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.
It's the new underpaid employee that you're training to replace you.
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.
If you can record a human doing anything on a computer, we'll soon have a way to automate it
> the new underpaid employee that you're training to replace you.
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
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.
Frankly I'm tired of metaphor-based attempts to explain LLMs.
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.
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.
A understanding-without-metaphors isn't easy -- it requires a grasp of math, computer science, linguistics and philosophy.
But if we're going to move forward instead of just finding slightly more useful tropes, we have to do it. Or at least to try.
Well since their capabilities change over time maybe it would be useful to assign it an age based on what a human can do at that age. Right now it could be like a 13 year old
> Humans don’t have an internal notion of “fact” or “truth.” They generate statistically plausible text.
This doesn't jive with reality at all. Language is a relatively recent invention, yet somehow Homo sapiens were able to survive in the world and even use tools before the appearance of language. You're saying they did this without an internal notion of "fact" or "truth"?
I hate the trend of downplaying human capabilities to make the wild promises of AI more plausible.
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.
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."
So save thinking and listen to intent - replace 90% of SWEs in near future (6-12 months according to Amodei).
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.
I sort of agree the random pontification and bad analogies aren't super useful, but I'm not sure why you would believe the intent of the AI CEOs has more bearing on outcomes than, you know, actual utility over time. I mean those guys are so far out over their skis in terms of investor expectations, it's the last opinion I would take seriously in terms of best-effort predictions.
If the goal is to reduce the need for SWE, you don’t need AI for that. I suspect I’m not alone in observing how companies are often very inefficient, so that devs end up spending a lot of time on projects of questionable value—something that seems to happen more often the larger the organization. I recall at one job my manager insisted I delegate building a react app for an internal tool to a team of contractors rather than letting me focus for two weeks and knock it out myself.
It’s always the people management stuff that’s the hard part, but AI isn’t going to solve that. I don’t know what my previous manager’s deal was, but AI wouldn’t fix it.
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.
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.
In the latest interview with Claude Code's author: https://podcasts.apple.com/us/podcast/lennys-podcast-product..., 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?
> Boris said that writing code is a solved problem
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.
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.
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)
He works for a company that crowed about an AI-generated C compiler that was so overfitted, it couldn't compile "hello world"
So if he tells me that "software engineering is solved", I take that with rather large grains of salt. It is far 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.
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.
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.
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.
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 linear combination.
> is the field of computer science plateaued to the point that most of what we do is linear combination of well established patterns?
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.
Just to add: this is only the prediction of someone who has a decent amount of information, not an expert or insider
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.
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.
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.
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.
Did he pick Excalidraw as the project to work on, or did the audience?
It's easy to be conned if you're not looking for the sleight of hand. You need to start channelling your inner Randi whenever AI demos are done, there's a lot of money at stake and a lot of money to prep a polished show.
To be honest, even if the audience "picked" that project, it could have been a plant shouting out the project.
I'm not saying they prepped the answer, I'm saying they prepped picking a project it could definitely work on. An AI solvable problem.
Sure, people did it for the fun and the credits, but the fun quickly goes out of it when the credits go to the IP laundromat and the fun is had by the people ripping off your code. Why would anybody contribute their works for free in an environment like that?
> Boris said that writing code is a solved problem.
No way, the person selling a tool that writes code says said tool can now write code? Color me shocked at this revelation.
Let's check in on Claude Code's open issues for a sec here, and see how "solved" all of its issues are? Or my favorite, how their shitty React TUI that pegs modern CPUs and consumes all the memory on the system is apparently harder to get right than Video Games! Truly the masters of software engineering, these Anthropic folks.
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.
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.
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.
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).
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.
The exoskeleton doesn't replace instinct. It just removes friction from execution so more cycles go toward the judgment calls that actually matter.
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[ 2.4 ms ] story [ 100 ms ] threadPeople 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.
If you can record a human doing anything on a computer, we'll soon have a way to automate it
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
Claude is that you? Why haven’t you called me?
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"
How typical!
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.
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.
A understanding-without-metaphors isn't easy -- it requires a grasp of math, computer science, linguistics and philosophy.
But if we're going to move forward instead of just finding slightly more useful tropes, we have to do it. Or at least to try.
Reliability comes from scaffolding: retrieval, tools, validation layers. Without that, fluency can masquerade as authority.
The interesting question isn’t whether they’re coworkers or exoskeletons. It’s whether we’re mistaking rhetoric for epistemology.
This doesn't jive with reality at all. Language is a relatively recent invention, yet somehow Homo sapiens were able to survive in the world and even use tools before the appearance of language. You're saying they did this without an internal notion of "fact" or "truth"?
I hate the trend of downplaying human capabilities to make the wild promises of AI more plausible.
And this write up is not an exception.
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."
So save thinking and listen to intent - replace 90% of SWEs in near future (6-12 months according to Amodei).
Historically when SWEs became more efficient then we just started making more complicated software (and SWE demand actually increased).
It’s always the people management stuff that’s the hard part, but AI isn’t going to solve that. I don’t know what my previous manager’s deal was, but AI wouldn’t fix it.
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.
Yet.
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.
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.
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)
He works for a company that crowed about an AI-generated C compiler that was so overfitted, it couldn't compile "hello world"
So if he tells me that "software engineering is solved", I take that with rather large grains of salt. It is far 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.
The constant hypefest is just vomit inducing.
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.
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.
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.
Just to add: this is only the prediction of someone who has a decent amount of information, not an expert or insider
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.
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.
It's easy to be conned if you're not looking for the sleight of hand. You need to start channelling your inner Randi whenever AI demos are done, there's a lot of money at stake and a lot of money to prep a polished show.
To be honest, even if the audience "picked" that project, it could have been a plant shouting out the project.
I'm not saying they prepped the answer, I'm saying they prepped picking a project it could definitely work on. An AI solvable problem.
sure is news for the models tripping on my thousands of LOC jquery legacy app...
Sure, people did it for the fun and the credits, but the fun quickly goes out of it when the credits go to the IP laundromat and the fun is had by the people ripping off your code. Why would anybody contribute their works for free in an environment like that?
No way, the person selling a tool that writes code says said tool can now write code? Color me shocked at this revelation.
Let's check in on Claude Code's open issues for a sec here, and see how "solved" all of its issues are? Or my favorite, how their shitty React TUI that pegs modern CPUs and consumes all the memory on the system is apparently harder to get right than Video Games! Truly the masters of software engineering, these Anthropic folks.
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.
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.
The exoskeleton doesn't replace instinct. It just removes friction from execution so more cycles go toward the judgment calls that actually matter.