Hacker News Reader

Comment by nwienert | Hacker News Reader

In before all the comments about how “most code is trivial” or “most programming is stuff that already exists” or “you’re missing the point look how it’s getting better”.

I really am in awe of how much work people seem willing to do to justify this as revolutionary and programmers as infantile, and also why they do that. It’s fascinating.

Thinking back to my first job out of college as a solid entry level programmer. ChatGPT couldn’t have done what I was doing on day 2. Not because it’s so hard or I’m so special. Just because programming is never just a snippet of code. Programming is an iterative process that involves a CLI, shell, many runtimes, many files, a REPL, a debugger, a lot of time figuring out a big codebase and how it all links together, and a ton of time going back and forth between designers, managers, and other programmers on your team, iterating in problems that aren’t fully clear, getting feedback, testing it across devices, realizing it feels off for reasons, and then often doing it and redoing it after testing for performance, feel, and feedback.

Often it’s “spend a whole day just reading code and trying to replicate something very tricky to find” and you only produce a single tiny change deep in the code somewhere. GPT is absolutely terrible at stuff like this.

And yes, often it is finding new solutions that aren’t anywhere on the internet. That’s the most valuable programming work, and a significant % of it.

Feel like there’s 10 more points I could make here but I’m on my phone and don’t like wasting too much time on HN. But man, what a disappointment of critical thinking I’ve seen in this specific topic.

View on HN

While I think there's truth to what you say, I'd also point our that workers in many pre-automated industries with an "artisan" approach also considered themselves irreplaceable because they figured, correctly, that nobody could build a machine with the capability of reproducing their workflow, with all its inherent uncertainty, flexibility and diverse physical and mental skills.

What they failed to predict was that some people wouldn't try to automate them like-for-like. Instead they would reconfigure their entire approach to fit with the specific advantages and limitations of the machinery. And this new approach might even be qualitatively worse in various ways, but not so much as to overwhelm the economic advantages that provided by the things machines were good at.

AI likely isn't going to slot into a developer-shaped hole in a software team. But it's possible we'll see new organisation approaches, companies, and development paradigms that say: How far can you get if you put prompt-generated code at the heart of the workflow and make everything else subservient to it. I'm not sure, right now, that that approach is feasible, but I'm not sure it won't be in a year or two.

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.

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.

For me, its mostly that I have used GPT-3.5 a little for programming C++, and I wasnt impressed.

For one, it made horrible, glaring mistakes (like defining extern functions which dont exist, using functions which are specific to a platform im not using, etc.), stuff beginners would do.

It also decided to sneak in little issues, such as off-by-one errors (calling write() with a buffer and a size that is off by one in a place where its very hard to tell), missing edge cases (such as writing a C++ concept which worked, but actually did everything in slightly the wrong way to actually ensure the concept was requiring exactly what I asked).

Even when asked to correct these mistakes, it often struggled, made me read paragraph after paragraph of "im sorry, ive been such a bad little machine" garbage, and didnt even correct the issue (or, in some cases, introduced new bugs).

Im utterly unimpressed by this. GPT is great for a lot of things, but not writing code better than I would, in the same time.

The time it took me to massage it to solve a nontrivial problem (write hello world with just syscalls) was way longer than reading the manual and writing it myself (and has less bugs).

Not everyone unfazed by these articles is simply in denial. I feel sorry for people who write copy paste code and find that ChatGPT or Clippy from 2000 can relace them, but not everyone writes trivial code.

Two points: GPT4 is significantly better in this regard, and you should be concerned about the rate of progress more than it’s actual capabilities today.

> you should be concerned about the rate of progress

Kind of agree?

On the one hand we don't even have a roadmap toward reliable AI.

On the other, if we ever plug an LLM into something that has memory, acquires experiences, does experiments, observes the outcome and adjusts its worldview in response, consciousness might fall out of that. And writing good code might not even require consciousness.

Epistemologically speaking, I think we can roughly break down the potential nature of consciousness into three categories:

- as a function of an independent human soul

- as the fundamental substrate on which the rest of the universe is built

- as a byproduct/secondary phenomenon of physical processes

In the latter two cases I believe that the question of whether GPT is conscious is immaterial. In either case it is functioning in the same medium we all are when we talk, think, write. In the first case it is not, and the question is thornier.

Consciousness in this context is often used as an imprecise but important bundle of very material concepts, including whether something can have wants (and therefore warrants our anticipation of them) and whether it deserves ethical status.

One can debate whether either those is necessarily a consequence of consciousness, but nonetheless those kinds of qualities are what people are aiming at when they wonder about conscious AI.

I suspect (and sort of hope) they're close to hitting a local maxima

presumably they've already fed it all the code in the world (including a load they probably aren't licensed to)

If you tried GPT4, you probably understood it's not about feeding all the code in the world. GPT4 analyzes and "understands" your code and will answer based on this. Clearly, it will read the variable names and make deductions based on this. It will actually read the comments, the function names and make decisions based on this. And it knows the rules of the language. I mean, I'm writing this because this is what I've witnessed since the time I spent playing with it.

The problem I've seen is that, maybe like the author has been writing, it's making sh*t up. That's not untrue, sometimes I didn't give it all dependent classes and it tried to think sometimes correctly, sometimes incorrectly what those were (such as method signatures, instance members, etc.) I wish it would have asked me some details rather than trying to figure things out. The guys at OpenAI have still a lot to do, but the current status is very impressive

it is entirely about feeding it all the code in the world

it doesn't understand anything, it doesn't make deductions

it's a probability model, and I understand how it's implemented perfectly well, thank you

No you obviously don't lol

Even view it as a simple probability model, you don’t need a million Python repos. The massive amount of English text + 1,000 repos + your codebase is very powerful. You can see this because you can make up a language, give it some examples and it’s surprisingly good.

Does a chess engine “understand” the position? If you define “understanding” as the ability to think like a human then Stockfish is obviously much worse at that. If you define understanding as the ability to choose the correct move, then Stockfish understands the position much better than any human.

The point being, you can choose to laden the word “understand” with the meaning of human-like thinking, in which case humans will always be superior by definition. Or you can choose a “many ways to Rome” definition of understanding that is purely focused on results.

Large language models understand language in their own way. Currently their results are inferior to humans’ but one day the results may be superior.

Don't you think it's going to improve much more now that it has hundreds of billions of pointed queries being typed into it every month?

This is data at a scale OpenAI did not have access to before.

My thoughts exactly.

There are so many non-CRUD complex disciplines involving programming such as signal processing, robotics, control theory, scientific computation to name a few, the current version, at least, of GPT is not even close to being a good supplement, let alone a substitute.

