People say something like this is an example of “AI is replacing engineers,” but that’s ridiculous. What’s actually happening is that one engineer with AI now does the work of ten engineers, which will simply lead to more exciting opportunities for the other nine to “focus on higher-level problems” such as updating their resumes.
That is a pretty good setup and delivery I must say
I think really things will just start shifting as things really do start to get better and step changes of capabilities where “I’m not even going to try to get opus to do X because I know it’s going to suck” moves to “oh wow it’s actually helpful” to “I don’t really even need to be that involved”.
Places where engineering labor is the bottleneck to better output will be where talent migrates towards and places where output is capped regardless of engineering labor are going to be where talent migrates from. I don’t really see this apocalyptic view really accurate at all, I think it’s really going to be cost / output will reduce. It’ll make new markets pop up where it wouldn’t be really possible to justify engineering expense today.
"I'm not joking and this isn't funny. We have been trying to build distributed agent orchestrators at Google since last year. There are various options, not everyone is aligned... I gave Claude Code a description of the problem, it generated what we built last year in an hour."
The real question is "could she have written that problem statement a year ago, or is a year's learning by the team baked into that prompt?"
There are lots of distributed agent orchestators out there. Rarely is any of them going to do exactly what you want. The hard part is commonly defining the problem to be solved.
The code it produces (svelte, typescript, python) is usually riddled with small errors , deprecated usage, reimplementation, and terrible segmentation. This is opus 4.5. Literally every single time if I did not review it carefully and edit it thoroughly it would be a mountain of extremely hard to debug tech debt. It’s a toss up if it’s actual faster to be honest. I think the phenomenon of people expressing their hopes of the technology as if they were the current reality is an extremely interesting phenomenon.
Your experience mirrors mine as well. I will say since I’ve got both data science and engineering workflows, data science is where I’ve been accelerated the most because opus can do ad hoc scripts and e.g. streamlit websites and data munging very well so all of the things that take time before decisioning are much faster.
For engineering, its extremely easy to not only acquire tech debt but to basically run yourself into the ground when opus cannot do a task but doesn’t know it cannot do a task.
BUT: what I will say is to me and to others the trajectory has been really impressive over the last 1.5 years, so I don’t view the optimism of “we’re nearly there!” As being kind of wishful or magical thinking. I can definitely believe by the end of 2026 we’ll have agents that break through the walls that it still can’t climb over as of today just based on the rate of improvements we’ve seen so far and the knowledge that we still have a lot of headroom just with current stack.
We've all used Claude, how much can it really do in one hour? Maybe 1-3kloc of quality bug free code that does what you want it to do? Ok, so are we to believe that a team of Google engineers write greenfield code at a rate of 10 lines per day? 1 line per person per day? Or did her team spent a year on some combination of iterating, learning, discarding versions, being blocked by other teams, being blocked by corporate processes etc?
Of course it's the latter, this article is silly clickbait. AI won't help you convince a peer to go with your design, won't clear your work with legal, yada yada.
We often hear claims like "do this and you'll make so much money" or "this is so good it's unbelievable." Occasionally, they turn out to be true. I've experienced a couple myself and feel fairly positive about the current one. The initial resistance comes from not wanting them to be true because then I know I either have to take action or risk missing out. It's a weird psychological thing that I now try hard to avoid.
I find Claude (and specifically Opus 4.5) insanely impressive and frankly scary, but I think there is some Pareto principle that people ignore when discussing it. Specifically I find it is great at the first 80%, but I struggle getting the last 20% over the line with it. I think this colours people’s judgement, because influencers/casual/beginner users only experience that first 80%
I love Claude Code, but this feels a bit like the perennial "our team spent a year on project X and then some intern built the same thing in a hackathon" claims. The hard part isn't writing the code, it's investigating, testing, integrating, etc. A rule of thumb I've seen in some places (Google certainly seems applicable) is the project will take a month for every day it takes to make the prototype.
Which is no shade on Claude Code, but given everything CC has already done, this seems pretty normal.
