8 ms·

Some uncomfortable truths about AI coding agents

80 points by borealis-dev 6mo ago

IgorGanapolsky 5mo ago
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palmotea 6mo ago
> The role change has been described by some as becoming a sort of software engineering manager, where one writes little or no code oneself but instead supervises a team of AI coding agents as if they are a team of human junior software engineers.... > In reality, though, the code review load for software engineers will gradually increase as fewer and fewer of them are expected to supervise an ever-growing number of coding agents, and they will inevitably learn to become complacent over time, out of pure necessity for their sanity. I’m a proponent of code review...but even I often consider it a slog to do my due diligence for a large code review (just because I think it’s important doesn’t mean I think it’s fun). If it’s your full-time job to review a swarm of agents’ work, and experience tells you they are good enough 95%+ of the time, you’re not going to pay as much attention as you should and bad changes will get through. Another way to look at this is that AI coding agents take the fun out of a software engineer's job. The machine takes many of the fun parts and leaves the human with more of the unenjoyable parts. Under our new ways of working, you are required to be excited an curious about this evolution three times per day.
- jonah 6mo ago
  Sounds a lot like "self-driving" cars - "they are good enough 95%+ of the time, you’re not going to pay as much attention as you should". Same thing happens here, you get complacent and miss critical failures or problems. It's also similar in that it "take[s away] many of the fun parts". When I can focus on simply driving it can be engaging and enjoyable - no matter the road or traffic or whatever.
  - deleted 6mo ago
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  - gruez 6mo ago
    >Sounds a lot like "self-driving" cars - "they are good enough 95%+ of the time, you’re not going to pay as much attention as you should". That might be an issue for supervised "self-driving" cars (eg. tesla FSD), but not really applicable to self driving cars as a whole. Waymo seems to be doing just fine for instance.
    - jonah 6mo ago
      Exactly why I put "self-driving" in quotes. Right now AI assisted coding might generally be at the equivalent of Level-2 or -3 self-driving. Getting to autonomous coding agents will be like the step change that is Level-4 or -5 driving.
    - teatro 6mo ago
      Aren’t Waymo‘s 5% covered by some people in the Philippines? [1] And aren’t they still failing occasionally? [2] [1](https://www.techspot.com/news/111233-waymo-admits-autopilot-often-guys-philippines.html https://www.techspot.com/news/111233-waymo-admits-autopilot-...) [2] (https://www.bloomberg.com/news/articles/2026-01-23/waymo-probed-by-us-safety-agency-over-austin-school-bus-mishaps https://www.bloomberg.com/news/articles/2026-01-23/waymo-pro...)
      - gruez 6mo ago
        >Aren’t Waymo‘s 5% covered by some people in the Philippines? [1] If you look at waymo's prior blog posts, you'll realize that the people in Philippines aren't making split second decisions that was implied by "you’re not going to pay as much attention as you should". https://waymo.com/blog/2024/05/fleet-response https://waymo.com/blog/2024/05/fleet-response >And aren’t they still failing occasionally? [2] What's the failure rate of humans? For both AI drivers and coders, if they make less mistakes than humans, that's still a win.
        6mo ago
- mark242 6mo ago
  > Another way to look at this is that AI coding agents take the fun out of a software engineer's job. Completely backwards - the fun in the job should be to solve problems and come up with solutions. The fun in the job is not knowing where to place a semicolon.
  - bluefirebrand 6mo ago
    > the fun in the job should be to solve problems and come up with solutions Who are you to tell anyone what the fun "should" be? Personally, I find writing code very fun, because building the solution is also very gratifying. Besides which, in my experience until you actually write the code you haven't proven that you solved anything. It's so easy to think you have solved a problem when you haven't, but you won't figure that out until you actually try to apply your solution > The fun in the job is not knowing where to place a semicolon. This can be solved with simple linters, no need for LLMs
  - chris_money202 6mo ago
    Exactly, the fun part is when the code works and does what you wanted it to do. Writing code itself is not fun. People forget this because they get small wins / dopamine hits along the way, a clever function, an elegant few lines of code, a bug fix, but the majority of that time coding is just a grind until the end where you get the big dopamine hit.
    - HendrikHensen 6mo ago
      Fun is not measured objectively. Different people find different things fun. I enjoy writing code very much (in addition to solving big problems; one can enjoy both).
      - deleted 6mo ago
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  - palmotea 6mo ago
    >> Another way to look at this is that AI coding agents take the fun out of a software engineer's job. > Completely backwards - the fun in the job should be to solve problems and come up with solutions. Aren't the coding agents supposed to be doing that too? You give them the problem, they code up a solution, then the engineer is left with the review it to see if it's good enough. > The fun in the job is not knowing where to place a semicolon. That's like such a minor and easy-to-do thing that I'm surprised you're even bringing it up.
- dybber 6mo ago
  I think it depends on what you find enjoyable. I think people who like the tinkering and the actual act of coding, debugging, etc. will find it less and less fun to be in this area, but people who like to look at the big picture, and solve problems, will see that they will now be better at both getting overview of larger and larger codebases and that technical debt that was never attainable to solve before can now be “outsourced” to LLM’s. I find that fun. I work in a 50 year old IT company, with lots of legacy code and technical debt which we have never been able to address - suddenly it’s within reach to really get us to a better place.
  - maplethorpe 6mo ago
    The best way to have a big picture view of a project is to build a mental model of that project in your head. Coding with LLMs removes that ability, and replaces it with an illusion.
    - stavros 6mo ago
      Does the thing work like I want it in the end? Is it fast, reliable, enjoyable to use, maintainable, cheap, efficient, resilient, etc? If so, I don't care if I wrote it by hand or with an LLM. People who think that building something with an LLM somehow dooms the something to mediocrity are engaging in magical thinking. I can simply use as much or as little LLM as will allow me to meet my quality criteria.
      - frizlab 6mo ago
        You forgot maintainable.
        stavros 6mo ago
        Added a few more adjectives and an "etc", to cover all pedantic bases that don't matter to my argument.
      - maplethorpe 6mo ago
        You listed "maintainable", but how do you know your project is maintainable, if you yourself have no understanding of the code base? Presumably the reason is that the AI has managed to maintain the project so far, so it follows that it will be able to do so in the future. But that's not a given. It's more of a prayer.
- raw_anon_1111 6mo ago
  The “fun” for me has never been “coding” and on the enterprise dev side that has been a commodity for a decade. If you look at the leveling guidelines for any tech company “codez real gud” will only get you to a mid level ticket taker.
  - catgary 6mo ago
    And even then - I still read the code it generates, and if I see a better way of doing something I just step in, write a partial solution, and then sketch out how the complete solution should work.
    - raw_anon_1111 6mo ago
      Unless the solution is going to be more secure, faster, more stable etc, why does it matter? Will the end user care? “Does it make the beer taste better”?
      - dijksterhuis 6mo ago
        in a word, maintainability > maintainability is inversely proportional to the amount of time it takes a developer to make a change and the risk that change will break something https://softwareengineering.stackexchange.com/a/134863 https://softwareengineering.stackexchange.com/a/134863 i could be wrong, but i'm pretty sure that end-users get upset when a change takes a long time or it ends up breaking something for them. just because people are finding that agents or whatever are speeding changes up now doesn't necessarily mean they won't encounter a slow-down later when the codebase becomes an un-maintainable mess. technical debt is always a thing, even with machines doing the work (the agent/machine still has to parse a codebase to make changes).
        raw_anon_1111 6mo ago
