throw1235435
Data centres also prefer being near major cities due to latency. This means to deliver the amount of power required there needs to be huge upgrades to infrastructure (long distance power lines) just to provide for data centres. Who does that? At the moment for many cities it is the taxpayer who will fork higher electricity bills for infra upgrades for a data centre few of them care or need.
On the water issue - who will give money to residents? The taxpayer? Just to subsidize data centres/AI construction? Water is also unlike electricity more scarce especially potable water. Do we build more dams? Desalination? Main line piping? Taxpayer funds that again?
To the average joe blow: I'm paying more taxes, for data centres I don't want, to pay more for electricity and have water shortages, for something that will take my job or someone's job I know. Not a great trade.
There's a lot of negative externalities to data centres due to their sheer scale to the point where they compete with households and other industries. My view is that the data centres should pay and compensate the community for those should they use grid or community based infra. The income could actually long term could help the community get a dividend from data centre construction as well.
Cheap power is one thing. Quality reliable power at mass scale is quite another. These things chew through a LOT of power and most people don't understand the sheer scale of it. I've seen a local one (a medium AI data centre) take up 2% of the whole cities grid and there's plenty more to come around here including a 1GW one (10% of the whole city's power in only 0.005% approx of the city's whole space) which cleared wildlife reserved land to build. Even a few of these things compete massively for trades people, commodities, power and other infrastructure pricing locals out. They are talking about using evaporative cooling as well putting pressure on water supply.
As you use more and more land as well the ability to provision renewables decreases - in general renewable power needs more land/resources per energy produced. You can't just mine it out of the ground; they just aren't as dense of a form of energy. Which means we either build less data centres to make room for renewables and transmission infrastructure associated with them, or more likely with lax regulation builders switch to more dense power sources (e.g. gas peakers, generators, etc) even if it is for supplementation.
I can see a future where data center wants are put ahead of communities paying tax on said infrastructure. In fact I think its happening in some places already.
AI, being the super hungry energy monster it is right now, in my view accelerates this trend not reverses it. Even with renewables the need for reliable, stable power in a dense form (data centres use A LOT of power per sqm) means lots of land clearing, energy for construction, cooling/pumping, chip manufacturing and other uses. All want stable quick to deploy power due to the AI race (e.g. fossil fuels).
Generally industries once they become a cheap commodity are at best cost based pricing. If you aren't charging to cost I will go to where it is; especially in a saturated market.
Ironically large corp, instead of tech companies, is probably where the SWE jobs of the future are at. Cost based pricing in cost based centre's. Creating own software with domain knowledge; rather than generic SaaS. Shared platforms will probably still have some value; but the value there isn't from the effort in code - more things like network effects, physical control, regulation, etc. Not an industry to get into anymore IMO -> AI is destroying SWE.
Software was always a means to an end; albeit an expensive way to get there that often paid off anyway at scale. The means is getting cheaper; the end remains.
There are also mixed people on these forums in different regions, countries, work experiences, etc. For example software in most places in the world had an above average salary but not extremely high (i.e. many other white collar professions would pay similar/more). For those people where it is a standard skilled role it probably hits even harder now than say the ones with lots of stock who can retire early and enjoy the new toy that is AI.
Most jobs don't have the same level of verification and/or repeatability. Some factors include:
* Physical constraints: Even the jobs that have productive output if they are physical it will take a long time for AI and more importantly energy density to catch up. Robots have a while to go as well - in the end human hands and your metabolism/energy density will be worth more than your brain/intelligence.
* Cost of failure/can't repeat: For things like building the cost of failure is high (e.g. disposal, cleanup, more resources, etc) -> even 70% of a "building bench" benchmark would be completely inadequate without low cost to repeat. Many jobs are also already largely automated but scaled (e.g. mining, manufacturing, etc) - they've already gone through the wave.
* Human need for its own sake: Other jobs cater not just for productive output, but for some human need where it hasn't been made more efficient ever (e.g. care jobs). There are jobs that a human is more effective in the medium term because the receiver needs it from a human.
No -> this just affects white collar STEM based roles. Thinking we are in it together is just another form of "cope" sadly. There's a rational reason why others have optimism while we SWE's are now full of anxiety and dread.
For the people who it doesn't affect given their current place in many societies (nurses, builders, etc etc) there will be little sympathy.
