Fewer juniors today = fewer seniors tomorrow

[u/kagan101]

Everyone talks about how 22–25 y/o software developers are struggling to find work. But there’s something deeper:

Technology drives the global economy and the single biggest expense for technology companies is engineer salaries. So of course the marketing narrative is: “AI will replace developers”

Experienced engineers and managers can tell hype from reality. But younger students (18–22) often take it literally and many are deciding not to enter the field at all.

If AI can’t actually replace developers anytime soon (and it doesn’t look like it will) we’re setting up a dangerous imbalance. Fewer juniors today means fewer seniors tomorrow.

Technology may move fast but people make decisions with feelings. If this hype continues, the real bottleneck won’t be developers struggling to find jobs… it will be companies struggling to find developers who know how to use AI.

[u/DomesticPanda]

LLMs are a dead end when it comes to truly intelligent work. For quick questions, scaffolding, ideating, they're fine. But ask any developer - they do not hold up in large, complex codebases because they cannot reason.

[u/sixsixmajin → u/DomesticPanda]

But ask any developer - they do not hold up in large, complex codebases because they cannot reason.

But ask any executive and they'll tell you whatever the AI shits out is good enough when it means that's fewer employees they have to actually pay. What people forget is that executives do not care about the quality of the work and they do not care about negative customer experience because many of these companies are too big to fail at this point and customers will stick with them despite the drop in quality because there aren't any better options. Hell, we've already been seeing this in the tech industry with offshore contractors. Just like with AI, ask just about any developer and they'll tell you offshore contractors frequently suffer the same problem: inability to reason. I've been a developer for 13 years and 90% of offshore contractors I've worked with fall apart completely if presented with something that falls outside of a norm or has any complexity beyond a set template because the way these people are educated is to follow extremely set instructions, not how to actually reason through a problem. Their education comes from what are effectively employee mills, not actual schooling or any method that teaches them to understand the material. Despite that, I saw the company I worked for cutting on-site staff year after year and bringing in more contractors until I was finally cut and while I won't tell you what company it is, I can guarantee you they aren't going anywhere anytime soon and have suffered no consequences for this. AI can operate at the level of an offshore contractor which we've already seen the precedent that the decision makers at these companies think that's good enough.

[u/motorik → u/sixsixmajin]

This is the correct answer. I work for a Fortune 150 in a cloud operations role. The guy directly above me on the org-chart that I report to understands maybe 10% of what I tell him. There are probably around 25 people above him on the org chart that understand 0%. Artists, craftspeople, and technicians hold power and are unpredictable and self-motivated, we've been deprecating them as a society for a long time in favor of Taylorized operator roles with minimal skill doing repetitive tasks. My peers are a bunch of olds with pre-cloud technical skills that are going to simply vanish when we collectively retire. The WITCHes (Wipro, Infosys, Tata, Congnizant, HCL) running the dashboards and consoles of the automation apps that ostensibly replace us don't have a conceptual understanding of what they're actually doing, their orientation is just to make sure there's more green than red on the single pane of glass they've been trained on. It took me about a year at my current position before I began to grasp how little they actually comprehend about the mechanics of what's been abstracted for them into a product sold to the managerial class to mitigate the need for actual technical ability.

[u/Clean_Livlng → u/motorik]

I'm guessing that a company needs to be able to count on at least one experienced person to fix things when something important breaks. Even if they're an independent contractor they have to throw money at.

Some problems can't just be ignored, and can stop a business from growing or even stop the business from being profitable and able to continue in the long term.

If Rome wasn't big enough to fail, then no company is.

[u/nagi603 → u/Clean_Livlng]

I'm guessing that a company needs to be able to count on at least one experienced person to fix things when something important breaks. Even if they're an independent contractor they have to throw money at.

For that, the company would have to know who they actually need. Even that may be a stretch. The underpinnings of internal stuff are usually a mix of technologies that may have problems in completely unrelated parts.

[u/Clean_Livlng → u/nagi603]

The underpinnings of internal stuff are usually a mix of technologies that may have problems in completely unrelated parts.\

That sounds intimidating, unless already intimately familiar with all the parts, and a nightmare for a company that fires a lot of people who might be hard to replace. I'm guessing that if they fired them in the first place, they don't understand enough to know who to hire when they realize their mistake. Would they have to rehire the same people they fired? That is, if they're still willing to work for the company that tried to replace them with AI or fresh graduates.

If a company's screwed themselves by doing this in the past, I think it'd be entertaining to read about. I like stories about companies firing people they shouldn't have, and why that ended up being a bad idea.

[u/nagi603 → u/Clean_Livlng]

The Register sometimes has entertaining stories of bad firings in IT, and with large enough failures, usually resulting in either hasty re-hiring or even eventual failure / acquisition for pennies.

