HN Reader

From my experience AI is only good with mainstream coding tasks. Javascript, node, react, crud. Whatever it has seen in overabundance, it is good with. Already with typescript it is less strong than with javascript. It is just a clever token generator without intelligence. Often it resembles intelligence but so can a human by learning quotes of famous people.

It is a new and exciting tool but immediately limited with medium complex tasks. Also we will see a lot more code with tricky bugs coming out of AI assistants and all of that needs to be maintained. If software development gets cheaper per line of code then there will be more demand. And someone has to clean up the mess created by people who have no clue whatsoever of SWE.

Once upon a time people developed software with punch hole cards. Even without AI a developer today is orders of magnitude more proficient than that.

The only thing I hope I am not going to see in my lifetime is real artificial intelligence.

For some reason miss two important points:

1) The AI code maintainence question - who would maintain the AI generated code 2) The true cost of AI. Once the VC/PE money runs out and companies charge the full cost, what would happen to vibe coding at that point ?

I honestly think middle management will be where AI causes job cuts.

In today's corporate environment, 70% of the costs are in management and admin muddlement, do we really think these "people skills" translate into anything useful in an AI economy?

The junior devs have far more hope.

My experience hasn't been LLMs automate coding, just speeds it up. It's like I know what I want the solution to be and I'll describe it to the LLM, usually for specific code blocks at a time, and then build it up block-by-block. When I read hacker news people are talking like it's doing much more than that. It doesn't feel like an automation tool to me at all. It just helps me do what I was gonna do anyways, but without having to look up library function calls and language specific syntax

TBH, it all feels like a huge gamble at this point. Neither skills, education, institutional ties, nor current employment can guarantee a stable foundation for life.

This hits harder depending on how much money, social capital, or debt you accumulated before this volatility began. If you’ve paid off your debts, bought a house, and stabilized your family life, you’re gambling with how comfortable the coming years will be. If you’re a fresh grad with student debt, no house, and no social network, you’re more or less gambling with your life.

> Addy Osmani is a Software Engineer at Google working on Google Cloud and Gemini

Ah, there it is.

> Senior developers: Position yourself as the guardian of quality and complexity.

Agreed but it's not an easy charge to fulfill.

> Junior developers: Make yourself AI-proficient and versatile. Demonstrate that one junior plus AI can match a small team’s output. Use AI coding agents (Cursor/Antigravity/Claude Code/Gemini CLI) to build bigger features, but understand and explain every line if not most. Focus on skills AI can’t easily replace: communication, problem decomposition, domain knowledge. Look at adjacent roles (QA, DevRel, data analytics) as entry points. Build a portfolio, especially projects integrating AI APIs. Consider apprenticeships, internships, contracting, or open source. Don’t be “just another new grad who needs training”; be an immediately useful engineer who learns quickly.

If I were starting out today, this is basically the only advice I would listen to. There will indeed be a vacuum in the next few years because of the drastic drop in junior hiring today.

Sometimes I wonder if I made the wrong choice with software development. Even after getting to a senior role, according to this article, you're still expected to get more education and work on side projects outside of work. Am I supposed to want to code all the time? When can I pursue hobbies, a social life, etc.

I have been telling people that, titles aside, senior developers were the people not afraid to write original code. I don’t see LLMs changing this. I only envision people wishing LLMs would change this.

> The bottom line: Junior developer hiring could collapse as AI automates entry-level tasks

If AI automated entry-level tasks from today, that just means "entry-level" means something different now. It doesn't mean entry-level ceases to exist. Entey-level as we know it, but not entry-level in general.

Funny that he mentions people not pivoting away from COBOL. My neighbors work for a bank, programming in COBOL every day. When I moved in and met them 14 years ago, I wondered how much longer they would be able to keep that up.

They're still doing it.

> Senior developers: Fewer juniors means more grunt work landing on your plate

I'm not sure I agree with that. Right now as a senior my task involves reviewing code from juniors; replace juniors with AI and it means reviewing code from AI. More or less the same thing.

This article suggests it is specialists who are "at risk", but as much more of a generalist I was thinking the opposite and starting to regret not specialising more.

My value so far in my career has been my very broad knowledge of basically the entire of computer science, IT, engineering, science, mathematics, and even beyond. Basically, I read a lot, at least 10x more than most people it seems. I was starting to wonder how relevant that now is, given that LLMs have read everything.

But maybe I'm wrong about what my skill actually is. Everyone has had LLMs for years now and yet I still seem better at finding info, contextualising it and assimilating it than a lot of people. I'm now using LLMs too but so far I haven't seen anyone use an LLM to become like me.

So I remain slightly confused about what exactly it is about me and people like me that makes us valuable.

