There's an undertone of self-soothing "AI will leverage me, not replace me", which I don't agree with especially in the long run, at least in software.
In the end it will be the users sculpting formal systems like playdoh.<p>In the medium run, "AI is not a co-worker" is exactly right.
The idea of a co-worker will go away.
Human collaboration on software is fundamentally inefficient.
We pay huge communication/synchronization costs to eek out mild speed ups on projects by adding teams of people.
Software is going to become an individual sport, not a team sport, quickly.
The benefits we get from checking in with other humans, like error correction, and delegation can all be done better by AI.
I would rather a single human (for now) architect with good taste and an army of agents than a team of humans.
This assumes every individual is capable of succinctly communicating to the AI what they want. And the AI is capable of maintaining it as underlying platforms and libraries shift.<p>And that there is little value in reusing software initiated by others.
> This assumes every individual is capable of succinctly communicating to the AI what they want. And the AI is capable of maintaining it as underlying platforms and libraries shift.<p>I think there are people who want to use software to accomplish a goal, and there are people who are forced to use software. The people who only use software because the world around them has forced it on them, either through work or friends, are probably cognitively excluded from building software.<p>The people who seek out software to solve a problem (I think this is most people) and compare alternatives to see which one matches their mental model will be able to skip all that and just build the software they have in mind using AI.<p>> And that there is little value in reusing software initiated by others.<p>I think engineers greatly over-estimate the value of code reuse. Trying to fit a round peg in a square hole produces more problems than it solves.
A sign of an elite engineer is knowing when to just copy something and change it as needed rather than call into it.
Or to re-implement something because the library that does it is a bad fit.<p>The only time reuse really matters is in network protocols. Communication requires that both sides have a shared understanding.
no but if the old '10x developer' is really 1 in 10 or 1 in 100, they might just do fine while the rest of us, average PHP enjoyers, may go to the wayside
Well, without the self soothing I think what's left is pitchforks.
You can't write "autonomous agents often fail" and then advertise "AI agents that perform complex multi-step tasks autonomously" on the same site.
The exoskeleton framing is comforting but it buries the real shift: taste scales now. Before AI, having great judgment about what to build didn't matter much if you couldn't also hire 10 people to build it. Now one person with strong opinions and good architecture instincts can ship what used to require a team.<p>That's not augmentation, that's a completely different game. The bottleneck moved from "can you write code" to "do you know what's worth building." A lot of senior engineers are going to find out their value was coordination, not insight.
In the latest interview with Claude Code's author: <a href="https://podcasts.apple.com/us/podcast/lennys-podcast-product-career-growth/id1627920305?i=1000750488631" rel="nofollow">https://podcasts.apple.com/us/podcast/lennys-podcast-product...</a>, Boris said that writing code is a solved problem. This brings me to a hypothetical question: what if engineers stop contributing to open source, in which case would AI still be powerful enough to learn the knowledge of software development in the future? Or is the field of computer science plateaued to the point that most of what we do is linear combination of well established patterns?
He is likely working on a very clean codebase where all the context is already reachable or indexed. There are probably strong feedback loops via tests. Some areas I contribute to have these characteristics, and the experience is very similar to his. But in areas where they don’t exist, writing code isn’t a solved problem until you can restructure the codebase to be more friendly to agents.<p>Even with full context, writing CSS in a project where vanilla CSS is scattered around and wasn’t well thought out originally is challenging. Coding agents struggle there too, just not as much as humans, even with feedback loops through browser automation.
It's funny that "restructure the codebase to be more friendly to agents" aligns really well with what we have "supposed" to have been doing already, but many teams slack on: quality tests that are easy to run, and great documentation. Context and verifiability.<p>The easier your codebase is to hack on for a human, the easier it is for an LLM generally.
I had this epiphany a few weeks ago, I'm glad to see others agreeing. Eventually most models will handle large enough context windows where this will sadly not matter as much, but it would be nice for the industry to still do everything to make better looking code that humans can see and appreciate.
Truth. I've had much easier time grappling with code bases I keep clean and compartmentalized with AI, over-stuffing context is one of the main killers of its quality.
<i>> Boris said that writing code is a solved problem</i><p>That's just so dumb to say. I don't think we can trust anything that comes out of the mouths of the authors of these tools. They are conflicted. Conflict of interest, in society today, is such a huge problem.
There are bloggers that can't even acknowledge that they're only invited out to big tech events because they'll glaze them up to high heavens.<p>Reminds me of that famous exchange, by noted friend of Jeffrey Epstein, Noam Chomsky: "I’m not saying you’re self-censoring. I’m sure you believe everything you say. But what I’m saying is if you believed something different you wouldn’t be sitting where you’re sitting."
Its all basically: Sensationalist take to shock you and get attention
There's so many timeless books on how to write software, design patterns, lessons learned from production issues. I don't think AI will stop being used for open source, in fact, with the number of increasing projects adjusting their contributor policies to account for AI I would argue that what we'll see is always people who love to hand craft their own code, and people who use AI to build their own open source tooling and solutions. We will also see an explosion is needing specs for things. If you give a model a well defined spec, it will follow it. I get better results the more specific I get about how I want things built and which libraries I want used.
