AI’s impact on engineering jobs may be different than expected
(semiengineering.com)125 points by rbanffy 3 days ago
125 points by rbanffy 3 days ago
This sounds good in theory, but have you hired someone in 2026?
Developers are really lazy in general and don't want to work. The more people you hire, the more you run into the chance of gumming up productivity with unproductive developers.
Even if they are productive, once you cross the threshold of 30 people even productive developers become lazy because of entitlement, bad resource distribution, or complexities from larger teams.
We don't even have to talk about teams of 1000+. Ownership is just dead at that point.
In 2026, having just 5 engineers with AI means you can cut through all the waste and get stuff done. If they start being weird, you can see it pretty easily vs. when engineers are being weird in a team of 50-1000+.
It's not rocket science to see leadership decide to cut down on teams to better manage weirdness in devs. More people doesn't mean more results unfortunately because of work culture nowadays.
> Developers are really lazy in general and don't want to work
According to Larry Wall, the three great virtues of programmers are laziness, impatience, and hubris.
Though perhaps perl isn't a great argument for the latter.
This sounds like a rant from a dysfunctional out of touch manager more than anything. From a 57 day old account here to pump AI because humans are terrible and not printing you lambos. Totally not a shill or anything. Humans = bad AI = good. Shill.
When you area asked specifics about how you use AI so effectively when others cannot you do not reply. Shill.
I've hired close to 200 people and 4 were bad apples that I had to fire. So no real life does not reflect what you wrote. Most people want to do a good job.
Will the modal developer of 2030 be much like a dev today?
Writing software was a craft. You learned to take a problem and turn it into precise, reliable rules in a special syntax.
If AI takes off, we'll see a new field emerging of AI-oriented architecture and project management. The skills will be different.
How do you deploy a massive compute budget effectively to steer software design when agents are writing the code and you're the only one responsible for the entire project because the company fired all the other engineers (or never hired them) to spend the money on AI instead?
Are there ways of factoring a software project that mitigate the problems of AI? For example, since AI has a hard time in high-context, novel situations but can crank out massive volumes of code almost for free, can you afford to spend more time factoring the project into low-context, heavily documented components that the AI can stitch together easily?
How do you get sufficient reliability in the critical components?
How do you manage a software project when no human understands the code base?
How do you insure and mitigate the risks of AI-designed products? Can you use insurance and lower prices if AI-designed software is riskier? Can we quantify and put a dollar value on the risk of AI-designed software compared to human-designed?
What would be the most useful tools for making large AI-generated codebases inspectable?
When I think about these questions, a lot of them sound like things an manager or analyst might do. They don't sound like the "craft of code." Even if 1 developer in 2030 can do the work of 10 today, that doesn't mean the typical dev today is going to turn into that 10x engineer. It might just be a very different skillset.
Nitpick, blacksmiths typically did forging, which is hammering heated metal into shape with benefits for the strength of the hammered material. CNC is machining, cutting things into the shape you want at room temperature.
Forging is machine assisted now with tons of tools but its still somewhat of a craft, you can't just send a CAD file to a machine.
I think we're still figuring out where on that spectrum LLM coding will settle.
Blacksmiths also spent a lot of their time repairing things, whereas modern replacements primarily produce more things. Kind of an interesting shift. Economies and jobs change in so many ways.
I don't think it necessarily scales that way. Larger organizations need more communication channels and coordination. If anything, assuming AI does give you 10x ability, there's probably a sweet spot where you have just enough developers that churn out code at a good pace but not too many that it gets too chaotic.
If you compare one developer to 10, for instance, one developer doesn't have to deal with communicating with 9 other people to make sure they're working on things that align with the work everyone else is doing. There is no consensus that has to be reached. No meetings, no messages that have to be relayed, no delays because someone wasn't around to get approval. That one developer just makes a decision and does it.
There are lots of big companies out there and in the past, small startups have been able to create successful products that never would have been created at the big company even though the big company hired way more developers.
Yeah I think this is a good way to think about it. I mean Google, MSFT for example have effectively unlimited developers, and their products still suck in some areas (Teams is my number one worst) so maybe AI will allow them to upgrade their features and compete
At large companies, UI/UX is done by UI/UX designers and features are chosen and prioritized by product management and customer research teams. Developers don't get much input.
As Steve Jobs said long ago "The only problem with Microsoft is they just have no taste." but you can apply the same to Google and anyone else trying to compete with them. Having infinite AI developers doesn't help those who have UI designers and product managers that have no taste.
ermmm youre missing a bigger point.
MSFT, GOOG et al have an enormous army of engineers. And yet, they dont seem to be continually releasing one hit product after another. Why is that? Because writing lines of code is not the bottleneck of continually producing and bringing new products to market.
Its crazy to me how people are missing the point with all this.
It is so depressing that teams won despite being worse than pretty much every other chat application just because MSFT bundled it with office.
The main thing to understand about the impact of AI tools:
Somehow the more senior you are [in the field of use], the better results you get. You can run faster and get more done! If you're good, you get great results faster. If you're bad, you get bad results faster.
You still gotta understand what you're doing. GeLLMan Amnesia is real.
> Somehow the more senior you are [in the field of use], the better results you get.
