On Being an Engineer in 2026 | Hacker News Reader

On Being an Engineer in 2026

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42 pointstalboren60 comments

> If anything, 10x engineers have become 100x engineers. And the average engineer is still average.

This is the real fear. We had a flood of engineers who entered the profession in the past 10-15 years who were decidedly average. No real interest in software development beyond "it's a cushy desk job, or better yet WFH job, that pays really well." No interest in whatever product was being developed by their company. Just show up, their PM assigns them a ticket to make a widget with a button, they use their TypeScript bootcamp skills to code up a widget with a button, ticket moved to In Review, job's done.

Now an agent can do that, and you need fewer of these types of people. Like, a lot fewer. Every engineer on your team who isn't a 10x-cum-100x engineer feels like a massive lost opportunity.

The "10x" engineers I know have never been more excited about their jobs. I'm really unsure what's going to happen to the average ones.

> We had a flood of engineers who entered the profession in the past 10-15 years who were decidedly average. No real interest in software development

Source?

you don't need a source for everything. it's pretty obvious that SWE, like anything else follows a bell curve and most folks land smack dab in the middle. average engineers are exactly as described, glorified ticket machines that don't care whatsoever about their work beyond maintaining the job. i don't need some rigorous study to tell me exactly what I've experienced in the last 15+ years of my profession across 5+ companies

Realistically you want fewer, higher-quality engineers who are educated in these tools and treat them with respect and caution. These tools are dangerous and you can’t play around. All it takes is one mistake and the blast radius is way, way larger. One mistake can do so much damage it undos all value of an entire year. Before downloading a malicious dependency was limited to deterministic behavior and could be scanned for. Now, you run a MCP server locally, it can prompt inject (in many different ways), and now your super powerful AI buddy is hacking you in ways a deterministic piece of malware could never. You could cripple entire companies through negligent use.

At the same time, having customized software has become cheap. We’ll be writing a ton more small software, and people will have to maintain it.

I feel like that’s where the average ones will go.

To be honest, I doubt the current state of things will hold much longer. Current AI can automate virtually all of my work as a pre-AI senior and is rapidly closing in on being able to automate the types of work that have emerged post-AI too. I have a hard time imagining a world where a drop-in end-to-end replacement for a full software team is more than a year away. I'm sure I'll have a job for a year or two past that, but there'll be some small stock market bump eventually and a bloodbath will begin the second the C-suite takes a closer look at what their engineers are even doing anymore.

C-suite is as likely to be automated as engineers. It will probably be AI firing engineers on behalf of shareholders.

> C-suite is as likely to be automated as engineers.

Nah. C-suite has (a) money and (b) legal and fiscal obligation. You literally need a human being certain legal operations, like incorporation, salary, and lawsuits.

You need *a* human. And that human will be delegated to clicking approve on AI decisions.

I think a lot of people thought that every step of the way with the progress of AI or automation in general. Unless that C-suite is all shareholders with majority or near majority control of a company, I would think the passive shareholders (in a public or private company) would want the savings in cutting all of those high salaries to zero for a likely superior outcome with AI.

I wouldn't count on lawyers being deemed necessary by the law for as many actions as they have been so far.

> would want the savings in cutting all of those high salaries to zero for a likely superior outcome with AI.

Maybe. Or maybe the C-suite salary will be cut to $50K/yr. That would make them marginally more expensive than AI, but for that price the shareholders get an extra layer of insulation should things go awry (and they won't be held personally responsible for corporate malfeasance).

> I wouldn't count on lawyers being deemed necessary by the law for as many actions as they have been so far.

Maybe, but that's pretty far down the road and would require restructuring the whole jurisprudence system.

Who is C-suite going to blame for fuck ups and failures if the company is just a suit and a laptop?

Middle management should be nervous. Plenty of people whose job it is to ask "is it done yet? when can I have it?!" -- they could be replaced today with better tooling and processes.

No. The C-Suite is safe, very safe. I participated in an AI survey of what is the information that a C-Suite operates upon, and it is largely unwritten information, it is a suspicion and manipulation game with all the other c-suite players, and the middle managers beneath them as the battle ground. There are Peter Principled members that maintain their positions by executing the corporations legally grey dirty work, and then there are serious 100x non-engineers that can launch $100M initiatives in short notice, with successful experience doing so to create divisions and more.

