I don't get the obsession with more code. Whatever happened to good development practices? Many of which, if followed allow LLMs to work more efficiently ironically enough. I would rather hear that you've fixed bugs that have stumped your dev team for years, and have had next to no outages, to me that's more valuable than just slopping features out the door.
One thing I've spotted several times throughout this year: the larger volume of code leads to exactly the sort of problems it always has. It just happens real fast now, with fewer people. Problems that used to take years to erupt now manifest before an MVP ships, but people just sweep it all under the rug in the name of moving fast.
While I agree with your sentiment, very strongly actually, there has always been a tension between what I'll call "engineering teams" and "product teams". The engineering teams want the long-term platform stability, performance, etc. The product teams want to push that new feature out, quick! The overall business needs both (types of thinking and doing) to survive, of course, but not all leadership sees the world through the lens engineering team lens. I'd argue most actually lean the other way, which is understandable though unfortunate. I'd also posit that since these product teams are often "closer to the business", those people typically win favor with the real decision makers.
Features don’t usually come from product teams. They come from above: some Head Of, VP Of, and the like. Those people only ask, they know very little about execution. They want their wishes come true in a timely manner
it really isn't a flex ppl think it is when they're burning tokens churning agents that are working for "hours" doing work
we don't need to read or write code anymore, but we do need to write (as humans) the instructions for agents.
having ADRs, product specs, acceptance criteria, etc. that are properly maintained leads to agents working for just a few minutes and pumping out reliable code.
the more vague the work for agents, the more randomness and inefficiency there will be. that leads to bugs, constantly re-writing code, etc.
imo ofc, i'm not yet running a billion dollar company so maybe i'm wrong.
I think the HN crowd overestimates the quality of software. There is a (very) long tail of truly abysmal software that people are forced to use all the time. The reason it gets to be abysmal is because it’s often back-office admin software that helps people file forms for, say, logistics paperwork or school district expense tracking. AI-native engineering teams can create dramatic improvements for that long tail because the people running that software are either incompetent or checked out, and a pull towards average quality by AIs would yield great improvement.
One of the most uncomfortable truths about our profession is that there is literally no floor to how shitty software can be and yet still make people billions of dollars.
none of these types of pieces of content focus on what was created for users, its always about how sky high their "shipping" metrics are (PRs merged, LoC, etc). it makes it extremely hard for me to care, as they all boil down to "im a dev who made some loops on loops on loops that writes a lot of code and spends a lot of tokens."
i will say, at least this article says "we merge 5x more and our revert rate has stayed the same." so thats at least a metric i can start to respect
Now the real question is "is the revert rate the same because you still have the same process, or do you fix-forward more often now, so your revert-rate is because of higher-severity incidents?"
That's why I'm sceptical about the value of ai in software development. Much more can now be done but I don't see costumers paying more for it. The only way it can work is by laying off software engineers and saving money on their salaries.
I've gone through a few recruiter screens lately and the number of people who mention "number of deploys per day" as a metric they are proud of causes my eyes to roll.
What can you possibly be doing as a product that justifies you releasing 60 times in a working day?
It's certainly better than the old models of releasing on a quarterly cadence with the test team and all that, but it feels like the metric has become the goal when you are releasing that often.
This post is funny to me, because I feel it falls well short of what a proper, forward-thinking "AI-native" engineering org would actually look like. That is, the author is presenting this as kind of a radical departure from normal engineering, but I just see it as normal engineering with more AI. I don't see that as AI-native.
To me, true "AI-native" engineering will be one where questions regarding how humans will do things like review code are irrelevant. You explicitly throw away any concept of typical software engineering and, instead, differ to the AI. Your job, as a human, is solely to do what the AI can't physically do. That's not the same as closing gaps between what AI is currently capable of doing and what you'd need it to be capable of doing. It's literally enforcing that anything an AI is able to do is treated as correct in itself, and you, as a human, only support it by being a human.
If that sounds outlandish, that's because it is. We don't have such capable AI systems yet. It's possible we never will. But that's beyond the point. The reality is trying to make an "AI-native" engineering org by just layering AI on top of existing processes and systems is futile. If your expectation is that AI will continue to improve in capabilities at an accelerating rate, then these stop-gaps are pointless. You might as well just ignore AI altogether until it is able to completely take over, because that's the inevitable goal, right?
If you're willing to buy that will be possible, then my guess is that it will occur in the form of small scale startups and actual mavericks rather than someone trying to run an existing business who will simply be too risk adverse to go all in when the time comes. Some guy in his basement is more likely to leverage AI "properly" to build an actual "AI-native" engineering org because that person won't have much to lose.
