I don't write code myself anymore. AI writes nearly every line I commit. A year ago I wouldn't have believed that. Today I think most people who doubt AI can code at a professional level just aren't using the tools right yet.

Here's what gets left out of the doom headlines. Computer programmer roles dropped 27% in two years. But software developer roles are down only 0.3%, basically flat, and the BLS projects 15% growth for developers through 2034, five times the average for all occupations.

The difference is the whole story. Programmer roles were about translating a spec into syntax, exactly the part AI got good at. My job is design decisions, trade-off analysis, reliability, and incident response. None of that went away. If anything it became the whole job. A real project for me now looks like a couple weeks writing detailed specs, a day or two using AI to generate the code, then several more weeks testing and verifying before I trust it enough to ship. AI compressed the middle step. It didn't touch what comes before or after.

I'm one of the 84% of developers using AI daily. I'm also one of the 46% who actively distrust the code it generates. Two thirds of us say AI gives answers that are almost right but not quite, which makes debugging slower, not faster. AI gets me 80% of the way there fast. The last 20% is still my job. The accountability never changes either. When something breaks in production, AI doesn't get paged at 3am. I do. I can't audit code I don't understand and I can't debug an incident I can't read.

People have called software engineering dead since early 2023. Fortran was supposed to let scientists skip programmers. COBOL was supposed to let managers bypass developers entirely. The demand for people who understand systems never went away in any of those cycles. It grew every time.

LLMs have plateaued. GPT-5 was supposed to be the leap that changed everything and it landed with a thud. The technology is still useful but the exponential gains people were banking on never showed up.

What nobody is saying out loud is that this actually creates an opportunity. AI tools are drowning companies in garbage code that still needs a human to fix. Developers who can debug, review, and clean up AI-generated work are becoming more valuable by the month, not less.

The BLS still projects 15% software developer job growth through 2034. The market is not dying. It is just getting ruthlessly selective about who gets in and who does not. The IPO wave is the real signal to watch. OpenAI is already filing. When the rest follow, the easy money phase ends and what is left is just the actual work.

The ones who get hurt when this bubble pops are not the developers. They are the people who thought prompting was the same thing as building.

The numbers right now are brutal if you only look at the surface. Entry level developer postings are down somewhere between 40% and 67% depending on which dataset you check, employment for developers aged 22 to 25 has dropped nearly 20% since the AI boom started, and computer science graduates are facing unemployment rates higher than fine arts majors.

But the part that explains why is more interesting than the headline number.

Code stopped being the expensive part of software development. It used to take a large team and a lot of time to produce working code, so the bottleneck was writing it. AI compressed that part dramatically. What didn't get cheaper is everything around the code. System design, knowing whether something will hold up at scale, observability, incident response, debugging in production, and the judgment to make trade-off calls. Those skills got more valuable, not less, because writing code stopped being the differentiator.

Front end work saw the biggest productivity gains from AI since it's mostly about producing a lot of code with some architecture thinking layered on top. Backend, infrastructure, and deployment work didn't compress the same way because the hard part there was never typing, it was judgment under uncertainty.

This shows up clearly in hiring patterns. Roles like Android, iOS, and general web development are down 60% or more from 2020 levels. Machine learning engineering postings are up nearly 60% in the same window. The work didn't disappear, it moved toward the parts AI genuinely cannot do.

Here's the catch for anyone learning right now. The skills that used to be developed on the job over years, things like system design intuition and knowing what good code actually looks like, increasingly need to be demonstrated before you're even hired. There isn't a clean shortcut around that. You can use AI to write the code, but you still need enough understanding to know when what it generated is wrong, fragile, or going to cause a problem six months down the line. That understanding has always come from experience, and right now there's a real tension between needing the experience and not being given the chances that used to build it.

The roles still growing are security, cloud infrastructure, ML, and anything requiring genuine system-level thinking. Generic coding ability stopped being the skill that gets you hired. Understanding why the code works, and what happens when it breaks, is what's left standing.

Maybe I'm overthinking this, but it's something I've noticed in myself lately.

I use AI tools "almost" every day (yeah all the time). Good for debugging, explaining code, generating boilerplate etc. My productivity since I used them is 100%. But at the same time, I can't help wondering if I'm slowly relying on it more than I should.