Or does the field <i>become</i> plateaued because engineers treat "writing code" as a "solved problem?"<p>We could argue that writing poetry is a solved problem in much the same way, and while I don't think we especially need 50,000 people writing poems at Google, we do still need poets.
> we especially need 50,000 people writing poems at Google, we do still need poets.<p>I'd assume that an implied concern of most engineers is how many software engineers the world will need in the future. If it's the situation like the world needing poets, then the field is only for the lucky few. Most people would be out of job.
"Writing code is a solved problem"
disagree.<p>Yes, there are common parts to everything we do, at the same time - I've been doing this for 25 years and most of the projects have some new part to them.
I think you mean software engineering, not computer science. And no, I don’t think there is reason for software engineering (and certainly not for computer science) to be plateauing. Unless we let it plateau, which I don’t think we will. Also, writing code isn’t a solved problem, whatever that’s supposed to mean. Furthermore, since the patterns we use often aren’t orthogonal, it’s certainly not a <i>linear</i> combination.
I assume that new business scenarios will drive new workflows, which requires new work of software engineering. In the meantime, I assume that computer science will drive paradigm shift, which will drive truly different software engineering practice. If we don't have advances in algorithms, systems, and etc, I'd assume that people can slowly abstract away all the hard parts, enabling AI to do most of our jobs.
I saw Boris give a live demo today. He had a swarm of Claude agents one shot the most upvoted open issue on Excalidraw while he explained Claude code for about 20 minutes.<p>No lines of code written by him at all. The agent used Claude for chrome to test the fix in front of us all and it worked. I think he may be right or close to it.
>writing code is a solved problem<p>sure is news for the models tripping on my thousands of LOC jquery legacy app...
> is the field of computer science plateaued to the point that most of what we do is linear combination of well established patterns?<p>Computer science is different from writing business software to solve business problems. I think Boris was talking about the second and not the first. And I personally think he is mostly correct. At least for my organization. It is very rare for us to write any code by hand anymore. Once you have a solid testing harness and a peer review system run by multiple and different LLMs, you are in pretty good shape for agentic software development. Not everybody's got these bits figured out. They stumble around and them blame the tools for their failures.
My prediction: soon (e.g. a few years) the agents will be the one doing the exploration and building better ways to write code, build frameworks,... replacing open source. That being said software engineers will still be in the loop. But there will be far less of them.<p>Just to add: this is only the prediction of someone who has a decent amount of information, not an expert or insider
If code was solved explain why Claude code is such a mess?
I don’t believe people who have dedicated their lives to open source will simply want to stop working on it, no matter how much is or is not written by AI. I also have to agree, I find myself more and more lately laughing about just how much resources we waste creating exactly the same things over and over in software. I don’t mean generally, like languages, I mean specifically. How many trillions of times has a form with username and password fields been designed, developed, had meetings over, tested, debugged, transmitted, processed, only to ultimately be re-written months later?<p>I wonder what all we might build instead, if all that time could be saved.
> I don’t believe people who have dedicated their lives to open source will simply want to stop working on it, no matter how much is or is not written by AI.<p>Yeah, hence my question can only be hypothetical.<p>> I wonder what all we might build instead, if all that time could be saved<p>If we subscribe to Economics' broken-window theory, then the investment into such repetitive work is not investment but waste. Once we stop such investment, we will have a lot more resources to work on something else, bring out a new chapter of the tech revolution. Or so I hope.
> If we subscribe to Economics' broken-window theory, then the investment into such repetitive work is not investment but waste. Once we stop such investment, we will have a lot more resources to work on something else, bring out a new chapter of the tech revolution. Or so I hope.<p>I'm not sure I agree with the application of the broken-window theory here. That's a metaphor intended to counter arguments in favor of make-work projects for economic stimulus: the idea here is that breaking a window always has a net negative on the economy, since even though it creates demand for a replacement window, the resources that are necessary to replace a window that already existed are just being allocated to restore the status quo ante, but the opportunity cost of that is everything else the same resources might have bee used for instead, if the window hadn't been broken.<p>I think that's quite distinct from manufacturing <i>new</i> windows for new installations, which <i>is</i> net positive production, and where newer use cases for windows create opportunities for producers to iterate on new window designs, and incrementally refine and improve the product, which wouldn't happen if you were simply producing replacements for pre-existing windows.<p>Even in this example, lots of people writing lots of different variations of login pages has produced incremental improvements -- in fact, as an industry, we haven't been writing the same exact login page over and over again, but have been gradually refining them in ways that have evolved their appearance, performance, security, UI intuitiveness, and other variables considerably over time. Relying on AI to design, not just implement, login pages will likely be the thing that causes this process to halt, and perpetuate the status quo indefinitely.
Even if you like them, I don't think there's any reason to believe what people from these companies say. They have every reason to exaggerate or outright lie, and the hype cycle moves so quickly that there are zero consequences for doing so.
