I use an unscientific rule of thumb that 10-100x scaling is about the most to plan for except for exceptional cases. Anything beyond that ends up with overcomplicated bloat for a "tomorrow" problem.
Being able to handle spikes and iterate quickly is probably more important.
Maybe on the perf rating, but on a bigger scale it could make it way more complex for analytics departments to function. Complexity adds real costs when that department exists to increase revenue and retention, and iterate quickly.
Those types of tests serve two very different purposes. UI is also unit testable.
Unit tests are more of a binary pass/fail. A-B tests are looking for cause-and-effect relationships by comparing some metric between a control group and a variant group.
> Part of the reason why many companies jumped on this bandwagon is also because 'customizability' is hard(er) to build in, and certainly more expensive to maintain.
It's also harder to A-B test with so many variables. If tests aren't statistics significant, the value of user analytics and UX experimentation decreases from a "% lift" perspective. It's harder to know if a feature change or a user defined config had a causal relationship to some other metric.
The ability to think independently and outside of the box nurtured by psychedelics actually turned out well for capitalism. It was not good for military recruiting and war support though.
The rise in software and hardware innovations that came out of the SF counter culture included Steve Jobs and Apple. He did quite a bit of acid.
Some recent parts of the government welcome the corruption.
https://www.justice.gov/opa/pr/us-citizens-and-russian-intel...
https://www.nytimes.com/2021/03/16/us/politics/election-inte...