Comment by simonw
4 hours ago
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?
I don't know that the "humans learn, LLMs don't" argument holds any more with coding agents.
Coding agents look at existing text in the codebase before they act. If they previously used a pattern you dislike and you tell them how to do differently, the next time they run they'll see the new pattern and are much more likely to follow that example.
There are fancier ways of having them "learn" - self-updating CLAUDE.md files, taking notes in a notes/ folder etc - but just the code that they write (and can later read in future sessions) feels close-enough to "learning" to me that I don't think it makes sense to say they don't learn any more.
In some ways these methods are similar to the model "learning", but it's also fundamentally different than how models are trained and how humans learn. If a human actually learns something, they're retain that even if they no longer have access to what they learned it from. And LLM won't (unless trained by the labs not to, which is out of scope). If you stop giving it the instructions, it won't know how to do the thing you were "teaching" it to do any more.
It is a matter of fact that LLMs cannot learn. Whether it is dressed up in slightly different packaging to trick you into thinking it learns does not make any difference to that fact.
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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.
Replace "intern" with "coworker" and my comment still holds.
It worked with interns because interns are temporary workers. It doesn’t work with coworkers because you get to know them over time, you can teach them over time, and you can pick which ones you work with to some degree.
To come up with an analogy that works at all for AI, it would have to be something like temporary workers who code fast, and read fast, but go home at the end of the day and never return.
You can make a lot of valuable software managing a team like that working on the subset of problems that the team is a good fit for. But I wouldn’t work there.
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.
> 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.
Yeah, I agree with that.
That thought crossed my mind as I was posting this comment, but I decided to go with it anyway because I think this is one of those cases where I think the comparison is genuinely useful.
We delegate work to humans all the time without thinking "this is gambling, these collaborators are unreliable and non-deterministic".
True. I think that's why my second point is much stronger. The main issue is not delegation, or human vs machine intelligence. It's the instant feedback.
Human collaboration has always been slow and messy. Large tech companies have always looked for ways to speed up the feedback loop, isolating small chunks of work to be delegated to contractors or offshore teams. LLMs have supercharged that. If you have a skilled prompter you can get to a solution of good enough quality by rapidly iterating, asking for output, correcting the prompt, etc.
That is good in that if you legitimately have good ideas and the block is execution speed. But if the real blocker is elsewhere, it might give you the illusion of progress.
I don't know. Everything is changing too fast to diagnose in real time. Let's check back in a year.
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.