Even as the field evolves, the phoning home telemetry of closed models creates a centralized intelligence monopoly. If open source atrophies, we lose the public square of architectural and design reasoning, the decision graph that is often just as important as the code. The labs won't just pick up new patterns; they will define them, effectively becoming the high priests of a new closed-loop ecosystem.<p>However, the risk isn't just a loss of "truth," but model collapse. Without the divergent, creative, and often weird contributions of open-source humans, AI risks stagnating into a linear combination of its own previous outputs. In the long run, killing the commons doesn't just make the labs powerful. It might make the technology itself hit a ceiling because it's no longer being fed novel human problem-solving at scale.<p>Humans will likely continue to drive consensus building around standards. The governance and reliability benefits of open source should grow in value in an AI-codes-it-first world.
> It might make the technology itself hit a ceiling because it's no longer being fed novel human problem-solving at scale.<p>My read of the recent discussion is that people assume that the work of far fewer number of elites will define the patterns for the future. For instance, implementation of low-level networking code can be the combination of patterns of zeromq. The underlying assumption is that most people don't know how to write high-performance concurrent code anyway, so why not just ask them to command the AI instead.
That is the same team that has an app that used React for TUI, that uses gigabytes to have a scrollback buffer, and that had text scrolling so slow you could get a coffee in between.<p>And that then had the gall to claim writing a TUI is as hard as a video game. (It clearly must be harder, given that most dev consoles or text interfaces in video games consistently use less than ~5% CPU, which at that point was completely out of reach for CC)<p>He works for a company that crowed about an AI-generated C compiler that was so overfitted, it couldn't compile "hello world"<p>So if he tells me that "software engineering is solved", I take that with rather large grains of salt. It is <i>far</i> from solved. I say that as somebody who's extremely positive on AI usefulness. I see massive acceleration for the things I do with AI. But I also know where I need to override/steer/step in.<p>The constant hypefest is just vomit inducing.
The amount of "It's not X it's Y" type commentary suggests to me that A) nobody knows and B) there is solid chance this ends up being either all true or all false<p>Or put differently we've managed to hype this to the moon but somehow complete failure (see studies about zero impact on productivity) seem plausible. And similarly kills all jobs seems plausible.<p>That's an insane amount of conflicting opinions being help in the air at same time
This reminds me of the early days of the Internet. Lots of hype around something that was clearly globally transformation, but most people weren't benefiting hugely from it in the first few years.<p>It might have replaced sending a letter with an email. But now people get their groceries from it, hail rides, an even track their dogs or luggage with it.<p>Too many companies have been to focused on acting like AI 'features' have made their products better, when most of them haven't yet. I'm looking at Microsoft and Office especially. But tools like Claude Code, Codex CLI, and Github Copilot CLI have shown that LLMs can do incredible things in the right applications.
You appear to have said a lot. Without saying anything.
> We're thinking about AI wrong.<p>And this write up is not an exception.<p>Why even bother thinking about AI, when Anthropic and OpenAI CEOs openly tell us what they want (quote from recent Dwarkesh interview) - "Then further down the spectrum, there’s 90% less demand for SWEs, which I think will happen but this is a spectrum."<p>So save thinking and listen to intent - replace 90% of SWEs in near future (6-12 months according to Amodei).
I don't think anyone serious believes this. Replacing developers with a less costly alternative is obviously a very market bullish dream, it has existed since as long as I've worked in the field. First it was supposed to be UML generated code by "architects", then it was supposed to be developers from developing countries, then no-code frameworks, etc.<p>AI will be a tool, no more no less. Most likely a good one, but there will still need to be people driving it, guiding it, fixing for it, etc.<p>All these discourses from CEO are just that, stock market pumping, because tech is the most profitable sector, and software engineers are costly, so having investors dream about scale + less costs is good for the stock price.
Ah, don't take me wrong - I don't believe it's possible for LLMs to replace 90% or any number of SWEs with existing technology.<p>All I'm saying is - why to think what AI is (exoskeleton, co-worker, new life form), when its owners intent is to create SWE replacement?<p>If your neighbor is building a nuclear reactor in his shed from a pile of smoke detectors, you don't say "think about this as a science experiment" because it's impossible, just call police/NRC because of intent and actions.
> If your neighbor is building a nuclear reactor in his shed from a pile of smoke detectors, you don't say "think about this as a science experiment" because it's impossible, just call police/NRC because of intent and actions.<p>Only if you're a snitch loser
The funny thing is I think these things would work much better if they WEREN'T so insistent on the agentic thing. Like, I find in-IDE AI tools a lot more precise and I usually move just as fast as a TUI with a lot less rework. But Claude is CONSTANTLY pushing me to try to "one shot" a big feature while asking me for as little context as possible. I'd much rather it work with me as opposed to just wandering off and writing a thousand lines. It's obviously designed for anthropic's best interests rather than mine.
Not without some major breakthrough. What's hilarious is that all these developers building the tools are going to be the first to be without jobs. Their kids will be ecstatic: "Tell me again, dad, so, you had this awesome and well paying easy job and you wrecked it? Shut up kid, and tuck in that flap, there is too much wind in our cardboard box."
