Misleading title. Has nothing to say about working, i.e paid employment, with AI apps.
The main claim in the post: Their portfolio companies have shown an improved rate of accumulating revenue ever since LLMs took off.
Weakest part of the post: No attempt at explaining how or why a LLM affects these numbers. They allude to 'shipping speed' and 'product iteration', but how an LLM helps these functions is left unexplored.
There's an implied deductive argument that a LLM can write some code, so obviously shipping speed is faster, so obviously revenue is faster. But the argument is never explored for magnitude of effect or defended against examples where shipping faster or using LLMs doesn't equal faster revenue.
Also, nothing about sampling bias, size or spread.
Overall: Probably meant as a confidence boost to the sleep-deprived founders out there. But teaches nothing.
Exactly this. They say increased delivery speed etc. No proof that it’s not at least as feasible that “the Covid + cheap money induced bubble” just started increasing in size faster… Because it’s just too painful of it wouldn’t.
> Probably meant as a confidence boost to the sleep-deprived founders out there. But teaches nothing.
The post insist 2 to 4 million ARR in 1 year is the new norm. My guess its meant for their own investors and get founders to undervalue their achievements (Or learn to get creative with what ARR means).
I can’t speak to the world of startups or venture capital, I’m way too far from that ecosystem, but I’d like to add a perspective from the sidelines.
What stands out to me right now is just how loud the expectations around AI have become, especially among non-technical folks. It’s not just “Bitcoin hype” loud, it’s bordering on “AI will solve everything” levels of noise. For those of us who’ve been around a bit longer (sorry, younger HN crowd), the current buzz feels reminiscent of Y2K or the first dot-com wave.
Back then, I was early in my career, but I vividly remember the headlines, the overpromises, and the sheer volume of attention. The difference now is, there’s a lot more substance under the surface. The tools are genuinely useful, and the adoption curve feels more practical, even inevitable. That’s what makes me think AI might become to this era what the smartphone was to the last, not just a novelty, but an everyday dependency.
That said, I’ve also learned a lot from voices here on HN, especially when it comes to the financial realities behind the tech. If there’s one throughline in many of these discussions, it’s that financial viability, not just hype or innovation, is what ultimately determines whether this all collapses or truly transforms the world.
>financial viability, not just hype or innovation, is what ultimately determines whether this all collapses or truly transforms the world.
That is some of the best wisdom on HN. Beating your competition's AI model at whatever goalposts you think is important means nothing until you have positive cash flow. All hype must encounter reality and survive to not only make a sale, but then go and do it very consistently.
I lived through the dotcom boom too. It's a poor point of comparison, because as thrilling as the moment was, there weren't any techs then that could think or reason. And right now are we at the furthest point in its development? From the extreme pace of improvements it looks more like its infancy.
Yeah, I totally agree, we’re still at the beginning, and that’s what makes it a little scary
My dotcom comparison wasn’t really about the tech, more about the noise and hype. Feels like that same kind of frenzy, but now the tech’s actually capable of doing something big. The financial viability is still a big question though. Thanks for the comment.
100% agree on this. It’s 1995 all over again. AI is as big (or bigger) as the Internet back then. Hype to totally insane levels but eventually all things that go up must come down.
In the meantime, the usual suspects are gonna make a whole lotta money.
It is insane. You should see my inbox, daily links to AI articles, sales execs panicking about falling behind. Honestly, it’s understandable with all the noise, but it’s also hard to keep up.
I’ve lost count of how many times I’ve had to explain, again and again
“No, AI can’t do that… and no, it’s definitely not drawing up your architectural building plans.”
Well, not yet, anyway.
The internet ended up just as big as predicted in 1995 (or bigger) - it just took a bit longer. What do we not have online today that was predicted in 1995?
I think discussions about AI hype miss a critical factor: there are two groups of people getting swept up in hype. One are the Investors[0]. The other are the Beneficiaries of the technology[1]. AI is over-hyped for the former, but not for the latter.
If AI hype is anything like dotcom boom - or like telecom, or building up railways in the US - well, it sucks for the Investors. For them, the hype is getting dangerous - if it's a bubble and it bursts, plenty of them will lose money, and many companies will fold.
But I'm not in that group, so I don't care.
For me, one of the Beneficiaries, the hype seems totally warranted. The capability is there, the possibilities are enormous, pace of advancement is staggering, and achieving them is realistic. If it takes a few years longer than the Investor group thinks - that's fine with us; it's only a problem for them.
