Machine Learning And Deflationary Contagions

Here’s the most common question I get from members of the press, analysts, and others I talk to about generative AI: can you describe what impact this will have on my industry?

Moore’s Law and deflation
For those of you who are used to thinking of Moore’s Law in terms of transistor size or even computer performance, which is how it’s typically presented, you may not be aware that Gordon Moore originally formulated this idea in terms of deflation in the cost of transistors.

When Moore published his famous paper [PDF] in 1965 — the paper that’s cited as the origin of “Moore’s Law” — he was thinking not about increased “computing power” or “performance” but about lowering the cost of circuitry and about the impact such deflation would have on society. He writes:

Integrated electronics will make electronic techniques more generally available throughout all of society, performing many functions that presently are done inadequately by other techniques or not done at all. The principal advantages will be lower costs and greatly simplified design payoffs from a ready supply of low-cost functional packages. [Emphasis added]

You can see this deflationary dynamic spelled out in the graph below from Moore’s famous 1965 paper, which illustrates how he expected the relative cost per transistor to decline as transistors got smaller and you could pack more of them onto a single integrated circuit:

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