Couldn't agree more, isn't that the bizarre thing? "We have this great intellectually challenging job where we as workers have leverage. How can we completely ruin that while also screwing up every other white collar profession"
I have a feeling they internally say "not me, I won't be replaced" and just keep moving...
"Well son, we made a lot of shareholder value."
The exoskeleton framing resonates, especially for repetitive data work. Parts where AI consistently delivers: pattern recognition, format normalization, first-draft generation. Parts where human judgment is still irreplaceable: knowing when the data is wrong, deciding what 'correct' even means in context, and knowing when to stop iterating.<p>The exoskeleton doesn't replace instinct. It just removes friction from execution so more cycles go toward the judgment calls that actually matter.
It's the new underpaid employee that you're training to replace you.<p>People need to understand that we have the technology to train models to do anything that you can do on a computer, only thing that's missing is the data.<p>If you can record a human doing anything on a computer, we'll soon have a way to automate it
Sure, but do you want abundance of software, or scarcity?<p>The price of having "star trek computers" is that people who work with computers have to adapt to the changes. Seems worth it?
Abundance of services before abundance of physical resources seems like the worst of both worlds.
My only objection here is that technology wont save us unless we also have a voice in how it is used. I don't think personal adaptation is enough for that. We need to adapt our ways to engage with power.
Both abundance and scarcity can be bad. If you can't imagine a world where abundance of software is a very bad thing, I'd suggest you have a limited imagination?
It's a strange economical morbid dependency. AI companies promises incredible things but AI agents cannot produce it themselves, they need to eat you slowly first.
> the new underpaid employee that you're training to replace you.<p>and who is also compiling a detailed log of your every action (and inaction) into a searchable data store -- which will certainly never, NEVER be used against you
Exactly. If there's any opportunity around AI it goes to those who have big troves of custom data (Google Workspace, Office 365, Adobe, Salesforce, etc.) or consultants adding data capture/surveillance of workers (especially high paid ones like engineers, doctors, lawyers).
Data clearly isn't the only issue. LLMs have been trained on orders of magnitude more data than any person has ever seen.
How much practice have you got on software development with agentic assistance. Which rough edges, surprising failure modes, unexpected strengths and weaknesses, have you already identified?<p>How much do you wish someone else had done your favorite SOTA LLM's RLHF?
I think we’re past the “if only we had more training data” myth now. There are pretty obviously far more fundamental issues with LLMs than that.
i've been working in this field for a very long time, i promise you, if you can collect a dataset of a task you can train a model to repeat it.<p>the models do an amazing job interpolating and i actually think the lack of extrapolation is a feature that will allow us to have amazing tools and not as much risk of uncontrollable "AGI".<p>look at seedance 2.0, if a transformer can fit that, it can fit anything with enough data
LLMs have a large quantity of chess data and still can't play for shit.
Not anymore. This benchmark is for LLM chess ability: <a href="https://github.com/lightnesscaster/Chess-LLM-Benchmark?tab=readme-ov-file" rel="nofollow">https://github.com/lightnesscaster/Chess-LLM-Benchmark?tab=r...</a>. LLMs are graded according to FIDE rules so e.g. two illegal moves in a game leads to an immediate loss.<p>This benchmark doesn't have the latest models from the last two months, but Gemini 3 (with no tools) is already at 1750 - 1800 FIDE, which is approximately probably around 1900 - 2000 USCF (about USCF expert level). This is enough to beat almost everyone at your local chess club.
They have literally every chess game in existence to train on, and they can't do better than 1800?
Yeah, but 1800 FIDE players don't make illegal moves, and Gemini does.
1800 FIDE players do make illegal moves. I believe they make about one to two orders of magnitude less illegal moves than Gemini 3 does here. IIRC the usual statistic for expert chess play is about 0.02% of expert chess games have an illegal move (I can look that up later if there's interest to be sure), but that is only the ones that made it into the final game notation (and weren't e.g. corrected at the board by an opponent or arbiter). So that should be a lower bound (hence why it could be up to one order lower, although I suspect two orders is still probably closer to the truth).<p>Whether or not we'll see LLMs continue to get a lower error rate to make up for those orders of magnitude remains to be seen (I could see it go either way in the next two years based on the current rate of progress).
That benchmark methodology isn't great, but regardless, LLMs can be trained to play Chess with a 99.8% legal move rate.
Wait, I may be missing something here. These benchmarks are gathered by having models play each other, and the second illegal move forfeits the game. This seems like a flawed method as the models who are more prone to illegal moves are going to bump the ratings of the models who are less likely.<p>Additionally, how do we know the model isn’t benchmaxxed to eliminate illegal moves.<p>For example, here is the list of games by Gemini-3-pro-preview. In 44 games it preformed 3 illegal moves (if I counted correctly) but won 5 because opponent forfeits due to illegal moves.<p><a href="https://chessbenchllm.onrender.com/games?page=5&model=gemini-3-pro-preview" rel="nofollow">https://chessbenchllm.onrender.com/games?page=5&model=gemini...</a><p>I suspect the ratings here may be significantly inflated due to a flaw in the methodology.<p>EDIT: I want to suggest a better methodology here (I am not gonna do it; I really really really don’t care about this technology). Have the LLMs play rated engines and rated humans, the first illegal move forfeits the game (same rules apply to humans).
