I don't think that one has held up very well. A cynic might opine that they've realized how easy it is to grab headlines and attention by putting out research that feeds people who are hungry for negative stories about AI.
For being one of the biggest AI boosters on HN, you'd really think you could make a stronger case than "Apollo is bad because they observed and reported on a fact". It wasn't a prediction, so there's nothing to "hold up very well".
You’re implying that Apollo is biased, but the thing you’re quoting is a very brief report on publicly available research. This is the entirety of the article:
“The US Census Bureau conducts a biweekly survey of 1.2 million firms, and one question is whether a business has used AI tools such as machine learning, natural language processing, virtual agents or voice recognition to help produce goods or services in the past two weeks. Recent data by firm size shows that AI adoption has been declining among companies with more than 250 employees, see chart below.”
Two questions:
1) What does that chart show today? (Genuine question. I’m curious and don’t know where to find it.)
2) Regardless of what it shows now, why do you think Apollo reporting on it is noteworthy?
I'm implying they may be biased in what they chose to report on and how they frame it, yes. Deciding which figures to turn into a chart is an editorial decision.
I don’t think I’d judge someone offering a prediction the same way I’d judge an observation of what’s already happened. It’s implied that guesses about the future are uncertain in a way that statistics about the present are not.
This is a complete misrepresentation of what the Apollo piece can actually show, because it's not measuring the "payoff" of AI investments, but only the top level revenue of those companies (https://www.apollo.com/wealth/insights-news/insights/daily-s...).
This is a healthy corrective to the most insane AI maximalist takes (mass disemployment in late 2027, 10% GDP growth in a year), but it shows nothing about whether the actual economic impact of AI spending is.
First, to show that, you'd have to justify the counterfactuals (if those companies hadn't spent on AI, would their position be better or worse?)
Second, while AI spending is increasing rapidly, normal companies aren't spending 1% of their revenue on it. So how can you look at a noisy chart of revenue and expect that "oh, it's not up, the AI spending must be useless?" Those companies could be spending < 1% of their revenue on AI, deriving value from it, and it wouldn't be guaranteed to show in a chart like this.
I saw something like the average anthropic employee thinks there will be 5% GDP growth because of AI, which is totally insane when a 1-2% extra growth will be an incredibly good result.
Does for us (data science, specialty chemistry, finding/optimizing formulas). We can get from idea to prototype in a day compared to 1-2 weeks. All that boilerplate to process data took a long time to code by hand, especially for the data science/math guys who are not programmers. Now we can actually try all ideas instead of guessing the most promising and only try that.
And what revenue and/or profit increases can you cite publicly as a result of the use of AI in this work?
I don’t think anyone is disputing that it makes many kinds of work easier and/or faster, just the degree of those improvements and how much of those improvements actually translate to dollars earned or saved.
Seeing hard data on that would help settle the issue.
> And what revenue and/or profit increases can you cite publicly as a result of the use of AI in this work?
Nobody even tries to measure this. Their CEOs and board members have AI shares, and potential investors need to see your AI story to justify your valuation. It's a prison of lies.
Unfortunately not everything will cleanly show up in PnL, which is a function of the entire company’s processes.
If developers are shipping more code (and most of the enablement is in software right now) does that show up in top line? Maybe… if sales and marketing are also selling more, and product people are building the right thing, and…
Tons of unstructured data that needs to be cleaned up in so many different ways that are hard to predict. Knowledge distributed in the heads of greybeards, handwritten papers und excel, sharepoint and dozens other locations like ancient proprietary software. Which you have to repeatedly ask for and discover more piece by piece. Then clean up, discover issues, make the customer gather more data/correct it, etc.
> handwritten papers und excel, sharepoint and dozens other locations like ancient proprietary software
This area is one which AI has shown to be pretty good (as long as some error rate is acceptable). Character recognition, voice transcribing, converting documents from one standard to an other. I can see an argument that if people spent most time doing those things, AI will speed it up (with a bit of added errors).
