Comment by Quothling
17 hours ago
Around here AI isn't really more of a threat to juniors than it is to seniors. It's a threat to the people who have been taught "recipies" rather than applied computer science. You can have excellent seniors who can do TDD, DRY, SOLID and so on, who also happen to have no idea what a L1 cache miss is. The current AI models know all of those things, but they struggle applying them correctly without someone piloting them. Even in the energy industry where I work, where you'd think it would be obvious from the context that you should prioritize runtime safety over debug safety, the current AI models struggle to do so. As far as seniority goes, though. If we can find a young developer with little experience who actually knows computer science, we're much more likely to hire them... Since they are cheaper.
This isn't something which is unique to software development though. We're currently building enterprise AI apps that we can deploy into the AI agents working for anyone of our employees. The key thing we're currently seeing is that the people in a team who are the ones that everyone turn to for advice, are the only people who aren't in "danger". Even people who are great at their jobs are being outperformed by AI in many cases.
I think it'll be a massive challenge for our society in the coming years. Maybe we're even going to get to the point where the AI will also be capable of replacing a lot of the "domain experts". Right now that seems far out, but then, if you had asked me about AI four months ago I would've told you it was all hype.
AI is a threat to everyone. People who claim that AI will never be able to do X have consistently been proven wrong.
The only people who are safe are those whose jobs depend in some way on their humanity. e.g. yoga teachers, bouncers, etc
Nobody knows.
It's not a zero sum game. You can have AI "senior engineers" working under humans building bigger things than we've been able to.
We also don't know where the capabilities of current AIs will plateau. The benchmarks aren't really telling the entire story. From my perspective of using the models there are certain axis where they're not making a lot of progress, like being able to have large accurate context on the scale that humans can. There are other dimensions where there is still a large gap between human capabilities and LLMs. It's true that relative to other areas (lessay chess) LLMs are more generalized but they are still not fully generalized (back to the chess example, LLMs are not good at chess).
> It's not a zero sum game.
Resources are, though. The planet cannot support a race of digital super-people, and us, and an continually growing economy.
It's the height of folly to think that, as things are going, we are going anywhere "good".
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This was sort of what I wanted to say, but I guess I should have worded it differently. I certainly didn't mean to say that I thought AI would stop improving. If anything I'm surprised at how much we have to fight the AI models to do what NASA has been doing for 60(?) years.
i'd take a yoga class from a bot
I'm now imagining Bender's Yoga class. I suspect it would hurt.
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How would this be different from taking a yoga class from a video?
I would be willing to be proven wrong, but I doubt the ability of LLMs to give useful corrections in yoga much more than their ability to write useful code.
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Your first two sentences were correct. The last one is already being proven false.
It's a threat to everyone. UBI is the only way.
Productivity improvements tend to increase employment. AI will not reduce employment.
(Also, the US budget deficit is way too high to afford a UBI.)
Interesting, thanks. I don't know where "around here" is, but the signals I've seen in a lot of articles is that the demand for junior software people has taken a dive since a year or two back, with student programs etc getting cancelled. One googler said they were getting a junior to their team and that was kind of a big deal because it hadn't happened in that whole department for a long time.
In relation to that, I guess my question becomes: if the same thing will happen in math research, who will write the ten page math proof prompts in the future?
There is definitely the effect of less or de-growth in the industry, which started before the current AI hype. And now there's the additional effect of companies hoping AI will replace their need for (junior) devs. Nobody knows if or to what degree this will work out (yes, we all have opinions, but no crystal balls), but they are holding back the hiring until they know how all this pans out.
I'm from Denmark and I've been an external examiner for various CS educations for the previous 13 years now. Some of them teach you a lot about how the hardware works, others mainly teach you design patterns. Five years ago the latter was in high demand, because a lot of software development frankly doesn't need computer science (until it does). Now there is almost no demand for them.