The LLMs do play rated engines (maia and eubos). They provide the baselines. Gemini e.g. consistently beats the different maia versions.<p>The rest is taken care of by elo. That is they then play each other as well, but it is not really possible for Gemini to have a higher elo than maia with such a small sample size (and such weak other LLMs).<p>Elo doesn't let you inflate your score by playing low ranked opponents if there are known baselines (rated engines) because the rated engines will promptly crush your elo.<p>You could add humans into the mix, the benchmark just gets expensive.
That’s a devastating benchmark design flaw. Sick of these bullshit benchmarks designed solely to hype AI. AI boosters turn around and use them as ammo, despite not understanding them.
> That’s a devastating benchmark design flaw<p>I think parent simply missed until their later reply that the benchmark includes rated engines.
Relax. Anyone who's genuinely interested in the question will see with a few searches that LLMs can play chess fine, although the post-trained models mostly seem to be regressed. Problem is people are more interested in validating their own assumptions than anything else.<p><a href="https://arxiv.org/abs/2403.15498" rel="nofollow">https://arxiv.org/abs/2403.15498</a><p><a href="https://arxiv.org/abs/2501.17186" rel="nofollow">https://arxiv.org/abs/2501.17186</a><p><a href="https://github.com/adamkarvonen/chess_gpt_eval" rel="nofollow">https://github.com/adamkarvonen/chess_gpt_eval</a>
I like this game between grok-4.1-fast and maia-1100 (engine, not LLM).<p><a href="https://chessbenchllm.onrender.com/game/37d0d260-d63b-4e41-9bba-6b25b922493f" rel="nofollow">https://chessbenchllm.onrender.com/game/37d0d260-d63b-4e41-9...</a><p>This exact game has been played 60 thousand times on lichess. The peace sacrifice Grok performed on move 6 has been played 5 million times on lichess. Every single move Grok made is also the top played move on lichess.<p>This reminds me of Stefan Zweig’s <i>The Royal Game</i> where the protagonist survived Nazi torture by memorizing every game in a chess book his torturers dropped (excellent book btw. and I am aware I just committed Godwin’s law here; also aware of the irony here). The protagonist became “good” at chess, simply by memorizing a lot of games.
Why do we care about this? Chess AI have long been solved problems and LLMs are just an overly brute forced approach. They will never become very efficient chess players.<p>The correct solution is to have a conventional chess AI as a tool and use the LLM as a front end for humanized output. A software engineer who proposes just doing it all via raw LLM should be fired.
Hm.. but do they need it.. at this point, we do have custom tools that beat humans. In a sense, all LLM need is a way to connect to that tool ( and the same is true is for counting and many other aspects ).
Yeah, but you know that manually telling the LLM to operate other custom tools is not going to be a long-term solution. And if an LLM could design, create, and operate a separate model, and then return/translate its results to you, that would be huge, but it also seems far away.<p>But I'm ignorant here. Can anyone with a better background of SOTA ML tell me if this is being pursued, and if so, how far away it is? (And if not, what are the arguments against it, or what other approaches might deliver similar capacities?)
This has been happening for the past year on verifiable problems (did the change you made in your codebase work end-to-end, does this mathematical expression validate, did I win this chess match, etc...). The bulk of data, RL environment, and inference spend right now is on coding agents (or broadly speaking, tool use agents that can make their own tools).<p>Recent advances in mathematical/physics research have all been with coding agents making their own "tools" by writing programs: <a href="https://openai.com/index/new-result-theoretical-physics/" rel="nofollow">https://openai.com/index/new-result-theoretical-physics/</a>
Are you saying an LLM can't produce a chess engine that will easily beat you?
Did you already forget about the AlphaZero?
If we find an AI that is truly operating as an independent agent in the economy without a human responsible for it, we should kill it. I wonder if I'll live long enough to see an AI terminator profession emerge. We could call them blade runners.
<p><pre><code> > an AI that is truly operating as an independent agent in the economy without a human responsible for it
</code></pre>
Sounds like the "customer support" in any large company (think Google, for example), to be honest.
It happened not too long ago! <a href="https://news.ycombinator.com/item?id=46990729">https://news.ycombinator.com/item?id=46990729</a>
I like this. This is an accurate state of AI at this very moment for me. The LLM is (just) a tool which is making me "amplified" for coding and certain tasks.<p>I will worry about developers being completely replaced when I see something resembling it. Enough people worry about that (or say it to amp stock prices) -- and they like to tell everyone about this future too. I just don't see it.
Amplified means more work done by fewer people. It doesn’t need to replace a single entire functional human being to do things like kill the demand for labor in dev, which in turn, will kill salaries.