Reminds me of a few things, like being a meat proxy, as well as books like Company of One or Million Dollar One Person Business, and these were published before AI so right now we will see companies with only one founder and hundreds to thousands of AI agents. There already was that drug delivery company with the one founder but apparently a lot of their revenue was fraudulent. Still, it proves the point that it is possible.
I think this is all fair, but somewhat just semantics.
Most engineers are working in "normal" engineering orgs, but can feel the ground underneath them shifting, and companies believe that if they are caught flat footed, they will be out competed in the marketplace, so they are all trying to make sure they are adopting it at least in a way that lets them keep pace if not accelerate revenue.
If you are not impacted by economic incentives, you can continue to not care about this transitional state, but most people do.
Are there any companies outside of the "AI shovels" business that make more money because of AI? Every time I ask someone why they went so hard on adopting AI, I hear the same story about the fear of losing the market share or becoming less competitive. Honest question, who is benefitting from all of this? The revenues at majority of companies seem flat at best. There is a growing concern about professionals getting deskilled, the job satisfaction is not improving, wages are stagnant. Mind-boggling that we are actively engaged and often push for this experiment that hurts our own profession. Cheering for our own demise.
It this point, a whole lot of teams have been leaning into AI-first, not-entirely-reviewed code since around March this year.
That means we are already 6-8 months in, and not far from having a full year of experience of what that actually looks like, albeit weirdly influenced by the increase in model capabilities during that period.
I'm looking forward to those retrospectives. Did this turn out to work OK for the teams doing it? What were the biggest failings and/or surprises? With hindsight, did this produce good quality software?
I think the biggest shift is in predictability. Will something be done today, next week, or next month - nobody knows. Will we converge on a bug free state? not sure.
So far AI works great at fast moving work which you will probably recode or throw away in a month or two anyway (e.g. a startup) but is weak at maintaining a large code base that you rely on. At least part of this is cultural, as the cost of misalignments between team members can be quickly translated into broken software.
Software robustness, at least in terms of "has the app crashed on any client machines today", is achievable. It's just expensive enough to achieve that most people (rightly or not) don't prioritize it, and the more moving parts (like third party libraries or language runtimes) your application has the harder it is to achieve. Maybe AI will lower the cost of achieving that.
When I worked on Guild Wars every server or user crash went to a shared inbox and was bucketed so we could identify and act on new crashes. IIRC we maintained very, very low production crash rates and could prove that many of the crashes were due to hardware faults.
Similarly, Mozilla tries to analyze and act on every crash report they get in the field. If a webpage visited by even a small % of the customer base was causing crashes, the team worked to try and fix the crash to deliver stability. If it was a high-traffic webpage it got treated as a high-priority task - I remember Farmville breaking was basically a 3-alarm fire back when it was extremely popular.
"No bugs at all" is an even higher bar, but it probably is possible for simpler applications with the use of stuff like theorem provers. I'd love to see someone try it in production but I think the return on investment for that is probably super super negative.
Have been at it since Nov 23rd 2025 with a first commit with Opus 4.5 - it was rough at first but after about year well worth it. You can also tell where so many just get stuck in the mud due to lack of direct experience with all the engineering silos (from bare metal sys admin to QA eng silos outside of standard software engineering core) it takes to roll out a new kind of technology company while scaling a new kind of commercialization model, also managed by AI and AI native from the ground up.
I was just looking at some user-facing error messages Claude generated for me which contained unnecessary/irrelevant technical details while failed to provide clear instructions or fix.
Earlier, I reviewed code that does way more than specified scope, changes public API and introduces new behavior. I had to almost redo the whole thing.
I thought to myself, no way I am going to ship the code as written without actually reading through it (or as some people put it, only looking at the "intent"). I'll get more complaints from users while having absolutely no idea what's going on with the code or actual behavior.
It's a puzzle to me how these orgs can pull it off.
The people / orgs that can actually codify their strong software engineering principles and practices into "a harness"[1] are the people / orgs that find success with these tools.
It describes a team spending 30x payroll on tokens - $3,000,000 in token spend per month - building a multiplayer game where incidents and bug reports from users are passed directly to Fable 5/5.1 agents who then act on them and deploy the changes to production.
"We consider it an outage if we don't ship every hour"
It's worth reading because it's either a complete disaster or a glimpse of the future, or quite possibly both.
This is a complete and utter disaster. They are making spam games. Like miniclips but for spam. A semi functional website to make what is basically youtube playables.
