ChatGPT loses money on every query their premium subscribers submit. They lose money when people use copilot, which they resell to Microsoft. And it’s not like they’re going to make it up on volume - heavy users are significantly more costly.
This isn’t unique to ChatGPT.
Yes, it has its uses; no, it cannot continue in the way it has so far. Is it worth more than $200/month to you? Microsoft is tearing up datacenter deals. I don’t know what the future is, but this ain’t it.
ETA I think that management gets the most benefit, by far, and that’s why there’s so much talk about it. I recently needed to lead a meeting and spent some time building the deck with a LLM; took me 20 min to do something otherwise would have taken over an hour. When that is your job alongside responding to emails, it’s easy to see the draw. Of course, many of these people are in Bullshit Jobs.
Entirely agree with that. Except to add that so is Dario Amodei.
I think it’s got potential, but the cost and the accuracy are two pieces that need to be addressed. DeepSeek is headed in the right direction, only because they didn’t have the insane dollars that Microsoft and Google throw at OpenAI and Anthropic respectively.
Even with massive efficiency gains, though, the hardware market is going to do well if we’re all running local models!
Alibaba’s QwQ 32B is already incredible, and runnable on 16GB GPUs! Honestly it’s a bigger deal than Deepseek R1, and many open models before that were too, they just didn’t get the finance media attention DS got. And they are releasing a new series this month.
Local, efficient ML is coming. That’s why Altman and everyone are lying through their teeth: scaling up infinitely is not the way forward. It never was.
I fucking hate AI, but an AI coding assistant that is basically a glorified StackOverflow search engine is actually worth more than $200/month to me professionally.
I don’t use it to do my work, I use it to speed up the research part of my work.
That’s the business model these days. ChatGPT, and other AI companies are following the disrupt (or enshittification) business model.
Acquire capital/investors to bankroll your project.
Operate at a loss while undercutting your competition.
Once you are the only company left standing, hike prices and cut services.
Ridiculous profit.
When your customers can no longer deal with the shit service and high prices, take the money, fold the company, and leave the investors holding the bag.
Now you’ve got a shit-ton of your own capital, so start over at step 1, and just add an extra step where you transfer the risk/liability to new investors over time.
I do think there will have to be some cutting back, but it provides capitalists with the ability to discipline labor and absolve themselves (I would never do such a thing, it was the AI what did it!) which might they might consider worth the expense.
Right, but most of their expenditures are not in the queries themselves but in model training. I think capital for training will dry up in coming years but people will keep running queries on the existing models, with more and more emphasis on efficiency. I hate AI overall but it does have its uses.
No, that’s the thing. There’s still significant expenditure to simply respond to a query. It’s not like Facebook where it costs $1 million to build it and $0.10/month for every additional user. It’s $1billion to build and $1 per query. There’s no recouping the cost at scale like previous tech innovation. The more use it gets, the more it costs to run, in a straight line, not asymptotically.
No way is it $1 per query. Hell a lot of these models you can run on your own computer, with no cost apart from a few cents of electricity (plus datacenter upkeep)
Theres more than just chatgpt and American data center/llm companies. Theres openAI, google and meta (american), mistral (French), alibaba and deepseek (china). Many more smaller companies that either make their own models or further finetune specialized models from the big ones. Its global competition, all of them occasionally releasing open weights models of different sizes for you to run your own on home consumer computer hardware. Dont like big models from American megacorps that were trained on stolen copyright infringed information? Use ones trained completely on open public domain information.
Your phone can run a 1-4b model, your laptop 4-8b, your desktop with a GPU 12-32b. No data is sent to servers when you self-host. This is also relevant for companies that data kept in house.
Like it or not machine learning models are here to stay. Two big points. One, you can self host open weights models trained on completely public domain knowledge or your own private datasets already. Two, It actually does provide useful functions to home users beyond being a chatbot. People have used llms to make music, generate images/video, integrate home automation like lighting control with tool calling, see images for details including document scanning, boilerplate basic code logic, check for semantic mistakes that regular spell check wont pick up on. In business ‘agenic tool calling’ to integrate models as secretaries is popular. Nft and crypto are truly worthless beyond grifting idiots with pump n dump and baseless speculative asset gambling. AI can at least make an attempt at a task you give it and either generally succeed or fail at it.
Models around 24-32b range in high quant are reasonably capable of basic information processing task and generally accurate domain knowledge. You can’t treat it like a fact source because theres always a small statistical chance of it being wrong but its OK starting point for researching like Wikipedia.
