Assigning work to an intern is gambling: they're inherently non-deterministic and it's a roll of the dice whether the work they do will be good enough or you'll have to give them feedback in order to get to what you need.

1. Interns learn. LLMs only get better when a new model comes out, which will happen (or not) regardless of whether you use them now.

2. Who here thinks that having interns write all/almost all of your code and moving all your mid level and senior developers to exclusively reviewing their work and managing them is a good idea?

That’s very true. But interns aren’t supposed to be doing useful work. The purpose of interns is training interns and identifying people who might become useful at a later date.

I’ve never worked anywhere where the interns had net productivity on average.

People don't write blog posts about how they wake up at 3AM to assign new tasks to their intern, nor do they build "orchestration frameworks" that involve N layers of interns passing tasks down between eachother

The only similarity is that they both say "you’re absolutely right" when you point out their obvious mistakes

exactly where my mind went as well. There aren't really levels to pulling a lever on a slot machine, other than the ability for each pull to result in more "plays" of the same potential outcome.

The reason i think this metaphor keeps popping up, is because of how easy it is to just hit a wall and constantly prompt "its not working please fix it" and sometimes that will actually result in a positive outcome. So you can choose to gamble very easily, and receive the gambling feedback very quickly unlike with an intern where the feedback loop is considerably delayed, and the delayed interns output might simply be them screaming that they don't understand.

There are two major mistakes here.

The first is equating human and LLM intelligence. Note that I am not saying that humans are smarter than LLMs. But I do believe that LLMs represent an alien intelligence with a linguistic layer that obscures the differences. The thought processes are very different. At top AI firms, they have the equivalent of Asimov's Susan Calvin trying to understand how these programs think, because it does not resemble human cognition despite the similar outputs.

The second and more important is the feedback loop. What makes gambling gambling is you can smash that lever over and over again and immediately learn if you lost or got a jackpot. The slowness and imprecision of human communication creates a totally different dynamic.

To reiterate, I am not saying interns are superior to LLMs. I'm just saying they are fundamentally different.

And, if we're being honest, the way people talk about interns is weirdly dehumanizing, and the fact that they are always trotted out in these AI debates is depressing.

You generally don’t assign work to an intern just for the output, though.

An intern can be taught. If you try to 'teach' a craps table, they'll drag you out of the casino.

Drawing parallels between AI and interns just shows you're a misanthrope

You should value assigning tasks to human interns more than AI because they are human

As someone who has worked with interns for year, expect feedback and reiterations always, be surprised if they get it the first time... which merits feedback as well!

But looks like the intern mafia is bombarding you with downvotes.