The AI labs use each other's models constantly. It's also pragmatic: there are cases where one model can't do something but a different model can blow right through it.
>> I gave Claude Code a description of the problem, it generated what we built last year in an hour.
How likely is it that the former year's code was in the training data, or that the (perhaps unreleased, beta version) of Claude Code that Dogan was using somehow had access to it, unbeknownst to the team?
Very unlikely. They were talking about internal Google software which would not have ever made it into Claude's training data.
This also plays into my own intuition of using the latest coding agents. They don't need to have seen a system before in order to build it for you if you know how to describe it.
I can describe a project I never released myself in a couple of detailed paragraphs and get a great implementation.
Nevertheless, regardless whether these are real results or just hypes, I do see a bigger existence of AI tools in all processes from planning to coding to wrapping up to even sending an email out. The writing is on the wall.
Every time someone says “AI built in an hour what took us a year,” what they really mean is that humans spent a year doing the hard thinking and the AI merely regurgitated it at silicon speed. Which is, of course, completely different from productivity.
Also, if it truly took your team a year, that probably says more about your process than about AI. But not in a way that threatens my worldview. In a different way. A safer way.
Let’s be clear: writing the code is the easy part. The real work is the meetings, the alignment, the architectural debates, the Jira grooming, the moral struggle of choosing snake_case vs camelCase. Claude didn’t do any of that. Therefore it didn’t actually do anything.
I, personally, have spent years cultivating intuition, judgment, and taste. These are things that cannot be automated, except apparently by a probabilistic text model that keeps outperforming me in domains I insist are “subtle.”
Sure, the output works. Sure, it passes tests. Sure, it replaces months of effort. But it doesn’t understand what it’s doing. Unlike me, who definitely understands everything I copy from Stack Overflow.
Also, I tried AI last year and it hallucinated once, so I’ve concluded the entire field has plateaued permanently. Technology famously never improves after an early bad demo.
Anyway, I remain unconcerned. If AI really were that powerful, it would have already made me irrelevant, and since I still have a job, this must all be hype. QED.
Now if you’ll excuse me, I need to spend the afternoon explaining why a tool that just invalidated a year of human labor is “just autocomplete.”
>I, personally, have spent years cultivating intuition, judgment, and taste.
exactly. I am using AI to make tons of good code and I love it. but the AI makes silly oversight or has gaps in logic that to someone with 'hands on' experience thinks of right away.
im debugging some web server stuff for hours and ai never ask me for the logs or --verbose output, which is insane. instead the ai comes up with hypothetical causes for the problem then confidently states the solution.
I would assume there are open source solutions to the problem it could have trained on, if so it would be interesting yo see how they had influenced what Claude produced here.
Where is the prompt? Are they going to hide behind "prompts are IP" now?
AI boosters are heralding this as a new age of programming, but they conveniently ignore that the prompt in these hyperbolic articles (or GASP the source code!) is never shared, because then we would be able to try and replicate it and call out this bullshit. There would be no more hiding behind "oh you are not using the correct model".
Shame on all these "reporters" as well who never ask "Okay can we see the code? Can we see the prompt, since it's just 3 paragraphs?"
i don't know if its just a bubble i'm in, but it seems like most of my day as an engineer is meetings/endless scrum events and tracking problems yet we sound more energy on automating code with llms than on automating all that other overhead...
i wonder, is coding really the bottleneck in most cases?
I routinely spend more time tweaking a prompt than Claude Code spends creating something. The better the prompt, the faster it seems to work (with better results). So I can totally relate to your comment.
It's awesome to be amazed by some cool new technology but let's not be naive.
Yes. Obviously no one is claiming that Claude Code literally made a year pass in an hour as if it were Superman spinning the Earth faster. Can we just keep the goalpost put for a second?
P.S. EDIT:
The big question will soon become - how technical do you need to be to build a system, because most of those learnings, concepts and associations are surely at the domain level. Or phrased differently: to what extent will future software development shift from hands-on engineering to hands-off technical guidance? Perhaps the future developer role would be much more similar to today's TPM (Technical Program Manager)?
