDIA) are making similar investments into companies that will have large (and exclusive) spending on their cloud.
There are many situations in which this ends poorly — companies raise money to train large models that ultimately won’t convert to commercial value. On the other hand, there’s an argument that this symbiotic relationship enables companies to attack markets quicker and more effectively than if they went alone.
GPU capacity is currently constrained. Those who have access to large clusters have a short-term advantage. But is this a long-term moat? Capital-intensive investments in a space that moves extremely fast feel riskier than the potential reward.
Designing with constraints is one of the greatest sources of creativity. Instead of CUDA, we’ll soon be able to run on other hardware (LLMs for software portability?). Maybe even CPU-based inference. Or maybe we find optimizations that make training or inference magnitudes cheaper or quicker.
I think we're going to see far more Yahoo!s and PETS dot coms than Amazons and Microsofts from this particular moment. NVDA in particular is a great and important company, but I'm not really convinced it has that much of a moat. Meanwhile, Apple has gotten out in front of its skis, and I'm not sure why everyone things trees will grow to the sky. There's a clear limit for the amount of money in the system vs the number of companies playing the same game.
On the other hand, there's a lot to be said about how "this time is different." Big tech is very likely to acquire the smaller companies before they get a chance to crash, meaning big tech is likely to crash HARD when it all comes down.
Don't blink!
Good take -- I was thinking about that circular relationship with OpenAI and Microsoft earlier today, along with the shortage of GPU's and the rush to fund / acquire the companies that are buying or have huge amounts of GPUs available to create foundational model competitors or similar. It seems the gold rush is on. Moves like Databricks buying Mosiac probably increase in the near term to make all the analytics / database infrastructure companies "AI powered".
It seems like NVDA has a nice "moat" short term while demand is raging and there are no great alternatives, but absent weird (and stupid) regulation creating a real long-term moat, competition will likely change that in the near term.
The crash effect is an interesting perspective on this. Thanks for sharing and getting me thinking more and differently about this!