The New Yorker had a piece by James Somers, A Coder Considers the Waning Days of the Craft, about how GPT4 is empowering non-coders to solve coding problems. Many parts of my career path echo his.
He points out how once upon a time Squarespace and other tools empowered non-techies to make websites just by clicking around, and a set of medium-low-effort, sometimes high-paying work went away.
There are some interesting challenges to bringing that same egalitarian nature to programming - many of them have to do with deployment and environmental context. There are some obvious risks to allowing half-baked code on your server! (Some of those to the host can be mitigated by proper containerization.) I wonder what ChatGPT would suggest for from scratch deployment for the non-programmer.
But Somers mentions the tie-in with how we seem to be cracking the long-pondered "natural language programming" problem - of which COBOL was one of first attempts
In a 1978 essay titled "On the Foolishness of 'Natural Language Programming,' " the computer scientist Edsger W. Dijkstra argued that if you were to instruct computers not in a specialized language like C++ or Python but in your native tongue you'd be rejecting the very precision that made computers useful. Formal programming languages, he wrote, are "an amazingly effective tool for ruling out all sorts of nonsense that, when we use our native tongues, are almost impossible to avoid."But it feels like that equation changes somewhat with AI. You're not solving unique challenges, you're solving problems very similar to what many people before you have, and LLMs are uniquely empowered to draw from that. They don't truly model the problem in their head, and so have all kinds of limitations, but they are able to get to "DWIMNWIS" ("Do What I Mean Not What I say") in a way previous systems have not.
He talks about Go champion Lee Sedol's retirement after losing to Alpha Go
But whenever I think about Sedol I think about chess. After machines conquered that game, some thirty years ago, the fear was that there would be no reason to play it anymore. Yet chess has never been more popular--A.I. has enlivened the game. A friend of mine picked it up recently. At all hours, he has access to an A.I. coach that can feed him chess problems just at the edge of his ability and can tell him, after he's lost a game, exactly where he went wrong. Meanwhile, at the highest levels, grandmasters study moves the computer proposes as if reading tablets from the gods. Learning chess has never been easier; studying its deepest secrets has never been more exciting.Near the end of the piece Somers sounds a hopeful note for the programmer:
Computing is not yet overcome. GPT-4 is impressive, but a layperson can't wield it the way a programmer can. I still feel secure in my profession. In fact, I feel somewhat more secure than before. As software gets easier to make, it'll proliferate; programmers will be tasked with its design, its configuration, and its maintenance. And though I've always found the fiddly parts of programming the most calming, and the most essential, I'm not especially good at them. I've failed many classic coding interview tests of the kind you find at Big Tech companies. The thing I'm relatively good at is knowing what's worth building, what users like, how to communicate both technically and humanely. A friend of mine has called this A.I. moment "the revenge of the so-so programmer." As coding per se begins to matter less, maybe softer skills will shine.Here's hoping! For folks caught on the outside of the current boom-to-bust cycle, these sea changes are frightening. But right now, where I've had ChatGPT write me some simple one page apps, but also fall on its face on some similar problems, I'm optimistic I'll at least be able to ride out the rest of my career doing this kind of thing, with ChatGPT as an ally instead of a foe. But, my previous advice to young people: "uh, I dunno, maybe try programming? It always worked for me" seems more precarious than ever.
Viral phrases from Chinese Work Culture. It's so easy to think of workers in China as just a big pile of "other", this can help give some insight and maybe empathy.