My local colleges are researching multimodal llms recognizing the subtle patterns in billions of cancer cell photos to possibly help doctors better screen patients. I would love a vision model trained on public domain botany pictures that helps recognize poisonous or invasive plants.
The problem is that theres too much energy being spent training them. It takes a lot of energy in compute power to cool a model and refine it. Its important for researchers to find more efficent ways to make them, Deepseek did this, they found a way to cook their models with way less energy and compute which is part of why that was exciting. Hopefully this energy can also come more from renewable instead of burning fuel.
Theres openAI, google and meta (american), mistral (French), alibaba and deepseek (china). Many more smaller companies that either make their own models or further finetune specialized models from the big ones
Which ones are not actively spending an amount of money that scales directly with the number of users?
I’m talking about the general-purpose LLM AI bubble , wherein people are expected to return tremendous productivity improvements by using a LLM, thus justifying the obscene investment. Not ML as a whole. There’s a lot there, such as the work your colleagues are doing.
But it’s being treated as the equivalent of electricity, and it is not.
Which ones are not actively spending an amount of money that scales directly with the number of users?
Most of these companies offer direct web/api access to their own cloud supercomputer datacenter, and All cloud services have some scaling with operation cost. The more users connect and use computer, the better hardware, processing power, and data connection needed to process all the users. Probably the smaller fine tuners like Nous Research that take a pre-cooked and open-licensed model, tweak it with their own dataset, then sell the cloud access at a profit with minimal operating cost, will do best with the scaling. They are also way way cheaper than big model access cost probably for similar reasons. Mistral and deepseek do things to optimize their models for better compute power efficency so they can afford to be cheaper on access.
OpenAI, claude, and google, are very expensive compared to competition and probably still operate at a loss considering compute cost to train the model + cost to maintain web/api hosting cloud datacenters. Its important to note that immediate profit is only one factor here. Many big well financed companies will happily eat the L on operating cost and electrical usage as long as they feel they can solidify their presence in the growing market early on to be a potential monopoly in the coming decades. Control, (social) power, lasting influence, data collection. These are some of the other valuable currencies corporations and governments recognize that they will exchange monetary currency for.
but its treated as the equivalent of electricity and its not
I assume you mean in a tech progression kind of way. A better comparison might be is that its being treated closer to the invention of transistors and computers. Before we could only do information processing with the cold hard certainty of logical bit calculations. We got by quite a while just cooking fancy logical programs to process inputs and outputs. Data communication, vector graphics and digital audio, cryptography, the internet, just about everything today is thanks to the humble transistor and logical gate, and the clever brains that assemble them into functioning tools.
Machine learning models are based on neuron brain structures and biological activation trigger pattern encoding layers. We have found both a way to train trillions of transtistors simulate the basic information pattern organizing systems living beings use, and a point in time which its technialy possible to have the compute available needed to do so. The perceptron was discovered in the 1940s. It took almost a century for computers and ML to catch up to the point of putting theory to practice. We couldn’t create artificial computer brain structures and integrate them into consumer hardware 10 years ago, the only player then was google with their billion dollar datacenter and alphago/deepmind.
Its exciting new toy that people think can either improve their daily life or make them money, so people get carried away and over promise with hype and cram it into everything especially the stuff it makes no sense being in. Thats human nature for you. Only the future will tell whether this new way of precessing information will live up to the expectations of techbros and academics.
There’s nothing wrong with using AI in your personal or professional life. But let’s be honest here: people who find value in it are in the extreme minority. At least at the moment, and in its current form. So companies burning fossil fuels, losing money spinning up these endless LLMs, and then shoving them down our throats in every. single. product. is extremely annoying and makes me root for the technology as a whole to fail.
It is definitely here to stay, but the hype of AGI being just around the corner is definitely not believable. And a lot of the billions being invested in AI will never return a profit.
AI is already a commodity. People will be paying $10/month at max for general AI. Whether Gemini, Apple Intelligence, Llama, ChatGPT, copilot or Deepseek. People will just have one cheap plan that covers anything an ordinary person would need. Most people might even limit themselves to free plans supported by advertisements.
These companies aren’t going to be able to extract revenues in the $20-$100/month from the general population, which is what they need to recoup their investments.
Specialized implementations for law firms, medical field, etc will be able to charge more per seat, but their user base will be small. And even they will face stiff competition.
I do believe AI can mostly solve quite a few of the problems of an aging society, by making the smaller pool of workers significantly more productive. But it will not be able to fully replace humans any time soon.
