Posted by florianherrengt 3 hours ago
This part of the article hits home for me. With AI, "bad" engineers can now amplify their "bad" engineering x10 across the organization. The most egregious of these cases for me is often long tenured engineers who have lost interest in the craft, creating a dangerous combination of having enough merit to ship but not enough interest to make what they ship _good_.
I am still a firm believer in garbage in -> garbage out, AI is only as good as the abstractions and contracts you put in place for it. I don't subscribe to the idea that AI generated code is fundamentally bad, just that people lack the right skills today to wrangle agents into writing good code.
Earlier in the year I put together a talk for my company on what the future of architecture & design means for us in the career, I'm very proud of it and will share here in case folks have their own thoughts to share on the topic: https://youtu.be/SIZrt9Rt05Q?si=W57eirniWmoSFeBu
We are pushing tons of code and now our CPU usage has grown exponentially over the past year because the bad engineers just ship whatever Claude gives them and do not think about the consequences.
Our biggest consumer of CPU right now is HTTP connection churn because engineers are creating new clients every request we handle. If the engineers would just think for a second, push back on Claude, even Claude would tell them this is bad. But they don't... Platform engineering is now 10x harder with terrible engineers and unlimited code machines.
Don't even get me started on ffmpeg usage, engineers act like the resources are unlimited.
He gave them a lengthy, grueling, hyper-detailed tour of the entire facility, encompassing the HVAC systems, electrical systems, network and computing systems, finishing with about 20 minutes where he had them stand inside a hot aisle that he was just outside of, giving a fantastic soliloquy on the importance of code efficiency, and the consequences of ignoring it. It was hilarious to watch from the comfort of the cold aisle, knowing full well what he was doing.
The term for that is "10x AI engineer." Anyone who has anything negative to say about such people is just jealous of their insane productivity and speed.
I'd try and push for some "lunch and learn" meeting where the engineers get lunch catered and in exchange sit in on a meeting where you explain your point of view. Without monetary incentive it'll be hard to change the culture, but not impossible (and food goes a long way in greasing the wheels).
Sometimes this can be a death by a thousand cuts. Any individual change may not impact performance to a noticeable degree but when they're pumping out a 10x increase in commits it can be a slow decline.
Just look at how they're merging ~300 commits a week into bun.
i've gone this route a few times in my career, it's very stressful and involves angry/panicked people and many all nighters. Also, the glory fades fast. would not recommend.
AI disease is encouraging "sketchy process and minimal checks on the software quality." QA has been eliminated from my team, and the QA engineers that are left have been declared to be developers now.
Gotta move fast, and I guess making sure the stuff we ship works was "slowing us down."
Hiring pipelines that tested the wrong thing have existed for years but the problem is magnified 10x when you test for something that weakly correlates with ability at best which an AI can do better than a human.
This is leading to stuff like incompetent junior-level engineers being hired as principals.
You assume those people haven't already left, been kicked out, or were hired to begin with. We're not in a rational job market right now.
Natural consequence: Then they never grasped the concept of computational complexity.
O(n) Vs O(n²)? They have n, what's the difference? Python is fast enough. The only thing that matters is shipping features fast! Features! Our competitor will have this next week, we need to write code fast, everything else is a matter of adding more compute, which we will pay with revenue!
The missing part of mathematics education, IMO, would be to focus more into developing the intuition of what something means, instead of the current focus on getting some (numeric) results.
But, right now I'm paralyzed with fear in how to make a successful career switch without starting from literally "new grad level." I have wisdom, so it doesn't feel like I should have to start at the bottom rung again. Egotistically, I don't even mind, it's just the salary hit that would be the main issue.
Maybe it's not even a paradigm shift (though, I've always wanted to work on film productions). I've sort of lost the passion for being an IC, but how can I make a transition to management without any management experience? Should I just apply for a managerial role and in the cover letter state management is my intended path for growth?
> With AI, "bad" engineers can now amplify their "bad" engineering x10 across the organization.
Yup, and that's going to be the comeuppance for a decade of aggresive overhiring.