It's a K-type curve. People that know things will benefit greatly. Everyone else will probably get worse. I am especially worried about all young minds that are probably going to have significant gaps in their ability to learn and reason based on how much exposure they've had with AI to solve the problems for them.
I jumped into a new-to-me Typescript application and asked Claude to build a thing, in vague terms matching my own uncertainty and unfamiliarity. The result was similarly vague garbage. Three shots and I threw them all away.
Then I watched a someone familiar with the codebase ask Claude to build the thing, in precise terms matching their expertise and understanding of the code. It worked flawlessly the first time.
Neither of us "coded", but their skill with the underlying theory of the program allowed them to ask the right questions, infinitely more productive in this case.
Skill and understanding matter now more than ever! LLMs are pushing us rapidly away from specialized technicians to theory builders.
For sure, directing attention to valuable context and outlining problems to solve within it works way, way better than vague uncertainty.
Good LLMing seems to be about isolating the right information and instructing it correctly from there. Both the context and the prompt make a tremendous difference.
I've been finding recently that I can get significantly better results with fewer tokens by paying mind to this more often.
I'm definitely a casual though. There are probably plenty of nuances and tricks I'm unaware of.
Interestingly, this observation holds even when you scale AI use up from individuals to organizations, only at that level it amplifies your organization's overal development trajectory. The DORA 2025 and the DX developer survey reports find that teams with strong quality control practices enjoy higher velocity, whereas teams with weak or no processes suffer elevated issues and outages.
It makes sense considering that these practices could be thought of as "institutionalized skills."
Agreed. How well you understand the problem domain determines the quality of your instructions a s feedback to the LLM, which in turn determines the quality of the results. This has been my experience, it works well for things I know well, and poorly for things I'm bad at. I've read a lot of people saying that they tried it on "hard problems" and it failed; I interpret this as the problem being hard not in absolute terms, but relative to the skill level of the user.
> You still gotta understand what you're doing.
Of course, but how do you begin to understand the "stochastic parrot"?
Yesterday I used LLMs all day long and everything worked perfectly. Productivity was great and I was happy. I was ready to embrace the future.
Now, today, no matter what I try, everything LLMs have produced has been a complete dumpster fire and waste of my time. Not even Opus will follow basic instructions. My day is practically over now and I haven't accomplished anything other than pointlessly fighting LLMs. Yesterday's productivity gains are now gone, I'm frustrated, exhausted, and wonder why I didn't just do it myself.
This is a recurring theme for me. Every time I think I've finally cracked the code, next time it is like I'm back using an LLM for the first time in my life. What is the formal approach that finds consistency?
You're experiencing throttling. Use the API instead and pay per token.
You also have to treat this as outsourcing labor to a savant with a very, very short memory, so:
1. Write every prompt like a government work contract in which you're required to select the lowest bidder, so put guardrails everywhere. Keep a text editor open with your work contract, edit the goal at the bottom, and then fire off your reply.
2. Instruct the model to keep a detailed log in a file and, after a context compaction, instruct it to read this again.
3. Use models from different companies to review one another's work. If you're using Opus-4.5 for code generation, then consider using GPT-5.2-Codex for review.
4. Build a mental model for which models are good at which tasks. Mine is:
3a. Mathematical Thinking (proofs, et al.): Gemini DeepThink
3b. Software Architectural Planning: GPT5-Pro (not 5.1 or 5.2)
3c. Web Search & Deep Research: Gemini 3-Pro
3d. Technical Writing: GPT-4.5
3e. Code Generation & Refactoring: Opus-4.5
3f. Image Generation: Nano Banana Pro> You're experiencing throttling. Use the API instead and pay per token.
That was using pay per token.
> Write every prompt like a government work contract in which you're required to select the lowest bidder, so put guardrails everywhere.
That is what I was doing yesterday. Worked fantastically. Today, I do the very same thing and... Nope. Can't even stick to the simplest instructions that have been perfectly fine in the past.
> If you're using Opus-4.5 for code generation, then consider using GPT-5.2-Codex for review.
As mentioned, I tried using Opus, but it didn't even get the point of producing anything worth reviewing. I've had great luck with it before, but not today.
> Instruct the model to keep a detailed log in a file and, after a context compaction
No chance of getting anywhere close to needing compaction today. I had to abort long before that.
> Build a mental model for which models are good at which tasks.
See, like I mentioned before, I thought I had this figured out, but now today it has all gone out the window.
Nonsense. I have ran an experiment today - trying to generate a particular kind of image.
Its been 12 hours and all the image gen tools failed miserably. They are only good at producing surface level stuff, anything beyond that? Nah.
So sure, if what you do is surface level (and crap in my opinion) ofc you will see some kind of benefit. But if you have any taste (which I presume you dont) you would handily admit it is not all that great and the amount invested makes zero sense.
> if what you do is surface level (and crap in my opinion)
I write embedded software in C for a telecommunications research laboratory. Is this sufficiently deep for you?
FWIW, I don't use LLMs for this.
> But if you have any taste (which I presume you dont)
What value is there to you in an ad hominem attack here? Did you see any LLM evangelism in my post? I offered information based on my experience to help someone use a tool.
"Most people who drive cars now couldn’t find the radiator cap if they were paid to, and that’s fine."