> junior developer employment drops by about 9-10% within six quarters, while senior employment barely budges. Big tech hired 50% fewer fresh graduates over the past three years.

This study showing 9-10% drop is odd[1] and I'm not sure about their identification critria.

> We identify GenAI adoption by detecting job postings that explicitly seek workers to implement or integrate GenAI technologies into firm workflows.

Based on that MIT study it seems like 90+% of these projects fail. So we could easily be seeing an effect where firms posting these GenAI roles are burning money on the projects in a way that displaces investment in headcount.

The point about "BigTech" hiring 50% fewer grads is almost orthogonal. All of these companies are shifting hiring towards things where new grads are unlikely to add value, building data centers and frontier work.

Moreover the TCJA of 2017 caused software developers to not count for R&D tax write offs (I'm oversimplifying) starting in 2022. This surely has more of an effect than whatever "GenAI integrator roles" postings correlates to.

[1] https://download.ssrn.com/2025/11/6/5425555.pdf

>The flip scenario: AI unlocks massive demand for developers across every industry, not just tech. Healthcare, agriculture, manufacturing, and finance all start embedding software and automation.

I find this one hard to believe. Software is already massively present in all these industries and has already replaced jobs. The last step is complete automation (ie drone tractors that can load up at a hub, go to the field and spray all by themselves) but the bottleneck for this isn't "we need more code", it's real-world issues that I don't see AI help solving (political, notably)

imo, The OP has bad ai-assisted takes on almost every single "critical question". This makes me doubt if he has breadth of experience in the craft. For example.

> Narrow specialists risk finding their niche automated or obsolete

Exactly the opposite. Those with expertise will oversee the tool. Those without expertise will take orders from it.

> Universities may struggle to keep up with an industry that changes every few months

Those who know the theory of the craft will oversee the machine. Those who dont will take orders from it. Universities will continue to teach the theory of the discipline.

> The flip scenario: AI unlocks massive demand for developers across every industry, not just tech. Healthcare, agriculture, manufacturing, and finance all start embedding software and automation. Rather than replacing developers, AI becomes a force multiplier that spreads development work into domains that never employed coders. We’d see more entry-level roles, just different ones: “AI-native” developers who quickly build automations and integrations for specific niches.

This is what I expect to happen, but why would these entry-level roles be "developers". I think it's more likely that they will be roles that already exist in those industries, where the responsibilities include (or at least benefit from) effective use of AI tools.

I think the upshot is that more people should probably be learning how to work in some specific domain while learning how to effectively use AI to automate tasks in that domain. (But I also thought this is how the previous iteration of "learn to code" should be directed, so maybe I just have a hammer and everything looks like a nail.)

I think dedicated "pure tech" software where the domain is software rather than some other thing, is more likely to be concentrated in companies building all the infrastructure that is still being built to make this all work. That is, the models themselves and all the surrounding tools, and all the services and databases etc. that are used to orchestrate everything.

The next two years of software engineering will be the last two years of software engineering (probably).

Is there a Jeapordy for guessing prompts? Give an executive summary of GenAI trends where GenAI is the destiny and everything reacts to it. Touch on all “problems”. Don’t be divisive by making hard proclamations. Summarize in a safe way by appealing to the trope of the enthusiastic programmer who dutifully adapts to the world around them in order to stay “up to date”; the passive drone that accepts whatever environment they are placed in and never tries to change it. But add insult to injury by paradoxically concluding that the only safe future is the one you (individual) “actively engineer”.

I’m not saying that this was prompted. I’m just summarizing it in my own way.

>> The skillset is shifting from implementing algorithms to knowing how to ask the AI the right questions and verify its output.

The question is, how much faster is verification only vs writing the code by hand? You gain a lot of understanding when you write the code yourself, and understanding is a prerequisite for verification. The idea seems to be a quick review is all that should be needed "LGTM". That's fine as long as you understand the tradeoffs you are making.

With today's AI you either trade speed for correctness or you have to accept a more modest (and highly project specific) productivity boost.

Something very odd about the tone of this article. Is it mostly AI written? There is a lot of references and info. But I am feeling far more disconnected with it.

For the record, I was genuinely trying to read it properly. But it is becoming unbearable by mid article.

The bottom up and top down don’t seem to match.

Where is all the new and improved software output we’d expect to see?

My question: are those people who were building crappy, brittle software, which was full of bugs and and orher suboptimal behavior, that were the main reasons of slowing down the evolution that software, will they now begin writing better software because of AI? Answering yes, implies that the main reason of those problems was that those developers didn't have enough time to spend on analyzing those problems or to build protection harnesses. I would stronly argue that was not the case, as the main reason is of intelectual and personal nature - inability to build abstractions, to follow up the route causes (thus not aquiring necessary knowledge), or to avoid being distracted by some new toy. In 2-5 years I expect the industry going into panic mode, as there will be a shortage of people who could maintain the drivel that is now being created en masse. The future is bright for those with the brains, just need to wait this out

Love the article, I had a struggle with my new identity and thus had to write https://edtw.in/high-agency-engineering/ for myself, but also came to the realisation that the industry is shifting too especially for junior engineers.