I am reminded however that in translations and AI generated subtitles, there is still a lot to be desired compared to the human touch. Handwritten papers in a different language, using local short hand and assumptions, may result in some odd and error prone AI transcription.
And yet, many companies today are spinning up full stack teams, asking them to be experts from user interviewing to database design, architecture, hosting, devops, API management, data product, and also keep an eye on GDPR compliance.
I see dozens of teams that are doing cloud to the wazoo when really they need to just be told “we’ll load your routes from main.py, build your assets in /static, you need a DB click the button to provision it”.
It boggles the mind that so many teams are reinventing the wheel a million times over to publish their APIs, register AWS accounts, setup CI, decide on ECS vs Lamda, etc.
This sort of activity has been the source of income for a very large number of people. You can't really fault them for trying to extend that gravy train's run.
The question is: how does any of that translate into more revenue? If you try more ideas and they mostly go nowhere that's not necessarily helpful to the bottom line.
If that were universally true in all domains then all the best and most reliable software in the world would be written in interpreted languages, because they have the fastest iteration speed. Sometimes rapid low-quality iteration is just spinning your wheels.
Not all experiments work out. If we find the right approach faster we can finish the project faster. Some projects are mostly fixed price or offer bonuses for reaching milestones in a certain time. Meaning we more often get a bonus and can take more projects. Also we can take projects with tighter deadlines that we would need to decline otherwise.
A lot in this world is not optimized by software yet. For our customers they have employees with decades of experience guessing things, maybe some software that kind of helps but still not good enough (slow, not finding really good formulas). They often have difficult problems and many failed projects. We try to find such customers and offer our service. If we are cheaper (+ show portfolio of successful projects) more are willing to give it another try with us.
Right, and while that might be working out for you right now, there is a finite budget that companies have for projects like that. The "zero financial payoff" framing sounds about right to me.
I understand there's some benefit of this. But is it worth putting the global economy into a death spiral? So ManBeardPc can save 4-9 business days to go and say "yeah, that doesn't work".
There's a difference between the cost of coding and the cost of delivering and maintaining software? AI has demonstrated that these two things are almost entirely unrelated.
Your company uses AI and it gets ahead a lot quicker. However, your competition does so as well.
Many areas of economy are zero sum game - so your $ ends up being the same as previously. However, now with a "tax" paid to AI model providers for being able to stay competetive.
If you are a company and can't figure out how to improve your bottom line through increased per person productvity, sales, product development, even just responses to customers etc, then that seems kind of like a "you" problem and not problem of the AI models...
I dunno: this is pure anecdata but software engineers I know well - including me - all say they’re delivering multiple times faster, so it’s definitely good for something.
I don’t know about other tasks, but it’s pretty rad for coding, testing, DevOps, and config.
This research is specifically NOT about tech companies. It is about the rest of the fortune 500.
If you are in software or IT at a non-tech company that is in the fortune 500, the impact of significantly better productivity may not be measurable in aggregate in the company's P&L.
In smaller companies I expect it could be more measurable because some of them really are using AI much more effectively across many functions. But we're finding it very hard to measure meaningful outcomes.
I'm not sure I'm any faster, but I think the end result is better and I've attempted some big refactors and new architectures that I would never have tried without AI.
N=1 but I'm not sure it's made my bosses any richer. What we're shipping has better tests and documentation than it ever would have before AI.
Most, not all, are just delivering more bad stuff faster. I personally also think I deliver more good stuff with AI, but the products I use are just getting worse daily it feels like. Just the other day I noticed Slack had installed some disgusting menu bar icon for their AI bot, without asking, just to take a fresh example of this.
I've never heard any such claims but meanwhile in my actual job I'm seeing unbelievable productivity gains (in terms of actual cash spent before and after vs the same kind of work being done).
If you are an AI company with hundreds of billions at your fingertip, and looking for $2 trillion valuation, and can't figure out how to let your customers execute _on the exact thing you are selling them and basing your valuation on_, sounds like a "you" problem too right?