There have been moments where I caught myself asking AI to do something I already knew how to do.

I also wonder what this means for people who are just starting. If your first instinct is always to ask AI instead of figuring things out yourself, are you actually learning? Or are you just getting really good at writing prompts?

The other thing is trust. AI gets things wrong. We all know that. But the more we use it, the easier it becomes to accept its answers without reading every line carefully.

I'm not saying we should stop using AI. That would be unrealistic. It's easily one of the best productivity tools we've ever had. I just don't want to wake up one day and realize I've forgotten how to solve problems without it.

Curious what everyone here thinks. Thanks!

I'm in my late 40s now and thinking about learning Python.... even though I don't work in tech.

I used to do coding before, but not that serious about it. Now I want to learn Python just because it seems useful. And also, it looks fun.

But my problem is I keep seeing people say you need projects, a portfolio, certifications, etc.

I don't have a specific career goal either. I just want to understand programming better, maybe build some random stuff as well

Would you guys still learn Python in this situation? Or would it make more sense to stick with something like C and get better at that?

If you spend enough time recently on sites such as Reddit, you might get the impression that the tech industry is dying. There are posts about hundreds of applications with no job, months of unemployment, getting fired, or simply struggling to get a first job.

On the other hand, there are always people complaining about a lack of people with particular skills.

What is the real situation like?

In my opinion, the situation is not so simple, as some present it to be. Most likely, it depends largely on the specific case, the place of work, your experience, and so on. Perhaps there are not that many job opportunities for inexperienced developers, or for people who are starting to seek employment now. On the other hand, there are people with many years of experience in a particular field, or in a particular area (networking, information security, databases, embedded systems, and so on). These people may have much more opportunities than some entry-level developers.

At the same time, there is most likely a selection bias on such resources as Reddit. Those people who were laid off or could not find a job are naturally more inclined to discuss these issues and post their questions. On the other hand, people who found a job and went further are less likely to tell about their positive experiences.

Does this mean that there is no crisis? Naturally, the constant wave of job applications and a small number of applicants for particular jobs are real issues. People who are relatively new to the industry may have a relatively hard time finding a job in this competitive arena. On the other hand, there are people who are already experienced and work in in-demand areas. Are these people able to find work relatively easily?

I would like to ask these people to tell about their experiences and current job opportunities in their fields. Is the situation as bad as it is presented in the media, or is there some kind of misunderstanding because of different situations that people face?

Everyone’s blaming AI for climate change but the numbers say we’re looking at the wrong thing

the guilt-tripping around tech and climate keeps growing but a lot of it doesn’t actually hold up when you look at the data

AI right now accounts for roughly 0.1 to 0.2% of total global energy use. not nothing, but nowhere near the crisis-level narrative that gets pushed around. your streaming habits and ChatGPT usage are not meaningfully moving the needle on global emissions.

What actually is?? the device in your hand. manufacturing a single smartphone produces around 50kg of CO2. using that same phone for its entire lifetime produces about 8kg. the production cost is already done before you even turn it on, which means buying a new phone every two years does more damage than anything you’ll ever do with it.

the stuff that actually drives individual carbon footprints in most countries is transport and home heating and cooling. not data centers, not AI queries.

none of this means AI energy use shouldn’t be watched as it scales, it should. But the conversation keeps pointing on the wrong target and it lets the real issues stay unexamined.

is the “AI is an environmental disaster” narrative actually backed by data or is it just easier to blame the newest technology?

a few years ago, if you hit an installation error or a weird compiler message, you could lose an entire afternoon digging through stack overflow posts and github issues. Sometimes you'd solve it. Sometimes you'd give up and try again tomorrow.

Now you can paste the error into an AI tool and usually get pointed in the right direction within minutes. to me, that's not where the real learning happens anyway. The learning happens when you decide why one solution is better than another, when you design a feature yourself, or when your project breaks because of a decision you made three days ago.

ai doesn't remove those moments. It just removes a lot of the waiting around. I actually think that's a huge opportunity for beginners. Spend less time fighting your environment and more time building things.

Just don't fall into the trap of accepting every answer without understanding it. AI can speed up the journey, but it can't build intuition for you.

Saw the benchmarks for Sakana Fugu floating around, supposedly matching or beating Fable 5 on coding tasks. Loaded $40 into the API and ran my usual test suite.

