How AI conquered the US economy: A visual FAQ(derekthompson.org)
derekthompson.org
How AI conquered the US economy: A visual FAQ
https://www.derekthompson.org/p/how-ai-conquered-the-us-economy-a
236 comments
I think it's more likely the assumption is you'd expect a far more diversified market. If we're really in a situation where the rational, good reasons move is to effectively ignore 98% of companies, that doesn't say good things about our economy (verging on some kind of technostate). You get into weird effects like "why invest in other companies" leading to "why start a company that will just get ignored" leading to even more consolidation and less dynamism.
But startups are thriving. That doesn't suggest decreasing dynamism to me. It suggests that there are abundant gains to be had by exploiting technological progress, and the legacy economy is not availing itself of these opportunities. A thriving tech startup sector is surely key to dynamism.
> But startups are thriving
I'm just some guy with an opinion. I worked in startups for 20 years. Startups are called exciting or thriving or good bets because a tiny few are successful and even fewer are trying to compete with established companies. Capital is pumped into lots of little ones regardless of the technology de-jour or market opportunity. They generally don't last. Statistically, if you were an AI startup from 6 years ago, you're long gone and you made out with what you could scrape together on the way out. Startups are thriving by feeding off dreams of grandeur, with very few happening upon the right combination of personality, capability and market enough to last for a decade. Is that thriving or thrashing? Don't confuse the velocity of gambling with the volume of opportunity.
I'm just some guy with an opinion. I worked in startups for 20 years. Startups are called exciting or thriving or good bets because a tiny few are successful and even fewer are trying to compete with established companies. Capital is pumped into lots of little ones regardless of the technology de-jour or market opportunity. They generally don't last. Statistically, if you were an AI startup from 6 years ago, you're long gone and you made out with what you could scrape together on the way out. Startups are thriving by feeding off dreams of grandeur, with very few happening upon the right combination of personality, capability and market enough to last for a decade. Is that thriving or thrashing? Don't confuse the velocity of gambling with the volume of opportunity.
This metric has obvious problems, but 120/134 of YC's S25 batch are AI-based [0]. 90% is, I guess, better than 98%, but woof. So, depends on what you mean by "thriving", but if diversity factors in there at all then at least YC is proving you wrong.
[0]: https://www.ycombinator.com/companies?batch=Summer%202025 (search for "AI" and it gives 120)
[0]: https://www.ycombinator.com/companies?batch=Summer%202025 (search for "AI" and it gives 120)
If you are still using sleds after the invention of the wheel, you are making a mistake. If you are innovating with pencil and paper after the development of the computer, you're making a grave error. There was no virtue in still sending faxes in 2010.
Tech has to believe this. We're all victims of the Upton Sinclair "it's difficult to get a man to understand something when his salary depends on his not understanding it" thing.
A lot of tech is just straight up bad. The machine gun was bad. The nuclear bomb was bad. Social media was bad. TV was bad. Cars were bad. C++ was bad.
> If you are innovating with pencil and paper after the development of the computer, you're making a grave error.
This is a great example. Study after study shows that writing on paper helps retention and analysis vs. typing. Study after study shows reading on paper helps retention and comprehension vs. a screen.
> There was no virtue in still sending faxes in 2010.
You can send a document with an authentic signature. This might not seem like a big deal, but "wet ink" clauses prevent many pro-democracy agendas (voter registration, etc), and the good faith reason for them (the typical reason is voter suppression) is that documents are too easy to forge without signatures.
But also, were fax machines bad? Did they enable an even-more-sprawling bureaucracy? We're well past the point where "new tech = good" is even close to default true.
A lot of tech is just straight up bad. The machine gun was bad. The nuclear bomb was bad. Social media was bad. TV was bad. Cars were bad. C++ was bad.
> If you are innovating with pencil and paper after the development of the computer, you're making a grave error.
This is a great example. Study after study shows that writing on paper helps retention and analysis vs. typing. Study after study shows reading on paper helps retention and comprehension vs. a screen.
> There was no virtue in still sending faxes in 2010.
You can send a document with an authentic signature. This might not seem like a big deal, but "wet ink" clauses prevent many pro-democracy agendas (voter registration, etc), and the good faith reason for them (the typical reason is voter suppression) is that documents are too easy to forge without signatures.
But also, were fax machines bad? Did they enable an even-more-sprawling bureaucracy? We're well past the point where "new tech = good" is even close to default true.
AI startups are getting a lot of money. "Thriving" is a stretch from there unless the acquisition of money itself is the success bar.
Meanwhile, I hear pretty much any startup not associated with AI is finding it harder to get funding.
Meanwhile, I hear pretty much any startup not associated with AI is finding it harder to get funding.
If "thriving" means "moving quickly towards being bought by one of the big companies" then that's an illusory diversity.
Even the acqui-hire route is ending, with BigCos just hiring the talent directly. I think this will be a drag on startup investment: why should I invest my capital just to build a throwaway resume builder for some AI engineers where I don't even get made whole at the end of it?
I agree, except that it seems dynamism is almost restricted to digital tech. I wish tech would spread its dynamism a little better into legacy industries, and give some productivity gains/disruption to those areas.
there's been plenty of disruption in traditional industries like retail, automotive, media, communications, etc.
part of the problem is that the remaining set of industries is pretty tough to make dynamic using technology simply because the explosive market size isn't there for various reasons. if you wanted to disrupt aviation, for example, a plane takes tens of billions of dollars to bring to market, and an airline requires outlaying billions in capex on planes.
part of the problem is that the remaining set of industries is pretty tough to make dynamic using technology simply because the explosive market size isn't there for various reasons. if you wanted to disrupt aviation, for example, a plane takes tens of billions of dollars to bring to market, and an airline requires outlaying billions in capex on planes.
Such a shame. The passenger aircraft industry could certainly stand to be shaken up with some of that "move fast and break things" startup dynamism!
I’m not really saying it should, more of an example.
I do wish there was more dynamism in the US airline market though, the mergers forming the big three were probably a mistake
I do wish there was more dynamism in the US airline market though, the mergers forming the big three were probably a mistake
I do not want my passenger aircraft to move fast and break things thank you very much.
But I understand what you meant
But I understand what you meant
> But startups are thriving.
What do you base this statement on? Is there data?
What do you base this statement on? Is there data?
1. People aren't going to take on risk and deploy capital if they can't get a return.
2. If people think they can get an abnormally high return, they will invest more than otherwise.
3. Whatever other money would've got invested would've gone wherever it could've gotten the highest returns, which is unlikely to have the same ratio as US AI investments - the big tech companies did share repurchases for a decade because they didn't have any more R&D to invest in (according to their shareholders).
So while it's unlikely the US would've had $0 investment if not for AI, it's probably even less likely we would've had just as much investment.
2. If people think they can get an abnormally high return, they will invest more than otherwise.
3. Whatever other money would've got invested would've gone wherever it could've gotten the highest returns, which is unlikely to have the same ratio as US AI investments - the big tech companies did share repurchases for a decade because they didn't have any more R&D to invest in (according to their shareholders).
So while it's unlikely the US would've had $0 investment if not for AI, it's probably even less likely we would've had just as much investment.
> it's probably even less likely we would've had just as much investment.
I doubt it. Investors aren't going to just sit on money and let it lose value to inflation.
On the other hand, you could claim non-AI companies wouldn't start a new bubble, so there'd be fewer returns to reinvest, and that might be true, but it's kind of circular.
I doubt it. Investors aren't going to just sit on money and let it lose value to inflation.
On the other hand, you could claim non-AI companies wouldn't start a new bubble, so there'd be fewer returns to reinvest, and that might be true, but it's kind of circular.
Correct - that's why you'd put it in Treasuries which have a positive real return for the first time in ~25 years - or, as I mentioned elsewhere - invest it somewhere else if you see a better option.
Which is an even better argument when you look at how yields have been behaving. AI is sucking the air out of the room.
From a certain macro perspective, of no one is going to beat the Treasury, where is the Treasury going to get that money?
From people who want to lend them money? Or do you mean where is the Treasury going to find production?
they’ll print it of course :)
Sadly, this is exactly what the administration unironically desires. We're arguably in a recession and Trump wants to speed it up to a depression, ransacking the US and making off with the money.
> 1. People aren't going to take on risk and deploy capital if they can't get a return.
> 2. If people think they can get an abnormally high return, they will invest more than otherwise.
Sounds like a good argument for wealth taxes to limit this natural hoarding of wealth absent unreasonably good returns.
> 2. If people think they can get an abnormally high return, they will invest more than otherwise.
Sounds like a good argument for wealth taxes to limit this natural hoarding of wealth absent unreasonably good returns.
we 1000% need all kinds of wealth taxes. The money's been hoarded for decades and I don't think that money is going to the next generation when the boomers kick the bucket. Inheritance tax, ultra high income tax, taxes on stocks if possible. The government wants to defund food stamps and healthcare while giving the robber barons trillions in tax breaks. Something's gotta give.
Sadly I think a wealth tax would make us all poorer in the end. Most of the wealth held by the top 0.01% is in the form of equities/investments in various industries. Taxing this would literally just suck capital out of the economy. I think taxing real estate holdings is a good idea though (something like Georgeism).
Why is it "unlikely" that the alternative is not US investment by these US companies?
The big US software firms have the cash and they would invest in whatever the market fad is, and thus, bring it into the US economy.
The big US software firms have the cash and they would invest in whatever the market fad is, and thus, bring it into the US economy.
No - traditionally they return it as share buybacks, because they don't have any good investments.
Or, like Apple they just sit on the cash waiting for better opportunities.
Apple had huge share buybacks.
Companies need to have a healthy pile of cash as a percentage of their expenses.
Apple just has enormous revenues and expenses.
Companies need to have a healthy pile of cash as a percentage of their expenses.
Apple just has enormous revenues and expenses.
It's not just Apple, they all sit on huge piles of cash.
A big dynamic in this is that the business models for two of the biggest players are at stake.
Google is facing a significant danger that its search advertising business is going to just disappear. If people are using AI to find stuff or get recommendations then then aren't using google. Why should a photographer continue to spend $200 a month on ads when their clients are coming from openAI? Especially when OpenAI is using the organic search results. Meta is facing the same sort of issue, if the eyeballs aren't on insta then the ad $$$ go somewhere else.
So if they have money, and can get money, they will invest it to protect their businesses - all of it.
MS and VC's are doing the opposite, they are investing with the idea of taking the attention that Google and Meta currently have, but they are also following the "I'm scared" signal that Google and Meta have put in the market.
Google is facing a significant danger that its search advertising business is going to just disappear. If people are using AI to find stuff or get recommendations then then aren't using google. Why should a photographer continue to spend $200 a month on ads when their clients are coming from openAI? Especially when OpenAI is using the organic search results. Meta is facing the same sort of issue, if the eyeballs aren't on insta then the ad $$$ go somewhere else.
So if they have money, and can get money, they will invest it to protect their businesses - all of it.
MS and VC's are doing the opposite, they are investing with the idea of taking the attention that Google and Meta currently have, but they are also following the "I'm scared" signal that Google and Meta have put in the market.
Every major consumer facing AI player (besides Anthropic and Apple) is incorporating advertisements into their product currently, or exploring how to, to include OpenAI.
So I’m not as optimistic that Google’s business of advertising is in danger, instead it’s just transforming.
So I’m not as optimistic that Google’s business of advertising is in danger, instead it’s just transforming.
> 1. People aren't going to take on risk and deploy capital if they can't get a return.
This doesn't seem to align with the behavior I've observed in modern VCs. It truly amazes me the kind of money that gets deployed into silly things that are long shots at best.
This doesn't seem to align with the behavior I've observed in modern VCs. It truly amazes me the kind of money that gets deployed into silly things that are long shots at best.
When you think about all of VC being like 1% of a mostly boring portfolio it makes more sense (from the perspective of the people putting the money in).
>1. People aren't going to take on risk and deploy capital if they can't get a return.
I'm sorry, what? This has happened all the time, and in increasing volleys, since 2008.
I'm sorry, what? This has happened all the time, and in increasing volleys, since 2008.
> > Without AI, US economic growth would be meager.
> The assumption here is that, without AI, none of that capital would have been deployed anywhere. That intuitively doesn't sound realistic.
That's the really damning thing about all of this, maybe all this capital could have been invested into actually growing the economy instead of fueling this speculation bubble that will burst sooner or later, bringing along any illusion of growth into its demise.
> The assumption here is that, without AI, none of that capital would have been deployed anywhere. That intuitively doesn't sound realistic.
That's the really damning thing about all of this, maybe all this capital could have been invested into actually growing the economy instead of fueling this speculation bubble that will burst sooner or later, bringing along any illusion of growth into its demise.