--
[0] - In a broad sense, to include both people funding it and people making big investments around the expectations - whether regular investments, or company strategy, or career plans.
[1] - People using it for work and personally, researchers, etc.; also people with defined hopes for the technology; also ultimately everyone who benefits from it when it matures (and possibly builds on top of it).
TL;DR: there are two groups of people mixed up in the hype: the people investing in it, and people using it. AI may indeed be overhyped for the former. It's not overhyped for the latter.
Makes me think of how railways were built across the US. AFAIK, the first generation of investors generally lost big. They funded a huge, capital-expensive infrastructure project, and didn't get a return on it in time. But even as they lost, the work they funded remained - subsequent waves of businesses built on top of it and became profitable, the society benefited, and the country was transformed. The only losers to this "bubble" were the first-movers and their backers.
So when someone wonders if AI is overhyped, I'd ask them: what's your stake in this? Are you an investor hoping for quick returns, or are you someone who stands to benefit from the technology existing?
I agree that financial viability is critical to the long-term prospects of a technology. It must deliver an ROI above other options. I'd recommend getting off the sidelines and jumping in to see what's happening. At the least, you'll have another perspective to inform your position. It's a pretty minimal investment to try it out.
The latest trend I've seen is blabber about AI super intelligence which will either kill us or lead to absolute utopia by 2030.
In the mean time, I try to enjoy the freely available LLMs for quick summaries on technical topics before the inevitable enshittification ruins them forever.
> just how loud the expectations around AI have become, especially among non-technical folks.
This. It's bordering on mass madness. I am taking 2-4 calls a week from "two guys from ..." with mad ideas and unrealistic expectations of what it takes to build and maintain an AI product. I've seen it with early internet rush, Web 2.0, and crypto before.
The article lists the many ways in which the bar for success - the minimum "table stakes" that you have to achieve in order to be considered success - have drastically risen, and then concludes with:
> we believe there’s never been a better time to build an application-layer software company.
Nothing could be a clearer indication that the primary desirable quality in a founder is the conviction that, against all odds, you are better than everyone else.
> The article lists the many ways in which the bar for success - the minimum "table stakes" that you have to achieve in order to be considered success - have drastically risen
Have seen how application processes for technical roles went, in less than a year, from considering AI cheating; to now requiring AI to do the take home or finish a live coding task
Your loss, then. There is a reason that "shoot the messenger" attacks are referred to as ad hominem fallacies.
There may be loads I don't like about a16z, but this article contained lots of interesting insights and actual data. You don't have to agree with their conclusions to get tons of value out of the information they present.
Maybe it's the fact that we've seen VCs and a16 specifically push for highly overhyped things that didn't really come true like WeWork, Crypto?
If we go beyond a16, the last decade has shown us VCs pushing for insane valuations for companies that went under after hiring sprees. We've seen Mark Zuckerberg sing similar odes to Metaverse.
At some point you stop believing the next big thing from a certain crowd.
This doesn't mean I am not exploring how to use AI and even have something I am working on in terms of an idea, it's just that caution is advised.
In general, it’s unwise to take corporate comms at face value. Think about what Andreessen Horowitz would like investors and founders to believe and how it would like them to behave. Then consider the post as if it were an attempt instil those beliefs and elicit those behaviours.
This hides one major caveat in AI which is, contrasting to "old" tech, your OpEx on compute doesn't scale the same way your engineered app traditionally did.
In other terms, as your revenue scales, your OpEx scales too. This breaks the idea that you need to grow revenue to "break off" as your margin as set due to compute.
The other issue is, I've been burning compute between Perplexity, Grok, Gemini, Claude and Deepseek. I pay nothing for these and they are good enough. It is easy to grow revenue to $1m when you are burning $2m of compute.
Anyone else a bit confused by the use of the word "working" used, given the content of the post? I thought this was going to be about how white collar work is changing, not about fundraising and growth strategies.
This seems like a case of selection bias, where they are looking at all the Gen AI startups and seeing that they are making revenue faster than previous startups. But Gen AI startups have mostly only started very recently, so it's obvious that all the successes must have grown fast, as they haven't been around long enough to grow slowly. Maybe in 5 years, we'll see a lot of cases of successful startups that took a slower growth trajectory instead.
But whether it's short-sighted for the investors or not, I think the takeaway for founders is "investors now expect you to make more revenue faster, and B2C applications are more interesting than before".
I think this has an interesting consequence for founders and employees at startups in this day and age, and I'm not quite sure how I feel about it.