I would disagree. Amplified meens me and you get more s** done.<p>Unless there a limited amount of software we need to produce per year globally to keep everyone happy, then nobody wants more -- and we happen to be at that point right NOW this second.<p>I think not. We can make more (in less time) and people will get more. This is the mental "glass half full" approach I think. Why not take this mental route instead? We don't know the future anyway.
In fact, there isn’t infinite demand for software. Especially not for <i>all kinds</i> of software.<p>And if corporate wealth means people get paid more, why are companies that are making more money than ever laying off so many people? Wouldn’t they just be happy to use them to meet the inexhaustible demand for software?
Jevon's paradox means this is untrue because it means more work not less.
Hm. More of what? Functionality, security, performance?<p>Current software is often buggy because the pressure to ship is just too high. If AI can fix some loose threads within, the overall quality grows.<p>Personally, I would welcome a massive deployment of AI to root out various zero-days from widespread libraries.<p>But we may instead get a larger quantity of even more buggy software.
This is incorrect. It’s basic economics - technology that boosts productivity results in higher salaries and more jobs.
That’s not basic economics. Basic economics says that salaries are determined by the demand for labor vs the supply of labor. With more efficiency, each worker does more labor, so you need fewer people to accomplish the same thing. So unless the demand for their product increases around the same rate as productivity increases, companies will employ fewer people. Since the market for products is not infinite, you only need as much labor as you require to meet the demand for your product.<p>Companies that are doing better than ever are laying people off by the shipload, not giving people raises for a job well done.
Well, that depends on whether the technology requires expertise that is rare and/or hard to acquire.<p>I'd say that using AI tools effectively to create software systems is in that class currently, but it isn't necessarily always going to be the case.
The more likely outcome is that fewer devs will be hired as fewer devs will be needed to accomplish the same amount of output.
The old shrinking markets aka lump of labour fallacy. It's a bit like dreaming of that mythical day, when all of the work will be done.
No it's not that.<p>Tell me, when was the last time you visited your shoe cobbler? How about your travel agent? Have you chatted with your phone operator recently?<p>The lump labour fallacy says it's a fallacy that automation reduces the net amount of human labor, importantly, across all industries. It does not say that automation won't eliminate or reduce jobs in specific industries.<p>It's an argument that jobs lost to automation aren't a big deal because there's always work somewhere else but not necessarily in the job that was automated away.
Jobs are replaced when new technology is able to produce an equivalent or better product that meets the demand, cheaper, faster, more reliably, etc. There is no evidence that the current generation of "AI" tools can do that for software.<p>There is a whole lot of marketing propping up the valuations of "AI" companies, a large influx of new users pumping out supremely shoddy software, and a split in a minority of users who either report a boost in productivity or little to no practical benefits from using these tools. The result of all this momentum is arguably net negative for the industry and the world.<p>This is in no way comparable to changes in the footwear, travel, and telecom industries.
When computers came onto the market and could automate a large percentage of office jobs, what happened to the job market for office jobs?
They changed, significantly.<p>We lost the pneumatic tube [1] maintenance crew. Secretarial work nearly went away. A huge number of bookkeepers in the banking industry lost their jobs. The job a typist was eliminated/merged into everyone else's job. The job of a "computer" (someone that does computations) was eliminated.<p>What we ended up with was primarily a bunch of customer service, marketing, and sales workers.<p>There was never a "office worker" job. But there were a lot of jobs under the umbrella of "office work" that were fundamentally changed and, crucially, your experience in those fields didn't necessarily translate over to the new jobs created.<p>[1] <a href="https://www.youtube.com/watch?v=qman4N3Waw4" rel="nofollow">https://www.youtube.com/watch?v=qman4N3Waw4</a>
It’s a tool like a linter. It’s a fancy tool, but calling it anything more than a tool is hype
Petition to make "AI is not X, but Y" articles banned or limited in some way.
I said this in 2015... just not as well!<p>"Automation Should Be Like Iron Man, Not Ultron"
<a href="https://queue.acm.org/detail.cfm?id=2841313" rel="nofollow">https://queue.acm.org/detail.cfm?id=2841313</a>
I agree. I call it my Extended Mind in the spirit of Clark (1).
One thing I realized while working a lot in the last weeks with openClaw that this Agents are becoming an extension of my self. They are tools that quickly became a part of my Being. I outsource a lot of work to them, they do stuff for me, help me and support me and therefore make my (work-)life easier and more enjoyable. But its me in the driver seat.<p>(1) <a href="https://www.alice.id.tue.nl/references/clark-chalmers-1998.pdf" rel="nofollow">https://www.alice.id.tue.nl/references/clark-chalmers-1998.p...</a>
<i>> “The AI handles the scale. The human interprets the meaning.”</i><p>Claude is that you? Why haven’t you called me?
I like this analogy, and in fact in have used it for a totally different reason: why I don't like AI.<p>Imagine someone going to a local gym and using an exosqueleton to do the exercises without effort. Able to lift more? Yes. Run faster? Sure. Exercising and enjoying the gym? ... No, and probably not.<p>I like writing code, even if it's boilerplate. It's fun for me, and I want to keep doing it. Using AI to do that part for me is just...not fun.<p>Someone going to the gym isn't trying to lift more or run faster, but instead improving and enjoying. Not using AI for coding has the same outcome for me.