But then I remember I'm on HN where the technical pinnacle of programming is Backend and DevOps.

Yup. It kept suggesting me properties in flyway (Java lib) which doesn’t exist. It actually threw me of the track and I made a mental note of programming without GPT.

GPT4 is very different from 3.5. I've asked it today to write some unit tests given the code of the class (~200 lines) and the methods I wanted to cover and it did that just perfectly. It put asserts where it made sense (without me asking to do it), and the unit test code was better written than some code I've seen written by (lazy) humans. It's not perfect sure and it's easy to get a bad response but give OpenAI a few more iterations and my job will be simply to copy paste the requirement to GPT and paste the generated code back to compile.

what kind of unit tests are these? is it `check_eq(add(1, 2), 3)` or "check for possible exceptions, cover edge cases, test extremes of this super important db function"

It's Salesforce unit tests, written in Apex, actually a niche language, so it's surprising that even on such a language, it was so good. And no, the unit tests were much more complex than this. It involves creating records, querying data, some business logic runs and then the data is updated. The asserts are after, checking that the business logic performed correctly.

The bot created the whole unit test involving the creation of data with test fields, then queried the output results and put some asserts. That's more than 100 lines of code which were written by GPT4. A (good) Salesforce developer would need a good 30 minutes to write those, and the result would not have been better.

Again, I also have some counter examples were it made some mistakes, but this is really shocking how... a program... figured all this out.

write addition python code

A Toolformer model integrated with Intellisense is the obvious next step.

Have you seen this video? https://www.youtube.com/watch?v=9QZlQMpNk-M

I think the author is onto something – while AI might not be able to program per se, it can certainly be handed a code snippet and then use its huge corpus of Internet Learning™ to tell you things about it, code that looks like it, and ways (people on the Internet think) it might be solved better.

In that sense, it isn't replacing the programmer; it's replacing IDE autocomplete.

> ways (people on the Internet think)

It reminds me of arguments that it's not the computer that plays chess, but its programmers.

You can describe a GPT's response as a statistical average of responses on the internet (for quite a contrived definition of average), but at some point it will be easier to describe it as analyzing a snippet and forming an opinion (based on what people on the Internet think). Are we past that point? I'm not sure yet, but we are close.

> quite a contrived definition of average

Sure, maybe an argmax mixed with an RNG isn't really an average, but some would say it's quite a contrived definition of forming an opinion.

argmax is doing all the heavy lifting there. Consider argmax is_proof_of_P_eq_NP(string)

You can describe solution of any well defined problem with argmax, it doesn't make it trivial.

> replacing IDE autocomplete

Co-pilot has been very useful the times I've used it. It's not perfect, but does cover a lot of boiler plate. It also makes it much easier to jump between languages.

I’ve been working with copilot a few months and the biggest surprise is how it has led to much better commented code. I used to comment tricky code to explain it. Now I comment trivial code instead of writing it. Often two lines of comment will get me 10 lines of copiloted code, faster than I could have typed it and with good comments to boot.

I really enjoyed the video, thanks for sharing.

I think the author is operating in what I consider to be the sweet spot of current LLMs - where I can ask a question I don't know the answer to but can reliably spot bullshit (either because I know enough or through other means). I think there's a lot of value to be had when those conditions are met, and not just for coding.

That is a good take on it. I have been saying this thing is like really good at summary and terrible on detail. So watch what it spits out.

Last night I sat down and tried using it to write an 8086 emulator. It got an simple emulation outline fairly quickly. But when it came to getting each of the instructions and interrupts correct. It fell very flat very quickly. What was interesting is that it made the exact same mistakes many early emulator writers make. You could then correct it and it would give it a shot at 'doing better'. I at one point got a bit bored with it and kept feeding it 'can you make that more compact/better'. It did an adequate job at that, eventually using templates and jump lists. It did not get very far using duffs device or dynrec but I am sure I could have guided it into doing that.

But probably for a majority of things like in emulation 'close enough is good enough'. That is an interesting finding of coding things up I think. This thing is also going to make seriously crazy amount of bugs we will be chasing for decades.

This is how I use it. It is the best rubber duck money can buy.

I spent this afternoon asking ChatGPT (3.5 not 4) to help me query AWS resources into a csv. It gave me a 90% correct answer but made up a native csv output option. When I told it that option didn't exist it got almost sassy insisting it was correct. Eventually it gave me a closer answer using json and jq after I prodded it.

I had a similar experience asking it to write an API client. It wrote something very plausible but just concocted an endpoint that looked real but didn't exist.

I don’t think you’ve tried GPT-4.

If you have, I don’t think you are like majority of devs (maybe not on HN, but in real life).

You sound lucky to have true, novel problems to solve each day. I’m with many here commenting that this is quite powerful stuff, especially when my day-to-day is writing simple CRUD apps, or transforming data from one format to another within an API, or configuring some new bit of infra or CI/CD.

I’d love to be challenged in some new way, and have access to truly fascinating problems that require novel solutions. But most enterprises aren’t really like that nor do that need that from majority of engineers.

There's not such thing as actually boring CRUD. I've worked at many companies and founded my own. Even when it felt like CRUD, a year+ in it was clear that tasteful decisions pay off and iteration and cost gradients matter. GPT doesn't sniff that.

I agree with GP - day 2 dev me outclasses it, which means it isn't replacing anyone.

HN echo chamber. Generally the better/best programmers hang around here; your day 2 was probably better than many coders hope to achieve in their whole lives. It is replacing people already; I personally know about 10 people who have been fired or assigned to a completely different position because gpt did a faster and better or equal job. So ‘not anyone’ is simply nonsense; I am only one person, there will be many many more who see the same thing. Of course they are not told they are replaced by ai when fired, but their managers or more skilled colleagues know.

I do agree that there is no boring crud; that’s why gpt or no code tools are not good for full solutions (yet), but it’ll get there I am sure.

> I personally know about 10 people who have been fired or assigned to a completely different position because gpt did a faster and better or equal job

Please elaborate.

And, if true, this would be a major news story that Vox or any number of major newspapers would love to write about - so have you approached the media about this? If not, why not?

> Please elaborate.

I simply know firsthand (i'm old, i have manager, cto, ceo friends who I go golf and play squash with) that people in data entry and programming have been let go in the past weeks because 1 person could take over their work using the gtp/chatgpt api's and do their work faster with less errors. I am recommending the same in my company as a lot of my colleagues are doing nothing anymore as the skilled seniors are doing it themselves with gpt now as it's faster, less communication etc. We feed jira issues into gpt and it generates code; we review and refine or fix ourselves. It works much much faster and with better results. Most things most of us do all day is integrating ancient API's of partners and so mapping xml/soap/... api's to our json schema's. With chatgpt that's really fast and mostly painless; it even renames the properties that need to be changed to our enums properly. With humans this is a painful and slow process, especially with people who are fast and loose (broken education seems to made many of those graduate just by cheer production speed & volume instead of quality; gpt can do that better too...).

