To the extent that the value of generative artificial intelligence models is based on computational power or speed, it is virtually inevitable that raw processing power will cease to be the driver of customer value as the differences in performance between models diminishes and as open source alternatives proliferate.
We have seen that shift in many types of computing products. Inference costs, for example, dropped 600 times between 2020 and 2026, for example. The price of the cheapest available output tokens fell from roughly $0.13 per million tokens in mid-2024 into the $0.01-$0.03 range in 2025-2026, according to one study.
In the personal computer industry, that meant marketing eventually shifted away from processor speed to other attributes, while the overall value shifted to applications.
So we might well predict that the cost of using models will continue to drop, while model value also shifts. The likely outcome is that, as cheaper computation expanded the addressable markets for computation, so cheaper inference will grow the addressable use cases for inference.
If typical computing product models also apply, then value will migrate “up the stack.” Instead of evaluating inputs (processor speed; model power), we shift to evaluating outputs “what does it do for me?” or “what are the economic results?”).
Eventually, we stop evaluating value in discrete ways, as capabilities are simply integrated into many other products. The analogy perhaps is electricity, an input used by many products, but not itself a user-relevant output.
The implications for value in the AI value chain would seem to be clear as well. Over time, value gets produced beyond workflows or even outcomes. At some point, AI becomes invisible, as electricity supply is invisible.
We assume its existence, as we assume networking exists, or computation exists.
At that point, AI becomes infrastructure for other products, the way electricity, computation and networking are available for use by many types of products.
It might take some time, but the PC analogy also suggests the evolution path for AI. When computation was scarce, computation itself was valuable.
When computation became abundant, software became valuable. When software became abundant, data, networks, platforms and workflows became increasingly valuable.
When intelligence becomes abundant, the scarce resource may become the ability to direct intelligence toward economically valuable outcomes, as arguably was true not only of PCs but also transistors and optical fiber networks.
Scarcity is the driver. Early on, inference capability is scarce, so that drives the value metrics. Later, when inference is plentiful, scarcity shifts elsewhere: “what are the outcomes?”
On the other hand, the value of some frontier models should remain, as commodity PCs coexist and embedded processors coexist with graphics processing units and servers. One popular example might be smartphones.
Smartphones illustrate that the physical device can remain the value-bearing product even after its underlying computing capabilities become commoditized. The reason is that the smartphone bundles computing with several other scarce things.
PCs remain “place based.” They sit on desks. We use them for work, learning or play. Smartphones are used ambiently and personally, with sensors, cameras and location awareness that make them a platform “for life.”
The point is that value migrates toward whatever remains scarce. For PCs, scarcity migrated toward software and applications.
For smartphones, it migrated toward ecosystems, connectivity, design, cameras, convenience and network effects.
For AI, the scarce things might be context, proprietary data, customer relationships, trust, workflow integration, distribution and the ability to turn intelligence into economically valuable action.
