For those around for the .com bust it does feel very similar. In both cases the tech is amazing and isn’t going away, but the business models of many/most companies “innovating” with the tech is simply unsustainable. A lot of “AI” currently looks like a dry forest waiting for lighting to strike and burn it to the ground. The latest round of PR puff from CEOs saying they’re doing layoffs because of AI (vs their poor performance or prior bad business decisions) is fueling the perception that the hype is a mile wide and a millimeter thick, just waiting for the moment when it all comes crashing down.
This is a longstanding predictable pattern in tech. Most of these “AI companies” will go bust or become a shell of their former self and sold off for parts. The tech will be commoditized and become pretty ubiquitous across the board but not a profit center in its own right.
In most implementations of generative AI, as of today, the use case is as a feature. People don't buy features, they buy products. If your company is built solely on this hot button you better be sure you either have some IP backing it up, are building and own it (models), or are the best mouse trap for your target market. Because, I'm watching an entire industry segment all show up with slight iterations of the exact same thing and those things are not great. They're unreliable and mostly mediocre.
Yes. But those feature companies are not IPO’d and listed on the exchanges. That is the key distinction with the markets this time. Most public companies in this space are actually growing their bottom lines. I’m not talking about just Nvidia here. You can see this effect in the entire food-chain. Everyone is benefitting.
> Elon dumped 80% of Twitter staff and it didn't collapse.
Have you looked at a graph of Twitter revenue and profit recently (hint: it is very low). Nobody has ever claimed that you cannot fire all the employees and keep Twitter online with a skeleton crew; the claim has always been that you can't do that _and remain a viable business_.
It didn't "collapse" but it lost a ton of users and stopped being a public service where anyone could freely read most tweets. The load on Twitter's servers and the scope of its services have gone down dramatically, and it's childish to conclude "Twitter was overstaffed." Musk made Twitter into a much smaller service.
It didn't collapse in the sense of ceasing to exist. It did collapse as a place where decent human beings cared to gather, and it lost over 75% of its market value.
Exactly this. Everyone else is doing what everyone else does. There's no direct relationship between AI adoption and layoffs, except for inflated board/CEO expectations.
He definitely started a trend, but the takeaway was a mess. The company makes less money, the technology/UX is objectively getting worse. Yes, some parts of the audience have proven sticky, but I don’t think that was ever a huge revelation. It’s always been possible to take some seeds off the bun.
I don’t think most (non social companies) were as overstaffed as twitter because most companies have valuations based on revenue/profit rather than “it’s internet/social so 1000x”. Many “traditional” companies have been running leaner since the 90s and can’t function at all with even a 25% headcount reduction.
> CEOs are doing layoffs because Elon dumped 80% of Twitter staff and it didn't collapse.
Anyone that ever worked in a sufficiently large tech company knows that it can keep the lights on with a skeleton crew. It doesn't mean the results will be good or desirable, but it can be done.
Over time cracks will start to show. New featurs are seldom delivered and half-assed. Bugs will take a long time to fix or just become part of the landscape. That sort of thing.
Twitter is a special case that Musk did not buy it to make it into a profitable company, or to make it more valuable, or to improve it in any way. He just bought it because he wanted to rid it of the "woke mind virus" (and factually tried to get out of the deal and was sued into actually buying it).
CEOs are doing layoffs for a variety of reasons, but mostly to improve profits by reducing costs. These cycles happen, and what it actually points to is that those companies are not seeing a viable path for growth at the moment that justifies their valuation. Layoffs with some vague gestures towards AI is a good smokescreen for the time being.
"For those around for the .com bust it does feel very similar."
I was around for the .com boom and it feels very different.
I experienced the boom as exuberant
without limits, the current situation is much more nuanced.
I was around as well and while tech financing is more sophisticated and mainstream, it feels like a similar cliff in regards to valuations - what are some of the nuances you see that separate these too time periods?
NVDA has a P/E of 55, which is definitely elevated, but nowhere near the 230+ that CSCO had at that time. TO say nothing of SUNW.
The big AI labs are definitely losing money, but they're doing it on the back of tens of (rapidly growing) billions of dollars in ARR, versus the dot com e-commerce and portal flameouts who would go public on (maybe) a million in revenue, at best.
We also have large AI teams at FAANG who are being funded directly by the fat margins of these companies, whose funding is not dependent on the whims of VC, PE or public markets.
I don't think Mark Zuckerberg salivating about data centers bigger than Manhattan is "nuanced." People gleefully predicting a 30% increase in national energy consumption strikes me as pretty darn exuberant.