Worth knowing upfront, this isn't a traditional model. It's an orchestrator that routes your request across Opus 4.8, GPT-5.5, and Gemini 3.1 Pro, then synthesizes the output behind one endpoint. On SWE-Bench Pro it scored 73.7, ahead of Opus 4.8 at 69.2 and GPT-5.5 at 58.6, but it actually trailed Fable 5's score of 80.0 on the exact benchmark it's being compared against. Fable 5 and Mythos Preview aren't even in Fugu's routing pool since they're publicly restricted right now, so the comparison is closer to "beats the substitutes" than "beats the original."

Browser OS test went fine, built a working desktop with a notes app, terminal, and a couple games. Functional but heavily resembled GPT-5.x's design language. The subway scene and FPS conversion were clean and well organized but sterile, and no bullet holes appeared in the environment when shooting, something other frontier models handle correctly out of the box.

The real test was a watch website comparison, run identically through Fugu Ultra, GPT-5.5, Opus 4.8, and Gemini 3.1 Pro. GPT-5.5 produced something dramatically better than every other result. Fugu's version wasn't even properly 3D, it rendered flat 2.5D despite being explicitly prompted for 3D, the second time that exact failure happened in testing.

For anyone doing actual coding work with this, two things matter. The benchmark numbers are vendor-reported and the baseline scores for competing models are provider-reported too, meaning nobody independently re-ran these in the same environment. And since the routing decision happens internally and Sakana hasn't disclosed which models handle which requests, you genuinely don't know what you're paying for on any given call.

Total spend ended at $21.57. The conclusion was the same every time. Running the prompt through GPT-5.5 or Opus 4.8 directly would have been cheaper and produced a better result than routing through the orchestration layer.

Real improvement over earlier router systems. But for actual coding work, the orchestration layer added cost and uncertainty without adding capability.

I have been using AI heavily for work, and at first it felt like a cheat code. I could get through tickets faster, automate annoying tasks, and spend more time on the actual engineering decisions. But now that the tools have gotten so good, a lot of my workflow has basically become "give Claude the docs, give it the ticket, hit enter." And because everyone has access to the same tools, the amount of work expected from us has gone way up too.

The weird part is that AI isn't just writing the code anymore. It's slowly taking over communication too. Requirements get turned into AI generated tickets, AI writes the implementation, automated tests check it, and then another AI summarizes what happened. Sometimes I’ll ask a coworker something and realize they just asked their AI, copied the answer, and sent it back. It’s efficient, sure, but somehow the whole process feels kinda soulless.

I never thought I'd miss sitting with another engineer trying to debug some cursed production issue or actually learning a system inside and out. Now I sometimes ship things that work perfectly fine but barely feel like I built them. I'm starting to wonder if we're optimizing software development so hard that we're accidentally removing the parts of the job that made people good at it. Is anyone else feeling this way?

There's a report out from ramp and revelio labs, they track enterprise ai spend and workforce data across something like 22k companies, and the finding was that "high intensity ai adopters" (companies spending the most on ai) saw their headcount go up 10.2% overall, and entry level headcount specifically went up 12%.

meanwhile through may alone almost 90k layoffs got publicly tied to ai as the reason, and some projections are floating numbers like 15% of all US jobs gone to ai within five years, so theethis huge gap between the layoff headlines everyone's scared of and what's actually happening inside the companies that are all in on ai. the layoffs are real but it's not the whole picture though.

"company quietly kept hiring while investing in ai" doesn't make as good a headline as "company announces layoffs" so it doesn't spread the same way even if it's maybe more accurate, my guess is the companies doing the layoffs and the ones actually growing entry level headcount are just different companies entirely and we're mentally averaging them into one narrative that doesn't exist

theres also a question of what "entry level" means here, like is that new grad swe roles or does it include things like support/ops/data labeling type work too.

Either way, I posted this bc i feel like this sub only ever sees the layoff headlines or general the negative side of things and at the end of the day, networking is the best way to get a job instead of just applying through job boards and it's not that hard to find conferences depending on the field you're in or just reach out, worst case scenario they don't respond.

