…What am I even reading? Am I crazy to think this is a crazy thing to say, or it’s actually crazy?
This is not an outrageous amount of money, if the productivity is there. More likely the AI would work like two $90k junior engineers, but without a need to pay for a vacation, office space, social security, etc. If the productivity ends up higher than this, it's pure profit; I suppose this is their bet.
The human engineer would be like a tech lead guiding a tea of juniors, only designing plans and checking results above the level of code proper, but for exceptional cases, like when a human engineer would look at the assembly code a compiler has produced.
This does sound exaggeratedly optimistic now, but does not sound crazy.
This looks like AI companies marketing that is something in line 1+1 or buy 3 for 2.
Money you don’t spend on tokens are the only saved money, period.
With employees you have to pay them anyway you can’t just say „these requirements make no sense, park for two days until I get them right”.
You would have to be damn sure of that you are doing the right thing to burn $1k a day on tokens.
With humans I can see many reasons why would you pay anyway and it is on you that you should provide sensible requirements to be built and make use of employees time.
The seem to be plenty of people willing to pay the AI do that junior engineer level work, so wouldn’t it make sense to defect and just wait until it has gained enough experience to do the senior engineer work?
You just reduced the supply of engineers from millions to just three. If you think it was expensive before ...
Google, OpenAI, Anthropic, Meta, Amazon, Reka AI, Alibaba (Qwen), 01 AI, Cohere, DeepSeek, Nvidia, Mistral, NexusFlow, Z.ai (GLM), xAI, Ai2, Princeton, Tencent, MiniMax, Moonshot (Kimi) and I've certainly missed some.
All of those organizations have trained what I'd class as a GPT-4+ level model.
aws and gcp's margins are legendarily poor
oh, wait
Big part of why clouds are expensive is not necessary hardware, but all software infra and complexity of all services.
I hear things like this all the time, but outside of a few major centers it's just not the norm. And no companies are spending anything like $1k / month on remote work environments.
What dystopia is this?
> $20/month Claude sub
> $20/month OpenAI sub
> When Claude Code runs out, switch to Codex
> When Codex runs out, go for a walk with the dogs or read a book
I'm not an accelerationist singularity neohuman. Oh well, I still get plenty done
Setting aside the absurdity of using dollars per day spent on tokens as the new lines of code per day, have they not heard of mocks or simulation testing? These are long proven techniques, but they appear bent on taking credit for some kind revolutionary discovery by recasting these standard techniques as a Digital Twin Universe.
One positive(?) thing I'll say is that this fits well with my experience of people who like to talk about software factories (or digital factories), but at least they're up front about the massive cost of this type of approach - whereas "digital factories" are typically cast as a miracle cure that will reduce costs dramatically somehow (once it's eventually done correctly, of course).
Hard pass.
Designing reliable, stable, and correct systems is already a high level task. When you actually need to write the code for it, it's not a lot and you should write it with precision. When creating novel or differently complex systems, you should (or need to) be doing it yourself anyway.
If it's not labelled it's in violation of FTC regulations, for both the companies and the individuals.
[ That said... I'm surprised at this example on LinkedIn that was linked to by the Washington Post - https://www.linkedin.com/posts/meganlieu_claudepartner-activ... - the only hint it's sponsored content is the #ClaudePartner hashtag at the end, is that enough? Oh wait! There's text under the profile that says "Brand partnership" which I missed, I guess that's the LinkedIn standard for this? Feels a bit weak to me! https://www.linkedin.com/help/linkedin/answer/a1627083 ]
- Factory, unconvinced. Their marketing videos are just too cringe, and any company that tries to get my attentions with free tokens in my DMs reduce my respect for them. If you're that good, you don't need to convince me by giving me free stuff. Additionally, some posts on Twitter about it have this paid influencer smell. If you use claude code tho, you'll feel right at home with the [signature flicker](https://x.com/badlogicgames/status/1977103325192667323).
+ Factory, unconvinced. Their videos are a bit cringe, I do hear good things in my timeline about it tho, even if images aren't supported (yet) and they have the [signature flicker](https://x.com/badlogicgames/status/1977103325192667323).
My bosses bosses boss like to claim that we're successfully moving to the cloud because the cost is increasing year over year.
Which is more or less creating a customized harness. There is a lot more that is possiible once we move past the idea that harnesses are just for workflow variations for engineers.
The only github I could find is: https://github.com/strongdm/attractor
Building Attractor
Supply the following prompt to a modern coding agent
(Claude Code, Codex, OpenCode, Amp, Cursor, etc):
codeagent> Implement Attractor as described by
https://factory.strongdm.ai/
Edit:
I did find some code. Commit history has been squashed unfortunately: https://github.com/strongdm/cxdb
There's a bunch more under the same org but it's years old.
(I'm continuing to try to learn Rust!)
- The StoreError type is stringly typed and generally badly thought out. Depending on what they actually want to do, they should either add more variants to StoreError for the difference failure cases, replaces the strings with a sub-types (probably enums) to do the same, or write a type erased error similar to (or wrapping) the ones provided by anyhow, eyre, etc, but with a status code attached. They definitely shouldn't be checking for substrings in their own error type for control flow.
- So many calls to String::clone [0]. Several of the ones I saw were actually only necessary because the function took a parameter by reference even though it could have (and I would argue should have) taken it by value (If I had to guess, I'd say the agent first tried to do it without the clone, got an error, and implemented a local fix without considering the broader context).