> As much as I had high hopes for F# I think its safe at this point, to not pursuit it any further
I find this attitude interesting; you wanted it to be more than it was. I don't have high hopes for any language; other than it building my software which it and many others can do. Right tool for right job. I'm not attached to my code, other than if it can be maintained, changed, has sane defaults/guardrails to introduce less defects, etc. F# can do this, as many others. Interestingly I've seen the same attitude eventually happen to all languages of this class other than Rust; Scala, OCaml, etc are in similar positions.
Funnily enough Opus/CC has a number of times for my projects has suggested Rust, and if that doesn't work (too much for the team) went F# even over Java based langs assuming domain modelling code and the need for more perf (e.g. value types, and other stuff) without explicit prompting. Its then generated fsx scripts to run experiments, etc that seem to be more reliable than the default Python ones it runs (package errors and static typing fixes mostly). `dotnet fsi` fits well into the agentic workflow.
> Rescript and that being said, Rescript is probably more of a competitor to gleam
Depends on your target. F# at least has a backdoor into the C# ecosystem - from a risk perspective that makes it more palatable. New languages have an initial "excitement" about them; but generally they are just tools.
Pick something that fits your team and build. F# does work, and so do many other tools. In the age of AI IMO the ecosystem and guardrails for the AI matter more than the language and its syntax IMO other than readability. In this regard F# still is "less risky" with its .NET interop.
Yes but not for the reason you think - more that those are the future customers. If you look closely most are pivoting slowly away from software and shifting more to AI + hardware. The slow layoffs and pivoting that capital to infra shows this. All that "vibed" software needs to run somewhere. Also the models that generate and also power all that software need compute which comes from somewhere.
If I can:
- Have large margin compute since GPU's, power, data centre, etc setup is expensive AND
- Models that outperform models you can have at home.
- Vibed software that derives a lot of functionality from the AI compute and wants to be hosted on compute.
The big companies are pivoting away from software to being more infrastructure like for the democratized software that is projected to be made. They will be fine but in 10 years they will be more cloud hyperscalers, AI compute agents, etc than software businesses. Any software they write will be more to package up their compute as higher margin products.
None of this IMV gives any hope to current SWE's.
The root question is: Will AI decentralise quicker than the disruption to this profession? I don't think so.
I've noticed us techies don't really understand economics and game theory all that well - we just see awesome toy and want to play with it and want others to enjoy it too. We have worked to democratize computing for years (e.g. OSS) now to our detriment. No one in society long term respects people who do this in a capitalist system; they find them naive. I can now understand why other professions find us a little immature like kids playing with tech toys.
I love solving problems with technology and love the field, but as I've gotten older I look back on a less technological life with nostalgia. Technology for all its benefit has disrupted the one thing humans do need and had for millions of years in our evolution - relative stability within their lifetimes. The mental health benefits to stability are massive and usually unmeasured. Technology, as evidenced by this thread, creates more and more anxiety about our future and our place within the community (e.g. social media, AI, and others). "Adaptability" isn't just a psychological trait; a wealthy person and secure person by definition is more adaptable too.
If the local unskilled job matters more than a SWE now these people have gone from being worth something to society to being less of worth than someone unskilled with a job. At that point following from your logic I can assume their long term value is one of an unemployed person which to some people is negative. That isn't just an identity crash; its a crash potentially on their whole lives and livelihood. Even smart people can be in situations where it is hard to pivot (as you say mortgages, families, lives, etc).
I'm sure many of the SWE's here (myself included) are asking the same questions; and the answers are too pessimistic to admit public ally and even privately. Myself the joy of coding is taken away with AI in general, in that there is no joy doing something that a machine will be able to do better soon for me at least.
Claude code making itself obsolete is banned.
I'm not sure what this means for the future of SWE's though yet. I don't see higher levels of staff in big large businesses bothering to do this, and at some scale I don't see founders still wanting to manage all of these agents, and processes (got better things to do at higher levels). But I do see the barrier of learning to code gone; meaning it probably becomes just like any other job.
The "sudden switch" since Opus 4.5 when many were saying just a few months ago "I enjoy actual coding" but now are praising LLM's isn't a one off occurrence. I do think underneath it is somewhat motivated by fear; not for the job however but for relevance. i.e. its in being relevant to discussions, tech talks, new opportunities, etc.
