It's not a binary thing. There are several types of failure.
Being locked up for fraud would be one failure that's unlikely to be a good sign for future investors.
Having a product offering that is ahead of its time, so there isn't a market yet would be an example of a good failure. You did everything right, apart from time things well.
The fact is, once you succeed, it's natural to stop trying. So there's way more data from failures than from success.
You can't really learn anything from one success. You could have just been lucky.
Just look at programming, you might iterate an algorithm a few times before you hit on something you think works well. Each iteration, you're learning from the failure of the last. You're assessing what you could make better, and implementing it the next time.
Exactly. This is what Jason seems to miss. No one sets out for failure nor do they think failure is a good thing. The axioms and other well-known phrases were designed to keep you going after failure, not to inspire you to fail.
One other thing I would like to add with respect to this article is that it reeks of a blog post designed to elicit a reaction by pitting two concepts with opposite meanings against one another. This sort of thing is useful when those concepts are concrete and discrete (lazy vs eager evaluation, for instance), but with something as fuzzy as success and failure you just make a mess by trying to place them in artificially constructed boxes.
This is in my view another prime example of people trying to think in binary in too many areas of their life. Success does not exist without failure, literally. Success is the absence of failure. They need one another to exist. Trying to write about how one is more important than the other is, IMO, a fool's errand.
I agree. Silicon valley's successes tend to never start companies again. So the mantra "learn from failure" has more to do with keeping motivated than directly useful advice.
One could argue that while Steve Jobs made a lot of money from cofounding Apple Computer, he essentially became a failure when he was ousted from the company.
If he didn't go on to start NeXT, his failure at Apple probably would have defined him.
One might consider that Apple's failure after Jobs' departure suggested Jobs was successful. We're getting into the crux of the interplay between success and failure. They are dependent on one another, and they change with time. Again, this totally undermines any sort of binary representation one might try and espouse.
I think the reason the glorification of failure is (and ought to be) popular is because there are more smart people with good ideas not starting companies for fear of failure than there are people starting companies aiming to fail.
Even that number comparison aside, for VC's, I think the cost of people starting a company which fails is far less than the benefit of people starting successful companies that wouldn't have been started if the founders didn't overcome their fear of failure.
Oh look! Another binary discussion on learning from failure vs success!
One word: Balance.
One is not better than the other. These concepts do not exist without eachother. Failure would be nothing without success, and vice versa. Learning that is the first step to not wasting your time reading anymore of this fluff.
I think it's important to emphasize that the beginnings of "failing one's way to success" started with many quotes from Thomas Edison. It's usually a bad idea to generalize any quotation to a rule, but I think in this specific case, it might be useful to say that there is no failure or success, only possibilities.
If anything, making judgments either way is overrated.
Being locked up for fraud would be one failure that's unlikely to be a good sign for future investors.
Having a product offering that is ahead of its time, so there isn't a market yet would be an example of a good failure. You did everything right, apart from time things well.
The fact is, once you succeed, it's natural to stop trying. So there's way more data from failures than from success.
You can't really learn anything from one success. You could have just been lucky.
Just look at programming, you might iterate an algorithm a few times before you hit on something you think works well. Each iteration, you're learning from the failure of the last. You're assessing what you could make better, and implementing it the next time.