> so have you approached the media about this? If not, why not?

Why would I do that? Even anonymous, it doesn't seem to make much sense for me to do that. Anyway; that'll come soon enough as it will be common soon.

How does this work from an IP perspective? Aren't you feeding OpenAI and Microsoft with your confidential information?

Also, what license is the code under when you get it back?

I take care not to feed it secrets ; this is just boring ERP stuff without the magic numbers (they are not needed to create or test; we use test data normally as well, as we cannot give that to outsourcing companies either, so there is no difference in work).

And the output? Are you allowed to use their code for your own projects or company?

Yes, we don’t all live in the US.

So you basically don't care about licensing.

That's ok for your use case but risky for anything open source of that might be sold around the world.

Not really, there is absolutely no way to show, let alone prove, any of it is written by AI.

> simply know firsthand (i'm old, i have manager, cto, ceo friends who I go golf and play squash with) that people in data entry and programming have been let go in the past weeks because 1 person could take over their work using the gtp/chatgpt api's and do their work faster with less errors. I am recommending the same in my company as a lot of my colleagues are doing nothing anymore as the skilled seniors are doing it themselves with gpt now as it's faster, less communication etc. We feed jira issues into gpt and it generates code; we review and refine or fix ourselves. It works much much faster and with better results.

This isn't software engineering work, this is 21st century data entry with some code. This is exactly the type of menial work that should be automated by AI.

If you have small self contained problems like map X -> Y then sure, ChatGPT will be sufficient. Where I disagree with you is calling these jobs "programming" jobs. These are the type of tasks that should've been written in a transform language like JOLT. This shouldn't even be code.

> With humans this is a painful and slow process, especially with people who are fast and loose (broken education seems to made many of those graduate just by cheer production speed & volume instead of quality; gpt can do that better too...).

Humans suck at repetitive menial tasks like this. It's not education's fault.

> This isn't software engineering work, this is 21st century data entry with some code. This is exactly the type of menial work that should be automated by AI.

Who said it was? This is what most programmers do all day, that's the point. These people can be replaced now without writing specialised software for the case. It is programming, not software engineering and it is what people are doing all day long (and longer) who are called programmers / software engineers in most companies all over the world. You can disagree with it, but that doesn't change much.

So you are now putting the bar higher which is not fair; the fact is that people who have the title 'programmer' and even 'software engineer' are now readily replaced by AI. If you don't agree with the title; I don't either, but reality is what it is.

I would say basically the point is ; there are way way too many people being 'programmers' (but not limited to this field) who can be replaced; only a few % should remain as the rest does what you wouldn't call programming, but the rest of the world does. Search twitter for 'html programmer' and such. Millions and millions will never be programmers and your definition, but have a high paying job (for their country) working as a programmer.

Manager here. Did not fire anyone (and hope not to), but I am starting to look into implementing GPT-4 as a part of our development practice and, potentially, reduce future hiring. This might have a positive impact on our engineering budgets, which are always tight. Many of my colleagues are doing the same - this has been a cooler topic with other managers for weeks now (strange how progress is now measured in weeks).

At this point, this would only affect engineers who don't understand our subject area (biotech) and are relatively junior (in the sense that their output is not much better than a GPT4 output reviewed by a more senior dev).

If your engineers are actually engineering then yes, really by all means keep them and integrate GPT in workflows as is suitable.

If all you're doing is very simple crud apps and transforming API responses, your coding is already replaceable with "no code" tools.

I'm still waiting for the no-code tools that can replace me, the API Glue Coder.

They've been promising those since the early 2000's, but I'm still employed.

Some stuff has been automated by Zapier, IFTTT and the like, but most of the systems I'm integrating with don't have public APIs.

"I'm still waiting for the automobile that can replace me, the Horse Carriage Driver. They've been promising those since the 1880s, but I've still got passengers in my carriage every day!"

Obviously it's a cheeky example, but this would not be the first time in history a previously well-established career was upended in a (relatively) short amount of time. I'm a FAANG dev, I've got skin in the game too and I'm trying to be optimistic, but I can't help but be at least a little worried. From Wikipedia -

"In 1890 there were 13,800 companies in the United States in the business of building carriages pulled by horses. By 1920, only 90 such companies remained."

I don't think we'll be out of the job entirely, but I can definitely imagine the bar being raised and the compensation stagnating as we now have to justify the time and cost of our work compared to the near-free, near-instantaneous output of an LLM.

All that being said, if you've been working since the 2000s, you've got nearly a 20 year head-start on me, so perhaps it makes sense for me to be a bit more worried.

> All that being said, if you've been working since the 2000s, you've got nearly a 20 year head-start on me, so perhaps it makes sense for me to be a bit more worried.

Yea, that's mostly why I get hired. Experience gives people a certain intuition on what kind of solutions work for which cases.

And when you've been working long enough, you don't (hopefully) feel the need to do cool bleeding edge shit at work, you just want the work code to ... work. You pick the simplest and most boring solution possible so you can clock out at 1600 and not think about services crashing because the newfangled thingamageek coded with the latest language du jour failed because of an edge case nobody has seen before.

That's a comforting perspective, I hope that you're right.

> I'm still waiting for the automobile that can replace me, the Horse Carriage Driver.

Wow. The hype around "AI"s has entered the crypto stages. Just like crypto was the new car, the new internet etc., so is AI now.

Well, at least AI is useful in certain applications.

Again? Last time it ended in a long winter.

And horse carriages have some serious advantages, maybe even will make a comeback. :)

I got into and out of Bitcoin back in 2010 when I was still in high school. Even then, I thought and still do that crypto as a whole is a complete ponzi scheme with little real world utility.

I feel completely differently about LLMs; I'd say we're closer to 2007 when the first iPhone was released. I believe LLMs will become a part of our day to day lives at a level that crypto never came close to.

But horse carriage drivers were never out of a job, because cars still needed people to drive them... (yes, people can drive their own car, but they can also drive their own carriage, so nothing is different there). In contrast, horses were out of a job; do you think we are more like horses than like the people who drive horses?

Except it isn't and it won't be until 10 layers of management learn what no-code tools are and why they should want them.

