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I don't think you are wrong. I find many tech people/founders excited by AI don't understand end game economics in general. Like kids excited by the new toy starting their new startup they don't see the end game if this all plays out; or they are hopeful that they are the lucky ones.

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.

Changed their opinion/public stance completely sometimes only months apart. For example "I love programming" to "if you aren't with AI you are behind" within maybe only 6 months time. Its almost like anyone with any public opinion in software is being paid to say it now and boost the AI hype.

Generally from a financial planning standpoint 35-50 is typically the "grinding years" where mortgage, family, and other life commitments means that typically your career investment needs to pay off to make it through. In some ways it is the "danger zone" financially. Hard to change careers (not young enough), but not yet worked enough to retire with large expenses coming in. This isn't unique to software engineers either -> this is most people in most jobs.

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.

Software, and most STEM based jobs, have a lot of determinism and verifiability + some way to reduce the cost of failure so brute force iteration can cover up the remaining. There is often "a correct answer". They've also yet to be truly disrupted until now which makes them particularly vulnerable than any other job.

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.

What's more interesting is how the big names in our industry, the ones who already made their money as you say, have turned quickly since the end of 2025. I think even the most old school names can see that the writing is on the wall now.

A lot of startups/small businesses are like "with AI we can build more than ever". The problem is so can everyone else and capitalism rewards scarcity not value. The bar for startups and small software business has risen quite substantially. I know we are avoiding buying software now where I work if possible unless we previously committed to it (contracts).

The language is still ahead of C#, and still receiving features and keeping up by and large with the .NET ecosystem. Tbh I don't get the sheer negativity; the same thing could be said for Gleam or any other functional language these days tbh especially with AI coming along w.r.t long term support. Eventually things just work and are mostly complete; things don't have to get reinvented or get better forever.

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.

They are laughing at them.

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.

There's a lot of this forum in exactly that position. The fear is real; there is a real risk this AI destroys families and people's lives in the disruption.

This is what will occur - the bad scenario that is. Labor and its knowledge distributes (hard to contain knowledge), capital centralises and compounds. Always been that way. With AI there will be a a tension between the two of course.

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.

Sure; I absolutely agree and more to the point SWE's and their ideologies compared to other professions have meant they are the first on the chopping block. But what do you tell those people; that they no longer matter? Do they still matter? How will they matter? They are no different than practitioners of any other craft - humans in general derive value partly from the value they can give to their fellow man.

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.

Software dev's training the model with their code making themselves obsolete is encouraged not banned.

Claude code making itself obsolete is banned.

If that is true; then all the commentary around software people having jobs still due to "taste" and other nice words is just that. Commentary. In the end the higher level stuff still needs someone to learn it (e.g. learning ASX2 architecture, knowing what tech to work with); but it requires IMO significantly less practice then coding which in itself was a gate. The skill morphs more into a tech expert rather than a coding expert.

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.

Not entirely disagreeing with your point but I think they've mostly been forced to pivot recently for their own sakes; they will never say it though. As much as they may seem eager the most public people tend to also be better at outside communication and knowing what they should say in public to enjoy more opportunities, remain employed or for the top engineers to still seem relevant in the face of the communities they are a part of. Its less about money and more about respect there I think.

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.

Not sure. As software becomes a commodity I can see the "old school" like tech slowing down (e.g. programming languages, frameworks frontend and backend, etc). The need for a better programming language is less now since LLM's are the ones writing code anyway more so these days - the pain isn't felt necessarily by the writer of the code to be more concise/expressive. The ones that do come out will probably have more specific communities for them (e.g. AI)

Maybe. There the cost of failure again is low. Its easier to destroy than to create. Economic disruption to workers will take a bit longer I think.

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.

The question is how rapid the adoption is. The price of failure in the real world is much higher ($$$, environmental, physical risks) vs just "rebuild/regenerate" in the digital realm.

But otherwise I think it's harmful to society, even if beneficial to the individuals

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.

How many pay? And out of that how many are willing to pay the amount to at least cover the inference costs (not loss leading?)

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.

Not really. I used to think more general with the first generation of LLM's but given all progress since o1 is RL based I'm thinking most disruption will happen in open productive domains and not closed domains. Speaking to people in these professions they don't think SWE's have any self respect and so in your example of law:

* 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.

I also believe this - I think it will probably just disrupt software engineering and any other digital medium with mass internet publication (i.e. things RLVR can use). For the short term future it seems to need a lot of data to train on, and no other profession has posted the same amount of verifiable material. The open source altruism has disrupted the profession in the end; just not in the way people first predicted. I don't think it will disrupt most knowledge work for a number of reasons. Most knowledge professions have "credentials' (i.e. gatekeeping) and they can see what is happening to SWE's and are acting accordingly. I'm hearing it firsthand at least locally in things like law, even accounting, etc. Society will ironically respect these professions more for doing so.

