I started a $50k+ ARR business in 3 months. While having a full time PE job at Amazon. I'm insanely more productive insane and outside of Amazon now.
Are you worried by any of those claims about SaaS being dead because of AI? lol
I've found AI to be a big productivity boost for myself, but I don't really use it to generate much actual code. Maybe it could do more for me, idk, but I also don't feel like I'm being left behind. I actually enjoy writing code, but hate most other programming tasks so it's been nice to just focus on what I like. Feels good to have it generate a UI skeleton for me so I can just fill out the styles and stuff. Or figure out stupid build config and errors. Etc etc.
Anyways congrats on the product. I know a lot of people are negative about productivity claims and I'm certainly skeptical of a lot of them too, but if you asked most programmers 5 years ago if a super-autocomplete which could generate working code snippets and debug issues in a project would boost productivity everyone would say yes lol. People are annoyed that its overhyped, but there should still be room for reasonable hype imo.
For me, I always had the ideas and even as a competent engineer, the speed of development annoyed me.
I think folks get annoyed when their reality doesn't match other people's claims. But I have friends who aren't engineers who have launched successful SaaS products. I don't know if it's jealousy or what but people are quite passionate about how it doesn't have productivity gains.
Hell, I remember Intellisense in Visual Studio being a big boon for me. Now I can have tasks asynchronous, even if not faster, it frees up my time.
Is the business 3 months old now?
I run an eComm business and have built multiple software tools that each save the business $1000+ per month, in measurable wage savings/reductions in misfires.
What used to take a month or so can now be spat out in less than a week, and the tools are absolutely fit for purpose.
It's arguably more than that, since I used to have to spread that month of work over 3-6 months (working part time while also doing daily tasks at the warehouse), but now can just take a week WFH and come back with a notable productivity gain.
I will say, to give credit to the anti-AI-hype crowd, that I make sure to roll the critical parts of the software by hand (things like the actual calculations that tell us what price an item at, for example). I did try to vibecode too much once and it backfired.
But things like UIs, task managers for web apps, simple API calls to print a courier label, all done with vibes.
This would have been a couple day+ unpleasant task before; possibly more. I had been putting it off because scouring datasheets and register maps and startup behavior is not fun.
It didn’t know how to troubleshoot the startup successfully itself, though. I had to advise it on a debugging strategy with sentinel values to bisect. But then once explained it fixed the defects and succeeded.
LLMs struggle in large codebases and the benefit is much smaller now. But that capability is growing fast, and not everything software developers do is large.
It someone can use AI to make a $50,000/year project in three months, then someone else can also do so.
Obviously some people hype and lie. But also obviously some people DID succeed at SEO/Affiliate marketing/dropshipping etc. AI resembled those areas in that the entry barrier is low.
To get actual reports you often need to look to open source. Simon Willison details how he used it extensively and he has real projects. And here Mitchell Hashimoto, creator of Ghostty, details how he uses it: https://mitchellh.com/writing/my-ai-adoption-journey
Update: OP posted their own project however. Looks nice!
It involves a whole raft of complex agents + code they've written, but that code and the agents were written by AI over a very short span of time. And as much as I'd like to stroke my own ego and assume it's one of a kind, realistically if I can do it, someone else can too.
Search for "Rivian Roamer".
Stop arguing on HN and get to building.
Which measure? Like when folk say something is more "efficient" it's more time-efficient to fly but one trades other efficiency. Efficiency, like productivity needs a second word with it to properly communicate.
Whtys more productive? Lines of code (a weak measure). Features shipped? Bugs fixed? Time by company saved? Time for client? Shareholders value (lame).
I don't know the answer but this year (2026) I'm gonna see if LLM is better at tax prep than my 10yr CPA. So that test is my time vs $6k USD.
I get that it takes a long time to make software, but people were making big promises a year ago and I think its time to start expecting some results.
Also weekend hackathon events have completely/drastically changed as an experience in the last 2-3 years (expectations and also feature-set/polish of working code by the end of the weekend).
And as another example, you see people producing CUDA kernels and MLX ports as an individual (with AI) way more these days (compared to 1-2 years ago), like this: https://huggingface.co/blog/custom-cuda-kernels-agent-skills
January numbers are out and there were fewer games launched this January than last.
This chart from a16z (scroll down to “App Store, Engage”) plots monthly iOS App Store releases each month and shows significant growth [1].
> After basically zero growth for the past three years, new app releases surged 60% yoy in December (and 24% on a trailing twelve month basis).
It’s completely anecdotal evidence but my own personal experience shows various sub-Reddit’s just flooded with AI assisted projects now, so much so that various pages have started to implement bans or limits of AI related posts (r/selfhosted just did this).
