You know when someone is singing the praises about AI and they get asked "if you're so much more productive with AI, what have you built with it"? Well I think a bunch of companies are asking this same question to their employees and realising that the productivity gains they are betting on were overhyped.
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!
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.
Time could be very expensive as mistakes on taxes can be fraud resulting in prison time. Mostly they understand people make mistakes - but they need to look like honest mistakes and llm may not. remember you sign your taxes as correct to the best of your knowledge - your CPA is admitting you outsourced understanding to an expert, something they accept. However if you sign alone you are saying you understand it all even if you don't.
These days productivity at a macroeconomic scale is usually cited in something like GDP per hour worked.
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.
Economy-wide productivity can be measured reasonably well, although there are a few different measures [1]. The big question I guess is whether AI will make a measurable impact there. Historically tech has had less impact than people thought it would, as noted in Robert Solow's classic quip that "You can see the computer age everywhere but in the productivity statistics". [2]
Number of features shipped. Traction metrics. Revenue per product. Ultimately business metrics. For example, tax prep effectiveness would be a proper experiment tied to specific metrics.
I hear this every day, and I'm sure its true sometimes, but where is the tsunami of amazing software LLM users are producing?
Where are the games that make the old games look like things from a bygone era?
Where are the updates to the software that I currently use that greatly increase it capabilities? I have seen none of this.
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.
Reddit and GitHub are littered with people launching new projects and appear to be way more feature-rich than new tool/app launches from previous years. I think it is a lot harder to get noticed with a new tool/app new because of this increase in volume of launches.
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).
I’d be interested where you’re getting your data. SteamDB shows an accelerating trend of game releases over time, though comparing January 2026 to January 2025 directly shows a marginal gain [0].
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.
I got the numbers swapped. Turns out there was an increase of about 40 games between last January and this. Which is exactly what you wouldn’t expect if the 5-10x claims are true.
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.
Specifically about custom CUDA kernels, I’ve implemented them with AI that significantly sped up the code in this project I worked on. Didn’t know how to code these kernels at all, but I implemented and tested a couple of variations and got it running fast in just two days. Basically impossible for me before AI coding (well not impossible but it would have taken me many weeks, so I wouldn’t have tried it).
The one thing AI is consistently better at than humans is shipping quickly. It will give you as much slop as you want right away, and if you push on it for a short period of time it will compile and if you run it a program will appear that has a button for each of the requested features.
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 Labs
Look somewhere outside of the AI hype space. You’re seeing more AI competitors because it’s easy to build on top of someone’s existing model or API and everyone is trying to cash in. You saw the same thing with new crypto currency.
That’s an incredibly niche area. From their website it looks like there are 4k modules available. Is there a way to see historical data. Also is number of users available, so that you can rule out popularity growth?
I bet you the predictions are largely correct but technology doesn't care about funding timelines and egos. It will come in its own time.
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.
Well, the predictions are tied to the timelines. If someone predicts that AI will take over writing code sometime in the future I think a lot of people would agree. The pushback comes from suggesting it's current LLMs and that the timeline is months and not decades.
> 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.
I think for a lot of folks it basically comes down to just using AI to make the tasks they have to do easier and to free up time for themselves.
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.
Because very few knows how to use AI. I teach AI courses on the side. I've done auditing supervised fine tuning and RLHF projects for a major provider. From seeing real prompts, many specifically from people who work with agents every day, people do not yet have the faintest clue how to productively prompt AI. A lot of people prompt them in ways that are barely coherent.
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.
You'd be surprised how low the bar is. What I'm seeing is down to the level of people not writing complete sentences.
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.
Not surprising. Many folks struggle with writing (hence why ChatGPT is so popular for writing stuff), so people struggling to coherently express what they want and how makes sense.
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.
This is exactly it. A lot of people use it that way. And it's still a vast improvement, but they could also generally do a lot better with some training. I think this is one of the areas where you'll unfortunately see a big gap developing between developers who do this well, and have the models work undisturbed for longer and longer while doing other stuff, and those who ends up needing a lot more rework than necessary.
> 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.
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.
You're right, they should know better, but I think a lot of them have gotten away with it because most of them are not expected to produce written material setting out missing assumptions etc. and breaking down the task into more detail before proceeding to work, so a lot have never gotten the practice.
