For instance, if an AI model could complete a one-hour task with 50% success, it only had a 25% chance of successfully completing a two-hour task. This indicates that for 99% reliability, task duration must be reduced by a factor of 70.
This is interesting. I have noticed this myself. Generally, when an LLM boosts productivity, it shoots back a solution very quickly, and after a quick sanity check, I can accept it and move on. When it has trouble, that’s something of a red flag. You might get there eventually by probing it more and more, but there is good reason for pessimism if it’s taking too long.
In the worst case scenario where you ask it a coding problem for which there is no solution—it’s just not possible to do what you’re asking—it may nevertheless engage you indefinitely until you eventually realize it’s running you around in circles. I’ve wasted a whole afternoon with that nonsense.
Anyway, I worry that companies are no longer hiring junior devs. Today’s juniors are tomorrow’s elites and there is going to be a talent gap in a decade that LLMs—in their current state at least—seem unlikely to fill.
In the worst case scenario where you ask it a coding problem for which there is no solution—it’s just not possible to do what you’re asking—it may nevertheless engage you indefinitely until you eventually realize it’s running you around in circles.
Exactly this, and it’s frustrating as a Jr dev to be fed this bs when you’re learning. I’ve had multiple scenarios where it blatantly told me wrong things. Like using string interpolation in a terraform file to try and set a dynamic source - what it was giving me looked totally viable. It wasn’t until I dug around some more that I found out that terraform init can’t use variables in the source field.
On the positive side it helps give me some direction when I don’t know where to start. I use it with a highly pessimistic and cautious approach. I understand that today is the worst it’s going to be, and that I will be required to use it as a tool in my job going forward, so I’m making an effort to get to grips when working with it.
This is interesting. I have noticed this myself. Generally, when an LLM boosts productivity, it shoots back a solution very quickly, and after a quick sanity check, I can accept it and move on. When it has trouble, that’s something of a red flag. You might get there eventually by probing it more and more, but there is good reason for pessimism if it’s taking too long.
In the worst case scenario where you ask it a coding problem for which there is no solution—it’s just not possible to do what you’re asking—it may nevertheless engage you indefinitely until you eventually realize it’s running you around in circles. I’ve wasted a whole afternoon with that nonsense.
Anyway, I worry that companies are no longer hiring junior devs. Today’s juniors are tomorrow’s elites and there is going to be a talent gap in a decade that LLMs—in their current state at least—seem unlikely to fill.
Exactly this, and it’s frustrating as a Jr dev to be fed this bs when you’re learning. I’ve had multiple scenarios where it blatantly told me wrong things. Like using string interpolation in a terraform file to try and set a dynamic source - what it was giving me looked totally viable. It wasn’t until I dug around some more that I found out that terraform init can’t use variables in the source field.
On the positive side it helps give me some direction when I don’t know where to start. I use it with a highly pessimistic and cautious approach. I understand that today is the worst it’s going to be, and that I will be required to use it as a tool in my job going forward, so I’m making an effort to get to grips when working with it.