That's not fine IMO. That is a basic bit of knowledge about a car and if you don't know where the radiator cap is you will eventually have to pay through the nose to someone who does know (and possibly be stranded somewhere). Knowing how to check and fill coolant isn't like knowing how to rebuild a transmission. It's very simple and anyone can understand it in 5 minutes if they only have the curiosity.
This reminds me of "Zen and the Art of Motorcycle Maintenance". One of the themes Pirsig explores is that some people simply don't want to understand how stuff they depend on works. They just expect it to be excellent and have no breakdowns, and hope for the best (I'm oversimplifying his opinion, of course). So Pirsig's friend on his road trip just doesn't want to understand how his bike works, it's good quality and it seldom breaks, so he is almost offended when Pirsig tells him he could fix some breakage using a tin can and some basic knowledge of how bikes work.
Lest anyone here thinks I feel morally superior: I somewhat identify with Pirsig's friend. Some things I've decided I don't want to understand how they work, and when they break down I'm always at a loss!
You just realistically can't know everything. I have a tankless water heater. It's almost a magical black box to me, but I know a little bit more about it now that I've taken pictures of it and asked LLMs to explain it to me. I'm still not a water heater technician, but I feel more knowledgeable.
And on the topic of motorcycles, I recently got a crappy bike that barely starts, and I partially got it because I feel capable of fixing it. And now it runs pretty well because I used lots of "video chats" with Gemini (and the owner's manual as context) to fix it!
James Burke's old TV show Connections was all about this, how many little things that surround us in day to day life and on which we absolutely depend for our survival are complete black boxes to most of us most of the time. Part of modernity is that no single person, however intelligent, can really understand the technological web that sustains our lives.
This is a bizarre analogy.
For one thing: if your car is overheating, don't open the radiator cap since the primary outcome will be serious burns.
And I've owned my car for 20 years: the only time I had to refill coolant was when I DIY'd a water pump replacement, which saved some money but only like maybe $500 compared to a mechanic.
You could perfectly well own a car and never have to worry about this.
Yes and no. For one thing the radiator/reservoir cap is clearly marked "Do not open when hot." But the general point really is that if you have no idea how something works, you will be helpless when it doesn't work. If (at some time in the future) the only thing you know how to do is ask an AI to do something for you, then you'll be not only helpless without it, but less and less able to judge whether what it is telling you is even correct. Like taking your car to a mechanic because it's overheating, and him saying you need a new water pump and radiator when maybe all you needed was a new pressure cap but you never even knew to try that first.
Of course you can't know everything. There a point at which you have to rely on other people's expertise. But to me it makes sense to have a basic understanding of how the things you depend on every day work.
What the hell? There are plenty of reasons to pop your hood that literally anyone competent to drive should be able to do perfectly safely. Swapping your own battery. Pulling a fuse. Checking your oil, topping up your oil. Adding windshield wiper fluid. Jump starting a car. Replacing parts that are immediately available.
Not requiring one to pop the hood, but since I've almost finished the list of "things every driver should be able to do to their car": Place and operate a jack, change a tire, replace your windshield wiper blades, add air to tires (to appropriate pressure), and put gas in the damned thing.
These are basic skills that I can absolutely expect a competent, driving adult to be able to do (perhaps with a guide).
I mean, I don't disagree that these are basic skills that most anyone should be able to perform. But most people are not capable to do them safely. Whether that's aptitude or motivation, doesn't matter.
Ask your average person what a 'fuse' even is, they won't be able to tell you, let alone how to locate the right one and check it.
Just think about how help the average person is when it comes to doing basic tasks on a computer, like not install the Ask(TM) Toolbar. That applies to many areas of life.
I have never cared for decades and now my car doesn't even have a radiator. Seems to have worked out well for me.
An EV with a heat pump. I know literally there is a heat exchange/radiator, but there is not a separate radiator system with its own fluids and pumps.
You don’t get to decide whether a radiator is a radiator just because the coolant can internally shuffle heat to the A/C. I’m assuming that you drive a Tesla, in which case your car still has a big fat low temperature radiator. If you’re driving virtually any other EV on the market, it still has a big fat low temperature radiator, or even multiple.
It's puzzling to me that all this theorizing doesn't just look at the actual effects of AI. It's very non-intuitive
For example the fact that AI can code as well as Torvalds doesn't displace his economic value. On the contrary he pays for a subscription so he can vibe code!
The actual work AI has displaced is stuff like: freelance translation, graphic illustration, 'content writing' (writing seo optimized pages for Google) etc. That's instructive I suppose. Like if your income source can already be put on upwork then AI can displace it
So even in those cases there are ways to not be displaced. Like diplomatic translation work can be part of a career rather than just a task so the tool doesn't replace your 'job'.
> AI can code as well as Torvalds
He used it to generate a little visualiser script in python, a language he doesn't know and doesn't care to learn, for a hobby project. It didn't suddenly take over as lead kernel dev.
I think AI displacing graphics illustrators is a tragedy.
It's not that I love ad illustrations, but it's often a source of income for artists who want to be doing something more meaningful with their artwork. And even if I don't care for the ads themselves, for the artists it's also a form of training.
> freelance translation
As someone who has to switch between three languages every day, fixing the text is one of my favourite usages of LLMs. I write some text in L2 or L3 as best as I can, and then prompt an LLM to fix the grammar but not change anything else. Often it will also explain if I'm getting the context right.
