As a lowly level 2 who remains skeptical of these software “dark factories” described at the top of this ladder, what I don’t understand is this:
If software engineering is enough of a solved problem that you can delegate it entirely to LLM agents, what part of it remains context-specific enough that it can’t be better solved by a general-purpose software factory product? In other words, if you’re a company that is using LLMs to develop non-AI software, and you’ve built a sufficient factory to generate that software, why don’t you start selling the factory instead of whatever you were selling before? It has a much higher TAM (all of software)
Why sell the factory when you can create automated software cloner companies that make millions off of instantly copying promising startups as soon as they come out of stealth?
If you could get a dark factory working when others don't have one, you can make much more money using it than however much you can make selling it
Was listening to a radio programme recently with 3 entrepreneurs talking about being entrepreneurs.
In relation to sales, there were two gems. For direct to consumer type companies - influencers are where it's at right now especially during bootstrap phase - and they were talking about trying to keep marketing budget under 20% of sales.
Another, who is mostly in the VC business, finds the best way to gain traction for his startups is to create controversy - ie anything to be talked about.
In both cases you are trying to be talked about - either by directly paying for people to do that, or by providing entertainment value so people talk about you.
You could argue that both of those activities are already been automated - and the nice thing about sales is there is that fairly direct feedback loop you can actively learn from.
Yeah I really would like to know how many bots are on reddit (and on particular subreddits/threads) and also how many are here!
The interesting thing though is that the bots are just cheaper versions of real human influencers. So nothing has changed aside from scale (and speed) - the underlying mechanisms of paying for word of mouth is the same as it's been for a long time.
You can do a lot of work with agents to remove a lot of manual work around the sales process. Sales is a lot of grinding on leads, contacts, follow ups, etc. And a lot of that is preparation work (background research, figuring out who to talk to, who the customer is, etc.), making sure follow ups are scheduled appropriately, etc.
You still should talk to people yourself and be very careful with communicating AI slop, cold outreach and other things that piss off more people than they get into your funnel. But a lot of stuff around this can be automated or at least supported by LLMs.
Most of the success with sales is actually having something that people want to buy. That sounds easy. But it's actually the hardest part of selling software. Getting there is a bit of a journey.
I've built a lot of stuff that did not sell well. These are hard earned lessons. I see a lot of startups fall into this trap. You can waste years on product development and many people do. Until it starts selling, it won't matter. Sales is not a person you hire to do it for you: you have to be able to sell it yourself. If you can't, nobody else will be able to either. Founder sales is crucial. Step back from that once it runs smoothly, not before.
Use AI to your advantage here. We use it for almost everything. SEO, wording stuff on our website, competitor analysis, staying on top of our funnel, analyzing and sharpening our pitches, preparing responses to customer questions and demands, criticizing and roasting our own pitches and ideas, etc. Confirmation bias is going to your biggest blindspot. And we also use LLMs to work on the actual product. This stuff is a lot of work. If you can afford a ten person team to work on this, great. But many startups have to prove themselves before the funding for that happens. And when it does, hiring lots of people isn't necessarily a good allocation of resources given you can automate a lot of it now. I'd recommend to hire fewer but better people.
Your points are all valid, but it doesn’t really change the situation that was being discussed: an AI company trying to enter completely new markets just because they can write software for it is hardly some sort of automatic win. They’re much more likely to fail than succeed.
I mentioned sales and marketing but there’s a whole lot more as well. Basically, it involves creating an entire subsidiary. Perhaps the time will come when that can be mostly done by a team of AI agents, but right now that’s a big hurdle in practice.
It does raise the question of where in the future will companies compete.
What's the balance going to be between,
'connecting customers to product' and
'making differentiated product'?
In theory, if customers have perfect information ( ignoring a very large part of sales is emotional ), then the former part will disappear. However the rise of the internet, and perhaps AI agents shopping on your behalf, hasn't really made much of a dent there [1] - marketing, in all it's forms, is still huge business - and you could argue still expanding ( cf google ).
[1] Perhaps because of the huge importance of the emotional component. Perhaps also because in many areas of manufacturing you've reached a product plateau already - is there much space to make a better cup and plate?
There's also a world where "all companies have access to the software factory so sales and entrepreneurship in software disappears entirely."
But in that scenario it's hard to see where the unwinding stops. What are these other companies doing and which parts of it actually need humans if the "agents" are that good? Marketing? No. Talking to customers? No. Support? No. Financial planning and admin? No. Manufacturing? Some, for now. Shipping physical goods? For now. What else...
>It does raise the question of where in the future will companies compete.
Exactly where current companies compete, rent seeking, IP control, and legal machinations.
Hence you'll see a few giant lumbering dinosaurs control most of the market, and a few more nimble companies make successful releases until they either get crushed by, get snapped up by the larger companies, or become a large company themselves.
ASML has a near monopoly on the most advanced chip machines. They maintain that by 'just' being the most advanced and having lots of patents.
They haven't branched off into making chips themselves. They keep their focus on selling the factories.
I think they haven't, because ASML itself doesn't have production lines. Every machine is one off. It even gets delivered with a team of engineers to keep it running.
The same probably holds true for software factories: the best ones are assembled by the smartest people (wielding AI in ways most of us don't). They are not in the business to produce software at scale, they are in the business to ensure others can do that using increasingly advanced software factories.
This relies on the premise that such a factory cannot produce a more advanced factory without significant human intervention (e.g. high ingenuity and/or lots of elbow grease). If this doesn't hold true, then we are in for some interesting times x100.