On one hand, it means you can "fail faster". That is, if you're a startup employee, and you don't see "hockey stick" growth that is looking crazy impressive at the end of year 1, you should know that the chances of your equity being worth more than a token are basically zero. Starting around the dot com boom, I worked in numerous startups, and for some of them we were still chugging along in years 3-4 with the hopes that our "semi-OK, decent growth" would turn vertical any day now. I've seen numerous startups that started in the 2015-2020 timeframe (so existed for 5-10 years) where they didn't outright fail but common got wiped in an acquisition. That's more a consequence of the rise in interest rates and difficult fundraising environment, but it's really rough to plug along at a company for 5-10 years, think you're doing OK, and then your stock is worth nothing. So from a startup founder/employee perspective, you get signal faster and don't have to waste time.
Simultaneously, though, it seems like any idea that would take a decent amount of upfront investment and time would be hella difficult to get funded, and I think that's unfortunate.
The metric the article
focuses on is “revenue,” but it seems like the foundation many of these startups build on (other people’s LLM APIs) are much more expensive than the last generation of startups.
Given the cost of training a SOTA model, it’s not clear these companies have sustainable businesses. If your primary expense is AWS you can always shift to your own hardware once you hit sufficient scale. If you’re Cursor, how big do you need to get to eliminate your 3rd party API dependency?
The cost of training a good model can be as low as $5.5m (DeepSeek v3) or "a few tens of millions of dollars" (Claude 3.7 Sonnet https://simonwillison.net/2025/Mar/2/ethan-mollick/) so once you have proven revenue against other models it might actually become a reasonable thing to do.
This article is about AI applications, not core foundation models. None of the initial 3 companies mentioned (Cursor, Lovable, Gamma) train there own models from scratch, nor do they need to. E.g. I get tons of value from Cursor, but I also still pay for ChatGPT Plus.
There is also enough competition in the core model space that these apps don't need to have an Achilles heel by being reliant on a single vendor. E.g. I think Cursor was smart to let you "bring your own API key".
I get that the article is about applications, but the applications have a dependency on 3rd party models, and that dependency is costing them most (all? More than all?) of their revenue.
Put another way, if I make $100M in annual revenue, but am paying out $110M to the API I wrap, it’s not nearly as compelling a business as that top-line $100M number makes it out to be.
In the previous generation of startups, expenses were mostly dominated by headcount, and the cost of actually delivering the service tended to be small. The story was “keep growing revenue, and if you need to show a profit, stop hiring.”
An AI startup built on other people’s models has to hope that the foundational models end up being fungible commodities, otherwise any margins you might gain will get squeezed out by your LLM provider. Alternatively, you can train your own model.
I don’t know what Cursor’s userbase looks like. If everyone is paying for Pro but using their own API key, that’s obviously a high margin business.
I read TFA. Quickly but I read it. It's short but I have no idea if they were saying "wow AI products are popular" or "AI helps startups reach higher levels of profitability faster" or simply "A company that says they are making an AI driven product receives more initial users and funding".
Each of those are so wildly different conclusions and require such wildly different data to support it.
I tried using Claude Code, was utterly disappointed with using it and it cost me a lot of money very quickly.
Just goes to show these tools can augment and improve your workflow and knowledge, but in their current state they cannot replace you - any CEO who says otherwise has no clue and is riding the hype train
Nowadays, I don't think work should be defined just by clocking in and clocking out. It’s about the ability to complete tasks and hit goals efficiently. This is how AI will redefine work.
If we want a feature we can write a two sentence prompt and get that feature. But the technical debt is going to grow exponentially, and I haven't seen a shred of focus on preventing that inevitable outcome.
Misleading title. Has nothing to say about working, i.e paid employment, with AI apps.
The main claim in the post: Their portfolio companies have shown an improved rate of accumulating revenue ever since LLMs took off.
Weakest part of the post: No attempt at explaining how or why a LLM affects these numbers. They allude to 'shipping speed' and 'product iteration', but how an LLM helps these functions is left unexplored.
There's an implied deductive argument that a LLM can write some code, so obviously shipping speed is faster, so obviously revenue is faster. But the argument is never explored for magnitude of effect or defended against examples where shipping faster or using LLMs doesn't equal faster revenue.
Also, nothing about sampling bias, size or spread.
Overall: Probably meant as a confidence boost to the sleep-deprived founders out there. But teaches nothing.
Exactly this. They say increased delivery speed etc. No proof that it’s not at least as feasible that “the Covid + cheap money induced bubble” just started increasing in size faster… Because it’s just too painful of it wouldn’t.