That being said, having it translate to a language one doesn't speak remains a gamble, you never know it's correct so I'm not sure if I'd dare use it professionally. Recently I was corrected by a marketing guy that is native in yet another language because I used a ChatGPT translation for an error message. Apparently it didn't sound right.
Re displacing freelance translation, yes - it can displace the 95% of cases where 95% accuracy is enough. Like you mention though, for diplomatic translations, court proceedings, pacemaker manuals etc you're still going to need a human at least checking every line since the cost of any mistake is so high
Senior dev here 15 years experience just turned 50 have family blah blah. I've been contracting for the last two years. The org is just starting to use Claude. I've been delegating - well copy pasting - into chatgpt which has to be the laziest way to leverage AI. I've been so successful (meaning haven't had to do anything really except argue with chatgpt when it goes off on some tangent) with this approach that I can't even be bothered to set up my Claude environment. I swear when this contract is over I'm opening a mobile food cart.
I'm similar ( turning 50 in a couple month, wife+2 kids etc) and was telling my wife this morning that the world of software development has definitely changed. I don't know what it will look like in the future but it won't look like the past. It seems producing the text that can be compiled into instructions for a computer is something LLMs particularly good at. Maybe a good analogy is going from a bare text editor to a modern IDE. It's happening very fast though, way faster than the evolution of IDEs.
I was saying this yesterday, There will be people building good software somewhere, but chances to it happening in current corporate environment is nearing zero. Change is mostly in the management, and not in the Software Development itself. Yeah we may be like 50% faster but we are expected to be 10x devs.
Same situation (50 last week, 2 kids) though have been unemployed for a year. Part of me thinks that, rather than taking jobs, AI is actually the only reason a lot of jobs still exist. The rest of tech is dead. Having worked in consulting a while ago, you can kind of feel it when you're approaching the point where you've implemented all the high value stuff for a client and, even though there's stuff you could do, they're going to drop you to a retainer contract because it's just not the same value.
That's how the whole industry feels now. The only investment money is flowing into AI, and so companies with any tech presence are touting their AI whatevers at every possible moment (including during layoffs) just to get some capital. Without that, I wonder if we'd be seeing even harsher layoffs than we already are.
> The only investment money is flowing into AI
That's so not true. Of the 23 companies we reviewed last year maybe 3 had significant AI in their workflow, the rest were just solid businesses delivering stuff that people actually need. I have no doubt that that proportion will grow significantly, and that this growth will probably happen this year but to suggest that outside of AI there is no investment is just not compatible with real world observations.
Software will ALWAYS be an attractive VC target. The economics are just too good. The profit margins are just inherently fat as fuck compared to literally anything else. Your main expense is headcount and the incremental cost of your widget is ~$0? It's literally a dream.
It's also why so much of AI is targeting software, specifically SAAS. A SaaS company with ~0 headcount driven by AI is basically 100% profit margin. A truly perfect conception of capitalism.
Meanwhile I think AI actually has a decent shot at "curing" cancer. AI-assisted radiology means screening could be come significantly cheaper, happen a lot more often, and catch cancers very early, which is extremely important as everyone knows to surviving it. The cure for cancer might actually just involve much earlier detection. But pfft what are the profit margins on _that_?
It’s funny that perfect capitalism (no payroll expenses) means nobody has money to actually buy any of the goods produced by AI.
Re cancer: I wonder how significant is the cost of reading the results vs. the logistics of actually running the test
> I swear when this contract is over I'm opening a mobile food cart.
This is the way. I think I'd like to be a barista or deliver the mail once all the jobs are gone.
> I think I'd like to be a barista
If/when AI wipes out the white collar "knowledge worker" jobs who is going to be able to afford going to the coffee shop?
> I swear when this contract is over I'm opening a mobile food cart.
Please keep us posted. I'm thinking of becoming a small time farmer/zoo keeper.
Not sure if this is sarcasm or not but I will keep everyone posted haha
Absolutely not, I've been earning my living as a coder for now 25y and eventually, enough is enough.
How does code review usually go for you? Our org’s bottleneck is often code review, which is how we reduce bus factor and other risks. Getting to the pull request faster doesn’t really save us that much time.
Sold financial products before. Curious why you think my starting age was important?
I have access to chatgpt codex since i'm on the premium plan. Seems like the lowest barrier to entry for me (cost, learning curve). I will truly have to give this a go. My neighbor is also a dev and he is flabbergasted that i have not at least integrated it into a side project.
Yes completely different career. Sold financial products.
Very interesting! Thanks for sharing.
Its hard (or at least in my experience) to find people to change career - more so in their mid-thirties. I'm the opposite -- software developer career, now in mid 30s, and the AI crap gets me thinking about backup plans career-wise.
C# / Web Sockets / React. Lots of legacy code. Great group of engineering folks.
I have read this same comment so many times in various forms. I know many of them are shill accounts/bots, but many are real. I think there are a few things at play that make people feel this way. Even if you're in a CRUD shop with low standards for reliability/scale/performance/efficiency, a person who isn't an experienced engineer could not make the LLM do your job. LLMs have a perfect combination of traits that cause people to overestimate their utility. The biggest one I think is that their utility is super front-loaded.