        What makes you think that AI couldn’t make the same changes without breaking it whether you modify the code or not? And you do have automated unit tests don’t you? Right now I have a 5000 line monolithic vibe coded internal website that is at most going to be used by 3 people. It mixes Python, inline CSS and Javasript with the API. I haven’t looked at a line of code. My IAM permissions for the Lambda runtime has limited permissions (meaning the code can’t do anything that the permissions won’t allow it to). I used AWS Cognito for authorization and validated the security of the endpoints and I validated the permissions of the database user. Neither Claude nor Codex have any issues adding pages, features and API endpoints without breaking changes. By definition, coding agents are the worse they will be right now.
- hax0ron3 6mo ago
  I like programming for fun, but professional software engineering has never been more than very occasionally fun to me. I do it because it pays well. Most companies use some variant of the sprint/"agile" methodology, which means that you as the programmer are similar to an assembly line worker. You don't control the pace, you rarely get the chance to actually finish anything as much as you would like to so you don't get the satisfaction of a finished product, you get little downtime in between tickets or projects to feel a sense of satisfaction at what you have done before you move on to something else. I totally understand why businesses operate this way. It's simple: if you try not to operate this way, you increase the likelihood that your competitors will release more rapidly and take all your market share. It's the basic evolutionary logic. If you can release a decent but buggy product six months faster than the competitor can release a better and less buggy product, there is a good chance that you will drive them out of business. It all makes sense, but it doesn't result in a pleasant experience for me as a programmer. The job is also very sedentary and it puts stress on your eyes and your hands. Of course I'm not going to compare myself to a coal miner, but the fact remains that in its own ways the job is more rough on the body than some people might expect. Meanwhile the intellectual and constantly changing nature of the field means that you can never rest on your laurels - the job requires constant mental engagement. It's hard to do it well while multitasking and thinking about other interesting things at the same time. If jobs in this field did not pay so well, I don't think I'd ever even consider doing this as a career. It just doesn't have nearly enough upsides for me. The only upside I can think of besides the money is that you get to spend time interacting with intelligent people. But one can get that in other places. Coding with the help of AI is a big improvement for me because just automating away the boilerplate and greatly reducing the time that needs to be spent in doing reading, research and experimentation already takes away some of the main time-sinks of the job. And while I appreciate code's deterministic and logical elegance, I've written more than enough code in my life to not miss writing it directly. I also enjoy writing English, after all. It's refreshing for me to spend my time composing precise English instructions for the AI instead of writing in a programming language. My English skills are above average, so this also helps me to leverage one of my talents that had previously been underutilized. Well, my English skills always came in handy when I needed to talk to coworkers or managers, but I meant that they had been underutilized in the act of actually creating software.
- linsys 6mo ago
  But isn't that what Linux admins said when Cloud and Platform Engineering became a "thing". Puppet, Chef, Ansible they are taking the fun out of system administration, Cloud it's going to take away my job. But what happened is roles changed, SRE, Platform Engineering, dare I say DevOps engineers (whatever those are) all emerged. Software engineering is going through he same transformation with the same knee jerk reactions from both sides of the argument. Either AI run rampant with no guardrails like OpenClaw people burning 1000s in tokens, OpenClaw calling them randomly at 2am, etc.. then on the other side of the argument we have a petition asking the Node.js Technical Steering Committee to ban AI-assisted code from Node.js core. AI is here, it's here to stay, I believe that those of us that will be successful will find the middle ground.
- az09mugen 6mo ago
  That's exactly the feeling in this quotation : "I want AI to do my laundry and dishes so that I can do art and writing, not for AI to do my art and writing so that I can do my laundry and dishes." -- Joanna Maciejewska, author and videogame enthusiast
adshotco 6mo ago
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abletonlive 6mo ago
These opinions about what is going on w/ LLM development always stop short at first order effects and fail to account for second/third order effects. > Skill atrophy If LLMs are so good that you no longer have use for the skill, why do we care about skill atrophy? That skill isn't that useful to most people. There are so many examples of this in human history where it was completely fine and we went on to do higher order things that were more useful. > Even if they set out fully intending to provide the highest level of scrutiny to all generated code, they will gradually lose the ability to tell a good change from a bad one If this (first order effect) is actually a problem then it follows that we will naturally exercise our skill of detecting good change from bad ones (second order effect) and the skill will not atrophy? (third order effect). Seems like your "problem" is self correcting? > At its core, the only defense I’ve got for that response is… this time feels different? Not a particularly rigorous defense, I admit, but I did warn you that this was the squishiest of the issues at hand. Well, if you knew this perhaps it was better just not to lead with it and spend so many paragraphs on it. > Some might argue that, even if that time comes eventually, that’s no reason not to make use of the tools that are available right now. But it should come as no surprise that I disagree. Better not to become overly dependent on AI coding agents in the first place so you’ll be better situated to weather the storm (and maybe even thrive) when it comes. Well this argument didn't turn out to be any less squishy than the first one. It's a self correcting "problem" but you disagree and we should do X because you said so. What was the point of all of this then? > Prompt Injection I also think this will likely always be a problem but you can pretty much point at ANY tool we use in software development. Your viewpoint would be similar to saying we should stop using libraries because there's always going to be a vulnerability when you distribute code that somewhere in the chain a bad actor can inject malicious code even if the library was created by a trusted source in the industry. We have plenty of examples of this happening in real life. So far, still squishy. > Copyright/licensing > I’m not a lawyer! I’m a legal layperson offering my unqualified assessment of some tricky legal questions. Let’s get to it. Sigh, this entire post is slop isn't it? Bad look for whatever "standup for me is". edit: Standup for me is something that is made entirely irrelevant by agentic LLMs, no surprise. The irony is rich. The author wants to be the gatekeeper of skill, quality, and how we develop while they hand feed us slop in the form of their blog posts.
- 6mo ago
polotics 6mo ago
i was kinda hoping for TFA to finally produce some research outputs or even statistics, but sadly the `uncomfortable truths` are your usual vague talking points.
- borealis-dev 6mo ago
  Sorry, I realize the headline perhaps implies something with more rigour than it actually delivers. I'm pretty new to writing blog posts! But if you want some actual factual data, you seriously should read Ed Zitron's blog: https://www.wheresyoured.at/ https://www.wheresyoured.at/
throwaway613746 6mo ago
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dijksterhuis 6mo ago
As someone who worked on “prompt injection” before it was called “prompt injection” for an (unfinished) phd… yeah there is only one surefire 100% fix for “prompt injection”: use deterministic solutions ie not machine learning. ---- addendum in case someone tries to make this commonly made point -- i don't use deterministic here to mean "i've pinned the ML model weights after training". i use it in reference to the probability theory stuff of training/models (the boring and complicated maths stuff).
- gitaarik 6mo ago
  Yeah there's no solution to prompt injection, but prompt injection in itself is not a security risk. It's about what you give the LLM access to. You can give it access to your complete DB and APIs, or you can only allow it to operate on a very specific piece of data.
  - dijksterhuis 6mo ago
    > but prompt injection in itself is not a security risk sorry, but this is wrong. the only time “prompt injection” is not a security risk is if no data is ever passed as input to a model, or the model output has no bearing on anything in the world anywhere. in which case, why bother with the model/system in the first place. you can exclude this security risk from your threat model. but what you’re saying there is you don’t believe it’s high likelihood that someone would want to or could run an attack, or that doing so would have sufficiently low impact/severity. possibly because you’ve put mitigations in place … > It's about what you give the LLM access to. You can give it access to your complete DB and APIs, or you can only allow it to operate on a very specific piece of data. aha! mitigations! limiting blast radius and/or limiting access to model input. neither of which remove the security risk, but they do reduce the possible impact/severity and/or likelihood. it’s all about the threat model ;)