I know the software engineering world like to think the C-Suite are disposable, but that is only because you really have no idea what they really do.

At which point the question will be: What are even the customers paying for? If they can get the product / service straight from their own chatbots, why pay a company for it? The companies that sell data, will probably somewhat safe - as long as they can have some moat between the users and data.

The death of SaaS could also be a thing in the (not so distant) future.

I mean, the same reason they do now. Time, taste, and amortized costs.

Just because it's faster and cheaper and requires less skill, doesn't mean it's instant and free and requires no skill.

I’m planning out my 2030 career in idle moments these days. I think it’ll be something akin to a carpenter who builds custom furniture.

I think we’ve barely scratched the surface of what software can do if it’s completely custom built, specifically for individuals or small businesses who could’ve never afforded it, pre-ai. Home automation, health and disease management, all sorts of administration, conversational ai over your life’s data - most people have no clue what’s possible once it’s custom built. With close attention, good design, creativity, and without giving away privacy or independence.

In my limited spare time I’m building up this tapestry of custom services for myself and my loved ones, and having a great time doing it, while trying to learn how I can offer this as a service.

Judging by the reactions I get when showing people what I can do with my phone, and no that’s not available on Google play, I think the demand will be there.

Woodworking and making furniture is fun! However, having made both furniture (and ceramics) myself - I think it’s hard to make a living this way. The overwhelming majority of people are used to paying little for these kinds of things. In the case of woodworking a large piece of furniture can take months so you might expect to only produce 3-4 / year unless you’re very good.

Not to say you shouldn’t! But it’s worth being realistic about income compared to (current) software salaries.

Sure. But you're taking my metaphor farther than I intended. Most people understand the basics of furniture, and still prefer to pay Ikea rather than a carpenter. In the case of custom ("bespoke", ugh) software, we'll have to both define and communicate a new category of service. And the customers are likely to be the same sorts of people who will pay a personal trainer or get a private tutor for their kids - and I think that includes a lot more people than are willing to pay for custom furniture. But generally I do agree, this vision is far more hand-wavy than concrete at this point. It is, though, a good time for hand-waving right now.

I think we're looking at the end of commodity software, the end of software companies, and birth of every single company writing their own everything. A proprietary soup everywhere, and 1-2 100X developers overseeing it all. And those few developers will spend their time in communications tutorials to get the staff to be able to explain what they want and need, so they can then make it happen for that company's exclusive use.

Accountability. Suppose you had the ability to have robotically built highway bridges. Who is going to sign off on the work? Who is going to be held accountable? The same with software. The people who don't like to write software today aren't going to suddenly develop a love for deep testing and validation.

And now we are controlling models that can be hacked in so many different ways and literally blow up companies. It used to be accountability the software wouldn’t break. Now it’s accountability that in creating the software we don’t ruin the business.

> To be honest, I doubt the current state of things will hold much longer. Current AI can automate virtually all of my work as a pre-AI senior and is rapidly closing in on being able to automate the types of work that have emerged post-AI too.

reading HN these days feels like constant gaslighting. this is so far from my reality that i wonder what folks who are saying this are working on

I think people who were in mostly bs kind of roles, honestly.

I'm guessing it's the copy-paste CRUD apps that used to be plagiarized from stackoverflow. I'm with you, I've never seen an AI agent do good work.

AI is the ultimate generalist. You can make it do a lot of things well by asking the right questions. In a generalist job, many people can ask those questions.

I don't use AI that much. When I do, it's often surprisingly competent in the topics I'm a specialist in, and it can do a lot of good work quickly. But other people in other organizations still continue building upon my work instead of asking AI directly. Apparently because they don't know what to ask.

Nobody knows how long this will last. Hopefully long enough that I can afford to retire, because I'm old enough that I would face ageism if I had to find a different job.

And if we get there, we will probably have a serious political debate on how many digits should there be in the wealth tax rate.

People have been saying this for years. They’ve also been saying “have you tried n” model yet as well as “wait till n+1” model is here as the author mentions.

I just had fable 5.2 (high effort) review a change. It hallucinated 2 problems that I was only able to correct because I also understand the code.