You can continue to do that for your personal projects. Nobody forces you to like AI. You may not have the choice at your job though, and you can't take Claude Code et al. from me. I've been programming for 30 years, and I still have fun with it, even with AI.
We've all been raised in a world where we got to practice the 'art' of programming, and get paid extraordinarily well to do so, because the output of that art was useful for businesses to make more money.<p>If a programmer with an exoskeleton can produce more output that makes more money for the business, they will continue to be paid well. Those who refuse the exoskeleton because they are in it for the pure art will most likely trend towards earning the types of living that artists and musicians do today. The truly extraordinary will be able to create things that the machines can't and will be in high demand, the other 99% will be pursing an art no one is interested in paying top dollar for.
You’re forgetting that the “art” part of it is writing sound, scalable, performant code that can adapt and stand the test of time. That’s certainly more valuable in the long run than banging out some dogshit spaghetti code that “gets the job done” but will lead to all kinds of issues in the future.
> the “art” part of it is writing sound, scalable, performant code that can adapt and stand the test of time.<p>Sure, and it's possible to use LLM tools to aid in writing such code.
Neither, AI is a tool to guide you in improving your process in any way and/or form.<p>The problem is people using AI to do the heavy processing making them dumber.
Technology itself was already making us dumber, I mean, Tesla drivers not even drive anymore or know how, coz the car does everything.<p>Look how company after company is being either breached or have major issues in production because of the heavy dependency on AI.
In the language of Lynch's Dune, AI is not an exoskeleton, it is a pain amplifier. Get it all wrong more quickly and deeply and irretrievably.
AI is not an exoskeleton, it's a pretzel: It only tastes good if you douse it in lye.
I like the analogy and will ponder it more. But it didn't take long before the article started spruiking Kasava's amazing solution to the problem they just presented.
Humans don’t have an internal notion of “fact” or “truth.” They generate statistically plausible text.<p>Reliability comes from scaffolding: retrieval, tools, validation layers. Without that, fluency can masquerade as authority.<p>The interesting question isn’t whether they’re coworkers or exoskeletons. It’s whether we’re mistaking rhetoric for epistemology.
> LLMs aren’t built around truth as a first-class primitive.<p>neither are humans<p>> They optimize for next-token probability and human approval, not factual verification.<p>while there are outliers, most humans also tend to tell people what they want to hear and to fit in.<p>> factuality is emergent and contingent, not enforced by architecture.<p>like humans; as far as we know, there is no "factuality" gene, and we lie to ourselves, to others, in politics, scientific papers, to our partners, etc.<p>> If we’re going to treat them as coworkers or exoskeletons, we should be clear about that distinction.<p>I don't see the distinction. Humans exhibit many of the same behaviours.
If an employee repeatedly makes factually incorrect statements, we will (or could) hold them accountable. That seems to be one difference.
Strangely, the GP replaced the ChatGPT-generated text you're commenting on by an even worse and more misleading ChatGPT-generated one. Perhaps in order to make a point.
There's a ground truth to human cognition in that we have to feed ourselves and survive. We have to interact with others, reap the results of those interactions, and adjust for the next time. This requires validation layers. If you don't see them, it's because they're so intrinsic to you that you can't see them.<p>You're just indulging in sort of idle cynical judgement of people. To lie well even takes careful truthful evaluation of the possible effects of that lie and the likelihood and consequences of being caught. If you yourself claim to have observed a lie, and can verify that it was a lie, then you understand a truth; you're confounding truthfulness with <i>honesty.</i><p>So that's the (obvious) distinction. A distributed algorithm that predicts likely strings of words doesn't do any of that, and doesn't have any concerns or consequences. It doesn't exist at all (even if calculation is existence - maybe we're all reductively just calculators, right?) <i>after</i> your query has run. You have to save a context and feed it back into an algorithm that hasn't changed an iota from when you ran it the last time. There's no capacity to evaluate <i>anything.</i><p>You'll know we're getting closer to the fantasy abstract AI of your imagination when a system gets more out of the second time it trains on the same book than it did the first time.
A much more useful tool is a technology that check for our blind spots and bugs.<p>For example fact checking a news article and making sure what's get reported line up with base reality.<p>I once fact check a virology lecture and found out that the professor confused two brothers as one individual.<p>I am sure about the professor having a super solid grasp of how viruses work, but errors like these probably creeps in all the time.
Ethical realists would disagree with you.
AI is the philosophers stone. It appears to break equivalence, when in reality you are using electricity for an entire town.
Make centaurs, not unicorns. The human is almost always going to be the strongest element in the loop, and the most efficient. Augmenting human skill will always outperform present day SOTA AI systems (assuming a competent human).
> Autonomous agents fail because they don't have the context that humans carry around implicitly.<p>Yet.<p>This is mostly a matter of data capture and organization. It sounds like Kasava is already doing a lot of this. They just need more sources.