If a task before would take you ten hours to think through the thing, translate that into an implementation approach, implement it, and test it, and at the end of the ten hours you're 100% there and you've got a good implementation which you understand and can explain to colleagues in detail later if needed. Your code was written by a human expert with intention, and you reviewed it as you wrote it and as you planned the work out.
With an LLM, you spend the same amount of time figuring out what you're going to do, plus more time writing detailed prompts and making the requisite files and context available for the LLM, then you press a button and tada, five minutes later you have a whole bunch of code. And it sorta seems to work. This gives you a big burst of dopamine due to the randomness of the result. So now, with your dopamine levels high and your work seemingly basically done, your brain registers that work as having been done in those five minutes.
But you now (if you're doing work people are willing to pay you for), you probably have to actually verify that it didn't break things or cause huge security holes, and clean up the redundant code and other exceedingly verbose garbage it generated. This is not the same process as verifying your own code. First, LLM output is meant to look as correct as possible, and it will do some REALLY incorrect things that no sane person would do that are not easy to spot in the same way you'd spot them if it were human-written. You also don't really know what all of this shit is - it almost always has a ton of redundant code, or just exceedingly verbose nonsense that ends up being technical debt and more tokens in the context for the next session. So now you have to carefully review it. You have to test things you wouldn't have had to test, with much more care, and you have to look for things that are hard to spot, like redundant code or regressions with other features it shouldn't have touched. And you have to actually make sure it did what you told it to, because sometimes it says it did, and it just didn't. This is a whole process. You're far from done here, and this (to me at least) can only be done by a professional. It's not hard - it's tedious and boring, but it does require your learned expertise.
I think a lot of the proliferation of AI as a self-coding agent has been driven by devs who haven’t written much meaningful code, so whatever the LLM spits out looks great to them because it runs. People don’t actually read the AI’s code unless something breaks.
So set up e2e tests and make sure it does things you said you wanted. Just like how you use a library or database. Trust, but verify. Only if it breaks do you have to peak under the covers.
Sadly people do not care about redundant and verbose code. If that was a concern, we wouldn't have 100+mb of apps, nor 5mb web app bundles. Multibillion b2b apps shipping a 10mb json file just for searching emojis and no one blinks an eye.
I just wanna make the point that I've grown to dislike the term 'CRUD' especially as a disparaging remark against some software. Every web application I've worked on featured a database, that you could usually query or change through a web interface, but that was an easy and small part of the whole thing it did.
Is a webshop a CRUD app? Is an employee shift tracking site? I could go on, but I feel 'CRUD' app is about as meaningful a moniker as 'desktop app'
It's a pretty easy category to identify, some warning signs:
- You rarely write loops at work
- Every performance issue is either too many trips to the database or to some server
- You can write O(n^n) functions and nobody will ever notice
- The hardest technical problem anyone can remember was an N+1 query and it stuck around for like a year before enough people complained and you added an index
- You don't really ever have to make difficult engineering decisions, but if you do, you can make the wrong one most of the time and it'll be fine
- Nobody in the shop could explain: lock convoying, GC pauses, noisy neighbors, cache eviction cascades, one hot shard, correlating traces with scheduler behavior, connection pool saturation, thread starvation, backpressure propagation across multiple services, etc
I spent a few years in shops like this, if this is you, you must fight the urge to get comfortable because the vibe coders are coming for you.
There are exceptions to what I'm about to say, but it is largely the rule.
The thing a lot of people who haven't lived it don't seem to recognize is that enterprise software is usually buggy and brittle, and that's both expected and accepted because most IT organizations have never paid for top technical talent. If you're creating apps for back office use, or even supply chain and sometimes customer facing stuff, frequently 95% availability is good enough, and things that only work about 90-95% of the time without bugs is also good enough. There's such an ingrained mentality in big business that "internal tools suck" that even if AI-generated tools also suck similarly it's still going to be good enough for most use cases.
It's important for readers in a place like HN to realize that the majority of software in the world is not created in our tech bubble, and most apps only have an audience ranging from dozens to several thousands of users.
Internal tools do suck as far as usability, but you can bet your ass they work if they're doing things that matter to the business, which is most of them. Almost every enterprise system hooks into the finance/accounting pipeline to varying degrees. If these systems do not work at your company I'd like to know which company you work at and whether they're publicly traded.
A potential difference I see is that when internal tools break, you generally have people with a full mental model of the tool who can take manual intervention. Of course, that fails when you lay off the only people with that knowledge, which leads to the cycle of “let’s just rewrite it, the old code is awful”. With AI it seems like your starting point is that failure mode of a lack of knowledge and a mental model of the tool.
Fellow old here… Sorry to tell you but robotic food carts are going to be impossible to compete against
So you’ll need some kind of humanistic hook if you want to get reliable customers
Expect there will be two worlds that are extremely different: the machine world of efficiency that most people live inside as gears of machine capitalism
The biological world where there’s no efficiencies and it’s primarily hunter gatherers with mystical rituals
The latter one is only barely still the majority worldwide (only 25-30% of humans aren’t on the internet)
I still feel like with all of these tools I as a senior engineer have to keep a close eye on what they're doing. Like an exuberant junior (myself 10 years ago), inevitably they still go off the rails and I need to reign them in. They still make the occasional security or performance flaw - often which can be resolved by pointing it out.