- A lot of errors are just ignored with Result::unwrap_or_default or the like. Sometimes that's the right choice, but from what I can see they're allowing legitimate errors to pass silently. They also treat the values they get in the error case differently, rather than e.g. storing a Result or Option.
- Their HTTP handler has an 800 line long closure which they immediately call, apparently as a substitute for the the still unstable try_blocks feature. I would strongly recommend moving that into it's own full function instead.
- Several ifs which should have been match.
- Lots of calls to Result::unwrap and Option::unwrap. IMO in production code you should always at minimum use expect instead, forcing you to explain what went wrong/why the Err/None case is impossible.
It wouldn't catch all/most of these (and from what I've seen might even induce some if agents continue to pursue the most local fix rather than removing the underlying cause), but I would strongly recommend turning on most of clippy's lints if you want to learn rust.
[0] https://rust-unofficial.github.io/patterns/anti_patterns/bor...
PS: TIL about "Canadian girlfriend", thanks!
For those of us working on building factories, this is pretty obvious because once you immediately need shared context across agents / sessions and an improved ID + permissions system to keep track of who is doing what.
The worst part is they got simonw to (perhaps unwittingly or social engineering) vouch and stealth market for them.
And $1000/day/engineer in token costs at current market rates? It's a bold strategy, Cotton.
But we all know what they're going for here. They want to make themselves look amazing to convince the boards of the Great Houses to acquire them. Because why else would investors invest in them and not in the Great Houses directly.
(Two people who's opinions I respect said "yeah you really should accept that invitation" otherwise I probably wouldn't have gone.)
I've been looking forward to being able to write more details about what they're doing ever since.
EDIT nvm just saw your other comment.
We’ve been working on this since July, and we shared the techniques and principles that have been working for us because we thought others might find them useful. We’ve also open-sourced the nlspec so people can build their own versions of the software factory.
We’re not selling a product or service here. This also isn’t about positioning for an acquisition: we’ve already been in a definitive agreement to be acquired since last month.
It’s completely fair to have opinions and to not like what we’re putting out, but your comment reads as snarky without adding anything to the conversation.
There are higher and lower leverage ways to do that, for instance reviewing tests and QA'ing software via use vs reading original code, but you can't get away from doing it entirely.
What I’m working on (open source) is less about replacing human validation and more about scaling it: using multiple independent agents with explicit incentives and disagreement surfaced, instead of trusting a single model or a single reviewer.
Humans are still the final authority, but consensus, adversarial review, and traceable decision paths let you reserve human attention for the edge cases that actually matter, rather than reading code or outputs linearly.
Until we treat validation as a first-class system problem (not a vibe check on one model’s answer), most of this will stay in “cool demo” territory.
We’ve spent years systematizing generation, testing, and deployment. Validation largely hasn’t changed, even as the surface area has exploded. My interest is in making that human effort composable and inspectable, not pretending it can be eliminated.
And “define the spec concretely“ (and how to exploit emerging behaviors) becomes the new definition of what programming is.
(and unambiguously. and completely. For various depths of those)
This always has been the crux of programming. Just has been drowned in closer-to-the-machine more-deterministic verbosities, be it assembly, C, prolog, js, python, html, what-have-you
There have been a never ending attempts to reduce that to more away-from-machine representation. Low-code/no-code (anyone remember Last-one for Apple ][ ?), interpreting-and/or-generating-off DSLs of various level of abstraction, further to esperanto-like artificial reduced-ambiguity languages... some even english-like..
For some domains, above worked/works - and the (business)-analysts became new programmers. Some companies have such internal languages. For most others, not really. And not that long ago, the SW-Engineer job was called Analyst-programmer.
But still, the frontier is there to cross..
Biological evolution overcomes this by running thousands and millions of variations in parallel, and letting the more defective ones to crash and die. In software ecosystems, we can't afford such a luxury.
However I guess that at least some of that can be mitigated by distilling out a system description and then running agents again to refactor the entire thing.
The problem with this is that the code is the spec. There are 1000 times more decisions made in the implementation details than are ever going to be recorded in a test suite or a spec.
The only way for that to work differently is if the spec is as complex as the code and at that level what’s the point.
With what you’re describing, every time you regenerate the whole thing you’re going to get different behavior, which is just madness.
>StrongDM’s answer was inspired by Scenario testing (Cem Kaner, 2003).
> You still have to have a human who knows the system to validate that the thing that was built matches the intent of the spec.
You don't need a human who knows the system to validate it if you trust the LLM to do the scenario testing correctly. And from my experience, it is very trustable in these aspects.
Can you detail a scenario by which an LLM can get the scenario wrong?
You and I disagree on this specific point.
Edit: I find your comment a bit distasteful. If you can provide a scenario where it can get it incorrect, that’s a good discussion point. I don’t see many places where LLMs can’t verify as good as humans. If I developed a new business logic like - users from country X should not be able to use this feature - LLM can very easily verify this by generating its own sample api call and checking the response.
Then it seems like the only workable solution from your perspective is a solo member team working on a product they came up with. Because as soon as there's more than one person on something, they have to use "lossy natural language" to communicate it between themselves.
On the plus side, IMO nonverbal cues make it way easier to tell when a human doesn't understand things than an agent.
You can't 100% trust a human either.
But, as with self-driving, the LLM simply needs to be better. It does not need to be perfect.
We do have a system of checks and balances that does a reasonable job of it. Not everyone in position of power is willing to burn their reputation and land in jail. You don't check the food at the restaurant for poison, nor check the gas in your tank if it's ok. But you would if the cook or the gas manufacturer was as reliable as current LLMs.