And it's devouring tens of millions. I know what tens of millions can make in the video game industry, it can keep a small team of good devs and designers and artists making something special that's _really_ enjoyed by tens of thousands of people.
We don't want more games, we want more great games. This will never, ever, make great games.
Look on ye mighty, and despair. I really don't care how this sausage was made because the only thing you need to know is that it shouldn't ever have been made.
well, i did go check out the company spawn from the article. its certainly interesting how hands off their process is and how much they ship, but holy shit the games were fucking horrible. there was a direct WoW ripoff (right down to the assets) that was unplayable, a racing simulator where my controls stopped working 10 second in, and some sort of RPG that i started and just never loaded. so i will take what they are saying with the heaviest possible grain of salt.
Yes exactly this is my standard for everything now. You can spend an unbelievable amount of tokens doing all kinds of things that are interesting as processes in their own right.
at the end of it: is anything of value delivered to customers? the company might say "well users make the games with AI, we just serve them and host the multiplayer." okay, but again, what value was created? they brag about being one of anthropic's top customers; congrats you are handing VC money to anthropic. why should i care? theres no answer for why anyone should care, because to them the system and clout is the product, the product doesnt actually matter.
at least thats my read on it. i dont know how else to understand whats going on here.
That actually sounds really cool. Assuming it has access to a well-tuned predictive analytics mechanism that allows it to derive that changing the game in this way would increase satisfaction and revenue from you and other players like you, sufficiently to offset development costs and dissatisfaction to others, then it really should.
I could definitely see potential in an async co-op game based on this idea. A sim harness, some pbt to avoid broken states, and something like a modified nomic ruleset might get pretty far.
This is amazing and somewhat insane. It's more like they are building an organism with an immune system than a code base. Interesting that they say only Fable works for them.
I’m building the same but with a malleable SaaS. Basically when you sign up, you get the software deployed and an agent that can control that deployment and code.
But the process is slow. The user wants to add a feature to their app so they ask the AI. The AI has access to a terminal where it can clone, fix, and push. This takes time and isn’t instant.
Once AI can abstract this part, software will change forever. It’s what this post alludes to, does code, PR, etc need to reinvented for this?
Once so, no one would ever use cookie cutter software because you could build your own and maintain it using just an agent.
Was thinking of that exact article while I was reading this, we must be following the same folks. Highly recommend folks reading it, even if it feels insane in some regards it's a fascinating look at what the limits of this stuff could be and I found myself with a lot of new ideas to consider after reading it. Interesting times.
Edit - after writing "we must be following the same folks" I realized that you are, in fact, Simon Willison - I saw this article because you posted about it on X, haha.
This works as we've been doing it since Opus 4.5 (Nov 2025) with every user request, feature and bug. It's the future without a doubt. The feedback loops evolve and get stronger depending on how well you fine-tune or train your system to optimize on its own.
> A default that made Savi think longer on every message, landed at 10:22pm and reverted by 10:40pm: that's not right, undo it it's too slow and not jazz.
I giggled on that one. It feels like children playing having a job. If they pay that much for tokens they have to have at least 80k customers paying 50$ a month. Seems huge for something like that. Or they have a lot of investors. Anyway does not feel like something going anywhere.
This from that tweet stood out to me: "In general, we've seen that OpenAI's models game their own checks"
In the past when working on agentic stuff I've observed models gaming checks aggressively, to the point that when we were validating log files to see what the agent had done, the agent responded by editing the log files to pass validation. I can only imagine how bad that kind of behavior is at scale, and it sounds like they ran into it.
Won't there also be a point were you're shipping so fast that that in itself deteriorates UX? What if a page literally changes every time I reload? What if it changes mid-checkout process and the deal I agreed to 5 seconds ago does not exist anymore?!
I have come to realize that code (and I think possibly productivity in general) being expensive was always a feature. I am sure that a few great ideas did get lost to obscurity due to lack of access to resources to build them out. However, we now have an absolute deluge of complete and utter shite.
Agreed, for the individual LLMs can be powerful to let less technical folks build and customize their software - at an org level I reckon we'll start seeing more sinking ships as that bottleneck was actually helpful at limiting the flow of lackluster features to prod.
Spent few minutes reading the infomercial -- regretted it. Engineering principles form the bedrock first, and AI-tools are made to fit in them, not the other way around as this infomercial propounds. Its linkedin vibe is a big turnoff too.
