GPU & computing power (compute)
Computing power ("compute") is the raw material of AI; GPUs are the specialised chips that make training and running AI models possible in the first place.
Explained simply
GPUs were originally developed for graphics in video games. They can execute thousands of simple calculations simultaneously, which is exactly what AI needs. A large language model is trained on tens of thousands of such chips.
The chips sit in data centres that require enormous amounts of electricity and cooling. Anyone wanting to run AI usually rents this computing from cloud providers rather than buying it.
Computing has thus become a scarce, tradable commodity with its own supply chain: chip design, manufacturing, data centres, energy, network connectivity.
From the familiar world
Electricity during industrialisation: anyone wanting to run factories needed power plants, lines and coal. Today anyone wanting to run AI needs chips, data centres and electricity. The supply chain behind it is the investment story.
An example
The world's leading maker of AI chips at times became the most valuable company in the world in 2024. Large technology groups announced data centre investments in the hundreds of billions.
Why it matters
The question "How do you invest in AI?" usually leads not to the models (owned by a few corporations) but to the infrastructure: chips, data centres, energy. That is the "shovels in the gold rush" logic clients grasp immediately.
To pass on
„Computing is the electricity of AI. Whoever builds the chips, data centres and lines is selling shovels in the gold rush.“
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