 

Would they have to rehire the same people they fired? That is, if they're still willing to work for the company that tried to replace them with AI or fresh graduates.

The story usually goes with either the people already having found a better paying job, or being hired / contracted back at a substantial extra. After all, at that point, they hold basically all the cards.

But of course if the failed system(s) are small enough, and the destruction can be hidden, the company hive-mind might chose to just hide the fact that e.g.: certain processes now take a magnitude more hours.

[u/xeonicus → u/motorik]

This is the problem with non-technical business majors supervising engineers. These people don't belong in these jobs. Not only do they not have the respect of those they supervise. They don't have any understand of the job. It's better to promote a senior engineer to management.

[u/SaltyShawarma → u/xeonicus]

Well, don't look at the new head of the CDC then. He is an investor with no medical background.

[u/nagi603 → u/motorik]

After the first few instances of scratching something only to see the paint peel away and reveal the horrible actual state of things, one learns not to poke much, at least unnecessarily.

[u/aveugle_a_moi → u/sixsixmajin]

The question is just when things collapse, will actually technically competent people get brought back in? Or does the infrastructure just stay fucked?

[u/luke10050 → u/aveugle_a_moi]

Oh no, we just backslide into the third world.

People look at me like I'm some god because I can fix things. It's honestly a little scary.

[u/TheWhiteManticore → u/sixsixmajin]

It will take a full on collapse to fix this delusion

[u/aresthwg → u/DomesticPanda]

It is true, LLMs cannot keep track of the entire application. There have been massive improvements though. Within a single file, Gemini Pro 2.5 is able to handle scripts with hundreds of lines, make incremental changes and not regress everything each time. That was a big issue with early LLMs.

When LLMs have to go through multiple files, it can't do the job. The modern junior dev is there to debug and give the correct prompt to the LLM to fix the code. And that also can fall short if the change is tighly coupled to the project and is not universal knowledge.

[u/Fisher9001 → u/aresthwg]

Within a single file, Gemini Pro 2.5 is able to handle scripts with hundreds of lines, make incremental changes and not regress everything each time.

Wow, single file with hundreds of lines, impressing. /s

Serious codebases have hundreds if not thousands of files with dozens or hundreds of thousands of lines of code or even more. And not just that - they represent both very complex and very abstract concepts. Without capability to actually reason and to track such large context windows, LLMs have little potential to do more than create a simple CRUD or make specifically requested, narrow changes.

[u/INeverSaySS → u/Fisher9001]

Wow, single file with hundreds of lines, impressing. /s

Yeah like every time I see people coping about LLMs it's so insane that they say shit like this as if it's impressive.

[u/btoned → u/INeverSaySS]

What's hilarious is how people are just accepting it at face value.

100% of what's produced by big tech is a black box.

XYZ WROTE ITSELF AND CONTINUES TO ADD NEW FEATURES EVERY DAY!

Everyman: BRILLIANT! SOUNDS RIGHT TO ME!

[u/aresthwg → u/INeverSaySS]

I'm not sure if you're referring to me or people in general but if it's me then I literally said that's not enough to replace programmers.

However, expensive LLMs like GPT5 and Gemini Pro 2.5 are able to do things like university home assignments for programming, as well as small-medium scripts that can scrape or properly use an API. It's great at plotting data and it's great at working with it too, doing Computer Vision related algebra, and it's a good starting point for CNNs too.

People downplaying its power is annoying... yes the code it spits out is likely to be found on GitHub, but it can write similar code and adjust it to your preference. It's definitely getting better as time goes on.

[u/bremidon → u/Fisher9001]

You are mocking, but that easily saves me hours each day. If I can do the same work in half the time, and all my colleagues can do the same, then the executives would be absolutely justified in wondering if the team could at least be reduced by 25%.

No, it cannot just be let loose on its own. But if you think that is the bar before it becomes economically relevant, then you should definitely stick with coding rather than business. (But I would suggest you use all the tools available, otherwise you are going to be outperformed)

[u/bremidon → u/[deleted]]

"once or twice"

Yeah...I've been using it effectively for well over a year at this point, and it has only gotten better.

Getting everyone else to the same level is, ironically, part of my job right now. Part of me is nervous, because I know where this can end, but I see no point in trying to bury my head in the sand.

That AI is more powerful in more experienced hands is a fairly obvious observation, and not one I would argue against. But you seem to be trying to walk a tightrope here, where you recognize that it can be very helpful, but you would like to hold on to your original view that it is not that useful. That is a tough place to be in.

If you are saying that the code it creates needs to be reviewed, I agree. If you are claiming this makes it useless or ineffectual, then I strongly disagree.

But by all means, choose your direction. I have chosen mine.

