That's an extremely interesting thought. Perhaps we will see organisations in the future structure themselves more like a suite of unit tests. Instead of getting a developer or software house to plug a specific need in their org and being entirely outside of the development process: they will reflect the development process organisationally to ensure they catch any problems with the current output and just feed the box new prompts to increase their efficiency or efficacy.

Their competitive advantage in their field then becomes the range of their tests (borne through experience), efficiency in running their pipeline of testing and ability to generate effective prompts.

I firmly believe that we should be studying model analysis and using that to create a field of peompt engineering. Both from a security standpoint and a productivity standpoint.

Tests and type signatures. Hopefully soon, dependently typed signatures.

This is already how automotive companies work. They are huge organizations which do four things: marketing, financing, design, and requirements. The supplier management and project management all fall under requirements management and enforcement.

Indeed, a programmer's job feels artisan oftentimes. I think a reason for it is that projects are often ill defined from day one. They are defined by people who do not know enough about the system to set good requirements. The engineer works both bottom-up from the existing primitives of an existing system and top-down from requirements and tries to solve the puzzle where both approaches meet. Such work is very hard to automate. I believe there is a very big opportunity for AI to be used in a workflow such that inconsistencies between requirements, on all levels, and actual systems become clear a lot faster. The feedback loop to the requirements will be shorter, prototypes will exist sooner. The current workflow has little space for an AI worker. I believe this will change and have a major impact on the art of developing products. The AI programmer is still at its infancy, let's talk again 5 years from now.

For that we need to make the AI deterministic or at least shape processes on specific error rates which are probably higher than that of the average smart human.

We had to do that for the industrial approach and it wasn't a simple, fast or intuitive process.

"How far can you get if you put prompt-generated code at the heart of the workflow and make everything else subservient to it"

OK, challenge accepted - will go down your suggested route - thanks :)

Iteration and integration, the tasks which take most of at least my time as a developer, could fade significantly - or become automated themselves.

We won't have understanding of our code, similar to how we don't understand the machine language being generated by our compilers now.

We will be using our intuition about GPT to bring into being fully designed and integrated systems with 10 paragraphs at the prompt.

Which could in the end greatly increase the influence of a programmer in a given organization. Our role will be a softer, almost cyborgian one.

But this will indeed require the destruction of all that came before it. Questions like "but does it work with this 3rd party API, or this platform?" must become irrelevant for this future to happen.

A bit similar to how the web destroyed mainframe, perhaps, by first creating its own compelling world, then making the mountain come to it.

I don’t understand the high level language to machine language comparison with AI. HLL to machine language is translation. We hardcode things. This literal translates to this thing. Machine language has it own mind (figuratively) and it’s not doing translation. It can put some silly bug by misunderstanding the requirements which may cause a billion dollar software meltdown. And who is going to be responsible for that?

The more black box programming becomes the more dumb human programmer gets. There will be stagnation. There won’t be any new “design patterns”.

A deep insight. Thanks.

Two problems with the analogy: The artisans fields where machines took over where hundreds and thousands of years old. We understood them very good. And maybe more importantly factory automation is deterministic and needs to be, as opposed to generative ML.

Even giving this point, GPT has two attributes I haven’t seen improve with scaling much at all:

1. The whole bullshitting thing. If I bullshitted even 1% as much as GPT I’d be fired within a few months.

2. Precision. Ask it to count, or do simple math. It seems to really struggle here, and despite us feeding it nearly all of humanity’s text, it isn’t getting better. Programming is extremely precise in a way painting isn’t. GPT is a painter, not a programmer.

Final note:

9 years ago I worked on an ML startup with a few people, some of which went on and now are at OpenAI. We saw the absolutely incredible progress ML was making and had many discussion with the brightest in the field.

I was much like you, I was convinced that ML would he automating basically everything within 5 years. I mean the rate of progress felt super-exponential and there was hard proof of amazing results. Especially things like radiology for example which seemed to almost already be there.

Turned out to be dramatically wrong. My 5 year predications were laughable. It did well on some things we didn’t expect (fuzzy stuff like language and drawing) but much worse in the precise areas. It still is struggling with precision, and radiology in the most straightforward cases.

I think my calibration on this is much better now. I realize it has limits, where they are defined, and I see now it doesn’t scale in some ways. It doesn’t produce novelties in the way humans do while also being a lot less conservative/precise. It may turn out that solving both of those things at once is the extremely difficult last 10% that is actually 90% of the work (and they feel like polarities that may train against each other - be very precise, also don’t lie, but still be creative - have you noticed the more you push GPT the more “distracted” it gets? I see limits in many ways as it’s scaled that don’t look good for this).

Look at Self Driving Cars. 7 years ago I predicted they’d be here within a year or two. Had to be. They still aren’t! Why? The last 10% is really, really hard. It’s also critical to success. We really seem to have trouble reconciling these two, both underestimating the difficulty, and the need.

Stuff like your “a year or two” note is a big red flag for me here, especially when the claim is so broad. Yes, it will integrate with things in a way that avoids a lot of complexity developers deal with, but needless to say I’d love to see a concrete bet for “in two years it will have (insert exact large effect)” so I can profit off the hype.

The artisan of old created one bowl at a time and made artisan pots. A machine that makes bowl can automate the artisan's job away.

However, the next challenge is that the machine itself is now an "artisan" device.

I'm sure the first bowl printing machine ( https://youtu.be/bD2DNSt8Wb4 ) was purely artisan... but now you can buy them on Alibaba for a few thousand dollars ( https://www.alibaba.com/product-detail/Printing-Machine-Cera... )

I am sure there is a (bowl printing machine) machine out there.

But if you say "I want a bowl printing machine that can do gradient colors" that first one (and probably the first few until it gets refined) are all going to be artisanal manufacturing processes again.

This all boils down to that at some point in the process, there will be new and novel challenges to overcome. They're moving further up the production chain, but there is an artisan process at the end of it.

The design of a new car has changed over time so that it is a lot more automated now than it was back then ( https://youtu.be/xatHPihJCpM ) but you're not going to get an AI to go from "create a new car design" to actually verifying that it works and is right.

There will always be an artisan making the first version of anything.

> There will always be an artisan making the first version of anything.

Until we reach the bootstrap point (the singularity?), i.e. when the GPT-making machine is GPT itself. Or maybe we're still one level behind, and the GPT-making machine will generate the GPT-making machine, as well as all the other machines that will generate everything else.