The year is 2026. The unemployment rate just printed 4.28%, AI capex is 2% of GDP (650bn), AI adjacent commodities are up 65% since Jan-23 and approximately 2,800 data centers are planned for construction in the US. In spite of the current displacement narrative – job postings for software engineers are rising rapidly, up 11% YoY. ... We wrote last week that we see the near-term dynamics around the AI capex story as inflationary, but given markets are focused on the forward narrative, we outline a more constructive take on the end state below. Before that, however, it’s worth reflecting that the imminent disintermediation narrative rests on the speed of diffusion.
The chart "Job Postings For Software Engineers Are Rapidly Rising" seems to show a rise from 65 to 71 for "Indeed job postings" from October 2025 to March 2025. That's a 9% increase. Then they inflate that by extrapolating it to a year. The graph exaggerates the change by depressing the zero line to way off the bottom and expanding the scale. This could just be noise.
The chart "Adoption Rate of Generative AI at Work and Home versus the Rate for Other Technologies" has one (1) data point for Generative AI.
This article bashes some iffy numbers into supporting their narrative.
Suggested reading: [1]
[1] https://en.wikipedia.org/wiki/How_to_Lie_with_Statistics
The options do seem a bit idiosyncratic, but I guess they are useful for the kind of data the site users usually look at.
What’s absolutely mind blowing to me though…the idea AI isn’t causing software engineering jobs to collapse…which you would think would make people here happy…is something that makes software engineers upset??
It’s almost as if everyone here has married their identity to the idea they are victims of AI progress and any suggestion otherwise is ego destruction.
”What??? You mean the job market is expanding and the reason I can’t find a job is…me? That can’t possibly be true I’m a genius, the data is clearly wrong!”
Then stuck in the 60-80 range since 2023. The sample period chosen by Citadel is wildly deceptive.
This is an important question and these crap stats are not helping.
The link you're responding to has the option to zoom out more to 2020. If you scroll down to view the other related graphs, you'll find that they also index 2020 as a starting point because they're all tracking this hiring bubble.
edit: nvm they probably pull in results from these ATS
Do they mean "published" or "latest" or what?
It’s continuously updated, and postings are still on the rise as of last week, so your criticism doesn’t make much sense.
But I do like folks calling out the OP for being AI spam.
They're addressing a very important question, and one for which there is surprisingly little hard data. It's too soon to try to see a trend from low-quality data. Three years of this data might be meaningful.
McKinsey reports are the original slop
However, if there will be, locally deployable, meaningfully capable AI models that can change the computing cost equation.
[1]: https://www.forbes.com/sites/jasonsnyder/2025/08/26/mit-find...
If you just mean “people who make software in any capacity”, it will probably grow (or has already grown) via product, marketing, etc folks making internal tools with AI (which may not work out, we’ll see).
Presuming we keep seeing LLM improvements, SWE will move up the stack like they did in the past. They used to work directly with hardware and software. Ops folks sprung up to do the hardware, and SWEs do basically all software using abstractions over hardware. This will be another step up where SWEs no longer work directly on software, but rather on the tooling that writes software which they hand over to marketing, HR, etc.
Again, presuming this all works out the way the AI folks plan.
Unlike people who take the extreme position that vibe coders are useless, I do think LLMs often write individual functions or methods better than I do. But in a way, that does not fundamentally change the nature of the work. Even before LLMs, many functions and methods were effectively assembled from libraries, Stack Overflow snippets, documentation examples, and copied patterns.
The real limitation comes from the nature of transformer-based LLMs and their context windows. Agentic coding has a ceiling. Once the codebase reaches a scale where the agent can no longer hold the relevant structure in context, you need a programmer again.
At that point, software engineering becomes necessary: knowing how to split things according to cohesion and coupling, using patterns to constrain degrees of freedom, and designing boundaries that keep the system understandable.
In my experience, agentic coding is useful for building skeletons. But if you let the agent write everything by itself, the codebase tends to degrade. The human role is to divide the work into task units that the agent can handle well.
Eventually, a person is still needed.
If you make an agent do everything, it tends to create god objects, or it strangely glues things together even when the structure could have been separated with a simpler pattern. Thinking about it now, this may be exactly why I was drawn to books like EIB: they teach how to constrain freedom in software design so the system does not collapse under its own flexibility.
If AI replaces everything, then I become unnecessary. So maybe I am simply trying to convince myself that developers like me are still needed.
That said, realistically, I still think there are limits unless the essence of architecture itself changes. I also acknowledge part of your perspective.
Those of us who are not in the AI field tend to experience AI progress not as a linear or continuous process, but as a series of discrete events, such as major model releases. Because of that, there is inevitably a gap in perspective.
People inside the industry, at least those who are not just promoting hype, often seem to feel that technological progress is exponential. But since we are not part of that industry, we experience it more episodically, as separate events.