    - hexaga 6mo ago
      Model output that has seen user input is user input. User input can be dealt with securely.
pugchat 6mo ago
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ineedasername 6mo ago
There were no uncomfortable truths there about code agents, save one of the 4 points which was that maybe they sometimes get prompt injected if you let them search for things online and don't pay attention to where they search and the code they write. That's not an uncomfortable truth in the normal sense of "I know you don't want to admit this but..." and more just the thing that, if you didn't know it already 8 months ago, you certainly should by now. The other truths that were not about coding agents: --Skill Atrophy. (Use it or lose it-- another thing we already know) --The economics of serving code agents at scale (Ungrounded in actual numbers, only OpenAI's miscellaneous statements and annecdotes. Actual cost of running code agents: last gen's mid-tier gaming gpu's will get you reasonably close to Claude Sonnet if you put just a little time in to an agent harness, and its getting cheaper and cheaper for better and better. So, at scale, with real sysadmins doing the hard engineering to eek out every last bit of performance-- well, infra needed for serving these isn't the cost center) --Copyright. (This passed on the same bad read of a court ruling half the press has been doing for a few years now. TLDR: The Thaler vs. Perlmutter case, which said nothing about output not being protected by copyright. It denied Thaler's attempt to register *the AI* as the owner of the copyright)
freetime2 6mo ago
The more immediate uncomfortable truth for me is that my company is requiring all developers to use LLMs, and laying off developers who won't make the switch. I'm not sure that "LLM-based AI coding agents have no place now, or ever, in generating production code for any software I build professionally" is a decision that most of us will have a choice in.
- borealis-dev 6mo ago
  You're right about that. It's something I should have addressed in the original article, but I don't really have a solution for those who aren't self-employed. Maybe just use up a bunch of tokens on junk tasks while continuing to secretly write the code yourself? It's super wasteful, but do what you have to do to survive until the bubble pops.
johnfn 6mo ago
The section on "artificially low costs" does not make a lot of sense to me. If anything I feel like the costs are inflated for the frontier models, not "artificially low". Easy proof: GLM-5 costs about 1/10 as much as Opus. I'm not going to tell you it's as good as Opus 4.6 -- it's not -- but it performs comparably to where frontier models were 6 months ago. (It's on par with Sonnet 4.5 on leaderboards, though in practice it's probably closer to Sonnet 4.0.) If I can switch to an open source model today, run it myself, and spend 1/10 as much as Opus, and get to about where frontier models were 6 months ago, fear-mongering about how we'll have to weather "orders-of-magnitude price hikes" and arguing that that one shouldn't even bother to learn how to use AI at all seems disconnected from reality. Who cares about the "shady accounting" OpenAI is doing, or that AI labs are "wildly unprofitable"? I can run GLM 5 right now, forever, for cheap.
- piker 6mo ago
  The post is factoring in training costs, not just inference.
  - johnfn 6mo ago
    But I don't need to pay training costs to use GLM-5?
    - piker 6mo ago
      Sure, but somebody needs to pay for GLM-6 unless you're happy to stop here.
      - InsideOutSanta 6mo ago
        If everybody stopped training models today and Anthropic and OpenAI were deleted from the universe, I'd be happy to just keep using GLM-5 at its current inference cost. The article's author assumes that there will be a point where we will no longer have access to good models at reasonable cost because current models are subsidized, but GLM-5 disproves that.
      - 6mo ago
fredolivier0 6mo ago
i just read this before - why is it 3hrs ago?
- deleted 6mo ago
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instig007 6mo ago
I find the other article that the author refers to in his text, to be more thorough and revealing: https://www.wheresyoured.at/the-ai-industry-is-lying-to-you/ https://www.wheresyoured.at/the-ai-industry-is-lying-to-you/
- borealis-dev 6mo ago
  Speaking as the author of the original article, I wholeheartedly agree! :D
rbalicki 6mo ago
The skill atrophy point strikes me as tenuous at best. Obviously, the plural of anecdote is not data, but I find myself able to work on projects of greater complexity than I would have been able to otherwise. 90% of my time is spent going back and forth on Markdown files, discussing the architecture, trade-offs, etc. I don't think it's necessarily impossible to use all this newfound power to ship more sloppier code. It's clearly possible to use all this newfound power to ship better code too.
- piker 6mo ago
  > I find myself able to work on projects of greater complexity than I would have been able to otherwise Yes. Now turn off the LLM and make an improvement to that code.
  - adamors 6mo ago
    Exactly, this is like watching youtubers code, ie backseat coding. It’s easy to follow along but taking control midway is anything but, especially in a codebase that has been written by an agent and you don’t have any muscle-memory in.
  - rbalicki 6mo ago
    I think what you're implying is that the agent ships unmaintainable slop. Certainly, if I don't pay attention and review the code line by line, it will ship slop. And even sometimes, when I'm certain that it is implemented one way, I'll come back to the code many days later and discover that it went a completely different route than I expected. Very frustrating. But it doesn't have to be that way. You just have to put an effort into shipping fewer, better features as opposed to more features. The projects I'm working on (e.g. agent orchestration, because who isn't nowadays) have a small surface area and high payoff and thus are uniquely well positioned for this. If I couldn't use an LLM, I would still work on this, and it would have roughly the same architecture. But because I'm able to go probably 100x as fast, I'm able to be much more ambitious. Or rather, I'm able to discover that my initial ideas were not on point and pivot, and not have any sense of sunk cost Anyway, to each their own.
  - 6mo ago
tao_oat 6mo ago
I didn't find this very convincing. Especially the argument around artificially low cost -- we know that training the next model is the biggest cost for these companies, and we've already seen inference costs fall drastically (https://epoch.ai/data-insights/llm-inference-price-trends https://epoch.ai/data-insights/llm-inference-price-trends).
- bigyikes 6mo ago
  Yes, Dario has publicly stated that models are already profitable if you exclude R&D for the next model. Even if that’s not true, given that hardware and software efficiency gains can be expected to continue it’s likely that this is the most expensive the current level of intelligence will ever be. The frontier models may increase in price, but only because they’re also more capable. If you hold intelligence constant, price should fall over time.
  - borealis-dev 6mo ago
    You can't trust anything Dario tells you: https://www.wheresyoured.at/the-ai-industry-is-lying-to-you/ https://www.wheresyoured.at/the-ai-industry-is-lying-to-you/
skybrian 6mo ago
These "truths" are more like concerns. Skills do atrophy if you don't practice them, but also, refreshing your memory about some technology you haven't used in a while, or even learning something new, is easier than ever. You can ask the AI questions and try things out yourself very easily. Maybe "just in time" learning isn't good enough, but that's more of a concern based on speculation than a truth. AI is being subsidized, but also, inference costs are dropping due to algorithmic improvements. For example, TurboQuant [1] looks pretty promising and even if it doesn't pan out, there are plenty of other potential advances like that. Competition might result in AI inference being available at good prices even without subsidies. So, again, more of a concern than a "truth." Prompt injection: an unsolved mess, but perhaps curated, trusted datasets will be good enough for many projects, so you don't have to expose your agent to the open Internet? It's a similar problem to the supply-chain vulnerabilities that downloadable open source libraries have. A valid concern, but it seems like we'll improve security and muddle through? Copyright: also an unsolved mess, but kind of similar. Search engines copy the web as part of how they work, but that didn't stop Google from becoming big tech. And sure, Napster was built on copying music and was shut down, but YouTube was also built on widespread copyright violation and it muddled through. It's unclear whether copyright law is load-bearing infrastructure for the software industry or for open source software. [1] https://github.com/tonbistudio/turboquant-pytorch https://github.com/tonbistudio/turboquant-pytorch
jjcm 6mo ago
