Maybe I'm naive, but I just don't feel threatened by this at all. As a software engineer, I'd love to have an AI engineer automate the boring stuff so I can work on higher-level architecture concerns.
You're already "inside". The danger with automation is that it can pull up the ladder of developing junior talent on nuts-and-bolts, especially if these platforms aren't accessible to students and small-time developers.
Architecture doesn't matter anymore. I can have the AI do general purpose computing. Who cares if its slow? Integration work between two systems? Why bother writing APIs when my robot can spawn 1000 tabs and do manual data entry. Eventually they can just speak English to each other
That is...exquisitely false. Especially under the tensions of scale, architecture matters a great deal. Yes, computers are ridiculously fast these days, but it is quite easy to architect yourself into a situation that cannot be solved by more hardware, not even theoretically. There are plenty of problems that would require more memory than there are fundamental particles in the universe if you model them wrong.
As for your offered examples, you seem to be thinking in terms of glue code and how well it could be replaced by an intelligent agent with its attention focused on a browser, reading one tab and entering data in another. This approach is going to break at any kind of scale greater than 1, and probably be quite brittle at 1. I'd argue that such an agent, if properly trained, will quickly realize that the cost/benefit ratio of it doing the job manually is not nearly as good as if it wrote a program to perform this mechanistic task. In which case, architecture still matters but the agent doing the architecting has changed.
The people and skillsets to successfully run a company have little overlap with strong software engineers. It happens, but no where near the numbers of engineers out there.
Plus if you kill off many engineers jobs, who is left to buy your products?
They can go back to work in the farms, or weave baskets, or pottery...
Still not good? Ok, maybe this is too radical but hear me out: how about we take this incredible increase in productivity as an opportunity to start paving the way for a true post-scarcity future that can benefit most people, eliminate intellectual property and take everything produced by robots to fund UBI, instead of worrying about our cushy jobs and (relative) wealth/status obtained through bullshit jobs?
I mean, yes, but also management is a wayyy less skilled job that’s far easier to automate. That goes away shortly after we get a competent AI engineer.
Business analysts are also incredibly vulnerable - why have a middle man if the machine understands your requirements in English/French/whatever?
> I mean, yes, but also management is a wayyy less skilled job that’s far easier to automate.
This is a pretty wild take. The job that is 99% dealing with human interactions is easier to automate than the job where you make a computer do what you want?
Even without any AI, every senior I know( 10+ years of experience) is pretty much trying to shift to architect or similar position and sees day to day programming almost on the same level as sweeping floors. I appreciate this may not be the case in shops doing something truly unique,but in an average CRED app world, this will happen much sooner than a lot of people might think.
It looks like with ai tools there will be less demand for engineers. For example, instead of keeping 300k people around, Google will replace 200k of them with ai-engineer ai-coder ai-tester.
Can you imagine how depressing it will be to your market salary?
Frankly it sounds not far off from the software job of the present. The correct code is largely the easy part in my experience, but this could be because I struggle immensely with learning each company/department/projects esoteric CI/CD pipelines
Well we don't use AI now, so most code is written by humans. CI/CD can be hard but it's not actually harder than anything else overall. If you think it's hard for an ordinary software project, I'd bet it's either not your field of expertise, or you're dealing with a system that was probably set up by inexperienced people.
It might not actually get that effective, but it's certainly wrong to expect to only get fun stuff to do. The AI might actually have decent output when it comes to architecture too. I personally haven't seen such amazing AI but I could accept that it might exist now or in my lifetime.
When AI gets good enough to really displace programmers, it probably won't require as much supervision as it does today. It might feel more like the AI is prompting you instead of the other way around... But idk, it remains to be seen just how good it will actually get.
I don‘t quite see why the high level architecture couldn‘t be done by AI either, once it get‘s good enough to build the low level one (which is what software development really is). Half of architecture is best practices anyway, which are explicitly in the training data, and the other half can be inferred from implicit principles in existing systems that are also in the training data.
As a software engineer, no, there's no reason to be worried. But what if you were a wordpress developer where a big chunk of your work can be automated?
Do you seriously think WP is just about blogs? I'd say majority of WP sites are now ecommerce, the amount of different plugins interacting based on local markets needs with custom tweaks, integrations to local courier APIs, payment systems and the general fudginess of the codebase I'll be very surprised if this can be automated. Your general FAANG proto pusher SWE will be the first to go.
Majority of WP sites are definitely not e-commerce. There are about 160M active domains and WP is estimated to run on 40% of the web. That's a whooping 60M+ web sites. I doubt more than 10M of them are e-commerce ones.
Honestly if you're letting yourself sink that deeply into a niche, and neglecting broader programming skills such that you're unemployable outside it, you have no one else to blame when that comes back to bite you.
If anything you should want the opposite. Most companies are fundamentally solving problems within a finite set of domains which could be represented with a finite set of "good" architectures.
The opinionated thing is how to implement these within the boundaries of the existing codebase, skills, etc.