If the economy in my life has taught me anything, it’s that there will always be another bubble. The Innovators Dilemma mentions that bubbles aren’t even a bad thing in the sense that useful technologies are often made during them, it’s just that the market is messy and lots of people end up invested in the bubble. It’s the “throw spaghetti at the wall” approach to market growth. Not too different than evolution, in which most mutations are useless but all mutations have the potential to be transformative.
I took their comment to mean that we could have given school children free lunch or implement universal childcare. Like actual useful things that would unlock tremendous economic value, but instead I was given the lying machine and told to make it productive.
Or all that money might have been churning around chasing other speculative technologies. Or it might have been sitting in US Treasuries making 5% waiting for something promising. Who knows what is happening in the parallel alternate universe? Right now, it feels like everyone is just spamming dollars and hoping that AI actually becomes a big industry, to justify all of this economic activity. I'm reminded of Danny DeVito's character's speech in the movie Other People's Money, after the company's president made an impassioned speech about why its investors should keep investing:
"Amen. And amen. And amen. You have to forgive me. I'm not familiar with the local custom. Where I come from, you always say "Amen" after you hear a prayer. Because that's what you just heard - a prayer."
At this point, everyone is just praying that AI ends up a net positive, rather than bursting and plunging the world into a 5+ year recession.
"Amen. And amen. And amen. You have to forgive me. I'm not familiar with the local custom. Where I come from, you always say "Amen" after you hear a prayer. Because that's what you just heard - a prayer."
At this point, everyone is just praying that AI ends up a net positive, rather than bursting and plunging the world into a 5+ year recession.
> Or it might have been sitting in US Treasuries making 5%
Nit: if this happened, I believe the treasury yield would plummet.
Nit: if this happened, I believe the treasury yield would plummet.
I agree, in any time in US history there has always been those 5-10 companies leading the economic progress.
This is very common, and this happens in literally every country.
But their CAPEX would be much smaller, as if you look at current CAPEX from Big Tech, most of it are from NVidia GPUs.
If a Bubble is happening, when it pops, the depreciation applied to all that NVidia hardware will absolute melt the balance sheet and earnings of all Cloud companies, or companies building their own Data centers like Meta and X.ai
This is very common, and this happens in literally every country.
But their CAPEX would be much smaller, as if you look at current CAPEX from Big Tech, most of it are from NVidia GPUs.
If a Bubble is happening, when it pops, the depreciation applied to all that NVidia hardware will absolute melt the balance sheet and earnings of all Cloud companies, or companies building their own Data centers like Meta and X.ai
And NVidia don't even make the GPUs! They're all made on a brave little island off the coast of China, while 7 huge US companies shuffle them around and exchange vast amounts of money for them.
Its also not fair to compare AI firms with others using growth because AI is a novel technology. Why would there be explosive growth in rideshare apps when its a mature niche with established incumbents?
I think the explosive growth that people want is in manufacturing. Ex: US screws, bolts, rivets, dies, pcbs, assembly and such.
The dollars are being diverted elsewhere.
Intel a chip maker who can directly serve the AI boom, has failed to deploy its 2nm or 1.8nm fabs and instead written them off. The next generation fabs are failing. So even as AI gets a lot of dollars it doesn't seem to be going to the correct places.
The dollars are being diverted elsewhere.
Intel a chip maker who can directly serve the AI boom, has failed to deploy its 2nm or 1.8nm fabs and instead written them off. The next generation fabs are failing. So even as AI gets a lot of dollars it doesn't seem to be going to the correct places.
They're not going to get it. The political economy of East Asia is simply better suited for advanced manufacturing. The US wants the manufacturing of East Asia without its politics. Sometimes for good reason - being an export economy has its downsides!
Taiwan isn't some backwater island making low skilled items.
USA lost mass manufacturing (screws and rivets and zippers), but now we are losing cream of the crop world class manufacturing (Intel vs TSMC).
If we cannot manufacture then we likely cannot win the next war. That's the politics at play. The last major war between industrialized nations shows that technology and manufacturing was the key to success. Now I don't think USA has to manufacture all by itself, but it needs a reasonable plan to get every critical component in our supply chain.
In WW2, that pretty much all came down to ball bearings. The future is hard to predict but maybe it's chips next time.
Maybe we give up on the cheapest of screws or nails. But we need to hold onto elite status on some item.
USA lost mass manufacturing (screws and rivets and zippers), but now we are losing cream of the crop world class manufacturing (Intel vs TSMC).
If we cannot manufacture then we likely cannot win the next war. That's the politics at play. The last major war between industrialized nations shows that technology and manufacturing was the key to success. Now I don't think USA has to manufacture all by itself, but it needs a reasonable plan to get every critical component in our supply chain.
In WW2, that pretty much all came down to ball bearings. The future is hard to predict but maybe it's chips next time.
Maybe we give up on the cheapest of screws or nails. But we need to hold onto elite status on some item.
> Taiwan isn't some backwater island making low skilled items.
Definitely not! Wasn't trying to imply this.
> If we cannot manufacture then we likely cannot win the next war.
If you think a war is imminent (a big claim!), then our only chance is to partner with specialized allies that set up shop here (e.g. Taiwan, Japan, South Korea). Trying to resurrect Intel's vertically integrated business model to compete with TSMC's contractor model is a mistake, IMO.
Definitely not! Wasn't trying to imply this.
> If we cannot manufacture then we likely cannot win the next war.
If you think a war is imminent (a big claim!), then our only chance is to partner with specialized allies that set up shop here (e.g. Taiwan, Japan, South Korea). Trying to resurrect Intel's vertically integrated business model to compete with TSMC's contractor model is a mistake, IMO.
I think this is a gross oversimplification and an incorrect assessment of the US’ economic manufacturing capabilities.
The US completely controls critical steps of the chip making process as well as the production of the intellectual property needed to produce competitive chips, and the lithography machines are controlled by a close ally that would abide by US sanctions.
The actual war planes and ships and missiles are of course still built in the USA. Modern warfare with stuff that China makes like drones and batteries only gets you so far. They can’t make a commercially competitive aviation jet engine without US and Western European suppliers.
And the US/NAFTA has a ton of existing manufacturing capability in a lot of the “screws and rivets” categories. For example, there are lots of automotive parts and assembly companies in the US. The industry isn’t as big as it used to be but it’s still significant. The US is the largest manufacturing exporter besides China.
The US completely controls critical steps of the chip making process as well as the production of the intellectual property needed to produce competitive chips, and the lithography machines are controlled by a close ally that would abide by US sanctions.
The actual war planes and ships and missiles are of course still built in the USA. Modern warfare with stuff that China makes like drones and batteries only gets you so far. They can’t make a commercially competitive aviation jet engine without US and Western European suppliers.
And the US/NAFTA has a ton of existing manufacturing capability in a lot of the “screws and rivets” categories. For example, there are lots of automotive parts and assembly companies in the US. The industry isn’t as big as it used to be but it’s still significant. The US is the largest manufacturing exporter besides China.
Indeed. Just now our kid's therapist told us they are moving out from current school district because some chemical plant is coming up near by. More than pollution it is the attitude that any kind physical product factory is blight on Disney-fied suburbia and its white collar folks.
>I think the explosive growth that people want is in manufacturing. Ex: US screws, bolts, rivets, dies, pcbs, assembly and such.
And the [only] way to get that explosive growth is robotics. That is the Post-Post-Industrial Revolution that we're stepping into - it is when manufacturing stops being separate from the knowledge-based economy and instead becomes a part of it aa a form of an output, specifically a physical-world form of output from the knowledge-based economy.
>The dollars are being diverted elsewhere.
The dollars are going in exactly right direction - AI. After LLM the companies like NVDA and Google are making next steps - foundational world models and robotics.
>Intel a chip maker who can directly serve the AI boom
Intel is a managers' gravy train - just like for example Sun Microsystems was 20 years ago. Forget about it.
>Intel ... has failed to deploy its 2nm or 1.8nm fabs and instead written them off. So even as AI gets a lot of dollars it doesn't seem to be going to the correct places.
The dollars go to NVDA instead of Intel. Seems exactly to correct place.
And the [only] way to get that explosive growth is robotics. That is the Post-Post-Industrial Revolution that we're stepping into - it is when manufacturing stops being separate from the knowledge-based economy and instead becomes a part of it aa a form of an output, specifically a physical-world form of output from the knowledge-based economy.
>The dollars are being diverted elsewhere.
The dollars are going in exactly right direction - AI. After LLM the companies like NVDA and Google are making next steps - foundational world models and robotics.
>Intel a chip maker who can directly serve the AI boom
Intel is a managers' gravy train - just like for example Sun Microsystems was 20 years ago. Forget about it.
>Intel ... has failed to deploy its 2nm or 1.8nm fabs and instead written them off. So even as AI gets a lot of dollars it doesn't seem to be going to the correct places.
The dollars go to NVDA instead of Intel. Seems exactly to correct place.
You don't need an LLM to run a Pick and Place machine, a lathe or a PCB line.
EDIT: https://youtu.be/SRu02F6AOmg?si=LZHZhbuB1ITjVdkw
While the USA spent a $trillion on LLMs, China spent the past two decades mass making chip shooters and pick and place machines like above.
Your LLMs aren't even fast enough to process or compete vs 15year old technology.
Now USA has pick and place machines and other such equipment. But we are slower and we don't have nearly as many. And despite better software here in the USA out setup costs are far higher (jlcpcb orders are prewired up with premade libraries because of huge volumes of orders. USA has fewer orders and thus requires a human to setup each line).
EDIT: https://youtu.be/SRu02F6AOmg?si=LZHZhbuB1ITjVdkw
While the USA spent a $trillion on LLMs, China spent the past two decades mass making chip shooters and pick and place machines like above.
Your LLMs aren't even fast enough to process or compete vs 15year old technology.
Now USA has pick and place machines and other such equipment. But we are slower and we don't have nearly as many. And despite better software here in the USA out setup costs are far higher (jlcpcb orders are prewired up with premade libraries because of huge volumes of orders. USA has fewer orders and thus requires a human to setup each line).
>assumption here is that, without AI, none of that capital would have been deployed anywhere. That intuitively doesn't sound realistic
For the longest time, capex at FAANG was quite low. These companies are clearly responding specifically to AI. I don't think it's realistic to expect that they would raise capex for no reason.
>a statement that's been broadly true since 2020, long before ChatGPT started the current boom
I guess it depends on your definition of "long before," but the ChatGPT release is about mid-way between now and 2020.
As for the startup vs. AI company point, have you read Stripe's whitepaper on this? They go into detail on how it seems like AI companies are indeed a different breed. https://stripe.com/en-br/lp/indexingai
For the longest time, capex at FAANG was quite low. These companies are clearly responding specifically to AI. I don't think it's realistic to expect that they would raise capex for no reason.
>a statement that's been broadly true since 2020, long before ChatGPT started the current boom
I guess it depends on your definition of "long before," but the ChatGPT release is about mid-way between now and 2020.
As for the startup vs. AI company point, have you read Stripe's whitepaper on this? They go into detail on how it seems like AI companies are indeed a different breed. https://stripe.com/en-br/lp/indexingai
The sunsetting of research tax breaks would explain why they threw everything into this.
They also view labor as a replaceable cost, as most accountant based companies that no longer innovate act. They forget that if you don't hire people, and pay people, you don't have any sales demand and this grows worse as the overall concentration or intensity of money in few hands grows. Most AI companies are funded on extreme leverage from banks that are money-printing and this coincided with 2020 where the deposit requirements were set to 0% effectively removing fractional banking as a system.
They also view labor as a replaceable cost, as most accountant based companies that no longer innovate act. They forget that if you don't hire people, and pay people, you don't have any sales demand and this grows worse as the overall concentration or intensity of money in few hands grows. Most AI companies are funded on extreme leverage from banks that are money-printing and this coincided with 2020 where the deposit requirements were set to 0% effectively removing fractional banking as a system.
>The assumption here is that, without AI, none of that capital would have been deployed anywhere.
With this recessionary behavior, it might not be that far fetched an assumption. I'm not sure where else it would flow outside of being hoarded up in assets if there wasn't this big speculation everyone wants to take advantadge of. Especially when you consider that there's so much money flowing into AI, but AI is not as of yet profitable.
With this recessionary behavior, it might not be that far fetched an assumption. I'm not sure where else it would flow outside of being hoarded up in assets if there wasn't this big speculation everyone wants to take advantadge of. Especially when you consider that there's so much money flowing into AI, but AI is not as of yet profitable.
That's it, it's unusual times and the AI boom could be covering things up. I personally completely divested from US businesses since the start of the year but I can understand the allure of wanting to ride that particular gravy train
I think the money is chasing growth in a market that is mostly mature. Tech is really the only hope in that situation, so that's where the dollars land.
“The assumption here is that, without AI, none of that capital would have been deployed anywhere.”
The question is could the capital be deployed somewhere more productive. If that was just used park it into REIT’s buying single family homes, would you be happier?