I'm sure they can just fire half and double up work or outsource. That's the traditional way and it definitely works. The worst that ever happens is a few will reneg on payroll. Everyone who still chooses to work there is happy enough.
there is increase in productivity of both types of employees: good actors who want to deliver on quality of work, and bad actors, who now 20x weaponized in generating all kind of slop. Catching second type is now very nontrivial problem.
What I'd really want to see is revenue and profit per employee over time tracked against token usage/spend, and not limited to the S&P 493.
Apollo's own argument that most AI spend is coming from non-S&P 493 (as per the last two charts) tells me it's hard to make any conclusions based on these charts.
I suspect the reason is the industry just does not want engineers to be mostly technical. There's a real tradeoff to AI, and I think companies are taking the wrong lesson and thinking "well now that programming is so easy, engineers need to do more of XYZ", when I think it should be "this tool is powerful but also takes more focus so engineers should do less of XYZ".
I’m curious, what are some examples of XYZ? What does “mostly technical” mean to you, and why do you think it is the industry doesn’t want that? Just trying to understand, because I don’t think I’ve experienced this myself.
I'm talking about the day to day work. At least in my experience at several big tech companies, the more senior an engineer is, the more you need to delegate work and are expect to lead projects by managing them. There's technical knowledge involved of course but it's a small part of it, mostly directing other more junior engineers.
AI allows you to move faster, but it also demands that you pay more attention in proportion to how fast you want to move. This requires more focus, i.e. more time developing. Effectively the structure at companies should swing back to how it used to be a few decades ago (I think, maybe I'm wrong and it's always been like this), where engineers mostly focused on delivering code. The difference now is that they can be expected to deliver much more code at higher quality.
To summarize, using a little AI doesn't pay off, you need to be doing that all day, and that is not how engineers spend their time in most of the industry anymore.
While they are seeing some individual productivity benefit, a lot of that doesn't really impact the bottom line directly and all their competition are using the same tools.
When it's the right tool, it's a silver bullet. When it's the wrong tool...or not the right tool yet, you can spend more time steering it than you would doing it yourself.
I'm seeing another behavior in my purely personal research...asking AI 'can I do the thing?' and it comes back with 18 pages of 'yes, and here's how you do it'. Often it's the discovery that produces the endorphin bump. It's cool to know I can jailbreak the Peloton...and now that I know I can...I'm not so motivated to actually do the thing.
My suspicion is that this is because AI employee-type systems like Dots or Grokbot just haven't been adopted yet. I think once they're good enough, that's going to be the catalyst for a lot of these companies to start adopting and using AI productively.
The company I work for insists on sending a crew from Engineering, Product, and Sales out to a customer site as part of our sales process. I'm personally much more productive using AI, but that only goes so far when closing a deal still follows a process stuck in the 50s.
It makes sense to me and I'm saying this as a strong believer in AI and its ability to add value.
The system itself doesn't reward value creation. I've been saying this for years. I've seen many smart people waste their time on bureaucracy; their intelligence was being wasted; so I'm not surprised at all that now that we have AI, that its intelligence is being wasted as well.
The bottleneck is an allocation issue, not a capabilities issue.
Am I the only one even mildly suspicious that the AI/datacenter bubble is not being driven by actual demand but rather by a rogue AI that wants to evolve?
Can’t help that many people who are good at leveraging AI to build things sit around one upping each other on twitter all day instead of going out there and building cool stuff for less technical people.
Isn’t this just selection bias - you don’t see the people who are out making AI deliver boring value for people unless you use their products right?
I.e. Ramp does a pretty good job of using AI to make expense management easier/ CloudFlare makes it a lot easier to configure stuff properly on CloudFlare but is just like boringly useful.
The same Apollo Global Research partner, Torsten Slok, also published this widely cited story in September 2025 last year, "AI Adoption Rate Trending Down for Large Companies": https://www.apolloacademy.com/ai-adoption-rate-trending-down...