As far as _amazing software_ goes, that’s all a bit subjective. But there is definitely an increase happening.
[0] https://steamdb.info/stats/releases/
[1] https://www.a16z.news/p/charts-of-the-week-the-almighty-cons...
Also the accelerating trend dates back to 2018 if you remove the early COVID dip. Which is exactly my point. You can look at the graph and there is no noticeable impact correlated to any major AI advancements.
The iOS data is interesting. But it’s an outlier because the Play Store and Steam show nothing similar. And the iOS App Store is weird because they’ve had numerous periods of negative growth follow by huge positive growth over the years. My guess is that it probably has more to do with all of the VC money flowing into AI startups and all the small teams following the hype building wrappers and post training existing models. If you look at a random sample of the iOS new apps that looks likely.
Seriously go to the App Store, search AI and scroll until you get bored. There are literally thousands of AI API wrappers.
Then you start asking questions like, does the button for each of the features actually do the thing? Are there any race conditions? Are there inputs that cause it to segfault or deadlock? Are the libraries it uses being maintained by anyone or are they full of security vulnerabilities? Is the code itself full of security vulnerabilities? What happens if you have more than 100 users at once? If the user sets some preferences, does it actually save them somewhere, and then load them back properly on the next run? If the preferences are sensitive, where is it saving them and who has access to it?
It's way easier to get code that runs than code that works.
Or to put it another way, AI is pretty good at writing the first 90% of the code:
"The first 90 percent of the code accounts for the first 90 percent of the development time. The remaining 10 percent of the code accounts for the other 90 percent of the development time." — Tom Cargill, Bell LabsI wrote a python DHCP server which connects with proxmox server to hand out stable IPs as long as the VM / container exists in proxmox.
Not via MAC but basically via VM ID ( or name)
Have you ever looked for, say, WisprFlow alternatives? I had to compare like 10 extremely similar solutions. Apps have no moat nowadays.
That's happening all over the place.
But the numbers of lfg is basically the same, maybe a few percent more. But not dozens of modules more per day more...
- Edsger Dijkstra
[1] https://www.oecd.org/en/topics/sub-issues/measuring-producti...
Most recent BLS for the last quarter ‘25 was an annualized rate of 5.4%.
The historic annual average is around 2%.
It’s a bit early to draw a conclusion from this. Also it’s not an absolute measure. GDP per hour worked. So, to cut through any proxy factors or intermediating signals you’d really need to know how many hours were worked, which I don’t have to hand.
That said, in general macro sense, assuming hours worked does not decrease, productivity +% and gdp +% are two of the fundamental factors required for real world wage gains.
If you’re looking for signals in either direction on AI’s influence on the economy, these are #s to watch, among others. The Federal Reserve, the the Chair reports after each meeting, is (IMO) one of the most convenient places to get very fresh hard #s combined with cogent analysis and usually some q&a from the business press asking questions that are at least some of the ones I’d want to ask.
If you follow these fairly accessible speeches after meetings, you’ll occasionally see how lots of the things in them end up being thematic in lots of the stories that pop up here weeks or months later.
It's like trying to make fusion happen only by spending more money. It helps but it doesn't fundamentally solve thr pace of true innovation.
I've been saying for years now that the next AI breakthrough could come from big tech but it also has just a likely chance of comming from a smart kid with a whiteboard.
It comes from the company best equipped with capital and infra.
If some university invents a new approach, one of the nimble hyperscalers / foundation model companies will gobble it up.
This is why capital is being spent. That is the only thing that matters: positioning to take advantage of the adoption curve.
The "limits of AI" bit is just smokescreen.
Firing seniors:
> Just a week after his comments, however, IBM announced it would cut thousands of workers by the end of the year as it shifts focus to high-growth software and AI areas. A company spokesperson told Fortune at the time that the round of layoffs would impact a relatively low single-digit percentage of the company’s global workforce, and when combined with new hiring, would leave IBM’s U.S. headcount roughly flat.
New workers will use AI:
> While she admitted that many of the responsibilities that previously defined entry-level jobs can now be automated, IBM has since rewritten its roles across sectors to account for AI fluency. For example, software engineers will spend less time on routine coding—and more on interacting with customers, and HR staffers will work more on intervening with chatbots, rather than having to answer every question.
Obviously they want new workers to use AI but I don't really see anything to suggest they're so successful with AI that they're firing all their seniors and hiring juniors to be meatbags for LLMs.
I suspect the gap is that you don't know enough about IBM's business model.