Curious about how the Specialist vs Generalist theme plays out, who is going to feel it more *first* when AI gets better over time?

I've been saying for a decade that one of the fundamental issues with SWE in the average company, is that management does not seem to understand that SWE is a management level job. Its not an assembly line worker. It requires reorganizing, streamlining, stake-holders, etc - in code and data - which directly affect people much the same that any other management role has. There are just fewer issues with HR and more with CDNs or CVEs.

> A CEO of a low-code platform articulated this vision: in an “agentic” development environment, engineers become “composers,”

I see we'll be twisting words around to keep avoiding the comparison.

On the junior developer question:

A humble way for devs to look at this, is that in the new LLM era we are all juniors now.

A new entrant with a good attitude, curiosity and interest in learning the traditional "meta" of coding (version control, specs, testing etc) and a cutting-edge, first-rate grasp of using LLMs to assist their craft (as recommended in the article) will likely be more useful in a couple of years than a "senior" dragging their heels or dismissing LLMs as hype.

We aren't in coding Kansas anymore, junior and senior will not be so easily mapped to legacy development roles.

All well-documented knowledge fields will be gone if software goes. Then the undocumented ones will become documented, and they too will go. The best advice to junior devs is get a hands on job before robotic articulating sausages are perfected and humans become irrelevant blobs of watery meat.

> Demonstrate that one junior plus AI can match a small team’s output.

I don't understand the take that a junior with AI is able to replace a small team. Maybe a horribly performing small team? Even then, wouldn't it just be logical to outfit the small team with AI and then have a small team of small teams?

The alleged increased AI output of developers has yet to be realized. Individuals perceive themselves as having greatly increased output, but the market has not yet demonstrated that with more products (or competitors to existing products) and/or improved products.

Please dear developers be as lazy as possible and use LLMs. The amount of bugs that get shipped enable me a comfortable life as opsec.

The most useful thing juniors can do now is use AI to rapidly get up to the speed with the new skill floor. Learn like crazy. Self learning is empowered by AI.

Engineers > developers > coders.

The most important question is who will get paid the most? I don't think the future of software engineering will be attractive if all you do is more work for same or even less pay. A second danger is too much reliance on AI tools will centralise knowledge and THAT is the scariest thing. Software systems will need to perform for a long time, having juniors on board and people who understand software architecture will be massively important. Or will all software crash when this generation retires?

Do companies hire less because of AI or try to rely more on AI because they can’t afford to hire?

Tech layoffs have been happening even before LLMs.

The outlook on CS credentials is wrong. You'll never be worse off than someone without those credentials, all other things equal. Buried in this text is some assumption that the relatively studious people who get degrees are going to fall behind the non-degreed, because the ones who didn't go to school will out-study them. What is really going to happen generally is that the non-degreed will continue to not study, and they will lean on AI to avoid studying even the few things that they might have otherwise needed to study to squeak by in industry.

This article reads like it was written by an AI.

One option that didn't seem to be discussed in TFA is turning away from AI.

There's an implicit assumption in the article that the coding models are here to stay in development. It's possible that assumption is incorrect for multiple reasons.

Maybe (as some research indicates) the models are as good as they are going to get. They're always going to be a cross between a chipper stochastic parrot and that ego inflated junior dev that refuses to admit a mistake. Maybe when the real (non-subsidized) economics present themselves, the benefit isn't there.

Perhaps the industry segments itself to a degree. There's a big difference in tolerance for errors in a cat fart app and a nuclear cooling system. I can see a role for certified 100% AI free development. Maybe vibe coders go in one direction, with lower quality output but rapid TTM, but a segment of more highly skilled developers focus on AI free development.

I also think it's possible that over time the AI hyper-productivity stuff is revealed to be mostly a mirage. My personal experience and a few studies seem to indicate this. The purported productivity boost is a result of confirmation bias and ridiculous metrics (like LOC generated) that have little to do with actual value creation. When the mirage fades, companies realize they are stuck with heaps of AI slop and no technical talent able to deal with it. A bitter lesson indeed.

Since we're reading tea leaves, I think the most likely outcome is that the massive central models for code generation fade due to enormous costs and increased endpoint device capabilities. The past 50 years have shown us clearly that computing will always distribute, and centralized mainframe style compute gets pushed down to powerful local devices.