I disagree with the author's interpretation of many of their points. > Skill Atrophy Very true, but in the same way moving from assembly -> scripting languages degraded the skill of programmers to manage memory. AI is another tool (albeit one that's on a different level than any other transformation we've had); what matters is whether the programmer understands the intent of the code, not the 0's and 1's that it turns into. That intent is still there, we're just writing it at a much higher level than before. > Artificially low cost This heavily depends model to model. IMO OpenAI/Anthropic are likely the outliers here, and I do agree that it's unlikely they'll recoup the training costs for their specific models, but they also legitimized an industry - something that's hard to tangibly price. Many of the models out of China however will almost certainly recoup cost. Qwen 3 had a training price of around $1.6m USD. In the first quarter of this year OpenRouter processed around 5 trillion tokens from it, landing at around 2.5m in revenue (very rough numbers). Assuming a 30% margin, that's already 40% of their training cost in revenue for a quarter from a single platform - their usage in china is almost certainly higher. The reality is training costs are getting cheaper. I agree that currently the top providers are heavily subsidizing costs, but that doesn't mean you can't drive revenue, they just choose not to as having the "best" model right now gives them clout.
- InsideOutSanta 6mo ago
  Yeah, I agree. The first point depends on the second: skill atrophy is only an issue if you expect that coding LLMs will essentially disappear or become much more expensive. But they won't. There are companies offering GLM-5 at a profit, and it's affordable, and a great model. The third point is moot if you run agents in a container and review their code, which you can and should do. The fourth point is real, but the chance of it actually ending up harming people who use LLMs to write code is IMO remote.
tusuegra 6mo ago
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ryguz 6mo ago
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jopsen 6mo ago
Lots of claims on cost, very little data.
- borealis-dev 6mo ago
  You're right. I did a fair amount of hand-waving, and I'm leaning pretty heavily on Ed Zitron's investigative reporting to make the case instead: https://www.wheresyoured.at/oai_docs/ https://www.wheresyoured.at/oai_docs/
0xbadcafebee 6mo ago
> There are four main issues contributing to the blanket ban on AI coding agents in my professional work: skill atrophy, artificially low cost, prompt injections and copyright/licensing. - Skill atrophy is not a realistic concern. Every single technological advancement in human history should have atrophied the skills of people working in those fields, to some kind of detriment. Yet that hasn't happened (in any way any of us would notice). You can still find people skilled in everything that is still produced, and everything still gets produced. If nobody's made it in 100 years and only 1 old man still knows how to make it by hand, sure, that knowledge will die. That isn't true of things still actively produced with advanced technology, like agriculture, textiles, metalworking, woodworking, printing, art, music. If this was a serious concern, we would have freaked out more that COBOL programmers were becoming rare to find to support IRS/bank systems, well before AI existed. AI is not the problem, and rejecting it isn't the solution. If you're concerned your skills will deteriorate, that's not something to fear. You can always get better at it again if you need to. Your brain isn't falling out of your head. - The artificially low cost is not something to worry about. We have already invested so many dollars into LLMs that we could ride the current SOTA models for decades and still get way more value out of it than the money we burned to get here. The only reason trillions are getting spent now is insane business people are being insane. You don't have to spend trillions. There are several very small teams of AI labs who produce very good open weight models with tiny budgets. You can make much more money using even local AI than from not using it. The economics are there. Do the math: cost of an AI dev team for 1 year, cost of 1 cluster of GPUs to train on, is an order of magnitude less than some companies pay for their advertising budget. There absolutely is an AI bubble, and when it pops, so will the US economy (causing a world recession, since the Saudis are also tied up in AI investment), but we will all keep using AI regardless. It just won't be Anthropic or OpenAI we're using. The AI world is already way bigger than that. - Prompt injections are just one security concern, but they are solveable. The trick is to not use LLMs for everything, like most people are today. You don't allow the control plane to be AI-dictated. You segregate tainted data. You practice defense-in-depth. You lean on deterministic, versioned software more than prompts. With these and more practices, you can do a lot of valuable work safely, even with tainted data. - I think copyright is dead. Unless there's a global rejection of AI in general (which I don't think will happen), the world will adapt to this new order, where everything is a mix of everything, and nobody really owns anything, IP wise. There is just no way to reject the immense value of AI trained on the world's content. The world will simply change its laws and conventions to fit around AI.
doug_durham 6mo ago
The copyright example is naive at best. Any AI work that has been meaningfully modified by a human can be copyrighted. So if LLM generated code is used a the starting point and it is then modified in the testing and review phases you are good. Code that has not been touched by humans is very rare.
- dijksterhuis 6mo ago
  > Code that has not been touched by humans is very rare. i mean... it's not the most common, but it's certainly not super rare. https://hn.algolia.com/?q=i+vibecoded https://hn.algolia.com/?q=i+vibecoded 508 results, yes not all of the hits are actual vibe-coded projects. but then this is just the people who are willing to admit it freely as well. so.
- yason 6mo ago
  I don't see how copyright would be any issue. Currently: human output is copyrighted so companies sign a transfer agreement with employees that anything they produce at work belongs to the employer. The employer now owns the copyright eventhough the employee, depending on jurisdiction, still owns the moral rights (which matter not much more than squat). With AI: the company uses AI to produce code that isn't copyrighted. The company can take it like any public domain piece of software and incorporate it into their product. Their product is copyrighted by the company. There are even no moral rights that are personal (no need to mention "This product is based on work by Claude").
norir 6mo ago
> I recognize that it is reminiscent of a few decades ago when old timers complained about the proliferation of high level programming languages and insisted they would lead to a generation of programmers lacking a proper understanding of how the system behaves beneath all that syntactic sugar and automatic garbage collection. They won’t have the foundational skills necessary to design and build quality software. And, for the most part, they turned out to be wrong. What if the old timers were actually right? I tend to think they were.
- gitaarik 6mo ago
  Yeah people in the past were always more right. The people that built the first processors were much more aware of low level stuff than those so called low level programmers with their fancy compilers. And before them, the electrical and mechanical engineers, without them we wouldn't even have these processors. We all ultimately are dependent on them. And like that you can go on and on
- yason 6mo ago
  The things is, they were and they were not. It's absolutely necessary that there's a line of people somewhere who will understand the path from garbage collection to assembly instructions. We can't build upon abstractions only as long as we still run stuff on physical cpus. But it's also unequivocally true that once we have enough long-bearded oldtimers and newtimers who do understand how writing a Python expression somewhere will end up with a register write elsewhere all the others just don't — have to. In old times, all you had was hardware and to program you had to understand hardware. But those who then did program and did understand were the few smart people who had access to hardware. Everyone else was left out. Now we have high-level languages, scripting languages, AI, what else. As long as we can maintain the link to hardware by some people, the rest can build on that.
jp57 6mo ago
Honest question: Is inference actually being sold at a loss? I.e. does inference itself cost more to run than we're being charged, or are the companies operating at a loss because they're spending more on training than the margins they get from inference?
- borealis-dev 6mo ago
  Companies like OpenAI and Anthropic are particularly sneaky about reporting numbers, but there's good reason to believe it is: https://www.wheresyoured.at/oai_docs/ https://www.wheresyoured.at/oai_docs/
  - ajam1507 6mo ago
    Even if an AI company spends more on inference than their revenue, that does not mean that they are selling inference at a loss.
bmd1905 6mo ago
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80 points by borealis-dev 6mo ago