The question is could the capital be deployed somewhere more productive. If that was just used park it into REIT’s buying single family homes, would you be happier?
It depends on what "more productive" means to them. e.g. pyramid schemes are very productive if you exit early enough.
The capital would have, should have maybe, been deployed in stock buybacks and dividends. Investment already doesn't happen in the US, and that's an expected thing for a well-developed country with a trade deficit.
Derek Thompson is not well suited to this kind of work. He is much better suited to his usual lame, predictable, centrist, lukewarm commentary on tired political and social topics.
I found this the most interesting part of the whole essay - "the ten largest companies in the S&P 500 have so dominated net income growth in the last six years that it’s becoming more useful to think about an S&P 10 vs an S&P 490" - which then took me here:
https://insight-public.sgmarkets.com/quant-motion-pictures/o...
Can anyone shed light on what is going on between these two groups. I wasn't convinced by the rest of the argument in the article, and I would like something that didn't just rely on "AI" as an explanation.
Can anyone shed light on what is going on between these two groups. I wasn't convinced by the rest of the argument in the article, and I would like something that didn't just rely on "AI" as an explanation.
It is a very complex phenomenon, with no single driving force. The usual culprit is uncertainty, which itself can have a ton of root causes (say, tariffs changing every few weeks, or higher inflation due to government subsidies).
In more uncertain scenarios small companies can't take risks as well as big companies. The last 2 years have seen AI, which is a large risk these big companies invested in, pay off. But due to uncertainty smallish companies couldn't capitalize.
But that's only one possible explanation!
In more uncertain scenarios small companies can't take risks as well as big companies. The last 2 years have seen AI, which is a large risk these big companies invested in, pay off. But due to uncertainty smallish companies couldn't capitalize.
But that's only one possible explanation!
> The last 2 years have seen AI, which is a large risk these big companies invested in, pay off
LOL. It's paying off right now, because There Is No Alternative. But at some point, the companies and investors are going to want to make back these hundreds of billions. And the only people making money are Nvidia, and sort-of Microsoft through selling more Azure.
Once it becomes clear that there's no trillion dollar industry in cheating-at-homework-for-schoolkids, and nvidia stop selling more in year X than X-1, very quickly will people realize that the last 2 years have been a massive bubble.
LOL. It's paying off right now, because There Is No Alternative. But at some point, the companies and investors are going to want to make back these hundreds of billions. And the only people making money are Nvidia, and sort-of Microsoft through selling more Azure.
Once it becomes clear that there's no trillion dollar industry in cheating-at-homework-for-schoolkids, and nvidia stop selling more in year X than X-1, very quickly will people realize that the last 2 years have been a massive bubble.
That's a very out of the money view! If you are right you could make some very good money!
No as you and I both know - I can't. Because it's a qualitative view, and not a quantitative one. I would need to know _when_, quite precisely, I will turn out to be right.
And I don't know, because I have about 60 minutes a week to think about this, and also good quantitative market analysis is really hard.
So whilst it may sound like a good reposte to go "wow, I bet you make so much money shorting!" knowing that I don't and can't, it's also facile. Because I don't mind if I'm right in 12, 24 or 60 months. Fwiw, I thought I'd be right in 12 months, 12 months ago. Oops. Good thing I didn't attempt to "make money" in an endeavor where the upside is 100% of your wager, and the downside theoretically infinite.
And I don't know, because I have about 60 minutes a week to think about this, and also good quantitative market analysis is really hard.
So whilst it may sound like a good reposte to go "wow, I bet you make so much money shorting!" knowing that I don't and can't, it's also facile. Because I don't mind if I'm right in 12, 24 or 60 months. Fwiw, I thought I'd be right in 12 months, 12 months ago. Oops. Good thing I didn't attempt to "make money" in an endeavor where the upside is 100% of your wager, and the downside theoretically infinite.
Your reasoning is correct if you think about negotiating options, or going all in on a trade, but its not quite right for stocks. The borrowing rates for MSFT and NVDA - even for a retail investor - are less than 1% yearly. So if your view is right you could hold a short on them for years. The market cap for these companies has already incorporated a large capex investment for AI DCs. As long as you use a reasonable rebalancing strategy, and you are right that their current investment in AI will not pay off, you will make money.
Mind you, this is a view that exists - a few large hedge funds and sell side firms currently hold negative positions/views on these companies.
However, the fact of the matter is, fewer people are willing to take that bet than the opposite view. So it is reasonable to state that view with care.
You might be right at the end of the day, but it is very much not obvious that this bet has not (or will not) pay off.
Mind you, this is a view that exists - a few large hedge funds and sell side firms currently hold negative positions/views on these companies.
However, the fact of the matter is, fewer people are willing to take that bet than the opposite view. So it is reasonable to state that view with care.
You might be right at the end of the day, but it is very much not obvious that this bet has not (or will not) pay off.
Won’t you get margin called if the stock goes up in the meantime?
They are 40% of the S&P 500, so it makes sense that they are primary drivers of its growth.
They are also all tech companies, which had a really amazing run during Covid.
They also resemble companies with growth potential, whereas other companies such as P&G or Walmart might’ve saturated their market already
They are also all tech companies, which had a really amazing run during Covid.
They also resemble companies with growth potential, whereas other companies such as P&G or Walmart might’ve saturated their market already
> They are also all tech companies, which had a really amazing run during Covid.
Only 8 out of the 10 are. Berkshire and JP Morgan are not. It is also arguable whether Tesla is a tech company or whether it is a car company.
Only 8 out of the 10 are. Berkshire and JP Morgan are not. It is also arguable whether Tesla is a tech company or whether it is a car company.
Berkshire holds ~$60B+ of Apple and is also exposed to AI through its power-utility arm, Berkshire Hathaway Energy.
> Berkshire holds ~$60B+ of Apple and is also exposed to AI through its power-utility arm, Berkshire Hathaway Energy.
Apple is 22% of BRK’s holdings. The next biggest of their investments are Amex, BoA, Coke, Chevron.
They are not a tech company.
Apple is 22% of BRK’s holdings. The next biggest of their investments are Amex, BoA, Coke, Chevron.
They are not a tech company.
BRK has significant AI exposure through both Apple and Berkshire Hathaway Energy. So while they are not a tech company, they have more exposure to the AI boom than basically any other non-tech company.
That which might be of additional interest... look at how the top 10 of the S&P 500 has changed over the decades[1].
At any point in time the world thinks that those top 10 are unstoppable. In the 90's and early 00's... GE was unstoppable and the executive world was filled with acolytes of Jack Welch. Yet here we are.
Five years ago I think a lot of us saw Apple and Google and Microsoft as unstoppable. But 5-10 years from now I bet you we'll see new logos in the top 10. NVDA is already there. Is Apple going to continue dominance or go the way of Sony? Is the business model of the internet changing such that Google can't react quick enough. Will OpenAI go public (or any foundational model player).
I don't know what the future will be but I'm pretty sure it will be different.
[1] https://www.visualcapitalist.com/ranked-the-largest-sp-500-c...
At any point in time the world thinks that those top 10 are unstoppable. In the 90's and early 00's... GE was unstoppable and the executive world was filled with acolytes of Jack Welch. Yet here we are.
Five years ago I think a lot of us saw Apple and Google and Microsoft as unstoppable. But 5-10 years from now I bet you we'll see new logos in the top 10. NVDA is already there. Is Apple going to continue dominance or go the way of Sony? Is the business model of the internet changing such that Google can't react quick enough. Will OpenAI go public (or any foundational model player).
I don't know what the future will be but I'm pretty sure it will be different.
[1] https://www.visualcapitalist.com/ranked-the-largest-sp-500-c...
There was always some subset of the S&P that mattered way more than the rest, just like the S&P matters way more than the Russel.
Typically, you probably need to go down to the S&P 25 rather than the S&P 10.
Typically, you probably need to go down to the S&P 25 rather than the S&P 10.
Winner takes most is now true at the global economy level.
The primary goal of big companies is (/has become) maintaining market dominance, but this doesn't always translate to a well run business with great profits, it depends on internal and external factors. Maybe profits should have actually gone down due to tarrifs and uncertainty but the big companies have kept profit stable.
> Maybe profits should have actually gone down due to tarrifs and uncertainty but the big companies have kept profit stable.
If you’re referencing Trump’s tariffs, they have only come into effect now, so the economic effects will be felt in the months and years ahead.
If you’re referencing Trump’s tariffs, they have only come into effect now, so the economic effects will be felt in the months and years ahead.
People have more room to buy more digital goods. There's far less room to buy more physical goods. People aren't going to double their stomach size to eat more McDonalds, but there's no limit to how much more data, software or AI tokens a person could require.
power law explains the distribution, but the distribution is getting more extreme over the years, likely due to (market structure, macro conditions, tech economics, etc)
Interesting that the profits of those bottom 490 companies of S&P 500 do not rise with the help of AI technology, which is supposedly sold to them at a reduced rate as AI vendors are bleeding money.
Other than NVIDIA, the profits of the S&P 10 haven't risen either. It's just that the market is pricing them very optimistically.
IMO this is an extremely scary situation in the stock market. The AI bubble burst is going to be more painful than the Dotcom bubble burst. Note that an "AI bubble burst" doesn't necessitate a belief that AI is "useless" -- the Internet wasn't useless and the Dotcom burst still happened. The market can crash when it froths up too early even though the optimistic hypotheses driving the froth actually do come true eventually.
IMO this is an extremely scary situation in the stock market. The AI bubble burst is going to be more painful than the Dotcom bubble burst. Note that an "AI bubble burst" doesn't necessitate a belief that AI is "useless" -- the Internet wasn't useless and the Dotcom burst still happened. The market can crash when it froths up too early even though the optimistic hypotheses driving the froth actually do come true eventually.
We are still in the "land grab" phase where companies are offering generous AI plans to capture users.
Once users get hooked on AI and it becomes an indispensable companion for doing whatever, these companies will start charging the true cost of using these models.
It would not be surprising if the $20 plans of today are actually just introductory rate $70 plans.
Once users get hooked on AI and it becomes an indispensable companion for doing whatever, these companies will start charging the true cost of using these models.
It would not be surprising if the $20 plans of today are actually just introductory rate $70 plans.
I'd be surprised because (free) open source are continually closing the gap, exerting downward pressure on the price.
Meaning the real value is in infra. Running local is super expensive as you need to provision 100% and there are high table stakes. So people will tend to use a cloud offering. If the model doesnt differentiate then its how you mix the models, the capacity and uptime, the "GPT wrappers" etc.
I don't think it will be much of an issue for large providers, anymore than open source software has ever been a concern for Microsoft. The AI market is the entirety of the population, not just the small sliver who knows what "VRAM" means and is willing to spend thousands on hardware.
You can get open source models hosted for cheap too; e.g., through OpenRouter, AWS Bedrock, etc. You do not have to run it yourself.
> anymore than open source software has ever been a concern for Microsoft.
So a big concern then? (Although not a death sentence)
So a big concern then? (Although not a death sentence)
The modern Microsoft with Azure, Office360, etc is not much threatened by open source software. Especially with Azure, open source is a fantastic compliment which they would like the world to produce as much of as possible. The same with AI models. They would look at charge for AI hosting and services, at premium due to already being integrated in businesses. They are going to bundle it with all their existing moneymakers, and then just jack up the price. No sale needed, just a bump in the invoices that are flowing anyway.
The phrasing was "has ever been" but even in the modern era, you're only looking at their winners.
Shouldn't something like Kubernetes or Android's flavor of open source be on the radar? Seems like there are plenty of large players that might turn their 4th place closed source API into a first place open ecosystem.
Shouldn't something like Kubernetes or Android's flavor of open source be on the radar? Seems like there are plenty of large players that might turn their 4th place closed source API into a first place open ecosystem.
They're definitely very threatened by open source - a lot of software infrastructure these days is built off of open source software. In the 2000s, it wasn't. It was Microsoft, MSS, COM, Windows server, etc all the way down. Microsoft has basically been earned alive by open source software, it's just hard to tell because they were so huge that, even taken down a few pegs, they're still big.
Even today, Azure and AWS are not really cheaper or better - for most situations, they're more expensive, and less flexible than what can be done with OS infrastructure. For companies who are successful making software, Azure is more of a kneecap and a regret. Many switch away from cloud, despite that process being deliberately painful - a shocking mirror of how it was switching away from Microsoft infrastructure of the past.
Even today, Azure and AWS are not really cheaper or better - for most situations, they're more expensive, and less flexible than what can be done with OS infrastructure. For companies who are successful making software, Azure is more of a kneecap and a regret. Many switch away from cloud, despite that process being deliberately painful - a shocking mirror of how it was switching away from Microsoft infrastructure of the past.
