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The vast majority of software is written for businesses and runs either on an intranet or is available on the internet to the users of that business.

That doesn't mean a thing in terms of scalability. One user can max out a fast machine with OLAP cubes or other DSS. Or any sort of simulation or modeling. Think in terms of datasets, not in terms of users (all many users are is a large dataset with a requirement for a quick turnaround of each computation).

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If an app only has one user, it doesn't need to scale, it needs to perform. Scalability is all about users and only loosely related to performance. Datasets are irrelevant.

I don't know what dictionary you're using, but here are a few definitions from mine:

scalable - How well a hardware or software system can adapt to increased demands.

scalable - the ability of a product or network to accommodate growth

The ability to scale hardware and software to support larger or smaller volumes of data and more or less users.

the ability of a computer system to shrink or grow as requirements change

That's not true. It needs to scale from 1M of data to 1G to 1T and beyond. You seem to think that scalability only refers to concurrency. It doesn't. It means that you're as close as possible to O(n) or better yet O(1) as your workload increases, whatever that workload is.

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