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> The role change has been described by some as becoming a sort of software engineering manager, where one writes little or no code oneself but instead supervises a team of AI coding agents as if they are a team of human junior software engineers.... > In reality, though, the code review load for software engineers will gradually increase as fewer and fewer of them are expected to supervise an ever-growing number of coding agents, and they will inevitably learn to become complacent over time, out of pure necessity for their sanity. I’m a proponent of code review...but even I often consider it a slog to do my due diligence for a large code review (just because I think it’s important doesn’t mean I think it’s fun). If it’s your full-time job to review a swarm of agents’ work, and experience tells you they are good enough 95%+ of the time, you’re not going to pay as much attention as you should and bad changes will get through. Another way to look at this is that AI coding agents take the fun out of a software engineer's job. The machine takes many of the fun parts and leaves the human with more of the unenjoyable parts. Under our new ways of working, you are required to be excited an curious about this evolution three times per day.

jonah 6mo ago
Sounds a lot like "self-driving" cars - "they are good enough 95%+ of the time, you’re not going to pay as much attention as you should". Same thing happens here, you get complacent and miss critical failures or problems. It's also similar in that it "take[s away] many of the fun parts". When I can focus on simply driving it can be engaging and enjoyable - no matter the road or traffic or whatever.
- deleted 6mo ago
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- gruez 6mo ago
  >Sounds a lot like "self-driving" cars - "they are good enough 95%+ of the time, you’re not going to pay as much attention as you should". That might be an issue for supervised "self-driving" cars (eg. tesla FSD), but not really applicable to self driving cars as a whole. Waymo seems to be doing just fine for instance.
  - jonah 6mo ago
    Exactly why I put "self-driving" in quotes. Right now AI assisted coding might generally be at the equivalent of Level-2 or -3 self-driving. Getting to autonomous coding agents will be like the step change that is Level-4 or -5 driving.
  - teatro 6mo ago
    Aren’t Waymo‘s 5% covered by some people in the Philippines? [1] And aren’t they still failing occasionally? [2] [1](https://www.techspot.com/news/111233-waymo-admits-autopilot-often-guys-philippines.html https://www.techspot.com/news/111233-waymo-admits-autopilot-...) [2] (https://www.bloomberg.com/news/articles/2026-01-23/waymo-probed-by-us-safety-agency-over-austin-school-bus-mishaps https://www.bloomberg.com/news/articles/2026-01-23/waymo-pro...)
    - gruez 6mo ago
      >Aren’t Waymo‘s 5% covered by some people in the Philippines? [1] If you look at waymo's prior blog posts, you'll realize that the people in Philippines aren't making split second decisions that was implied by "you’re not going to pay as much attention as you should". https://waymo.com/blog/2024/05/fleet-response https://waymo.com/blog/2024/05/fleet-response >And aren’t they still failing occasionally? [2] What's the failure rate of humans? For both AI drivers and coders, if they make less mistakes than humans, that's still a win.
      - 6mo ago
mark242 6mo ago
> Another way to look at this is that AI coding agents take the fun out of a software engineer's job. Completely backwards - the fun in the job should be to solve problems and come up with solutions. The fun in the job is not knowing where to place a semicolon.
- bluefirebrand 6mo ago
  > the fun in the job should be to solve problems and come up with solutions Who are you to tell anyone what the fun "should" be? Personally, I find writing code very fun, because building the solution is also very gratifying. Besides which, in my experience until you actually write the code you haven't proven that you solved anything. It's so easy to think you have solved a problem when you haven't, but you won't figure that out until you actually try to apply your solution > The fun in the job is not knowing where to place a semicolon. This can be solved with simple linters, no need for LLMs
- chris_money202 6mo ago
  Exactly, the fun part is when the code works and does what you wanted it to do. Writing code itself is not fun. People forget this because they get small wins / dopamine hits along the way, a clever function, an elegant few lines of code, a bug fix, but the majority of that time coding is just a grind until the end where you get the big dopamine hit.
  - HendrikHensen 6mo ago
    Fun is not measured objectively. Different people find different things fun. I enjoy writing code very much (in addition to solving big problems; one can enjoy both).
    - deleted 6mo ago
      [deleted]
- palmotea 6mo ago
  >> Another way to look at this is that AI coding agents take the fun out of a software engineer's job. > Completely backwards - the fun in the job should be to solve problems and come up with solutions. Aren't the coding agents supposed to be doing that too? You give them the problem, they code up a solution, then the engineer is left with the review it to see if it's good enough. > The fun in the job is not knowing where to place a semicolon. That's like such a minor and easy-to-do thing that I'm surprised you're even bringing it up.
dybber 6mo ago
I think it depends on what you find enjoyable. I think people who like the tinkering and the actual act of coding, debugging, etc. will find it less and less fun to be in this area, but people who like to look at the big picture, and solve problems, will see that they will now be better at both getting overview of larger and larger codebases and that technical debt that was never attainable to solve before can now be “outsourced” to LLM’s. I find that fun. I work in a 50 year old IT company, with lots of legacy code and technical debt which we have never been able to address - suddenly it’s within reach to really get us to a better place.
- maplethorpe 6mo ago
  The best way to have a big picture view of a project is to build a mental model of that project in your head. Coding with LLMs removes that ability, and replaces it with an illusion.
  - stavros 6mo ago
    Does the thing work like I want it in the end? Is it fast, reliable, enjoyable to use, maintainable, cheap, efficient, resilient, etc? If so, I don't care if I wrote it by hand or with an LLM. People who think that building something with an LLM somehow dooms the something to mediocrity are engaging in magical thinking. I can simply use as much or as little LLM as will allow me to meet my quality criteria.
    - frizlab 6mo ago
      You forgot maintainable.
      - stavros 6mo ago
        Added a few more adjectives and an "etc", to cover all pedantic bases that don't matter to my argument.
    - maplethorpe 6mo ago
      You listed "maintainable", but how do you know your project is maintainable, if you yourself have no understanding of the code base? Presumably the reason is that the AI has managed to maintain the project so far, so it follows that it will be able to do so in the future. But that's not a given. It's more of a prayer.
raw_anon_1111 6mo ago
The “fun” for me has never been “coding” and on the enterprise dev side that has been a commodity for a decade. If you look at the leveling guidelines for any tech company “codez real gud” will only get you to a mid level ticket taker.
- catgary 6mo ago
  And even then - I still read the code it generates, and if I see a better way of doing something I just step in, write a partial solution, and then sketch out how the complete solution should work.
  - raw_anon_1111 6mo ago
    Unless the solution is going to be more secure, faster, more stable etc, why does it matter? Will the end user care? “Does it make the beer taste better”?
    - dijksterhuis 6mo ago
      in a word, maintainability > maintainability is inversely proportional to the amount of time it takes a developer to make a change and the risk that change will break something https://softwareengineering.stackexchange.com/a/134863 https://softwareengineering.stackexchange.com/a/134863 i could be wrong, but i'm pretty sure that end-users get upset when a change takes a long time or it ends up breaking something for them. just because people are finding that agents or whatever are speeding changes up now doesn't necessarily mean they won't encounter a slow-down later when the codebase becomes an un-maintainable mess. technical debt is always a thing, even with machines doing the work (the agent/machine still has to parse a codebase to make changes).
      - raw_anon_1111 6mo ago
        What makes you think that AI couldn’t make the same changes without breaking it whether you modify the code or not? And you do have automated unit tests don’t you? Right now I have a 5000 line monolithic vibe coded internal website that is at most going to be used by 3 people. It mixes Python, inline CSS and Javasript with the API. I haven’t looked at a line of code. My IAM permissions for the Lambda runtime has limited permissions (meaning the code can’t do anything that the permissions won’t allow it to). I used AWS Cognito for authorization and validated the security of the endpoints and I validated the permissions of the database user. Neither Claude nor Codex have any issues adding pages, features and API endpoints without breaking changes. By definition, coding agents are the worse they will be right now.
hax0ron3 6mo ago
I like programming for fun, but professional software engineering has never been more than very occasionally fun to me. I do it because it pays well. Most companies use some variant of the sprint/"agile" methodology, which means that you as the programmer are similar to an assembly line worker. You don't control the pace, you rarely get the chance to actually finish anything as much as you would like to so you don't get the satisfaction of a finished product, you get little downtime in between tickets or projects to feel a sense of satisfaction at what you have done before you move on to something else. I totally understand why businesses operate this way. It's simple: if you try not to operate this way, you increase the likelihood that your competitors will release more rapidly and take all your market share. It's the basic evolutionary logic. If you can release a decent but buggy product six months faster than the competitor can release a better and less buggy product, there is a good chance that you will drive them out of business. It all makes sense, but it doesn't result in a pleasant experience for me as a programmer. The job is also very sedentary and it puts stress on your eyes and your hands. Of course I'm not going to compare myself to a coal miner, but the fact remains that in its own ways the job is more rough on the body than some people might expect. Meanwhile the intellectual and constantly changing nature of the field means that you can never rest on your laurels - the job requires constant mental engagement. It's hard to do it well while multitasking and thinking about other interesting things at the same time. If jobs in this field did not pay so well, I don't think I'd ever even consider doing this as a career. It just doesn't have nearly enough upsides for me. The only upside I can think of besides the money is that you get to spend time interacting with intelligent people. But one can get that in other places. Coding with the help of AI is a big improvement for me because just automating away the boilerplate and greatly reducing the time that needs to be spent in doing reading, research and experimentation already takes away some of the main time-sinks of the job. And while I appreciate code's deterministic and logical elegance, I've written more than enough code in my life to not miss writing it directly. I also enjoy writing English, after all. It's refreshing for me to spend my time composing precise English instructions for the AI instead of writing in a programming language. My English skills are above average, so this also helps me to leverage one of my talents that had previously been underutilized. Well, my English skills always came in handy when I needed to talk to coworkers or managers, but I meant that they had been underutilized in the act of actually creating software.
linsys 6mo ago
But isn't that what Linux admins said when Cloud and Platform Engineering became a "thing". Puppet, Chef, Ansible they are taking the fun out of system administration, Cloud it's going to take away my job. But what happened is roles changed, SRE, Platform Engineering, dare I say DevOps engineers (whatever those are) all emerged. Software engineering is going through he same transformation with the same knee jerk reactions from both sides of the argument. Either AI run rampant with no guardrails like OpenClaw people burning 1000s in tokens, OpenClaw calling them randomly at 2am, etc.. then on the other side of the argument we have a petition asking the Node.js Technical Steering Committee to ban AI-assisted code from Node.js core. AI is here, it's here to stay, I believe that those of us that will be successful will find the middle ground.
az09mugen 6mo ago
That's exactly the feeling in this quotation : "I want AI to do my laundry and dishes so that I can do art and writing, not for AI to do my art and writing so that I can do my laundry and dishes." -- Joanna Maciejewska, author and videogame enthusiast