LLMs can do any job _in theory_. It’s not a very interesting argument. Their effectiveness at various jobs is much more interesting.

> I also understand the code.

and the product, the team, and (no matter how shallowly) the organizational pressures and politics. Whether we like it or not, those are part of the problems developers solve. Even something as rudimentary as knowing what is feasible to test given your deadline isn't something you can encode in a model[1]. Code does not exist in a vacuum.

I say this even with the latest models, it seems as soon as your development practice falls out of the ideals written up in blogposts of the previous ZIRP decade, you have to course correct. I use this term because the AI is technically correct (the best kind of correct) but practically useless, if not also irresponsible.

Recent anecdote (literally today): we've been having Claude write exhaustive test suites to eat at the coverage backlog. Lately, we noticed that tests in develop are even more flakier than usual. Our whole suite runs in 30-40min but lately it could exceed an hour and then fail.

It seems Claude took "exhaustive tests" to mean "exhaust the compute resources of the Jenkins node". What's worse is it wrote those tests for features that have been disabled and would soon be rewritten anyway.[1] I've always said that LLMs are great for generating test suites and this is the first time an LLM "failed" this assignment for me.

Code is cheap. The right code (or lack thereof) for the right situation takes meatspacebrainpower.

[1] The solution here is to run your company/team 100% as agents so they have the full context of project timelines, resources, customer feedback, etc. ala stevey and gtan but my reasons why I don't believe in that has already been articulated elsewhere.

What happened to all that "RTO is to keep to C-suite happy" talk around here? How are they going to prop up real estate or feed their egos or whatever the conspiracy was.

That's where my 1-2 year buffer comes from.

And your reaction is to accelerate the process by saying exactly what BigAI wants to hear? Totally believable.

You know, Elon and the "superhero" DOGE team said they "couldnt imagine" why most of Govt was required. What happened?

Security will always be a risk with AI tooling. The only way to avoid that risk is intelligent human reasoning. No, not human-in-a-loop. It’s human defining boundaries, carefully, and allowing agents to operate within them. Failure to do so will always lead to disaster given enough time.

Then who directs the swarm to create the most value? An executive can’t. They have other things to do so they delegate. That person will be an engineer and their attention is finite too so they will delegate. To other engineers.

Here’s the best part. You write back and say I’m wrong. Guess what? That’s engineering too. Some people have to sort out all this nonsense and the stakes are higher than they’ve ever been and now engineers are supposedly not relevant.

But yeah when you get everything under control, what’s the day to day look like? Pretty freaking cushy. And then it relies on intelligence to figure out what next. Clients always want things done and having those conversations won’t get much easier with AI. Keeping up to date. Jesus I’ve exhausted myself writing all this. So much work!!

https://nitter.cf/shaharglazner/article/2119796029256032278

A readable link for those of us without Twitter/X account

If you can’t make things right when your agent goes wrong, then yeah you’re not adding value. If you can’t tell the difference between the agent hallucinations and the useful stuff, then yeah you’re not adding value. If you can’t steer the agent in a useful direction instead of just standing back, then yeah…

You sound dismissive, but taking your description to its conclusion, the engineer value can be approximated as:

  {(P(AI mistake) * P(human spots the mistake | AI mistake) * P(human fixes mistake | given AI mistake, human spots it) - P(AI mistake)} * cost of AI mistake

For many engineering jobs, P(AI mistake) is very low and decreasing, and cost of AI mistakes is also usually (consider the cost of a bug for an average engineer; unless you’re building avionics or working on MRIs or such, usually very low).

So even if you can “make things right” or “tell the difference” as you say, the value of any given engineer is dropping.

> For many engineering jobs, P(AI mistake) is very low

Not remotely the case. LLMs make mistakes very frequently, much more frequently than humans do.

source? I'm wondering at the curve of those mistakes actually, I doubt modern LLMs make mistakes at the level of the worst engineers I've worked with.