There are so many "bad engineers" filling the ranks now that in many teams and divisions there's not even anyone left around who can recognize them as such.
This was already manifesting as a rapid decline in software quality and worsening practices, and the amplification effect of AI is mostly going to make everything worse for a while as we wait for all these declining projects to buckle under their weight.
If you are a good engineer, it's a good time to work on small teams with other good engineers and rigorous practices. You can be using AI to amplify what you do (and probably should), but you need to be rigorously considering your processes and guarding yourself from seduction by blind-leading-blind hype you see on social media or in iconference talks.
Prove who's good and bad.
And you can't really, because there's always tradeoffs you're making as an engineer. The really self confident ones think their tradeoffs win, and maybe they do, though often they don't and are self aggrandizing and stroking their large egos.
The issue is that with no reins, the LLM is a fire hose of bad code compared to the garden hose of bad code orgs had before. A good engineer, with a good model and harness will produce great stuff. A bad engineer with a good model and harness will produce something faster, but it will be worse.
A good engineer, without LLM assistance, will still produce great stuff.
So yes, garbage in -> garbage out, but framed in a way that makes it clear what is garbage. The ideas, not the engineer themselves :-)
This suggests we need to be doing more designing and planning; introducing that friction intentionally to make sure bad ideas get culled, viable ideas get refined. Critical thinking becomes the bottleneck; the quality of the idea becomes the deciding factor in success.
The past 5-10 years or so saw me pretty beaten down though with software engineering, and AI was the final nail in the coffin that caused me to leave the profession in later middle age. I thought software as a business had "lost its way" from building great products with attention to detail to "how do we addict as many people as possible as quickly as possible". Think about the degradation in Apple software from say the "it just works" era to now.
With respect to AI, I'm not really against it, and I find it extremely valuable in my personal projects. I just feel in a large group/enterprise context that it's replaced a lot of tasks I actually enjoy doing with becoming an editor for what feels like a slightly inebriated junior developer. Or maybe it's better to say a junior developer on a mild amount of meth, because as you say this developer can churn out semi-but-not-fully-working code at an astonishing rate, and then I feel like it's often my job to mop the slop off the floor. Pass, not interested.
I feel lucky to have had my career during what I consider the golden age of software engineering, but I'd note I don't think that golden age lasted even a full career of one person.
To me, the best time was late 90s, early 2000s. We had a lot of autonomy. People would just trusted that we knew what and how to build it. I could focus on building a great product. Overtime, we lost control, to the point that we now work based on jira tickets made by managers or product owners with one tenth of the experience that we have.
Paying for software was the norm, so it was more clear what the "product" was, and software writers had a direct incentive to write better software.
Updates could not be pushed out so you had to be pretty sure your code worked before you shipped it. Having to ship physical media to all your customers with a bug fix was very expensive. Any new release was a big deal, so you had to put some real thought into what features it should contain.
Stack churn was much slower. The feeling of constantly trying to keep up with a treadmill was much less.
Users, while often not technology experts, were a much more competent slice of people than the general public who showed up when they got internet service and a computer at home.
The only ads were in print in trade magazines or publications like Computer Shopper. Yes, people actually used to buy a magazine that was nothing but ads.
It seems pretty clear that the current crop of executives strongly prefer the latter scenario.
This is where the quality magnification seems to be occurring.
Goal-driven loops can make good code great, or bad code worse.
If you're a good dev, you can totally prompt Claude to not do this and correct itself, that's not an issue. The issue is that bad devs won't even notice this is happening in the first place.
I've often had it test and benchmark against the wrong things = no test.
It also writes over-engineered code. So yes sort, maybe.
i think i fall into this bucket. our "leaders" and executives have told us they dont care about 'shipping good' . we are simply responding to incentives.
It's much easier for people to pump out absolute garbage, you know the kind of "just get it done fast" slop that management types cry out for. And then they wonder why everything end ups broken, not being maintained etc.
