4 ms·

I think it's, by far, the least compelling use case. AI first and foremost will be focused on singular tasks that are fairly complex, in order to automate. The

I think it's, by far, the least compelling use case. AI first and foremost will be focused on singular tasks that are fairly complex, in order to automate. These "catch all" use cases sound cool, but in practice are nearly worthless. Because you'll spend more time verifying than if you just did it yourself. The compelling use cases are the human-less use cases. Imagine ridding your entire accounting department because it can be handled by highly specific LLMs and calculators.

adamtaylor_13 2y ago
I have never, in 2+ years of using AI spent more time verifying the output lol. It takes like 10 seconds. You’re just using it to point you in a direction, not solve the entire problem.
- consteval 2y ago
  Then I'm questioning if the output is actually correct. That's just not a compelling use case. It makes far more sense to just hire junior engineers. Because then they're actual engineers, and their knowledge base will grow and transfer over time. If you have proprietary systems, then it may make sense to train an LLM on those. But I would consider that a more "focused" use case. I just don't see the main value of AI being a glorified, generalized google search. There's potential here to automate tasks with MUCH less friction. Now business folk and people who understand processes (but not algorithms) can potentially automate business processes.
  - adamtaylor_13 2y ago
    It makes sense to hire junior engineers for big businesses, sure. I think you underestimate how much work a solo developer can get done with GPT. Yesterday, I implemented a recursive PostgreSQL function to crunch some pretty complex tables to produce a new output. As a senior engineer, I knew the output I wanted and understood the trade offs, but I didn’t want to spend 3 whole days trying to figure out how to build the recursive function myself. Claude did it correctly in about 5 minutes, and then I spent another hour or two just tweaking and exploring alternate options. I think it also goes without saying that I could not handle this to a junior engineer. We’re quite clearly past the “GPT is only as good as a junior engineer” phase. It’s so different from anything else that’s ever existed. You could not do what I did yesterday without spending a LOT more time. Google would not have solved that problem, nor would a junior engineer.
airstrike 2y ago
> Because you'll spend more time verifying than if you just did it yourself. This hasn't been the case in my experience.
- consteval 2y ago
  In my experience as a software engineer, reading code is much more difficult than writing it. The trick is you have to understand it, understand the implications, and understand the business impact. If you skip all three of those then it might be faster.

adamtaylor_13 2y ago

I have never, in 2+ years of using AI spent more time verifying the output lol. It takes like 10 seconds. You’re just using it to point you in a direction, not solve the entire problem.

consteval 2y ago
Then I'm questioning if the output is actually correct. That's just not a compelling use case. It makes far more sense to just hire junior engineers. Because then they're actual engineers, and their knowledge base will grow and transfer over time. If you have proprietary systems, then it may make sense to train an LLM on those. But I would consider that a more "focused" use case. I just don't see the main value of AI being a glorified, generalized google search. There's potential here to automate tasks with MUCH less friction. Now business folk and people who understand processes (but not algorithms) can potentially automate business processes.
- adamtaylor_13 2y ago
  It makes sense to hire junior engineers for big businesses, sure. I think you underestimate how much work a solo developer can get done with GPT. Yesterday, I implemented a recursive PostgreSQL function to crunch some pretty complex tables to produce a new output. As a senior engineer, I knew the output I wanted and understood the trade offs, but I didn’t want to spend 3 whole days trying to figure out how to build the recursive function myself. Claude did it correctly in about 5 minutes, and then I spent another hour or two just tweaking and exploring alternate options. I think it also goes without saying that I could not handle this to a junior engineer. We’re quite clearly past the “GPT is only as good as a junior engineer” phase. It’s so different from anything else that’s ever existed. You could not do what I did yesterday without spending a LOT more time. Google would not have solved that problem, nor would a junior engineer.

airstrike 2y ago

> Because you'll spend more time verifying than if you just did it yourself. This hasn't been the case in my experience.

consteval 2y ago
In my experience as a software engineer, reading code is much more difficult than writing it. The trick is you have to understand it, understand the implications, and understand the business impact. If you skip all three of those then it might be faster.