And i guess this will never happen as management at the first sign of trouble making AI do that they want, will gladly pay someone else to do it for them. Of course as little as possible, but either way they will be glad to delegate.

It's same for me. I can learn how to tile my bathroom or repair my car, but i just don't feel like and am happy to pay someone else.

I've been a programmer for 23 years now. In all these years every year was the year of no-code tools.

And you know what? We went from having actual no- or low-code tools (UI builders, forms in Access and FoxPro etc.) to zero no-code tools worth a damn [1]. There was a brief dream of Yahoo! Pipes in mid-to-late 2000s, but it's dead as well.

[1] Except some applications like Unreal Blueprints and similar node-based tools in audio and graphical software

I think the reason Unreal Blueprints and cousins work and are useful is: they are used in a relatively narrow domain, the primitives of that domain are well understood/defined, and the primitives are easily compossible.

Once you create a general purpose no-code option, it is so complicated and sprawling that the mental burden to understand it is just as great (if not greater) as just using plain old code again. Or conversely, it is so constraining (for the sake of "simplicity") that it can't do anything useful.

> If all you're doing is very simple crud apps and transforming API responses,

I think all of us here conflate simple with easy. It's simple in theory yes - you get some JSON from service X, maybe tweak service Y that talks to X and then feed it into some front end. In practice even very experienced engineers can take days writing or changing a simple end point or some front end because unclear requirements/bugs/micro service hell/unclear existing code/etc etc.

If it was that easy the pace and quality would have been much higher than what I'm seeing in tech companies.

On the flip side, I am having a great time charging enterprises exorbitant hourly rates to come in and help them fix their crappy crud programms.

But I still don’t know if I am thrilled by the prospect of being called in to make their monkeypatched, GPT generated project work.

"Hey GPT-X: Can you refactor the codebase in gpt@monkeypatched-crap.git for me? Preferably in the style of gpt@crappycrud.git that I worked on last year."

Token cap will probably be the biggest problem here.

After validation.

After getting the changes to disk, documented, actually compiling, etc…

But the biggest problem is that transferring the nuance that is external to the code base is typically really tiresome and lengthy grunt work and again token cap.

They went from 4K to 32k in a few months. Wait a few years.

GPT-3 is 3 years old, not a few months.

Sure but the token jump to 32k is new isn’t it? Even 8k to 32k is significant and that was this year.

Yeah, but the jump to 32k didn't take a few months, it was years in the making. Otherwise you could extrapolate with "yesterday we had 4k, today we have 32k, tomorrow we will have 256k", that isn't how we do it. If we follow the same exponential pace 256k would have to wait 3 years, and even that is unlikely.

I think it is better to assume that we are at the beginning of the s-curve, not the end.

Wow. I never expected to be downvoted for such a comment. Downvotes please elucidate as to why we are in the asymptomatic phase with LLMs.

"Luddites" refusing to accept that we might be onto something that is going to change humanity forever...

The sad thing is that real luddites would go out and actively sabotage AI development because they think it's a real threat. Yet these people just makes bold and false claims in online forums and continue to move the goalposts once they're proven wrong. Sad. Pathetic. (and obviously, I don't mind being downvoted. Whatever! :)

Seems very man of these here for some reason, trying to find whatever reasons and ways (gonna be licensing mostly) to spread FUD.

Few years, yes. I’m with you “the change is coming” but we still need to transfer millions of tokens in and out to cater for context and out of repo intricacies.

Large enterprises will just pay to fine tune over their documents/code/etc.

we don't know what that jump means, did they increased model proportionally, or slapped some projection layer with quality degradation?..

> their monkeypatched

You mean, like almost every outsourcing company pops over? So the type of code that infests companies who hired some sweatshop to do ‘some simple crud’? What’s the difference? Can you see the difference? Besides the gpt code will be far better commented as comments come for almost free with gpt while humans hate writing them.

I think you can trust the code of both those worlds exactly the same, not at all.

I’ve seen GPT 3 and 4 hallucinate the most amazing commentary about their own output. Maybe we will get dependable, out of process, guidance at some point about how factual the model thinks it is on an output per output basis but until that point you should trust every LOC and comment exactly the same as code gifted to you by an adversary.

My modest 2¢

Exactly... and then the question becomes: why do I interact with an adversary?

I'm working on an outsourced codebase right now and boy, I wish they hadn't commented their code at all

Also if all the company is doing is boring crud app as a service then the whole company is going to disappear once anyone can ask ChatGPT to create a version of the service for themselves

Most CRUD apps in enterprise settings relate to something in the real world. E.g. ordering systems, warehouse management and so on.

The hard part here isn’t really the coding - but understanding the business logic and getting it out of the customer what they need.

Often the customer hasn’t even got a good enough grasp on their own business to be able to formulate what they need.

In the short term this ai tools will make the programming part easier but the planning part is still hard.

Excellent response.

I also wanted to add about the myriad of incoming data formats that need to be processed and the myriad of data exports that one has to implement for most of those "boring" CRUD apps.

If one hasn't written code that includes comments like "Special case, California does it this way" or "Alberta needs an .xml export, not an .xsl one", with a link to a .pdf spec that points to somewhere on the internet, then he/she hasn't got to know what it really means to write a true and "boring" CRUD app.

And n+1 things that HAVE to be done against the spec. Or that for some region some field has to be hidden, but only for that region - not any else.

‘Understanding the business logic and getting it out of the customer’ is precisely what a lot of programmers are bad at doing. Many would rather talk to a compiler than a human being. For them, ChatGPT is a real existential threat.

Some folks grew up without practicing code- did business / operational /technical things for 20+ years. For someone like that, chatGPT is manna from heaven. 0>0>

I point chatgpt at the general problem and ask about the tech. Check it's answers, drill into specifics. As more questions, get it to write a prototype. (obviously with different parameters to prod- I don't trust open ai not to harvest from my interactions)- ok, now I have proof of concept, if it does the thing I want to do, then go to the engineers and say - hey I want to "X", here is RFC and rough code, .. any problems? if not, how long to push a localised version?

I guess you might call this scripting or prototyping not "real coding" but, damn it's useful not have to fill my head with python etc. Or bore/waste a guy earning 150k for a half day plus to get the basics, then never get my prototype.. because, priority, resources, etc

These statements can definitely be simultaneously true:

* ChatGPT is revolutionary - honestly, it's genuinely impressive how much of a leap ChatGPT is compared to the attempts that came before it.

* Programmers write a lot of simple code that has been written before - there are genuinely tons of cases of "write a web endpoint that takes an ID, looks it up in a database table, pulls an object through an ORM, and returns a JSON serialization of it." Most programmers? Doubt it. But tons of programmers write CRUD stuff and tons of IT admins do light scripting, and a lot of it is repeated code.

