The writing is on the wall. It's not right now, but it will be in my lifetime. What are some AI-proof(ish) industries we can jump into after software engineering is drastically reduced?
AI will never write software for the same reason professional chefs will never be replaced by robots. Sure, certain parts of our jobs (the fast food equivalent work) will be assisted, and then replaced, with AI help. Ops is already benefitting from AI assistance with things like log correlation and managing incidents, and all of that is great! But the fact is that AI will still require the input of humans to solve problems, and specifying a problem completely enough that a machine can write the code for it is equivalent in effort to just writing the code yourself.
I see the introduction of AI to the SWE workforce as roughly equivalent to the introduction of digital kitchen scales, thermometers, etc. to professional kitchens. It's going to help us up our game, which is awesome! It's going to make a lot of the boring, mechanical stuff simpler so we can focus on the creative aspects of our jobs. But a burger flipping robot isn't replacing a michelin star chef anytime soon, and AI isn't replacing you anytime soon.
I’m hopeful this is the right answer, but I’m not optimistic it is. AI seems more like the introduction of the assembly line for software workers, minus huge blockers for physical labor. It’s not there now, but at this rate? Not infeasible either
How will you tell AI what you want to build? I'll take an example from my current work: I'm currently working on adding producing data to a kafka feed from an API server that will be aggregated downstream into a big huge in-memory datastore for fast access. That's a moderately sized problem, one that a reasonably competent engineer can solve in a week or two, but how much work will you have to do to tell the AI that this is what you want? And if you tell it at a higher level than this, how will it know what tools it has available? How will it deploy stuff?
There are some fairly intractable problems in the "AI writes software" space. I don't think they're getting solved any time soon.
If software engineers have been replaced, it won't be any of us telling the AI anything, it would be a manager. Ask a manager about Kafka, I recon more would respond with something about Franz than anything about Apache.
> And if you tell it at a higher level than this, how will it know what tools it has available? How will it deploy stuff?
Step 1. Ask it to come up with a plan, after giving it read access to your corporate documents.
Step 2. Ask it to execute the plan it came up with in step 1, after giving it write access.
Of course right now there's a step 3: "Get real engineer to read and then perform business continuity/disaster recovery plan because current AI is about as good at this as someone fresh out of university, what on earth were you thinking, …" etc.
I don't know how long we've got in these jobs, but based on the rate of change (and the training cost for the larger models and supply shortage for the chips used to train the better models) I'm thinking at least 2 and no more than 15 years — though I'm hoping more towards the latter.
Sounds more like hope to me. There's nothing special about creativity.
The real reason chefs won't be released by robots sooner than AI and software devs is a lot more boring. Sensorimotor and physical perception is harder than reasoning and "creativity".
It doesn't help that we don't know how to digitize taste so any model with a good sense of taste will have to develop it indirectly incentivized by something else (eg a language model training on recipes).
GPT is a predictor. It will just continue to reduce loss until it has modelled the data entirely.
I see GPT and similar LLMs as basically like managing an over-eager intern. Have you ever tried to do that?
They're overflowing with ideas and knowledge and passion, and "all" you have to do is point them in the right direction. Except that when you review their code, you find that they didn't consider about a thousand different edge cases. Oh, and they didn't follow the style guide. Oh, and they are way over-focused on the wrong parts of the problem, prematurely optimizing performance in places where it doesn't matter. Oh, and their code is nigh-on unreadable. Oh, and they wrote a bunch of code that is already provided in libraries, but they just didn't know about it, and their implementation is probably full of subtle bugs so they should just use the library version. Oh, and they forgot to update the CI because now they need to pull in a new dependency to run their tests. Oh, and... the list goes on.
I don't ever see AI progressing past the regurgitation stage; you can give it as much knowledge as you want, and it can rearrange and reproduce and restate that knowledge in a thousand different ways; but we're so far from AI being able to handle all the details of our work, that as I said above, fully specifying the problem is going to be just as much work as doing the work yourself. And you'll still need expertise to do so, because our software systems are complex and full of nuance that can't be easily communicated to a machine.
AI doomers always strike me as people who have never had to try to corral interns; maybe that's an overly specific life experience to expect someone to have, but it's a really useful proxy for how much "more productive" we're going to get with AI. A really good intern can solve simple problems given exact constraints, but anything requiring lateral thinking will take them a lot of coaxing to get to the right solution. And that's okay! People can learn and get better. But I don't see AI getting better "enough" to take on anything beyond that first rung of the ladder of complexity.
>I see GPT and similar LLMs as basically like managing an over-eager intern. Have you ever tried to do that?
So? Assuming you're right, GPT-3 was not at the level of intern. GPT-2 could not even write coherent text.
I bet you didn't expect any of those developments either.
It's interesting how much people struggle to look forward. I guess we never needed to for nearly all of our evolutionary history. Like very few people genuinely think they'll be replaced right before they are.
Your argument basically boils down to "I have a hunch language models will stop improving".
Your hunch is unfounded, backed by nothing except vague assertions that "surely this time" these goals won't be reached.