Self-conscious efforts to formalize and concentrate information in systems controlled by firm management, known as "scientific management" by its proponents and "Taylorism" by many of its detractors, are a century old[1]. It has proven to be a constantly receding horizon.<p>[1]: <a href="https://en.wikipedia.org/wiki/Scientific_management" rel="nofollow">https://en.wikipedia.org/wiki/Scientific_management</a>
I'll guess we'll se a lot of analogies and have to get used to it, although most will be off.<p>AI <i>can</i> be an exoskeleton. It can be a co-worker and it can also replace <i>you</i> and your whole team.<p>The "Office Space"-question is what are <i>you</i> particularly within an organization and concretely when you'll become the bottleneck, preventing your "exoskeleton" for efficiently doing its job independently.<p>There's no other question that's relevant for any practical purposes for your employer and your well being as a person that presumably needs to earn a living based on their utility.
> It can be a co-worker and it can also replace you and your whole team.<p>You drank the koolaide m8. It fundamentally cannot replace a single SWE and never will without fundamental changes to the model construction. If there is displacement, it’ll be short lived when the hype doesn’t match reality.<p>Go take a gander at openclaws codebase and feel at-ease with your job security.<p>I have seen zero evidence that the frontier model companies are innovating. All I see is full steam ahead on scaling what exists, but correct me if I’m wrong.
My worry is if we treat code as "AI wrote it" we choose to not be responsible for what it does.<p>I'm worried we're going to see a serious safety issue at some point. Therac 25 sort of issue where something goes really bad with code that absolutely cannot fail. Would a human make mistakes? Of course. But if the culture goes even harder to "move fast break things", and organizational pressure to ship the slop increases, bad things will happen.
I prefer the term "assistant". It can do some tasks, but today's AI often needs human guidance for good results.
not AI, but IA: Intelligence Augmentation.
Closer to a really capable intern. Lots of potential for good and bad; needs to be watched closely.
Or software engineers are not coachmen while AI is diesel engine to horses. Instead, software engineers are mistrels -- they disappear if all they do is moving knowledge from one place to another.
It's funny developing AI stuff eg. RAG tools and being against AI at the same time, not drinking the kool aid I mean.<p>But it's fun, I say "Henceforth you shall be known as Jaundice" and it's like "Alright my lord, I am now referred to as Jaundice"
The exoskeleton metaphor is closer than most analogies but it still undersells one thing: exoskeletons augment existing capability along the same axis. AI augments along orthogonal axes too.<p>Running 17 products as an indie maker, I've found AI is less "do the same thing faster" and more "attempt things you'd never justify the time for." I now write throwaway prototypes to test ideas that would have died as shower thoughts. The bottleneck moved from "can I build this" to "should I build this" — and that's a judgment call AI makes worse, not better.<p>The real risk of the exoskeleton framing is that it implies AI makes you better at what you already do. In practice it makes you worse at deciding what to do, because the cost of starting is near zero but the cost of maintaining and shipping is unchanged.
This take lands for me. I'm a busy dad working a day job as a developer with a long backlog of side project ideas.<p>Hearing all the news of how good Claude Opus is getting, I fired it up with some agent orchestrator instruction files, babysat it off and on for a few days, and now have 3 projects making serious progress that used to be stale repos from a decade ago with only 1 or 2 commits.<p>On one of them, I had to feed Claude some research papers before it finally started making real headway and passing the benchmark tests I had it write.
An electric bicycle for the mind.
Maybe more of a mobility scooter for the mind.
Owners intent is more like electric chair (for SWEs), but some people are trying to use it as office chair.
An electric chair for the mind?
I prefer mind vibe-rator.
Exoskeletons sound cool but somebody please put an LLM into a spider tank.
Frankly I'm tired of metaphor-based attempts to explain LLMs.<p>Stochastic Parrots. Interns. Junior Devs. Thought partners. Bicycles for the mind. Spicy autocomplete. A blurry jpeg of the web. Calculators but for words. Copilot. The term "artificial intelligence" itself.<p>These may correspond to a greater or lesser degree with what LLMs are capable of, but if we stick to metaphors as our primary tool for reasoning about these machines, we're hamstringing ourselves and making it impossible to reason about the frontier of capabilities, or resolve disagreements about them.<p>A understanding-without-metaphors isn't easy -- it requires a grasp of math, computer science, linguistics and philosophy.<p>But if we're going to move forward instead of just finding slightly more useful tropes, we <i>have</i> to do it. Or at least to try.
blogger who fancies themselves an ai vibe code guru with 12 arms and a 3rd eye yet can't make a homepage that's not totally broken<p>How typical!
an exoskeleten made of cheese
This utterly boring AI writing. Go, please go away...
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By reading the title, I already know you did not try OpenClaw. AI employees are here.
What are your digital 'employees' doing? Did they replace any humans or was there nobody before?
Looking into OpenClaw, I really do want to believe all the hype. However, it's frustrating that I can find very few, concrete examples of people showcasing their work with it.<p>Can you highlight what you've managed to do with it?