You are thinking growth in product and opportunities. But that doesn't always require or translate to more engineering especially in the face of AI efficiency if that work to support that growth can come from AI (NOTE: I'm not saying it can; I believe some people believe and are acting like it can which is enough). For these people the new bottleneck to growth may be new market segments, adoption and integration which could mean more sales like staff - AI seems more of a boon to the non technical, AI hypers and sales pitchers IMO than it is to engineers. On a side note personally this makes the industry less desirable to work in - it devalues effort/intelligence/craftsmanship/product and values connections, marketing, status and "the hustle" a lot more.
As the supply curve of software becomes more vertical due to AI the argument that growth equals a proportional amount of engineering demand may be violated. We may see 2x growth in some companies even as the "people engineers" are cut. They could still be pursuing growth; it just that engineering costs are now lower or are less of a need to pursue that growth in general.
AI is the first technology that I've seen that has potentially hurt technology engineering demand rather than creating it; which is why the usual arguments don't always apply here.
My opinion is that it is because the measure isn't aimed at technical people. It's aimed at clients that just want to specify what they want -> then get what they want without an engineer. They are pitching to their investors, VC's, etc who just want to "will their stuff into existence" without engaging professional staff. Their end goal isn't augmentation, or a faster tool. It's replacement of expertise and capturing of that value to the AI labs. Similar to how I would love a new house to be magically built just by prompting it without having to engage anyone; or follow the process. Its great for marketing having the AI genie.
> A better move would be to stop allowing people to graduate in CS.
Its a free market if you are the one paying for it solely. If you are willing to pay for the degree then the demand is there for them to sell you the product. Buyer beware sadly. Its a question of what you see the purpose of the course is - to prep people for the job market or to teach knowledge? If it is the former to you then yes they should at least warn people of the competition and the contraction of the industry they may face - they can decide if it is worth it for them.
For many on this forum if they are honest with themselves given the pace of AI and the future risk/uncertainty they may not have taken this risk on. They've misallocated their own funds which is always not ideal - and sadly expected when there is rapid change; not all investments pay off. The only thing I can say is if you are young the time horizon is there to invest in something else and get back on your feet. There is more to life then work IMV especially as you get older; and an investment in your career needs enough time horizon to pay off. For many a job is a means to an end - they may enjoy it but being able to earn a long term living is the primary goal.
In other countries where the education is subsidized for the purposes of skill building for the economy then yes I actually think it might be pragmatic for them to offer less places. Otherwise its tax money going to waste with a misallocation of public resources without the associated society wide economic benefits.
If the industry is to shrink this is the best way it can. Stop people entering while they are young and can pivot into something with better returns. Keep the experienced people who are older and may find it harder to pivot and had some "good days" to help them ride them through these bad times. I've seen similar dynamics in other industries as they slowly die/move on (e.g. manufacturing, niche trades, etc). A slow decline is better than a boom/bust. If it ends up that we need software engineers later training is an easier problem than mid career death for the juniors in a few years time.
Eventually the market finds a new equilibrium of staff to demand ratio. You prefer that happen sooner so people don't make bad investments of their time (e.g. studying the wrong course based on inaccurate market signals).
My personal anecdote when I talk to people - everyone when talking about their job w.r.t AI is like "at least I'm not a software engineer!". To give a hint this isn't just a US phenomenon - seen this in other countries too where due to AI SWE and/or tech as a career with status has gone down the drain. Then they always go on trying to defend why their job is different. For example "human touch", "asking the right questions" etc not knowing that good engineers also need to do this.
The truth is we just don't know how things will play out right now IMV. I expect some job destruction, some jobs to remain in all fields, some jobs to change, etc. We assume it will totally destroy a job or not when in reality most fields will be somewhere in between. The mix/coefficient of these outcomes is yet to be determined and I suspect most fields will augment both AI and human in different ratios. Certain fields also have a lot of demand that can absorb this efficiency increase (e.g. I think health has a lot of unmet demand for example).
I find algo performance is a consideration, but so is overall system performance especially in the face of concurrency, staleness, update rate, data processing size, consistency of data, etc. I think persistent collections are just another tool which is sometimes appropriate; and it has saved me over the standard Concurrent collections in some interesting cases. There are significantly faster immutable collection libraries than the standard F# Map class though online you can use if I recall from awhile back - still not quite mutable perf though. It tends to be appropriate to use for almost the opposite case than a single thread in a tight loop which is the usual benchmark I guess. As usual YMMV/depends on problem at hand.
I actually think F# has received some "love" over the recent years contrary to some on this forum; that feature being an example. My view, maybe unpopular but in the age of AI maybe less so, is there is a diminishing returns to language features anyway w.r.t complexity and the use cases that new feature will actually apply for. F# in my mind and many other languages now for that matter is pretty much there or are almost there; the languages are converging. When I used F# I liked how it unified features and tried to keep things simple. Features didn't feel "tacked on" mostly with some later exceptions.
Last time I used F# a few libraries started adopting this for their CE's (e.g. IcedTasks library, etc).
Agree. Anthrophic in particular have been quite clear in what they are trying to do. Every blog post about every new model almost dismisses every other use case other than coding - every other use case seems almost a footnote in their communication.
The question really is what you think the long term direction of SWE as a profession is. If we need juniors later and senior's become expensive that's a nice problem to have mostly and can be fixed via training and knowledge transfer. Conversely people being hired and trained, especially when young into a sinking industry isn't doing anyone any favors.