Assertions that many people parroted just a few years ago and have continued to be proven wrong.
The text is data. Gradient descent and prediction will strive the model the data. The data will be modelled. That's really all there is to it.
Is it though? It is not having a significant impact in most fields, despite all the hype. Companies will use it to continue to make customer services worse so they can spend less money on it. People writing marketing copy that no one wants to read anyways will lose their jobs. Semi-competent software engineers will use it instead of searching Stack Overflow five times per day and still write questionable code.
To be clear, I'm referring to LLM. Obviously ML is used for a lot of analytics, but I also think that most of the hype around ML has died down as companies realized that not every problem has a ML solution. In a few years the same will happen with LLM.
> It is not having a significant impact in most fields, despite all the hype. Companies will use it to continue to make customer services worse so they can spend less money on it.
There's some delicious irony in this: tech-savvy HN posters are so bullish on AI replacing everyone's jobs, yet there's been so many posts here about how people have lost their Google/Apple/bank/whatever accounts due to AI accidentally flagging, then being unable to recover because they can't get in touch with an actual human, with begging for help on social media as their last resort.
It seems like a variant of Gell-Mann amnesia[0]: people believe AI is capable of replacing humans in so many aspects of daily life, except for the aspects of daily life which they personally experience.
This type of sentiment is not valuable at all and overly pessimistic. Current AI tech is not very likely to replace work that requires a modicum of logical reasoning and the tech to do so has no known path forward as of now.
Why do you think those things? Logic & theorem provers massively predate LLMs, and getting LLMs to use them is as easy as asking the LLM to write a proof in the language of your theorem prover of choice, which you can then copy-paste into that theorem prover and execute. And if/when it doesn't work, the error message itself helps with a significant fraction of most other programming problems, so my guess is they would also help here.
Also, there have been substantial new developments and discoveries about what transformer models do (both internally and in terms of capacity) every week or two for most of this year, so why do you think there's no known path forward?
Thinking of logic, I just tried the following with gpt-3.5, gpt-4, and gpt-4-1106-preview. The newest model spotted the trick (and then still got it wrong), the older two didn't even spot the trick. Can you spot the trick?
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A person is in Nairobi. They board a plane, fly 9000 km north, then 1000 km east, then 9000 km south, then 1000 km west. Where are they now?
Highly reducing just means you can do far more with the same resources.
I don't know about you but my team's backlog is virtually endless. If AI/LLM/whatever can automate away a ton of dev work, that doesn't mean we're going to fire 90% of our team. It means we might have a glimpse of being able to actually make a dent in all the work that we want to do but can't.
Are you currently functionally limited by your typing speed? No? You're probably fine. I'd take a team of juniors that have full autonomy to make informed decisions with some hand holding than a tool that has no logical reasoning and just spits out what the statistically average next thing to do is.
In Fifth Element, the bartender is a robot. (Works of fiction may or may not reflect our future!)
Whether customers prefer that or not may or may not matter to the business decisions that are made (given that it's not a perfect customer-driven market.)
Have you been to McDonald's? They really want you to use a touch screen in-person, or an app even though you're in your car, talking to a person.
Perhaps an underground movement can maintain a grass roots database of "real human service" locations, after the revolution.
A bar is not McDonald’s. Robot bartenders have existed for some time but people go to bars for the human connection not simply to consume alcohol. Even here, a human bartender could probably benefit from a robot doing the work of cleaning glassware and restocking ingredients for them.
>What are some AI-proof(ish) industries we can jump into after software engineering is drastically reduced?
Hospitality will always be a thing. There's a reason why the Enterprise-D still has a human bartender that makes real drinks, even though it's completely superfluous. People will always want to be served by other humans, even if AI Android tech became perfect.
The Doylist reason was "we need a role for Whoopi Goldberg". There is no Watsonian reason, as Data did bar duty in the episode they had their memories wiped.
Looking back at them, the taking points about AI are basically the same now as in each episode of TNG, DS9, and VOY that is focused on AI, in the form of Data, Moriarty, The Doctor (and if he has copyright over his holonovel), that Irish village in Voyager, Wesley Crusher's nanites or the ship's main computer becoming sentient…
>The Doylist reason was "we need a role for Whoopi Goldberg"
Fair, but the point still stands. A better example would be Quark's from DS9. Even in a world of perfect replication and perfect androids, people are always going to want a human touch for these things.
This in turn may or may not be sufficient for work once the issues with bioprinting and/or tissue/organ culture get solved and someone prints/grows a brainless body, connects its organic lower nervous system to a silicon chip, and has the higher functions all performed by an AI on that chip.
And by "someone" I mainly mean The Thought Emporium given they're already trying to do exactly that.
I see the introduction of AI to the SWE workforce as roughly equivalent to the introduction of digital kitchen scales, thermometers, etc. to professional kitchens. It's going to help us up our game, which is awesome! It's going to make a lot of the boring, mechanical stuff simpler so we can focus on the creative aspects of our jobs. But a burger flipping robot isn't replacing a michelin star chef anytime soon, and AI isn't replacing you anytime soon.