It’s kinda like email or the web. You can make money using these technologies, but by itself it’s not a big money maker.
Does it really boost productivity? In my experience, if a long email can be written by an AI, then you should just email the AI prompt directly to the email recipient and save everyone involved some time. AI is like reverse file compression. No new information is added, just noise.
If you’re using the thing to write your work emails, you’re probably so bad at your job that you won’t last anyway. Being able to write a clear, effective message is not a skill, it’s a basic function like walking. Asking a machine to do it for you just hurts yourself more than anything.
That said, it can be very useful for coding, for analyzing large contracts and agreements and providing summaries of huge datasets, it can help in designing slide shows when you have to do weekly power-points and other small-scale tasks that make your day go faster.
I find it hilarious how many people try to make the thing do ALL their work for them and end up looking like idiots as it blows up in their face.
See, LLM’s will never be smarter than you personally, they are tools for amplifying your own cognition and abilities, but few people use them that way, most people think it’s already alive and can make meaning for them. It’s not, it’s a mirror. You wouldn’t put a hand-mirror on your work chair and leave it to finish out your day.
I’m not a coder by any means, but when updating the super fucking outdated excel files my old company used, I’d usually make a VBA script using an LLM. It wasn’t always perfect, but 99% of the time, it was waaaay faster than me doing it myself. Then again, the things that company insisted was done in Excel could easily have been done better with other software. But the reality is that my field is conservative as fuck, and if it worked for the boss in 1994, it has to work for me.
If that email needs to go to a client or stakeholder, then our culture won’t accept just the prompt.
Where it really shines is translation, transcription and coding.
Programmers can easily double their productivity and increase the quality of their code, tests and documentation while reducing bugs.
Translation is basically perfect. Human translators aren’t needed. At most they can review, but it’s basically errorless, so they won’t really change the outcome.
Transcribing meetings also works very well. No typos or grammar errors, only sometimes issues with acronyms and technical terms, but those are easy to spot and correct.
As a programmer, there are so very few situations where I’ve seen LLMs suggest reasonable code. There are some that are good at it in some very limited situations but for the most part they’re just as bad at writing code as they are at everything else.
Programmers can double their productivity and increase quality of code?!? If AI can do that for you, you’re not a programmer, you’re writing some HTML.
We tried AI a lot and I’ve never seen a single useful result. Every single time, even for pretty trivial things, we had to fix several bugs and the time we needed went up instead of down.
Every. Single. Time.
Best AI can do for programmers is context sensitive auto completion.
Another thing where AI might be useful is static code analysis.
Not really. As a programmer who doesn’t deal with math like at all, just working on overly-complicated CRUD’s, and even for me the AI is still completely wrong and/or waste of time 9 times out of 10. And I can usually spot when my colleagues are trying to use LLM’s because they submit overly descriptive yet completely fucking pointless refactors in their PR’s.
AI is a commodity but the big players are losing money for every query sent. Even at the $200/month subscription level.
Tech valuations are based on scaling. ARPU grows with every user added. It costs the same to serve 10 users vs 100 users, etc. ChatGPT, Gemini, copilot, Claude all cost more the more they’re used. That’s the bubble.
Unlike NFTs, it’s actually used by ordinary people
Yeah, but i don’t recall every tech company shoving NFTs into every product ever whether it made sense or if people wanted it or not. Not so with AI. Like, pretty much every second or third tech article these days is “[Company] shoves AI somewhere else no one asked for”.
It’s being force-fed to people in a way blockchain and NFTs never were. All so it can gobble up training data.
That’s because it died out before they all could, Reddit had the nft like aliens thing twitter used to let you use your nft as a profile picture. It just died out way too quick for the general tech companies to get in on it.
If it stayed longer Samsung would have worked out how to put nft tech in their phones
What you described literally happened with blockchain, not with NFTs because by then everyone knew blockchain is fucking stupid and NFTs were just a layer of full retard on top.
In a recent study, Jain and Jain (2019) measure the valuation effect of including the words “blockchain” or “bitcoin” in corporate names using a set of ten publicly listed firms. They found that these firms earn significant positive abnormal returns that persist for 2 months after the name change announcement.
For better or worse, AI is here to stay. Unlike NFTs, it’s actually used by ordinary people - and there’s no sign of it stopping anytime soon.
ChatGPT loses money on every query their premium subscribers submit. They lose money when people use copilot, which they resell to Microsoft. And it’s not like they’re going to make it up on volume - heavy users are significantly more costly.