For the argument lets say that this approach works and results in high quality apps. But will be users happy that app is updated 3 times a week and all updates add major changes? Because code generation is so cheap and fast it means we should even more thing about what to build to not overwhelm our users and actually solve their problems.
Here we are again, thirty years after I was taught the "waterfall" paradigm at business school.
When asked about "customer satisfaction" we were told, awkwardly, that became "political"
The paradigm did not survive its encounter with reality, and I do not think this one will either
The "only" 175% increase in productivity is very impressive, and seems realistic to me, when you remove the chores of writing code (thank you to coding agents)
What you build is still very dependant on what you need, and you do not know that until you build it
It is a paradox only for the "waterfall" paradigm. We rejected it, in the 1990s for a reason
First you take your Blendtech and "will it blend" your engineers, then upload the result to the cloud inside what is called an S3 bucket (don't forget to set access permissions on your buckets kids!) to be digested by the clankers, sit back and profit!
Dark humor aside I think disconnecting a bunch of problem solvers from their computing equipment could really enhance the world, putting that analytical power into the world in person could move a lot of cogs, rather then marshalling JSON.
Only three metrics went up: LOC, token spend and presumably release speed/frequency.
What's the markup (profit/COGS)? is it up?
Customer satisfaction? Is NPS moving one way or another?
These new tools only exacerbate this issue.
it really isn't a flex ppl think it is when they're burning tokens churning agents that are working for "hours" doing work
we don't need to read or write code anymore, but we do need to write (as humans) the instructions for agents.
having ADRs, product specs, acceptance criteria, etc. that are properly maintained leads to agents working for just a few minutes and pumping out reliable code.
the more vague the work for agents, the more randomness and inefficiency there will be. that leads to bugs, constantly re-writing code, etc.
imo ofc, i'm not yet running a billion dollar company so maybe i'm wrong.
i will say, at least this article says "we merge 5x more and our revert rate has stayed the same." so thats at least a metric i can start to respect
What’s the point of doing 5x more?
1) You can merge a "new change" to fix a merge, instead of reverting
2) It's unclear whether the substance of their individual PRs are the same. Never thought I'd want an LOC metric.
3) Really, I'm going to double down on the idea that merges could be fixing merges. Also happens to increase the merge metric.
What can you possibly be doing as a product that justifies you releasing 60 times in a working day?
It's certainly better than the old models of releasing on a quarterly cadence with the test team and all that, but it feels like the metric has become the goal when you are releasing that often.
To me, true "AI-native" engineering will be one where questions regarding how humans will do things like review code are irrelevant. You explicitly throw away any concept of typical software engineering and, instead, differ to the AI. Your job, as a human, is solely to do what the AI can't physically do. That's not the same as closing gaps between what AI is currently capable of doing and what you'd need it to be capable of doing. It's literally enforcing that anything an AI is able to do is treated as correct in itself, and you, as a human, only support it by being a human.
If that sounds outlandish, that's because it is. We don't have such capable AI systems yet. It's possible we never will. But that's beyond the point. The reality is trying to make an "AI-native" engineering org by just layering AI on top of existing processes and systems is futile. If your expectation is that AI will continue to improve in capabilities at an accelerating rate, then these stop-gaps are pointless. You might as well just ignore AI altogether until it is able to completely take over, because that's the inevitable goal, right?
If you're willing to buy that will be possible, then my guess is that it will occur in the form of small scale startups and actual mavericks rather than someone trying to run an existing business who will simply be too risk adverse to go all in when the time comes. Some guy in his basement is more likely to leverage AI "properly" to build an actual "AI-native" engineering org because that person won't have much to lose.
Most engineers are working in "normal" engineering orgs, but can feel the ground underneath them shifting, and companies believe that if they are caught flat footed, they will be out competed in the marketplace, so they are all trying to make sure they are adopting it at least in a way that lets them keep pace if not accelerate revenue.
If you are not impacted by economic incentives, you can continue to not care about this transitional state, but most people do.
That means we are already 6-8 months in, and not far from having a full year of experience of what that actually looks like, albeit weirdly influenced by the increase in model capabilities during that period.
I'm looking forward to those retrospectives. Did this turn out to work OK for the teams doing it? What were the biggest failings and/or surprises? With hindsight, did this produce good quality software?
So far AI works great at fast moving work which you will probably recode or throw away in a month or two anyway (e.g. a startup) but is weak at maintaining a large code base that you rely on. At least part of this is cultural, as the cost of misalignments between team members can be quickly translated into broken software.
> Will we converge on a bug free state?
