The commercialization of any era-defining computing technology typically features a race between suppliers: how long can new suppliers live off of scarcity before abundance eliminates the profit margins?
In other words, how long can business strategies based on scarcity flourish until abundance is reached, and how many of the early suppliers can make the jump from “commodity” product supply to some form of new value add?
That is one question investors have to ask themselves about the rise of third-party high-performance computing facilities operated by the likes of CoreWeave, Nebius and others.
A related question for investors in artificial intelligence firms is whether the capital intensity of AI changes the traditional sources of value for computing products, which has tended to migrate “up the stack” over time.
The capital intensity of the AI model training and inference operations might suggest there is a new and possibly somewhat lasting new “moat” value based on infrastructure ownership and control, which has tended not to be the case for most other computing innovations.
This tension between scarcity-driven value and abundance-driven commoditization arguably has occurred in every new major wave of technology.
A new resource (compute, bandwidth, or power) is initially scarce, allowing early movers to capture "bottleneck rents."
However, incumbents and new entrants eventually respond by flooding the market with supply, destroying those rents and forcing value to migrate to a different layer of the stack.
Google (TPU), Amazon (Trainium), and Microsoft (Maia) have responded to Nvidia’s dominance by building their own silicon, starting to alleviate the physical scarcity of graphics processor untis for their internal workloads.
Open source has in the past functioned as a method of alleviating scarcity. The issue is how well this strategy will work in a capital-intensive AI industry.
This has played out in past computing technology eras.
In the late 1970s, computing was defined by integrated, proprietary systems like the Apple II or the Commodore PET. If you wanted the software, you had to buy specific hardware.
For a brief moment, hardware manufacturers enjoyed high margins because they controlled the entire "stack."
In 1981, IBM entered the market using off-the-shelf parts (Intel CPUs, Microsoft OS). That eliminated “scarcity” as other firms flooded the market with "IBM-compatible" PCs, turning the hardware into a low-margin commodity.
As the hardware became a commodity, the value migrated to the two components that remained proprietary and scarce: the Intel microprocessor and the Microsoft operating system (the "Wintel" monopoly).
In the era of cloud computing, server ownership ceased to be a barrier to compute hardware costs. As a practical matter, that meant software or app startups did not need to invest in their own compute facilities, but could rent them from AWS, for example.
The point is that, over time, scarcity is eliminated in the value chain, shifting value creation elsewhere in the product stack.
So the issue is how neocloud providers such as CoreWeave and Nebius, which essentially have democratized access to high-performance clusters, will flourish going forward, as high-performance computing becomes less scarce and more commodity-like.
The rise of "Neoclouds" (specialized AI infrastructure providers like CoreWeave, Nebius, and Lambda Labs) challenged the assumption that only the "Big Three" hyperscalers (AWS, Azure, Google) owned the “AI as a service” infrastructure business.
But history suggests the "compute moat" goes away over time, forcing suppliers to find new sources of value.
So investors must make decisions about where scarcity is in the value chain, how long that scarcity will last, and where value shifts within the value chain as a result.
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