And I think the contrast between good and bad code output is much more impactful. Someone can pump out 10x the bad code they used to before, never test it never read through it just push push push baby. And then for good code, sure it's increased my output for slop tasks like repetitive unit tests, but a lot of TLC and review is required for good code and I'd say I've had maybe a 2-3x speed up on a lot of things. But not 10x; you only get that when you don't give a fuck.
I for one tend to care less about the minutiae of solutions implemented by AI as long as it gets the job done, I do care about architecture and design decisions and correctness and I have ways to steer and verify these when working with LLMs but I couldn't care less about it writing "good" code. Bad engineers also produce better results with AI at least when they're working in established frameworks, AI doesn't really need a lot of high level architecture input when designing or building a web app with a common stack, so as long as you're not working on something that's completely novel I don't think it will make a strong difference.
Maybe designers think the same way about the AI generated web designs I have Claude Code do for me but to be honest I don't care, I just know that before this tool existed it would have taken me weeks or months to come up with a good design and I would have to rely on prefabricated UI libraries and stuff like that or pay a designer tens of thousands of USD to make one for me, now I can get a (for me and my customers) perfectly acceptable and professional design within a few hours. So maybe I'm also a bad designer that amplifies my bad design taste 10x in my company, but the fact is the stuff ships and makes money and the customer is happy! And I can tell you customers or users don't give a shit about how good your code is, they only care if the software works and does what they want!
There are valid situations where the best code you can write is code you never look at and throw out the next month; there are equally valid situations where the best code is well thought through and reasoned abstractions for an area you expect to become core to the business in the near future.
Doing the right thing for the customer is independent from good design and good code. It’s a problem of requirements and project management. This is an excuse some poor programmers use, that they can’t write good code, but at least they fulfilled the customer requirements :D
I am just tired of typing and looking up syntax for every line of code.
This, however, is a slippery slope, and one has to be mindful of falling into cognitive surrender.
Hopefully when there's bad devs, there's a bit less making a mess where someone doesn't know better and as a result some amount of average or general best practices start happening.
This can be true not just for software development, but making a mess in anything, including a spreadsheet.
A big part of this is ensuring that a "bad" engineers can still write solid code and also investing in systems that make it easier for us to review code.
When it comes to writing code, we have this entire library of coding standards that we've moved from one project to another. It describes, sometimes in excruciating detail, exactly how we want our code structured, antipatterns, best practices, etc.
On the review side we have invested equally into skills that split up code into readable chunks, take screenshots of any UI changes for quick validation, and a whole battery of tests to ensure that we're not generating slop.
If you were to look at just our development process, you would conclude that we're very lazy engineers. We seldom write code by hand, we seldom ask for corrections and our reviews are more of a cursory look at the PR rather than a deep review.
But the real work is not in the "development layer", it's now in the "agent layer". Making sure the agent knows how to write solid code so we don't need to write code by hand, making sure it doesn't make dumb mistakes so we don't have to correct it, and structuring our review process in such a way where an engineer only has to take a cursory look at the code.
The key difference we noticed between the "old way" and the "new way" is the "new way" is way more scalable and we're able to move way faster than we ever could before.
Were you only relying on the difficulty of producing "working" from replacement skill/rate "software engineers" and their level of disinterest being the only real circuit breaker?
Generative AI breaks that agreement. It took me over a year to realize my previous CTO actually didn’t care too much about the system design he shipped . And the expectation was actually to just throw it away, have AI reimplement “what wasn’t working”. Use AI to ship, AI to learn what shipped, and AI to fix what shipped.
I quit because of it. Hell is working on other people’s AI code.
> Hell is working on other people's NoSQL code.
> Hell is working on other people's Python slop code.
> Hell is working on other people's enterprise Java code.
> Hell is working on other people's Windows Forms/GUI Builder code.
To quote Jean-Paul Sartre: Hell is other people.
This isn't coming solely from engineers wanting to produce more stuff faster.
In general, the way we still do it is: some basic static analysis finding anti-patterns, but the meat of it is, and always will be, code review. Except you can't review code at the pace AI generates it.
Where I work someone is still responsible for the output. We expect developers to examine the code the LLM produces before burdening someone else with it.