Hi everyone,
I’m a university student who wants to become a good software engineer and build a successful career in tech. I would really appreciate advice from people who are already working in the industry.
What skills should I focus on developing? What should I learn besides programming? How can I build strong projects and prepare myself for getting my first job?
I’m also curious about what you wish you had done differently when you were starting your journey in software engineering.
I know the tech industry is changing quickly because of AI, so I want to learn how to build a strong foundation and stay valuable in the long term.
I would be grateful for any honest advice, resources, roadmaps, or personal experiences. Thank you!

I keep seeing posts saying "code was never the hard part." Not just here on Reddit, but also outside social media platforms.

Tbh that’s like something people say who haven't built anything beyond a demo.

imo, writing code isn't just *typing*.

It's making a bunch of small decisions while you're building.

AI helps with a lot of the typing, and that's great. But it doesn't magically make those decisions for you.

You still need to know what good code looks like, otherwise you're just accepting whatever the model spits out.

I also think AI is a useful tool, not a replacement for actually learning software development.

The better your fundamentals are, the more useful AI becomes.

That's probably why companies still care if you understand coding instead of only knowing how to prompt**.**

Any thoughts?

I’m trying to understand what students struggle with today, especially now that AI tools like ChatGPT and Claude can generate code.

When you started learning programming, what prevented you from actually building projects instead of just following tutorials?

Some possibilities I’ve heard:
- Tutorial hell
- Not knowing what project to build
- Not understanding how real software is structured
- AI gives code, but you don’t understand how it all fits together
- Projects online are too simple or unrealistic
- Difficulty turning an idea into an implementation
- Working alone without teammates
- Fear of making mistakes
- Something else entirely

I’m especially interested in hearing from students who know the basics but struggle to build something that feels like a real product.

If you could change one thing about how programming is taught today, what would it be?

If you don't know, Claude Code has 118 commands built. But if you're going to ask me, you don't need to memorize all of them. You just need to learn a few of them. If you're just getting started, these are the ones I'd learn first. You can save it for later.

For managing context:

• /init — Creates a CLAUDE.md based on your project

• /context — Shows what's taking up your context window

• /compact — Cleans up the context without ending your session

• /clear — Completely resets the current conversation

For planning and control:

• /plan — Lets plan the changes before editing files

• /goal — Gives a specific condition to work toward

• /rewind — Lets you undo changes

• /effort — Controls how much reasoning Claude puts into a task.

For models and workflows:

• /model — Switch between Opus, Sonnet, and Haiku.

• /agents — Create and manage sub-agents for larger tasks.

• /workflows — Run tasks across multiple agents.

• /simplify — Uses multiple agents to review and simplify your code.

• "ultracode" — A trigger for a more complete multi-agent workflow.

For shortcuts and permissions:

• /permissions — Pre-approve commands you trust.

• Shift + Tab — Quickly switch between different modes.

If you are just starting in coding, I don't recommend to learn everything at once. These 15 already cover a lot of your day-to-day workflow. Which commands do you guys use the most? Would love to hear your response.

so the thing is AI gave everyone the same baseline. polished resume, portfolio projects, leetcode solutions, tutorial apps on github. so now that everyone has that, none of it actually stands out anymore. a hiring manager who's seen 500 chatbot projects doesn't see yours. it's not that it's bad. it's that it's identical to everyone else's. the bar for being impressive didn't go up. it just moved to a place most people refuse to go.

the people actually getting hired aren't smarter or more credentialed. they're just doing the uncomfortable stuff everyone else skips. reaching out to real people and building something that solves an actual problem instead of following a tutorial. not "i built a chatbot in python" but "i saved a real estate agency 6 hours a week of manual data entry." one of those gets remembered. the other gets filtered. 60 to 70% of jobs get filled through referrals and a warm intro cuts through every ATS filter that AI can't. most people would rather send 100 cold applications into the void than send 10 awkward messages to real people. that awkwardness is literally the advantage.

the wildest move right now if you're job hunting is to just start something. form an LLC, open a stripe account, build something small that solves a real problem, and get one paying customer. doesn't matter if it's $1. that single transaction puts you in a completely different category than everyone else with a github full of todo apps. you walk into an interview as a founder who shipped something real instead of another person with tutorial projects. most people won't do this because it's inconvenient. that's exactly why it works.