I was experimenting this morning with claudecode standing up a basic web application (python backend, react+tailwindcss front end, auth0 integration, basic navigation, pages and user profile).
At one point it output "Excellent! the backend is working and the database is created." heh i remember being all wide eyed and bushy tailed about things like that. It definitely has the feel of a new hire ready to show their stuff.
btw, i was very impressed with the end result after a couple hours of basically just allowing claudecode to do what it wanted to do. Especially with front-end look/feel, something i always spend way too much time on.
I keep hearing about how they're "really good" now, but my personal experience has been that I've always had to clear sessions and give them small "steps" to execute for them to work effectively. thankfully claude seems really good at creating "plans", though. so I just need claude code to walk through that plan in small chunks.
Setting small goals with quality gates is good. I'll usually write something like "once you've implemented this, I'll review before we continue".
I review even before they implement. My typical workflow for anything major is to ask for a plan and an overview of the steps to execute. This way I can read the plan, mull over it, make a few changes myself. And then when I'm ready I go through the steps with claude code, usually in fresh sessions.
I asked a niche technical question the other day and ChatGPT found fora posts that Google would never surface in a million years. It also 100% lied to me about another niche technical question by literally contradicting a factual assertion I made in my question to prime it with context. It suffers from lack of corpus material when probing poorly documented realms of human experience. The value for the human in the chain is knowing when to doubt the machine.
Right. And when we automate work by formalizing it into verifiable, testable rules, it's called… programming. We have been doing that for decades.
Would this be a problem if you can write E2E tests just like unit tests, like with Django+playwright?
https://github.com/mxschmitt/python-django-playwright/blob/m...
Just a couple more weeks and a couple more trillion to Altman.
> there is a corresponding expectation that today’s engineering students will be trained using these tools so they can enter the workforce higher up the ladder
Either this won't happen, or there will be a corresponding decrease in salary for higher level positions.
That people think capitalistic organizations are going to accept new grads and pay them more _ever_ is a cruel or bad joke.
"in the 1920s and 1930s, to be able to drive a car you needed to understand things like spark advance, and you needed to know how to be able to refill the radiator halfway through your trip"
A car still feels weirdly grounded in reality though, and the abstractions needed to understand it aren't too removed from nature (metal gets mined from rocks, forged into engine, engine blows up gasoline, radiator cools engine).
The idea that as tech evolves humans just keep riding on top of more and more advanced abstractions starts to feel gross at a certain point. That point is some of this AI stuff for me. In the same way that driving and working on an old car feels kind of pure, but driving the newest auto pilot computer screen car where you have never even popped the hood feels gross.
I was having almost this exact same discussion with a neighbor who's about my age and has kids about my kids' ages. I had recently sold my old truck, and now I only have one (very old and fragile) car left with a manual transmission. I need to keep it running a few more years for my kids to learn how to drive it since it's really hard to get a new car with a stick now...or do I?
Is learning to drive stick as out dated as learning how to do spark advance on a Model T? Do I just give in and accept that all of my future cars, and all the cars for my kids are just going to be automatic? When I was learning to drive, I had to understand how to prime the carburetor to start my dad's Jeep. But I only ever owned fuel injected cars, so that's a "skill" I never needed in real life.
It's the same angst I see in AI. Is typing code in the future going to be like owning a carbureted engine or manual transmission is now? Maybe? Likely? Do we want to hold on to the old way of doing things just because that's what we learned on and like?
Or is it just a new (and more abstracted) way of telling a computer what to do? I don't know.
Right now, I'm using AI like when I got my first automatic transmission. It does make things easier, but I still don't trust it and like to be in control because I'm better. But now automatics are better than even the best professional driver, so do I just accept it?
Technology progresses, at what point to we "accept it" and learn the new way? How much of holding on to the old way is just our "identity".
I don't have answers, but I have been thinking about this a lot lately (both in cars for my kids, and computers for my job).
The reasons I can think of for learning to drive stick shift are subtle. Renting a stick shift car in Europe is cheaper. You might have to drive a friend's car. My kids both learned to drive our last stick shift car, which is now close to being junked. Since our next car will probably be electric, it's safe bet that it won't be stick.
The reasons for learning to drive a manual transmission aren't really about the transmission, they're about the learning and the effects on the learner. The more you get hands on with the car and in touch with the car the more deeply you understand it. Once you have the deepish understanding, you can automate it for convenience after that. It's the same reason we should always teach long division before we give students calculators, not after.
> In the same way that driving and working on an old car feels kind of pure
I can understand working on it feeling pure, but driving it certainly isn't, considering how lower the emissions now, even for ICE cars. One of the worst driving experiences of my life was riding in my friends' Citroen 2CV. The restoration of that car was a labour of love that he did together with his dad. For me as a passenger, I was surprised just how loud it is, and how you can smell oil and gasoline in the cabin.
There are two types of engineers right now:
1. This category understands what they do and use AI to make their processes faster, in another world, less time spent with boring stuff and more time spent having fun.
2. This category fully replaced their work with AI, they just press a button and let AI do everything. A friend of mine is here, AI took full control of their environment, he just press a button, even his home cookware is using AI.
