The trouble with “quality versus quantity” is that it implies a strict trade-off between how good something is and how many things there are. This widespread misconception that this trade-off even exists probably explains why novices are often surprised when experts advise them to focus on quantity first, not on quality.
Quantity always trumps quality. That’s why the one bit of advice I always give aspiring bloggers is to pick a schedule and stick with it. It’s the only advice that matters, because until you’ve mentally committed to doing it over and over, you will not improve.
– Jeff Atwood
It turns out that this advice is stunningly common between fields. I’ve heard it for painting, writing, and especially programming. Novices who are just starting out don’t have a system that reliably produces work as output, and that system is critical to even do any work at all, let alone have any improvement occur. Quantity has to be solved first, and only after can you start worrying about quality.
This also explains why people who do a lot of work actually also tend to be the people who do the best work.
If you want to be a writer, you must do two things above all others: read a lot and write a lot. There’s no way around these two things that I’m aware of, no shortcut.
– Stephen King, On Writing
So if “quantity vs quality” is wrong, what’s next?
An alternative is the higher fidelity “babble/prune” model. It describes, very roughly, two stages. First, babbling means to generate a lot of possibilities at random, only weakly ensuring that they’re relevant. After comes prune, which is to rapidly destroy bad possibilities, ones which don’t work, don’t sound good, and don’t rise to the level of quality.
How much to prune depends on what the punishment for getting it wrong is. This was certainly a relevant concern for writers, like Stephen King, whose reputation would take a nose dive if he released a half-baked novel. Importantly, it’s very hard to edit a book that’s already been printed. But I, on the other hand, can probably release half-baked posts without consequence for a long time. I already have, though you don’t see them because I’ve just deleted the ones that I grew to recognise as noticeably bad.
Blogs in particular are this weird art form that appear to be rewarded strongly for great content and punished weakly for bad content. This punishment is really quite weak, since you don’t generally hear of bad blogs. Going for both a high volume and a high variance strategy makes sense in this light, because you’ll eventually find some good ideas that you’ll be recognised for, even if all the other work was only merely okay.
This is probably what’s hard about being well known or well respected. The punishment for bad work goes up, and so you feel the loss aversion kick in and start to prune more strongly. Sometimes, this seems to choke the entire system.
Most famous people, sooner or later, tend to think they can only work on important problems — hence they fail to plant the little acorns which grow into the mighty oak trees. I have seen it many times, from Brattain of transistor fame and a Nobel Prize to Shannon and his information theory. Not that you should merely work on random things, but on small things which seem to you to have the possibility of future growth.
– Richard Hamming, You and Your Research
This is the sense in which being unknown ends up being an asset. There may indeed be danger in being well known, at least if you aren’t impervious to the pressure to prune more strongly.
Note that the babel/prune model is still just a model, and every model is flawed in its own unique way. You can prune very little when writing and then prune a lot when choosing what to publish, for example. So a full conception of the system behind your work might contain multiple nested babel/prune steps.
Babbling can also sometimes improve without even having any visible pruning take place. For instance, programmers sometimes become good by just doing a lot of programming. Kids also seem to be able to learn how to do stuff without anyone giving them much critique.
Babble/prune is higher fidelity than quantity vs. quality, but I don’t yet know how to account for the 99th percentile of performance. I would guess that this is where explanations of outcomes start to come tricky, or luck becomes dominant. But if you want to know how to get to the 95th percentile, it’s simple: optimize for quantity.