the big thing to me is programming isn't hard

I've worked on adtech, crypto, fintech, gamedev, startup founder, BigCo. Not once was programming something that was a time sink.

Makes me feel like GPT is marketing to the incompetent or something.

> Not once was programming something that was a time sink.

Can you please expound on that? Do you mean not hard as in comparison to something else?

I think all it shows is that GP has never worked on complicated problems that require anything more than gluing libraries together.

If you do nontrivial coding then GPT wont help you, so that isn't relevant for the discussion.

I disagree. GP, by their statement and appeal "I've worked on adtech, crypto, fintech, gamedev, startup founder, ...", implies that he has worked on complicated software issues, and that he thinks GPT is a promising replacement for developers working on those problems.

> implies that he has worked on complicated software issues

No it doesn't, there is a lot of simple "gluing APIs together" to do in all of those. The hard part then is figuring out what you want to do, not getting the code to do it.

Opposite.

I did a startup for years and the backend was all boring Golang talking to Postgres. I am confident that if GPT had produced identical code it would've been worse. Because shit hit the fan many times due to misunderstandings or other things that cause bugs. Because I wrote the bugs, I was able to fix them. Making coding more of a blackbox and treating coding as menial labor would have definitely caused us to lose customers and miss important deadlines.

Maybe the next way to move the goalposts is "GPT will debug for you someday and be trained on the entire transitive closure of your dependencies."

That sort of thing could actually be useful as a sort of hint, but it isn't replacing devs any more than ripgrep did. To be honest, ripgrep through the source of my deps is already highly efficient and points me to things often.

I have built plenty of complicated things that were much more than glue. Even for those, coding was far from a bottleneck. Because coding is easy.

In fact, all the complicated software anecdotes I could give were things that ChatGPT wouldn't even touch. In the realm of design and scaling and fault tolerance and other such things.

Exactly, we know how much code gets written per programmer hour, its in the single digits on average. Speeding that up wont make a big difference.

Single digits what? What are you measuring here? Files, lines, KLOCs?

> Makes me feel like GPT is marketing to the incompetent or something.

Absolutely. The common constant I can see in people who are really blown away by GPT's performance at [task] is that they are bad at [task].

Programmers who describe their job as copying from StackOverflow think it's great at coding. People who don't read fiction think it's great at writing fiction, and so on.

That's not accurate at all for me. I'm not impressed with the output when rating it against humans who are good at said task. It's obviously not as good as those who are good in that relevant field.

But it's certainly far better than humans who are not skilled at those tasks, and that is what I find to be very impressive. I just didn't realise that these models could be this good, and they're not even as good as they will be.

I guess if you were expecting something that's going to be as good as those who are skilled in a particular field, you'll be unimpressed -- but I wasn't even expecting mediocrity.

I think we're talking at cross-purposes here. I'm not underwhelmed by it being not as good as good humans at [task]; that's not a surprise.

I'm saying that most people I see who are impressed by it for any given task are people who are not well-equipped to judge performance on that task.

I'm impressed by the talking dog because it's a dog that can talk, not because I think it's a great orator.

I trust that you're familiar enough with dogs to tell barking apart from talking.

Many people are blown away by GPT for the leverage it provides, not because it makes the impossible possible.

Programming is generally not hard. Producing 5x - 10x as much programming output at the same quality is quite hard. And keeping the most interesting 10% for yourself while offloading the 90% that is basically just typing? That’s what people are excited about.

> you can coerce it to do surprising things

Yeah, I've gotten it to write some pretty decent contracts, but only because I have written said contracts and can ask it all the right questions/prod it into adding what I need.

And the next iteration of ChatGPT will likely generate a better contract because your interaction with it will be used as training data

Huh. There’s something scary about that in my lizard brain. Not sure what.

Its like feeding a baby polar bear. When it grows up it is going to eat you.

To extend the analogy...Will the polar bear community eat enough expert food providers that their quality of food and eventually overall community health declines?

Will GPTs eat themselves, or more correctly, will they dilute their training corpuses until there are no gains to make? Seems possible.

Like how we've relied on fossil fuels that are a limited resources, to get further we have to go beyond such fuels. It seems like we're in the process of locking in our highest attainable level of text-or-image based advancement.

We mine the expertise in industries (programming, music production, graphic design, ...) and simultaneously cause the replacement of many future experts. Thus leading to a lack of expert output that we can mine?

Now, currently, the problem seems to be "we're putting people out of work" -- but that's progress. The real problem is that we are unevenly distributing the benefits.

It will only be a better contract if it better meets the actual real world requirement, rather than the requirement it was literally given. That means inferring information that isn't there. Now that is possible for common situations but not reliably even for a human expert. The way a human lawyer would do that is by interrogating the client and asking about likely additional requirement and iterating on them to agree on a final requirement. Current LLM architectures aren't capable of this, and it's hard to see how they could be re-architected to do it because it's a very different kind of task, but if that ever is achieved then there may be no limit to what such a system can do.