Hopefully we see enough efficiency gains over time that this is true. The models I can run on my (expensive) local hardware are pretty terrible compared to the free models provided by Big LLM. I would hate to be chained to hardware I can't afford forever.
The breakthrough of diffusion for tolken generation bumped down compute alot. But there are no local open sources versions yet.
Distillation for specialisation can also raise the capacity of the local models if we need it for specific things.
So its chugging along nicely.
Distillation for specialisation can also raise the capacity of the local models if we need it for specific things.
So its chugging along nicely.
They're not really free, someone still has to pay for the compute cost.
I'm curious to see the bubble burst. I personally don't think it will be anything like the dotcom era.
The benefits have just not been that wide ranging to the average person. Maybe I'm wrong but, I don't AI hype as a cornerstone of US jobs, so there's no jobs to suddenly dry up. The big companies are still flush with cash on hand, aren't they?
If/when the fad dies I'd think it would die with a wimper.
The benefits have just not been that wide ranging to the average person. Maybe I'm wrong but, I don't AI hype as a cornerstone of US jobs, so there's no jobs to suddenly dry up. The big companies are still flush with cash on hand, aren't they?
If/when the fad dies I'd think it would die with a wimper.
It's definitely more like the railways of the 1880s. Lots of companies, with more-or-less the same product, competing for a highly extrapolated theoretical demand. Out of all that excitement comes an eventual crash and rationalization. Railways today are much more boring, regulated and utilitarian affairs. Many are state owned and still often loss-making.
I think AI has great potential to change as much as the internet. But i dont consider LLMs to be the right type to do that.
Self driving cars and intelligent robotics is the real goldmine. But we still don't seem to have the right architecture or methods.
I say that because self driving cars are entirely stagnant despite the boom AI interest and resources.
Personally i think we need a major breakthrough in reinforcement learning, computer vision (which are still mostly stuck at feed forward CNNs) and few shot learning. The tranformer is a major leap, but its not enough on its own.
Self driving cars and intelligent robotics is the real goldmine. But we still don't seem to have the right architecture or methods.
I say that because self driving cars are entirely stagnant despite the boom AI interest and resources.
Personally i think we need a major breakthrough in reinforcement learning, computer vision (which are still mostly stuck at feed forward CNNs) and few shot learning. The tranformer is a major leap, but its not enough on its own.
I'm not saying things couldn't change. I'm only looking at the landscape as it is now and imagining what would happen if the funding stops because of lack of consumer interest.
In general I do not agree that the economy is overleveraged on AI just like it is not overleveraged on cyrpto currency. If the money dries up, I don't expect economy wide layoffs.
In general I do not agree that the economy is overleveraged on AI just like it is not overleveraged on cyrpto currency. If the money dries up, I don't expect economy wide layoffs.
If money dries up, a bunch of startups living on speculative assets will trip, and the s&p will fall in evaluation. But that doesn't actually affect the core economy much.
I think we're in a massive AI bubble, but its a bubble that doesn't affect normal consumers much, so it's not too dangerous or concerning.
As for my prediction of what is needed for truly useful AI automation... i suspect the current bubble will pop before we solve it.
I think we're in a massive AI bubble, but its a bubble that doesn't affect normal consumers much, so it's not too dangerous or concerning.
As for my prediction of what is needed for truly useful AI automation... i suspect the current bubble will pop before we solve it.
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Are self driving cars stagnant? Waymo seems to continue to chug along.
The benefits have just not been that wide ranging to the average person
I think you might be underestimating how many non-technical people are using LLM’s daily.
I think you might be underestimating how many non-technical people are using LLM’s daily.
> Other than NVIDIA, the profits of the S&P 10 haven't risen either.
That’s not correct. Did you mean something else?
That’s not correct. Did you mean something else?
We'll never know what would've happened without AI.
1. There profits could otherwise be down.
2. The plan might be to invest a bunch up front in severance and AI Integration that is supposed to pay off in the future.
3. In the future that may or may not happen, and it'll be hard to tell, because it may pay off at the same time an otherwise recession is hitting, which smoothes it out.
It's almost as if it's not that simple.
1. There profits could otherwise be down.
2. The plan might be to invest a bunch up front in severance and AI Integration that is supposed to pay off in the future.
3. In the future that may or may not happen, and it'll be hard to tell, because it may pay off at the same time an otherwise recession is hitting, which smoothes it out.
It's almost as if it's not that simple.
Predicting the future is always hard.
But the only thing I've seen in my life that most resembles what is happening with AI, the hype, its usefulness beyond the hype, vapid projects, solid projects, etc, is the rise of the internet.
Based on this I would say we're in the 1999-2000 era. If it's true what does it mean for the future?
But the only thing I've seen in my life that most resembles what is happening with AI, the hype, its usefulness beyond the hype, vapid projects, solid projects, etc, is the rise of the internet.
Based on this I would say we're in the 1999-2000 era. If it's true what does it mean for the future?
Well, there’s a fundamental difference: the Internet blew up because it enabled people to connect with each other more easily: culturally, economically, politically.
AI is more-or-less replacing people, not connecting them. In many cases this is economically valuable, but in others I think it just pushes the human connection into another venue. I wouldn’t be surprised if in-person meetup groups really make a comeback, for example.
So if a prediction about AI involves it replacing human cultural activities (say, the idea that YouTube will just be replaced by AI videos and real people will be left out of a job), then I’m quite bearish. People will find other ways to connect with each other instead.
AI is more-or-less replacing people, not connecting them. In many cases this is economically valuable, but in others I think it just pushes the human connection into another venue. I wouldn’t be surprised if in-person meetup groups really make a comeback, for example.
So if a prediction about AI involves it replacing human cultural activities (say, the idea that YouTube will just be replaced by AI videos and real people will be left out of a job), then I’m quite bearish. People will find other ways to connect with each other instead.
Businesses are overly optimistic about AI replacing people.
For very simple jobs, like working in a call center? Sure.
But the vast majority of all jobs aren't ones that AI can replace. Anything that requires any amount of context sensitive human decision making, for example.
There's no way that AI can deliver on the hype we have now, and it's going to crash. The only question is how hard - a whimper or a bang?
For very simple jobs, like working in a call center? Sure.
But the vast majority of all jobs aren't ones that AI can replace. Anything that requires any amount of context sensitive human decision making, for example.
There's no way that AI can deliver on the hype we have now, and it's going to crash. The only question is how hard - a whimper or a bang?
As a customer, nothing infurates me like an AI call center. If I have to call, it's because I have an account problem that requires me to speak with someone to resolve it.
I moved states and Xfinity was billing me for the month after I cancelled. I called, pressed 5 (or whatever) for billing. "It looks like your cable modem is disconnected. Power-cycling your modem resolves most problems. Would you like to do that now?" No. "Most problems can be resolved by power-cycling your modem, would you like to try that now?" No, my problem is about billing, and my modem is off-line because I CANCELLED MY SERVICE! They asked three more times (for a total of five) before I could progress. For reasons I have now forgot I had to call back several times, going through the whole thing again.
There is a name for someone who pays no attention to what you say, and none of them are complimentary. AI is, fundamentally, an inhuman jerk.
(It turned out that they can only get their database to update once a month, or something, and despite the fact that nobody could help me, they issued me a refund in a month when their database finally updated. The local people wanted to help, but could not because my new state is in a different region and the regions cannot access each other.)
I moved states and Xfinity was billing me for the month after I cancelled. I called, pressed 5 (or whatever) for billing. "It looks like your cable modem is disconnected. Power-cycling your modem resolves most problems. Would you like to do that now?" No. "Most problems can be resolved by power-cycling your modem, would you like to try that now?" No, my problem is about billing, and my modem is off-line because I CANCELLED MY SERVICE! They asked three more times (for a total of five) before I could progress. For reasons I have now forgot I had to call back several times, going through the whole thing again.
There is a name for someone who pays no attention to what you say, and none of them are complimentary. AI is, fundamentally, an inhuman jerk.
(It turned out that they can only get their database to update once a month, or something, and despite the fact that nobody could help me, they issued me a refund in a month when their database finally updated. The local people wanted to help, but could not because my new state is in a different region and the regions cannot access each other.)
> For very simple jobs, like working in a call center? Sure.
Klarna would like a word.
> Anything that requires any amount of context sensitive human decision making, for example.
That describes a significant percentage of call center work.
Klarna would like a word.
> Anything that requires any amount of context sensitive human decision making, for example.
That describes a significant percentage of call center work.
There's also the difference that the internet worked.
In a classically disruptive way, the internet provided an existing service (information exchange) in a way that was in many ways far less pleasant than existing channels (newspapers, libraries, phone). Remember that the early Internet was mostly text, very low resolution, un credentialed, flaky, expensive, and too technical for most people.
The only reason that we can have such nice things today like retina display screens and live video and secure payment processing is because the original Internet provided enough value without these things.
In my first and maybe only ever comment on this website defending AI, I do believe that in 30 or 40 years we might see this first wave of generative AI in a similar way to the early Internet.
The only reason that we can have such nice things today like retina display screens and live video and secure payment processing is because the original Internet provided enough value without these things.
In my first and maybe only ever comment on this website defending AI, I do believe that in 30 or 40 years we might see this first wave of generative AI in a similar way to the early Internet.
Connecting to other people, I think, is going to see a surge of desire. I've been feeling it, many I talk to are feeling. One strange piece of anecdata is that I've been working out (with my son) at a gym for the past 3 years, very consistently. Everybody has always just done their thing, almost entirely individuals working out, with a handful of pairs. I'm old, so I remember when working out at gyms would be a much more social situation, but times change.
There are a number of regulars that we've seen there for years now. Barely have interacted with them. Suddenly, the past week, through a chance interaction, two of them individually have talked to us and ended up introducing themselves. Plus I see other people talking more and more.
I think we've hit a on-your-own saturation point and the pendulum might swing the other way.
But I'm really staying away from many "predict the AI future" because we just don't know where it is going. I've read thousands of scifi books and nonfiction and futurist and any number of options are open and no one, and I mean no one, knows what will happen with any kind of certainty.
There are a number of regulars that we've seen there for years now. Barely have interacted with them. Suddenly, the past week, through a chance interaction, two of them individually have talked to us and ended up introducing themselves. Plus I see other people talking more and more.
I think we've hit a on-your-own saturation point and the pendulum might swing the other way.
But I'm really staying away from many "predict the AI future" because we just don't know where it is going. I've read thousands of scifi books and nonfiction and futurist and any number of options are open and no one, and I mean no one, knows what will happen with any kind of certainty.
There's also an element of economic drag caused by AI that did not exist with the internet: for example, the proliferation of slop content and AI-driven scams. The internet did not almost immediately make previous modes of doing business more difficult but instead slowly replaced them with more efficient alternatives. AI has already enshittified things like Google search and product reviews. In many ways AI is undoing some of the gains of the internet without providing a replacement.
Classic repeat of the Gartner Hype Cycle. This bubble pop will dwarf the dot-bomb era. There's also no guarantee that the "slope of enlightenment" phase will amount to much beyond coding assistants. GenAI in its current form will never be reliable enough to do so-called "Agentic" tasks in everyday lives.
This bubble also seems to combine the worst of the two huge previous bubbles; the hype of the dot-com bubble plus the housing bubble in the way of massive data center buildout using massive debt and security bundling.
This bubble also seems to combine the worst of the two huge previous bubbles; the hype of the dot-com bubble plus the housing bubble in the way of massive data center buildout using massive debt and security bundling.
Mm. Partial agree, partial disagree.
These things, as they are right now, are essentially at the performance level of an intern or recent graduate in approximately all academic topics (but not necessarily practical topics), that can run on high-end consumer hardware. The learning curves suggest to me limited opportunities for further quality improvements within the foreseeable future… though "foreseeable future" here means "18 months".
I definitely agree it's a bubble. Many of these companies are priced with the assumption that they get most of the market; they obviously can't all get most of the market, and because these models are accessible to the upper end of consumer hardware, there's a reasonable chance none of them will be able to capture any of the market because open models will be zero cost and the inference hardware is something you had anyway so it's all running locally.
Other than that, to the extent that I agree with you that:
> GenAI in its current form will never be reliable enough to do so-called "Agentic" tasks in everyday lives
I do so only in that not everyone wants (or would even benefit from) a book-smart-no-practical-experience intern, and not all economic tasks are such that book-smarts count for much anyway. This set of AI advancements didn't suddenly cause all cars manufacturers to suddenly agree that this was the one weird trick holding back level 5 self driving, for example.
But for those of us who can make use of them, these models are already useful (and, like all power tools, dangerous when used incautiously) beyond merely being coding assistants.
These things, as they are right now, are essentially at the performance level of an intern or recent graduate in approximately all academic topics (but not necessarily practical topics), that can run on high-end consumer hardware. The learning curves suggest to me limited opportunities for further quality improvements within the foreseeable future… though "foreseeable future" here means "18 months".