I've heard some refer to this as a "nature is healing" scenario for the industry where if you only signed up for a high paycheck and didn't care to think critically about any of the work you're doing then this will be painful because that previously manual process has been automated. The floor of what's necessary to be considered valuable has been raised.
This is the theory, but I’ve never worked anywhere (and I’ve worked at a lot of places over 20 years) that actually did it like that in practice.
What tends to happen is that the EMs and PMs look at who’s free and give that person the task. This means that sometimes you get a senior leading a simple project and sometimes you get a junior leading/designing a complex project (usually with help from a very minorly technical PM).
Then the senior/staff/principal (often on a different “special” team) gets pulled in at the last minute to rescue the project.
If your company is highly product driven, you’ll often find that the juniors end up leaving projects more often than not because they will tell the PM exactly what they want to hear.
>What tends to happen is that the EMs and PMs look at who’s free and give that person the task.
The gp you replied to qualified it with "enterprise software" (LOB, CRUD, etc aka "cost center") so your observations where senior -vs- junior engineers being more fungible can be true.
However, in "engineering" type of software products (game engines, RDBMS engines, operating systems, etc) where the software is more often a "product" that's sold (aka profit center) ... there are definitely different layers of complexity where senior and junior engineers are not fungible at all.
One way to describe the differences of complexity and criticality in various parts of the source tree is the "core" parts vs the "leaf" parts. E.g. in a game engine or Linux kernel, the deep parts of the engine or os process scheduler where the tight loops are located are the "core" parts. They most likely would be worked on by the most senior people.
But the game engine may have some less complex code for handling text width on a menu for different foreign languages. Or the os needs a new menu option on the installer to ask for the users age. Those would be more "leaf" functionality in the source tree. A junior could get assigned to those parts with less risk. After a few years of experience, he might be trusted enough to work on the deep "core" logic without screwing things up in catastrophic ways for a million customers.
A product with several million lines of source code will invariably have both the "hard parts" and "easy parts" so the new hires and juniors will work on the easier stuff first.
There's also a spectrum of hard-to-easy in enterprise software but it's much more narrow than engineering code bases.
That's great and all, but that's a small minority of all software written. I'd love to work on projects like that but ultimately I have to pay the bills. Many software engineers are in the same boat I am.
Now I guess I'm just up shit creek because I built a career on SAAS work that was available instead of holding out hope I could get in at my dream job working on game engines or some other non-SAAS product?
Sorry if I sound bitter. I'm variations tired of hearing variations "If you only worked in SAAS you were always close to meaningless and now we've automated you so you don't deserve to earn anything anymore". Feels like every day.
Sure you might lean more on Google or SO if it's something you haven't seen before, or it's something deep in a stack that isn't code you actually wrote. But as the noob eventually learns when he thinks he's found a bug in a runtime or compiler that thousands of other people are using every day: No. It's probably your code.
I suppose there are people who never advanced beyond the "type (or copy/paste); run; google the errors" loop, but I've never seen anyone with more than a year or two of experience doing that very often.
Unfortunately, looking back, I could easily see Claude replacing 3 out of 4 of those developers. Myself + one other dev + AI would probably would've shipped a bit more a little faster. With that said tokens aren't free so the net cost savings would've been 2 dev salaries maximum.
I would have said the same thing for the first 15 years of my career across several jobs and acquisitions.
Then I took a job at a company that fit this description. They had so many managers and PMs that every task was talked about, broken down, and documented so much that every Jira ticket was a little piece of work that a junior could handle by Googling things.
The quirk was that they had started hiring a lot of experienced and staff level engineers, too, but then tried to force this same framework on to everyone. We spent more time discussing tasks than doing them by a factor of 2-50X. There are some situations where this is appropriate, but none of our work was actually high scale or difficult. Your day might be spent writing design docs and collective sign offs as you worked through committees until the tickets at the end were so simple that any junior could do them.
It didn’t lead to better software. It was one big cargo cult game of performative management. A frequent outcome was that someone would get into the micromanaged tickets and realize there was a better way to handle something, but it wasn’t worth doing all of the fighting and meetings involved to do the meeting and Jira ticket dance all over again.