While I think both sides have an argument on the eventual SWE career viability there is a problem. The downsides of hiring now (costs, uncertainity of work velocity, dry backlogs, etc) are certain; the risk of paying more later is not guaranteed and maybe not as big of an issue. Also training juniors doesn't always benefit the person paying.
* If you think long term that we will need seniors again (industry stays same size or starts growing again) given the usual high ROI on software most can afford to defer that decision till later. Goes back to pre-AI calculus and SWE's were expensive then and people still payed for them.
* If you think that the industry shrinks then its better to hold off so you get more out of your current staff, and you don't "hire to fire". Hopefully the industry on average shrinks in proportion to natural retirement of staff - I've seen this happen for example in local manufacturing where the plant lives but slowly winds down over time and as people retire they aren't replaced.
Domain knowledge and gatekeeping. We don't know what is required in their role fully, but we do know what is required in ours. We also know that we are the target of potentially trillions in capital to disrupt our job and that the best and brightest are being paid well just to disrupt "coding". A perfect storm of factors that make this faster than other professions.
It also doesn't help that some people in this role believe that the SWE career is a sinking ship which creates an incentive to climb over others and profit before it tanks (i.e. build AI tools, automate it and profit). This is the typical "It isn't AI, but the person who automates your job using AI that replaces you".
I think it's pretty clear that Anthrophic was the main AI lab pushing code automation right from the start. Their blog posts, everything just targeted code generation. Even their headings for new models in articules would be "code". My view if they weren't around, even if it would of happened eventually, code would of been solved with cadence to other use cases (i.e. gradually as per general demand).
AI Engineers aren't actually SWE's per se; they use code but they see it as tedious non-main work IMO. They are happy to automate their compliment and raise in status vs SWE's who typically before all of this had more employment opportunities and more practical ways to show value.
All I can say to that is "I hope so too"; but logic is telling me otherwise at this point. Because the alternative, as evidenced by this thread, isn't all that good. The fear/dread in people since the holidays has been sad to see - its overwhelmed everything else in tech now.
> disrupting others careers is why you have a career in the first place.
Not every software project has or did this. In fact I would argue many new businesses exist that didn't exist before software and computing and people are doing things they didn't beforehand. Especially around discovery of information - solving the "I don't know what I don't know" problem also expanded markets and demand to people who now know.
Whereas the current AI wave seems to be more about efficiency/industrialization/democratizing of existing use cases rather than novel things to date. I would be more excited if I saw more "product orientated" AI use cases other than destroying jobs. While I'm hoping that the "vibing" of software will mean that SWE's are needed to productionise it I'm not confident that AI won't be able to do that soon too nor any other knowledge profession.
I wouldn't be surprised with AI if there's mass unemployment but we still don't cure cancer for example in 20 years.
They commoditized their complement to their hardware/infra, that being software. Good for them and the value of tech will shift to what is still scarce relatively.
Because of point 3 most SWE's are also hesistant to pay for software. The positive feedback loop of "I did well out of this so i will support others as well" is over.
When you are thinking your days are numbered any cost to develop software (even token budget) is measured. As coding becomes commoditized the ROI in code will drop of that code (capitalism rewards scarcity; not value delivered) and you suddenly become cost conscious. We are moving from a monopoly-moat like market to a competitive cost based market in SWE as AI improves.
I think AI has come as the industry was somewhat maturing and most frameworks/software had previous incarnations that mostly did the same thing or could be done adhoc anyway. The need for libraries as the models get better probably declines as well.
Not all open source but a lot of it is fundamentally for humans to consume. If AI can, at its extreme (still remains to be seen), just magic up the software then the value of libraries and a lot of open source software will decline. In some ways its a fundamentally different paradigm of computing, and we don't yet understand what that looks like.
As AI gets better OSS contributes to it; but in its source code feeding the training data not as a direct framework dependency. If the LLM's continue to get better I can see the whole concept of frameworks being less and less necessary.
My theory is that this (juniors unable to get in) is generally how industries/jobs die and phase out in a healthy manner that causes the least pain to its workers. I've seen this happen to a number of other industries with people I know and when it phases out this way its generally less disruptive to people.
The seniors who have less leeway to change course (its harder as you get older in general, large sunk costs, etc) maintain their positions and the disruption occurs at the usual "retirement rate" meaning the industry shrinks a bit each year. They don't get much with pay rises, etc but normally they have some buffer from earlier times so are willing to wear being in a dying field. Staff aren't replaced but on the whole they still have marginal long term value (e.g. domain knowledge on the job that keeps them somewhat respected there or "that guy was around when they had to do that; show respect" kind of thing).
The juniors move to other industries where the price signal shows value and strong demand remains (e.g. locally for me that's trades but YMMV). They don't have the sunk cost and have time on their side to pivot.
If done right the disruption to people's lives can be small and most of the gains of the tech can still come out. My fear is the AI wave will happen fast but only in certain domains (the worst case for SWE's) meaning the adjustment will be hard hitting without appropriate support mechanisms (i.e. most of society doesn't feel it so they don't care). On average individual people aren't that adaptable, but over generations society is.
As the supply curve of software becomes more vertical due to AI the argument that growth equals a proportional amount of engineering demand may be violated. We may see 2x growth in some companies even as the "people engineers" are cut. They could still be pursuing growth; it just that engineering costs are now lower or are less of a need to pursue that growth in general.
AI is the first technology that I've seen that has potentially hurt technology engineering demand rather than creating it; which is why the usual arguments don't always apply here.