I definitely agree it's a bubble. Many of these companies are priced with the assumption that they get most of the market; they obviously can't all get most of the market, and because these models are accessible to the upper end of consumer hardware, there's a reasonable chance none of them will be able to capture any of the market because open models will be zero cost and the inference hardware is something you had anyway so it's all running locally.
Other than that, to the extent that I agree with you that:
> GenAI in its current form will never be reliable enough to do so-called "Agentic" tasks in everyday lives
I do so only in that not everyone wants (or would even benefit from) a book-smart-no-practical-experience intern, and not all economic tasks are such that book-smarts count for much anyway. This set of AI advancements didn't suddenly cause all cars manufacturers to suddenly agree that this was the one weird trick holding back level 5 self driving, for example.
But for those of us who can make use of them, these models are already useful (and, like all power tools, dangerous when used incautiously) beyond merely being coding assistants.
> GenAI in its current form will never be reliable enough to do so-called "Agentic" tasks in everyday lives
No, but GenAI in it's current form is insanely useful and is already shifting the productivity gears into a higher level. Even without 100% reliable "agentic" task execution and AGI, this is already some next level stuff, especially for non-technical people.
No, but GenAI in it's current form is insanely useful and is already shifting the productivity gears into a higher level. Even without 100% reliable "agentic" task execution and AGI, this is already some next level stuff, especially for non-technical people.
Very simple question:
How do people trust the output of LLMs? In the fields I know about, sometimes the answers are impressive, sometimes totally wrong (hallucinations). When the answer is correct, I always feel like I could have simply googled the issue and some variation of the answer lies deep in some pages of some forum or stack exchange or reddit.
However, in the fields I'm not familiar with, I'm clueless how much I can trust the answer.
How do people trust the output of LLMs? In the fields I know about, sometimes the answers are impressive, sometimes totally wrong (hallucinations). When the answer is correct, I always feel like I could have simply googled the issue and some variation of the answer lies deep in some pages of some forum or stack exchange or reddit.
However, in the fields I'm not familiar with, I'm clueless how much I can trust the answer.
There's a few cases:
1. For coding, and the reason coders are so excited about GenAI is it can often be 90% right, but it's doing all of the writing and researching for me. If I can reduce how much I need to actually type/write to more reviewing/editing, that's a huge improvement day to day. And the other 10% can be covered by tests or adding human code to verify correctness.
2. There are cases where 90% right is better than the current state. Go look at Amazon product descriptions, especially things sold from Asia in the United States. They're probably closer to 50% or 70% right. An LLM being "less wrong" is actually an improvement, and while you might argue a product description should simply be correct, the market already disagrees with you.
3. For something like a medical question, the magic is really just taking plain language questions and giving concise results. As you said, you can find this in Google / other search engines, but they dropped the ball so badly on summaries and aggregating content in favor of serving ads that people immediately saw the value of AI chat interfaces. Should you trust what it tells you? Absolutely not! But in terms of "give me a concise answer to the question as I asked it" it is a step above traditional searches. Is the information wrong? Maybe! But I'd argue that if you wanted to ask your doctor about something that quick LLM response might be better than what you'd find on Internet forums.
1. For coding, and the reason coders are so excited about GenAI is it can often be 90% right, but it's doing all of the writing and researching for me. If I can reduce how much I need to actually type/write to more reviewing/editing, that's a huge improvement day to day. And the other 10% can be covered by tests or adding human code to verify correctness.
2. There are cases where 90% right is better than the current state. Go look at Amazon product descriptions, especially things sold from Asia in the United States. They're probably closer to 50% or 70% right. An LLM being "less wrong" is actually an improvement, and while you might argue a product description should simply be correct, the market already disagrees with you.
3. For something like a medical question, the magic is really just taking plain language questions and giving concise results. As you said, you can find this in Google / other search engines, but they dropped the ball so badly on summaries and aggregating content in favor of serving ads that people immediately saw the value of AI chat interfaces. Should you trust what it tells you? Absolutely not! But in terms of "give me a concise answer to the question as I asked it" it is a step above traditional searches. Is the information wrong? Maybe! But I'd argue that if you wanted to ask your doctor about something that quick LLM response might be better than what you'd find on Internet forums.
This is really strange to me...
Of course you don't trust the answer.
That doesn't mean you can't work with it.
One of the key use cases for me other than coding is as a much better search engine.
You can ask a really detailed and specific question that would be really hard to Google, and o3 or whatever high end model will know a lot about exactly this question.
It's up to you as a thinking human to decide what to do with that. You can use that as a starting point for in depth literature research, think through the arguments it makes from first principles, follow it up with Google searches for key terms it surfaces...
There's a whole class of searches I would never have done on Google because they would have taken half a day to do properly that you can do in fifteen minutes like this.
Of course you don't trust the answer.
That doesn't mean you can't work with it.
One of the key use cases for me other than coding is as a much better search engine.
You can ask a really detailed and specific question that would be really hard to Google, and o3 or whatever high end model will know a lot about exactly this question.
It's up to you as a thinking human to decide what to do with that. You can use that as a starting point for in depth literature research, think through the arguments it makes from first principles, follow it up with Google searches for key terms it surfaces...
There's a whole class of searches I would never have done on Google because they would have taken half a day to do properly that you can do in fifteen minutes like this.
Such as
I went through my ChatGPT history to pick a few examples that I'm both comfortable sharing and that illustrate the use-case well:
> There are some classic supply chain challenges such as the bullwhip effect. How come modern supply chains seem so resilient? Such effects don't really seem to occur anymore, at least not in big volume products.
> When the US used nuclear weapons against Japan, did Japan know what it was? That is, did they understood the possibility in principle of a weapon based on a nuclear chain reaction?
> As of July 2025, equities have shown a remarkable resilience since the great financial crisis. Even COVID was only a temporary issue in equity prices. What are the main macroeconomic reasons behind this strength of equities.
> If I have two consecutive legs of my air trip booked on separate tickets, but it's the same airline (also answer this for same alliance), will they allow me to check my baggage to the final destination across the two tickets?
> what would be the primary naics code for the business with website at [redacted]
I probably wouldn't have bothered to search any of these on Google because it would just have been too tedious.
With the airline one, for example, the goal is to get a number of relevant links directly to various airline's official regulations, which o3 did successfully (along with some IATA regulations).
For something like the first or second, the goal is to surface the names of the relevant people / theories involved, so that you know where to dig if you wish.
> There are some classic supply chain challenges such as the bullwhip effect. How come modern supply chains seem so resilient? Such effects don't really seem to occur anymore, at least not in big volume products.
> When the US used nuclear weapons against Japan, did Japan know what it was? That is, did they understood the possibility in principle of a weapon based on a nuclear chain reaction?
> As of July 2025, equities have shown a remarkable resilience since the great financial crisis. Even COVID was only a temporary issue in equity prices. What are the main macroeconomic reasons behind this strength of equities.
> If I have two consecutive legs of my air trip booked on separate tickets, but it's the same airline (also answer this for same alliance), will they allow me to check my baggage to the final destination across the two tickets?
> what would be the primary naics code for the business with website at [redacted]
I probably wouldn't have bothered to search any of these on Google because it would just have been too tedious.
With the airline one, for example, the goal is to get a number of relevant links directly to various airline's official regulations, which o3 did successfully (along with some IATA regulations).
For something like the first or second, the goal is to surface the names of the relevant people / theories involved, so that you know where to dig if you wish.
This is true.
But I've seen some harnesses (i.e., whatever Gemini Pro uses) do impressive things. The way I model it is like this: an LLM, like a person, has a chance to produce wrong output. A quorum of people and some experiments/study usually arrives to a "less wrong" answer. The same can be done with an LLM, and to an extent, is being done by things like Gemini Pro and o3 and their agentic "eyes" and "arms". As the price of hardware and compute goes down (if it does, which is a big "if"), harnesses will become better by being able to deploy more computation, even if the LLM models themselves remain at their current level.
Here's an example: there is a certain kind of work we haven't quite yet figured how to have LLMs do: creating frameworks and sticking to them, e.g. creating and structuring a codebase in a consistent way. But, in theory, if one could have 10 instances of an LLM "discuss" if a function in code conforms to an agreed convention, well, that would solve that problem.
There are also avenues of improvement that open with more computation. Namely, today we use "one-shot" models... you train them, then you use them many times. But the structure, the weights of the model aren't being retrained on the output of their actions. Doing that in a per-model-instance basis is also a matter of having sufficient computation at some affordable price. Doing that in a per-model basis is practical already today, the only limitation are legal terms, NDAs, and regulation.
I say all of this objectively. I don't like where this is going; I think this is going to take us to a wild world where most things are gonna be way tougher for us humans. But I don't want to (be forced to) enter that world wearing rosy lenses.
But I've seen some harnesses (i.e., whatever Gemini Pro uses) do impressive things. The way I model it is like this: an LLM, like a person, has a chance to produce wrong output. A quorum of people and some experiments/study usually arrives to a "less wrong" answer. The same can be done with an LLM, and to an extent, is being done by things like Gemini Pro and o3 and their agentic "eyes" and "arms". As the price of hardware and compute goes down (if it does, which is a big "if"), harnesses will become better by being able to deploy more computation, even if the LLM models themselves remain at their current level.
Here's an example: there is a certain kind of work we haven't quite yet figured how to have LLMs do: creating frameworks and sticking to them, e.g. creating and structuring a codebase in a consistent way. But, in theory, if one could have 10 instances of an LLM "discuss" if a function in code conforms to an agreed convention, well, that would solve that problem.
There are also avenues of improvement that open with more computation. Namely, today we use "one-shot" models... you train them, then you use them many times. But the structure, the weights of the model aren't being retrained on the output of their actions. Doing that in a per-model-instance basis is also a matter of having sufficient computation at some affordable price. Doing that in a per-model basis is practical already today, the only limitation are legal terms, NDAs, and regulation.
I say all of this objectively. I don't like where this is going; I think this is going to take us to a wild world where most things are gonna be way tougher for us humans. But I don't want to (be forced to) enter that world wearing rosy lenses.
I think the primary benefit of LLMs for me is as an entrypoint into an area I know nothing about. For instance, if I’m building a new kind of system which I haven’t built before, then I’m missing lots of information about it — like what are the most common ways to approach this problem, is there academic research I should read, what are the common terms/paradigms/etc. For this kind of thing LLMs are good because they just need to be approximately correct to be useful, and they can also provide links to enough primary sources that you can verify what they say. It’s similar if I’m using a new library I haven’t used before, or something like that. I use LLMs much less for things that I am already an expert in.
We place plenty of trust with strangers to do their jobs to keep society going. What’s their error rate?
It all ends up with the track record, perception and experience of the LLMs. Kinda like self-driving cars.
Strangers have an economic incentive to perform. AI does not. What AI program is currently able to modify its behavior autonomously to increase its own profitablity? Most if not all current public models are simply chat bots trained on old data scraped off the web. Wow we have created an economy based on cultivated Wikipedia and Reddit content from the 2010s linked together by bots that can make grammatical sentences and cogent sounding paragraphs. Isn't that great? I don't know, about 10 years ago before google broke itself, I could find information on any topic easily and judge its truth using my grounded human intelligence better than any AI today.
For one thing AI can not even count. Ask google's AI to draw a woman wearing a straw hat. More often than not the woman is wearing a well drawn hat while holding another in her hand. Why? Frequently she has three arms. Why? Tesla self driving vision can't differentiate between the sky and a light colored tractor trailer turning across traffic resulting in a fatality in Florida.
For something to be intelligent it needs to be able to think and evaluate the correctness of its thinking correctly. Not just regurgitate old web scrapings.
It is pathetic realy.
Show me one application where black box LLM ai is generating a profit that an effectively trained human or rules based system couldn't do better.
Even if AI is able to replace a human in some tasks this is not a good thing for a consumption based economy with an already low labor force participation rate.
During the first industrial revolution human labor was scarce so machines could economically replace and augnent labor and raise standards of living. In the present time labor is not scarce so automation is a solution in search of a problem and a problem itself if it increasingly leads to unemployment without universal bssic income to support consumption. If your economy produces too much with nobody to buy it then economic contraction follows. Already young people today struggle to buy a house. Instead of investing in chat bots maybe our economy should be employing more people in building trades and production occupations where they can earn an income to support consumption including of durable items like a house or a car. Instead because of the fomo and hype about AI investors are looking for greater returns by directing money toward scifi fantasy and when that doesn't materialize an economic contraction will result.
For one thing AI can not even count. Ask google's AI to draw a woman wearing a straw hat. More often than not the woman is wearing a well drawn hat while holding another in her hand. Why? Frequently she has three arms. Why? Tesla self driving vision can't differentiate between the sky and a light colored tractor trailer turning across traffic resulting in a fatality in Florida.