adshotco 6mo ago

[flagged]

abletonlive 6mo ago

These opinions about what is going on w/ LLM development always stop short at first order effects and fail to account for second/third order effects. > Skill atrophy If LLMs are so good that you no longer have use for the skill, why do we care about skill atrophy? That skill isn't that useful to most people. There are so many examples of this in human history where it was completely fine and we went on to do higher order things that were more useful. > Even if they set out fully intending to provide the highest level of scrutiny to all generated code, they will gradually lose the ability to tell a good change from a bad one If this (first order effect) is actually a problem then it follows that we will naturally exercise our skill of detecting good change from bad ones (second order effect) and the skill will not atrophy? (third order effect). Seems like your "problem" is self correcting? > At its core, the only defense I’ve got for that response is… this time feels different? Not a particularly rigorous defense, I admit, but I did warn you that this was the squishiest of the issues at hand. Well, if you knew this perhaps it was better just not to lead with it and spend so many paragraphs on it. > Some might argue that, even if that time comes eventually, that’s no reason not to make use of the tools that are available right now. But it should come as no surprise that I disagree. Better not to become overly dependent on AI coding agents in the first place so you’ll be better situated to weather the storm (and maybe even thrive) when it comes. Well this argument didn't turn out to be any less squishy than the first one. It's a self correcting "problem" but you disagree and we should do X because you said so. What was the point of all of this then? > Prompt Injection I also think this will likely always be a problem but you can pretty much point at ANY tool we use in software development. Your viewpoint would be similar to saying we should stop using libraries because there's always going to be a vulnerability when you distribute code that somewhere in the chain a bad actor can inject malicious code even if the library was created by a trusted source in the industry. We have plenty of examples of this happening in real life. So far, still squishy. > Copyright/licensing > I’m not a lawyer! I’m a legal layperson offering my unqualified assessment of some tricky legal questions. Let’s get to it. Sigh, this entire post is slop isn't it? Bad look for whatever "standup for me is". edit: Standup for me is something that is made entirely irrelevant by agentic LLMs, no surprise. The irony is rich. The author wants to be the gatekeeper of skill, quality, and how we develop while they hand feed us slop in the form of their blog posts.

6mo ago

polotics 6mo ago

i was kinda hoping for TFA to finally produce some research outputs or even statistics, but sadly the `uncomfortable truths` are your usual vague talking points.

borealis-dev 6mo ago
Sorry, I realize the headline perhaps implies something with more rigour than it actually delivers. I'm pretty new to writing blog posts! But if you want some actual factual data, you seriously should read Ed Zitron's blog: https://www.wheresyoured.at/ https://www.wheresyoured.at/

throwaway613746 6mo ago

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dijksterhuis 6mo ago

As someone who worked on “prompt injection” before it was called “prompt injection” for an (unfinished) phd… yeah there is only one surefire 100% fix for “prompt injection”: use deterministic solutions ie not machine learning. ---- addendum in case someone tries to make this commonly made point -- i don't use deterministic here to mean "i've pinned the ML model weights after training". i use it in reference to the probability theory stuff of training/models (the boring and complicated maths stuff).

gitaarik 6mo ago
Yeah there's no solution to prompt injection, but prompt injection in itself is not a security risk. It's about what you give the LLM access to. You can give it access to your complete DB and APIs, or you can only allow it to operate on a very specific piece of data.
- dijksterhuis 6mo ago
  > but prompt injection in itself is not a security risk sorry, but this is wrong. the only time “prompt injection” is not a security risk is if no data is ever passed as input to a model, or the model output has no bearing on anything in the world anywhere. in which case, why bother with the model/system in the first place. you can exclude this security risk from your threat model. but what you’re saying there is you don’t believe it’s high likelihood that someone would want to or could run an attack, or that doing so would have sufficiently low impact/severity. possibly because you’ve put mitigations in place … > It's about what you give the LLM access to. You can give it access to your complete DB and APIs, or you can only allow it to operate on a very specific piece of data. aha! mitigations! limiting blast radius and/or limiting access to model input. neither of which remove the security risk, but they do reduce the possible impact/severity and/or likelihood. it’s all about the threat model ;)
  - hexaga 6mo ago
    Model output that has seen user input is user input. User input can be dealt with securely.