I did as well, and then we had a few layoff rounds after having "positive" results when the VC money dried out, and those that stayed like myself, had several months of delayed salaries.
The .com bubble casualties were companies acting like "the internet means we don't need a viable business model". I see echoes of that in today's "AI means we don't need (m)any human experts anymore, now everyone is a 10x engineer".
Unless it's leveraged by skilled experts, AI-generated code is the payday loan / high-interest credit card of tech debt.
I was around for the dot com boom and bust, and this does not feel similar. The issue with the internet was that much of the value came from network effects that were not there in the late 90s and early aughts when personal computing was desktop boxes with 56k dial up connections. Very much, “if you build it, they will come.” It was the mass rollout of cable modems and then smart phones that changed the math.
There is no cart before the horse here. AI is coming for you, not the other way around. The pessimistic takes are underestimating the impact by at least a couple orders of magnitude. Think smart phones as a lower bound.
I have no idea what capacity people here work with AI, but given my view and experience the pessimistic takes I commonly see on here do not seem realistic.
With the internet, there was a clear value proposition for the vast majority of use cases. Even if some of the specific businesses were poorly-conceived or overly optimistic, the underlying technology was very obviously a) growing organically, b) going to be something everyone used & wanted, and c) a commodity.
All three of those parts are vital for a massive boom like that.
Generative AI is growing some, yes, but a lot of the growth is being pushed by the companies creating or otherwise massively invested in gen-AI. And yes, many people try out ChatGPT's webapp, but that's mostly a gimmick—and frankly, many of the cases where people are attempting to use it for more are fairly awful cautionary tales (eg, the people trying to use it as a therapist, and instead getting a cheerleader that confirms their worst impulses).
Gen-AI may be useful to some people, but it's not going to be a central feature of most people's lives—at least not in the forms it exists in today, or what can be clearly extrapolated from them. Yes, it can help some with coding—with mixed results—but not everyone's a programmer. Not everyone's even an office worker. The internet has obvious useful applications for a plumber or a lawyer; if I hired one of those and they said they were using generative AI to help them in their work, I'd fire them instantly. There are already a bunch of (both amusing and harrowing) stories of lawyers getting reamed out in court for using gen-AI to help them write legal filings.
OpenAI may or may not have a robust moat—I've seen people arguing both ways; personally I suspect lean slightly toward the "not" side—but generative AI as a whole is not something that's an interchangeable commodity the way internet access, or even hosting, is. First of all, in order to use the models that are touted as being advanced enough to actually look like more than spicy autocorrect, you need a serious GPU farm. Second of all, AFAIK, those models are being kept private by the big players like Google and OpenAI. That means that if you build your business on generative AI, unless you're able to both fork out for a massive hardware investment and do your own training to match what the big boys are already doing, you're going to be 100% dependent on another specific for-profit company for your entire business model. That's not a sound business decision, especially during this time where both the technology and the legal aspect of generative AI are still so much in flux.
Generative AI may be here to stay, but it's not going to take over the world the way the internet did.
>For those around for the .com bust it does feel very similar.
Not it doesn't. I was around and we didn't have an entire group of GenX gang warning about the .com crash everywhere, everytime. Compared to nonsense metrics like eyeballs, this time we have real revenue and the biggest companies are tech. It might end in some crash but nothing like the .com one.
Some of the most valuable companies in the world right now are remnants of the dot com crash, Facebook (Meta), Google, Microsoft, Oracle, etc. By this logic the "AI companies" will go bust and coalesce into a few winners who will go on to become the worlds first multi trillion dollar companies and dominate the economic landscape for the next couple of decades.
on the other hand, it's easier to "copy" telecommunications equipment than state of the art chips. Not saying there won't be competitions to nvidia's dominance, but so far, not a pip from anyone (realistically that is).
I keep coming back to this thought that the ability for computation itself sets the stage for speculation.. or rather, widely available and cheap computation.
Do you think FANG companies inflate AI on purpose in order to create a scenario for a bust to happen, they can survive it given their vast warchest of cash
It’s one of those scenarios where the high level value prop is obvious and compelling, just like the dotcom bubble.
80% of the hype is about 20% of the bullshit. And the bullshit attracts 80% of the dollars. The current cohort of leaders are Jedi at separating sovereign wealth and markets from their treasure.
Disagree, because the very few very big AI players are (in contrast to the 90s) very solid. Yes, there is a breadth of absolute bullshit built on top of current AI, but if it were only ChatGPT-alikes and LLMs for coding from here on out, that in itself is enough real value, that requires very little imagination, a lot of implementation and there's more demand than can easily be satisfied right now for both.