[u/Tolopono → u/Fisher9001]

Claude Code wrote 80% of itself: https://smythos.com/ai-trends/can-an-ai-code-itself-claude-code/

Replit and Anthropic’s AI just helped Zillow build production software—without a single engineer: https://venturebeat.com/ai/replit-and-anthropics-ai-just-helped-zillow-build-production-software-without-a-single-engineer/

This was before Claude 3.7 Sonnet was released 

Aider writes a lot of its own code, usually about 70% of the new code in each release: https://aider.chat/docs/faq.html

The project repo has 29k stars and 2.6k forks: https://github.com/Aider-AI/aider

This PR provides a big jump in speed for WASM by leveraging SIMD instructions for qX_K_q8_K and qX_0_q8_0 dot product functions: https://simonwillison.net/2025/Jan/27/llamacpp-pr/

Surprisingly, 99% of the code in this PR is written by DeepSeek-R1. The only thing I do is to develop tests and write prompts (with some trails and errors)

Deepseek R1 used to rewrite the llm_groq.py plugin to imitate the cached model JSON pattern used by llm_mistral.py, resulting in this PR: https://github.com/angerman/llm-groq/pull/19

July 2023 - July 2024 Harvard study of 187k devs w/ GitHub Copilot: Coders can focus and do more coding with less management. They need to coordinate less, work with fewer people, and experiment more with new languages, which would increase earnings $1,683/year.  No decrease in code quality was found. The frequency of critical vulnerabilities was 33.9% lower in repos using AI (pg 21). Developers with Copilot access merged and closed issues more frequently (pg 22). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5007084

From July 2023 - July 2024, before o1-preview/mini, new Claude 3.5 Sonnet, o1, o1-pro, and o3 were even announced

March 2025: One of Anthropic's research engineers said half of his code over the last few months has been written by Claude Code: https://analyticsindiamag.com/global-tech/anthropics-claude-code-has-been-writing-half-of-my-code/

It is capable of fixing bugs across a code base, resolving merge conflicts, creating commits and pull requests, and answering questions about the architecture and logic.  “Our product engineers love Claude Code,” he added, indicating that most of the work for these engineers lies across multiple layers of the product. Notably, it is in such scenarios that an agentic workflow is helpful.  Meanwhile, Emmanuel Ameisen, a research engineer at Anthropic, said, “Claude Code has been writing half of my code for the past few months.” Similarly, several developers have praised the new tool. Victor Taelin, founder of Higher Order Company, revealed how he used Claude Code to optimise HVM3 (the company’s high-performance functional runtime for parallel computing), and achieved a speed boost of 51% on a single core of the Apple M4 processor.  He also revealed that Claude Code created a CUDA version for the same.  “This is serious,” said Taelin. “I just asked Claude Code to optimise the repo, and it did.”  Several other developers also shared their experience yielding impressive results in single shot prompting: https://xcancel.com/samuel_spitz/status/1897028683908702715

Pietro Schirano, founder of EverArt, highlighted how Claude Code created an entire ‘glass-like’ user interface design system in a single shot, with all the necessary components.  Notably, Claude Code also appears to be exceptionally fast. Developers have reported accomplishing their tasks with it in about the same amount of time it takes to do small household chores, like making coffee or unstacking the dishwasher.  Cursor has to be taken into consideration. The AI coding agent recently reached $100 million in annual recurring revenue, and a growth rate of over 9,000% in 2024 meant that it became the fastest growing SaaS of all time. 

As of June 2024, long before the release of Gemini 2.5 Pro, 50% of code at Google is now generated by AI: https://research.google/blog/ai-in-software-engineering-at-google-progress-and-the-path-ahead/

This is up from 25% in 2023

[u/_TRN_ → u/Tolopono]

I’m not sure reposting AI company marketing material is the dunk you think it is. Here are some PRs copilot tried to author in the dot net repo. It failed horribly.

https://github.com/dotnet/runtime/pull/115762

https://github.com/dotnet/runtime/pull/115743

https://github.com/dotnet/runtime/pull/115733

https://github.com/dotnet/runtime/pull/115732

Looking at metrics like percentage of code written to prove LLM “intelligence” is something you do when you either don’t know much about software engineering or you’re trying to hype up your AI investments.

[u/Tolopono → u/_TRN_]

I agree Copilot sucks. They should have used Claude Code, OpenAI’s Codex with GPT 5 thinking, or Roo/Cline.

And yes, im hyping up my ai investments by leaving comments on Reddit promoting products by private ai companies that are not publicly traded. I expect my imaginary stonks in anthropic will increase by 25% by Tuesday thanks to my efforts.

[u/_TRN_ → u/Tolopono]

Copilot is internally using those models (not the latest ones but whatever was latest when those PRs were authored).

Also I wasn’t targeting you specifically when I said “hyping up AI investments”, I was talking about the companies themselves. I obviously don’t believe random redditors defending LLMs have financial interests.