I just realized you can slightly tweak his comment to fit almost all articles on AI/LLMs/etc lately
> Probably meant as a confidence boost to the sleep-deprived founders out there. But teaches nothing.
The post insist 2 to 4 million ARR in 1 year is the new norm. My guess its meant for their own investors and get founders to undervalue their achievements (Or learn to get creative with what ARR means).
I can’t speak to the world of startups or venture capital, I’m way too far from that ecosystem, but I’d like to add a perspective from the sidelines.
What stands out to me right now is just how loud the expectations around AI have become, especially among non-technical folks. It’s not just “Bitcoin hype” loud, it’s bordering on “AI will solve everything” levels of noise. For those of us who’ve been around a bit longer (sorry, younger HN crowd), the current buzz feels reminiscent of Y2K or the first dot-com wave.
Back then, I was early in my career, but I vividly remember the headlines, the overpromises, and the sheer volume of attention. The difference now is, there’s a lot more substance under the surface. The tools are genuinely useful, and the adoption curve feels more practical, even inevitable. That’s what makes me think AI might become to this era what the smartphone was to the last, not just a novelty, but an everyday dependency.
That said, I’ve also learned a lot from voices here on HN, especially when it comes to the financial realities behind the tech. If there’s one throughline in many of these discussions, it’s that financial viability, not just hype or innovation, is what ultimately determines whether this all collapses or truly transforms the world.
Just my 2 cents.
>financial viability, not just hype or innovation, is what ultimately determines whether this all collapses or truly transforms the world.
That is some of the best wisdom on HN. Beating your competition's AI model at whatever goalposts you think is important means nothing until you have positive cash flow. All hype must encounter reality and survive to not only make a sale, but then go and do it very consistently.
I lived through the dotcom boom too. It's a poor point of comparison, because as thrilling as the moment was, there weren't any techs then that could think or reason. And right now are we at the furthest point in its development? From the extreme pace of improvements it looks more like its infancy.
Yeah, I totally agree, we’re still at the beginning, and that’s what makes it a little scary
My dotcom comparison wasn’t really about the tech, more about the noise and hype. Feels like that same kind of frenzy, but now the tech’s actually capable of doing something big. The financial viability is still a big question though. Thanks for the comment.
100% agree on this. It’s 1995 all over again. AI is as big (or bigger) as the Internet back then. Hype to totally insane levels but eventually all things that go up must come down.
In the meantime, the usual suspects are gonna make a whole lotta money.
It is insane. You should see my inbox, daily links to AI articles, sales execs panicking about falling behind. Honestly, it’s understandable with all the noise, but it’s also hard to keep up.
I’ve lost count of how many times I’ve had to explain, again and again “No, AI can’t do that… and no, it’s definitely not drawing up your architectural building plans.” Well, not yet, anyway.
The internet ended up just as big as predicted in 1995 (or bigger) - it just took a bit longer. What do we not have online today that was predicted in 1995?
IPv6 :P
Timeline is important in a world where AI is under VC capital drip IVs.
Now that depends, doesn't it?
I think discussions about AI hype miss a critical factor: there are two groups of people getting swept up in hype. One are the Investors[0]. The other are the Beneficiaries of the technology[1]. AI is over-hyped for the former, but not for the latter.
If AI hype is anything like dotcom boom - or like telecom, or building up railways in the US - well, it sucks for the Investors. For them, the hype is getting dangerous - if it's a bubble and it bursts, plenty of them will lose money, and many companies will fold.
But I'm not in that group, so I don't care.
For me, one of the Beneficiaries, the hype seems totally warranted. The capability is there, the possibilities are enormous, pace of advancement is staggering, and achieving them is realistic. If it takes a few years longer than the Investor group thinks - that's fine with us; it's only a problem for them.
--
[0] - In a broad sense, to include both people funding it and people making big investments around the expectations - whether regular investments, or company strategy, or career plans.
[1] - People using it for work and personally, researchers, etc.; also people with defined hopes for the technology; also ultimately everyone who benefits from it when it matures (and possibly builds on top of it).
Good explanation. Thanks.
Posted this downthread, but it's also a reply to your points:
https://news.ycombinator.com/item?id=44208831
TL;DR: there are two groups of people mixed up in the hype: the people investing in it, and people using it. AI may indeed be overhyped for the former. It's not overhyped for the latter.