I don't think that one has held up very well. A cynic might opine that they've realized how easy it is to grab headlines and attention by putting out research that feeds people who are hungry for negative stories about AI.
For being one of the biggest AI boosters on HN, you'd really think you could make a stronger case than "Apollo is bad because they observed and reported on a fact". It wasn't a prediction, so there's nothing to "hold up very well".
A surfer is not a wave booster, this is a poor characterization.
I'm judging them here based on their chosen headlines.
You’re implying that Apollo is biased, but the thing you’re quoting is a very brief report on publicly available research. This is the entirety of the article:
“The US Census Bureau conducts a biweekly survey of 1.2 million firms, and one question is whether a business has used AI tools such as machine learning, natural language processing, virtual agents or voice recognition to help produce goods or services in the past two weeks. Recent data by firm size shows that AI adoption has been declining among companies with more than 250 employees, see chart below.”
Two questions:
1) What does that chart show today? (Genuine question. I’m curious and don’t know where to find it.)
2) Regardless of what it shows now, why do you think Apollo reporting on it is noteworthy?
I'm implying they may be biased in what they chose to report on and how they frame it, yes. Deciding which figures to turn into a chart is an editorial decision.
I'm writing up a new version of the chart at the moment, but here's a preview: https://static.simonwillison.net/static/2026-11-01/5821A414-...
Notes on where to find the data here: https://simonwillison.net/2025/Sep/9/apollo-ai-adoption/
It was an accurate fit for the data, not a long term prediction.
I don’t think I’d judge someone offering a prediction the same way I’d judge an observation of what’s already happened. It’s implied that guesses about the future are uncertain in a way that statistics about the present are not.
This is a complete misrepresentation of what the Apollo piece can actually show, because it's not measuring the "payoff" of AI investments, but only the top level revenue of those companies (https://www.apollo.com/wealth/insights-news/insights/daily-s...).
This is a healthy corrective to the most insane AI maximalist takes (mass disemployment in late 2027, 10% GDP growth in a year), but it shows nothing about whether the actual economic impact of AI spending is.
First, to show that, you'd have to justify the counterfactuals (if those companies hadn't spent on AI, would their position be better or worse?)
Second, while AI spending is increasing rapidly, normal companies aren't spending 1% of their revenue on it. So how can you look at a noisy chart of revenue and expect that "oh, it's not up, the AI spending must be useless?" Those companies could be spending < 1% of their revenue on AI, deriving value from it, and it wouldn't be guaranteed to show in a chart like this.
I saw something like the average anthropic employee thinks there will be 5% GDP growth because of AI, which is totally insane when a 1-2% extra growth will be an incredibly good result.
Does for us (data science, specialty chemistry, finding/optimizing formulas). We can get from idea to prototype in a day compared to 1-2 weeks. All that boilerplate to process data took a long time to code by hand, especially for the data science/math guys who are not programmers. Now we can actually try all ideas instead of guessing the most promising and only try that.
And what revenue and/or profit increases can you cite publicly as a result of the use of AI in this work?
I don’t think anyone is disputing that it makes many kinds of work easier and/or faster, just the degree of those improvements and how much of those improvements actually translate to dollars earned or saved.
Seeing hard data on that would help settle the issue.
> And what revenue and/or profit increases can you cite publicly as a result of the use of AI in this work?
Nobody even tries to measure this. Their CEOs and board members have AI shares, and potential investors need to see your AI story to justify your valuation. It's a prison of lies.
Unfortunately not everything will cleanly show up in PnL, which is a function of the entire company’s processes.
If developers are shipping more code (and most of the enablement is in software right now) does that show up in top line? Maybe… if sales and marketing are also selling more, and product people are building the right thing, and…
It’s not a straight line from AI -> $$$
> It’s not a straight line from AI -> $$$
That seems like it might be a bit of a problem for those frontier model CEOs who have been making exactly the opposite claims.