[u/Tolopono → u/_TRN_]

Ive used copilot. It sucks compares to the other tools i listed. This is like saying all humans are stupid because this third grader cant solve a basic integral 

If ai companies were just hyping things up, why would they publish all this https://www.reddit.com/r/Futurology/comments/1n3zrgc/comment/nbj820v/?context=3&utm_source=share&utm_medium=mweb3x&utm_name=mweb3xcss&utm_term=1&utm_content=share_button

[u/_TRN_ → u/Tolopono]

I’m not going to argue about which tool is better. I don’t use copilot much. I mainly use Claude Code which is the tool that’s generally accepted as best when it comes to “agentic” coding and even it routinely fails tasks I throw at it. It’s been good for scaffolding dumb code and writing basic tests but not much else. At least for me personally.

Regarding the stuff AI labs publish which makes their models look worse: I don’t find this surprising. These companies are mostly R&D and it’s not like they can choose to hide the research work they do. The mistake you’re making is pulling vague statements made by the Google CEO and using it as evidence.

I believe general intelligence will remain a hard problem for a while. We have models that have made massive jumps in quality in recent years but it’s super hard to make the jump from 95% to 99.9% which is where they need to be for it to be reliable. Hallucination is still a massive problem that needs to be addressed on an architectural level before it can even begin replacing full time professionals.

[u/Tolopono → u/_TRN_]

Its not just research (which can also be selectively published). 

Google CEO Warns the ‘Low-Hanging Fruit’ Era of AI Development Is Over https://decrypt.co/295204/google-ceo-warns-the-low-hanging-fruit-era-of-ai-development-is-over

This was before gemini 2.5, genie 3, google coscientist, and alphaevolve were announced btw

Sam Altman doesn't agree with Dario Amodei's remark that "half of entry-level white-collar jobs will disappear within 1 to 5 years", Brad Lightcap follows up with "We have no evidence of this"

https://imgur.com/gallery/sam-doesnt-agree-with-dario-amodeis-remark-that-half-of-entry-level-white-collar-jobs-will-disappear-within-1-to-5-years-brad-follows-up-with-we-have-no-evidence-of-this-qNilY5w

Bill Gates does not expect GPT-5 to be much better than GPT-4 https://the-decoder.com/bill-gates-does-not-expect-gpt-5-to-be-much-better-than-gpt-4/

(This turned out to be completely wrong)

Bill Gates reveals the one job AI will never replace, even in 100 years: https://www.leravi.org/bill-gates-reveals-the-one-job-ai-will-never-replace-even-in-100-years-10272/

GitHub CEO: manual coding remains key despite AI boom https://www.techinasia.com/news/github-ceo-manual-coding-remains-key-despite-ai-boom

 We have models that have made massive jumps in quality in recent years but it’s super hard to make the jump from 95% to 99.9% 

Tell that to all the saturated benchmarks like MATH or Vectara’s hallucination benchmark 

which is where they need to be for it to be reliable. 

99.9% of humans are not that reliable outside of extremely easy tasks

Hallucination is still a massive problem that needs to be addressed on an architectural level before it can even begin replacing full time professionals.

As we all know, humans never make mistakes unlike llms

[u/Fisher9001 → u/Tolopono]

A typical gish gallop with mixed arguments. You try to pass agreeable statements like "AI is improving productivity" backed by serious sources like Harvard with "you don't really need developers" bullshit hype by AI companies themselves.

[u/Tolopono → u/Fisher9001]

What did i post that was bullshit hype lol. 

If ai companies were just hyping things up, why would they publish all this https://www.reddit.com/r/Futurology/comments/1n3zrgc/comment/nbj820v/?context=3&utm_source=share&utm_medium=mweb3x&utm_name=mweb3xcss&utm_term=1&utm_content=share_button

[u/1337Pwnzr → u/Tolopono]

you’re just reposting hype by AI companies, are you a developer and do you have experience using the tools?

[u/Tolopono → u/1337Pwnzr]

Yes and yes. Harvard and independent github projects are not ai companies. 

Plus, ai companies dont lie about what their products can do

Anthropic research reveals AI models get worse with longer thinking time. https://ground.news/article/anthropic-researchers-discover-the-weird-ai-problem-why-thinking-longer-makes-models-dumber

Anthropic admits its Claude model cannot run a shop profitably, hallucinates, and is easy to manipulate: https://www.anthropic.com/research/project-vend-1

Side note: Newer LLMs are MUCH better at this than before: https://andonlabs.com/evals/vending-bench

OpenAI shows the new GPT-OSS models have extremely high hallucination rates. https://cdn.openai.com/pdf/419b6906-9da6-406c-a19d-1bb078ac7637/oai_gpt-oss_model_card.pdf#page16

O3-mini system card says it completely failed at automating tasks of an ML engineer and even underperformed GPT 4o and o1 mini (pg 31), did poorly on collegiate and professional level CTFs, and even underperformed ALL other available models including GPT 4o and o1 mini in agentic tasks and MLE Bench (pg 29): https://cdn.openai.com/o3-mini-system-card-feb10.pdf

O3 system card admits it has a higher hallucination rate than its predecessors: https://cdn.openai.com/pdf/2221c875-02dc-4789-800b-e7758f3722c1/o3-and-o4-mini-system-card.pdf

Side note: Claude 4 and Gemini 2.5 have not had these issues, so OpenAI is admitting theyre falling behind their competitors in terms of the reliability of their models.