Makes me think of how railways were built across the US. AFAIK, the first generation of investors generally lost big. They funded a huge, capital-expensive infrastructure project, and didn't get a return on it in time. But even as they lost, the work they funded remained - subsequent waves of businesses built on top of it and became profitable, the society benefited, and the country was transformed. The only losers to this "bubble" were the first-movers and their backers.
So when someone wonders if AI is overhyped, I'd ask them: what's your stake in this? Are you an investor hoping for quick returns, or are you someone who stands to benefit from the technology existing?
I agree that financial viability is critical to the long-term prospects of a technology. It must deliver an ROI above other options. I'd recommend getting off the sidelines and jumping in to see what's happening. At the least, you'll have another perspective to inform your position. It's a pretty minimal investment to try it out.
The latest trend I've seen is blabber about AI super intelligence which will either kill us or lead to absolute utopia by 2030.
In the mean time, I try to enjoy the freely available LLMs for quick summaries on technical topics before the inevitable enshittification ruins them forever.
I can only hope AI somehow kills the smartphone.
> just how loud the expectations around AI have become, especially among non-technical folks.
This. It's bordering on mass madness. I am taking 2-4 calls a week from "two guys from ..." with mad ideas and unrealistic expectations of what it takes to build and maintain an AI product. I've seen it with early internet rush, Web 2.0, and crypto before.
The article lists the many ways in which the bar for success - the minimum "table stakes" that you have to achieve in order to be considered success - have drastically risen, and then concludes with:
> we believe there’s never been a better time to build an application-layer software company.
Nothing could be a clearer indication that the primary desirable quality in a founder is the conviction that, against all odds, you are better than everyone else.
> The article lists the many ways in which the bar for success - the minimum "table stakes" that you have to achieve in order to be considered success - have drastically risen
Have seen how application processes for technical roles went, in less than a year, from considering AI cheating; to now requiring AI to do the take home or finish a live coding task
[flagged]
That was implied :)
If you don’t believe in yourself, no one else will.
I think there's a difference between "believe in yourself" and "have an ego the size of Jupiter" though
I don't take anything that comes from a16z seriously, specially since their crypto craze
Your loss, then. There is a reason that "shoot the messenger" attacks are referred to as ad hominem fallacies.
There may be loads I don't like about a16z, but this article contained lots of interesting insights and actual data. You don't have to agree with their conclusions to get tons of value out of the information they present.
[flagged]
I'm still curious, then. What it is it about the specifics of the article that make you want to dismiss it out of hand?
Maybe it's the fact that we've seen VCs and a16 specifically push for highly overhyped things that didn't really come true like WeWork, Crypto?
If we go beyond a16, the last decade has shown us VCs pushing for insane valuations for companies that went under after hiring sprees. We've seen Mark Zuckerberg sing similar odes to Metaverse.
At some point you stop believing the next big thing from a certain crowd.
This doesn't mean I am not exploring how to use AI and even have something I am working on in terms of an idea, it's just that caution is advised.
In general, it’s unwise to take corporate comms at face value. Think about what Andreessen Horowitz would like investors and founders to believe and how it would like them to behave. Then consider the post as if it were an attempt instil those beliefs and elicit those behaviours.
Exactly.
Reading your enemy’s propaganda may be useful sometimes, but taking it seriously is foolish.
This hides one major caveat in AI which is, contrasting to "old" tech, your OpEx on compute doesn't scale the same way your engineered app traditionally did.
In other terms, as your revenue scales, your OpEx scales too. This breaks the idea that you need to grow revenue to "break off" as your margin as set due to compute.
The other issue is, I've been burning compute between Perplexity, Grok, Gemini, Claude and Deepseek. I pay nothing for these and they are good enough. It is easy to grow revenue to $1m when you are burning $2m of compute.
Anyone else a bit confused by the use of the word "working" used, given the content of the post? I thought this was going to be about how white collar work is changing, not about fundraising and growth strategies.
I thought this was going to be about apps barely working if created by vibe coding.
I thought the same thing. Maybe "working" as in "providing value to investors." Or maybe "getting clicks on hacker news."
I'm confused by the whole title. Very strange
This seems like a case of selection bias, where they are looking at all the Gen AI startups and seeing that they are making revenue faster than previous startups. But Gen AI startups have mostly only started very recently, so it's obvious that all the successes must have grown fast, as they haven't been around long enough to grow slowly. Maybe in 5 years, we'll see a lot of cases of successful startups that took a slower growth trajectory instead.
But whether it's short-sighted for the investors or not, I think the takeaway for founders is "investors now expect you to make more revenue faster, and B2C applications are more interesting than before".