I think it settles at an improved intellisense running locally. The real value of the "better search engine" that LLMs hold today reduces as hard economics drive up subscription fees and content is manipulated by sponsors (same thing that happened to the Google search results).

For end users, I think the models get shoved into a box to do things they're really good at, like giving a much more intuitive human-computer interface, but structured data from that is handed off to a human developer to reason about, MCP will expand and become the glue.

I think that over time market forces will balance between AI and human created content, with a premium placed on the latter. McDonalds vs a 5 star steakhouse.

I mean it's pretty simple: management will take bad quality (because they don't understand the field) over having and paying more employees any day. Software engineer positions will shrink and be unrecognizable: one person expected to be doing the work of multiple departments to stay employed. People may leave the field or won't bother learning it. When the critical mass is reached, AI will be paywalled and rug pulled. Then the field evens itself out again over a long, expensive period of time for every company that fell for it, lowering the expectations back to reality.

The points mentioned in the article, regarding the things to focus on, is spot on.

I recently started as a developer advocate - I have similar opinions to the author - junior devs have a hard time getting hired and flipping to something like devrel makes a lot of sense.

Maybe a harsh criticism. The article seemed to be all over the place, maybe because the subject is also all over the place. I agree with everything, its just that it seemed like the same story we've been in for awhile.

Wasn't the main take away generally "study everything even more than you were, and talk/network to everybody even more than you were, and hold on. Work more more more"

Ah, time to drop out and shoot myself in the woods

I would like to see how things will be when using AI would require half of a devs current paycheck.

I don't know, we hired a junior developer and are about to hire another. Not sure it collapsed. I just think it's really hard to get a job across the board right now.

The author has a bizarre idea of what a computer science degree is about. Why would it teach cloud computing or dev ops? The idea is you learn those on your own.

This was an amazing read! The entire thought of being so versatile as the article mentions is similar to the book Homo Deus by Harari.

In my opinion we always needed to be versatile to stand any chance of being comfortable in these insanely rapid changing times.

Change is a constant for software engineers. It always has been. If your job is doing stuff that should be automated, either you are automating it or you are not a very good software engineer.

A few key fallacies at play here.

- Assuming a closed world assumption: we'll do the same amount of work but with less people. This has never been true. As soon as you meaningfully drop the price of a unit of software (pick your favorite), demand goes up and we'll need more of them. Also it opens the door to building software that previously would have been too expensive. That's why the amount of software engineers has consistently increased over the years. This despite a lot of stuff getting a lot easier over time.

- Assuming the type of work always stays the same. This too has never been true. Stuff changes over time. New tools, new frameworks, new types of software, new jobs to do. And the old ones fade away. Being a software engineer is a life of learning. Very few of us get to do the same things for decades on end.

- Assuming people know what to ask for. AIs do as you ask, which isn't necessarily what you want. The quality of what you get correlates very much to your ability you ask for it. The notion that you get a coherent bit of software in response to poorly articulated incoherent prompts is about as realistic as getting a customer to produce coherent requirements. That never happened either. Converting customer wishes into maintainable/valuable software is still a bit of a dark art.

The bottom line: many companies don't have a lot of in house software development capacity or competence. AI doesn't really help these companies to fix that in exactly the same way that Visual Basic didn't magically turn them into software driven companies either. They'll use third party companies to get the software they need because they lack the in house competence to even ask for the right things.

Lowering the cost just means they'll raise the ambition level and ask for more/better software. The type of companies that will deliver that will be staffed with people working with AI tools to build this stuff for them. You might call these people software engineers. Demand for senior SEs will go through the roof because they deliver the best AI generated software because they know what good software looks like and what to ask for. That creates a lot of room for enterprising juniors to skill up and join the club because, as ever, there simply aren't enough seniors around. And thanks to AI, skilling up is easier than ever.

The distinction between junior and senior was always fairly shallow. I know people that were in their twenties that got labeled as senior barely out of college. Maybe on their second or third job. It was always a bit of a vanity title that because of the high demand for any kind of SEs got awarded early. AI changes nothing here. It just creates more opportunities for people to use tools to work themselves up to senior level quicker. And of course there are lots of examples of smart young people that managed to code pretty significant things and create successful startups. If you are ambitious, now is a good time to be alive.

One of the better analysis of this question I think.

On the optimistic take side - I suspect it might end up being true that software might be infused into more niches but not sure it follows that this helps on the jobs market side. Or put different demand for software and SWE might decouple somewhat for much of that additional software demand.

One thing that fucks with juniors is the expecration of paying for subscriptions for AI models. If you need to know how the AI tools work, you need to learn them with your own money.

Not everyone can afford it, and then we are at the point of changing the field that was so proud about just needing a computer and access to internet to teach oneself into a subscription service.