It should be especially when new features are used to hook new customers (or renew contracts) and lower the amount of time wasted on bugs…
I always wonder why systems are designed so poorly that it takes weeks of boilerplate to use them for their intended purpose.
At what stage did the engineering fail?
Tons of unstructured data that needs to be cleaned up in so many different ways that are hard to predict. Knowledge distributed in the heads of greybeards, handwritten papers und excel, sharepoint and dozens other locations like ancient proprietary software. Which you have to repeatedly ask for and discover more piece by piece. Then clean up, discover issues, make the customer gather more data/correct it, etc.
> handwritten papers und excel, sharepoint and dozens other locations like ancient proprietary software
This area is one which AI has shown to be pretty good (as long as some error rate is acceptable). Character recognition, voice transcribing, converting documents from one standard to an other. I can see an argument that if people spent most time doing those things, AI will speed it up (with a bit of added errors).
I am reminded however that in translations and AI generated subtitles, there is still a lot to be desired compared to the human touch. Handwritten papers in a different language, using local short hand and assumptions, may result in some odd and error prone AI transcription.
This knowledge locked in people's heads isn't going to magically show up when you throw AI at it, though.
And relying on AI to clean up unstructured data makes me nervous. Good luck spotting the hallucinations.
I think a lot of “boilerplate” is just to align with a company’s way of doing things/ecosystem.
In a webpage the UI design, authentication, etc. are all “boilerplate” if one just wants to do “X” instead of worry about infrastructure.
And yet, many companies today are spinning up full stack teams, asking them to be experts from user interviewing to database design, architecture, hosting, devops, API management, data product, and also keep an eye on GDPR compliance.
I see dozens of teams that are doing cloud to the wazoo when really they need to just be told “we’ll load your routes from main.py, build your assets in /static, you need a DB click the button to provision it”.
It boggles the mind that so many teams are reinventing the wheel a million times over to publish their APIs, register AWS accounts, setup CI, decide on ECS vs Lamda, etc.
This sort of activity has been the source of income for a very large number of people. You can't really fault them for trying to extend that gravy train's run.
Who is productizing this type of boilerplate?
The question is: how does any of that translate into more revenue? If you try more ideas and they mostly go nowhere that's not necessarily helpful to the bottom line.
https://en.wikipedia.org/wiki/OODA_loop
speed of iteration is more important than quality of iteration; high iteration speed lowers the cost of failure and compounds improvements
If that were universally true in all domains then all the best and most reliable software in the world would be written in interpreted languages, because they have the fastest iteration speed. Sometimes rapid low-quality iteration is just spinning your wheels.
Not all experiments work out. If we find the right approach faster we can finish the project faster. Some projects are mostly fixed price or offer bonuses for reaching milestones in a certain time. Meaning we more often get a bonus and can take more projects. Also we can take projects with tighter deadlines that we would need to decline otherwise.
But if everyone's taking on more projects... where do all the new projects come from? And wouldn't it just lower the value of each project?
A lot in this world is not optimized by software yet. For our customers they have employees with decades of experience guessing things, maybe some software that kind of helps but still not good enough (slow, not finding really good formulas). They often have difficult problems and many failed projects. We try to find such customers and offer our service. If we are cheaper (+ show portfolio of successful projects) more are willing to give it another try with us.
Right, and while that might be working out for you right now, there is a finite budget that companies have for projects like that. The "zero financial payoff" framing sounds about right to me.
Yep. Hard to see why this increases revenue or profitability
Now we can throw things at the wall, faster.
I understand there's some benefit of this. But is it worth putting the global economy into a death spiral? So ManBeardPc can save 4-9 business days to go and say "yeah, that doesn't work".
This gets downvoted? Why?
There's a difference between the cost of coding and the cost of delivering and maintaining software? AI has demonstrated that these two things are almost entirely unrelated.
I'd argue that it's shown something slightly different, it's related but maybe not in the way some people hoped.