pugchat 6mo ago

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ineedasername 6mo ago

There were no uncomfortable truths there about code agents, save one of the 4 points which was that maybe they sometimes get prompt injected if you let them search for things online and don't pay attention to where they search and the code they write. That's not an uncomfortable truth in the normal sense of "I know you don't want to admit this but..." and more just the thing that, if you didn't know it already 8 months ago, you certainly should by now. The other truths that were not about coding agents: --Skill Atrophy. (Use it or lose it-- another thing we already know) --The economics of serving code agents at scale (Ungrounded in actual numbers, only OpenAI's miscellaneous statements and annecdotes. Actual cost of running code agents: last gen's mid-tier gaming gpu's will get you reasonably close to Claude Sonnet if you put just a little time in to an agent harness, and its getting cheaper and cheaper for better and better. So, at scale, with real sysadmins doing the hard engineering to eek out every last bit of performance-- well, infra needed for serving these isn't the cost center) --Copyright. (This passed on the same bad read of a court ruling half the press has been doing for a few years now. TLDR: The Thaler vs. Perlmutter case, which said nothing about output not being protected by copyright. It denied Thaler's attempt to register *the AI* as the owner of the copyright)

freetime2 6mo ago

The more immediate uncomfortable truth for me is that my company is requiring all developers to use LLMs, and laying off developers who won't make the switch. I'm not sure that "LLM-based AI coding agents have no place now, or ever, in generating production code for any software I build professionally" is a decision that most of us will have a choice in.

borealis-dev 6mo ago
You're right about that. It's something I should have addressed in the original article, but I don't really have a solution for those who aren't self-employed. Maybe just use up a bunch of tokens on junk tasks while continuing to secretly write the code yourself? It's super wasteful, but do what you have to do to survive until the bubble pops.

johnfn 6mo ago

The section on "artificially low costs" does not make a lot of sense to me. If anything I feel like the costs are inflated for the frontier models, not "artificially low". Easy proof: GLM-5 costs about 1/10 as much as Opus. I'm not going to tell you it's as good as Opus 4.6 -- it's not -- but it performs comparably to where frontier models were 6 months ago. (It's on par with Sonnet 4.5 on leaderboards, though in practice it's probably closer to Sonnet 4.0.) If I can switch to an open source model today, run it myself, and spend 1/10 as much as Opus, and get to about where frontier models were 6 months ago, fear-mongering about how we'll have to weather "orders-of-magnitude price hikes" and arguing that that one shouldn't even bother to learn how to use AI at all seems disconnected from reality. Who cares about the "shady accounting" OpenAI is doing, or that AI labs are "wildly unprofitable"? I can run GLM 5 right now, forever, for cheap.

piker 6mo ago
The post is factoring in training costs, not just inference.
- johnfn 6mo ago
  But I don't need to pay training costs to use GLM-5?
  - piker 6mo ago
    Sure, but somebody needs to pay for GLM-6 unless you're happy to stop here.
    - InsideOutSanta 6mo ago
      If everybody stopped training models today and Anthropic and OpenAI were deleted from the universe, I'd be happy to just keep using GLM-5 at its current inference cost. The article's author assumes that there will be a point where we will no longer have access to good models at reasonable cost because current models are subsidized, but GLM-5 disproves that.
    - 6mo ago

fredolivier0 6mo ago

i just read this before - why is it 3hrs ago?

deleted 6mo ago
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instig007 6mo ago

I find the other article that the author refers to in his text, to be more thorough and revealing: https://www.wheresyoured.at/the-ai-industry-is-lying-to-you/ https://www.wheresyoured.at/the-ai-industry-is-lying-to-you/

borealis-dev 6mo ago
Speaking as the author of the original article, I wholeheartedly agree! :D

rbalicki 6mo ago

The skill atrophy point strikes me as tenuous at best. Obviously, the plural of anecdote is not data, but I find myself able to work on projects of greater complexity than I would have been able to otherwise. 90% of my time is spent going back and forth on Markdown files, discussing the architecture, trade-offs, etc. I don't think it's necessarily impossible to use all this newfound power to ship more sloppier code. It's clearly possible to use all this newfound power to ship better code too.

piker 6mo ago
> I find myself able to work on projects of greater complexity than I would have been able to otherwise Yes. Now turn off the LLM and make an improvement to that code.
- adamors 6mo ago
  Exactly, this is like watching youtubers code, ie backseat coding. It’s easy to follow along but taking control midway is anything but, especially in a codebase that has been written by an agent and you don’t have any muscle-memory in.
- rbalicki 6mo ago
  I think what you're implying is that the agent ships unmaintainable slop. Certainly, if I don't pay attention and review the code line by line, it will ship slop. And even sometimes, when I'm certain that it is implemented one way, I'll come back to the code many days later and discover that it went a completely different route than I expected. Very frustrating. But it doesn't have to be that way. You just have to put an effort into shipping fewer, better features as opposed to more features. The projects I'm working on (e.g. agent orchestration, because who isn't nowadays) have a small surface area and high payoff and thus are uniquely well positioned for this. If I couldn't use an LLM, I would still work on this, and it would have roughly the same architecture. But because I'm able to go probably 100x as fast, I'm able to be much more ambitious. Or rather, I'm able to discover that my initial ideas were not on point and pivot, and not have any sense of sunk cost Anyway, to each their own.
- 6mo ago

tao_oat 6mo ago

I didn't find this very convincing. Especially the argument around artificially low cost -- we know that training the next model is the biggest cost for these companies, and we've already seen inference costs fall drastically (https://epoch.ai/data-insights/llm-inference-price-trends https://epoch.ai/data-insights/llm-inference-price-trends).

bigyikes 6mo ago
Yes, Dario has publicly stated that models are already profitable if you exclude R&D for the next model. Even if that’s not true, given that hardware and software efficiency gains can be expected to continue it’s likely that this is the most expensive the current level of intelligence will ever be. The frontier models may increase in price, but only because they’re also more capable. If you hold intelligence constant, price should fall over time.
- borealis-dev 6mo ago
  You can't trust anything Dario tells you: https://www.wheresyoured.at/the-ai-industry-is-lying-to-you/ https://www.wheresyoured.at/the-ai-industry-is-lying-to-you/

skybrian 6mo ago

These "truths" are more like concerns. Skills do atrophy if you don't practice them, but also, refreshing your memory about some technology you haven't used in a while, or even learning something new, is easier than ever. You can ask the AI questions and try things out yourself very easily. Maybe "just in time" learning isn't good enough, but that's more of a concern based on speculation than a truth. AI is being subsidized, but also, inference costs are dropping due to algorithmic improvements. For example, TurboQuant [1] looks pretty promising and even if it doesn't pan out, there are plenty of other potential advances like that. Competition might result in AI inference being available at good prices even without subsidies. So, again, more of a concern than a "truth." Prompt injection: an unsolved mess, but perhaps curated, trusted datasets will be good enough for many projects, so you don't have to expose your agent to the open Internet? It's a similar problem to the supply-chain vulnerabilities that downloadable open source libraries have. A valid concern, but it seems like we'll improve security and muddle through? Copyright: also an unsolved mess, but kind of similar. Search engines copy the web as part of how they work, but that didn't stop Google from becoming big tech. And sure, Napster was built on copying music and was shut down, but YouTube was also built on widespread copyright violation and it muddled through. It's unclear whether copyright law is load-bearing infrastructure for the software industry or for open source software. [1] https://github.com/tonbistudio/turboquant-pytorch https://github.com/tonbistudio/turboquant-pytorch

