Yup, same experience with me too (while observing customers' tests and doing my own A/B tests). Noah provided great counter-examples. I mostly publish only positive A/B test results as case studies but this doesn't mean every A/B test produces spectacular results.
Testing involves paying a price in example counts in order to buy analytical power at some unknown rate. Insignificant results arise when either (1) people react with a lot of variance and that power/example rate is small or (2) the effect you're trying to analyze is so tiny that you need a great deal of power to see it at all.
Unfortunately, it's difficult to determine the difference between those two cases. If many of your tests are failing to be significant, it could be that you're simply never investing enough to get the power you need: your users are somewhat inflexible to the design changes you're making.
It could also be that many of your changes simply are pretty insignificant and by the time you wait a full year gathering simply infinite numbers of impressions, you'll find it was just a waste of time.
Again, practice statistics with a great deal of self-awareness. They're only meant to inform.
I wouldn't call them failed tests. In science a null result mean that you didn't learn anything but in this case who cares if p is 0.6? It is still more likely to make you money.
The thing about non-statistically-significant tests is you can't, in fact, say "it is still more likely to make you money." You can say that the B set did in fact make you more money this time, but you can't rule out that being luck of the draw. It might even have a (very small) bias AGAINST making you money, but that bias got lost in the noise. That's why it's important to discard non-statistically-significant tests (or expand the sample size if you _really_ want to know...)
I think precisely the point of not being able to reject the null hypothesis is that you can't be sure it's more likely to make you more money; otherwise you could have required a lower confidence level and still rejected the null hypothesis.
To add insult to injury, half of the remainder were significant... in the wrong direction.
(Edit to add: null results aren't failures, though. What is the Edison quote: you now know one more thing that didn't work.)