I hear over and over from the staff engineers where i work that ai churns nothing but slop. When they know its ai they refuse to approve PRs even when it works cause its not the way they would do it.
Prime example was a quick set of scripts to run some tasks that were commonly performed. The ai version took a couple days to get the patterns right but worked and i was ready to not have to think of it anymore. A solid POC i thought. Well along comes the old hat saying its not the way he wouldve done it. Says he’s going to take a crack at it over the weekend. A month later and he still wont approve my pr, still not done with his better version and the code he has checked in is wayyyy more complicated using self-coded routines rather than preexisting libraries or even forked versions of them cause they are “security risks”. Then theres the things he straight up replicated what i produced with new file and variable names. Im still using what i put together aince his is still not done but in the mean time talking with powers that be about how we balance time, cost and quality.
[+] [-] threethirtytwo|2 months ago|reply
[+] [-] aspenmartin|2 months ago|reply
I think really things will just start shifting as things really do start to get better and step changes of capabilities where “I’m not even going to try to get opus to do X because I know it’s going to suck” moves to “oh wow it’s actually helpful” to “I don’t really even need to be that involved”.
Places where engineering labor is the bottleneck to better output will be where talent migrates towards and places where output is capped regardless of engineering labor are going to be where talent migrates from. I don’t really see this apocalyptic view really accurate at all, I think it’s really going to be cost / output will reduce. It’ll make new markets pop up where it wouldn’t be really possible to justify engineering expense today.
[+] [-] xnx|2 months ago|reply
https://x.com/rakyll/status/2007239758158975130
[+] [-] yodon|2 months ago|reply
There are lots of distributed agent orchestators out there. Rarely is any of them going to do exactly what you want. The hard part is commonly defining the problem to be solved.
[+] [-] imbusy111|2 months ago|reply
[+] [-] arresin|2 months ago|reply
[+] [-] aspenmartin|2 months ago|reply
For engineering, its extremely easy to not only acquire tech debt but to basically run yourself into the ground when opus cannot do a task but doesn’t know it cannot do a task.
BUT: what I will say is to me and to others the trajectory has been really impressive over the last 1.5 years, so I don’t view the optimism of “we’re nearly there!” As being kind of wishful or magical thinking. I can definitely believe by the end of 2026 we’ll have agents that break through the walls that it still can’t climb over as of today just based on the rate of improvements we’ve seen so far and the knowledge that we still have a lot of headroom just with current stack.
[+] [-] martythemaniak|2 months ago|reply
Of course it's the latter, this article is silly clickbait. AI won't help you convince a peer to go with your design, won't clear your work with legal, yada yada.
[+] [-] tiku|2 months ago|reply
[+] [-] xnx|2 months ago|reply
[+] [-] gaigalas|2 months ago|reply
[+] [-] andreygrehov|2 months ago|reply
[+] [-] alecco|2 months ago|reply
[+] [-] vagab0nd|2 months ago|reply
[+] [-] jeltz|2 months ago|reply
[+] [-] unknown|2 months ago|reply
[deleted]
[+] [-] oncallthrow|2 months ago|reply
[+] [-] norseboar|2 months ago|reply
Which is no shade on Claude Code, but given everything CC has already done, this seems pretty normal.
[+] [-] i7l|2 months ago|reply
[+] [-] alwillis|2 months ago|reply
[+] [-] treetalker|2 months ago|reply
How likely is it that the former year's code was in the training data, or that the (perhaps unreleased, beta version) of Claude Code that Dogan was using somehow had access to it, unbeknownst to the team?
[+] [-] simonw|2 months ago|reply
This also plays into my own intuition of using the latest coding agents. They don't need to have seen a system before in order to build it for you if you know how to describe it.
I can describe a project I never released myself in a couple of detailed paragraphs and get a great implementation.
[+] [-] markus_zhang|2 months ago|reply
[+] [-] andrekandre|2 months ago|reply
[+] [-] threethirtytwo|2 months ago|reply
Every time someone says “AI built in an hour what took us a year,” what they really mean is that humans spent a year doing the hard thinking and the AI merely regurgitated it at silicon speed. Which is, of course, completely different from productivity.