jjcm 6mo ago

I disagree with the author's interpretation of many of their points. > Skill Atrophy Very true, but in the same way moving from assembly -> scripting languages degraded the skill of programmers to manage memory. AI is another tool (albeit one that's on a different level than any other transformation we've had); what matters is whether the programmer understands the intent of the code, not the 0's and 1's that it turns into. That intent is still there, we're just writing it at a much higher level than before. > Artificially low cost This heavily depends model to model. IMO OpenAI/Anthropic are likely the outliers here, and I do agree that it's unlikely they'll recoup the training costs for their specific models, but they also legitimized an industry - something that's hard to tangibly price. Many of the models out of China however will almost certainly recoup cost. Qwen 3 had a training price of around $1.6m USD. In the first quarter of this year OpenRouter processed around 5 trillion tokens from it, landing at around 2.5m in revenue (very rough numbers). Assuming a 30% margin, that's already 40% of their training cost in revenue for a quarter from a single platform - their usage in china is almost certainly higher. The reality is training costs are getting cheaper. I agree that currently the top providers are heavily subsidizing costs, but that doesn't mean you can't drive revenue, they just choose not to as having the "best" model right now gives them clout.

InsideOutSanta 6mo ago
Yeah, I agree. The first point depends on the second: skill atrophy is only an issue if you expect that coding LLMs will essentially disappear or become much more expensive. But they won't. There are companies offering GLM-5 at a profit, and it's affordable, and a great model. The third point is moot if you run agents in a container and review their code, which you can and should do. The fourth point is real, but the chance of it actually ending up harming people who use LLMs to write code is IMO remote.

tusuegra 6mo ago

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ryguz 6mo ago

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jopsen 6mo ago

Lots of claims on cost, very little data.

borealis-dev 6mo ago
You're right. I did a fair amount of hand-waving, and I'm leaning pretty heavily on Ed Zitron's investigative reporting to make the case instead: https://www.wheresyoured.at/oai_docs/ https://www.wheresyoured.at/oai_docs/

0xbadcafebee 6mo ago

> There are four main issues contributing to the blanket ban on AI coding agents in my professional work: skill atrophy, artificially low cost, prompt injections and copyright/licensing. - Skill atrophy is not a realistic concern. Every single technological advancement in human history should have atrophied the skills of people working in those fields, to some kind of detriment. Yet that hasn't happened (in any way any of us would notice). You can still find people skilled in everything that is still produced, and everything still gets produced. If nobody's made it in 100 years and only 1 old man still knows how to make it by hand, sure, that knowledge will die. That isn't true of things still actively produced with advanced technology, like agriculture, textiles, metalworking, woodworking, printing, art, music. If this was a serious concern, we would have freaked out more that COBOL programmers were becoming rare to find to support IRS/bank systems, well before AI existed. AI is not the problem, and rejecting it isn't the solution. If you're concerned your skills will deteriorate, that's not something to fear. You can always get better at it again if you need to. Your brain isn't falling out of your head. - The artificially low cost is not something to worry about. We have already invested so many dollars into LLMs that we could ride the current SOTA models for decades and still get way more value out of it than the money we burned to get here. The only reason trillions are getting spent now is insane business people are being insane. You don't have to spend trillions. There are several very small teams of AI labs who produce very good open weight models with tiny budgets. You can make much more money using even local AI than from not using it. The economics are there. Do the math: cost of an AI dev team for 1 year, cost of 1 cluster of GPUs to train on, is an order of magnitude less than some companies pay for their advertising budget. There absolutely is an AI bubble, and when it pops, so will the US economy (causing a world recession, since the Saudis are also tied up in AI investment), but we will all keep using AI regardless. It just won't be Anthropic or OpenAI we're using. The AI world is already way bigger than that. - Prompt injections are just one security concern, but they are solveable. The trick is to not use LLMs for everything, like most people are today. You don't allow the control plane to be AI-dictated. You segregate tainted data. You practice defense-in-depth. You lean on deterministic, versioned software more than prompts. With these and more practices, you can do a lot of valuable work safely, even with tainted data. - I think copyright is dead. Unless there's a global rejection of AI in general (which I don't think will happen), the world will adapt to this new order, where everything is a mix of everything, and nobody really owns anything, IP wise. There is just no way to reject the immense value of AI trained on the world's content. The world will simply change its laws and conventions to fit around AI.

doug_durham 6mo ago

The copyright example is naive at best. Any AI work that has been meaningfully modified by a human can be copyrighted. So if LLM generated code is used a the starting point and it is then modified in the testing and review phases you are good. Code that has not been touched by humans is very rare.

dijksterhuis 6mo ago
> Code that has not been touched by humans is very rare. i mean... it's not the most common, but it's certainly not super rare. https://hn.algolia.com/?q=i+vibecoded https://hn.algolia.com/?q=i+vibecoded 508 results, yes not all of the hits are actual vibe-coded projects. but then this is just the people who are willing to admit it freely as well. so.
yason 6mo ago
I don't see how copyright would be any issue. Currently: human output is copyrighted so companies sign a transfer agreement with employees that anything they produce at work belongs to the employer. The employer now owns the copyright eventhough the employee, depending on jurisdiction, still owns the moral rights (which matter not much more than squat). With AI: the company uses AI to produce code that isn't copyrighted. The company can take it like any public domain piece of software and incorporate it into their product. Their product is copyrighted by the company. There are even no moral rights that are personal (no need to mention "This product is based on work by Claude").

norir 6mo ago

> I recognize that it is reminiscent of a few decades ago when old timers complained about the proliferation of high level programming languages and insisted they would lead to a generation of programmers lacking a proper understanding of how the system behaves beneath all that syntactic sugar and automatic garbage collection. They won’t have the foundational skills necessary to design and build quality software. And, for the most part, they turned out to be wrong. What if the old timers were actually right? I tend to think they were.

gitaarik 6mo ago
Yeah people in the past were always more right. The people that built the first processors were much more aware of low level stuff than those so called low level programmers with their fancy compilers. And before them, the electrical and mechanical engineers, without them we wouldn't even have these processors. We all ultimately are dependent on them. And like that you can go on and on
yason 6mo ago
The things is, they were and they were not. It's absolutely necessary that there's a line of people somewhere who will understand the path from garbage collection to assembly instructions. We can't build upon abstractions only as long as we still run stuff on physical cpus. But it's also unequivocally true that once we have enough long-bearded oldtimers and newtimers who do understand how writing a Python expression somewhere will end up with a register write elsewhere all the others just don't — have to. In old times, all you had was hardware and to program you had to understand hardware. But those who then did program and did understand were the few smart people who had access to hardware. Everyone else was left out. Now we have high-level languages, scripting languages, AI, what else. As long as we can maintain the link to hardware by some people, the rest can build on that.

jp57 6mo ago

Honest question: Is inference actually being sold at a loss? I.e. does inference itself cost more to run than we're being charged, or are the companies operating at a loss because they're spending more on training than the margins they get from inference?

borealis-dev 6mo ago
Companies like OpenAI and Anthropic are particularly sneaky about reporting numbers, but there's good reason to believe it is: https://www.wheresyoured.at/oai_docs/ https://www.wheresyoured.at/oai_docs/
- ajam1507 6mo ago
  Even if an AI company spends more on inference than their revenue, that does not mean that they are selling inference at a loss.

bmd1905 6mo ago

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