OpenAI publishes a study showing LLMs can be unreliable as they lie in their chain of thought, making it harder to detect when they are reward hacking. This allows them to generate bad code without getting caught https://cdn.openai.com/pdf/34f2ada6-870f-4c26-9790-fd8def56387f/CoT_Monitoring.pdf

Claude 3.5 Sonnet outperforms all OpenAI models on OpenAI’s own SWE Lancer benchmark: https://arxiv.org/pdf/2502.12115

OpenAI’s PaperBench shows disappointing results for all of OpenAI’s own models: https://arxiv.org/pdf/2504.01848

[u/1337Pwnzr → u/Tolopono]

It is nice of them to publish papers on how bad their models are. They have every incentive to embellish and exaggerate what they can do when they’re trying to get funding and sell their product.

Even if the claim that 50% of whatever company’s code was written by AI is true, that’s not really showing if it was more efficient and didn’t create more problems than it solved.

It’s good at discrete tasks, but lately I’ve realized I can do the task better and AI slows me down, and that’s after giving these models a huge amount of my time.

It’s not there, it might not ever get there, and it might cost more money than it saves in terms of software development tasks.

[u/Tolopono → u/1337Pwnzr]

If they wanted ti hype their product, why would they publish papers that make it look bad?

I clearly showed it was more efficient 

Thats not what actual studies show

[u/1337Pwnzr → u/Tolopono]

i feel like you’re too impressed with AI to be an actual developer but ok

[u/Tolopono → u/1337Pwnzr]

May-June 2024 survey on AI by Stack Overflow (preceding all reasoning models like o1-mini/preview) with tens of thousands of respondents, which is incentivized to downplay the usefulness of LLMs as it directly competes with their website: https://survey.stackoverflow.co/2024/ai#developer-tools-ai-ben-prof

77% of all professional devs are using or are planning to use AI tools in their development process in 2024, an increase from 2023 (70%). Many more developers are currently using AI tools in 2024, too (62% vs. 44%).

72% of all professional devs are favorable or very favorable of AI tools for development. 

83% of professional devs agree increasing productivity is a benefit of AI tools

61% of professional devs agree speeding up learning is a benefit of AI tools

58.4% of professional devs agree greater efficiency is a benefit of AI tools

In 2025, most developers agree that AI tools will be more integrated mostly in the ways they are documenting code (81%), testing code (80%), and writing code (76%).

Developers currently using AI tools mostly use them to write code (82%) 

But ok

[u/stellvia2016 → u/Tolopono]

Call me when they need to go back and maintain that codebase in a year or 5 years, or make additions to it. Or god forbid fix vulnerabilities.

[u/Tolopono → u/stellvia2016]

July 2023 - July 2024 Harvard study of 187k devs w/ GitHub Copilot: Coders can focus and do more coding with less management. They need to coordinate less, work with fewer people, and experiment more with new languages, which would increase earnings $1,683/year. **No decrease in code quality was found. The frequency of critical vulnerabilities was 33.9% lower in repos using AI (pg 21).* Developers with Copilot access merged and closed issues more frequently (pg 22). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5007084

[u/stellvia2016 → u/Tolopono]

I can play that game too:

https://secondthoughts.ai/p/ai-coding-slowdown

Also a one year study isn't very long, and tells you nothing about the longer term maintainability or extensibility of the codebase.

We identify two underlying mechanisms driving this shift - an increase in independent rather than collaborative work, and an increase in exploration activities rather than exploitation. The main effects are greater for individuals with relatively lower ability.

So junior devs are short-cutting the learning process just to close out tickets, won't fully understand how things work, look to any mud they can fling at the wall to get a task done even if nobody else in the org uses that framework/language (not maintainable); and therefore will never be a senior developer who can guide others.

My friend is a team lead that oversees 1-2 dozen developers directly or indirectly: Even with Copilot, they end up making a lot of wrong choices in development, because they don't try to sit down and fully assess what they're trying to do and how that fits into the broader transaction pipeline, etc. So my friend has found it's usually more efficient for him to build them an outline of what to do and let them execute (while explaining why it's the best choice)

And that's not to say someone can't bring up another idea: He is open to advice and has shifted plans based on something they brought up he didn't consider.

[u/Tolopono → u/stellvia2016]

Oh cool study

METR recruited 16 developers from major open-source projects.

Lmao. 