I have never witnessed this state in my two decades of software work. Why would we think AI will magically achieve this?
When I worked on Guild Wars every server or user crash went to a shared inbox and was bucketed so we could identify and act on new crashes. IIRC we maintained very, very low production crash rates and could prove that many of the crashes were due to hardware faults.
Similarly, Mozilla tries to analyze and act on every crash report they get in the field. If a webpage visited by even a small % of the customer base was causing crashes, the team worked to try and fix the crash to deliver stability. If it was a high-traffic webpage it got treated as a high-priority task - I remember Farmville breaking was basically a 3-alarm fire back when it was extremely popular.
"No bugs at all" is an even higher bar, but it probably is possible for simpler applications with the use of stuff like theorem provers. I'd love to see someone try it in production but I think the return on investment for that is probably super super negative.
Earlier, I reviewed code that does way more than specified scope, changes public API and introduces new behavior. I had to almost redo the whole thing.
I thought to myself, no way I am going to ship the code as written without actually reading through it (or as some people put it, only looking at the "intent"). I'll get more complaints from users while having absolutely no idea what's going on with the code or actual behavior.
It's a puzzle to me how these orgs can pull it off.
The rest are just marketers.
[1] Whatever that means to them
> no way I am going to ship the code as written without actually reading through it
Interestingly agents can help, not replace, this process
These tools will make us more productive, they will disrupt (have disrupted) our industry, but they will not replace us because they cannot
It describes a team spending 30x payroll on tokens - $3,000,000 in token spend per month - building a multiplayer game where incidents and bug reports from users are passed directly to Fable 5/5.1 agents who then act on them and deploy the changes to production.
"We consider it an outage if we don't ship every hour"
It's worth reading because it's either a complete disaster or a glimpse of the future, or quite possibly both.
And it's devouring tens of millions. I know what tens of millions can make in the video game industry, it can keep a small team of good devs and designers and artists making something special that's _really_ enjoyed by tens of thousands of people.
We don't want more games, we want more great games. This will never, ever, make great games.
https://www.spawn.co
Look on ye mighty, and despair. I really don't care how this sausage was made because the only thing you need to know is that it shouldn't ever have been made.
But is the outcome good? Is the game fun?
at least thats my read on it. i dont know how else to understand whats going on here.
https://en.wikipedia.org/wiki/Nomic
But the process is slow. The user wants to add a feature to their app so they ask the AI. The AI has access to a terminal where it can clone, fix, and push. This takes time and isn’t instant.
Once AI can abstract this part, software will change forever. It’s what this post alludes to, does code, PR, etc need to reinvented for this?
Once so, no one would ever use cookie cutter software because you could build your own and maintain it using just an agent.
One would argue that the effort to build and maintain it is why we have cookie cutter software, agentic built or not.
Edit - after writing "we must be following the same folks" I realized that you are, in fact, Simon Willison - I saw this article because you posted about it on X, haha.
I giggled on that one. It feels like children playing having a job. If they pay that much for tokens they have to have at least 80k customers paying 50$ a month. Seems huge for something like that. Or they have a lot of investors. Anyway does not feel like something going anywhere.
Isn't there a point where you're shipping too fast that the product hasn't even caught on enough yet to know what to build?
I looked at some of the games and it just looks like a Roblox clone
In the past when working on agentic stuff I've observed models gaming checks aggressively, to the point that when we were validating log files to see what the agent had done, the agent responded by editing the log files to pass validation. I can only imagine how bad that kind of behavior is at scale, and it sounds like they ran into it.
I really mean this. Does it benefit the business? Does it benefit the users? When you optimize for the wrong thing, you inevitably fail.
If that's the benchmark, developers are incentivized to produce over-engineered and bloated code.
When asked about "customer satisfaction" we were told, awkwardly, that became "political"
The paradigm did not survive its encounter with reality, and I do not think this one will either
The "only" 175% increase in productivity is very impressive, and seems realistic to me, when you remove the chores of writing code (thank you to coding agents)
What you build is still very dependant on what you need, and you do not know that until you build it
It is a paradox only for the "waterfall" paradigm. We rejected it, in the 1990s for a reason
It did not work
First you take your Blendtech and "will it blend" your engineers, then upload the result to the cloud inside what is called an S3 bucket (don't forget to set access permissions on your buckets kids!) to be digested by the clankers, sit back and profit!
Dark humor aside I think disconnecting a bunch of problem solvers from their computing equipment could really enhance the world, putting that analytical power into the world in person could move a lot of cogs, rather then marshalling JSON.