3 ms·
Maybe it's because I am not in "the Valley", but this line of thinking makes absolutely no sense to me. You are speaking in generalities, so you are essentially
by vp8989 6y ago
Maybe it's because I am not in "the Valley", but this line of thinking makes absolutely no sense to me. You are speaking in generalities, so you are essentially implying that most people are working on software that has not achieved product market fit?
How could the majority of people collect a salary working on software that has no users? That makes no sense.
- karpierz 6y agoIf you have 10 companies to invest in, each of which has a 10% chance to produce 20x returns, 90% of the engineers at these companies wouldn't really have users. Basically, if you think that software is feast-or-famine as an investment, then this would make sense.
- DylanDmitri 6y agoIf all engineers worked at early-stage startups, this would be the case. However most engineers (upwards of 95% outside of Bay Area) work at established companies.
- theptip 6y agoHacker News is affiliated with YC, a startup accelerator. While there are lots of FAANG engineers and engineers from all parts of the spectrum here too, this community biases towards startups. If there is a place to discuss strategy for pre-PMF startups on the internet, this is it. I’m not intending to generalize, quite the opposite. I’m arguing against a generalization that “speed is the killer feature [for all products]”. In my post I presented a few cases where this over-generalization is not true (pre-PMF startup and potentially large B2B product) and suggested a more nuanced and objective analysis of the trade-offs. > You are speaking in generalities, so you are essentially implying that most people are working on software that has not achieved product market fit? Perhaps try parsing my post as “here are a few examples of common cases where speed is not the killer feature, and a sketch of a more flexible thought process that will get you to a better answer”. Product managers at both large and small companies use ROI and experimentation to figure out what to build (though in some ways it’s actually harder at a startup as your sample size can be too small to get statistical significance, or at least to run as many experiments as you’d like).