Could ChatGPT do my job? Not even close. But it's still really impressive to me.

I feel safe too, and I'm amused at the dynamic. GPT could do a lot of the things I do, but it would take someone who knows what I know in order to explain the task in sufficient detail for it to do that.

I started learning how to code about 6 months ago, mostly to build prototypes of a couple of app ideas. I have no intention of getting a coding job - if the prototype is successful, I'll seek a technical co-founder.

Last couple of months, I've been using chatGPT to write a lot of features and functions. I don't think it has made me a better coder, but it has made me massively more productive. Things like scraping data from a URL - something I would have had to sit through an hour long tutorial to learn - is accessible with a single query.

I also think that the code quality has improved over the last few iterations. It makes fewer mistakes now.

This is exactly what GPT is great for, accelerating learning.

I predict there will be an explosion of productivity and small scale entrepreneurship. These tools are going to give so many smart but technically unskilled people a path towards realizing their ideas and vision.

Exciting times

Eh, it's still impressive that these systems can write such good code despite pretty much just predicting the next word. I guess it's a matter of perspective. You can either be astounded by how much it can do relative to your expectations from 2018 or you can be skeptical relative to the volume of excitement. I think some excitement is also people extrapolating to the future: if predicting the next word gets you this far, what happens when you actually try to make it good?

> despite pretty much just predicting the next word

Is GPT really like the old school NLP trigram stuff?

"predicting the next word" sounds trivial until you realize the value and complexity of "predicting the next word[s] by a world leading expert in a particular domain".

Quoting famous people sounds smart until you realize they just memorized a ton of trivia. These models have demonstrated that they don't learn logical models, instead they learn to generate text that looks logical at first glance but is nonsense.

I asked GPT-4 something fairly niche that I happen to know a fair amount about: to explain the concept of Xenon poisoning in a nuclear reactor. Other than skipping Te-135 being the initial fission product that starts the decay chain (and tbf, operationally it can be skipped since the half-life is 19 seconds), it got everything correct.

I'm sure if I kept probing on smaller and smaller details it would eventually fail, but I'd argue for _most_ people, on _most_ subjects, it performs incredibly well.

I couldn't get ChatGPT to do Wordle. I don't have access to GPT-4. See if it can do that.

Ok, but it has all this information already. So why is this surprising.

A real test of it's abilities would be synthesizing a NEW type of nuclear reactor.

> what happens when you actually try to make it good?

1. Do you think that companies paying millions of dollars to ML researchers aren't already trying to make it good?

2. I think it will take a real revolution in AI/ML to do what people here are extrapolating into the future. That revolution will eventually come, but I doubt it'll be as quick as people think. Just think about the excitement people had about Siri 10+ years ago, or about Full Self Driving 5+ years ago. In my opinion, in 5-10 years from now GPT will be in the same place where Siri and Full Self Driving currently are. Eventually we will make the leap we're dreaming of, but that leap isn't happening yet.

This post feels like the people that go into linux forums and say linux sucks because I can't get it to do X but microsoft can, but then get 400 replies and several that show <how to do thing>

GPT has limited reasoning but given enough knowledge of the problem you can coerce it to do surprising things so long as you can relate it to something in else in the knowledge base. Given how big that knowledge base is, you can get lucky surprises where things just work if you fish around enough

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.

Extending this analogy into a question: Could the polar bear community eat enough expert food providers that their quality of food and eventually overall 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.

I think most people see where the puck is going, and even where it is right now is very impressive. It's not hard to see that it will be likely less than 5 years before it will be able to do what you did on day 2, and much more, at a tiny fraction of the cost, with no downtime, no attitude problems, sick days, etc. The things you mentioned (taking on board feedback, testing across devices, iterating on solutions) doesn't seem very far away at all.

The rate of increase in capabilities is also unpredictable, which is what is amazing & terrifying.

That's what they said about Watson and Wolfram Alpha. You only hear this nonsense on HN.

Demonstrably untrue, it’s taken up semi-residence in everything from oped pages to boardrooms since the New Year. Welcome to the mid singularity, my friend.

You can’t say “singularity” on HN without a ton of downvotes. It’s not because people disagree (or they’d say so), but because they literally shit their pants when they think about it. Hard to blame them.

I'm saving my money, so I can live enough off savings to learn a new profession. I'm not sure if that will happen, but I consider this option as likely enough to affect my life decisions. The world is changing at alarming rate.

Though I think that it's more likely that I'll become shaman who talks to the spirits. Managers hate computers and they'd prefer to pay someone to deal with them.

Who's not disagreeing? Singularity feels like "man facing existential dread confuses saturation curve for exponential".

When I say singularity, I mean “changes the nature of human life irreversibly and to a greater degree than any prior technology through a rapid progression of self-reinforcing growth.”

That rapid progression can be modeled with an exponential growth curve, like Moore’s law, even though nothing material can have a sustained exponential growth. Regardless of how the curve is modeled, the steepness is such that it serves as a step function in evolution.

With that clarified, there is first the question of “is the technological progression going to level off soon?” And I think the answer is no. The second question is, “how are we going to deal?” And the answer to that question can only be addressed once we answer the question: “what kind of world do we want to live in?”

I think you just declared radio, television, the internet, the macaroon, and mobile phones to be singularities.

People generally use the term to mean the point at which progress is automatic, unbounded, and instant. E.g. AIs that can produce smarter AIs themselves, in short order.

'step change' sounds less exciting than 'singularity' but in terms of the original definition there's very little distinction.

Hmm. I see the singularity as a recursive step change; the last step where the average slope becomes vertical.

I’m not a singularity believer (singularitist?); from the perspective of 1000 years ago we’re already at the point of infinite progress in zero time. I think individuals, the culture, and the species adapt to accelerating progress, and I intuit, maybe wrongly, that Gödel‘s theorem means that technology will never become runaway and recursive.

But I think that’s what people mean, more than just a step/paradigm change like the internet.

I think everywhere except HN the expectations on Watson’s and Wolfram’s second seasons where even more inflated

Who? Crying wolf makes sense to discredit an individual but not a whole argument. Almost everything is being predicted by someone at any given time. If your criteria for predictions is “no one can have made a similar prediction and been wrong in the past”, then you’ll disbelieve all predictions.

And seriously, how many people were actually saying “WolframAlpha will destroy a huge amount of programming jobs”?

Except Watson and Alpha weren’t useful out of the box to everyone. They weren’t the fastest growing app of all time and couldn’t even remotely play that way. ChatGPT isn’t hype because it is so useful — it’s already on the “Plateau of Productivity”.

