Posted by delichon 5 hours ago
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
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.
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.
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…
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.
Maybe LLMs oneshotted the right way, maybe it needs fixes, maybe some fundamentals are misunderstood, in any case it’s easier for me to know what I need to build, for the PM to be aware of some limitations (LLMs do the job of pushing back and explaining) and overall for us to have to the point conversations.
It is somewhat orthogonal to what you say, when you focused on dev seniority, so that part stands true.
But I think “PMs armed with an LLM” can, when properly used, add a lot of value to the dev process.
When I use them myself, I just see them crushing it and think, this thing is now doing my job for basically $0, I am no longer economically relevant. But I've spent a lifetime learning to program, so it's possible I only get good results because of the way I think to prompt it.
I really can't get the outside view so I can't decide whether AI is going to make me homeless or not. I think we need the videos.
I'm under no illusions about the goals of AI company execs to justify their valuations (and expenses!) by capturing a huge chunk of global employment value, and the CEOs of many big companies whose financials are getting squeezed for all sorts of reasons and are all too happy to jump on the efficiency narrative of AI to justify layoffs that would have been necessary anyway. Also, AI will keep getting better and it could certainly will move up the food chain—it's already replaced a lot of what I did and I assume capabilities will continue improving for a while even after model capabilities plateau as we improve harnessing, tooling and practice.
So yeah, it can replace a lot of what we do, but I'm not running scared because every step of the way I've seen software people are the ones who actually get the most out of LLMs. Sure it can write all the code so the job changes, but even our workflows completely change, it's giving us more of an edge (if we're open to it) than it does to anyone non-technical. At this stage it still feels empowering on an individual level.
Now I do worry about the consolidation of power and wealth in a tech oligarchy, but that's an issue we need to deal with at a societal and government policy level. Essentially, I can see AI as having radically different outcome potential based on how it's governed. In one way it can be very empowering to small teams, and reduce coordination costs, and increase competition by allowing smaller groups of people to make more scalable companies. But it could also lead to unprecedented concentration of wealth and power if a small set of AI companies are allowed to capture all the economic gains. I don't think there are any easy answers, but I do feel hopeful that we can figure something out as a society—it certainly seems to be creating some unified sentiment across political lines that have been so polarized and divisive over the last decade.
Once upon a time the US had visionaries steering DARPA and making useful bets on the future.
Now strategy is defined by stonks-go-up, quarterly returns, democracy bad, and CEO narcissism, and that's a potently catastrophic combination.
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.
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.
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.
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.
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.