It merely depends what we point the AI at whether we get full blown software engineering out it. I mean right now people only point it at tasks that can be written/watched, reasoned about easily, and then verified. There probably needs to be a jump to the visual spectrum like a wireframe overlay for code/projects which will allow humans to quickly verify what it is "thinking" architecturally. There just needs to be a lot more imagining of ghost features and trade offs somehow. People barely comment code let alone speak of all things that are being done away with.
Some random team at Berkeley beat them to it by a couple days: https://largeworldmodel.github.io/ . It's just a matter of throwing compute at it, nothing fancy. OpenAI could probably do 1M token context too but they haven't yet found a way to make it profitable (neither have Google; the most they actually offer customers is 256k).
This brings a very interesting question: if you could have an AI software engineer today but it would cost you 1 trillion dollars, would you want and be able to afford it?
There is a reason why we still have people working at McDonald's even though fully automating it has been possible for a couple of decades now.
It's the same reason that for 80 years after the invention of the commercial icemaker in 1842, the American ice-harvesting industry produced more frozen water than manufacturing plants. And the ice trade did not exist until 1806.
It was more economical to send people out to cut ice from a lake in Maine and ship it by rail to Chicago than it was to just freeze water from a local supply. It was also more reliable since the technology was mature, versus ice plants that often broke down when meatpackers needed a consistent supply.
There's no reason why this won't be the case for AI unless semiconductor manufacturing continues its exponential performance/cost growth. The demand for technologically obsolete goods and services do not instantly disappear when a superior product enters the market.
Human software engineers right now are more reliable than AIs for most price-points. This is true for most industries in which machine learning is present.
How did you come up with this number? It seems pretty unrealistic.
> There is a reason why we still have people working at McDonald's even though fully automating it has been possible for a couple of decades now.
Maybe the low salary is the reason? If it is a bit more costly to automate certain aspects of manual labor, then the low salaries might remove the incentive to do so. This is not the case for software engineering.
Beyond the snark, this is basically it. It's the same reason the Roman Empire, despite all its technological prowess, never tried hard to automate relatively low-hanging-fruit tasks: because slaves were cheap, plentiful, and more flexible ("reprogrammable") than anything mechanical could ever be.
If it costs $1m p/y to run a machine that cooks burgers and fries, or $30k for an employee who can do that _and_ cover something else when someone else is ill, it's a no-brainer. But businesses had to discover that the hard way; until the 80s, most people were still convinced automation would win everywhere, because it had won (and won big) in manufacturing. A combination of factors, from the '80s onwards, made labor costs effectively fall, which created our reality where certain jobs are so cheap that automating them makes no sense.
The "problem" is that, in certain regions, software development costs reached a point where automation looks very, very appealing. If a machine costs 500k p/y to replace a few 150k p/y SWEs without all those pesky employment complications, businesses will happily choose "AWS AI CloudDeveloper"...
Do you mean an AI programmer would cost $500k per year? If so I think you greatly overestimate the cost.
Recently I did some text processing with GPT-4 turbo (128k context) and I reached the daily limit of 5 million tokens. IIRC it cost me around $70 bucks for the day.
I think $70 is the hourly rate of a SE with $150k salary working 40 hours per week. Note that we are at early stages with this tech, it will probably only get cheaper from here.
And it was meant to highlight that even if you have the tech (which we don't - the cheap tricks chatgpt or copilot do are impressive but still cheap tricks - are super expensive when it comes to actually training the models) it may not make economic sense to deploy them.
Even if it makes sense to deploy them the social unrest and volatility that will result in society may not end up well. (What's the point if all the consumers go away or they cannot actually buy the shit you're producing)
I started using it recently. $30, 50, or even $100 a month is litterly nothing for most companies in wester world. They'll hike up the prices eventually.
I believe that is where they are implying they do it without increasing memory utilization dramatically.
If 1M context uses 32x the memory of 32k, its a non-starter. Even a smallish LLM like Mixtral uses 4-8gb of memory just for your prompt. You would have 256+GiB at 1M...
GPT4, as smart and impressive as it is, starts forgetting or confusing key instructions with as few as 500 tokens (in my experimentation). Practically speaking the advertised 32k context window could be a few orders of magnitude smaller depending on what you're asking it to do!
Yeah, LLMs seem exceptionally good at summarizing large amounts of structured data with a prompt at the end, like that YT demonstrates.
If you have a back-and-forth conversation, with the previous conversation chunks prepended as context to the next interaction, it will rapidly lose track of where you instructed it to spend its attention.
The manner in which the context is used seems to make a huge difference.
I am feeling the same. Paying 100EUR/month for a competent AI junior programmer which I could offload simple or repetitive tasks on would be absolute banger. But so far AI is hype and crap code.
Those who do worry do so because they assume (rightly or wrongly) that it will be possible to automate everything you do, including ‘higher-level architecture concerns’.
Providing detailed instructions to computers to accomplish human/business objectives is the hard part about being a programmer.
But the level of abstraction has been increasing. It started as physically flipping switches then machine code then assembly then structured programming then object oriented programming and so on.
I remember during the 1990s with objects and VB custom controls people were talking that businesses would just hire a bunch of high school students to work part time just snapping components together like Legos.