Also, if it truly took your team a year, that probably says more about your process than about AI. But not in a way that threatens my worldview. In a different way. A safer way.
Let’s be clear: writing the code is the easy part. The real work is the meetings, the alignment, the architectural debates, the Jira grooming, the moral struggle of choosing snake_case vs camelCase. Claude didn’t do any of that. Therefore it didn’t actually do anything.
I, personally, have spent years cultivating intuition, judgment, and taste. These are things that cannot be automated, except apparently by a probabilistic text model that keeps outperforming me in domains I insist are “subtle.”
Sure, the output works. Sure, it passes tests. Sure, it replaces months of effort. But it doesn’t understand what it’s doing. Unlike me, who definitely understands everything I copy from Stack Overflow.
Also, I tried AI last year and it hallucinated once, so I’ve concluded the entire field has plateaued permanently. Technology famously never improves after an early bad demo.
Anyway, I remain unconcerned. If AI really were that powerful, it would have already made me irrelevant, and since I still have a job, this must all be hype. QED.
Now if you’ll excuse me, I need to spend the afternoon explaining why a tool that just invalidated a year of human labor is “just autocomplete.”
[+] [-] htx80nerd|2 months ago|reply
exactly. I am using AI to make tons of good code and I love it. but the AI makes silly oversight or has gaps in logic that to someone with 'hands on' experience thinks of right away.
im debugging some web server stuff for hours and ai never ask me for the logs or --verbose output, which is insane. instead the ai comes up with hypothetical causes for the problem then confidently states the solution.
[+] [-] colin_jack|2 months ago|reply
[+] [-] feverzsj|2 months ago|reply
[+] [-] 63stack|2 months ago|reply
AI boosters are heralding this as a new age of programming, but they conveniently ignore that the prompt in these hyperbolic articles (or GASP the source code!) is never shared, because then we would be able to try and replicate it and call out this bullshit. There would be no more hiding behind "oh you are not using the correct model".
Shame on all these "reporters" as well who never ask "Okay can we see the code? Can we see the prompt, since it's just 3 paragraphs?"
[+] [-] strange_quark|2 months ago|reply
In the Xitter thread she literally says it’s a toy version and needs to be iterated on.
How much longer are we going to collectively pretend this is so revolutionary and worth trillions of dollars of investment?
[+] [-] rolph|2 months ago|reply
people learned, explored concepts, and discovered lateral associations, developed collective actions, consolidated future solidarity.
claude just output some code.
[+] [-] andrekandre|2 months ago|reply
i wonder, is coding really the bottleneck in most cases?
[+] [-] Deegy|2 months ago|reply
[+] [-] gtirloni|2 months ago|reply
It's awesome to be amazed by some cool new technology but let's not be naive.
[+] [-] kvgr|2 months ago|reply
[+] [-] falcor84|2 months ago|reply
P.S. EDIT:
The big question will soon become - how technical do you need to be to build a system, because most of those learnings, concepts and associations are surely at the domain level. Or phrased differently: to what extent will future software development shift from hands-on engineering to hands-off technical guidance? Perhaps the future developer role would be much more similar to today's TPM (Technical Program Manager)?
[+] [-] NuclearPM|2 months ago|reply
[+] [-] lvspiff|2 months ago|reply
Prime example was a quick set of scripts to run some tasks that were commonly performed. The ai version took a couple days to get the patterns right but worked and i was ready to not have to think of it anymore. A solid POC i thought. Well along comes the old hat saying its not the way he wouldve done it. Says he’s going to take a crack at it over the weekend. A month later and he still wont approve my pr, still not done with his better version and the code he has checked in is wayyyy more complicated using self-coded routines rather than preexisting libraries or even forked versions of them cause they are “security risks”. Then theres the things he straight up replicated what i produced with new file and variable names. Im still using what i put together aince his is still not done but in the mean time talking with powers that be about how we balance time, cost and quality.