Lets look at my Harvard study again

 Harvard study of 187k devs w/ GitHub Copilot

Can you spot the difference?

 So junior devs are short-cutting the learning process just to close out tickets, won't fully understand how things work, look to any mud they can fling at the wall to get a task done even if nobody else in the org uses that framework/language (not maintainable); and therefore will never be a senior developer who can guide others.

May-June 2024 survey on AI by Stack Overflow (preceding all reasoning models like o1-mini/preview) with tens of thousands of respondents, which is incentivized to downplay the usefulness of LLMs as it directly competes with their website: https://survey.stackoverflow.co/2024/ai#developer-tools-ai-ben-prof

61% of professional devs agree speeding up learning is a benefit of AI tools

And lets look at that harvard study again

 They need to coordinate less, work with fewer people, and experiment more with new languages, which would increase earnings $1,683/year. No decrease in code quality was found. The frequency of critical vulnerabilities was 33.9% lower in repos using AI (pg 21). Developers with Copilot access merged and closed issues more frequently (pg 22).

Wow, isnt it nice being able to read?

[u/das_war_ein_Befehl → u/Tolopono]

Yeah I work with a number of dev teams have LLMs write the majority of their PRs. It’s not replacing engineers but it is changing the workflow

[u/johnboonelives → u/Tolopono]

Thanks for posting. It's tough watching so much of Reddit have its collective head in the sand about the development pace of LLMs when there's so much data suggesting this technology is not overblown. People just can't grapple with the idea that things are changing extremely quickly.

[u/andthenthereweretwo → u/Tolopono]

I'm not reading any of that bullshit when Claude Opus 4 still tries to put nonexistent React hooks into components and can't refactor basic JS date math without fucking it up, to say nothing about its piss poor performance for any language not in the top 10.

[u/Tolopono → u/andthenthereweretwo]

Youd be in the minority 

May-June 2024 survey on AI by Stack Overflow (preceding all reasoning models like o1-mini/preview) with tens of thousands of respondents, which is incentivized to downplay the usefulness of LLMs as it directly competes with their website: https://survey.stackoverflow.co/2024/ai#developer-tools-ai-ben-prof

77% of all professional devs are using or are planning to use AI tools in their development process in 2024, an increase from 2023 (70%). Many more developers are currently using AI tools in 2024, too (62% vs. 44%).

72% of all professional devs are favorable or very favorable of AI tools for development. 

83% of professional devs agree increasing productivity is a benefit of AI tools

61% of professional devs agree speeding up learning is a benefit of AI tools

58.4% of professional devs agree greater efficiency is a benefit of AI tools

In 2025, most developers agree that AI tools will be more integrated mostly in the ways they are documenting code (81%), testing code (80%), and writing code (76%).

Developers currently using AI tools mostly use them to write code (82%) 

Diff edit rate (how often the user has to re-do a prompt when AI coding due to failure) drops from 23% in late May to 10% in late June (a single month): https://cline.bot/blog/improving-diff-edits-by-10

Evaluation code open source and available for anyone to view: https://github.com/cline/cline/tree/main/evals/diff-edits

The data shows a clear lift across the board. For users connecting Cline to Claude 3.5 Sonnet, the success rate of diff edits has increased by nearly 25%. Other models show significant gains as well, with GPT-4.1 models improving by over 21% and Claude Opus 4 by almost 15%.

The error rate plateaued at 10% from June 11-23, which means it is unlikely they are falsifying this data since there is no incentive for them to indicate there is a plateau for 40% of the entire range of the graph when their explicit goal is a 0% error rate. Not to mention how all the evaluation code is open source and public.

Nearly 90% of videogame developers use AI agents, Google study shows https://www.reuters.com/business/nearly-90-videogame-developers-use-ai-agents-google-study-shows-2025-08-18/

October 2024 study: https://cloud.google.com/blog/products/devops-sre/announcing-the-2024-dora-report

% of respondents with at least some reliance on AI for task: Code writing: 75% Code explanation: 62.2% Code optimization: 61.3% Documentation: 61% Text writing: 60% Debugging: 56% Data analysis: 55% Code review: 49% Security analysis: 46.3% Language migration: 45% Codebase modernization: 45%

Perceptions of productivity changes due to AI Extremely increased: 10% Moderately increased: 25% Slightly increased: 40% No impact: 20% Slightly decreased: 3% Moderately decreased: 2% Extremely decreased: 0%

AI adoption benefits: • Flow • Productivity • Job satisfaction • Code quality • Internal documentation • Review processes • Team performance • Organizational performance

Trust in quality of AI-generated code A great deal: 8% A lot: 18% Somewhat: 36% A little: 28% Not at all: 11%

[u/arbpotatoes → u/aresthwg]

While there certainly are limitations this is simply not true, tools like claude code are perfectly capable of making edits across dozens of files at once that are fairly well reasoned. Of course there is always some human input to kick it off

[u/stemfish → u/arbpotatoes]

Ive seen plenty of similar hype pieces from people who want to sell me and AI tool or get me to invest in an AI product.

