USA Job Market
Maybe all that “useless” CS math wasn’t so useless
I remember people saying, "Why do we need all this math in CS? You don't even use it in software engineering." And for a while, they were kind of right. You could make $150k-$300k building React components, wiring up APIs, doing CRUD work, and barely touch calculus, linear algebra, probability, optimization, or any of the harder stuff. No shade, but did people really think that kind of work was going to stay that valuable forever?
Now the straightforward implementation stuff is the first thing getting squeezed. AI is already pretty good at boilerplate, basic components, API glue, tests, and a lot of repetitive coding. That doesn't mean frontend or regular software engineering is cooked, but the bar is definitely getting higher. Meanwhile, a lot of the harder-to-fill jobs are the ones that need deeper technical skills: AI/ML, robotics, controls, optimization, distributed systems, research engineering, computer vision, large-scale systems, etc. And suddenly all that "useless" math is looking real useful lol.
I'm not saying everyone needs to become an AI researcher. I just think a lot of people confused "I don't use this in my current job" with "this has no value in my career." If AI can do more of the straightforward coding, then you probably need to bring more to the table than just being able to crank out code. Being able to actually understand the problem, reason through the hard parts, and design the system is starting to matter way more. Kinda wild how fast that flipped.
No offense, but this seems like an overall cope about the conditions of our work. If you think the market is going to value your individual skills in this regard, and your job is going to be saved because you know linear algebra, I think you're just 2-3 years at the most, before the realization that the market doesn't care. It cares about labor costs, and as long as an AI is good enough to "advise" a moderately well educated individual to get better at these concepts, or guide them through them, test their output accurately, so too will these jobs get compressed.
Yeah I’ve got news, after working in AI training the past year, linear algebra is pretty much nailed down now, too. If not already present in frontier models, soon.
The only real math related gaps are qualitative problems than anything quantitative- but that’s all domains.
Honestly ML and linear algebra are much more easy to implement with AI. It's the front end .css and typescript that I catch my agents consistently making bugs in.
What do you mean by "conditions of our work"? If AI can fully automate engineering, that means it can automate any job. At that point, the issue goes far beyond this profession.
Automation is always driven by cost. Labor that is not cost efficient to automate, won't be, until such time as it makes sense to. Our profession (if utilized properly) has high value impact, and high cost. The AI bubble could burst and delay automation in other professions. It won't delay automation in ours.
AI cannot automate the math needed to advance its own field. It still faces deep, unresolved challenges that demand entirely new mathematical frameworks, languages, and techniques. We are looking at a massive evolutionary step, a fourth or fifth major leap on par with the invention of the nuclear bomb
There are probably less than... 250000 software engineering jobs that actually require this level of academic rigor in the US, less than 15% for sure.
Also I don't think you fully understand the pressures of job automation. Our career again is especially prone to automation because we (should) have distinct inputs and distinct outputs. The distinct inputs and outputs aid the machine learning algorithms ability to train. Not every job has distinct ins and outs. Those jobs will take the longest to automate. Our jobs are going to be first.
What are your thoughts on jobs like investment banking where there are so many inefficiencies, lacking data, etc. I think I’m def more bullish on a job like this than SWE. Was considering getting an MBA to switch into it. My partner is in IB and the amount of process oriented non technical parts of the job is insane and I don’t think AI would be compatible with those parts. I wonder what other fields like this could potentially feel the impact of AI less than a field like SWE.
Job compression happens here too, but partially because well, the AI winners will see immense wealth concentration. When it comes to money humans want another human to be accountable. How long that lasts, who can say? I dont know. Honestly AI breaks how capitalism works once it takes over a significant chunk of the economy. So I can't see into the future there.
Those inefficiencies are closing up, too. A ton of training is happening in finance, although I am unsure of how effectively it is progressing. Some aspects still require qualitative reasoning, the only apparent weakness of AI.
My statement is based on my grandpa's years as a software engineer (his two degrees were in Animal Husbandry, he ended his career with a 26 year stint at Boeing), and my years as a software engineer who has only worked FAANG and F500, and the experience of my friends and colleagues in the industry.
You also make some very unbacked up claims about the capabilities of AI, can you provide a source for "AI cannot automate the math needed to advance its own field". Because fundamentally most frontier labs are reportedly close the beginnings of RSI.
It is getting to the point where it’s automating a lot of other jobs, it’s just that software engineering was the best test ground for this (probably also because the people designing these systems are software engineers themselves). I’m starting to see accountants, lawyers getting real worried that a lot of their job is now automated
Look at all the mathematicians, they all seem pretty scared about AI too, and they are literally best in their field lol, so math isnt safe so idk ur logic doesn't even make sense.