My last project had 2 guys from India and one guy from a consulting company that I take any LLM over them. The guy from the consulting company spent more than a month trying to implement a design that actually was not implementable. I obviously also blame the supposed professional design company that produced the design, and I mean it took me about 6 hours spread over a day and a half to figure out waitaminute this is actually not doable! But I have to wonder how badly would an LLM have done. I sort of think the same, because probably an LLM is never going to announce - hey, this design as stated is impossible, some parameter needs to be changed.

it's worse than that, as you need to also compare

human mistake, AI spots the mistake

Human mistake, human spots mistake

AI mistake, AI spots the mistake.

Everybody is always talking as if they don't make mistakes, but every software engineer does; it used to be that human caught those mistakes and fixed them, but if AI catches those mistakes and fixes them, then that is also a decrease in value of human.

Human - Human and AI - AI mistake and spotting thereof basically cancel each other out, or can be considered no mistake. Human - AI just speaks in favor of AI, and my comment was responding to somebody dismissive of how crushed engineers can feel by AI.

I'm a fan of the 'sandwich' model: someone needs to say what to do, someone needs to do it, and someone needs to say if it's been done [well enough]. Even if AI can do that middle bit entirely, we still need humans at the beginning and the end. Replace the humans at the start and you have a paperclip-optimizer; replace them at the end and you have slop.

The problem is that software in corporations has typically been product managers at the front, software engineers in the middle, and QA at the end. Software developers who also have good product sense and do diligent QA are still needed. Programmers who treated their job as leetcode exercises are not needed anymore.

> someone needs to say what to do, someone needs to do it, and someone needs to say if it's been done

I agree, but if you zoom out, "someone needs to say what to do" and "someone needs to say if it's been done" are the customers and end users. Will they still need product managers, software engineers, or QA in the middle? I think so because many customers will need help from experts with domain knowledge.

> "someone needs to say if it's been done"

If that's the end user, they're not happy. End users generally don't like doing unpaid involuntary QA work.

Customers are notoriously bad representatives for 'what to do', per "my customers would have asked for a faster horse". And they also hate being used as QA; most quality issues (which can be reliability, UX, latency, etc) need to be addressed before the customer sees it and forms their durable impression of the software.

And then also it's as you say, customers do need help from domain experts (whether they admit it or not).

What if customers have conflicting requirements? Or they change their mind midway through implementation? IMO agents are bad at handling ambiguity, and often make assumptions and tradeoffs that are lost after the conversation. This is why there's a lot of compounding technical debt in projects where humans don't inspect the code.

I have had significant success applying LLMs to the start of that sandwich.

The sheer amount of time that gets wasted when people who are awful at expressing what they want just slap together a ticket with little understanding of what they're asking, what the software does, or what the customer wants or needs. Which is far, far too many of them.

Previously, eventually I or another person who does know that would get to it, cross out all the utterly irrelevant parts, strip out the parts that were clearly a completely unrelated ticket, point out to them that they'd asked for the font to be coloured both blue and red simultaneously (neither of which matched the style guide, the company or product colour scheme, or any other part of the UI) and so on and so on... it might takes weeks from start to end for a ticket to actually get expressed well.

Now, the LLM reviews it, adds a whole lot of questions, points out a bunch of nonsense, and sends it back to them. Even in cases where the LLM makes some mistakes, it's 100% fine because the human is forced to think about what they want. The LLM is great at natural language, and it doesn't matter what mistakes the LLM makes.

The quality of the software is going up and up, purely by finally getting a handle on awful requirement and ticket writing.

The human isn't replaced at the start of your sandwich, but now the LLM makes that human do it properly, at speed. Massive, massive reduction in time wasted and significant improvement in software quality.

At first I thought tacit knowledge would be a differentiator: LLMs are trained on writing, so they can’t learn things that have never been written down. But tacit knowledge is exactly what reinforcement learning post-training is about. So (just as with humans) the limitation is in providing a rich enough environment, with good enough feedback, to train in the tacit knowledge.

The trick is to remain intellectually stimulated and challenged.

AI is NOT coming for your job. Not your whole job...

It's coming to take away the roles in your job that can be done by AI.

It happened before and it'll happen again. Humans adapt - there's more work now than ever. Some examples from the 20th century...

Big companies used to have printing departments to which you'd send a print job, walk over and pick it up, nicely stapled. Nowadays the office printer sits there 24/7 awaiting your print job. You have to staple it yourself though.