Don't get me wrong; I hope that we do see it in physical work as well. There is more value to society there; and consists of work that is risky and/or hard to do - and is usually needed (food, shelter, etc). It also means that the disruption is an "everyone" problem rather than something that just affects those "intellectual" types.
I disagree a little in that stability/predictability to people also adds some benefit to society - constant disruption/change for the sake of efficiency I believe at extreme levels would be bad for mental health at the very least and probably cause some level of outrage and dysfunction. I know as an SWE tbh I'm feeling a bit of it - can't imagine if it was everyone.
I personally think there is a tradeoff; people on average have limits to adaptability in their lifetimes and so it needs to be worth it for people to invest and enter in a given profession (some level of economic profit that makes their limited time worth spending in it). It shouldn't be excessive though - it should be where both client and producer get fair/equal value for the time/effort they both need to put in.
Outside the verifiable domains I think the impact is more assistance/augmentation than outright disruption (i.e. a novelty which is still nice). A little tiny bit of value sprinkled over a very large user base but each person deriving little value overall.
Even as they use it as search it is at best an incrementable improvement on what they used to do - not life changing.
* Context is debatable/result isn't always clear: The way to interpret that/argue your case is different (i.e. you are paying for a service, not a product)
* Access to vast training data: Its very unlikely that they will train you and give you data to their practice especially as they are already in a union like structure/accreditation. Its like paying for a binary (a non-decompilable one) without source code (the result) rather than the source and the validation the practitioner used to get there.
* Variability of real world actors: There will be novel interpretations that invalidate the previous one as new context comes along.
* Velocity vs ability to make judgement: As a lawyer I prefer to be paid higher for less velocity since it means less judgement/less liability/less risk overall for myself and the industry. Why would I change that even at an individual level? Less problem of the commons here.
* Tolerance to failure is low: You can't iterate, get feedback and try again until "the tests pass" in a court room unlike "code on a text file". You need to have the right argument the first time. AI/ML generally only works where the end cost of failure is low (i.e can try again and again to iron out error terms/hallucinations). Its also why I'm skeptical AI will do much in the real economy even with robots soon - failure has bigger consequences in the real world ($$$, lives, etc).
* Self employment: There is no tension between say Google shareholders and its employees as per your example - especially for professions where you must trade in your own name. Why would I disrupt myself? The cost I charge is my profit.
TL;DR: Gatekeeping, changing context, and arms race behavior between participants/clients. Unfortunately I do think software, art, videos, translation, etc are unique in that there's numerous examples online and has the property "if I don't like it just re-roll" -> to me RLVR isn't that efficient - it needs volumes of data to build its view. Software sadly for us SWE's is the perfect domain for this; and we as practitioners of it made it that way through things like open source, TDD, etc and giving it away free on public platforms in numerous quantities.
Any data, verifiability, rules of thumb, tests, etc are being kept secret. You pay for the result, but don't know the means.
There's an element of arms race between players, and the genie is out of the bottle now so have to move with it. Game theory is more driving this than economics in the short term.
Marginal gains on top of these investments probably have a ROI now (i.e. new investments from this point).
Also your notion of "better tools" may of not happened, or happened more slowly without open source, AI, etc which would of meant higher salaries for longer most probably. That's where I disagree with the parent poster's claim of higher salaries - AI seems to be a great recent example of "better tools" disrupting the premium SWE's enjoy rather than improving their salaries. Whether that's fair or not is a different debate.
I was just doubting the notion of the parent comment that "open source software" and "automated testing" create higher salaries. Usually efficiency economically (some exceptional cases) creates lower salaries for the people who are made more efficient all else being equal - and the value shifts from them to either consumers or employers.
Making software easier and more abstract has allowed less technical people into the profession, allowed easier outsourcing, meant more competition/interview prep to filter out people (even if the skills are not used in the job at all), more material for AI to train on, etc. To the parent comment's point I don't think it has boosted salaries and/or conditions on average for the SWE - in the long run (10 years +) it could be argued that economically the opposite has occurred.
With AI at least locally I'm seeing the opposite now - less hiring, less wage pressure and in social circles a lot less status when I mention I'm a SWE (almost sympathy for my lot vs respect only 5 years ago). While I don't care for the status aspect, although I do care for my ability to earn money, some do.
At least locally inflation adjusted in my city SWE wages bought more and were higher in general compared to others in the 90's-2000's than on wards (ex big tech). Partly because this difficulty and low level knowledge meant only very skilled people could participate.