Very salient concept in regards to LLM's and the idea that one can encode a program one wishes to see output in natural English language input. There's lots of room for error in all of these LLM transformations for same reason.
I think this will act as a brake on the agentic shift as a whole.
At that point, outside of FAANG and their salaries, you are spending more on AI than you are on your humans. And they consider that level of spend to be a metric in and of itself. I'm kinda shocked the rest of the article just glossed over that one. It seems to be a breakdown of the entire vision of AI-driven coding. I mean, sure, the vendors would love it if everyone's salary budget just got shifted over to their revenue, but such a world is absolutely not my goal.
Edit: here's that section: https://simonwillison.net/2026/Feb/7/software-factory/#wait-...
Assuming 20 working days a month: that's 20k x 12 == 240k a year. So about a fresh grad's TC at FANG.
Now I've worked with many junior to mid-junior level SDEs and sadly 80% does not do a better job than Claude. (I've also worked with staff level SDEs who writes worse code than AI, but they offset that usually with domain knowledge and TL responsibilities)
I do see AI transform software engineering into even more of a pyramid with very few human on top.
> At that point, outside of FAANG and their salaries, you are spending more on AI than you are on your humans
You say
> Assuming 20 working days a month: that's 20k x 12 == 240k a year. So about a fresh grad's TC at FANG.
So you both are in agreement on that part at least.
And it might be the tokens will become cheaper.
Future better models will both demand higher compute use AND higher energy. We cannot underestimate the slowness of energy production growth and also the supplies required for simply hooking things up. Some labs are commissioning their own power plants on site, but this is not a true accelerator for power grid growth limits. You're using the same supply chain to build your own power plant.
If inference cost is not dramatically reduced and models don't start meaningfully helping with innovations that make energy production faster and inference/training demand less power, the only way to control demand is to raise prices. Current inference costs, do not pay for training costs. They can probably continue to do that on funding alone, but once the demand curve hits the power production limits, only one thing can slow demand and that's raising the cost of use.
which sounds more like if you haven't reached this point you don't have enough experience yet, keep going
At least that's how I read the quote
Wouldn’t they start to evolve to be able to reproduce more and eat more tokens? And then they’d be mature agents to take further human prompts to gain more tokens?
Would you see certain evolutionary strategies reemerge like carnivores eating weaker agents for tokens, eating of detritus of old code, or would it be more like evolution of roles in a company?
I assume the hurdles would be agents reproducing? How is that implemented?
Apart from being a absolutely ridiculous metric, this is a bad approach, at least with current generation models. In my experience, the less you inspect what the model does, the more spaghetti-like the code will be. And the flying spaghetti monster eats tokens faster than you can blink! Or put more clearly: implementing a feature will cost you a lot more tokens in a messy code base than it does in a clean one. It's not (yet) enough to just tell the agent to refactor and make it clean, you have to give it hints on how to organise the code.
I'd go do far as to say that if you're burning a thousand dollars a day per engineer, you're getting very little bang for your tokens.
And your engineers probably look like this: https://share.google/H5BFJ6guF4UhvXMQ7
I wrote a bunch more about that this morning: https://simonwillison.net/2026/Feb/7/software-factory/
This one is worth paying attention to to. They're the most ambitious team I've see exploring the limits of what you can do with this stuff. It's eye-opening.
> If you haven’t spent at least $1,000 on tokens today per human engineer, your software factory has room for improvement
Seems to me like if this is true I'm screwed no matter if I want to "embrace" the "AI revolution" or not. No way my manager's going to approve me to blow $1000 a day on tokens, they budgeted $40,000 for our team to explore AI for the entire year.
Let alone from a personal perspective I'm screwed because I don't have $1000 a month in the budget to blow on tokens because of pesky things that also demand financial resources like a mortgage and food.
At this point it seems like damned if I do, damned if I don't. Feels bad man.
I don't think you need to spend anything like that amount of money to get the majority of the value they're describing here.
Edit: added a new section to my blog post about this: https://simonwillison.net/2026/Feb/7/software-factory/#wait-...
I built a tool that writes (non shit) reports from unstructured data to be used internally by analysts at a trading firm.
It cost between $500 to $5000 per day per seat to run.
It could have cost a lot more but latency matters in market reports in a way it doesn't for software. I imagine they are burning $1000 per day per seat because they can't afford more.
Another skill called skill-improver, which tries to reduce skill token usage by finding deterministic patterns in another skill that can be scripted, and writes and packages the script.
Putting them together, the container-maintenance thingy improves itself every iteration, validated with automatic testing. It works perfectly about 3/4 of the time, another half of the time it kinda works, and fails spectacularly the rest.
It’s only going to get better, and this fit within my Max plan usage while coding other stuff.
If the tokens that need to attend to each other are on opposite ends of the code base the only way to do that is by reading in the whole code base and hoping for the best.
If you're very lucky you can chunk the code base in such a way that the chunks pairwise fit in your context window and you can extract the relevant tokens hierarchically.
If you're not. Well get reading monkey.
Agents, md files, etc. are bandaids to hide this fact. They work great until they don't.
I would expect cost to come down over time, using approaches pioneered in the field of manufacturing.
As for me, we get Cursor seats at work, and at home I have a GPU, a cheap Chinese coding plan, and a dream.
Right in the feels
Make a "systemctl start tokenspender.service" and share it with the team?