Big companies used to have secretaries & clerical staff who'd type up your letter and send it off for you. Now we have keyboards and email. You have to send the email yourself though.

Big companies used to have "code typists" who typed out huge amounts of code. Now they have agent managers who delegate the typing. Getting good output is a challenge, but asking for the output you need is so much better than RSI from typing and so much more effective than debugging based only on one's puny human knowledge, that we're suffering through. You have to assure the quality of the work yourself though.

It's strange seeing so many people take to their blogs and loudly declare themselves unemployable. I doubt anyone'd hire this author and that'll make their prophecy come true - for them.

A cautionary tale indeed.

I don't buy the reductio ad absurdum, which hinges on the assertion that some developers are still 10x more productive than others due to differences in their engineering potential. I think that A) however you're measuring productivity is probably not capturing the full story, and B) one of the most important reasons people have differences in productivity is because not everyone puts the same amount of effort in. It's not necessarily because they don't have the ability to stay off of reddit and work longer hours and prompt more furiously, it's because not everyone is equally motivated to do so (hence the entire field of management).

I also don't buy the idea that there is special human stardust that gives us permanent irreplaceable economic value. Even if the tacit knowledge that the current generation of engineers were able to build up in the years prior to ~2025 does give us value that won't be completely replaced for a long time, why would you assume that the delta between what you can do and what claude can do will still be worth $200K in 5 years?

What if the market for human consumption becomes human made in the next 5 years? I could replace grand oak furniture with IKEA night stands worldwide until the world regrew its desire for quality

On Being an [Software] Engineer (i.e. AI agent manager) in 2026

It's all about trust

Something I was thinking to myself last week – "if I believe SWE is more or less dead then why am I so busy?"

Clearly there is still some work to do, but the work I do now is very different, and for me, much less enjoyable.

I'm not solving challenging logic or algorithmic problems anymore, but basically just iterating on features with agents or getting them to look into things for me.

It sucks because I used to be very good at these things, but my edge against less competent SWEs doesn't really exist anymore.

More annoyingly, people I work with are producing a lot of code that I think the design of kinda sucks, but agents can force the design to work and so it works. I suggest we do things another way, but I'm now just slowing things down and it's hard to argue against the comment, "if that becomes a problem we can just get Claude to refactor later".

I guess what I'm saying is that I think think there's a really crappy job that will still exist for a while around managing and contributing to these agent-generated codebases. But skill is less about your technical abilities, but how good you are at hooking up agents to things and getting them to solve problems. This doesn't interest me at all though. My desire to spend a lot of time design and thinking about problems, and deeply understanding the codebase I'm maintaining now just work against me.

I think there will be SWEs by some definition, but the job as it existed a few years ago is gone. Not even saying that's a bad thing, but for someone like myself, it may as well be dead.

The move now is toward "software factories" where sucky design is no one's responsibility.

Eventually the whole thing will grind to a halt or fail catastrophically (that's life), but it will seem very good while it's working.

I don't know if there's a real role for engineers beyond designing and tuning the factory itself.

Of course, such a thing is only tractable on highly factorable platforms. Well designed incumbents have an edge for the next couple of years or so, until others catch up.

It gives me no pleasure to say this, but I think it will all be automated in the end. Engineers will be more like rare consultants who inspect and validate very high level solutions.

> Maybe one day agents will have their own experiences, too. Maybe they'll develop intuitions they can't quite explain, just like we do.

Machine / program / software cannot have these things in my opinion because this stuff is produced by human consciousness which we cannot even fully explain yet. Someone has "to teach" the agent to have this stuff, but we don't have that knowledge. And also "intuition" is a feeling (like love, hate, sadness, joy) so no agent can have that until they have the limbic system, prefrontal cortex and other stuff.

Not everyone knows how the brain works and many believe it to be one organ.

"We can't explain it therefore we can't create it" doesn't seem like a strong argument, emergent behavior and whatnot.

Hell, the fact we don't know what consciousness is means that some LLM systems (or trees! or planets!) might already have it, and we'd be none the wiser. Not that I believe this, but as these systems get larger and more sophisticated it's an increasingly concerning proposition that we have no way to validate either way.

We don't know why do many medicines work, and that doesn't stop us from using them to good results.

Oh he means programmers. I thought he meant real engineers.

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