Honestly, i've received a formal MSc education in the hardware aspects, including for designing embedded electronics products. Spent the most part ofy career in the software industry designing enterprise software and feel like i never needed to use them, except maybe early in my career when i was reviewing tech stacks and determined that .NET would be among the winning horses, precisely because it'd take care of that for me almost all the time.
What i see today is the opposite of what you see : product owners not knowing a thing about software engineering but being able to vibe code prototypes handed over to the dev team are rock stars.
They are closely followed by senior software developers having more of an architecture & design background than a low-level computer science background. Most businesses are looking for builders these days.
Where what you say may converge with my observation is that to be able to do to things such as proper database query optimization, even using AI assistance, you need to be able to understand the concepts of working memory set, cache misses etc...
I've found huge problems, like database servers being grossly underprovisioned (like, 60% cache hit, 4gb RAM server for a 700gb dataset with an 50gb circa hot data set). SSD were used and only latency was measured, so no one realized how problematic the situation was (including a consulting shop they hired to help them manage their DBs - backup, maintenance etc...).
However, having a high affinity with hardware is not a driver / computer science of hiring decisions from what i can see in the enterprise software world. But it would make sense for it to become the case within 10 years. I suspect that you work in a niche where performance optimization matters a lot.
It's funny how you applied on your own argument several logical fallacies about why ai is only a threat to people who have been taught "recipies" versus who know what L1 cache miss is.
Actually it's sad there are people out there dumb enough to believe knowing L1 cache is any different than knowing recipies when it comes to the story which jobs AI will take. I'm convinced by now it will be the jobs of those people believing such crap.
So... The AIs with no model of the world are replacing software developers that have no model of the world?
Unless you’re claiming that AIs will suddenly (and very soon) stop improving, they are obviously a threat to everyone’s job.
Calling notable conjectures that have been open for decades “low-hanging fruit” is an act of desperation. Most professional mathematicians couldn’t have proved those conjectures if their lives depended on it.
I wouldn’t call it “low hanging fruit” but it’s easy to think of problems that seem harder. Apparently solving notable math conjectures is easier than building a practical robot to deliver a package to someone’s porch?
So, yes, AI is a big deal and we don’t know what it’s going to affect, but the goal of replacing everyone’s job is extremely ambitious and there’s a long way to go.
This has to be assessed separately for each kind of job.
Moravec's Paradox strikes again!
Moravec must be at some level gratified things are arriving close to his predicted timeline.
The thought that anything could improve without bounds would be absurd. We are living in the physical world after all. The (open, interesting) question is how close we are to the limit.
It’s safe to assume that after less than a decade of LLM development, we’re nowhere close to the limit yet. In fact, progress still seems to be accelerating at the moment.
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Types of technology - of which we can include intelligence - move along S curves, but it's more absurd to think that humans are near the top of that curve rather than right at the bottom.
There might be a thing beyond intelligence that we can't even conceive of.
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>Unless you’re claiming that AIs will suddenly (and very soon) stop improving
Most technologies level off sharply after bouts of boundless improvements.
In 1968 they thought we'd be flying to the moon by now but instead we're flying across the ocean in planes not that different from the 747 that existed back then.
They sometimes start improving again. In the context of your comment, look how the cost/kg to LEO has suddenly dropped radically. This was mostly due to institutional change that allowed previous non-technological barriers to improvement to be bypassed.
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Even if AI stops merely at solving Erdos problems (merely...), metabolizing it would take decades.
> COUNTEREXAMPLE TO EULER'S CONJECTURE ON SUMS OF LIKE POWERS
> BY L. J. LANDER AND T. R. PARKIN
> A direct search on the CDC 6600 yielded:
> as the smallest instance in which four fifth powers sum to a fifth power. This is a counterexample to a conjecture by Euler that at least n nth powers are required to sum to an nth power, n>2.
https://www.ams.org/journals/bull/1966-72-06/S0002-9904-1966...
It is a conjecture whether grinding it out on Lean is a difference in kind, rather than degree. I say degree. But it remains to be seen.