To be fair, I’ll bet many embracing concerning advice like that have never worked for the same company for a full year.
I didn't read that as you need to be spending $1k/day per engineer. That is an insane number.
EDIT: re-reading... it's ambiguous to me. But perhaps they mean per day, every day. This will only hasten the elimination of human developers, which I presume is the point.
At home on my personal setup, I haven't even had to move past the cheapest codex/claude code subscription because it fulfills my needs ¯\_(ツ)_/¯. You can also get a lot of mileage out of the higher tiers of these subscriptions before you need to start paying the APIs directly.
Takes like this are just baffling to me.
For one engineer that is ~260k a year.
The thing with AI is that it ranges from net-negative to easily brute forcing tedious things that we never have considered wasting human time on. We can't figure out where the leverage is unless all the subject matter experts in their various organizational niches really check their assumptions and get creative about experimenting and just trying different things that may never have crossed their mind before. Obviously over time best practices will emerge and get socialized, but with the rate that AI has been improving lately, it makes a lot of sense to just give employees carte blanche to explore. Soon enough there will be more scrutiny and optimization, but that doesn't really make sense without a better understanding of what is possible.
1) Engineering investment at companies generally pays off in multiples of what is spent on engineering time. Say you pay 10 engineers $200k / year each and the features those 10 engineers build grow yearly revenue by $10M. That’s a 4x ROI and clearly a good deal. (Of course, this only applies up to some ceiling; not every company has enough TAM to grow as big as Amazon).
2) Giving engineers near-unlimited access to token usage means they can create even more features, in a way that still produces positive ROI per token. This is the part I disagree with most. It’s complicated. You cannot just ship infinite slop and make money. It glosses over massive complexity in how software is delivered and used.
3) Therefore (so the argument goes) you should not cap tokens and should encourage engineers to use as many as possible.
Like I said, I don’t agree with this argument. But the key thing here is step 1. Engineering time is an investment to grow revenue. If you really could get positive ROI per token in revenue growth, you should buy infinite tokens until you hit the ceiling of your business.
Of course, the real world does not work like this.
But my point is moreso that saying 1k a day is cheap is ridiculous. Even for a company that expects an ROI on that investment. There’s risks involved and as you said, diminishing returns on software output.
I find AI bros view of the economics of AI usage strange. It’s reasonable to me to say you think its a good investment, but to say it’s cheap is a whole different thing.
The best you can say is “high cost but positive ROI investment.” Although I don’t think that’s true beyond a certain point either, certainly not outside special cases like small startups with a lot of funding trying to build a product quickly. You can’t just spew tokens about and expect revenue to increase.
That said, I do reserve some special scorn for companies that penny-pinch on AI tooling. Any CTO or CEO who thinks a $200/month Claude Max subscription (or equivalent) for each developer is too much money to spent really needs to rethink their whole model of software ROI and costs. You’re often paying your devs >$100k yr and you won’t pay $2k / yr to make them more productive? I understand there are budget and planning cycle constraints blah blah, but… really?!
Their page looks to me like a lot of invented jargon and pure narrative. Every technique is just a renamed existing concept. Digital Twin Universe is mocks, Gene Transfusion is reading reference code, Semport is transpilation. The site has zero benchmarks, zero defect rates, zero cost comparisons, zero production outcomes. The only metric offered is "spend more money".
Anyone working honestly in this space knows 90% of agent projects are failing.
The main page of HN now has three to four posts daily with no substance, just Agentic AI marketing dressed as engineering insight.
With Google, Microsoft, and others spending $600 billion over the next year on AI, and panicking to get a return on that Capex....and with them now paying influencers over $600K [1] to manufacture AI enthusiasm to justify this infrastructure spend, I won't engage with any AI thought leadership that lacks a clear disclosure of financial interests and reproducible claims backed by actual data.
Show me a real production feature built entirely by agents with full traces, defect rates, and honest failure accounting. Or stop inventing vocabulary and posting vibes charts.
Repeating for emphasis, because this is the VERY obvious question anyone with a shred of curiosity would be asking not just about this submission but about what is CONSTANTLY on the frontpage these days.
There could be a very simple 5 question questionnaire that could eliminate 90+% of AI coding requests before they start:
- Is this a small wrapper around just querying an existing LLM
- Does a brief summary of this searched with "site:github" already return dozens or hundreds of results?
- Is this a classic scam (pump&dump, etc) redone using "AI"
- Is this needless churn between already high level abstractions of technology (dashboard of dashboards, yaml to json, python to java script, automation of automation framework)
I will reformulate my question to ask instead if the page is still 100% correct or needs an update?
However I would argue there are significant gaps:
- You do not name your consulting clients. You admit to do ad-hoc consulting and training for unnamed companies while writing daily about AI products. Those client names are material information.
- You have non payments that have monetary value. Free API credits, and weeks of early preview access, flights, hotels, dinners, and event invitations are all compensation. Do you keep those credits?
- The "I have not accepted payments from LLM vendors" could mean receiving things worth thousands of dollars. Please note I am not saying you did.
- You have a structural conflict. Your favorable coverage will mean preview access, then exclusive content then traffic, then sponsors, then consulting clients.
- You appeared in an OpenAI promotional video for GPT-5 and were paid for it. This is influencer marketing by any definition.
- Your quotes are used as third-party validation in press coverage of AI product launches. This is a PR function with commercial value to these companies.
The FTC revised Endorsement Guides explicitly apply to bloggers, not just social media influencers. The FTC defines material connection to include not only cash payments but also free products, early access to a product, event invitations, and appearing in promotional media all of which would seem to apply here.