That’s not true. Even if we assume LLMs can generate the code needed to support the next Facebook, one still has to: buy/rent tons of hardware (virtual or baremetal), put tons of money in marketing, break the network effect, pay for 3rd party services for monitoring, alerting and what not. That’s money, and LLMs don’t help with that
We are not there yet. While there are teams applying dark factory models to specific domains with self-reported success, it's yet to be proven, or generalizable enough to apply everywhere.
Also a measly level 2er. I'm curious what kind of project truly needs an autonomous agent team Ralph looping out 10,000 LOCs per hour? Seems like harness-maxxing is a competitive pursuit in its own right existing outside the task of delivering software to customers.
Feels like K8s cult, overly focused on the cleverness of _how_ something is built versus _what_ is being built.
essentially any enterprise software for example, surprisingly, that needs to be custom tailored and not scaled for millions of views.
e.g. anything that has a high context.
Youtube's of this world will not enjoy it, they will use rules of scale for billions of users.
Every Dashboard Chart, Security review system, Jira, ERP, CRM, LMS, chatbot, you name it. The problem that will win from a customization per smaller unit ( company, group of people or even more so an indvidual, like CEO, or CxO group) will win from such software.
The level 6 and and 7 is essentially death of enterprise software.
>The level 6 and and 7 is essentially death of enterprise software
Enterprise software that you sell, or enterprise software you use internally?
The amount of self created, self used software in enterprises is staggering, that software will still exist, and still have a massive maintenance cost. So maybe we need a better definition of enterprise software here, like externally sold software? Also a huge amount of that software still has regulatory requirements, so someone will have to sign off on it. Maybe it will be internal certification, but very often there is separation of duties on things like that where it's easier to come from a different company.
Codex and Claude Code are these (proto)factories you talk about - almost every programmer uses them now.
And when they will be fully dark factories, yes, what will happen is that a LOT of software companies will just disappear, they will be dis-intermediated by Codex/Claude Code.
In a lot of ways, Frank Ocean feels like the millennial Brian Wilson. Not sure if any zoomer musicians have had enough time in the spotlight to develop that sort of persona.
I'm not one that laughs at others' auto-correct bloopers. I have made quite a lot of mistakes myself. But your quick reply made me laugh for a good 90-sec or so.
Right? To me it's more than just a cheap joke because it opens such possibilities. My first thought was of Amazon expanding out under Puget Sound in Bioshock-esque sunken office buildings.
> The government already has the power to do this — just ask any trans person in the South
Any citations on every trans person in the South being singled out for denial of services by the government? Because that sounds like inflammatory nonsense.
Banning gender affirming care effectively is singling out transgender people and denying them services[0]. Additionally there was a recent Supreme Court decision allowing businesses to discriminate against people[1].
Additionally in living memory, many businesses and government services were segregated by race. In some cases some races were denied access to services or those services were severely underfunded. The legacy of that legal system still has impacts today.
> Banning gender affirming care effectively is singling out transgender people
For children. Or for state funding, but the state doesn't pay for the vast majority of plastic surgery.
> Additionally in living memory, many businesses and government services were segregated by race.
Race doesn't need affirming medical care, or identification to single out. Also, businesses and government services weren't abstractly segregated by race, they were specifically discriminatory toward black people.
1. The grandparent comment asked for “Any citations on every trans person in the South being singled out for denial of services by the government.” The great grandparent comment only mentions asking the opinion of a transgender person if the government has the power to deny services to them. By banning gender affirming care, even just for children, wouldn’t that be an example of government discrimination against transgender people?
2. The example of racial segregation was to point out an additional time that a minority group was discriminated against. Black people getting less than adequate medical treatment is not them needing specific affirmative care and being denied it but an example of discrimination that was government sanctioned. Beyond pointing to the two sets of laws and their discriminatory natures the link is superficial, just another example of legal discrimination.
I cannot speak for you but denying the government the power to discriminate or oppress minorities and empowering the rights of individuals to life, liberty, and the pursuit of happiness is worthwhile to me. Which is why seeing all these states pass these discriminatory laws is disheartening.
Honestly, a solid take! It’s more of an optimization problem — find the best balance of quality, proximity, and price, and you’ve got your favorite slice shop. If you’re at a friend’s or a loved one’s, that solution changes, and you begin to associate the flavor of that slice with them and their home. Sometimes it’s worth it to make a trip out to one of the pizza Meccas—Lucali, Joe’s on Carmine, L&B Spumoni Gardens, etc.—but more often than not, you’re set with your local slice.
Unless you live in Downtown Brooklyn, where there is no good pizza. Then you’re just fucked.
> Unless you live in Downtown Brooklyn, where there is no good pizza. Then you’re just fucked
There used to be a place in the Atlantic center food court that deep-fried their pizza base. It was unconventional, but pretty great. Unfortunately it closed somewhere last year I think.
When I was a teen I had a guitar amp that would, if plugged into an effects pedal with certain settings, pick up a Spanish-language radio station. No clue how!
One of the transistors in the early stages of the amplifier (or effects pedal) acted as a detector for the AM signal probably being picked up by a cable that worked as an antenna. It worked essentially as a untuned crystal radio followed by a powerful audio amplifier. The problem usually is a result of bad grounding or screening.
There is a massive library of analysis on the effects of broadcasting, from McLuhan to Eco. Most of it was put together in the 60s/70s and became popular outside academic circles in the 80s.
AWS Professional Services here -- we all study for our cert exams using the site acloud.guru. They're not always perfect, but generally pretty good and handy for spot study. May be a good choice if you want to go deeper than these flashcards. AWS also offers in-person training sessions that are quality, but I'm not sure what the status of those are given the ongoing pandemic.
I taught a semester of this company's Minecraft course in college. My students all had a pre-existing interest in the game and seemed interested in learning how to code their own experiences into it, for what it's worth.
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