But it looks like an exponentially steep climb from here. indefinitely

They said the same thing about transistor scaling

Being that you and I are talking to each other by forming thoughts in meat that us causing other meat to move around tells me that digital thought still has a lot of scaling room ahead of it.

Maybe superintelligence isn't possible for some reason, but the fact we exist should tell you that general intelligence is not magic.

Your brain is analog. Trying to emulate this with a digital computer is bound to come with some computational cost.

Good thing we aren’t trying, then. GPT works in a completely different fashion.

The world created by human thought has given us such excess means of power production that we have literal gigawatts of electricity we can use in a trade off where it doesn't take us 4 billion years to make efficient circuits.

> I think most people see where the puck is going

Overinflated hype about “where the puck is going” being wrong is…not a new phenomenon. And the non-tech media (traditional and social, and particularly a whole lot of the elite commentariat that spans both more than the “hard” news side of traditional media, though that is influenced too) perspective on this is driven quite disproportionately by the marketing message of the narrow set of people with the most financial stake in promoting the hype. Even the cautionary notes being sounded there are exactly the ones that are being used by the same people to promote narrow control.

You know the shoeshine boy indicator story [1]? That's the vibe I've been getting with ChatGPT lately, I have multiple old high school teachers with 0 computational background who hardly ever spoke about ML before posting regularly on LinkedIn about ChatGPT the last couple months.

The part that gets me is they aren't just aimlessly making posts, they're getting involved with webinars targeted at educators in their respective topics, speaking at regional events about the future with ChatGPT, etc.

One of them was a really excellent teacher but like this dude absolutely does not have any qualifications to be speaking on ChatGPT, and I hope it hasn't actually changed his teaching style too much because I'm having trouble imagining how ChatGPT could've fit in well with the way he used to teach.

Don't get me wrong, hype often surrounds something legitimately good, but I think ChatGPT is being taken way out of context in both the ML achievement it is and in what things the tool is actually useful for. I guess it is easy for a layperson to make mistaken assumptions about where the puck is going when they see what ChatGPT can do.

[1] "The story took place in 1929: Joseph Patrick Kennedy Sr., JFK's father, claimed that he knew it was time to get out of the stock market when he got investment tips from a shoeshine boy."

I can see why educators are getting involved. They are seeing it change how their students do work in real time.

I grew up and using a pocket calculator was verboten. You just did not do it. You better learn and memorize all of that stuff. Spin on 10 years after me and all kids have them now. If you have the right app on your phone the thing will OCR the problem and auto solve it and show you the steps. ChatGPT and its ilk are here, now. How we learn and create things has dramatically changed in under a year. It is not clear how much though.

Teachers are trying to get ahold of what does it mean to teach if you can just ask some device to summarize something as complex as the interactions of the 6 major countries in WWII and what caused it. Then the thing thinks for 2 seconds and spits out a 1000 word essay on exactly that. Then depending on which one you use it will footnote it all and everything.

This style of learning is going to take some getting used to. This tool is in their classrooms right now. Right or wrong. It will be there. The teachers are going to have to figure out what this means to their class planning. Not 5 years from now, today.

Yeah there is a broader issue here for sure. I don't think that means within a few weeks of the launch of the pocket calculator there should have been dozens of random talks from people that have no understanding of what a pocket calculator does trying to pitch a curriculum based around it.

These posts ooze hype bullshit, not nuanced talk about pros and cons and how it will affect education. ChatGPT should be thought of as a writing tool and perhaps an information source on very surface level topics. Trying to teach an advanced research course at a top school with ChatGPT heavily involved is a terrible idea on the other hand.

I have tested asking it about a number of specific topics in biology research/asking questions about particular papers, and it gives horrible answers the majority of the time. If someone submitted that as a paper to me I'd give it a bad grade because it is dumb, I wouldn't need to know if it were ChatGPT or not. I would be alarmed if my kid's teacher went from organically teaching how to dissect a scientific paper to suggesting that a major part of the curriculum can be replaced with talking to GPT.

I've seen articles about teachers taking the other extreme against ChatGPT too, but I haven't personally seen anything that was a realistic take on what LLMs can do. Maybe it boils down again to disagreement on "where the puck is going" but to me most of the hype is making ridiculous assumptions about what is imminent meanwhile ignoring the things worth discussing now.

Which sounds a lot like bubbles. The dot com crash didn't mean the internet was a bad idea or that it wasn't worth discussing at that time.

The thing was when pocket calculators came out they were expensive to get ahold of. Like 500-1000 bucks for one (in 1970s terms that is a lot). This sort of NLM is effectively free. All you need is an account and the cell phone you already have. This is disrupting classrooms right now. Everyone I talk to that has a kid is saying their kids are using it for coursework. I get why teachers are 'oh no...' as of right now it is exploding on them. They have not had time to digest it. Unfortunately at this point chatgpt is like an reporter summing up information it has no idea what it is talking about and putting it into nice prose that looks good. So while in your area of expertise you can spot out the issues. But you flip the page and it is on a different subject it will be tough to spot out the errors. Simply because you do not know and attribute expertise they do not really have onto it. This NLM is that but turned up to 11.

"where the puck is going" is something to consider and we will 100% get that wrong, but right now this thing is creating waves and making issues now.

Teaching is going to be a career affected early by chatgpt because students will use it (are using it) to do assignments. A good teacher doesn't need to know much about machine learning to think about how that will affect his job, he just needs to know his students and have an idea how to work with them given the new tools they have.

> Teaching is going to be a career affected early by chatgpt because students will use it (are using it) to do assignments.

Already happening. A student in Russia submitted his thesis written with the help of GPT, it was accepted, then rejected, then accepted: https://www.themoscowtimes.com/2023/02/02/russian-student-al...

Yes for generic 9th grade English or poorly taught science classes it is easy to just use ChatGPT. His class would've been pretty immune to GPT plagiarized assignments though, assuming a serious grader were evaluating them, which one used to at least. Yes GPT could've been used to assist in the writing itself, but that wouldn't disrupt the educational goals achieved by developing the content, which required synthesizing multiple modern scientific papers in a coherent way.

In any event, he isn't concerned at all about students using it for assignments, it's the degree to which he seems to think it can be integrated in his current curriculum that alarms me. I think he is misunderstanding the capabilities and therefore pitching something that doesn't actually make sense.

The other teacher I didn't follow as closely, I just got a kick out of seeing she was now advertising some sort of webinar about ChatGPT filled with buzzwords too.