They also say in the FTC own "Disclosures 101" guide that states [2]: "...Disclosures are likely to be missed if they appear only on an ABOUT ME or profile page, at the end of posts or videos, or anywhere that requires a person to click MORE."
https://www.ftc.gov/business-guidance/resources/disclosures-...
[2] - https://www.ftc.gov/system/files/documents/plain-language/10...
I would argue an ecosystem of free access, preview privileges, promotional video appearances, API credits, and undisclosed consulting does constitute a financial relationship that should be more transparently disclosed than "I have not accepted payments from LLM vendors."
I have a very strong policy that I won't write about someone because they paid me to do so, or asked me to as part of a consulting engagement. I guess you'll just have to trust me that I'll hold to that. I like to hope I've earned the trust of most of my readers.
I do have a structural conflict, which is one of the reasons my disclosures page exists. I don't value things like early access enough to avoid writing critically about companies, but the risk of subtle bias is always there. I can live with that, and I trust my readers can live with it too.
I've found myself in a somewhat strange position where my hobby - blogging about stuff I find interesting - has somehow grown to the point that I'm effectively single-handedly running an entire news agency covering the world's most valuable industry. As a side-project.
I could commit to this full-time and adopt full professional journalist ethics - no accepted credits, no free travel etc. I'd still have to solve the revenue side of things, and if I wrote full time I'd give up being a practitioner which would damage my ability to credibly cover the space. Part of the reason people trust me is that I'm an active developer and user of these tools.
On top of that, some people default to believing that the only reason anyone would write anything positive about AI is if they were being paid to do so. Convincing those people otherwise is a losing battle, and I'm trying to learn not to engage.
So I'm OK with my disclosures and principles as they stand. They may not get a 100% pure score from everyone, but they're enough to satisfy my own personal ethics.
I have just added disclosures links to the footer to make them easier to find - thanks for the prod on that: https://github.com/simonw/simonwillisonblog/commit/95291fd26...
These aren't tools they're asking $25,000 upfront for, that they can trick us that it for sure definitely works and get the huge lump sum then run
Nah.. at best they get a few dollars upfront for us to try it out. Then what? If it doesn't deliver on their promise, it flops
The hyperscalers are spending 600 billion a year, and literally betting their companies future, on what will happen over the next 24 months...but the bloggers are all doing it for philanthropy and to play with cool tech....Got it...
Let's say super popular blogger x is paid a million dollars to shill for AI and they convince you it's revolutionary. What then? Well of course you try it! You pay OpenAI $20 for a month
What happens after that, the actual experience of using the product, is the only important thing. If it sucks and provides no value to anyone, OpenAI fails. Sleezy marketing and salesmen can only get you in the door. They can't make a shit product amazing
A $10,000 get rich quick course can be made successful on hopes, dreams and sales tactics. A monthly subscription tool to help people with their work crashes and burns if it doesn't provide value
It doesn't matter how many people shill for it
I don't think it's unreasonable to say that your enumerated list would be considered beyond simply being enthusiastic about a new technology
The moats here are around mechanism design and values (to the extent they differ): the frontier labs are doomed in this world, the commons locked up behind paywalls gets hyper mirrored, value accrues in very different places, and it's not a nice orderly exponent from a sci-fi novel. It's nothing like what the talking heads at Davos say, Anthropic aren't in the top five groups I know in terms of being good at it, it'll get written off as fringe until one day it happens in like a day. So why be secretive?
You get on the ladder by throwing out Python and JSON and learning lean4, you tie property tests to lean theorems via FFI when you have to, you start building out rfl to pretty printers of proven AST properties.
And yeah, the droids run out ahead in little firecracker VMs reading from an effect/coeffect attestation graph and writing back to it. The result is saved, useful results are indexed. Human review is about big picture stuff, human coding is about airtight correctness (and fixing it when it breaks despite your "proof" that had a bug in the axioms).
Programming jobs are impacted but not as much as people think: droids do what David Graeber called bullshit jobs for the most part and then they're savants (not polymath geniuses) at a few things: reverse engineering and infosec they'll just run you over, they're fucking going in CIC.
This is about formal methods just as much as AI.
This is one of the clearest takes I've seen that starts to get me to the point of possibly being able to trust code that I haven't reviewed.
The whole idea of letting an AI write tests was problematic because they're so focused on "success" that `assert True` becomes appealing. But orchestrating teams of agents that are incentivized to build, and teams of agents that are incentivized to find bugs and problematic tests, is fascinating.
I'm quite curious to see where this goes, and more motivated (and curious) than ever to start setting up my own agents.
Question for people who are already doing this: How much are you spending on tokens?
That line about spending $1,000 on tokens is pretty off-putting. For commercial teams it's an easy calculation. It's also depressing to think about what this means for open source. I sure can't afford to spend $1,000 supporting teams of agents to continue my open source work.
I don't take your comment as dismissive, but I think a lot of people are dismissing interesting and possibly effective approaches with short reactions like this.
I'm interested in the approach described in this article because it's specifying where the humans are in all this, it's not about removing humans entirely. I can see a class of problems where any non-determinism is completely unacceptable. But I can also see a large number of problems where a small amount of non-determinism is quite acceptable.
Something like "approve this PR and I will generate some easy bugs for you to find later"
I think people are burning money on tokens letting these things fumble about until they arrive at some working set of files.
I'm staying in the loop more than this, building up rather than tuning out
And it’s not unreasonable to assume it’s going there.