I’m an AI enthusiast, and it’s not clear to me that selling inference to a prop model is a winning business model, which is what Anthropic and OpenAI are doing. The open models are good enough today for many things, and are likely to only get better. Feels like inference is a commodity, and not clear how much money there is in it.
I would love to know how much of the inference I pay for is being paid for by VC cash: I suspect a lot of it.
If I’m not mistaken they are using high valuations of top companies to conclude AI is overhyped?
Sorry, but weren’t these valuations escalated because of low interest rates and quantitative easing? Perhaps combined with increased concentration in Top 10 by investors navigating uncertainty?
Typical BS coming from a mega fund only supported by management fees. not saying AI isn’t hyped but this is laughable.
Good rule of thumb: When everybody talks about bubbles while rates are going down, it's a good time to invest. When everybody's talking about investing and rates are going up, it's a good time to drop out. Right now we are in the former timeframe. As long as cash remains cheap, there is no good reason from a financial market perspective for this to not go on. Is it sustainable indefinitely? No. But almost nothing in our current economy is. AI nowadays just generates easy clicks for opinion pieces like this looking at a single data point. That doesn't mean there is any reason to act on it or even just to read too much into it.
Better rule of thumb: have an automated investment strategy that takes a set percentage of your income every paycheck and invest it, regardless of current rates or what anyone's saying.
Note that this applies to vanilla investing, like index funds. You can easily automate that if you want. If you're really just looking for modest stable yields, you may as well invest in bonds right now. The US GOV 12 month is at >4%. With inflation significantly below, that's like free money (if you've been an adult in the 2010s you'll know what I mean). But don't expect to make a lot of money in less than a generational timeframe either way.
I think that at the moment the One Big Beautiful Bill ensures that the spending spree will continue and the world will stay afloat with cheap money so I would assume that we are about to see the last part of the bubble. But, I wouldn't bet on my assumptions.
With the amount of money being tossed around I am convinced this is going to be 10x worse than the dotcom bubble when it pops. And it will pop. You simply can't have pre-product companies valued at 10s of billions of dollars and expect a good outcome.
Everyone knows it's mostly bullshit, but that _someone_ is going to end up coming out as the Amazon-level winner.
Every single one of these "fake it till you make it" AI CEO/Founders are betting they are the Amazon.com and not the Pets.com... but if they are the Pets.com then what is the downside?
The CEO of pets.com certainly didn't end up out on the streets for being the biggest disaster of one of the largest bubbles and effectively burning billions of investor dollars (including institutions investing pensions and retirements funds).
The “talent wars” and VCs with a really nasty case of FOMO clouding investment fundamentals is throwing more dry kindling in the pile. For anyone that’s been around a while we’ve seen this movie before
I find AI useful, I use it most days to write snippets of code or to rubber duck with. It hasn't changed my workflows that much, just replaced Stackoverflow with ChatGPT. Feels like the sweet spot for me, everything else is noise.
Chat is the obvious application but the real value imho is using LLM to bridge a gap non-deterministically you couldn’t bridge deterministically before. Entity extraction for example allows us to connect two workflows that often required a human in the loop. Not anymore. I see this everywhere in our SaaS product.
For the majority of the questions asked and answered on StackOverflow, LLMs are undeniably better. I’m thinking of things like ‘How do I do X in Python’, ‘How do I do X on Linux’, etc., questions that are small in scope, not open ended, and can be easily verified. For everything else LLMs range from rarely useful to outright misleading and counterproductive.
LLMs also don’t really enable coincidental discovery like search engines do. Having to RTFM or read a spec or a book or a blog post to figure out the answer to your question sometimes also teaches you about related and important concepts that you wouldn’t have come across otherwise, and usually there will be suggestions for further reading or a side bar with other interesting topics etc. Completely replacing search feels like a a bit like a trap, where what you get in immediate answers you lose in an unseen opportunity cost.
Yep, replaces Google, Stackoverflow and autocomplete for coding with a much superior experience.
But anyone taking a vibe coded project with no human understanding of the code produced and puts it straight into production is going to have a bad time.
The LLM/AI tech has clear use cases and benefits. However, no, I do not need a shoehorned, dedicated AI in every single product and service I use. That is where is the bubble is in my opinion, everywhere the AI is built or applied in cases where it does not work or does not make sense.
A lot of the comments here are talking about startups. But the chart in the article is the forward P/E of the top 10 companies in the S&P.