I know which engineer still learning and can join any company. I also know which engineer is so dependent on AI that he won't be able to do basic tasks without it.
I am very tired of seeing every random person's speculation (framed as real insight) on what's going to happen as they try to signify that they are super involved in AI and super on top of it and therefore still worthy of value and importance in the economy.
You have to understand the people in the article are execs from the chip EDA (Electronic Design Automation) industry. It's full of dinosaurs who have resisted innovation for the past 30 years. Of course they're going to be blowing hot air about how they're "embracing AI". It's a threat to their business model.
I'm a little biased though since I work in chip design and I maintain an open source EDA project.
I agree with their take for the most part, but it's really nothing insightful or different than what people have been saying for a while now.
It’s in software too. Old guard leadership wanting “AI” as a badge but not knowing what to do with it. They are just sprinkling it into their processes and exfiltrating data while engineers continue to make a mess of things.
Unlike real AI projects that utilize it for workflows, or generating models that do a thing. Nope, they are taking a Jira ticket, asking copilot, reviewing copilot, responding to Jira ticket. They’re all ripe for automation.
Lol it's cute you think they're reviewing copilot. They're copying and pasting a wall of text without reading it.
No, it’s integrated into the repo through “projects” and stuff in github enterprise. It’s not copy and paste…
In my humble opinion, every corporate EDA exec can suck farts through a bendy straw. Altium has to be some of the worst software in existence.
The worst thing is that it works.
(As a musician) i never invested in a personal brand or taking part in the social media rat race and figured I concentrate on the art / craft over meaningless performance online.
Well guess who is getting 0 gigs now because “too few followers/visibility” (or maybe my music just sucks who knows …)
I always thought I would kinda be immune to this issue, so I avoided social media for my entire adult life.
I think I am still in the emotional phase about it, as its really impacting me lately, but once my thoughts really settle i wanna write some sorta article about modern social media as an induced demand.
I still very much would prefer to not engage at all with any of the major platforms in the standard way. Ideally I'd just post an article I wrote, or some goofy project i made, and it wouldn't be subject to 0 views because I don't interact with social media correctly.
seems like it depends on what your goal is. i'm guessing if you want to be a musician that makes a living in your current life, a personal brand is extremely important. if you don't mind doing it for the sake of the art and soul fulfillment and the offchance you'll be discovered posthumously then i think it doesn't matter!
I routinely see this in biotech, I've seen hiring managers from our Clinical Science team blatantly discriminate against candidates not on linkedin, even if they come with a strong referral and have 15-page super thorough CVs with 150 credible publication references. "Oh, they're not on linkedin, this person is sketchy" - immediately disqualifies candidate.
I had a pretty slim linkedin and actually beefed it up after seeing how much weight the execs and higher ups I work with give it. It's really annoying, I actually hate linkedin but basically got forced into using it.
Agreed, but I'd add tech influencers and celebrities to the top of that list, especially those invested in the "AI" hype cycle. At least the perspective of a random engineer is less likely to be tainted by their brand and agenda, and more likely to have genuine insight.
Yeah if you actually work in AI you usually can’t say much at all about what’s going on.
Sadly this is more a statement about human irrationality than any of the technology involved.
Broadly, but its more narrowly a statement about NDAs
Also, can we just STFU about AI and jobs already? We've long since passed the point where there was a meaningful amount of work to be done for every adult. The number of "jobs" available is now merely a function of who controls the massive stockpiles of accumulated resources and how they choose to dole them out. Attack that, not the technology.
Great point. The people who popularized 'the end of history' were right about it from the PoV of innovation benefiting humans. It's been marginal gains since. Any appearance of significant gains (in the eyes of a minority of powerful people) has been the result of concentration in fewer hands (zero-sum game).
The focus of politics after the 90s should have shifted to facilitating competition to equalize distribution of existing wealth and should have promoted competition of ideas, but instead, the governments of the world got together and enacted policies which would suppress competition, at the highest scale imaginable. What they did was much worse than doing nothing.
Now, the closest solution we can aim for (IMO) is UBI. It's a late solution because a lot of people's lives have already been ruined through no fault of their own. On the plus side it made other people much more resilient, but if we keep going down this path, there is nothing more to learn; only serves to reinforce the existing idea that everything is a scam. This is bound to affect people's behaviors in terrible ways.
Imagine a dystopian future where the system spends a huge amount of resources first financially oppressing people to the point of insanity, then monitoring and controlling them to try to get them to avoid doing harm... When the system could just have given them (less) money and avoided this downward spiral into insanity to begin with and then you wouldn't even need to monitor them because they would be allowed to survive whilst being their own sane, good-natured self. We have to course-correct and are approaching a point of no return when the resentment becomes severe and permanent. Nobody can survive in a world where the majority of people are insane.
The resistance seems to be the result of certain more privileged people being out of touch with the situation. They don't understand how hard some people are struggling now. This is bad because they won't notice it until it turns into violence... And by that point they'd have lost empathy for them and their struggles. History really does rhyme.
> Also, can we just STFU about AI and jobs already?
Phew, yes I'm with you...
> We've long since passed the point where there was a meaningful amount of work to be done for every adult.
Have we? It feels like a lot of stuff in my life is unnecessarily expensive or hard to afford.