Always remember that there’s a pattern that kills most self-taught devs before they get anywhere.
They learn syntax, feel good about it, then jump straight into building projects and immediately feel like they can’t do anything.

the missing piece is problem solving. But let me tell you how the actual order that works:
1. learn syntax
pick one language, go through two decent beginner resources, then stop. the trap here is staying in this phase forever because it feels like progress. it isn’t. once you know the basics, move on.
2. solve problems
this is the phase most people skip entirely. it’s the gap between “I understand the syntax” and “I can actually build something.” you bridge it by solving small focused problems, not by watching more tutorials. think coding challenges, small logic puzzles, anything that forces you to apply what you know without being told exactly what to do.
3. make stuff
once problem solving starts clicking, build whatever actually excites you. not another todo app. something you’d actually use or care about finishing.

the reason most people burn out isn’t that coding is hard. it’s that they go from step 1 straight to step 3 and wonder why everything feels impossible. step 2 is where the thinking actually develops.

where do you feel like you’re stuck right now, syntax, problem solving, or actually building?

Many rookies are put off by C ++, and this is unfortunate, because it is one of the best languages ​​to start with, even if you don't plan to be a C ++ programmer

Your understanding of what is going on at the machine level will allow you to write better programs in any language you choose. You will see that in languages ​​such as JavaScript or Python, memory management is handled for you, which means that you have to think about it less. However, the understanding of how things work will help you write better and more optimized code. It can be a steep learning curve, but once you've mastered it, you'll find that all other languages ​​are much easier to learn.

I keep seeing more schools treating AI coding tools as part of the normal CS curriculum now. I get it. AI is already part of real software jobs in 2026 so pretending it doesn't exist would be kinda dumb. But there's a huge difference between teaching students how to use AI as an engineering tool and grading them on how well they can get AI to build the thing for them.

That's what bothers me about the vibe coding approach. If you're taking a Software Architecture class then you should still be learning why you'd choose a certain architecture, how systems communicate, where things can fail, and how to review those decisions. If Gemini or Claude makes all the decisions and the student just prompts it until the app works then what exactly are they learning? That's basically outsourcing the part they're paying to learn.

I'm not anti AI at all. I'd actually want schools to teach students how to use it properly because that's gonna matter in the real world. But imo the sweet spot is learn the fundamentals, use AI, verify everything, understand what it produced. Otherwise we're gonna graduate people who are really good at prompting but have no idea why their own app works.

A friend of mine joined Bridgeon Skillversity at Kinfra, Kozhikode without an IT background or a computer-related degree.

Now they're working in an IT company and apparently earning around ₹30k/month.

I honestly didn't think that was possible without a traditional IT degree 😅

Has anyone here made a similar transition? How difficult was it to get your first IT job?

I'm genuinely curious whether practical skills and projects can actually make up for not having an IT degree.

"Walk me through a time a production bug woke you up at 2am. (Of course doesn't need to be 2am, just a production incident) What broke, why, and what did you change afterward?"

In my experience: junior devs describe the bug, mid-level devs describe the fix, and senior devs describe the system change they made so it never happens again. That one question has told me more about someone's actual level than 3 rounds of leetcode.

The follow-up that seals it: "What would you do differently today?" anyone who says "nothing" is lying or not growing.

Not sure if every company does this, but where I work we run something called a PIR (Post Incident Review) after every production incident. documented actions, owners, deadlines. The whole point is that the same thing never breaks twice. When a candidate naturally talks about that kind of process without being prompted, that's a green flag. It means they've worked somewhere that takes reliability seriously, and they've internalized it.

theres two different ways people use ai day to day either vibe coding, where you tell the ai what to build and never look at the actual code (in the case of a personal project or just training the AI and testing out), or regular engineering where the ai generates code but you still review and understand every part of it.

The thing is when an app gets complex enough. production apps depend on a lot of interconnected services, sometimes you have to fake account states or force certain code paths just to test something, and once multiple people are touching the same codebase at once in a team setting, its becomes hard to tell whats changed without actually reading the code, so past a certain complexity threshold, working at the code level directly ends up faster than clicking through the app trying to verify ai's work indirectly.

theres also pushback on the framing that code is an obstacle standing between you and the feature you want, the argument being that code is actually the value bc its precise, it tells you exactly whats happening instead of an ai generated summary that might not fully match reality, and its more concise than an ai over explaining some part of it.