For reference, those 10 companies are: Nvidia, Microsoft, Apple, Amazon, Meta, Braodcom, Alphabet (Class A), Alphabet again (Class C), Tesla, Berkshire.
This isn't a pets.com situation.
These companies are ENORMOUS cash engines with incredibly well-proven moats operating in an extremely monopoly-friendly political climate. Nothing like this existed in the 90s. Microsoft, but anti-trust still had some teeth.
The author makes a comparison between these companies and the rest of corporate America, arguing (implicitly) that the forward P/E of these ten symbols is too high relative to the rest of the S&P 500 index.
So let's look at the flip side. Many of the other companies in the S&P are vulnerable to these exact players' moats and pricing power. It's a zero-sum game and the winner is clear, so of course the winner's P/E looks really high compared to the expected loser.
Every single one of them has an AWS bill. Every single one of them has a big Windows/Office install base. Every single one of them probably has a huge apple install base. Every single one of them needs to pay to play in the App Store.
And many of them are also in the unenviable position of being on the losing side of an unfair competition in their actual core business. Walmart/HD/Coca-Cola vs Amazon. IBM/Oracle vs AWS. Or other complicated market dynamics that pose only upside to the big guys and potential downside to the rest (Biotechs vs Amazon Pharmacy).
The remainders are competing margins away from one another, are vulnerable to disruption of mid-market non-S&P players (or similarly sized companies that just aren't on the public markets -- see the huge size of privacy capital relative to the 90s). Some also face significant tariff risk. Think banks, consumer goods.
What percent of the difference in P/Es between the best and rest is justifiable on the thesis that we are entering a multi-decade period of (1) tech feudalism and (2) unpredictable populist fits that wreck havoc on everyone except the tippy top of the echelon who can blow enough cash to control the narrative?
Also, the chart doesn't take into account that the biggest companies have more power, are bigger right now, and it's not inherent to them using AI. If not AI, it would be something else. Shares and revenue are growing, and people are getting fired. They will not collapse.
We've been in a bubble ever since people starting believing "data is the new oil".
Data has only driven advertising, and it's done it in such a botched way that it's tearing down the whole discipline of advertising. These companies know all the little tidbits of information about all of us that they need to put the right products directly in front of our eyes multiple times per day, and they still get it wrong.
Advert engagement goes down, people who use advertising realise their budgets are being wasted on the wrong audience and the whole thing will pop. It was naïve to ever believe that data really means anything. At a certain scale it just becomes loads of noise.
This is not remotely true. I mean it's so incredibly not true I wonder how you came to believe this.
Haven't you ever heard of how hedge funds pay for cellular data to understand retail store traffic, or how satellite photos help them estimate the fullness of gas tanks at ports to predict pricing?
Or how data about predicted electrical pricing based on usage helps factories schedule energy-intensive production during times of low pricing?
Or how aircraft maintenance companies like AAR rely on "big data" to position replacement parts in a globally distributed system of warehouses to reduce the time it takes to repair aircraft (their contracts are based on airline uptime), thereby reducing passenger delays due to mechanical issues?
Or how farms use weather and satellite data to deal with droughts, identify areas to spray, and estimate competitor yields for the purposes of planning?
Or how governments now conduct surveillance of pathogens and drug use through sewer water data?
Or how semiconductor companies use massive amounts of data collected from production line sensors to massively increase yields and reduce chip prices, despite the complexity of chip production having increased massively?
You benefit directly or indirectly from companies using data all the time.
Similar to dot-com, part of the reason is the multiplier effect of all the AI investments. If these investments prove to be uneconomic, which I strongly suspect, the backend of this investment cycle is going to be brutal.
Readit News
This is a longstanding predictable pattern in tech. Most of these “AI companies” will go bust or become a shell of their former self and sold off for parts. The tech will be commoditized and become pretty ubiquitous across the board but not a profit center in its own right.
Those layoffs were a make or break moment for tech.
Have you looked at a graph of Twitter revenue and profit recently (hint: it is very low). Nobody has ever claimed that you cannot fire all the employees and keep Twitter online with a skeleton crew; the claim has always been that you can't do that _and remain a viable business_.
That was because Twitter was massively overstaffed. Are all these other businesses also overstaffed? The result of cheap money? Are they all atu
Anyone that ever worked in a sufficiently large tech company knows that it can keep the lights on with a skeleton crew. It doesn't mean the results will be good or desirable, but it can be done.