That being said, the models are not there yet. If you care about quality, you still need humans in the loop.
Even when given high quality specs, and existing code to use as an example, and lots of parallelism and orchestration, the models still make a lot of mistakes.
There’s lots of room for Software Factories, and Orchestrators, and multi agent swarms.
But today you still need humans reviewing code before you merge to main.
Models are getting better, quickly, but I think it’s going to be a while before “don’t have humans look at the code” is true.
Is this the quality we should expect from agentic? From my experiments with claude code, yes, the UX details are never there. Especially for bigger features. It can work reasonably well independently up to a "module" level (with clear interfaces). But for full app design, while technically possible, the UX and visual design is just not there.
And I am very not attracted to the idea of polishing such an agentic apps. A solution could be: 1. The boss prompts the system with what he wants. 2. The boss outsources to india the task of polishing the rough edges.
===
More on the arrow keys navigation: Pressing right on the last "Products" page loops to the first "Story" page, yet pressing left on the first page does nothing. Typical UX inconsistency of vibe coded software.
as a previous strongDM customer, i will never recommend their offering again. for a core security product, this is not the flex they think it is
also mimicking other products behavior and staying in sync is a fools task. you certainly won't be able to do it just off the API documentation. you may get close, but never perfect and you're going to experience constant breakage
From what I've heard the acquisition was unrelated to their AI lab work, it was about the core business.
Oh, to have the luxury of redefining success and handwaving away hard learned lessons in the software industry.
If their focus is to only show their productivity/ai system but not having built anything meaningful with it, it feels like one of those scammy life coaches/productivity gurus that talk about how they got rich by selling their courses.
If everyone can do this, there won't be any advantage (or profit) to be had from it very soon. Why not buy your own hardware and run local models, I wonder.
No local model out there is as good as the SOTA right now.
You should have led with that. I think that's actually more impressive; anyone can spend tokens.
This is still the same problem -- just pushed back a layer. Since the generated API is wrong, the QA outcomes will be wrong, too. Also, QAing things is an effective way to ensure that they work _after_ they've been reviewed by an engineer. A QA tester is not going to test for a vulnerability like a SQL injection unless they're guided by engineering judgement which comes from an understanding of the properties of the code under test.
The output is also essentially the definition of a derivative work, so it's probably not legally defensible (not that that's ever been a concern with LLMs).
Heat death of the SaaSiverse
As an example: imagine someone writing a data pipeline for training a machine learning model. Anyone who's done this knows that such a task involves lots data wrangling work like cleaning data, changing columns and some ad hoc stuff.
The only way to verify that things work is if the eventual model that is trained performs well.
In this case, scenario testing doesn't scale up because the feedback loop is extremely large - you have to wait until the model is trained and tested on hold out data.
Scenario testing clearly can not work on the smaller parts of the work like data wrangling.
What we have instead are many people creating hierarchies of concepts, a vast “naming” of their own experiences, without rigorous quantitative evaluation.
I may be alone in this, but it drives me nuts.
Okay, so with that in mind, it amounts to heresay “these guys are doing something cool” — why not shut up or put up with either (a) an evaluation of the ideas in a rigorous, quantitative way or (b) apply the ideas to produce an “hard” artifact (analogous, e.g., to the Anthropic C compiler, the Cursor browser) with a reproducible pathway to generation.
The answer seems to be that (b) is impossible (as long as we’re on the teet of the frontier labs, which disallow the kind of access that would make (b) possible) and the answer for (a) is “we can’t wait we have to get our names out there first”
I’m disappointed to see these types of posts on HN. Where is the science?
There are plenty of papers out there that look at LLM productivity and every one of them seems to have glaring methodology limitations and/or reports on models that are 12+ months out of date.
Have you seen any papers that really elevated your understanding of LLM productivity with real-world engineering teams?
Further, I’m not sure this elevates my understanding: I’ve read many posts on this space which could be viewed as analogous to this one (this one is more tempered, of course). Each one has this same flaw: someone is telling me I need to make a “organization” out of agents and positive things will follow.
Without a serious evaluation, how am I supposed to validate the author’s ontology?
Do you disagree with my assessment? Do you view the claims in this content as solid and reproducible?
My own view is that these are “soft ideas” (GasTown, Ralph fall into a similar category) without the rigorous justification.
What this amounts to is “synthetic biology” with billion dollar probability distributions — where the incentives are setup so that companies are incentivized to convey that they have the “secret sauce” … for massive amounts of money.
To that end, it’s difficult to trust a word out of anyone’s mouth — even if my empirical experiences match (along some projection).
StrongDM's implementation is the most impressive I've seen myself, but it's also incredibly expensive. Is it worth the cost?
Cursor's FastRender experiment was also interesting but also expensive for what was achieved.
I think my favorite current example at the moment was Anthropic's $20,000 C compiler from the other day. But they're an AI vendor, demos from non-vendors carry more weight.
I've seen enough to be convinced that there's something there, but I'm also confident we aren't close to figuring out the optimal way of putting this stuff to work yet.
The only reason I'm not dismissing it out of hand is basically because you said this team was worth taking a look at.
I'm not looking for a huge amount of statistical ceremony, but some detail would go a long way here.
What exactly was achieved for what effort and how?
Either you have faith and every post like this fills you with fervor and pious excitement for the latest miracles performed by machine gods.
Or you are a nonbeliever and each of these posts is yet another false miracle you can chalk up to baseless enthusiasm.