For something to be intelligent it needs to be able to think and evaluate the correctness of its thinking correctly. Not just regurgitate old web scrapings.
It is pathetic realy.
Show me one application where black box LLM ai is generating a profit that an effectively trained human or rules based system couldn't do better.
Even if AI is able to replace a human in some tasks this is not a good thing for a consumption based economy with an already low labor force participation rate.
During the first industrial revolution human labor was scarce so machines could economically replace and augnent labor and raise standards of living. In the present time labor is not scarce so automation is a solution in search of a problem and a problem itself if it increasingly leads to unemployment without universal bssic income to support consumption. If your economy produces too much with nobody to buy it then economic contraction follows. Already young people today struggle to buy a house. Instead of investing in chat bots maybe our economy should be employing more people in building trades and production occupations where they can earn an income to support consumption including of durable items like a house or a car. Instead because of the fomo and hype about AI investors are looking for greater returns by directing money toward scifi fantasy and when that doesn't materialize an economic contraction will result.
My point is humans make mistakes too, and we trust them, not because we inspect everything they say or do, but from how society is set up.
I'm not sure how up to date you are but most AIs with tool calling can do math. Image generation hasn't been generating weird stuff since last year. Waymo sees >82% fewer injuries/crashes than human drivers[1].
RL _is_ modifying its behavior to increase its own profitability, and companies training these models will optimize for revenue when the wallet runs dry.
I do feel the bit about being economically replaced. As a frontend-focused dev, nowadays LLMs can run circles around me. I'm uncertain where we go, but I would hate for people to have to do menial jobs just to make a living.
[1]: https://www.theverge.com/news/658952/waymo-injury-prevention...
I'm not sure how up to date you are but most AIs with tool calling can do math. Image generation hasn't been generating weird stuff since last year. Waymo sees >82% fewer injuries/crashes than human drivers[1].
RL _is_ modifying its behavior to increase its own profitability, and companies training these models will optimize for revenue when the wallet runs dry.
I do feel the bit about being economically replaced. As a frontend-focused dev, nowadays LLMs can run circles around me. I'm uncertain where we go, but I would hate for people to have to do menial jobs just to make a living.
[1]: https://www.theverge.com/news/658952/waymo-injury-prevention...
> My point is humans make mistakes too, and we trust them,
We trust them because they are intrinsically and extrinsically motivated not to mess up
AI has no motivation
We trust them because they are intrinsically and extrinsically motivated not to mess up
AI has no motivation
I get around this by not valuing the AI for its output, but for its process.
Treat it like a brilliant but clumsy assistant that does tasks for you without complaint – but whose work needs to be double checked.
Treat it like a brilliant but clumsy assistant that does tasks for you without complaint – but whose work needs to be double checked.
Your internal verifier model in your head is actually good enough and not random. It knows how the world works and subconsciously applies a lot of sniff tests it has learned over the years.
Sure a lot of answers from llms may be inaccurate - but you mostly identify them as such because your ability to verify (using various heuristics) is good too.
Do you learn from asking people advice? Do you learn from reading comments on Reddit? You still do without trusting them fully because you have sniff tests.
Sure a lot of answers from llms may be inaccurate - but you mostly identify them as such because your ability to verify (using various heuristics) is good too.
Do you learn from asking people advice? Do you learn from reading comments on Reddit? You still do without trusting them fully because you have sniff tests.
> You still do without trusting them fully because you have sniff tests
LLMs produce way too much noise and way too inconsistent quality for a sniff test to be terribly valuable in my opinion
LLMs produce way too much noise and way too inconsistent quality for a sniff test to be terribly valuable in my opinion
The problem is that content is dead. You can’t find answers any more on Google because every website is ai generated and littered with ads.
YouTube videos aren’t much better. Minutes of fluff are added to hit a juicy 10 minute mark so you can see more ads.
The internet is a dead place.
YouTube videos aren’t much better. Minutes of fluff are added to hit a juicy 10 minute mark so you can see more ads.
The internet is a dead place.
The problem isn't that content is AI generated, the problem is that the content is generated to maximize ad revenue (or some other kind of revenue) rather than maximize truth and usefulness. This has been the case pretty much since the Internet went commercial. Google was in a lot of ways created to solve this problem and it's been a constant struggle.
The problem isn't AI, the problem is the idea that advertising and PR markets are useful tools for organizing information rather than vaguely anarchist self-organizing collectives like Wikipedia or StackOverflow.
The problem isn't AI, the problem is the idea that advertising and PR markets are useful tools for organizing information rather than vaguely anarchist self-organizing collectives like Wikipedia or StackOverflow.
I have zero belief that AI won't follow this trend as well
that's where i disagree. the noise is not that high at all and is vastly exaggerated. of course if you go too deep into niche topics you will experience this.
Yeah niche topics like the technical questions I have left over after doing embedded development for more than a decade. Mostly questions like “can you dig up a pdf for this obsolete wire format.” And google used to be able to do that but now all I get is hundreds of identical results telling me about the protocol’s existence but nothing else.
One of the most amusing things to me is the amount of AI testimonials that basically go "once I help the AI over the things I know that it struggles with, when it gets to the things I don't know, wow, it's amazing at how much it knows and can do!" It's not so much Gell-Mann amnesia as it is Gell-Mann whiplash.
If you are a subject matter expert, as is expected to be of the person working on the task, then you will recognise the issue.
Otherwise, common sense, quick google search or let another LLM evaluate it.
Otherwise, common sense, quick google search or let another LLM evaluate it.
> especially for non-technical people.
The people who use llms to write reports for other people who use llms to read said reports ? It may alleviate a few pain points but it generates an insane amount of useless noise
The people who use llms to write reports for other people who use llms to read said reports ? It may alleviate a few pain points but it generates an insane amount of useless noise
Considering they were already creating useless noise, they can create it faster now.
But once you get out of the tech circles and bullshit jobs, there is a lot of quality usage, as much as there is shit usage. I've met everyone from lawyers and doctors to architects and accountants who are using some form of GenAI actively in their work.
Yes, it makes mistakes, yes, it hallucinates, but it gets a lot of fluff work out of the way, letting people deal with actual problems.
But once you get out of the tech circles and bullshit jobs, there is a lot of quality usage, as much as there is shit usage. I've met everyone from lawyers and doctors to architects and accountants who are using some form of GenAI actively in their work.
Yes, it makes mistakes, yes, it hallucinates, but it gets a lot of fluff work out of the way, letting people deal with actual problems.
> I've met everyone from lawyers and doctors to architects and accountants who are using some form of GenAI actively in their work
I can't wait for the first massive medical mistakes from LLM reliance
I can't wait for the first massive medical mistakes from LLM reliance
I would love to figure out why I don't see this at my company. People still shipping at the same rate as before, customers bringing up more and more bugs, problems that require planning for scale are not being thought about (more bugs), zero tests still being written. All the code I am seeing generated is like one shot garbage, with no context around our system or the codebase as a whole.
I fully agree that there will be a pop, there must be. Current evaluations and investments are based on monumentally society destroying assumptions. But with every disappointing, incremental and non evolutionary model generation the chance increases that the world at large realizes that those assumptions are wrong.
What should I do with my ETF? Sell now, wait for the inevitable crash? Be all modern long term investment style: "just keep invested what you don't need in the next 10 years bro"?
This really keeps me up at night.
What should I do with my ETF? Sell now, wait for the inevitable crash? Be all modern long term investment style: "just keep invested what you don't need in the next 10 years bro"?
This really keeps me up at night.
If you're sure enough that there is going to be a big crash I would move the money into gold, bonds or other more secure assets. After a crash you can invest again.
I don't know why buffet sold a lot of shares over the last few years to sit on a huge pile of cash, but I could guess.
The Job market looks like shit, people have no money to buy stuff and credit card debt is skyrocketing. When people can't buy stuff it is bad for the economy. Even if AI is revolutionary then we would need people spending money to keep the economy going, and with more AI taking jobs that wouldn't happen.
If AI doesn't work out the market is going to crash and the only companies keeping the market growing are going to wipe out all that growth.
No matter how I look at it I don't see a thriving market.
I don't know why buffet sold a lot of shares over the last few years to sit on a huge pile of cash, but I could guess.
The Job market looks like shit, people have no money to buy stuff and credit card debt is skyrocketing. When people can't buy stuff it is bad for the economy. Even if AI is revolutionary then we would need people spending money to keep the economy going, and with more AI taking jobs that wouldn't happen.
If AI doesn't work out the market is going to crash and the only companies keeping the market growing are going to wipe out all that growth.
No matter how I look at it I don't see a thriving market.
Last week I tested out the agent mode of chatGPT by asking it to plan a week's meals, then add all the ingredients to an online shopping basket for me. It worked pretty much flawlessly, the only problem was it ran out of time before it could add the last few ingredients, which doesn't exactly seem like an unsolvable problem.
The Internet in its 1999 form was never going to be fast enough or secure enough to support commerce, banking, or business operations.
Exactly, it took an evolution, but there was no discontinuity. At some point, things evolved enough for people like Tim O'Reilly to say that we know have "Web 2.0", but it was all just small steps by people like those of us here on this thread, gradually making things better and more reliable.
"It is difficult to make predictions, especially about the future" - Yogi Berra (?)
But let’s assume we can for a moment.
If we’re living in a 1999 moment, then we might be on a Gartner Hype Cycle like curve. And I assume we’re on the first peak.
Which means that the "trough of disillusionment" will follow.
This is a phase in Hype Cycle, following the initial peak of inflated expectations, where interest in a technology wanes as it fails to deliver on early promises.
But let’s assume we can for a moment.
If we’re living in a 1999 moment, then we might be on a Gartner Hype Cycle like curve. And I assume we’re on the first peak.
Which means that the "trough of disillusionment" will follow.
This is a phase in Hype Cycle, following the initial peak of inflated expectations, where interest in a technology wanes as it fails to deliver on early promises.
I too lived through the dot.com bubble and AI feels identical in so many ways.
AI is real just like the net was real, but the current environment is very bubbly and will probably crash.
AI is real just like the net was real, but the current environment is very bubbly and will probably crash.
It definitely feels identical. We had companies that never had any hope of being profitable (or even doing anything related to the early internet to begin with), but put .com in your name and suddenly you are flooded with hype and cash.
Same thing now with AI. The capital is going to dry up eventually, no one is profitable right now and its questionable whether or not they can be at a price consumers would be willing or able to pay.
Models are going to become a commodity, just being an "AI Company" isn't a moat and yet every one of the big names are being invested in as if they are going to capture the entire market, or if there even will be a market in the first place.
Investors are going to get nervous, eventually, and start expecting a return, just like .com. Once everyone realizes AGI isn't going to happen, and realize you aren't going to meet the expected return running a $200/month chatbot, it'll be game over.
Same thing now with AI. The capital is going to dry up eventually, no one is profitable right now and its questionable whether or not they can be at a price consumers would be willing or able to pay.
Models are going to become a commodity, just being an "AI Company" isn't a moat and yet every one of the big names are being invested in as if they are going to capture the entire market, or if there even will be a market in the first place.
Investors are going to get nervous, eventually, and start expecting a return, just like .com. Once everyone realizes AGI isn't going to happen, and realize you aren't going to meet the expected return running a $200/month chatbot, it'll be game over.
I lived through dot-com, and there are so many parallels. Large amount of money is chasing a dream which wont materialize in the near term.
Recent deja-vus are articles like this:
"The 20-Somethings Are Swarming San Francisco’s A.I. Boom" and
"Tech Billboards Are All Over San Francisco. Can You Decode Them?"
If I recall correctly, after the 2000 bust, folks fled silicon valley abandoning their leased BMWs at the SFO airport. 101 had no traffic jams. I wonder if that will repeat this time around.
Recent deja-vus are articles like this:
"The 20-Somethings Are Swarming San Francisco’s A.I. Boom" and
"Tech Billboards Are All Over San Francisco. Can You Decode Them?"
If I recall correctly, after the 2000 bust, folks fled silicon valley abandoning their leased BMWs at the SFO airport. 101 had no traffic jams. I wonder if that will repeat this time around.
Almost three years ago I gave a talk to a Staff+ group where I work and told them that AI felt like the internet in 1995 timeframe. It still does, and I agree we seem to be in the late-90s now. However, we need to be careful, this feels similar in a world-changing way, but it is different on so many levels. Amongst other things, the world is far more tech savvy than in the 90s. Plus nearly everyone alive and making decisions now were alive and making decisions in the 90s. That's why we're seeing this insane "all in" mentality - there is almost certainly going to be new Googles/Facebooks/Apples/Amazons that come out of this, or at least that is what capital believes.
I hate, hate, hate the Gartner Hype Cycle thing, it's just a dumb statement that things can get overhyped. Instead, I see it as a Cambrian explosion of everybody everywhere trying to find use cases for this new tech. Nearly all of them will fail, but many won't and we'll have a different world in a few years. There will be crashes and crazes and (hopefully) generally positive changes in the world.