Over time cracks will start to show. New featurs are seldom delivered and half-assed. Bugs will take a long time to fix or just become part of the landscape. That sort of thing.
Twitter is a special case that Musk did not buy it to make it into a profitable company, or to make it more valuable, or to improve it in any way. He just bought it because he wanted to rid it of the "woke mind virus" (and factually tried to get out of the deal and was sued into actually buying it).
CEOs are doing layoffs for a variety of reasons, but mostly to improve profits by reducing costs. These cycles happen, and what it actually points to is that those companies are not seeing a viable path for growth at the moment that justifies their valuation. Layoffs with some vague gestures towards AI is a good smokescreen for the time being.
I was around for the .com boom and it feels very different. I experienced the boom as exuberant without limits, the current situation is much more nuanced.
NVDA has a P/E of 55, which is definitely elevated, but nowhere near the 230+ that CSCO had at that time. TO say nothing of SUNW.
The big AI labs are definitely losing money, but they're doing it on the back of tens of (rapidly growing) billions of dollars in ARR, versus the dot com e-commerce and portal flameouts who would go public on (maybe) a million in revenue, at best.
We also have large AI teams at FAANG who are being funded directly by the fat margins of these companies, whose funding is not dependent on the whims of VC, PE or public markets.
These times are not really comparable.
Unless it's leveraged by skilled experts, AI-generated code is the payday loan / high-interest credit card of tech debt.
There is no cart before the horse here. AI is coming for you, not the other way around. The pessimistic takes are underestimating the impact by at least a couple orders of magnitude. Think smart phones as a lower bound.
I have no idea what capacity people here work with AI, but given my view and experience the pessimistic takes I commonly see on here do not seem realistic.
With the internet, there was a clear value proposition for the vast majority of use cases. Even if some of the specific businesses were poorly-conceived or overly optimistic, the underlying technology was very obviously a) growing organically, b) going to be something everyone used & wanted, and c) a commodity.
All three of those parts are vital for a massive boom like that.
Generative AI is growing some, yes, but a lot of the growth is being pushed by the companies creating or otherwise massively invested in gen-AI. And yes, many people try out ChatGPT's webapp, but that's mostly a gimmick—and frankly, many of the cases where people are attempting to use it for more are fairly awful cautionary tales (eg, the people trying to use it as a therapist, and instead getting a cheerleader that confirms their worst impulses).
Gen-AI may be useful to some people, but it's not going to be a central feature of most people's lives—at least not in the forms it exists in today, or what can be clearly extrapolated from them. Yes, it can help some with coding—with mixed results—but not everyone's a programmer. Not everyone's even an office worker. The internet has obvious useful applications for a plumber or a lawyer; if I hired one of those and they said they were using generative AI to help them in their work, I'd fire them instantly. There are already a bunch of (both amusing and harrowing) stories of lawyers getting reamed out in court for using gen-AI to help them write legal filings.
OpenAI may or may not have a robust moat—I've seen people arguing both ways; personally I suspect lean slightly toward the "not" side—but generative AI as a whole is not something that's an interchangeable commodity the way internet access, or even hosting, is. First of all, in order to use the models that are touted as being advanced enough to actually look like more than spicy autocorrect, you need a serious GPU farm. Second of all, AFAIK, those models are being kept private by the big players like Google and OpenAI. That means that if you build your business on generative AI, unless you're able to both fork out for a massive hardware investment and do your own training to match what the big boys are already doing, you're going to be 100% dependent on another specific for-profit company for your entire business model. That's not a sound business decision, especially during this time where both the technology and the legal aspect of generative AI are still so much in flux.
Generative AI may be here to stay, but it's not going to take over the world the way the internet did.
Not it doesn't. I was around and we didn't have an entire group of GenX gang warning about the .com crash everywhere, everytime. Compared to nonsense metrics like eyeballs, this time we have real revenue and the biggest companies are tech. It might end in some crash but nothing like the .com one.
Curiously, nvidia's P/E ratio is lower than it was two years ago!
80% of the hype is about 20% of the bullshit. And the bullshit attracts 80% of the dollars. The current cohort of leaders are Jedi at separating sovereign wealth and markets from their treasure.
I would love to know how much of the inference I pay for is being paid for by VC cash: I suspect a lot of it.
Most of the LLM applications are either entirely useless or trivially reproduced with much simpler free models (or even entirely non-“AI” methods).
Sorry, but weren’t these valuations escalated because of low interest rates and quantitative easing? Perhaps combined with increased concentration in Top 10 by investors navigating uncertainty?