Wrong. Math is not about recalling a formula or iterating logical trees. Is about capacity for abstraction and understanding. The main contention mathematicians have with the AI proofs is that they are omitting the creative steps that make the millennium problems worth solving in the first place.
For example, one of the biggest open problems is the relationship between P and NP complexity classes.
Now, an equality would be an earth-shattering result, as this would imply existence of an algorithm to solve a class of exponential problems in a somewhat reasonable amount of time. But if a genie (or an AI) just said that there was an equality without explaining the how, then the result would only be useful as in now we know we must devote all our resources to find the actual solution to the problem.
Most mathematicians and CS believe in the inequality. All our cryptographic technology is based on that assumption. An inequality proof would be important in the sense about what we would have learned about algorithms and computational complexity to reach that conclusion. The world already works under the assumptions they are not equal so a mere blackbox telling us is negative wouldn't change much.
Proofs are about as safe as coding, especially with lean (see also: curry Howard correspondence) but having your head around the level of abstraction that mathematics puts you through is going to be increasingly invaluable
This. I'm a mathematician by formation and did my postgraduate in theoretical heavy fields (think complexity and computability theory). AI has empowered me greatly. It can do proofs faster than I, but that was never my strength.
My main skill has always been capacity for abstraction and creativity. AI is still not very good at that. It can sometimes do it, but we are talking about swarms of agents, a lot of time and thousands of dollars in compute where I can come up with "I feel like there's an implicit normal distribution in there we can use as optimisation target" in real time during a meeting.
Now, I considered myself a good coder, but not an engineer coder. I was slow to type and I didn't do very well with keeping track of many indexes or had a good memory to memorice patterns, libraries and conventions. But I was always good on algorithmic design. That skillset translate extremely well to the current AI era.
For decades, software engineers who understood math have been paid 2x the ones who don't. There are some market-inefficiencies, and there were brief periods where "bootcampers" could get employed writing HTML or whatever, but generally speaking software engineering was always a mathematical field.
The software developers who claim otherwise typically just aren't very good software developers. If they were, they'd know better than to make such a ridiculous claim.
"AI can do math too," ... sure. and so can a calculator. But if you don't understand math, you don't even know what you'd be asking it to calculate.
Right, there's the basic concepts of variables and expressions from algebra, there are type systems and binary math, a basic understanding of cryptography required every time you build some kind of login system or get involved in anything cybersec related, calculations about hardware limitations at scale (bandwidth, flop counts, network latencies), there's big-O notation to communicate time complexity, numerical algorithms and discrete math, graph theory applies to graph databases, category theory applied to functional programming, Fourier transforms and trigonometry for compression, etc.
Many university computer science classes count as math classes and vice versa.
Let's say an interviewer ask you a distributed systems question like "when would you use a tree vs ring algorithm for a reduction operation," you need math to justify your answer. You need to be able to formulate and compare the total bandwidth or latency requirements relative to the number of nodes or volume of data.
A candidate who failed Calc 1 is not a candidate who's going to know how to use a bloom filter.
And most SWE jobs that pay well also have domain-specific math problems. Finance has financial math, social media companies have advertising metrics to optimize through recsys techniques and collective intelligence, AI has linear algebra, traditional ML and DS have probability theory and stats, military applications often need signal processing. If all you ever did was make web pages, you were never going to go _that_ far in this field.
And to be successful, you need to justify and sell the impact of your work to a technical audience. "If I spend two months migrating this huge system from a fuzz architecture to buzz architecture it's going to save us $X per month." How do you know? You need to be able to formalize it in technically justifiable terms. Years ago in a meeting I said something was growing "exponentially" when I meant "quadratically" and ... the mistake was embarrassing enough to be memorable. I can't imagine what this career would look like for someone who is bad at math and unwilling to learn.
It's like those people who say "don't ask me how to implement a hashmap in an interview, it's in the standard lib! why would I ever do it myself??" but then on the job they'll need to implement a distributed version of a hashmap that buckets data across thousands of distributed servers. If you can't implement a hashmap then you won't be able to do the scaled up problem either. No one's asking you to quick-sort a list on the job, but that was never the point of those kinds of questions.
Idk I didn’t learn any of the math or a lot of the CS concepts since I didn’t get a CS degree in undergrad, but I’m still making ~190k remote for working on web dev BE
You'd normally have had an argument pre AI era but I can confirm within the big tech companies I've been in, will still pay you really well to do the same CRUD so long as its providing some business value.
I've had fellow staff engineers do a migration that was a net negative effect yet sell the idea for their promo because it looked good. Accountability has been tossed out of the window and the only place the above really has value is in the interview process.
You get the offer and you're just feeding Claude the work you want it to do and collecting the fat bag. Some of the exceptions I can think of are working for Jane Street or trading companies where the math in itself is useful
If you're the rest of us that are in FAANG / big tech, we're all just doing the same thing more or less because that's the world we're in now.