When something doesn't make sense, a very common cause is a lack of context: many things can be extremely sensible for a business to do; things which appear insane from an outsider's point of view.
https://www.sciencefocus.com/science/is-water-wet https://centreforinquiry.ca/keiths-conundrums-is-water-wet https://www.theguardian.com/notesandqueries/query/0,5753,-17... http://scienceline.ucsb.edu/getkey.php?key=6097 https://parknotes.substack.com/p/is-water-wet-or-does-it-jus...
...etc. Turns out, it's not a solved question!
If my boss asked me a question like this my reply would be "exactly what you told me to build, check jira".
If you want to know if I'm more productive - look at the metrics. Isn't that what you pay Atlassian for? Maybe you could ask their AI...
I’d argue the majority use AI this way. The minority “10x” workers who are using it to churn through more tasks are the motivated ones driving real business value being added - but let’s be honest, in a soulless enterprise 9-5 these folks are few and far between.
Why are there fewer games launched in steam this January than last?
I’ve worked with a few folks who have been given AI tools (like a designer who never coded in his life, a or video/content creator) who have absolutely taken off with creating web apps and various little tools and process improvements for themselves thanks by just vibecoding what they wanted. The key with both these individuals is high agency, curiosity, and motivation. That was innate, the AI tooling just gave them the external means to realise what they wanted to do with more ease.
These kinds of folks are not the majority, and we’re still early into this technological revolution imo (models are improving on a regular basis).
In summary, we’ve given the masses to “intelligence” but creativity and motivation stay the same.
If you look at every game dev forum in existence, or you’ve ever talked to people about why they got into CS there are probably 1000x more people who want to publish a game than have done it.
If there was a a tool that provided a 10x-100x speed boost it would push enough of those people over the edge and make a significant impact on number of games released.
That’s to say nothing of boosting existing game devs.
Even if models stopped improving today, it'd take years before we see the full effects of people slowly gaining the skills needed to leverage them.
There doesn't need to be any "magic" there. Just clearly state your requirements. And start by asking the model to plan out the changes and write a markdown file with a plan first (I prefer this over e.g. Claude Code's plan mode, because I like to keep that artefact), including planning out tests.
If a colleague of yours not intimately familiar with the project could get the plan without needing to ask followup questions (but able to spend time digging through the code), you've done pretty well.
You can go over-board with agents to assist in reviewing the code, running tests etc. as well, but that's the second 90%. The first 90% is just to write a coherent request for a plan, read the plan, ask for revisions until it makes sense, and tell it to implement it.
But the big models have come a long way in this regard. Claude + Opus especially. You can build something with a super small prompt and keep hammering it with fix prompts until you get what you want. It's not efficient, but it's doable, and it's much better than having to write a full spec not half a year ago.
LOL: especially with Claude this was only in 1 out of 10 cases?
Claude output is usually (near) production ready on the first prompt if you precisely describe where you are, what you want and how you get it and what the result should be.
Nothing new here. Getting users to clearly state their requirements has always been like pulling teeth. Incomplete sentences and all.
If the people you are teaching are developers, they should know better. But I'm not all that surprised if many of them don't. People will be people.
Once people have had the experience of being a lead and having to pass tasks to other developers a few times, most seem to develop this skill at least to a basic level, but even then it's often informal and they don't get enough practice documenting the details in one go, say by improving a ticket.
Maybe it’s needing to step back and even ask for design doc before a plan, but even then…
But there are thousands of people on social media claiming huge productivity gains. Surely at least 5% of devs are holding it right.
If a 10x boost is possible, we’d notice that. There are only 20k games a year released on steam.
If my hypothesis is true and the real final output boost is somewhere near 20%, we’re seeing exactly what you’d expect.
There aren’t noticeably more total startups or projects though.
Eg, ai is a big multiplier but that doesnt mean it will translate to “more” in the way people think.
As an independent game developer myself, who has extensively tried to use LLMs in every which way for desire of a productivity boost, what they actually do is introduce code debt so quickly that the code collapses in on itself before you even get far enough done to ship an update, unless you handhold it so tightly that it would be faster to write the code yourself.
Now if it’s something closer to 20%, we’re seeing exactly what you’d expect.
But my guess would be: games are closed sourced and need physics. Which AI is bad at.
Many games don’t need physics, and there are a billion hobby projects on GitHub.
Does not look like less games.
Individuals make mistakes in air traffic control towers, but as a cumulative outcome it's a scandal if airplanes collide midair. Even in contested airspace.
The current infrastructure never gets there. There is no improvement path from MCP to air traffic control.
It's hard work and patience and math.
LLM's can be a very useful tool and will probably lead to measurable productivity increases in the future, at their current state they are not capable of replacing most knowledge workers. Remember, even computers as a whole didn't measurably impact the economy for years after their adoption. The real world is a messy place and hard to predict!