Without proper empirical method, we simply do not know.
What's even funnier about it is that large-scale empirical testing is actually necessary in the first place to verify that a stochastic processes is even doing what you want (at least on average). But the tech community has become such a brainless atmosphere totally absorbed by anecdata and marketing hype that no one simply seems to care anymore. It's quite literally devolved into the religious ceremony of performing the rain dance (use AI) because we said so.
One thing the papers help provide is basic understanding and consistent terminology, even when the models change. You may not find value in them but I assure you that the actual building of models and product improvements around them is highly dependent on the continual production of scientific research in machine learning, including experiments around applications of llms. The literature covers many prompting techniques well, and in a scientific fashion, and many of these have been adopted directly in products (chain of thought, to name one big example—part of the reason people integrate it is not because of some "fingers crossed guys, worked on my query" but because researchers have produced actual statistically significant results on benchmarks using the technique) To be a bit harsh, I find your very dismissal of the literature here in favor of hype-drenched blog posts soaked in ridiculous language and fantastical incantations to be precisely symptomatic of the brain rot the LLM craze has produced in the technical community.
One challenge we have here is that there are a lot of people who are desperate for evidence that LLMs are a waste of time, and they will leap on any paper that supports that narrative. This leads to a slightly perverse incentive where publishing papers that are critical of AI is a great way to get a whole lot of attention on that paper.
In that way academic papers and blogging aren't as distinct as you might hope!
Taking the time to point a coding agent towards the public (or even private) API of a B2B SaaS app to generate a working (partial) clone is effectively "unblocking" the agent. I wouldn't be surprised if a "DTU-hub" eventually gains traction for publishing and sharing these digital twins.
I would love to hear more about your learnings from building these digital twins. How do you handle API drift? Also, how do you handle statefulness within the twins? Do you test for divergence? For example, do you compare responses from the live third-party service against the Digital Twin to check for parity?
Of course, you can't always tell the model what to do, especially if it is a repeated task. It turns out, we already solved this decades ago using algorithms. Repeatable, reproducible, reliable. The challenge (and the reward) lies in separating the problem statement into algorithmic and agentic. Once you achieve this, the $1000 token usage is not needed at all.
I have a working prototype of the above and I'm currently productizing it (shameless plug):
However - I need to emphasize, the language you use to apply the pattern above matters. I use Elixir specifically for this, and it works really, really well.
It works based off starting with the architect. You. It feeds off specs and uses algorithms as much as possible to automate code generation (eg. Scaffolding) and only uses AI sparsely when needed.
Of course, the downside of this approach is that you can't just simply say "build me a social network". You can however say something like "Build me a social network where users can share photos, repost, like and comment on them".
Once you nail the models used in the MVC pattern, their relationships, the software design is pretty much 50% battle won. This is really good for v1 prototypes where you really want best practices enforced, OSWAP compliant code, security-first software output which is where a pure agentic/AI approach would mess up.
> How do you clone the important parts of Okta, Jira, Slack and more? With coding agents!
This is what's going to gut-punch most SaaS companies repeatedly over the next decade, even if this whole build-out ultimately collapses in on itself (which I expect it to). The era of bespoke consultants for SaaS product suites to handle configuration and integrations, while not gone, are certainly under threat by LLMs that can ingest user requirements and produce functional code to do a similar thing at a fraction of the price.
What a lot of folks miss is that in enterprise-land, we only need the integration once. Once we have an integration, it basically exists with minimal if any changes until one side of the integration dies. Code fails a security audit? We can either spool up the agents again briefly to fix it, or just isolate it in a security domain like the glut of WinXP and Win7 boxen rotting out there on assembly lines and factory floors.
This is why SaaS stocks have been hammered this week. It's not that investors genuinely expect huge players to go bankrupt due to AI so much as they know the era of infinite growth is over. It's also why big AI companies are rushing IPOs even as data center builds stall: we're officially in a world where a locally-run model - not even an Agent, just a model in LM Studio on the Corporate Laptop - can produce sufficient code for a growing number of product integrations without any engineer having to look through yet another set of API documentation. As agentic orchestration trickles down to homelabs and private servers on smaller, leaner, and more efficient hardware, that capability is only going to increase, threatening profits of subscription models and large AI companies. Again, why bother ponying up for a recurring subscription after the work is completed?
For full-fledged software, there's genuine benefit to be had with human intervention and creativity; for the multitude of integrations and pipelines that were previously farmed out to pricey consultants, LLMs will more than suffice for all but the biggest or most complex situations.
Stuff comes in from an API goes out to a different API.
With a semi-decent agent I can build what took me a week or two in hours just because it can iterate the solution faster than any human can type.
A new field in the API could’ve been a two day ordeal of patching it through umpteen layers of enterprise frameworks. Now I can just tell Claude to add it, it’ll do it up to the database in minutes - and update the tests at the same time.
So much of enterprise IT nowadays is spent hammering or needling vendors for basic API documentation so we can write a one-off that hooks DB1 into ServiceNow that's also pulling from NewRelic just to do ITAM. Consultants would salivate over such a basic integration because it'd be their yearly salary over a three month project.
Now we can do this ourselves with an LLM in a single sprint.
That's a Pandora's Box moment right there.
I’m happy to answer any questions!
> Those of us building software factories must practice a deliberate naivete
This is a great way to put it, I've been saying "I wonder which sacred cows are going to need slaughtered" but for those that didn't grow up on a farm, maybe that metaphor isn't the best. I might steal yours.