This isn’t unique to ChatGPT.
Yes, it has its uses; no, it cannot continue in the way it has so far. Is it worth more than $200/month to you? Microsoft is tearing up datacenter deals. I don’t know what the future is, but this ain’t it.
ETA I think that management gets the most benefit, by far, and that’s why there’s so much talk about it. I recently needed to lead a meeting and spent some time building the deck with a LLM; took me 20 min to do something otherwise would have taken over an hour. When that is your job alongside responding to emails, it’s easy to see the draw. Of course, many of these people are in Bullshit Jobs.
OpenAI is massively inefficient, and Atlman is a straight up con artist.
The future is more power efficient, smaller models hopefully running on your own device, especially if stuff like bitnet pans out.
Entirely agree with that. Except to add that so is Dario Amodei.
I think it’s got potential, but the cost and the accuracy are two pieces that need to be addressed. DeepSeek is headed in the right direction, only because they didn’t have the insane dollars that Microsoft and Google throw at OpenAI and Anthropic respectively.
Even with massive efficiency gains, though, the hardware market is going to do well if we’re all running local models!
Alibaba’s QwQ 32B is already incredible, and runnable on 16GB GPUs! Honestly it’s a bigger deal than Deepseek R1, and many open models before that were too, they just didn’t get the finance media attention DS got. And they are releasing a new series this month.
Microsoft just released a 2B bitnet model, today! And that’s their paltry underfunded research division, not the one training “usable” models: https://huggingface.co/microsoft/bitnet-b1.58-2B-4T
Local, efficient ML is coming. That’s why Altman and everyone are lying through their teeth: scaling up infinitely is not the way forward. It never was.
I fucking hate AI, but an AI coding assistant that is basically a glorified StackOverflow search engine is actually worth more than $200/month to me professionally.
I don’t use it to do my work, I use it to speed up the research part of my work.
That’s the business model these days. ChatGPT, and other AI companies are following the disrupt (or enshittification) business model.
Now you’ve got a shit-ton of your own capital, so start over at step 1, and just add an extra step where you transfer the risk/liability to new investors over time.
I do think there will have to be some cutting back, but it provides capitalists with the ability to discipline labor and absolve themselves (I would never do such a thing, it was the AI what did it!) which might they might consider worth the expense.
Might be cheaper than CEO fall guys, now that anti-die is stopping them from using “first woman CEOs” with their lower pay as the scapegoats.
So far courts have held companies responsible for AI decision-making.
Right, but most of their expenditures are not in the queries themselves but in model training. I think capital for training will dry up in coming years but people will keep running queries on the existing models, with more and more emphasis on efficiency. I hate AI overall but it does have its uses.
No, that’s the thing. There’s still significant expenditure to simply respond to a query. It’s not like Facebook where it costs $1 million to build it and $0.10/month for every additional user. It’s $1billion to build and $1 per query. There’s no recouping the cost at scale like previous tech innovation. The more use it gets, the more it costs to run, in a straight line, not asymptotically.
No way is it $1 per query. Hell a lot of these models you can run on your own computer, with no cost apart from a few cents of electricity (plus datacenter upkeep)
Theres more than just chatgpt and American data center/llm companies. Theres openAI, google and meta (american), mistral (French), alibaba and deepseek (china). Many more smaller companies that either make their own models or further finetune specialized models from the big ones. Its global competition, all of them occasionally releasing open weights models of different sizes for you to run your own on home consumer computer hardware. Dont like big models from American megacorps that were trained on stolen copyright infringed information? Use ones trained completely on open public domain information.
Your phone can run a 1-4b model, your laptop 4-8b, your desktop with a GPU 12-32b. No data is sent to servers when you self-host. This is also relevant for companies that data kept in house.
Like it or not machine learning models are here to stay. Two big points. One, you can self host open weights models trained on completely public domain knowledge or your own private datasets already. Two, It actually does provide useful functions to home users beyond being a chatbot. People have used llms to make music, generate images/video, integrate home automation like lighting control with tool calling, see images for details including document scanning, boilerplate basic code logic, check for semantic mistakes that regular spell check wont pick up on. In business ‘agenic tool calling’ to integrate models as secretaries is popular. Nft and crypto are truly worthless beyond grifting idiots with pump n dump and baseless speculative asset gambling. AI can at least make an attempt at a task you give it and either generally succeed or fail at it.