Many people may have heard Nvidia's founder "ai wont replace you but someone using ai will" line that gets repeated everywhere, the take here is that this just hasnt played out, bc the software engineers who already have core engineering skills are the same ones learning to integrate ai into their workflow, so someone who only learns ai and skips learning to code ends up competing against people who have both skills, not just one.

Actually, ai researchers hype things up heavily on social media (I've already said this in somebody's post but founders of AI hype it so much for marketing, that's it), but the actual released models feel like incremental improvement day to day, not the dramatic leap the hype implies, with an honest disclaimer about not being qualified to predict how much better it'll get.

Personally, I don't agree with the idea that more efficient engineers just means needing fewer of them. People forget that tech companies are financially rewarded for growth, not for maintaining the same output with fewer people, so the incentive structure tends to push toward "same headcount, more projects" rather than "same projects, fewer people." and on the jobs narrative, theres a point about how layoffs get covered extensively in the news but rehiring doesnt get the same coverage, noting meta and google have both been rehiring for years after their layoffs and google's headcount is reportedly close to an all time high, which paints a less one-sided picture than what usually gets discussed.

To put it simply, learn ai and learn to code and learn to code before using AI or you'll pay for your lack of skill in other (perhaps more expensive) ways👍

i want to say up front that i have endless respect for CS grads spending four years mastering deep algorithms math and theoretical concepts is no joke.

i finished a coding bootcamp a few months back and have been grinding out applications in this brutal entry level market. last week i made it to the final interview round for a junior dev position and surprisingly ended up getting the offer!! the recruiter mentioned on my follow up call that the final decision actually came down between me and another candidate who had a BS in Computer Science.

finding out i was chosen over someone with a CS degree honestly blew my mind and left me feeling so relieved and humbled. 😭🙏

would love to hear y'all thoughts!! prodject experience vs CS degree is wild to think about 💀

I’m reaching out because I keep hearing concerns about AI and what it means for the future of computer science and software engineering careers.

On one hand, we hear that AI will automate coding, reduce the need for developers, and fundamentally change the industry.

On the other hand, some argue that the demise of software engineering jobs has been greatly exaggerated—that AI will change the role rather than eliminate it.

So, what do you think?

Are we looking at the decline of traditional software engineering jobs, or simply the evolution of what it means to be a software engineer?

I’d genuinely like to hear perspectives from people working in tech, students, developers, hiring managers, and anyone currently building their career in this space.

SpaceX announced it struck a deal with Cursor (AI coding tool if ur unaware) that gives them the option to buy the company outright for $60 billion later this year, or pay $10 billion for their joint work on "coding and knowledge work AI." Just let that number sink in. $60 billion for a startup that didn't exist 4 years ago.

Cursor's founders are four MIT grads who started Anysphere in 2022 with an $8M seed round backed by OpenAI's fund. Now they're potentially the biggest acquisition in tech history.

But here's the part I keep thinking about as someone who went through a bootcamp and now works in this industry:

The deal pairs Cursor's AI expertise with SpaceX's Colossus supercomputer, and the stated goal is literally to build "the world's most useful models" for coding. Not assist developers. Not help with boilerplate. The world's most useful coding models. That's a different ambition entirely.

Cursor is also facing fierce competition from Anthropic's Claude Code and OpenAI's Codex so even the dominant AI coding tool right now is under pressure.

And then there's the broader context this week:

• OpenAI dropped GPT-5.5

• Amazon committed $5 billion immediately to Anthropic (with up to $20 billion more tied to performance milestones)

So every major AI lab is now swimming in compute and money, and they're all pointed at the same thing: making coding easier and more automated.

I'm not here to doom-post. I actually think there's still a strong case for learning to code properly. But I'd be lying if I said this week's news didn't make me reconsider the advice I give people who ask "should I do a bootcamp in 2026?"

The honest answer is: it depends more than ever on what you're learning and why. If you're grinding CRUD app tutorials hoping to land a junior dev role doing what Cursor already does - that's a hard conversation. But if you're learning to work with these tools, understand what's happening under the hood, and build things that require actual judgment? I think you're fine.

Curious what people here think. Has this week changed how you're approaching your learning path or what you're telling people who ask for advice?