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Comment by hatefulheart | Hacker News Reader

Survivorship bias, you don’t know of all the failed projects that couldn’t get off the ground because of incompetent development team and practices that lead a product to its demise, or a product that is possible within constraints that otherwise could have been a success, but not realised by sloppy work and incompetence.

Furthermore the dependencies you choose to build your product are presumably filtered for engineering practices or world class engineers. So given the choice you yourself prefer top quality engineering, so do your customers. Much in the same way you are a customer of your projects dependencies. Difference being, as developers we get to see how the sausage is made, our customers only see second and third order effects.

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> you don’t know of all the failed projects that couldn’t get off the ground because of incompetent development team and practices that lead a product to its demise

Trying to ignore the nuance is hard in your position or the following one I’ll give is difficult.. but is the opposite potentially true as well? We don’t know how many projects failed because of over optimizing, too much time spent on design and engineering decisions. It’s of getting out and MVP to market. I only say this because I have been apart of a few of these.

Over optimizing and spending too much time on engineering decisions is also something an incompetent development team would do.

Well, that's the other side of incompetence: they know how to spin their tools, but they don't know when to stop, or how to stop the change requests, the balance between shippable, maintainable, and what the market wants at that time.

I understand and of course I am familiar with the hypothetical you are trying to set up here but I was specifically pointing out a logical fallacy I see banded round all the time by people who should know better or educate themselves.

I will say that if “good engineering practices” comes up in your root cause analysis for a failure to launch a product you are not thinking critically.

> Survivorship bias

The statistical problem is small sample size, not survivorship bias, as I got to see things before failure. These two examples are merely illustrative of things I've seen.

I think both are at play here and I don’t know about Musk’s programming skills. But it seems that they had other very good programmers (including levchin). So maybe business success can buy good engineers to clean the messed up code. I’m not sure how it goes with AI now though.

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