Also if a teacher is really serious about wanting to plan around what GPT can and can't do to the point they want to be a teaching authority on it, they should be consulting with people in the relevant domain. When I want to use a new tool in the lab I talk with colleagues that have a great deal of relevant experience before I start working it into experiments. I can't imagine starting to give advice to others on the matter when I don't actually know the current nuances.

Soon, a lot of people will realize that the puck doesn't travel in a straight line. Eventually, it will veer to the right in a logarithmic fashion. When it does is up for debate, but - in my experience - it always happens very short of where the hype claims it was heading.

The puck can also hit you in the face and break your jaw.

Certainly a likelihood for everyone too lazy to write their own code.

Personally I think it's 99% hype. The current iteration and architecture of these systems means they will never be at the level where they can actually replace a programmer. The best they will ever get is barfing up snippets for a programmer (who still needs the industry-specific knowledge to VERIFY the snippets).

Additionally, "the rate of increase in capabilities" is very much a false flag. Past performance (especially for second-order things like 'rate of improvement') is an absolute batshit insane metric for predicting future success.

https://russellinvestments.com/us/blog/past-performance-no-g...

The advancements have eaten low hanging fruit. Once all the low hanging fruit is gone, we'll all realize GPT will never be tall enough to reach the good stuff.

That's why I said it was unpredictable, in both directions.

But obviously increasing avg and sr programmer productivity by many x implies many fewer jobs for programmers. That’s the point we’re interested in.

Is that obvious? The history of programming has been full of things that have at least claimed to increase programmer productivity, everything from high level languages to smart refactoring IDEs, debuggers, PaaS, etc and in all that time the trend has been towards more jobs for programmers not fewer.

Productivity with modern tools (SPAs, microservices) is often a lot lower than oldschool stuff (django/rails, maybe even Visual Basic, Delphi)

You seem to be saying that there is fixed demand for programmers and oversupply means less hiring.

But the history of technology says that demand is recursive; the more tech produced, the more demand there is for producers.

There may be a time when we hit the old “the whole world only needs 5 computers”[0] limit, but I don’t think we’re anywhere close. AI is providing leverage to create net more programming; it is not replacing programmers in a zero sum game.

0. https://www.theguardian.com/technology/2008/feb/21/computing...

Sounds a bit too much like a perpetual motion machine to me.

Isn’t that true of any economy? Think of how many people are employed in general. How could this number of people have been employed 200 years ago?

I’ve got a computer in all of my light switches. There’s a limit to this, but I’m not quite sure where it is.

https://en.wikipedia.org/wiki/Lump_of_labour_fallacy

> In economics, the lump of labour fallacy is the misconception that there is a fixed amount of work—a lump of labour—to be done within an economy which can be distributed to create more or fewer jobs.

The lump of labor fallacy says nothing about a particular type of job. A category of jobs can become non-productive versus a technology and the workers within those jobs should get reallocated to more productive jobs.

The fact that we’re even having this conversation should concern you. Move the goalposts if you want, but the rate of progress should concern you.

> The things you mentioned (taking on board feedback, testing across devices, iterating on solutions) doesn't seem very far away at all.

This is a very bold claim IMO.

Modeling/understanding interactions in a complex system of potential black boxes is much, much more computationally difficult problem that source code to source code operations.

I think we're seeing the early phases a prediction I made in my first book come true: That computers will be more suitable than humans for most of the functions humans currently use their left brain half for.

Best case, that will have a whole lot more humans using their right brain halves on things like defining the problem. I like the thought of that, it's more pleasant work. But a lot of intelligent people define their intelligence by how well their left brain half works and uncomfortable with how good Chatgpt is at those tasks. I think you're pointing out there's more to programming than left-brain activities, and I think you're right that silicon will never eclipse carbon at those challenges, but I think a lot of people are also feeling threatened by the fact that chatgpt is getting better and better at the thing they used to be better than all humans at.

> But a lot of intelligent people define their intelligence by how well their left brain half works and uncomfortable with how good Chatgpt is at those tasks.

I think 'left brain' work also has a lot more predictability (knowing you can churn out ~X widgets/hr) so having only uncertain 'right brain' work can be uncomfortable for people to build their livelihoods upon.

That being said. 'right brain' work is certainly more fulfilling for me.

> can be uncomfortable for people to build their livelihoods upon

Absolutely. Not everyone can be consistently creative, and without a large enough financial buffer, it's a recipe for disaster.

Great point. That's one reason I'm increasingly a single-issue voter focused on UBI.

I feel like there's a slight inaccuracy here that this article covers. GPT-like models excel at logical problems that have already been solved. As the article suggests the models (at least at the moment) are extremely keen to utilise existing solutions as opposed to inventing new ones. The existing solutions end up somewhat being far too distracting when problem complexity compounds beyond the common. This implies that those truly talented at solving logical problems will still be spearheading the development of novel solutions.

We might be able to state that GPT will easily trim away all the average workloads for both the left and right. It can perform "creative writing" or draw pictures to an average or even beyond average extent, but it continues to currently struggle to hit the exceptional examples that humanity are capable of.

  > I really am in awe of how much work people seem willing to do to justify this as revolutionary and programmers as infantile, and also why they do that. It’s fascinating.

Equally fascinating is all of the "this is fine" posts from programmers suddenly realizing they are not the gods they once thought.

But fret not, programming is not the first industry that has been automated into a shell of itself. Yes, the industry is going to shrink massively, but this is what new skills are for. Just as farmers had to learn industrial jobs and then miners and autoworkers had to "learn to code", most programmers will have to learn to do something else. Humans are resilient and will adapt.

And there will still be jobs in development for the most talented and in niche areas, but when the largest tech companies can layoff hundreds of thousands of employees without skipping a beat that should tell you all you need to know about the value of most "programming" jobs.

> Just as farmers had to learn industrial jobs and then miners and autoworkers had to "learn to code"

The transition from agrarian to industrial societies was extremely painful and arguably it was centuries before the people affected were better off.

> Humans are resilient and will adapt.

Based on recent events I think it's more likely people will elect extremist politicians who promise quick fixes while blaming a convenient out-group for everything.

"Yes, the industry is going to shrink massively"

You meant "expand massively" i think. Did all the programmers manually making machine code get fired and the job of programmer disappear when compilers were invented and totally replaced these jobs? No, it just changed to use the new tool.

There wont be any unprecedented mass layoffs, despite what the jaded STEM-hating twitter crowd wants. Companies will simply make their programmers use these tools to increase the amount of produce per employee, and make software that would have been otherwise financially impossible to be made. Because the competition will do so too to get ahead.

  > You meant "expand massively" i think.