Same but with 10 years, and I don't think I would do well in a place like that. That removes you from writing code and there is a similar complaint about relying too much on AI and not writing enough code on your own.
I worked in a company where this happened, and it wasn't pretty. Product managers used their experience and seniority to intimidate junior engineers and exert control over project management of engineering projects, which they predictably used to move as much work as possible to post-launch, including testing and security. They also gaslit junior engineers into agreeing that issues with contradictory or impossible product asks could be figured out later, leading to software getting released to customers that fundamentally could not be made reliable, performant, or even secure.
Even after the entire company went on site visits where customers told us they weren't using any of the features released in the last year because they were all buggy, and the only information they wanted about upcoming releases was assurance that their use cases wouldn't be impacted, product still kept claiming that the time it took to release new features was the biggest problem facing the company and kept fighting back against engineers who said we were in a quality crisis and desperately needed to make time for better testing and design.
The only thing that can shield you is good engineering management. Weak management will end up getting rolled by product and start promoting the bad behavior that product insists on. At that company I had a boss whose attitude towards us was "engineers in a startup should make decisions independently and stand by their work" but made sure engineers felt unsafe making engineering decisions that product wasn't happy with, likely because they felt unsafe themselves.
I've seen a twist - not juniors but just offshore engineers.
Primarily in Silicon Valley or outside of it?
I worked most of my two decades in the Valley but also spent time outside of it. There is in many cases a vast gulf between the dev cultures. What the parent comment is describing I've seen many many times.
Before AI, most big software was desgined and originally writen by a few key (founder-esq) architects. But they can only add functionality at human speed, so the team grows, and the quality of engineer drops as the functionality footprint grows. Eventually the functionality is much bigger, but most of it was written by a much lower quality engineer, and thus the code (even if functionally correct) is less efficient and less clean, and the big refactors a talented architect would have done never happened, so it just got bigger and bigger and more unweildy and bug prone and inflexible - until some new talented architects see the waste and build a startup to displace it.
With AI, big software can be entirely written by 1-3 people, plus alot of agent usage. Which means it can be refactored more, and the design kept cleaner. But this only happens if those senior architects are actually paying attention and controlling the design. If the agents are heavily automated and subtasaking and controlling the design, then nothing much changed, because the agents themselves are no better than the "stackoverflow engineer".
So without a training pipeline to provide experience to jr. Devs, in 15 years we will be hurting for senior devs to replace all the graybeards. Kind of like the fortran crisis of the last decade or so.... Just across an entire industry that supports every other industry....
I’ve worked with two junior devs recently who are very good precisely because they’re trying to understand what they’re doing, rather than just producing code.
I’ve also worked with senior devs who basically gave up and stopped trying to understand the code. They became much worse engineers as a result. At this point I would much rather work with those 2 juniors.
They use it to explore things they don’t understand, ask me questions to clarify their reasoning, double-check assumptions. They generally use the tools available to increase their understanding.
Those people are doomed. Which is fine in a lot of ways, but will be devastating for their economic prospects.
I’m convinced the only reason there’s a Fortran crisis is because nobody wants to work on Fortran or make a career out of it.
Has nothing to do with willingness to hire juniors. Good luck finding promising juniors who survey their career opportunities and decide that Fortran is a good technology for building their resume.
It's like complaining that no mechanics work on carburetors anymore. Well some do, but they are expensive and not worth it to keep your old hoopty running. Might be worth it if you have a show-quality 1960's era muscle car.
Think about all the open source the world got from some random person in Finland or Sicily or wherever hacking on a cool idea. Will that still happen with a high (for someone who is not employed... maybe a student) monthly cost?
I have worked in startups before and currently work at bkng, and have friends at uber. Everywhere it is expected that the engineers will create their own tickets and refine them with the team. If you are in a product facing team, your PM could even help you with a PRD but I have not seen one in the last few years.