It could be Terminator or Handmaid's Tale or 1984, too. The main difference between the 90s and now is that the world is much more educated on the downsides of tech and so there isn't that giddy sense of coolness from the 90s, and that bums me out.
I hate, hate, hate the Gartner Hype Cycle thing, it's just a dumb statement that things can get overhyped. Instead, I see it as a Cambrian explosion of everybody everywhere trying to find use cases for this new tech. Nearly all of them will fail, but many won't and we'll have a different world in a few years. There will be crashes and crazes and (hopefully) generally positive changes in the world.
It could be Terminator or Handmaid's Tale or 1984, too. The main difference between the 90s and now is that the world is much more educated on the downsides of tech and so there isn't that giddy sense of coolness from the 90s, and that bums me out.
It means you should hold on to your investments and be prepared to exit the market.
See "Spending on AI data centers is so massive that it’s taken a bigger chunk of GDP growth than shopping—and it could crash the American economy"[1]
The US is having a giant AI boom and a recession in the rest of the economy.
Historically, the classic example of this is railway mania of the mid 19th century.[2] That started in 1830, with the Liverpool and Manchester Railway.[3] This was when the industrial revolution got out of beta. There were earlier railroads, but with dual tracks, stations, signals, tickets, schedules, and reasonably good steam engines, the Liverpool and Manchester worked like a real service. It was profitable. Then lots of others started building and over-building railroads, with varying degrees of success. See Panic of 1847.
It was really too early for good railroads. Volume production of steel didn't exist. Early railroads were built with wood and iron, not very well. Around 1880-1900, everything was rebuilt in steel, and got bigger, better, and safer.
Consider the early Internet. We had TCP/IP working across the US in the early 1980s, but it wasn't a big deal commercially for another 10-15 years, it wasn't everywhere until the early 2000s, and it wasn't out of bubble mode until 2010 or so.
[1] https://fortune.com/2025/08/06/data-center-artificial-intell...
[2] https://en.wikipedia.org/wiki/Railway_Mania
[3] https://youtu.be/pDEnsraYx3k?t=505
Historically, the classic example of this is railway mania of the mid 19th century.[2] That started in 1830, with the Liverpool and Manchester Railway.[3] This was when the industrial revolution got out of beta. There were earlier railroads, but with dual tracks, stations, signals, tickets, schedules, and reasonably good steam engines, the Liverpool and Manchester worked like a real service. It was profitable. Then lots of others started building and over-building railroads, with varying degrees of success. See Panic of 1847.
It was really too early for good railroads. Volume production of steel didn't exist. Early railroads were built with wood and iron, not very well. Around 1880-1900, everything was rebuilt in steel, and got bigger, better, and safer.
Consider the early Internet. We had TCP/IP working across the US in the early 1980s, but it wasn't a big deal commercially for another 10-15 years, it wasn't everywhere until the early 2000s, and it wasn't out of bubble mode until 2010 or so.
[1] https://fortune.com/2025/08/06/data-center-artificial-intell...
[2] https://en.wikipedia.org/wiki/Railway_Mania
[3] https://youtu.be/pDEnsraYx3k?t=505
The really scary thing is all the value in the AI boom is predicated on the belief that the technology is "early" and that it will improve over time. We're seeing the opposite. Instead, all the competing models are basically converging on the same benchmark performance numbers, as we saw yesterday with the gpt-5 debacle. This suggests that performance is actually topping out, which makes intuitive sense if advancements in LLMs are proportional to their training data. They've already used up all the data. So it very well could be what we see right now is basically as good as it gets, or at least approximately so. The market is not ready for that.
> We're seeing the opposite. Instead, all the competing models are basically converging on the same benchmark performance numbers...
That question is really important. Comments on that? Maybe LLMs are asymptotically approaching some fundamental limit for that technology.
What goes on inside an LLM hasn't changed much in years. More data and more compute is thrown at that little kernel that makes it all go. Additional gimmicks are added around the core algorithm. But there's not much progress down at the bottom. There's no reaching into the net and extracting a reliable "don't know". Is there an upper limit to what that architecture can do? AGI may not be reachable by this route.
Maybe someone will find a way around that limit. The field has way too much money, too much visibility, and too many people. The technology works, although it has limits.
That question is really important. Comments on that? Maybe LLMs are asymptotically approaching some fundamental limit for that technology.
What goes on inside an LLM hasn't changed much in years. More data and more compute is thrown at that little kernel that makes it all go. Additional gimmicks are added around the core algorithm. But there's not much progress down at the bottom. There's no reaching into the net and extracting a reliable "don't know". Is there an upper limit to what that architecture can do? AGI may not be reachable by this route.
Maybe someone will find a way around that limit. The field has way too much money, too much visibility, and too many people. The technology works, although it has limits.
Money can't (necessarily) buy a breakthrough. It could take 10 or 100 or 1000 years, we just can't know. This kind of technical risk isn't usually so prominent. It's usually more diffuse with fallbacks, offramps, less palatable but workable alternatives.. This time it seems like the technical risk is looming very, very large.
I don't think AI is having much impact on the bits of the economy that have to do with labor and consumption. Folk who are getting displaced by AI are, for now, probably being re-hired to fix AI mess-ups later.
But if, or when AI gets a little better, then we will start to see a much more pronounced impact. The thing competent AIs will do is to super-charge the rate at which profits don't go to labor nor to social security, and this time they will have a legit reason: "you really didn't use any humans to pave the roads that my autonomous trucks use. Why should I pay for medical expenses for the humans, and generally for the well-being of their pesky flesh? You want to shutdown our digital CEO? You first need to break through our lines of (digital) lawyers and ChatGPT-dependent bought politicians."
But if, or when AI gets a little better, then we will start to see a much more pronounced impact. The thing competent AIs will do is to super-charge the rate at which profits don't go to labor nor to social security, and this time they will have a legit reason: "you really didn't use any humans to pave the roads that my autonomous trucks use. Why should I pay for medical expenses for the humans, and generally for the well-being of their pesky flesh? You want to shutdown our digital CEO? You first need to break through our lines of (digital) lawyers and ChatGPT-dependent bought politicians."
Well, if you don't use humans, then you're using machine labor that will drive prices down to at-cost in a competitive environment and strip profits in the end. Profits come from underpaying labor.
>Nobody can say for sure whether the AI boom is evidence of the next Industrial Revolution or the next big bubble.
Like the Internet boom, it's both. The rosy predictions of the dotcom era eventually came true. But they did not come true fast enough to avoid the dotcom bust. And so it will be with AI.
Like the Internet boom, it's both. The rosy predictions of the dotcom era eventually came true. But they did not come true fast enough to avoid the dotcom bust. And so it will be with AI.
My suspicion is that there's a there there, but it doesn't align with the predictions. This is supported by the tension between AI doom articles and the leading models experiencing diminishing performance gains while remaining error-prone. This is to speak nothing of the apparent LLM convergence limit of a ketamine-addled junior developer. Which is a boundary the models seem destined to approach indefinitely without ever breaching.
The "bust" in this scenario would hit the valuations (P/E ratio) of both the labs and their enterprise customers, and AI businesses dependant on exponential cost/performance growth curves with the models. The correction would shake the dummies (poorly capitalized or scoped businesses) out of the tree, leaving only the viable business and pricing models still standing.
That's my personal prediction as of writing.
The "bust" in this scenario would hit the valuations (P/E ratio) of both the labs and their enterprise customers, and AI businesses dependant on exponential cost/performance growth curves with the models. The correction would shake the dummies (poorly capitalized or scoped businesses) out of the tree, leaving only the viable business and pricing models still standing.
That's my personal prediction as of writing.
Although that is true, the big players just have enough money to go through it this time.
>“The top 100 AI companies on Stripe achieved annualized revenues of $1 million in a median period of just 11.5 months—four months ahead of the fastest-growing SaaS companies.”
This chart is extremely sparse and very confusing. Why not just plot a random sample of firms from both industries?
I'd be curious to see the shape of the annualized revenue distribution after a fixed time duration for SaaS and AI firms. Then I could judge whether its fair to filter by the top 100. Maybe AI has a rapid decay rate at low annualized revenue values but a slower decay rate at higher values, when compared to SaaS. Considering that AI has higher marginal costs and thus a larger price of entry, this seems plausible to me. If this is the case, this chart is cherry picking.
This chart is extremely sparse and very confusing. Why not just plot a random sample of firms from both industries?
I'd be curious to see the shape of the annualized revenue distribution after a fixed time duration for SaaS and AI firms. Then I could judge whether its fair to filter by the top 100. Maybe AI has a rapid decay rate at low annualized revenue values but a slower decay rate at higher values, when compared to SaaS. Considering that AI has higher marginal costs and thus a larger price of entry, this seems plausible to me. If this is the case, this chart is cherry picking.
I see hardware and AI companies‘ revenues rise.
Shouldn’t the customers‘ revenue also rise if AI fulfills its productivity promises?
Seems like the only ones getting rich in this gold rush are the shovel sellers. Business as usual.
Shouldn’t the customers‘ revenue also rise if AI fulfills its productivity promises?
Seems like the only ones getting rich in this gold rush are the shovel sellers. Business as usual.
If it's automation it could also reduce costs of the customers. But that is a very complex question. It could be that there isn't enough competition in AI and so the customers are getting only marginal gains while AI company gets the most. It could also be that for customers the revenue / profits will be delayed as implementation will take time, and it could be upfront investment.
> Shouldn’t the customers‘ revenue also rise if AI fulfills its productivity promises
Not necessarily, see the Jevons paradox.
Not necessarily, see the Jevons paradox.
Applying the Jevons paradox to this scenario should still result in revenues going up, assuming the employee labor being optimized adds value to the company. (they would add more)
Jevron is about higher resource consumption and costs but the output and therefore the revenue should rise too.
Maybe not the profit but at least the revenue.
Maybe not the profit but at least the revenue.
The biggest problem is the inability of corporate middle management to actually leverage GenAI.
What about food and housing? Why can't America invest in food and housing instead?
America has spent a century investing in food. We invested in food so hard we now have to pay farmers not to grow things, because otherwise the price crash would cause problems. Food in America is very cheap.
It's reassuring to be reminded that every child in America must justify their existence or starve to death.
Okay, that's too far. That's not true at all.
Children in America do not starve to death. There is no famine, economically manmade or otherwise.
This is America. We will happily allow and encourage your child to go into arbitrary amounts of debt from a young age to be fed at school.
Children in America do not starve to death. There is no famine, economically manmade or otherwise.
This is America. We will happily allow and encourage your child to go into arbitrary amounts of debt from a young age to be fed at school.
There is definitely a type of famine going on in the US right now, 40 million are struggling with food insecurity. There is also a crises of healthy food being unaffordable, so people are essentially eating 2x their daily calories while getting 0.5x their daily nutrients. Then there are multiple political decisions that have already been made this year that will result in various forms of crop loss (migrants scared to show up at work) and disease outbreak (RFKJr's raw milk). The results of which are likely to impact food prices. The people who are hit hardest by this will already have been hit the hardest by greedflation in rent.
Words have meanings and it cheapens them to use them outside those meanings.
Either there is no famine in America, or I experienced famine in America yesterday when I was hungry because we were busy and had a late dinner.
I agree with everything you said except the use of the word "famine".
Either there is no famine in America, or I experienced famine in America yesterday when I was hungry because we were busy and had a late dinner.
I agree with everything you said except the use of the word "famine".
Christ…
What does that even mean and what do you want changed?
What does that even mean and what do you want changed?
Ironically, this is currently second in HN: https://news.ycombinator.com/item?id=44836219 ("Food, housing, & health care costs are a source of major stress for many people")
Fair, I stand corrected.
> Food in America is very cheap.
And that's a whole different problem. Cheap != inexpensive.
And that's a whole different problem. Cheap != inexpensive.
Whether or not cheap and inexpensive mean the same thing, is contextually dependent. In some usages they have provide nuanced differences, while in others they are entirely identical.
In the context in which I was using it, they are identical.
In the context in which I was using it, they are identical.
the US systematically taxes and forbids new housing in many ways as local voters desire. Setback requirements, 100K+ hookup costs, stairway standards, density limits, parking minimums and regulations, community input, allowing rejection of new housing despite it following all rules, abuse of environmental regulations (which ends up hurting the environment by blocking density), affordable housing requirements (a tax on each new housing block to fund affordable units on the side) all prevent new housing form being built.
Having food and housing would make the population too comfortable. They want everyone a little on edge. You don't work, you don't eat.
Because investing in housing means actually changing things. There's a "Don't just do something, stand there!" strategy of maximizing comfort and minimizing effort, that must be overcome.
Is anyone starving in America? Why would there need to be focus on food production? We have huge food commodities.