Models around 24-32b range in high quant are reasonably capable of basic information processing task and generally accurate domain knowledge. You can’t treat it like a fact source because theres always a small statistical chance of it being wrong but its OK starting point for researching like Wikipedia.
My local colleges are researching multimodal llms recognizing the subtle patterns in billions of cancer cell photos to possibly help doctors better screen patients. I would love a vision model trained on public domain botany pictures that helps recognize poisonous or invasive plants.
The problem is that theres too much energy being spent training them. It takes a lot of energy in compute power to cool a model and refine it. Its important for researchers to find more efficent ways to make them, Deepseek did this, they found a way to cook their models with way less energy and compute which is part of why that was exciting. Hopefully this energy can also come more from renewable instead of burning fuel.
Which ones are not actively spending an amount of money that scales directly with the number of users?
I’m talking about the general-purpose LLM AI bubble , wherein people are expected to return tremendous productivity improvements by using a LLM, thus justifying the obscene investment. Not ML as a whole. There’s a lot there, such as the work your colleagues are doing.
But it’s being treated as the equivalent of electricity, and it is not.
Most of these companies offer direct web/api access to their own cloud supercomputer datacenter, and All cloud services have some scaling with operation cost. The more users connect and use computer, the better hardware, processing power, and data connection needed to process all the users. Probably the smaller fine tuners like Nous Research that take a pre-cooked and open-licensed model, tweak it with their own dataset, then sell the cloud access at a profit with minimal operating cost, will do best with the scaling. They are also way way cheaper than big model access cost probably for similar reasons. Mistral and deepseek do things to optimize their models for better compute power efficency so they can afford to be cheaper on access.
OpenAI, claude, and google, are very expensive compared to competition and probably still operate at a loss considering compute cost to train the model + cost to maintain web/api hosting cloud datacenters. Its important to note that immediate profit is only one factor here. Many big well financed companies will happily eat the L on operating cost and electrical usage as long as they feel they can solidify their presence in the growing market early on to be a potential monopoly in the coming decades. Control, (social) power, lasting influence, data collection. These are some of the other valuable currencies corporations and governments recognize that they will exchange monetary currency for.
I assume you mean in a tech progression kind of way. A better comparison might be is that its being treated closer to the invention of transistors and computers. Before we could only do information processing with the cold hard certainty of logical bit calculations. We got by quite a while just cooking fancy logical programs to process inputs and outputs. Data communication, vector graphics and digital audio, cryptography, the internet, just about everything today is thanks to the humble transistor and logical gate, and the clever brains that assemble them into functioning tools.
Machine learning models are based on neuron brain structures and biological activation trigger pattern encoding layers. We have found both a way to train trillions of transtistors simulate the basic information pattern organizing systems living beings use, and a point in time which its technialy possible to have the compute available needed to do so. The perceptron was discovered in the 1940s. It took almost a century for computers and ML to catch up to the point of putting theory to practice. We couldn’t create artificial computer brain structures and integrate them into consumer hardware 10 years ago, the only player then was google with their billion dollar datacenter and alphago/deepmind.
Its exciting new toy that people think can either improve their daily life or make them money, so people get carried away and over promise with hype and cram it into everything especially the stuff it makes no sense being in. Thats human nature for you. Only the future will tell whether this new way of precessing information will live up to the expectations of techbros and academics.
Companies will just in house some models and train it on their own data, making it both more efficient and more relevant to their domain.
There’s nothing wrong with using AI in your personal or professional life. But let’s be honest here: people who find value in it are in the extreme minority. At least at the moment, and in its current form. So companies burning fossil fuels, losing money spinning up these endless LLMs, and then shoving them down our throats in every. single. product. is extremely annoying and makes me root for the technology as a whole to fail.
I don’t use it much myself, but I’m often surprised how many others use ChatGPT in their job. I don’t believe it’s an extreme minority.
It is definitely here to stay, but the hype of AGI being just around the corner is definitely not believable. And a lot of the billions being invested in AI will never return a profit.
AI is already a commodity. People will be paying $10/month at max for general AI. Whether Gemini, Apple Intelligence, Llama, ChatGPT, copilot or Deepseek. People will just have one cheap plan that covers anything an ordinary person would need. Most people might even limit themselves to free plans supported by advertisements.
These companies aren’t going to be able to extract revenues in the $20-$100/month from the general population, which is what they need to recoup their investments.
Specialized implementations for law firms, medical field, etc will be able to charge more per seat, but their user base will be small. And even they will face stiff competition.