Tickets came from the product side, after being evaluated and prioritized. Engineers got involved with estimating effort (in "points") and doing the actual implementation of course.
I wish this were the extent of it nowadays. But for whatever reason, I've seen multiple really promising engineers get a hold of AI tools, and just outsource all of their thinking to it.
The self-infantalization is so bad that I was pair programming with a guy one day (partly to see how he was using AI) and watched him ask Claude if it makes sense to do X for every single decision.
I get using it as a form of linting... But you'll never learn how to be an independent developer if you constantly outsource your decision making. And it's disappointing too, because he was developing so rapidly as an engineer before Copilot came out.
I wonder if there's an agent harness that would work well for requirements gathering.
I feel like this is a bit of cope.
Right now you still need to have some skill to guide these things correctly to produce cohesive and functional products, but at the rate we've seen them improve I question how long this will remain true.
Personally speaking (and I'd love to hear others' takes on this): when using an LLM for work-related and development tasks, I never use the "full-auto" mode, and I never manually approve of something that I don't understand. When I don't understand something an agent wants to do, I go on a side-quest to learn more about said thing and to educate myself first. This takes extra time, but I feel that it's the right thing to do, so I can at least approve/deny/redirect from a more informed position, rather than flying blind and hoping for the best.
In addition to what the author discusses, I think skill-atrophy, stagnation due to complacency (i.e.: "why grow and learn if an agent can do it" mindset), and cognitive laziness are additional risks that come with overrelying on LLMs. Humans were meant to think. LLMs are a tool.
I know it's a cliche but the old IBM adage holds very true today
Machines should work, people should think
Maybe that's part of the problem I have with LLMs writing code. It would be nice if they just did the work, but there's a lot of underlying thinking and decision making with software work that is being offloaded any time you have the LLM do it
It seems like those decisions are being handwaved off as "not important" nowadays. Just let the LLM make those choices! But that is not sitting right with me for whatever reason. Something I should think about I guess
This is the challenge of the middle class developer. They aren't accustomed to working with constraint thinking and they're past the junior stage where one is expected to still be shaping their thinking so they are struggling to find a fit.
We could end up with a gulf between those who use AI for cognitive inquiry vs passive delegation. Those who accelerate learning by asking "help me understand this" vs those who relinquish understanding and analytical thinking to AI to complete their work.
Which means our pipeline to senior engineer is completely broken.
Wages are certainly going down for most software roles
We keep hearing this, but where is the Photoshop killer written by a gas station attendant? Where is the PayPal alternative written by a retired nurse?
Over and over we're told that software is now trivial to write, and Claude is evidently writing some billions of lines of code a day, but where are the results? Where is the vibe coded app toppling the legacy hand-coded monster product from 20 years ago?
photoshop, paypal, and many more companies etc. have software of vast complexity that cannot be reproduced or maintained today solely with llms, ipso facto you still need software developers or some similar role to build and maintain the complexity of _yesterday_
some guy shipping an iOS app to zero users doesn't change that. although i think it is good we can have more bespoke software.
If its so good why aren't you rich?
There are definitely more apps developed these days but I haven't seen anything much vibe coded which actually seems to have staying power. Everything is prototype quality.
A lot of AI believers (me too fwiw) seem to forget it’s a lot harder to ‘close the loop’ on non-code projects. I think this is because they are usually coders.
Which is kinda proof they're populated by sociopaths with extremely little empathy. The people who are building that stuff are programmers, and if they had a bit of functioning empathy they'd stop or slow down or at least be unenthusiastic.
And we're not talking about very high level empathy here, but the very basic kind of thinking about the effects on someone like yourself.
There is a budget and when it is gone, no contractors until next budget round.
If maintenance and security is fully automated and for small changes or a small feature you will only dump some spec document into your agent platform, for sure.
If you're in a product company and your development backlog is empty, run. That company is about to go out of business.
I have stupid question here. Why they sell tokens instead of AI generated full application and AI fixes to your code without you needing a single dev?
But despite me saying that, I think it's going to take longer than four years. It takes a while for people to realize that a dream is dead.