At the same time, capital has a self-fulfilling quality. If enough capital concentrates in one direction, what looked like linear progress may suddenly accelerate in an almost exponential way.
However, even that kind of model can eventually hit a specific limit. I do not know when that limit will arrive, because I am not an AI industry insider. More precisely, I am closer to someone who uses Hugging Face models, builds around them, and serves them, rather than someone working on AI R&D itself.
“people like me are still needed” is just a desperate form of self-persuasion.
Most of what these PMs can produce nowadays turns boardroom heads, sure. But it's just that: visuals and just enough prototype functionality that it fools the people you're demoing to. Seen enough of these in the recent past.
Will there be some PMs that can become "software developers" while armed with an LLM? Sure!
But that's not the majority. On the other hand, yes there are going to be "software developers" that will be out of a job because of LLMs, because the devs that were FS and could take an idea from 0-1 with very little overhead even in the past can now do so much faster and further without handing off to the intermediates and juniors. They mentor their LLM intern rather than their intermediates and juniors. The perpetual intermediate devs with 20 years of experience are the ones that are gonna have a larger and larger problem I'd say.
The Staff engineer that was able to run circles around others all along? They'll teach their LLM intern into an intermediate rather than having to "10 times" a bunch of perpetual intermediates with 20 years of experience.
Day to day, the resolution of our work is probably different. We're zooming out and spending more time strategizing and managing the AI tooling. This might mean less jobs. It might also mean we just get more done.
I don't work on AI directly either, but I'm finding a lot of value in learning the new tooling. I think being able to competently leverage these tools is going to be a key skill from now on.
I think my reasoning is you still need a tech person to translate from feature to architecture. AI can do both but not everyone knows they need the latter.
Vibe coding, to me, means having an LLM, with or without agents, do everything after an initial vague prompt. Which is why "anyone" can vibe code (because anyone can write general hand-waving imprecise instructions). This inevitably results in pointless demos and/or unmaintainable monsters.
the scale of code doesnt really matter that much, as long as a programmer can point it at the right places.
i think actually you want to be really involved in the skeleton, since from what ive seen the agent is quite bad at making skeletons that it can do a good job extending.
if you get the base right though, the agent can make precise changes in large code bases
I mostly agree with the general tendency that it starts to break down as the context grows. But there is also a difference in how people evaluate it. Some people say agents are good at building the skeleton, while others say they are better at extending an existing structure.
I think this depends on the setup, and it is ultimately a trade-off.
In my case, I usually work on codebases around 60,000 LoC. The programs I deliver are generally between 60,000 and 80,000 lines of code. I think I can fairly call myself a specialist at that scale, since I have personally delivered close to 40 projects of that size.
At that scale, I felt that agentic coding was actually very good at building the initial skeleton.
I do not know what kind of work you usually do, but if your work involves highly precise, low-level tasks, then I can understand why you might feel differently.
In my case, I mostly assemble high-level libraries and frameworks into working systems, so that may be why I experience it this way.
Like a child growing up!
Also, like a cancer.
Similar process, different outcomes.
1m lines of html are infinitely more conducive for a language model to work in than 10k lines of complex multithreaded low level code.
A lot of coding is just rehashing the same concepts in slightly novel ways, language models work great in this context as code gen machines.
The hope is that we can focus our efforts on harder problems, using language models as a tool to make us more productive and more powerful, and with the advancements open weight models have made, also less reliant on big tech companies to do so.
Or, it there a ceiling that we can't go passed?
If you organize your product into a collection of appropriately scoped libraries (the library is the right size for the LLM to be able to comprehend the whole thing) then the project size is not limited by the LLM comprehension.
Your task management has to match, the organization of your ticketing system has to parallel the codebase.
With this the LLM can think at different scales at different times.
Of course you can break things down into the right atomic units where a code gen machine becomes useful. Because you are an expert. People who aren't literally have no clue.
In any task, you can break it down no matter how complex into units where a language model can output useful code. The more complex, the smaller the units. At some point it's faster to write it yourself, thats the limit on the codegen.
I still don't see how it's anything else than a tool that experienced and knowledgeable workers can use to save time and energy to focus on the hard parts.
Like, it's not surprising that the developers who frequently talk about +90% of their work being delegated to LLMs are web developers. That is a field with very little innovative or complex code, it's mostly just grunt work translating knowledge of style rules and markup to code, or managing CRUD. I'm really thankful I can have a language model do that drudgery for me.
But compare that to eg. writing a multithreaded multiplayer networking service in Rust, they fall woefully short at generating code for me. They can be used in auxiliary aspects, like search or debugging, but the code it produces without substantial steering is not usable. It's often faster for me to write the code myself, because it's not a substantial amount of low impact code required, but a small amount of complex high impact code which needs to satisfy many invariants. This is fast to type, the majority of the work is elsewhere. At the end of the day, they work really well to replace typing the boilerplate, which is much appreciated.