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.
Because ai doesnt work like this “make me money” or “make stardew valley in space”. The hard part is the painful exploration and necessary taste to produce something useful. The number of these kind of people did not increase with ai.
Eg, ai is a big multiplier but that doesnt mean it will translate to “more” in the way people think.
It doesn’t need to be useful or a good game to launch on steam. Surely if it was a “big multiplier” 5-10x, it would be noticeably impacting steam launches.
Now if it’s something closer to 20%, we’re seeing exactly what you’d expect.
It comes down back to that whole discussion around intelligence becoming cheaper and more accessible but motivation and agency remaining stable.
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.
Yeah but if it’s 100x easier to do something, it takes much less motivation to push through and finish it.
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.
My guess is that the true impact of this will be difficult to measure for a while. Most "single-person start-ups" will probably not be high-visibility VC-backed, YC affairs, and rather solopreneurs with a handful of niche moonlighted apps each making 3-4 digit monthly revenue.
95% of all new startups have the word AI in the description, so of course there are lots of new API wrappers and people trying to build off of existing models.
There aren’t noticeably more total startups or projects though.
Sorry, I swapped the numbers. It's actually 1447 this year vs 1413 last year so 34 more games this year. So essentially now growth. Despite there being a clearly accelerating growth trend since 2018.
No. They're firing high paid seniors and replacing them with low pay juniors. This is IBM we're talking about.
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.
Where does it say those cuts were senior software developers?
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.
This just doesn't make any sense. Juniors + AI just does not equal seniors, except for prototyping greenfield projects. Who knows about 2 months from now, it moves fast and stuff, but not right now.
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.
“AI will steal your job” never made sense. If your company is doing bad, sure maybe you fire people after automating their job. But we’re in a growth oriented economic system. If the company is doing good, and AI increases productivity, you actually will hire more people because every person is that much more of a return on investment
As a senior engineer sometimes the system shows I did nothing because I was helping others. sometimes I get the really hard problem -'the isn't speller teh' type bugs are more common than thread race conditions - but a lot faster to solve.
No one has built business AI that is flat correct to the standards of a high redundancy human organization.
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.
Historically in a lot of niches such as search marketing etc, people would not name their successful projects because the barrier to entry is low.
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!
This is definitely the case. I have a project that while not wildly profitable yet, is producing real revenue, but that I will not give details of because the moat is so small. The main moat is that I know the potential is real, and hopefully not enough other people do, yet. I know it will disappear quickly, so I'm trying to make what I can of it while it's there. I may talk about it once the opportunity is gone.
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.
I only started charging customers in September. Super-linear growth. I launched annual subscriptions and within less than a week > 15% of customers switched.
I'm with you. I own a business and have created multiple tools for myself that collectively save me hours every month. What were boring, tedious tasks now just get done. I understand that the large-scale economic data are much less clear about productivity benefits, in my individual case they could not be more apparent.
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.
The only thing the comments told me is that people lake judgement and taste to do it themselves. It's not hard, identify a problem that's niche enough for a problem you can solve.
Every hype AI post is like this. “I’m making $$$ with these tools and you’re ngmi”
I completely understand the joys of a few good months but this is the same as the people working two fang jobs at the start of Covid. Illusionary and not sustainable.
I built and debugged an embedded stub loader for
Rp2350 to program MRAM and validate hardware status for a satellite. About 2.5 hours of my time, a lot of it while supervising students/doing other things.
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.
I'm not doubting of you or anything, but you just proved point above by saying you have a successful project without even mentioning which project is that.
Thanks! I used to own a Tesla and there were similar platforms out there. Bought a Rivian and wanted something like that. I started building this before AI-assisted coding was very popular. But it greatly increased my productivity.
There is that quote "there are cathedrals everywhere for those with the eyes to see". I feel like there is a solid variation with solid business opportunities instead of cathedrals haha.
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.
First of all, thank you. I've always been told I have a back for seeing opportunities others don't.
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.
Fair. I've had super-linear growth since launching in September. Zero marketing outside of a referral program. People genuinely love what I'm building. I get multiple emails per week about how people appreciate the software and how I send out weekly emails about everything I've launched.
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!