Typical BS coming from a mega fund only supported by management fees. not saying AI isn’t hyped but this is laughable.
Every single one of these "fake it till you make it" AI CEO/Founders are betting they are the Amazon.com and not the Pets.com... but if they are the Pets.com then what is the downside?
The CEO of pets.com certainly didn't end up out on the streets for being the biggest disaster of one of the largest bubbles and effectively burning billions of investor dollars (including institutions investing pensions and retirements funds).
https://www.quiverquant.com/insiders/1780458/Julie%20Wainwri...
LLMs also don’t really enable coincidental discovery like search engines do. Having to RTFM or read a spec or a book or a blog post to figure out the answer to your question sometimes also teaches you about related and important concepts that you wouldn’t have come across otherwise, and usually there will be suggestions for further reading or a side bar with other interesting topics etc. Completely replacing search feels like a a bit like a trap, where what you get in immediate answers you lose in an unseen opportunity cost.
But anyone taking a vibe coded project with no human understanding of the code produced and puts it straight into production is going to have a bad time.
- a better summarizing google for some queries
- snippet generator
but has not changed any workflows.
For reference, those 10 companies are: Nvidia, Microsoft, Apple, Amazon, Meta, Braodcom, Alphabet (Class A), Alphabet again (Class C), Tesla, Berkshire.
This isn't a pets.com situation.
These companies are ENORMOUS cash engines with incredibly well-proven moats operating in an extremely monopoly-friendly political climate. Nothing like this existed in the 90s. Microsoft, but anti-trust still had some teeth.
The author makes a comparison between these companies and the rest of corporate America, arguing (implicitly) that the forward P/E of these ten symbols is too high relative to the rest of the S&P 500 index.
So let's look at the flip side. Many of the other companies in the S&P are vulnerable to these exact players' moats and pricing power. It's a zero-sum game and the winner is clear, so of course the winner's P/E looks really high compared to the expected loser.
Every single one of them has an AWS bill. Every single one of them has a big Windows/Office install base. Every single one of them probably has a huge apple install base. Every single one of them needs to pay to play in the App Store.
And many of them are also in the unenviable position of being on the losing side of an unfair competition in their actual core business. Walmart/HD/Coca-Cola vs Amazon. IBM/Oracle vs AWS. Or other complicated market dynamics that pose only upside to the big guys and potential downside to the rest (Biotechs vs Amazon Pharmacy).
The remainders are competing margins away from one another, are vulnerable to disruption of mid-market non-S&P players (or similarly sized companies that just aren't on the public markets -- see the huge size of privacy capital relative to the 90s). Some also face significant tariff risk. Think banks, consumer goods.
What percent of the difference in P/Es between the best and rest is justifiable on the thesis that we are entering a multi-decade period of (1) tech feudalism and (2) unpredictable populist fits that wreck havoc on everyone except the tippy top of the echelon who can blow enough cash to control the narrative?
https://www.youtube.com/watch?v=IUK6zjtUj00
From that, any number of conclusions are possible, including perhaps:
* The level of innovation at those companies is high. Certainly the 90s tech booms were actually very innovative and profitable.
Data has only driven advertising, and it's done it in such a botched way that it's tearing down the whole discipline of advertising. These companies know all the little tidbits of information about all of us that they need to put the right products directly in front of our eyes multiple times per day, and they still get it wrong.
Advert engagement goes down, people who use advertising realise their budgets are being wasted on the wrong audience and the whole thing will pop. It was naïve to ever believe that data really means anything. At a certain scale it just becomes loads of noise.
This is not remotely true. I mean it's so incredibly not true I wonder how you came to believe this.
Haven't you ever heard of how hedge funds pay for cellular data to understand retail store traffic, or how satellite photos help them estimate the fullness of gas tanks at ports to predict pricing?
Or how data about predicted electrical pricing based on usage helps factories schedule energy-intensive production during times of low pricing?
Or how aircraft maintenance companies like AAR rely on "big data" to position replacement parts in a globally distributed system of warehouses to reduce the time it takes to repair aircraft (their contracts are based on airline uptime), thereby reducing passenger delays due to mechanical issues?
Or how farms use weather and satellite data to deal with droughts, identify areas to spray, and estimate competitor yields for the purposes of planning?
Or how governments now conduct surveillance of pathogens and drug use through sewer water data?
Or how semiconductor companies use massive amounts of data collected from production line sensors to massively increase yields and reduce chip prices, despite the complexity of chip production having increased massively?
You benefit directly or indirectly from companies using data all the time.