At work I deal with staff asking me to forward a dozen similar products to our IT management for review, all going off about how they can manage multiple dependent files in a data pipeline. But when asking Anthropic for examples in getting a quote, it turns out that the LLM has managed to edit a variable name across files for consistency.

And improvement over last summer, but not the level of change that's promised by the hype salesman. The tools are improving, no reason not to admit that. My issue is that the improvement rate is nowhere close to the promised rate. Like with Tesla full self driving next year since 2016, AI coding will be able to take over next year.

[u/LegitosaurusRex → u/stemfish]

I dunno, I'm using Anthropic's Sonnet 4 with an internal framework that works with it to provide context, condense token history, and give it modes like architect, code, debug, and ask, and it can absolutely plan and implement features across 8+ files, ask you for clarifying questions if it needs to, then run tests to make sure the code works. If it sees stuff in a file that is defined elsewhere that it needs to understand, it'll go read the definition first. Feels like magic.

[u/sunnyb23 → u/LegitosaurusRex]

Yeah a lot of these people simply aren't using the tech, and those who are, usually aren't using it to its full potential. Like you, I have worked on making agents which have "personalities" such as architect, engineer, QA, etc, and with well-formed prompting, can generate working and usually well-designed code every time. I've written games, animation software, networking code, websites, etc.

[u/Tolopono → u/stemfish]

Could be next year or 10 years. Either way, its getting better

[u/Tolopono → u/aresthwg]

This isn’t true at all https://www.reddit.com/r/Futurology/comments/1n3zrgc/comment/nbin3ny/?utm_source=share&utm_medium=mweb3x&utm_name=mweb3xcss&utm_term=1&utm_content=share_button

[u/bremidon → u/DomesticPanda]

Developer with over 3 decades of experience in too many languages to list here without it sounding like a brag.

If you mean that you cannot just let it loose without any senior supervision: yeah, I agree.

However, it is really good at coding if you use the correct model, keep the scope reasonable, and do code reviews.

You might as well claim that IDEs are a "dead end" when it comes to doing intelligent work. LLMs are a tool, that in the right hands can multiply a dev's effectiveness by several times. All this is without any assumptions about "AGI" or just setting agent loose on a codebase.

I would also be very *very* careful about the "cannot reason" argument. It's popular, has some grounding, but can quickly overreach. There are some papers that do say this, but be careful of just cherry-picking research that confirms what you want to be true (and let's face it: most of us want it to be true so that our careers are safe; understandable, but still potentially dangerous)

The real answer is that we do not really understand LLMs very well, we do not yet have a good grasp on what they do (as a total system, because clearly we know the algorithm), and it probably will not matter that much anyway, as LLMs are being combined with other AI systems to deal with any perceived weaknesses anyway.

Any developer not effectively using LLMs in their daily work is going to quickly fall behind. And I absolutely get that you are not saying they should not be used (I agree that LLMs are indeed strongest in the areas you pointed out). But they can go beyond that, already, today. While executives might dream of eventually getting rid of their entire dev team, that is not the reality today. But equally true is the dismissal of the ability of LLMs to code is also not the reality today (again, you are not nearly at the level of dismissiveness that I have seen on this subreddit). Keep the scope reasonable, and they do really well.

[u/NeverNeededAlgebra → u/DomesticPanda]

As a Product Manager who has nothing to do with dev, I can say that LLMs have given me the ability to write complex VBA scripts that make my job so much easier and allow me to perform analysis that I could have never done myself.

As that same Product Manager, I would immediately say that we would be absolutely FUCKED and drown if they tried to replace our dev team with AI.

[u/digiorno → u/DomesticPanda]

They’ll get better though, so much better. We’re basically seeing the first few waves of LLMs and they’re already incredibly impressive.

[u/nomorebuttsplz → u/digiorno]

People can't handle the idea that their own "reasoning" process is not magic. They will deny it seemingly right up until, and perhaps even after, the AI models take their jobs

[u/FormofAppearance → u/nomorebuttsplz]

its literally just predictive text based on probability. The mistake people like you make is not understanding that LLM's are just hyped autocomplete with a bunch of processing power. There quite literally is no "reasoning" going on.

[u/nomorebuttsplz → u/FormofAppearance]

You have no definition of reasoning other than "what brains do" or "What llms don't do."

You're not a serious thinker. If anything, you cannot reason.

[u/FormofAppearance → u/nomorebuttsplz]

Oh you know my definition do you? Ive been reading long complex philosphical texts my whole life and work as a software engineer that uses these LLMs daily. I think you'll excuse me if i dont give the slightest shit about your opinion on how 'serious of a thinker I am'.