There are around 100k U.S CS graduates . You're telling me 100k H1-Bs don't make any impact on them?
There's also only 60,000 H1-Bs a year for private companies.
Lastly the majority of H1-Bs doesn't go to software engineering jobs, it's around 30-50%.
There's also basically no US company hiring juniors via H1-Bs, so yeah, I'm saying it doesn't make any meaningful impact.
30K H1-Bs versus 400K grads...
Actually, more like over 80,000. They have a separate pool for people with advanced degrees (MS or higher) in tech fields.
Agree with everything else.
It's not an unreasonable thing for people of a country to think that the government should do more to protect their livelihoods over a corporation's balance sheet.
The correct framing is "The reason you're not getting a job at Amazon isn't because of people willing to work for less money,"
Big tech companies aren't like a Government job with rigorous pay grades based on role and seniority. You can have people even within the same role and level at big tech with multiple $100k pay discrepancies.
If someone's continued ability to stay in the country where they currently live is contingent on employment then they will accept worse conditions and pay.
That's the entire reason why tech companies use H1B, it's not as though there's a dearth of Java developers or DevOps people who are US citizens.
also nothing impressive about a senior SDE there, maybe 10 years ago it was impressive, best talent has either moved on or is L7+
I mean, think about it; just a few short years ago there was a very strong market for software engineers, despite outsourcing and immigration being very much part of the landscape.
I mean, I wrote this 12 years ago in response to similar discussions on this same site, and in the intervening years, we had a great market for software engineers: https://journal.dedasys.com/2014/12/29/people-places-and-job...
What has changed is 1) the zero interest rate environment went away and 2) LLM's.
I felt like a greybeard walking around google at 32. That's all I'll say. And that was a decade ago. I don't want to work for any of these companies anymore. They've all gone downhill.
I was very fortunate to get a job in a NOC a little bit after high school where I learned things one'd expect to learn in an entry level job. The team I worked on was all about mentorship, learning, and building people to the next level.
Haven't seen that attitude elsewhere in my entire career except in the non-profit world where I reside these days. It blows me away that people are surprised that these industries have failed to build entry level positions.
Which of those things changed (along with higher interest rates) in the past few years though? All the other variables did not change. They were present 5 years ago and present 10 years ago too. To me that's indicative that those factors are not the cause of a soft market for software engineers in 2026.
I am talking about the weak market for software engineers right now and what caused it.
I agree with most of this except this. Think there’s some rose tinted glasses here or I’ve got bad luck over time.
Life before ai was bad as well. There wasn’t any one to explain to you anything! You had to figure it out yourself. The people either already left or was busy with something else.
No one wrote tests (to my standard). Most of the ops works was skipped. Docs were just not there. Nobody linted properly. Just bad mannnn
>There used to be a time when people sat down and talked about how they'd do something. Now they can just prompt an agent for a few hours and open a PR.
>The most tragic aspect of this way of working is that, to the untrained eye, it works.
>If you pull the branch and test it, you'll probably get something somewhat functional. So what do they do? They keep going. Again and again. Until the project reaches a point where no one knows how anything works.
Isn't this the opposite of failing faster, it lets people who would have never made it past the first few hurdles and give up get deep in and then the false hope that they can then prompt their way out of whatever mess resulting in deeper and deeper spaghetti.
The thing is I've worked on plenty of code like this written by humans. AI just accelerates the process and puts this type of code within reach of more people. It could be seen as a step backwards in good software engineering practices we've painfully learned over the last two decades. I see it as a cycle repeating itself in a new iteration, with the hope that we'll end up with still better tools and processes in another few years. But I agree it can and will result in real harm in the meantime.
Bruh..I haven't consistently sat at my computer for even a hour at a time to code for a decade..people spend that much time in Claude??
But, moreover, the demand your company subscribes. But not your company anymore, because you'll probably be replaced by one engineer (and an AI subscription) who's now trying to do your job and about four or five other jobs.
And this one engineer will become more and more reliant on AI companies, AI companies that'll go for the throat once they've hollowed out all the skills that used to exist in the engineering market.