I'm a pretty huge proponent for AI-assisted development, but I've never found those 10x claims convincing. I've estimated that LLMs make me 2-5x more productive on the parts of my job which involve typing code into a computer, which is itself a small portion of that I do as a software engineer.
That's not too far from this article's assumptions. From the article:
> I wouldn't be surprised to learn AI helps many engineers do certain tasks 20-50% faster, but the nature of software bottlenecks mean this doesn't translate to a 20% productivity increase and certainly not a 10x increase.
I think that's an under-estimation - I suspect engineers that really know how to use this stuff effectively will get more than a 0.2x increase - but I do think all of the other stuff involved in building software makes the 10x thing unrealistic in most cases.
What will happen is over time this will become the new baseline for developing software.
It will mean we can deliver software faster. Maybe more so than other advances, but it won't fundamentally change the fact that software takes real effort and that effort will not go away, since that effort is much more than just coding this or that function.
I could create a huge list of things that have made developing and deploying quality software easier: linters, static type checkers, code formatters, hot reload, intelligent code completion, distributed version control (i.e., Git), unit testing frameworks, inference schema tools, code from schema, etc. I'm sure others can add dozens of items to that list. And yet there seems to be an unending amount of software to be built, limited only by the people available to build it and an organizations funding to hire those people.
In my personal work, I've found AI-assisted development to make me faster (not sure I have a good estimate for how much faster.) What I've also found is that it makes it much easier to tackle novel problems within an existing solution base. And I believe this is likely to be a big part of the dev productivity gain.
Just an example, lets say we want to use the strangler pattern as part of our modernization approach for a legacy enterprise app that has seen better days. Unless you have some senior devs who are both experienced with that pattern AND experienced with your code base, it can take a lot of trial and error to figure out how to make it work. (As you said, most of our work isn't actually typing code.)
This is where an AI/LLM tool can go to work on understanding the code base and understanding the pattern to create a reference implementation approach and tests. That can save a team of devs many weeks of trial & error (and stress) not to mention guidance on where they will run into roadblocks deep into the code base.
And, in my opinion, this is where a huge portion of the AI-assisted dev savings will come from - not so much writing the code (although that's helpful) but helping devs get to the details of a solution much faster.
It's that googling has always gotten us to generic references and AI gets us those references fit for our solution.
We don't care about which came first or second, only what gender each child is.
Thus the answer, to the question given the information you have, is 50%. The only possible outcomes are girl-girl or girl-boy (where order is irrelevant.)
And this is absolutely NOT the Monty Hall Problem. I don't know why some people are making that reference. The Monty Hall Problem contains three possible choices and one is eliminated by the host, this is what makes the statistical math interesting in that problem. None of that is happening here.
Lets look a the exact wording of the question:
> a family has two children. You're told that at least one of them is a girl. What's the > probability both are girls?
We have a family with two children. Assume we don't know their gender. We'll represent them as XX.
We are told one of them is a female. So now they are represented as GX (remember GX = XG, since order doesn't matter.)
You are left with the question what is the probability that X is female? Well there are only two choices, F and M, and we are told elsewhere that the probability of having a girl is 50/50.
> Assume that the probability of having a girl or boy is 50% and that the birth order has > no effect on the probability.
So the chance of X being female is 50%. Thus the answer is 50%.
You can't say birth order doesn't matter and then use birth order to say the FM and MF are different results. The only possible results are FM and FF (since birth order is irrelevant.)