Let them eat cake, right?
https://www.ers.usda.gov/topics/food-nutrition-assistance/fo...
https://www.ers.usda.gov/topics/food-nutrition-assistance/fo...
Or let them refuse to learn how to make cake, right?
I found this analysis insightful:
https://x.com/dampedspring/status/1953070287093731685
> However, this pace is likely unsustainable going forward. The sharp acceleration in capex is likely behind us, and the recent growth rate may not be maintained. Any sustained weakness in final demand will almost certainly affect future investment, as AI demand ultimately depends on business revenues and profits, which are tied to nominal GDP. Realized and forecasted capex remain elevated, while free cash flow and cash and cash equivalents are declining for hyperscalers.
https://x.com/dampedspring/status/1953070287093731685
> However, this pace is likely unsustainable going forward. The sharp acceleration in capex is likely behind us, and the recent growth rate may not be maintained. Any sustained weakness in final demand will almost certainly affect future investment, as AI demand ultimately depends on business revenues and profits, which are tied to nominal GDP. Realized and forecasted capex remain elevated, while free cash flow and cash and cash equivalents are declining for hyperscalers.
The take away from the teacher graphs were very different than my take aways. The author was bullish because ~55% used AI to help prep lessons. However, looking at the graph, I would be very bearish about the adoption of AI. Most of the graphs show that a vast majority of teachers aren't using AI. This leads me to see a huge discrepancy between real world usage and the amount of money that's being invested.
Remember, the appropriate way to parse use of "the economy" in popular press is to read it as "rich peoples' yatch money".
Related:
AI is propping up the US economy
https://news.ycombinator.com/item?id=44802916
AI is propping up the US economy
https://news.ycombinator.com/item?id=44802916
I'm noticing how that article is myopically discussing equity valuations rather than actual economic output and worker productivity.
This is going to end badly, I am afraid.
Could all pop today if GPT5 doesn’t benchmark hack hard on some new made up task.
I don't see how it would "all pop" - same as with the internet bubble, even if the massive valuations disappear, it seems clear to me that the technology is already massively disruptive and will continue growing its impact on the economy even if we never reach AGI.
Exactly like the internet bubble. I've been working in Deep Learning since 2014 and am very bullish on the technology but the trillions of dollars required for the next round of scaling will not be there if GPT-5 is not on the exponential growth curve that sama has been painting for the last few years.
Just like the dot com bubble we'll need to wash out a ton of "unicorn" companies selling $1s for $0.50 before we see the long term gains.
Just like the dot com bubble we'll need to wash out a ton of "unicorn" companies selling $1s for $0.50 before we see the long term gains.
> Exactly like the internet bubble.
So is this just about a bit of investor money lost? Because the internet obviously didn't decline at all after 2000, and even the investors who lost a lot but stayed in the game likely recouped their money relatively quickly. As I see it, the lesson from the dot-com bust is that we should stay in the game.
And as for GPT-5 being on the exponential growth curve - according to METR, it's well above it: https://metr.org/blog/2025-03-19-measuring-ai-ability-to-com...
So is this just about a bit of investor money lost? Because the internet obviously didn't decline at all after 2000, and even the investors who lost a lot but stayed in the game likely recouped their money relatively quickly. As I see it, the lesson from the dot-com bust is that we should stay in the game.
And as for GPT-5 being on the exponential growth curve - according to METR, it's well above it: https://metr.org/blog/2025-03-19-measuring-ai-ability-to-com...
I wouldn't say "well above" when the curve falls well within the error bars. I wonder how different the plot would look if they reported the median as their point estimate rather than mean.
I don't expect GPT-5 to be anything special, it seems OpenAI hasn't been able to keep its lead, but even current level of LLMs to me justifies the market valuations. Of course I might eat my words saying that OpenAI is behind, but we'll see.
> I don't expect GPT-5 to be anything special
because ?
because ?
Because everything past GPT 3.5 has been pretty unremarkable? Doubt anyone in the world would be able to tell a difference in a blind test between 4.0, 4o, 4.5 and 4.1.
I would absolutely take you on a blind test between 4.0 and 4.5 - the improvement is significant.
And while I do want your money, we can just look at LMArena which does blind testing to arrive at an ELO-based score and shows 4.0 to have a score of 1318 while 4.5 has a 1438 - it's over twice likely to be judged better on an arbitrary prompt, and the difference is more significant on coding and reasoning tasks.
And while I do want your money, we can just look at LMArena which does blind testing to arrive at an ELO-based score and shows 4.0 to have a score of 1318 while 4.5 has a 1438 - it's over twice likely to be judged better on an arbitrary prompt, and the difference is more significant on coding and reasoning tasks.
Well word on the street is that the OSS models released this week were Meta-Style benchmaxxed and their real world performance is incredibly underwhelming.
Everywhere I use "A.I.", I am forced to use "A.I.", and the thing I am forced to use it for has been made worse, as a result of the "A.I."
For those comparing the "A.I." bubble to the .com bubble, they are missing the point that even a mostly normie user such as myself, logging on with my 14.4 kbps modem I instantly had something that was new and useful. With "A.I.", I haven't found anything, at least for myself, that is useful.
For those comparing the "A.I." bubble to the .com bubble, they are missing the point that even a mostly normie user such as myself, logging on with my 14.4 kbps modem I instantly had something that was new and useful. With "A.I.", I haven't found anything, at least for myself, that is useful.
> METR also conducted an in-depth study that asked experienced developers to code with a popular AI assistant. After they finished their tasks, the developers claimed that using the AI had made them 20 percent more productive. But independent evaluators in the study actually concluded that using AI did the opposite: it increased task completion time by about 20 percent.
This somewhat reflects my experience... I can totally see the back-and-forth dance taking longer in some cases.
But I also think there is more than efficiency being unlocked here. Sure, a developer might have cranked out a rough MVP in less time, but with this they're also often continuously updating a README, tests and other infrastructure as they go.
One could argue about whether that's its own footgun - relying on tests that don't really test what they should, and let critical bugs through later.
This somewhat reflects my experience... I can totally see the back-and-forth dance taking longer in some cases.
But I also think there is more than efficiency being unlocked here. Sure, a developer might have cranked out a rough MVP in less time, but with this they're also often continuously updating a README, tests and other infrastructure as they go.
One could argue about whether that's its own footgun - relying on tests that don't really test what they should, and let critical bugs through later.
> “They’re generating unprecedented amounts of free cash flow,” Cembalest told me. “They make oodles and oodles of money, which is why they can afford to be pouring hundreds of billions of dollars of capital spending each year into AI-related R&D and infrastructure.”
IMO this should be a trigger for investors that they have not been receiving their profits, and instead the profits are being dumped into CEOs next big bets that will fuel their stockbased compensation gains. Also to blame is the government's culpability here for creating both tax incentives and a lack of laws that say profits must be returned as dividends (they can always be DRIP'd back into the company as new shares if desired, it's absurd to say its better for investors, when the alternative actually gives more choice).
IMO this should be a trigger for investors that they have not been receiving their profits, and instead the profits are being dumped into CEOs next big bets that will fuel their stockbased compensation gains. Also to blame is the government's culpability here for creating both tax incentives and a lack of laws that say profits must be returned as dividends (they can always be DRIP'd back into the company as new shares if desired, it's absurd to say its better for investors, when the alternative actually gives more choice).
One argument I often hear in my corner of the internet is that AI is one of the only remaining sectors of unregulated, meritocratic activity. The smart people have been pushed out of the real economy, so we're congregating here.
EXIT blog talks about this a lot. Eg in this article, "Sanctuary Cities are Special Economic Zones - Just Not For You": https://blog.exitgroup.us/p/sanctuary-cities-are-special-eco...
Anyone else knows fun writing like this, please recommend it to me
EXIT blog talks about this a lot. Eg in this article, "Sanctuary Cities are Special Economic Zones - Just Not For You": https://blog.exitgroup.us/p/sanctuary-cities-are-special-eco...
Anyone else knows fun writing like this, please recommend it to me
AI didn't conquer the US economy. What conquered the US economy is a small number of large companies. If Apple started rolling out an iTurd app, and Google started offering gTurd, and Amazon started shipping free turds to everyone's doorstep, and so on, then we would be talking about how turds have conquered the US economy. The economy has become divorced from any pretense of meeting actual consumer needs. The suppliers are creating their own demand.
> Nobody can say for sure whether the AI boom is evidence of the next Industrial Revolution or the next big bubble. All we know is that it’s happening.
In hindsight, it will be clear, and future generations (if any exist) will ask: "Why didn't you understand what was happening at the time"
My answer: Noise. Just because you can find someone who wrote down the answer at the time, doesn't mean that they really understood the answer, at least not to the extent that we will understand with hindsight.
Future history is contingent.
In hindsight, it will be clear, and future generations (if any exist) will ask: "Why didn't you understand what was happening at the time"
My answer: Noise. Just because you can find someone who wrote down the answer at the time, doesn't mean that they really understood the answer, at least not to the extent that we will understand with hindsight.
Future history is contingent.
This matches the tech job market: if you are not in top corp or labs, your hard work is most likely subsidizing the $ 1.5M paycheck for OpenAI employees.
> Artificial intelligence has a few simple ingredients: computer chips, racks of servers in data centers, huge amounts of electricity, and networking and cooling systems that keep everything running without overheating.
What about the software? What about the data? What about the models?
What about the software? What about the data? What about the models?
> What about the data?
What, you want them to pay for all the copyrighted data they consume and regurgitate? They can't afford that!
What, you want them to pay for all the copyrighted data they consume and regurgitate? They can't afford that!
AI bills are in the thousands of dollars per day for some small AI startups, and in the 10-100k range per day for large companies.
So as many people have speculated that OpenAI has no moat - are wrong.
So as many people have speculated that OpenAI has no moat - are wrong.
A moat is protection from competition. What you’ve described is not a moat.
What is the moat you are implying? (affordability?)
The fact that there is massive spending in AI in the tech sector,
isn't that just a -possible- sign of a other bust coming down the road?
We have seen it before, again and again.
We have seen it before, again and again.
Measuring such things in dollars rubs me the wrong way. Is using inflation adjusted dollar common or do people just compare nominal values?
Please stop using stacked bar charts where individual lines(plus a Total) line would help the poor reader comprehend the data better.
On the face of it, it looks positively habsburgian.
All I see is B-U-B-B-L-E
> because the oligarchs demanded it
There, summed it up for you.
There, summed it up for you.
Not sure why you were down voted. It's true and it actually answers the question in the article title, which the article doesn't even explore, let alone answer.
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They mention rate of adoption, compared to the internet. Consider the barriers to entry. Before we all got sick of receiving AOL CDs, the prospect of 'going online' was incredibly expensive and sometimes laborious.
More people subscribe to/play with a $20/m service than own/admin state-of-the-art machines?! Say it ain't so /s
More people subscribe to/play with a $20/m service than own/admin state-of-the-art machines?! Say it ain't so /s
> More people subscribe to/play with a $20/m service than own/admin state-of-the-art machines?! Say it ain't so /s
The problem is, $20/m isn't going to be profitable without better hardware, or more optimized models. Even the $200/month plan isn't making money for OpenAI. These companies are still in the "sell at a loss to capture marketshare" stage.
We don't even know if being an "AI Company" is viable in the first place - just developing models and selling access. Models will become a commodity, and if hardware costs ever come down, open models will win.
What happens when OpenAI, Anthropic, etc. can't be profitable without charging a price that consumers won't/can't afford to pay?
The problem is, $20/m isn't going to be profitable without better hardware, or more optimized models. Even the $200/month plan isn't making money for OpenAI. These companies are still in the "sell at a loss to capture marketshare" stage.
We don't even know if being an "AI Company" is viable in the first place - just developing models and selling access. Models will become a commodity, and if hardware costs ever come down, open models will win.
What happens when OpenAI, Anthropic, etc. can't be profitable without charging a price that consumers won't/can't afford to pay?
Pop :) For some, at least. Barons tend to leave remnants
The assumption here is that, without AI, none of that capital would have been deployed anywhere. That intuitively doesn't sound realistic. The article follows on with:
>In the last two years, about 60 percent of the stock market’s growth has come from AI-related companies, such as Microsoft, Nvidia, and Meta.
Which is a statement that's been broadly true since 2020, long before ChatGPT started the current boom. We had the Magnificent Seven, and before that the FAANG group. The US stock market has been tightly concentrated around a few small groups for a decades now.
>You see it in the business data. According to Stripe, firms that self-describe as “AI companies” are dominating revenue growth on the platform, and they’re far surpassing the growth rate of any other group.
The current Venn Diagram of "startups" and "AI companies" is two mostly concentric circles. Again, you could have written the following statement at any time in the last four decades:
> According to [datasource], firms that self-describe as “startups” are dominating revenue growth on the platform, and they’re far surpassing the growth rate of any other group.