Come back to this comment in 5 years. Everyone's that's fully bought into the AI hype is on serious crack. This is not my first (or 2nd) rodeo.
When tulips suddenly became fashionable a few years back, articles like this were rife.
This article even smells ... generative.
I think this has an interesting consequence for founders and employees at startups in this day and age, and I'm not quite sure how I feel about it.
On one hand, it means you can "fail faster". That is, if you're a startup employee, and you don't see "hockey stick" growth that is looking crazy impressive at the end of year 1, you should know that the chances of your equity being worth more than a token are basically zero. Starting around the dot com boom, I worked in numerous startups, and for some of them we were still chugging along in years 3-4 with the hopes that our "semi-OK, decent growth" would turn vertical any day now. I've seen numerous startups that started in the 2015-2020 timeframe (so existed for 5-10 years) where they didn't outright fail but common got wiped in an acquisition. That's more a consequence of the rise in interest rates and difficult fundraising environment, but it's really rough to plug along at a company for 5-10 years, think you're doing OK, and then your stock is worth nothing. So from a startup founder/employee perspective, you get signal faster and don't have to waste time.
Simultaneously, though, it seems like any idea that would take a decent amount of upfront investment and time would be hella difficult to get funded, and I think that's unfortunate.
The metric the article focuses on is “revenue,” but it seems like the foundation many of these startups build on (other people’s LLM APIs) are much more expensive than the last generation of startups.
Given the cost of training a SOTA model, it’s not clear these companies have sustainable businesses. If your primary expense is AWS you can always shift to your own hardware once you hit sufficient scale. If you’re Cursor, how big do you need to get to eliminate your 3rd party API dependency?
The cost of training a good model can be as low as $5.5m (DeepSeek v3) or "a few tens of millions of dollars" (Claude 3.7 Sonnet https://simonwillison.net/2025/Mar/2/ethan-mollick/) so once you have proven revenue against other models it might actually become a reasonable thing to do.
This article is about AI applications, not core foundation models. None of the initial 3 companies mentioned (Cursor, Lovable, Gamma) train there own models from scratch, nor do they need to. E.g. I get tons of value from Cursor, but I also still pay for ChatGPT Plus.
There is also enough competition in the core model space that these apps don't need to have an Achilles heel by being reliant on a single vendor. E.g. I think Cursor was smart to let you "bring your own API key".
I get that the article is about applications, but the applications have a dependency on 3rd party models, and that dependency is costing them most (all? More than all?) of their revenue.
Put another way, if I make $100M in annual revenue, but am paying out $110M to the API I wrap, it’s not nearly as compelling a business as that top-line $100M number makes it out to be.
In the previous generation of startups, expenses were mostly dominated by headcount, and the cost of actually delivering the service tended to be small. The story was “keep growing revenue, and if you need to show a profit, stop hiring.”
An AI startup built on other people’s models has to hope that the foundational models end up being fungible commodities, otherwise any margins you might gain will get squeezed out by your LLM provider. Alternatively, you can train your own model.
I don’t know what Cursor’s userbase looks like. If everyone is paying for Pro but using their own API key, that’s obviously a high margin business.
I read TFA. Quickly but I read it. It's short but I have no idea if they were saying "wow AI products are popular" or "AI helps startups reach higher levels of profitability faster" or simply "A company that says they are making an AI driven product receives more initial users and funding".
Each of those are so wildly different conclusions and require such wildly different data to support it.
It means doing your normal work and then staying late to finish your mandatory use of AI to meet management checkboxes.
I tried using Claude Code, was utterly disappointed with using it and it cost me a lot of money very quickly. Just goes to show these tools can augment and improve your workflow and knowledge, but in their current state they cannot replace you - any CEO who says otherwise has no clue and is riding the hype train
Difficult to know the size of the pool of companies they're talking about
[dead]
Nowadays, I don't think work should be defined just by clocking in and clocking out. It’s about the ability to complete tasks and hit goals efficiently. This is how AI will redefine work.
I don't disagree, but god help us.
If we want a feature we can write a two sentence prompt and get that feature. But the technical debt is going to grow exponentially, and I haven't seen a shred of focus on preventing that inevitable outcome.
How long until this starts biting companies en mass?
They would need to remain around long enough for the privilege of failing due to unmaintainable systems.
That's not the kind of "working" that the title refers to.
It really is about "How do you know your startup is 'working' (i.e. doing the right things to be successful) in this AI era".