Do you have model to back the claim up of inexperienced software engineers not contributing? Like an economic modelling claim or similar?
I’m just curious as I’m not sure if this is the case or not? (Like if a really good manager who is shit at coding learns how to understand code this can for example give large speedups)
Do you have model to back the claim up of inexperienced software engineers not contributing?
Kinda sorta? I was basing it off a vague intuition that productivity is probably distributed as a power law, so the top few percent of people would account for most of the useful (economic/research) output. Looking it up, if we use income as a proxy for productivity, that seems to be the case.
Now, granted, what this says is “the few most productive developers account for most of the value”, not “the few most competent developers account for most of the value”. But I think it’s reasonable to assume that the two are strongly correlated. Alternative models would imply a software industry in which the bulk of the gains is generated by superstar novices in their first years of programming, who then burn out and stop contributing much. Pretty sure that’s not how it works.
Another intuition I had is that the bulk of programmer-hours is probably experienced-programmer-hours, because, again, novices either quit or quickly become experienced programmers. So again, unless we assume that the value-generation is skewed towards a person’s first months/years of programming, we have to assume that most of the value is generated by experienced people.
But that’s all admittedly pure intuitive theorizing. I did say “presumably” in my initial statement.
I mean you’re obviously correct about the value distribution of experienced software engineers.
I should have made it more clear but I was more considering things like upskilling management or like more direct operational research on how it affects other parts of the firm itself.
Operational research can be quite complex but power laws are a thing and as a first approximation I would agree. It is just that I think it might be a bit more complex than that in reality since a manager without coding experience might be helped by it still.
Why would we use income as a proxy for productivity, given that a) companies’ pay grades are only half matching each other, b) there exists an open source community?
Now, granted, what this says is “the few most productive developers account for most of the value”, not “the few most competent developers account for most of the value”. But I think it’s reasonable to assume that the two are strongly correlated.
I don’t think that holds either. Say, existence of Windows is a large chunk of value (which enables other software and so on), but Windows is not written competently—e.g. from what we see when it, upon an update, crashes a bunch of computers.
Another intuition I had is that the bulk of programmer-hours is probably experienced-programmer-hours, because, again, novices either quit or quickly become experienced programmers.
What I’m saying is that ‘experienced’ is not precisely equal to ‘competent’; as long as your code works somehow, you are not under large pressure to make it maintainable or even valid for all cases.
So, how does that relate to the general productivity of the firm? If I look at this from a perspective of someone like Stafford Beer or other types of operational research then I could say that the smoothness of the delegation between top level and bottom level defines how good the operations are.
For example, you can have lots of cracked engineers but that doesn’t matter if management doesn’t know what to do?
One can think of a manager as an inexperienced software engineer for example. Sorry if I didn’t make that clear before.
I know a bunch of people with more experience in other areas who now have a lot easier time understanding code and that literacy might then lead to increases in precision at management level.
Big Tech headcounts grow, as they hire more people both to flatter the egos of managers—they are drowning in cash anyway—and in particular many product managers to oversee the AI codegen agents that are unleashing a massive series of new products now that they’re mostly no longer constrained by development taking lots of time. Internal company office politics becomes even more of a rate-limiter: if teams are functional, the AI codegen boost means more products shipped, whereas if teams are not, the gains are eaten up by employees working less or by factional fights within companies.
Do you have model to back the claim up of inexperienced software engineers not contributing? Like an economic modelling claim or similar?
I’m just curious as I’m not sure if this is the case or not? (Like if a really good manager who is shit at coding learns how to understand code this can for example give large speedups)
Kinda sorta? I was basing it off a vague intuition that productivity is probably distributed as a power law, so the top few percent of people would account for most of the useful (economic/research) output. Looking it up, if we use income as a proxy for productivity, that seems to be the case.
Now, granted, what this says is “the few most productive developers account for most of the value”, not “the few most competent developers account for most of the value”. But I think it’s reasonable to assume that the two are strongly correlated. Alternative models would imply a software industry in which the bulk of the gains is generated by superstar novices in their first years of programming, who then burn out and stop contributing much. Pretty sure that’s not how it works.
Another intuition I had is that the bulk of programmer-hours is probably experienced-programmer-hours, because, again, novices either quit or quickly become experienced programmers. So again, unless we assume that the value-generation is skewed towards a person’s first months/years of programming, we have to assume that most of the value is generated by experienced people.
But that’s all admittedly pure intuitive theorizing. I did say “presumably” in my initial statement.
I mean you’re obviously correct about the value distribution of experienced software engineers.
I should have made it more clear but I was more considering things like upskilling management or like more direct operational research on how it affects other parts of the firm itself.
Operational research can be quite complex but power laws are a thing and as a first approximation I would agree. It is just that I think it might be a bit more complex than that in reality since a manager without coding experience might be helped by it still.
Why would we use income as a proxy for productivity, given that
a) companies’ pay grades are only half matching each other,
b) there exists an open source community?
I don’t think that holds either. Say, existence of Windows is a large chunk of value (which enables other software and so on), but Windows is not written competently—e.g. from what we see when it, upon an update, crashes a bunch of computers.
What I’m saying is that ‘experienced’ is not precisely equal to ‘competent’; as long as your code works somehow, you are not under large pressure to make it maintainable or even valid for all cases.
Well, the top labs pretty much only higher really cracked coders, and it seems like the top labs are primarily responsible for pushing the frontier.
I do not know if Thane had a more rigorous argument, but mine seems pretty likely to work.
So, how does that relate to the general productivity of the firm? If I look at this from a perspective of someone like Stafford Beer or other types of operational research then I could say that the smoothness of the delegation between top level and bottom level defines how good the operations are.
For example, you can have lots of cracked engineers but that doesn’t matter if management doesn’t know what to do?
What does this have to do with inexperienced software engineers?
I don’t think I understand what you’re getting at anymore.
One can think of a manager as an inexperienced software engineer for example. Sorry if I didn’t make that clear before.
I know a bunch of people with more experience in other areas who now have a lot easier time understanding code and that literacy might then lead to increases in precision at management level.
You reminded me of this part of Rudolf’s story: