Posted by napolux 19 hours ago
This study showing 9-10% drop is odd[1] and I'm not sure about their identification critria.
> We identify GenAI adoption by detecting job postings that explicitly seek workers to implement or integrate GenAI technologies into firm workflows.
Based on that MIT study it seems like 90+% of these projects fail. So we could easily be seeing an effect where firms posting these GenAI roles are burning money on the projects in a way that displaces investment in headcount.
The point about "BigTech" hiring 50% fewer grads is almost orthogonal. All of these companies are shifting hiring towards things where new grads are unlikely to add value, building data centers and frontier work.
Moreover the TCJA of 2017 caused software developers to not count for R&D tax write offs (I'm oversimplifying) starting in 2022. This surely has more of an effect than whatever "GenAI integrator roles" postings correlates to.
On the optimistic take side - I suspect it might end up being true that software might be infused into more niches but not sure it follows that this helps on the jobs market side. Or put different demand for software and SWE might decouple somewhat for much of that additional software demand.
This is really just another form of automation, speeding things up. We can now make more customized software more quickly and cheaply. The market is already realizing that fact, and demand for more performant, bespoke software at lower costs/prices is increasing.
Those who are good at understanding the primary areas of concern in software design generally, and who can communicate well, will continue to be very much in demand.
1) Senior developers are more likely to know how to approach a variety of tasks, including complex ones, in ways that work, and are more likely to (maybe almost subconsciously) stick to these proven design patterns rather than reinvent the wheel in some novel way. Even if the task itself is somewhat novel, they will break it down in familar ways into familar subtasks/patterns. For sure if a task does require some thinking outside the box, or a novel approach, then the senior developer might have better intuition on what to consider.
The major caveat to this is that I'm an old school developer, who started professionally in the early 80's, a time when you basically had to invent everything from scratch, so certainly there is no mental block to having to do so, and I'm aware there is at least a generation of developers that grew up with stack overflow and have much more of a mindset of building stuff using cut an paste, and less having to sit down and write much complex/novel code themselves.
2) I think the real distinction of senior vs junior programmers, that will carry over into the AI era, is that senior developers have had enough experience, at increasing levels of complexity, that they know how to architect and work on large complex projects where a more junior developer will flounder. In the AI coding world, at least for time being, until something closer to AGI is achieved (could be 10-20 years away), you still need to be able to plan and architect the project if you want to achieve a result where the outcome isn't just some random "I let the AI choose everything" experiment.
The distinguishing behavior is not about the quantity of effort involved but the total cost after consideration of dependency management, maintenance time, and execution time. The people that reinvent wheels do so because they want to learn and they also want to do less work on the same effort in the future.
I think this is really underappreciated and was big in driving how a lot of people felt about LLMs. I found it even more notable on a site named Hacker News. There is an older generation for whom computing was new. 80s through 90s probably being the prime of that era (for people still in the industry). There constantly was a new platform, language, technology, concept to learn. And nobody knew any best practices, nobody knew how anything "should work". Nobody knew what anything was capable of. It was all trying things, figuring them out. It was way more trailblazing / exploring new territory. The birth of the internet being one of the last examples of this from that era.
The past 10-15 years of software development have been the opposite. Just about everything was evolutionary, rarely revolutionary. Optimizing things for scale, improving libraries, or porting successful ideas from one domain to another. A lot of shifting around deck chairs on things that were fundamentally the same. Just about every new "advance" in front-end technology was this. Something hailed as ground breaking really took little exploration, mostly solution space optimization. There was almost always a clear path. Someone always had an answer on stack overflow - you never were "on your own". A generation+ grew up in that environment and it felt normal to them.
LLMs came about and completely broke that. And people who remembered when tech was new and had potential and nobody knew how to use it loved that. Here is a new alien technology and I get to figure out what makes it tick, how it works how to use it. And on the flip side people who were used to there being a happy path, or a manual to tell you when you were doing it wrong got really frustrated as their being no direction - feeling perpetually lost and it not working the way they wanted.
I found it especially ironic being on hacker news how few people seemed to have a hacker mindset when it came to LLMs. So much was, "I tried something it didn't work so I gave up". Or "I just kept telling it to work and it didn't so I gave up". Explore, pretend you're in a sci-fi movie. Does it work better on Wednesdays? Does it work better if you stand on your head? Does it work differently if you speak pig-latin? Think side-ways. What behavior can you find about it that makes you go "hmm, that's interesting...".
Now I think there has been a shift very recently of people getting more comfortable with the tech - but still was surprised of how little of a hacker mindset I saw on hacker news when it came to LLMs.
LLMs have reset the playing field from well manicured lawn, to an unexplored wilderness. Figure out the new territory.
Bashing kludgy things together until they work was always part of the job, but that wasn't the motivational payoff. Even if the result was crappy, knowing why it was crappy and how it could've been better was key.
LLMs promise an unremitting drudgery of the "mess around until it works" part, facing problems that don't really have a cause (except in a stochastic sense) and which can't be reliably fixed and prevented going forward.
The social/managerial stuff that may emerge around "good enough" and velocity is a whole 'nother layer.
Louder for those turned deaf by LLM hype. Vibe coders want to turn a field of applied math into dice casting.
You keep using the word "LLMs" as if Opus 4.x came out in 2022. The first iterations of transformers were awful. Gpt-2 was more of a toy and Gpt-3 was an eyebrow-raising chatbot. It has taken years of innovations to reach the point of usable stuff without constant hallucinations. So don't fault devs for the flaws of early LLMs
Talk is cheap, let's see the money :D
Last year was, as it seems, just a normal year in terms of global software output.
But on product hunt, the amount of projects is First week of Jan: 5000+, Entire Jan 2018: 4000 approx.
Has the output of existing companies/products increased substantially?
Have more products proven successful and started companies?
hard to say but maybe a little
Would be impossible to tell.
Not to mention agent capabilities at the end of last year were vastly different to those at the start of the year.
Even if LLMs became better during the year, you'd still expect an increase in releases.
Exactly my thoughts lately ... Even by yesterday's standards it was already very difficult to land a job and, by tomorrow's standards, it appears as if only the very best of the best will be able to keep their jobs and the ones in a position of decision making power.
> A CEO of a low-code platform articulated this vision: in an “agentic” development environment, engineers become “composers,”
I see we'll be twisting words around to keep avoiding the comparison.
The question IMO is, who will be creating the demand on the other side for all of these goods produced if so many people will be left without the jobs? UBI, redistribution of wealth through taxes? I'm not so convinced about that ...
There is no reason why people will left without jobs. Ultimately, "job" is simply a superstructure for satisfying people's needs. As long as people have needs and the ability to satisfy them, there will be jobs in the market. AI change nothing in those aspects.
The people who lose their jobs prove this was always the case. No job comes with a guarantee, even ones that say or imply they do. Folks who believe their job is guaranteed to be there tomorrow are deceiving themselves.
For the record, I was genuinely trying to read it properly. But it is becoming unbearable by mid article.
It resembles an article, it has the right ingredients (words), but they aren't combined and cooked into any kind of recognizable food.
Its hard to put my finger on it. But it lacks soul, it factor or whatever you want to call it. Feels empty in a way.
I mean, this is not the first AI assisted article am reading. But usually, it's to a negligible level. Maybe it's just me. :)
intro... Problem... (The Bottom line... What to do about it...) Looped over and over. and then Finally...
I want to read it, but I can't get myself to.
Curious about how the Specialist vs Generalist theme plays out, who is going to feel it more *first* when AI gets better over time?
A humble way for devs to look at this, is that in the new LLM era we are all juniors now.
A new entrant with a good attitude, curiosity and interest in learning the traditional "meta" of coding (version control, specs, testing etc) and a cutting-edge, first-rate grasp of using LLMs to assist their craft (as recommended in the article) will likely be more useful in a couple of years than a "senior" dragging their heels or dismissing LLMs as hype.
We aren't in coding Kansas anymore, junior and senior will not be so easily mapped to legacy development roles.
I think of it a bit like ebike speed limits. Previously to go above 25mph on a 2-wheeled transport you needed a lot of time training on a bicycle, which gave you the skills, or you needed your motorcycle licence, which required you to pass a test. Now people can jump straight on a Surron and hare off at 40mph with no handling skills and no license. Of course this leads to more accidents.
Not to say LLMs can't solve this eventually, RL approaches look very strong and maybe some kind of self-play can be introduced like AlphaZero. But we aren't there yet, that's for sure.
But the comparison I made was between the junior with a good attitude and expert grasp on LLMs, and the stick-in-the-mud/disinterested "senior". Those are where the senior and junior roles will be more ambiguous in demarcation as time moves forward.