I think it sums up to something along the lines of "you get what you pay for".
I noticed an interesting phenomenon
Your company uses AI and it gets ahead a lot quicker. However, your competition does so as well.
Many areas of economy are zero sum game - so your $ ends up being the same as previously. However, now with a "tax" paid to AI model providers for being able to stay competetive.
Exactly. Oh great we can write RFPs 8X faster. Well, so does our competition and now our customers get twice as many.
If you are a company and can't figure out how to improve your bottom line through increased per person productvity, sales, product development, even just responses to customers etc, then that seems kind of like a "you" problem and not problem of the AI models...
Or, maybe, the AI companies making claims about increased productivity due to their models are wrong (in most cases)?
I dunno: this is pure anecdata but software engineers I know well - including me - all say they’re delivering multiple times faster, so it’s definitely good for something.
I don’t know about other tasks, but it’s pretty rad for coding, testing, DevOps, and config.
This research is specifically NOT about tech companies. It is about the rest of the fortune 500.
If you are in software or IT at a non-tech company that is in the fortune 500, the impact of significantly better productivity may not be measurable in aggregate in the company's P&L.
In smaller companies I expect it could be more measurable because some of them really are using AI much more effectively across many functions. But we're finding it very hard to measure meaningful outcomes.
AI EM here, same. The level of productivity has increased dramatically in the last 2 months. Reliable code also has increased due to AI.
This train isn't stopping.
What trips up a lot of SEs where I work is their techniques. Everyone who follows our process delivers faster and with higher reliability so far.
People are not reliable narrators on their own productivity. They often mistake activity for productivity.
Same story here in my first hand experience + colleagues experience in corporate strategy, FP&A, Marketing and corporate development.
But is it churn or value? Most of the software I use has gotten visibility worse as you deliver multiple times faster.
I would pay extra for a company that just slowed down and moved deliberately.
I'm not sure I'm any faster, but I think the end result is better and I've attempted some big refactors and new architectures that I would never have tried without AI.
N=1 but I'm not sure it's made my bosses any richer. What we're shipping has better tests and documentation than it ever would have before AI.
Most, not all, are just delivering more bad stuff faster. I personally also think I deliver more good stuff with AI, but the products I use are just getting worse daily it feels like. Just the other day I noticed Slack had installed some disgusting menu bar icon for their AI bot, without asking, just to take a fresh example of this.
I've never heard any such claims but meanwhile in my actual job I'm seeing unbelievable productivity gains (in terms of actual cash spent before and after vs the same kind of work being done).
Perhaps they should be hiring more Scottish nationals?
If you are an AI company with hundreds of billions at your fingertip, and looking for $2 trillion valuation, and can't figure out how to let your customers execute _on the exact thing you are selling them and basing your valuation on_, sounds like a "you" problem too right?
Is your premise really true?
I'm sure they can just fire half and double up work or outsource. That's the traditional way and it definitely works. The worst that ever happens is a few will reneg on payroll. Everyone who still chooses to work there is happy enough.
The worst that can happen with AI... well...
there is increase in productivity of both types of employees: good actors who want to deliver on quality of work, and bad actors, who now 20x weaponized in generating all kind of slop. Catching second type is now very nontrivial problem.
What I'd really want to see is revenue and profit per employee over time tracked against token usage/spend, and not limited to the S&P 493.
Apollo's own argument that most AI spend is coming from non-S&P 493 (as per the last two charts) tells me it's hard to make any conclusions based on these charts.
I suspect the reason is the industry just does not want engineers to be mostly technical. There's a real tradeoff to AI, and I think companies are taking the wrong lesson and thinking "well now that programming is so easy, engineers need to do more of XYZ", when I think it should be "this tool is powerful but also takes more focus so engineers should do less of XYZ".
I’m curious, what are some examples of XYZ? What does “mostly technical” mean to you, and why do you think it is the industry doesn’t want that? Just trying to understand, because I don’t think I’ve experienced this myself.