Any data, verifiability, rules of thumb, tests, etc are being kept secret. You pay for the result, but don't know the means.

Tbh this whole AI thing is probably a negative ROI but it will pay off. Even if the debt is written off the AI enhancements that this whole misallocation of capital created are now "sunk" and are here to stay - the assets and techniques have been built.

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).

Sure - I agree with that, and I agree its good for society but as you state probably not as good for the SWE who has to work harder for the same which was my point and I think you agree. Other professions have done what you have stated (i.e. certification) and seen higher wages than otherwise which also proves my point. They see this as the "street smart" thing to do, and generally society respects them for it putting their profession on a higher pedestal as a result. People respect people who take care of themselves first generally I find as well. Personally I think there should be a balance between the two (i.e. a fair go for all parties; a fair day's work with some job security over a standard career lifetime but not extortionary).

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.

I'm talking about what I perceive to be the median salary/conditions with big tech being only a part of that. My point is more that I remember back in that period good salaries could be had outside big tech too even in the boring standard companies that you state. I remember banks, insurance, etc paying very well for example compared to today for an SWE/tech worker - the good opportunities seemed more distributed. For example I've seen contract rates for some of the people we hire haven't really changed for 10 years for developers. Now at best they are on par with other professional white collar workers; and the competition seems fiercer (e.g. 5 interviews for a similar salary with leetcode games rather than experienced based interviews).

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.

There's also the effect of different models. Until the most recent models, especially for concise algorithms, I felt it was still easier to sometimes do it myself (i.e. a good algo can be concise/more concise than a lossy prompt) and leave the "expansion/repetitive" boilerplate code to the LLM. At least for me the latest models do feel like a "step change" in that the problems can be bigger and/or require less supervision on each problem depending on the tradeoff you want.

Indeed it did; I remember those times. All else being equal I still think SWE salaries on average would of been higher if we kept it like that given basic economics - there would of been a lot less people capable of doing it but the high ROI automation opportunities would of still been there. The fact that "it sucked" usually creates more scarcity on the supply side; which all being equal means higher wages and in our capitalist society - status. Other professions that are older as to the parent comment already know this and don't see SWE as very "street smart" disrupting themselves. I've seen articles recently like "at least we aren't in coding" from law, accounting, etc an an anecdote to this.

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.

Sure. I just know in most companies (seeing the numbers on projects in a number of them across industries now) funding projects which give time for people to think, ponder, publish white papers of new techniques is rare and economically not justifiable against other investments.

Put it this way - to have a project where people have the luxury to scratch their heads for awhile and to bet on something that may not actually be possible yet is something most companies can't justify to finance. Listening to the story of the transformer invention it sounds like one of these projects to me.

They may stand on the shoulders of giants that is true (at the very least they were trained in these institutions) but putting it together as it was - that was done in a commercial setting with shareholder funds.

In addition given the disruption to Google in general LLM's have done I would say, despite Gemini, it may of been better cost/benefit wise for Google NOT to invent the transformer architecture at all/yet or at least not publish a white paper for the world to see. As a use of shareholders funds the activity above probably isn't a wise one.

Expertise and craft and specialized knowledge can become irrelevant in a heartbeat, so your meaning and joy and purpose should be in higher principles.

My meaning could be in higher purposes; however I still need a job to be enable/pursue those things. If AI takes the meaning out of your craft it takes out the ability to use it to pursue higher order principles as well for most people, especially if you aren't in the US/big tech scene with significant equity to "make hay while the sun is still shining".

Jevons paradox I doubt applies to software sadly for SWE's; or at least not in the way they hope it does. That paradox implies that there are software projects on the shelf that have a decent return on investment (ROI) but aren't taken up because of lack of resources (money, space, production capacity or otherwise). In general unlike physical goods usually the only resource lacking is now money and people which means the only way for more software to be built is lower value projects now stack up.

AI may make low ROI projects more viable now (e.g. internal tooling in a company, or a business website) but in general the high ROI and therefore can justify high salary projects would of been done anyway.

If we had done what you say (distribute wealth more evenly between people/corporations) more to the point I don't know if AI would of progressed as it has - companies would of been more selective with their investment money and previously AI was seen at best as a long shot bet. Most companies in the "real economy" can't afford to make too many of these kind of bets in general.

The main reason for the transformer architecture, and many other AI advancements really was "big tech" has lots of cash that they don't know what to do with. It seems the US system punishes dividends as well tax wise; so companies are incentivized to become like VC's -> buy lots of opportunities hoping one makes it big even if many end up losing.