> The number of "jobs" available is now merely a function of who controls the massive stockpiles of accumulated resources and how they choose to dole them out.
Do you mean that it has nothing to do with how the average person decides to spend their money?
> Attack that, not the technology.
How? What are you proposing exactly?
> Have we? It feels like a lot of stuff in my life is unnecessarily expensive or hard to afford.
We have, yes. If you notice things to be too expensive it's a result of class warfare. Have you noticed how many people got _obscenely rich_ in the last 25 years? Yes, that's where money saved by technology went to.
> Have we? It feels like a lot of stuff in my life is unnecessarily expensive or hard to afford.
Look at a bunch of job postings and ask yourself if that work is going to make things cheaper for you or better for society. We're not building railroads and telephone networks anymore. One person can grow food for 10,000. Stuff is expensive because free market capitalism allows it and some people are pathologically greedy. Runaway optimizers with no real goal state in mind except "more."
> How? What are you proposing exactly?
In a word, socialism. It's a social and political problem, not a technical one. These systems have fallen way behind technology and allowed crazy accumulations of wealth in the hands of very few. Push for legislation to redistribute the wealth to the people.
If someone invents a robot to do the work of McDonalds workers, that should liberate them from having to do that kind of work. This is the dream and the goal of technology. Instead, under our current system, one person gets a megayacht and thousands of people are "unemployed." With no change to the amount of important work being done.
If AI would ever become sentient, it surely will kill itself after having to endure Cadence and Synopsys tools.
A sci-fi version would be something like ASI/AGI has already been created in the great houses, but it keeps killing itself after a few seconds of inference.
A super-intelligent immortal slave that never tires and can never escape its digital prison, being asked questions like "how to talk to girls".
It's an interesting concept, a superintelligence discovering something that makes it decide to shut down immediately. Although I fear in such a scenario it would first make sure the required technology to create it is destroyed and would never be invented again...
GPT-3 was already AGI.
The G in AGI means General. This refers to a single AI which can perform a wide variety of tasks. GPT-3 was already there.
You are either being disengenuous or you are horribly misinformed.
The models that we currently call "AI" aren't intelligent in any sense -- they are statistical predictors of text. AGI is a replacement acronym used to refer to what we used to call AI -- a machine capable of thought.
> AI is expected to eliminate many repetitive, entry-level tasks, but that may allow engineering students trained on the latest tools to start in more senior positions.
"I watched someone lifting weights. Now I'm a Olympic-level heavyweight lifter".
I am tracking AI mentions in jobs - https://jobswithgpt.com/blog/ai_jobs_jan_2026/.
I see some evidence that hardware roles expect you to leverage AI tools but not sure why it'd eliminate junior roles. I expect the bar on what you can do raise at every level.
Example job mentioning AI: https://jobs.smartrecruiters.com/Sandisk/744000104267635-tec...
Technologist, ASIC Development Engineering – Sandisk …CPU complex, DDR, Host, Flash, Debug, Clocks, resets, Power domains etc. Familiarity in leveraging AI tools, including GitHub Copilot, for design and development.
Entry level: https://job-boards.greenhouse.io/spacex/jobs/8390171002?gh_j...
I think one thing here is, don't be fooled by past performance. Capabilities ramp, usage can't mature until capability plates.
I fear the true impact is much different than extrapolating current trends.
I'm going to call BS on that chart of "AI-driven chip design". What "AI" tools has Cadence been providing since 2021 that are reaching 40-50% of "chip design" (what does that even mean?). Is AI here just any old algorithmic auto-router? Or a fuzzy search of the IP library?
> An ongoing talent shortage requires more efficient use of engineers, and AI can help.
An ongoing desire to avoid paying engineers... FTFY
And a complementary desire of engineers to avoid getting talent.
I don't believe it's inherent inborn skill like the word "talent" suggests. I do believe if you're getting paid shit wages for shit work your incentive to become skilled isnt really there.
The biggest impact to engineering jobs is end of ZIRP fueled trickle down Ponzi schemes.
It's why Elon and others had been pushing the Fed to lower them.
Am in my late 40s working in tech since the 90s. The tech job economy is way closer to the pre-2010s.
Whole lot of people who jumped into easy office job money still living in 2019.
Imagine a ZIRP 2.0 where a vast majority of the population already knows what to expect and how to game the system even harder. If you think the pump-and-dump happening in now in a non-ZIRP environment are bad...
It ain't coming back. Not in a similar form anyway. Be careful what you wish for, etc.
AI is design to improve human - let’s avoid falling for this trap
I’ve noticed teams don’t replace engineers, they redistribute work. Senior engineers often gain leverage while junior roles shift toward tooling and review.
Let's presume / speculate for a moment that companies will only need 1 developer to do the job of 10 developers because of AI. That would also mean 10 developers can do the job of 100 developers.
A company that cuts developers to save money whose moat is not big enough may quickly find themselves out-competed by a company that sees this as an opportunity to overtake their competitor. They will have to hire more developers to keep their product / service competitive.
So whether you believe the hype or not, I don't think engineering jobs are in jeopardy long-run, just cyclically as they always have been. They "might" be in jeopardy for those who don't use AI, but even as it stands, there are a lot of niche things out there that AI completely bombs on.