All behavior of backend code can at least be described with automated tests.
I guess my point is more that we have a lot of code being written that probably should have been automated already in some way, but it was simply more practical to just have people writing it. I dont see much harm in automating it with AI - the people doing the grunt work are largely capable of more, but at the end of the day someone has to dig the ditches. Now that we have a backhoe they can go do more interesting stuff.
However when I see people who were largely writing meaningless boilerplate now claiming that software development is dead because they've become automated, I think it's important that people are being realistic about the different contexts in which AI is either useful or not. There is a wide range of experiences, some people believe AI is useful in completely automating their jobs and others feel it's mostly useless, and of course most people are in the middle somewhere. They're all correct, but the context is crucial.
As far as I'm concerned it's just another tool in the toolbox.
Otherwise good luck getting things done in a business environment where people and processes depend on the software you produce.
Thugs of AI thought reality won't catch up to them they're untouchables.
Many firms are implicitly assuming the models will keep improving to the point where all these problems go away. But what if they don’t?
At first I found the AI recruiters impressive, because they tricked me. I thought the recruiter had really done their homework and read my profile deeply!
Now I know that an AI is reading it, picking random things to highlight, and then composing a message. But they're not real. They're just trying to connect to you so that they can say you are in their network when they go to sell their services to hiring managers.
An overwhelmingly large number of engineers have close to zero satisfaction with their work. A lot of firefighting happens across the board. There is a ubiquitous use of AI everywhere in reading documents, writing documents, and wherever hallucinations occur, critical information is also being missed. It's not a surprise at the end of the day, but this entire situation has put us in a very messy overall circumstance.
Both types expect you to spend as many tokens as possible so that the AI bubble doesn't burst (presumably because leadership has a financial interest in this).
Your actual productivity isn't important. If you point out that you're much faster writing code on your own in 90% of cases, you will be told you're not good at AI, you're not prompting it correctly and that generally you're not AI-native and that you'll be left behind. To be precise, token usage is a performance metric, so you'll be let go if Claude is not running continuously 8 hours a day.
I'd like to know how many places have mandates to write 100% of your code using AI, as well as to max out your AI agent's plan. For some reason nobody talks about it even though I know several companies around the world that are forcing this on their employees.
If you're looking for a job then you don't have a choice, it's better to have an income. But if you're looking to change jobs to get away from AI to actually be productive and gain experience then it's a very bad job market.
[edit 25 years not 20]
I'm searching for a job for many months, and I do see the uptick quite clearly.
Fashion is when developers jump on the next web framework because they got bored of the old one.
But when you get fired for not enough token usage, that's something else. When bosses start demanding you write 100% of your code using AI, and then a few months later Anthropic reports 30% increase in usage, that's not fashion. People who invested in AI are putting a lot of pressure on developers to ensure their investment pays off.
Token billing is coming very-very soon, there won't be a "plan".
What will these companies do then?
I have personally never been busier or more productive. It's like all the "work" of my work has disappeared. There are no more blockers and I can just run free and get as much done as I want and the only thing slowing me down is Jira.
The real downturn is going to be the SaaS apocalypse. In the next year or two there will be a reckoning where all these expensive low-code/no-code middleware applications suddenly don't make sense.
So I think it will be less about the ranks of engineers being thinned out unilaterally, and more about large swathes of products being obsolete.
None of these are really because of cost. But more because we can get a superior product by doing so.
It's honestly tiresome to keep having to debunk this with people who have no clue at all how large companies operate.
I think the SaaS landscape will look vastly different in five years.
I suspect hiring will pick up when capability of the models stops growing so quickly or gaps between start widening. Obviously the problem capabilities are not slowing down and gaps get shorter…
The chart is continuously updated, and postings are still on the rise as of last week. Your criticism is moot.
Unavoidable AI-based productivity growth, in software and in all the other industries, will lead to the software, specifically AI in this case, not just eating the wold, it would be devouring it. Such AI revolution will mean even more need for software engineers, just like the Personal Computer revolution and the Internet revolution did in their times. Of course the software engineering will get changed like it did in those previous revolutions.
There is no productivity growth attributed to AI. In fact, serious attempts to measure AI performance show that even if AI makes some code entry tasks faster, total product delivery times are, in fact, increased.
(This should be obvious once you realize coding AIs are technical debt generation machines.)
I think we've gone beyond anecdotal evidence of experience engineers finding true value in this new tech. It may not have registered yet, but skilled people are unequivocally finding value in these tools.
I agree that we have yet to settle down on the true costs involved (which will probably end up at "slightly less than a junior engineer" or something like that) - but we are months beyond the idea that it's all smoke and mirrors and no one is getting value out of it.
It can be true that the engineer is more productive but the end result is the firm is in a net negative state.
- AI will replace all workers (unlikely today) - AI speeds up programming (yes today)
Sure, whatever. That would be anecdotal evidence.