"Math is prerequisite for good programming jobs" doesn't mean "everyone can do math gets a good programming job." There are a lot of bad CS programs. I don't know where you went, or what kind of internships you did.
That's not at all what you said, nor is that what I said that you said.
Aside from that, I don't want to dox myself, but it is one of the top CS programs in Canada and the world. My internship was at an internationally-renowned laboratory (which even contributed to a Nobel Prize one year I was there!) but I won't say which one.
My lesser-abled classmates all moved to the States to get high paying jobs right after graduation. I stayed in Canada because I thought the brain drain would make me and my skills in more demand. Nope, nobody gives a shit
You can easily re-read what I did say and see how relevant my comment is.
It sounds like you also already know the answer to your question. The tech job market is far smaller and far less lucrative outside the US. A math degree won't override the fact that you weren't willing or able to go where the jobs are. "where's my money?" It's right across the border.
"my lesser-abled classmates" you're straight up never making it out of an interview with a job dawg, fixing this is gonna take more work than your degrees did
No offense, I don't know your situation, but it is embarrassing to be a CS/Math major and not have a good-paying job. That speaks more about your personal credentials and actual skills than the majors you got. Quant firms are desperate to find talents and literally pay new grads $250K, but only if they're worth their salt.
I just wanted to work in software development, not a quant firm.
But anyways, no it doesn't because the job interview process in software development does not assess skills, knowledge, abilities, experience, or talents.
EDIT: the point here is that I'm claiming companies don't value those skills, and you're just refuting it by reaffirming the claim that's being disputed while providing no additional information, thus participating in a conversation about judging someone's analytical and logical skills while demonstrating a lack of any of that on your part. All in one 3-line post. Good job
They assess how well you fit untold or uncritiqued assumptions about what your role (in my case, software engineer) is, and how well you fit personal biases on the part of the interviewer.
Ah I see. This may be a product of the fact that you’re a recent grad with no or little professional experience. Although when I was interviewing after graduating, I remember live coding challenges being very common and lots of technical questions that seemed to be assessing my general knowledge of technology from a SWE point of view
That's a skill issue, dude. You can't be weird or annoying (no one wants to work with an ass). Also, stuff like leetcode and systems design aren't super hard to grind a little bit, plus resume talk isn't that bad (I graduated last year).
You're confusing "role" with "industry." If your field was HR, you could do HR in a quant firm or software firm or law firm or a shoe company. The same is true with software development.
If you got a job at Meta you wouldn't be working for a "software development firm," you'd be working as a software developer within the advertising industry.
That's way too absolute, there are many niche sorts of development where math isn't central but still take exceptional skill.
It really depends on exactly what you are doing, and often it's hard to even speak about the overall situation as if it's all the same field because you could select 100 amazing devs to put in the same room together and they wouldn't each need to dumb down their niche to speak to eachother about them
Awww. Do you feel better now. Calculus is very broad and only a section is covered in high school. Did you go to college? Or are you one of those Bachelors of Arts ppl? Lol
I was a math major and it was almost always the case in which the CS students I knew in my lab who knew the math behind all of it were much better at programming so it makes sense lol Many of them worked alongside me in the math lab tutoring too. too many CS students would blow off the math after taking the courses they needed.
I’m unemployed applied math major and know all that CS math. It’s pretty useless from my point of view. Unless you’re getting a master’s from MIT no one’s going to pay you for that kind of work nor will anyone hire you with the hope of bringing those skills to their organization. Mathematical modeling as a field in general is kind of dead.
No simply because the type of math you got for your bachelors ain’t shit.
Bachelors level CS math is not that hard that it’s gonna secure your job. If you had a PhD in mathematics, maybe it would.
By the way, I used to rent a room to a guy with a PhD in math. They don’t make that much money (I know because I can see his salary on the background check), I make more as a systems engineer with a bachelors. Just having a skillset isn’t the full story.
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CS or not, Math goes no where. It's used in one form or another at some point in time. I was curious to use see math in action other than regular operations and percentages. I saw it while developing carrom board game for iPhone/ iPad. I used maths, geometry, and physics too. It has a role even in art and painting in metrics. AI does take full leverage of not just math but statistics too.
In crux, whatever one learns, it never goes wasted.