Nope, I meant what I said.

> There wont be any unprecedented mass layoffs

Baghdad Bob strikes again.

Then youre wrong. There is no market incentive for layoffs due to GPT-like technology, as I have demonstrated above. Similar breakthroughs "replacing jobs" have happened before in the field of software engineering, this is nothing new or unprecedented. Its merely another tool that will become in widespread use to increase production.

What i think will lead to mass layoffs is the current recession rather.

  > this is nothing new or unprecedented

This is both new and unprecedented.

Huh? I literally just gave you an example of tech that automated away what 90% of programmers were doing.

> most programmers will have to learn to do something else. Humans are resilient and will adapt.

Like what? Seriously, which cognitive occupation is safe then? I think if one wants to stop competing with the machines (who appear very close to becoming superior to humans by what you are saying), it's some kind of child care / social work job. We still don't want robots in those (for now. Eventually they may do those better than us as well).

The problem is that many people here have an extreme point of view. It's either "this is going to make all develops jobless" or "it's useless, my job isn't that".

I think It'll help with some tasks, which is always good to take. After all, people tweak their vim settings because they feel it makes them more productive.

Software development seems safe for the time being, but as someone who has both paid professional translators and used ChatGPT for translation, I'm certain GPT is obliterating some jobs.

This tech is so powerful, cutting so close to the last of human output that’s uncopied, advancing so fast, I have an incredibly hard time imagining it ending in a space that deserves a muted a reaction. It seems like it will either dramatically change society or fail. And I have quite a hard time imagining failure.

It seems on par with the Industrial Revolution, at least. Which, easy to forget, was a huge deal that totally changed society.

I feel it's the average programmer who is enamoured with GPT. The programmers working on hard stuff know that AI writing programs is still far away.

It is not just those who are working on hard problems who are of that opinion.

Good luck having a ML model understand a 20 year old undocumented dataformat developed inhouse at a specific research lab to be used in their proprietary systems which are also undocumented and are a part of a spiderweb of interconnected systems at that lab (I have worked at this particular lab).

It will be a long time (if ever) until a ML model will be able to handle these situations (and I hazard a guess that most of the worlds active code is something akin to this).

As a supporting tool for the software engineers working there, sure. Just like a linter.

Have you tried giving it a skeleton example of said undocumented data format? I've created some arbitrary ones and asked it to parse them, and it succeeded.

>It will be a long time (if ever) until a ML model will be able to handle these situations (and I hazard a guess that most of the worlds active code is something akin to this).

ChatGPT can do that right now. Just provide it example data in the input and it can extrapolate the format and interact with this abstraction (i.e. describe the format, write code to it, etc). LLMs don't just remix data it has seen before, they perform in-context learning. This means abstracting out patterns in the input and then leveraging it in generating output.

It’s also programmers who work with average and bad programmers and finally have a way to not waste time with them anymore. I work on the hard stuff, gpt generate the rest (I don’t have to waste time and energy to try to pull dead horses) and my colleagues go do something else. It’s a reality today and will improve further. Sure it cannot do what I do, but that might improve; gpt4 is noticeably better than 3.5 at slightly harder things, especially if you work properly iterative with it and having it fix it’s own errors. But no, it cannot do hard things it never saw; however it can give me all the boilerplate around the hard things so I only have to do the hard things. My days are simply 3-5 in 1 now, as I also don’t have the overhead of explaining jira tasks to others who are never going to really grasp what they are doing anyway.

So I am not average and I am enamoured with gpt, simply because it presents high value to me now, more than some actual real humans. For me that’s enough revolutionary.

Very far imo.. I mean it delivers what it is, an average of much content out there.. and it is good at it, yes. So in a way, maybe the better future stack overflow with a nicer interface (however what could it do if there weren't stackoverflow to start with?)

But on the other hand in new uncharted territory, it sometimes fails on the simplest shit: Asked it recently how to do one thing with enlighten (that I knew was possible with tqdm, but was almost sure not possible with enlighten). It just hallucinated up parameters to functions that didn't exist. Several rounds continued where it had that from, if different version. I asked it even for the reference where it meant it had that from.. and it referenced me fully confident a readthedocs url with tqdm and enlighten mixed, that didn't exist.. it is hilarious how it confidenlty can tell you one bullshit answer after the next.. dialogues always "hey are you really sure about xxx, did you look it up" "yeees, very certain, I did!" "But this doesn't exist" "Oooh, Im very sorry, you are correct and I am wrong, the next bullshit answer is: ..."

The history disappeared I hope I get it back once, but the dialogue til getting to "No, it may be not possible with this library" was amazing, I'm really scared for our futures building up on that and what will happen if everything from business presentations to lawyer letter exchanges will build up on that..(:

OK, most of us are average by definition, what's your point again? And - I think if it can replace my average ass it will sooner than you imagine be able to solve Linux Kernel bugs. I just don't see a huge difference in the computation required between CRUD and Kernel development: It's just statistics for the machine.

> I really am in awe of how much work people seem willing to do to justify this as revolutionary and programmers as infantile [...]

Well it is revolutionary. And it isn't just where it is today, but how fast these models are improving - with no saturation in ability evident at this time.

On the other hand, I am not sure anyone is saying programmers are infantile - although poorly written software is as at least as prevalent as poorly compensated or managed software development positions.

It's a similar waste of my time to respond to comments like this... but I will at least try to give some pointers so that those who encounter this comment won't immediately fall into that line of thinking:

- a lot of programmers, including experienced ones, are absolutely infantile and they only have a job because there is a big shortage of programmers; not all of them get better with experience... hence a significant part of software development is dealing with problematic programmers and problems created by them.

- GPT is not that great a programmer but a great thing about it is that it is not a human... and one can get thousands of instances of them for the price of one human. You only need one of those instances to produce usable code.

- there have been many changes throughout the years which have definitely replaced a lot of programmers: library distribution services (pypi, npmjs), better software development tools and practices, SaaS delivery model, better programming languages etc.; so far, because the market need for programmers has continued to increase, most programmers continue to have jobs; this won't last forever.

The reason is easy to imagine. Most non-programmers, are living like analphabets in a world were reading is valuable super power. They grudgingly accept this power assemtry, but ocassionally rebel - with "easier" visual programming languages made and excel.

This is another one of those rebellions, non-programers hoping to avoid reading the book and closing it for good, while keeping the awesome around. The code-bases we will see, were the commits are basically chatgpt tags and tasks for each document.

I think this clip section says a lot - if videos are permitted as replies.

https://youtube.com/watch?v=MNtjSMvcjzs&t=211s

cope

Reply on news.ycombinator.com