This stuff is very interesting and I'm really interested to see how it goes for you, I'll eagerly read whatever you end up putting out about this. Good luck!
EDIT: oh also the re-implemented SaaS apps really recontextualizes some other stuff I’ve been doing too…
Even though all three of us have very different working styles, we all seem to be very happy with the arrangement.
You definitely need to keep an open mind, though, and be ready to unlearn some things. I guess I haven’t spent enough time in the industry yet to develop habits that might hinder adopting these tools.
Jay single-handedly developed the digital twin universe. Only one person commits to a codebase :-)
Or a vegan or Hindu. Which ethics are you willing to throw away to run the software factory?
I eat hamburgers while aware of the moral issues.
To name one probable area of involvement: how do you specify what needs to be built?
Your intuition/thinking definitely lines up with how we're thinking about this problem. If you have a good definition of done and a good validation harness, these agents can hill climb their way to a solution.
But you still need human taste/judgment to decide what you want to build (unless your solution is to just brute force the entire problem space).
For maximal leverage, you should follow the mantra "Why am I doing this?" If you use this enough times, you'll come across the bottleneck that can only be solved by you for now. As a human, your job is to set the higher-level requirements for what you're trying to build. Coming up with these requirements and then using agents to shape them up is acceptable, but human judgment is definitely where we have to answer what needs to be built. At the same time, I never want to be doing something the models are better at. Until we crack the proactiveness part, we'll be required to figure out what to do next.
Also, it looks like you and Danvers are working in the same space, and we love trading notes with other teams working in this area. We'd love to connect. You can either find my personal email or shoot me an email at my work email: navan.chauhan [at] strongdm.com
Not just code review agents, but things like "find duplicated code and refactor it"?
* DRYing/Refactoring if needed
* Documentation compaction
* Security reviews
I wonder what the security teams at companies that use StrongDM will think about this.
My hunch is that the thing that's going to matter is network effects and other forms of soft lockin. Features alone won't cut it - you need to build something where value accumulates to your user over time in a way that discourages them from leaving.
If I launch a new product, and 4 hours later competitors pop up, then there's not enough time for network effects or lockin.
I'm guessing what is really going to be needed is something that can't be just copied. Non-public data, business contracts, something outside of software.
You can see the first waves of this trend in HN new.
My content revenue comes from ads on my blog via https://www.ethicalads.io/ - rarely more than $1,000 in a given month - and sponsors on GitHub: https://github.com/sponsors/simonw - which is adding up to quite good money now. Those people get my sponsors-only monthly newsletter which looks like this: https://gist.github.com/simonw/13e595a236218afce002e9aeafd75... - it's effectively the edited highlights from my blog because a lot of people are too busy to read everything I put out there!
I try to keep my disclosures updated on the about page of my blog: https://simonwillison.net/about/#disclosures
https://m.youtube.com/watch?v=4xgx4k83zzc&pp=ygUOdGhlc2UgZ28...
In this model the spec/scenarios are the code. These are curated and managed by humans just like code.
They say "non interactive". But of course their work is interactive. AI agents take a few minutes-hours whereas you can see code change result in seconds. That doesn't mean AI agents aren't interactive.
I'm very AI-positive, and what they're doing is different, but they are basically just lying. It's a new word for a new instance of the same old type of thing. It's not a new type of thing.
The common anti-AI trope is "AI just looked at <human output> to do this." The common AI trope from the StrongDM is "look, the agent is working without human input." Both of these takes are fundamentally flawed.
AI will always depend on humans to produce relevant results for humans. It's not a flaw of AI, it's more of a flaw of humans. Consequently, "AI needs human input to produce results we want to see" should not detract from the intelligence of AI.
Why is this true? At a certain point you just have Kolmogorov complexity, AI having fixed memory and fixed prompt size, pigeonhole principle, not every output is possible to be produced even with any input given specific model weights.
Recursive self-improvement doesn't get around this problem. Where does it get the data for next iteration? From interactions with humans.
With the infinite complexity of mathematics, for instance solving Busy Beaver numbers, this is a proof that AI can in fact not solve every problem. Humans seem to be limited in this regard as well, but there is no proof that humans are fundamentally limited this way like AI. This lack of proof of the limitations of humans is the precise advantage in intelligence that humans will always have over AI.
THIS FRIGHTENS ME. Many of us sweng are either going be FIRE millionaires, or living under a bridge, in two years.
I’ve spent this week performing SemPort; found a ts app that does a needed thing, and was able to use a long chain of prompts to get it completely reimplemented in our stack, using Gene Transfer to ensure it uses some existing libraries and concrete techniques present in our existing apps.
Now not only do I have an idiomatic Python port, which I can drop right into our stack, but I have an extremely detailed features/requirements statement for the origin typescript app along with the prompts for generating it. I can use this to continuously track this other product as it improves. I also have the “instructions infrastructure” to direct an agent to align new code to our stack. Two reusable skills, a new product, and it took a week.
Is it really that hard to write “developer” or “engineer”?
I was just trying to share the same patterns from OPs documentation that I found valuable within the context of agentic development; seeing them take this so far is was scares me, because they are right that I could wire an agent to do this autonomously and probably get the same outcomes, scaled.
Funnily enough, the marketing department even ran a campaign asking, “What does DM stand for?!”, and the answer was “Digital Metropolis,” because we did a design refresh.
I just linked the website because that’s what the actual company does, and we are just the “AI Lab”
Code must not be written by humans
Code must not be reviewed by humans