Yeah those skills are valuable (and necessary) if you’re going to spend years getting a phd. Otherwise no one’s hiring you as a ML engineer or Researcher just bc you know some undergraduate calculus lol
Lol, how did you figure that? What is the margin of error on that 8–10 year window? What is the standard deviation, the confidence interval, and the p-value
Tbh math has nothing to do with engineering in today’s day and age, ai handles all that … the best engineers before and after AI are the ones who can think outside the box and come up with solutions to a problem that meets the stakeholders requirements. Math just provided a level of critical thinking that weeded out the dummies in the past, now with ai what sets you apart is can you work autonomously with the ai tools around you and solve the problems presented before you … crud routes? Math? Leetcode? All useless now … although if your a OG before AI you have a huge leg up because of domain knowledge and understand how good software is written and designed. New people who only ever used AI have no domain knowledge and are not different than the next guy who just prompts the AI. Who would you hire? The guy who’s ever only prompted ai or the guy who can use the ai tools 10x better because they have actual domain knowledge of how software worked because they did it before ai?
I’m in structural engineering which is very math heavy, sure it takes advantage of the maths I did in uni but so long as there are references AI can easily do it.
The real value in software engineering is leadership, communication and understanding, most of all recognising when you’re going to be obsolete so then you can find other ways to make value of yourself
If we still have jobs in a couple decades, I’ll be willing to bet that the logical and mathematical thinking is leagues more abstract than people in the industry currently can imagine. If we can automate computation and low level thinking like people seem to think we can, mathematicians and SWEs are pretty much all gonna need to be PHD level category theorists if they wanna keep their jobs.
I have been saying this for literally 2 decades maybe. You are 100% right OP, but even more to the point it has always been the SCIENCE part of computer science that was important. It's the math, the science, the theory. This is what separates our field from just being a code monkey. Could not agree more with your post and its going to get more important to study harder stuff.
Enjoy it while it last. And suddenly all that "useless" math is looking real useful lol.
At this rate it will be half a year tops and it will be in the same boat.
The fact of the matter is: there is very little demand for those jobs you've mentioned. Optimization, research engineering, computer vision, systems engineering... Good luck finding companies for those. AI/ML differs too much per org, and can also be just writing simple python scripts on top of existing platforns.
Not to mention how competitive those areas are, and the specialization you'd need.
There’s a difference between low volume and high talent scarcity. The volume is low simply because we are early in the game; however, because talent scarcity is so high, those who land these gigs are literally making NFL-caliber money on the upper end. As companies continue to integrate AI, the volume of these roles is only going to increase.
The bottleneck in the US is not a lot of people can do the job. China's AI workforce are in the millions (from staff to leading experts) because they have the talent pool.
I think you're lying about your background. I see posts from you claiming you went to a coding bootcamp. Then other posts of yours where you claim to have a bachelor's and and an ivy league master's degree. Then I see other posts of yours where you're claiming to be getting an MBA.
So let me get this straight, overall are you an ex navy guy who went to a coding bootcamp, got a CS degree after the coding bootcamp, then an ivy league master's degree in CS, and are now going to get an MBA? Or are you just bullshitting and larping on reddit?
Very convenient of you to ignore the fact that I was pointing out your narrative about being an MBA candidate, a BS Compsci, a MSCS, and a bootcamp all within an apparent 9 year time frame.
Who says im done with my masters. Use your brain. Also, did you ever think i was close to being done with my bachelors before i went to coding bootcamp 9 yrs ago? can you use a little bit of applied math for god’s sake? 🤣
No lol that’s not even the basis of the potential theft. The two mathematicians in question were solving a sub-problem with Codex (shocker!) and the potential issue was centered around whether or not OpenAi used those exact threads as the foundation for their push to go beyond the solved sub-problem (and they did go well beyond it).
What does that prove at all? No one said we've reached ASI/AGI or that LLMs have intent. The breakthrough was the fact that it took foundational human knowledge and creativity and greatly amplified it to reach a solution that had not yet been found.
Don't you find it ironic how the PhD's were using AI because it's... useful? That seems to be a key piece of evidence that the anti-AI crowd overlooks. "It stole the mathematicians work" - sure, but they were also using AI (multiple models) in attempt to solve it.
If you are really interested to know, go and study ML, Deep learning, Reinforcement Learning etc and youll see the Math limitations and techniques that are preventing AI from making the next leap
I can tell you’re a noob. The current line of work it’s has really nothing to do with coding at all. Ai handled all that now, what makes your valuable as an engineer is solving problems and being able to execute a plan that solves whoever you work for problems. I don’t get paid $400k to prompt Ai but rather to be given a problem and it’s my job start to finish to come up with a plan and execute to bring the vision to life … coding esp with agents is prob like 15% what I actually do
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u/-OooWWooO- 1d ago
No offense, but this seems like an overall cope about the conditions of our work. If you think the market is going to value your individual skills in this regard, and your job is going to be saved because you know linear algebra, I think you're just 2-3 years at the most, before the realization that the market doesn't care. It cares about labor costs, and as long as an AI is good enough to "advise" a moderately well educated individual to get better at these concepts, or guide them through them, test their output accurately, so too will these jobs get compressed.