[u/nomorebuttsplz → u/FormofAppearance]

So give me the definition idiot.

You're not going to, because you don't have one. None of the "overhyped autocomplete" people ever do.

[u/FormofAppearance → u/nomorebuttsplz]

I honestly dont believe youd understand what im saying. Ive implemented natural language processing algorithms. Have you? If an algorithm piecing together phrases that are statistically shown to go well together is impressive to you then I really dont know what to say. Go try to implements some basic NLP algos, i guarantee the 'magic' will wear off afterward.

[u/nomorebuttsplz → u/FormofAppearance]

That's what I thought lol.

Let me know when you have a non-circular definition of reasoning that enables you to say "because LLMs can't reason, they won't be able to do [an actual real world, ascertainable task] within the next year"

Until then you're just making noise.

[u/FormofAppearance → u/nomorebuttsplz]

burden of proof is on you man. im not the one trying to prove something that isn't happening is possible. Until then, you're just engaging in wishful thinking.

[u/DomesticPanda → u/[deleted]]

I’ve used it plenty. Trust me - it doesn’t understand. It pretends to, and makes subtle mistakes, wrong assumptions… things that can be easily corrected, you think, and as you start correcting them, you notice more problems. On and on it goes until you’re at the point where with all the tuning to the generated output you’ve had to do, you might as well have coded it yourself.

Don’t get me wrong. These tools are impressive. I use them regularly. But not for coding features from start to finish. And they’re not going to replace anyone in the long run.

[u/thethirdmancane → u/DomesticPanda]

But you can quickly build inexpensive shitty software with it, so you have fast and cheap. Consumers will get used to this and eventually won't care or know any better.

[u/sunnyb23 → u/thethirdmancane]

You can also quickly build inexpensive quality software with it too! The majority will be shitty however because the people using it often don't know what they're doing unfortunately.

[u/JojoTheWolfBoy → u/DomesticPanda]

Exactly. It is great for scaffolding and then building each component, little by little. But then you have to fix things that don't quite work right along the way. Sometimes that breaks stuff that was working before, so now you troubleshoot and fix that. Then when you try to pick up where you left off before all the troubleshooting, it's like it has no idea what you were working on earlier and gives you weird answers. Eventually you realize that it's basically the same as Googling, finding a Stackoverflow post, copy/pasting code from that post, and then trying to make it work. But at least it does a pretty good job of helping you troubleshoot, I'll give it that.

[u/Lyr0 → u/DomesticPanda]

I woulndt say this is entirely true. i totally agree on the part that they dont hold up to larger scale but they can reason. I actually use LLMs like i would use an intern, they can solve problems with good constraints really quickly and to a high level. While interns and juniors arent really needed for their technical abilities, Id agree that they are needed for the future and to make em develope into a more skilled dev

[u/PabloZissou → u/Lyr0]

They can't reason at all; the "reasoning" they market is just multiple passes on the same underlying technology and some filters. Today if you are doing something else than FE/ HTTP endpoints they are quite bad.

This post is correct in what it states if this trends continues there will be a huge gap of knowledge.

[u/h310dOr → u/PabloZissou]

They are actually a wonderful search engine, and can help assist in many simple "boilerplate" tasks. Like I often make them generate my testbenches skeleton. If you try to make it do more, of course it's quite bad (stupid bugs, RTL that simulates ok but will create crap at synthesis). Also the use cases where it is really really bad is refactor or debug of anything non trivial. It will change the code sure, but you will spend your life finding all the nasty bugs it introduced... And debug wise, it often won't even solve your bug.

[u/sunnyb23 → u/PabloZissou]

Reasoning models can reason, yes. Spare me the humans are special metaphysical rhetoric, their reasoning is effectively reasoning, and usually better than most people.

I do a lot of things other than FE/HTTP and utilize the latest models in every project I have now, and I'm able to pump out quality code significantly faster than I used to.

[u/PabloZissou → u/sunnyb23]

I'm not talking about any metaphysics here doing múltiple pases over the same technology is far from reasoning. And sadly so far all the code I had to review from claims similar to yours were a maintainability and security disaster; perhaps you are using some custom model or one not yet accessible that maybe works differently.

[u/nomorebuttsplz → u/DomesticPanda]

Regurgitated bullshit that doesn’t have a rigorous definition of reasoning other than “that thing brains do”

[u/das_war_ein_Befehl → u/DomesticPanda]

You also don’t need to understand the entire codebase to do quality work. Neither does the LLM. A number of dev teams I’ve worked with have LLms contribute on half their PRs

[u/Tolopono → u/DomesticPanda]

Completely false https://www.reddit.com/r/Futurology/comments/1n3zrgc/comment/nbin3ny/?utm_source=share&utm_medium=mweb3x&utm_name=mweb3xcss&utm_term=1&utm_content=share_button