I do believe AI can mostly solve quite a few of the problems of an aging society, by making the smaller pool of workers significantly more productive. But it will not be able to fully replace humans any time soon.
It’s kinda like email or the web. You can make money using these technologies, but by itself it’s not a big money maker.
Does it really boost productivity? In my experience, if a long email can be written by an AI, then you should just email the AI prompt directly to the email recipient and save everyone involved some time. AI is like reverse file compression. No new information is added, just noise.
If you’re using the thing to write your work emails, you’re probably so bad at your job that you won’t last anyway. Being able to write a clear, effective message is not a skill, it’s a basic function like walking. Asking a machine to do it for you just hurts yourself more than anything.
That said, it can be very useful for coding, for analyzing large contracts and agreements and providing summaries of huge datasets, it can help in designing slide shows when you have to do weekly power-points and other small-scale tasks that make your day go faster.
I find it hilarious how many people try to make the thing do ALL their work for them and end up looking like idiots as it blows up in their face.
See, LLM’s will never be smarter than you personally, they are tools for amplifying your own cognition and abilities, but few people use them that way, most people think it’s already alive and can make meaning for them. It’s not, it’s a mirror. You wouldn’t put a hand-mirror on your work chair and leave it to finish out your day.
I’m not a coder by any means, but when updating the super fucking outdated excel files my old company used, I’d usually make a VBA script using an LLM. It wasn’t always perfect, but 99% of the time, it was waaaay faster than me doing it myself. Then again, the things that company insisted was done in Excel could easily have been done better with other software. But the reality is that my field is conservative as fuck, and if it worked for the boss in 1994, it has to work for me.
If that email needs to go to a client or stakeholder, then our culture won’t accept just the prompt.
Where it really shines is translation, transcription and coding.
Programmers can easily double their productivity and increase the quality of their code, tests and documentation while reducing bugs.
Translation is basically perfect. Human translators aren’t needed. At most they can review, but it’s basically errorless, so they won’t really change the outcome.
Transcribing meetings also works very well. No typos or grammar errors, only sometimes issues with acronyms and technical terms, but those are easy to spot and correct.
As a programmer, there are so very few situations where I’ve seen LLMs suggest reasonable code. There are some that are good at it in some very limited situations but for the most part they’re just as bad at writing code as they are at everything else.
Programmers can double their productivity and increase quality of code?!? If AI can do that for you, you’re not a programmer, you’re writing some HTML.
We tried AI a lot and I’ve never seen a single useful result. Every single time, even for pretty trivial things, we had to fix several bugs and the time we needed went up instead of down. Every. Single. Time.
Best AI can do for programmers is context sensitive auto completion.
Another thing where AI might be useful is static code analysis.
Not really. As a programmer who doesn’t deal with math like at all, just working on overly-complicated CRUD’s, and even for me the AI is still completely wrong and/or waste of time 9 times out of 10. And I can usually spot when my colleagues are trying to use LLM’s because they submit overly descriptive yet completely fucking pointless refactors in their PR’s.
AI is a commodity but the big players are losing money for every query sent. Even at the $200/month subscription level.
Tech valuations are based on scaling. ARPU grows with every user added. It costs the same to serve 10 users vs 100 users, etc. ChatGPT, Gemini, copilot, Claude all cost more the more they’re used. That’s the bubble.
Of course, I totally agree with that
Yeah, but i don’t recall every tech company shoving NFTs into every product ever whether it made sense or if people wanted it or not. Not so with AI. Like, pretty much every second or third tech article these days is “[Company] shoves AI somewhere else no one asked for”.
It’s being force-fed to people in a way blockchain and NFTs never were. All so it can gobble up training data.
That’s because it died out before they all could, Reddit had the nft like aliens thing twitter used to let you use your nft as a profile picture. It just died out way too quick for the general tech companies to get in on it.
If it stayed longer Samsung would have worked out how to put nft tech in their phones
Ubisoft went all in on that shit. Square still dreams of nft for whatever reason, as their shitty Symbiogenesis game shows
What you described literally happened with blockchain, not with NFTs because by then everyone knew blockchain is fucking stupid and NFTs were just a layer of full retard on top.
https://www.sciencedirect.com/science/article/abs/pii/S0165176519304148#%3A~%3Atext=In+a+recent+study%2C+Jain%2Cafter+the+name+change+announcement.
https://qz.com/1175701/putting-bitcoin-or-blockchain-in-a-company-name-is-sometimes-enough-for-a-pop-on-the-stock-market
“AI” doesn’t exist. You’re just recycling grifter hype.