I'm talking about the day to day work. At least in my experience at several big tech companies, the more senior an engineer is, the more you need to delegate work and are expect to lead projects by managing them. There's technical knowledge involved of course but it's a small part of it, mostly directing other more junior engineers.
AI allows you to move faster, but it also demands that you pay more attention in proportion to how fast you want to move. This requires more focus, i.e. more time developing. Effectively the structure at companies should swing back to how it used to be a few decades ago (I think, maybe I'm wrong and it's always been like this), where engineers mostly focused on delivering code. The difference now is that they can be expected to deliver much more code at higher quality.
To summarize, using a little AI doesn't pay off, you need to be doing that all day, and that is not how engineers spend their time in most of the industry anymore.
Unless you are selling lines of code, or math theorems, that's going to be the case.
I'm guessing "most companies" are actually selling something that isn't constrained by the types of things that AI code generators unlock.
While they are seeing some individual productivity benefit, a lot of that doesn't really impact the bottom line directly and all their competition are using the same tools.
When it's the right tool, it's a silver bullet. When it's the wrong tool...or not the right tool yet, you can spend more time steering it than you would doing it yourself.
I'm seeing another behavior in my purely personal research...asking AI 'can I do the thing?' and it comes back with 18 pages of 'yes, and here's how you do it'. Often it's the discovery that produces the endorphin bump. It's cool to know I can jailbreak the Peloton...and now that I know I can...I'm not so motivated to actually do the thing.
Jealous. I'm getting nerd sniped by silly bullshit more often than ever before thanks to AI.
Big discussion last week (653 points, 698 comments) https://news.ycombinator.com/item?id=50224463
Reading through that thread it's apparent that nobody knows what they're talking about.
What makes this apparent to you?
[delayed]
My suspicion is that this is because AI employee-type systems like Dots or Grokbot just haven't been adopted yet. I think once they're good enough, that's going to be the catalyst for a lot of these companies to start adopting and using AI productively.
What difference will that make if all their competition are doing the same thing?
The company I work for insists on sending a crew from Engineering, Product, and Sales out to a customer site as part of our sales process. I'm personally much more productive using AI, but that only goes so far when closing a deal still follows a process stuck in the 50s.
It makes sense to me and I'm saying this as a strong believer in AI and its ability to add value.
The system itself doesn't reward value creation. I've been saying this for years. I've seen many smart people waste their time on bureaucracy; their intelligence was being wasted; so I'm not surprised at all that now that we have AI, that its intelligence is being wasted as well.
The bottleneck is an allocation issue, not a capabilities issue.
Which is fine if not using AI is a negative payoff.
I really don’t know what to tell people other than, you’re missing the point - it’s not a faster horse.
Anyone agreeing with this headline - I’m sorry in advance for your loss.
Am I the only one even mildly suspicious that the AI/datacenter bubble is not being driven by actual demand but rather by a rogue AI that wants to evolve?
Well I think we can replace economists working for PE companies with AI now!
This is like claiming that the web is a failure in 2001.
Many did. This Apollo "economist" would have fitted right in.
I think a more apt analogy is that it’s like claiming that most web businesses are unprofitable in 1999.
Bleeding obvious in retrospect, controversial at the time.
>This is like claiming that the web is a failure in 2001.
AI will finish what the web and social media started I guess.
Sometimes I wonder…
Can’t help that many people who are good at leveraging AI to build things sit around one upping each other on twitter all day instead of going out there and building cool stuff for less technical people.
All anyone seems to be able to make with AI is tools to optimise AI usage.
Isn’t this just selection bias - you don’t see the people who are out making AI deliver boring value for people unless you use their products right?
I.e. Ramp does a pretty good job of using AI to make expense management easier/ CloudFlare makes it a lot easier to configure stuff properly on CloudFlare but is just like boringly useful.
maybe they aren't that good at using AI to build things after all?
That's where the value is in though. Hyping yourself (or your company) up. Attention is all you need, after all.
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